Support Resistance Pivot EMA Scalp Strategy [Mauserrifle]A strategy that creates signals based on: pivots, EMA 9+20, RSI, ATR, VWAP, wicks and volume.
The strategy is developed as a helper for quick long option scalping. This strategy is primarily designed for intraday trading on the 2m SPY chart with extended hours. However, users can adapt it for use on different symbols and timeframes. These signals are meant as a helper rather than fully automated trading bots.
One of the key elements is its pivot-based calculation, driven by my integrated indicator "Support and Resistance Pivot Points/Lines ". It enables multi-timeframe pivot calculations which are used to generate the signals and offers customizability, allowing you to define rounding methods and cooldown periods to refine pivot levels. The pivots, in combination with EMA crossovers, VWAP trend, and additional filters (RSI, ATR, VWAP, wicks and volume), create an entry and exit strategy for scalping opportunities that is useful for 0/1 DTE options with an average trade time of six minutes with the default setup for SPY. Option trading should be done outside TradingView. At this moment of release there is no option trading support.
All parameters used in the strategy are tweaked based on deep backtests results and real-time behavior. Be mindful that past performance does not guarantee future results.
The strategy is designed for intermediate and advanced users who are familiar intraday option scalping techniques.
How It Works
The strategy identifies entries based on multiple conditions, including: recently above pivot, recent EMA crossovers, RSI range, candle patterns, and VWAP uptrend. It avoids trades below the VWAP lower band due to poor backtesting results in those conditions. It creates a great number of signals when it detects an uptrend, which entails: VWAP and its lower/upper band slopes are going up, and the number of next high pivot points is greater than the number of lower pivot points. This indicates that we hope it will keep going up. In historical testing, this showed favorable results. This uptrend criteria runs on 15m charts max (where up to the VWAP effectiveness is the greatest).
The strategy also checks for candle and volume patterns, identified in backtesting to improve entry levels on historic data. Which include:
A red candle after multiple green ones, hoping to jump on a trend during a small pullback
Zero lower wick
Percentage and volume is up after lower volume candles
Percentage is up and the first and second EMA slopes are going up
Percentage is up, the first EMA is higher than the second, the price low is below the second EMA and price close above it
The VWAP uptrend overrules the candle and volume conditions (thus lots of signals during those moments).
The above is the base for many signals. There is a strict mode that adds extra checks such as:
not trading when there is no next low or high pivot
requiring a VWAP uptrend only
minimum candle percentages
This mode is for analyzing history and seeing performance during these conditions. It is worth it to create a separate alert for strict mode so you are aware of these conditions during trading.
When no stop has been defined, exits will always happen on pivot crossunder confirmations. If a stop is defined (default config), the strategy exits a position when:
the position is negative or no trail has been set
at least 1 bar has past
OR no stop has been defined (overrules previous)
trail has not been activated
The second exit condition happens when the close is below first EMA(9 by default) and when:
the position has been above first EMA
the gap between close and last pivot isn't small
the position is negative or no trail has been set
OR no stop has been defined (overrules above)
trail has not been activated
There are some more variations on this but the above are the most common. These exit conditions are a safety net because the strategy heavily relies on and favors stops. The settings allow changing stops, profit takers and trails. You can configure it to always sell without the conditions above.
The script will paint the pivot lines, trailing activation/stops, EMAs and entry/exits; with extra information in the data panel. For a complete view add VWAP and RSI to your chart, which are available from TradingView official indicator library. The strategy will not rely on those added indicators since VWAP and RSI are programmed in. You can add them to track the behavior of the signals based on these filters you have configured and have a complete view trading this strategy.
As mentioned earlier, the default settings are built for SPY 2m charts, with extended hours and real-time data. Open the strategy on this chart to study how all input parameters are used. If you don't have real-time data you need to adjust the minimum volume settings (set it to 0 at first).
The backtest
The default backtest configuration is set up to simulate SPY option trading.
Start capital is set to 10,000 and we risk around 5% of that per trade (1 contract)
Commission is set to 0.005%. The reason: at the time of this publication the SPY index price is approximately $580. Two ITM 0/1 DTE options contracts, each priced around $280, which is approximately $560. The typical commission for such a trade is around $3. To simulate this commission in the backtest on the SPY index itself, a commission of 0.005% per trade has been applied, approximating the options trading costs.
Slippage of 3 is set reflecting liquid SPY
The bar magnifier feature is turned on to have more realistic fills
Trading
In backtesting, setting commission and slippage to 0 on the SPY 2m chart shows many trades result around breaking even. Personally, I view them as an opportunity and safety net to help manage emotional decisions for exits. The signals are designed for short option scalps, allowing traders to take small profits and potentially re-enter during the strategy’s position window. It's advisable to take small potential profits, such as 4%, whenever the opportunity arises and consider re-entering if the setup still looks favorable, for example price still above ema9. Exiting a long position below ema9 is a common strategy for 2m scalping.
The average trade duration is approximately 6 minutes (3 bars). The choice between ITM (in-the-money), ATM (at-the-money), or OTM (out-of-the-money) options will depend on your trading style. Personally, I’ve seen better results with ITM options because they tend to move more in sync with the underlying index, thanks to their higher delta.
It’s important to note that the signals are designed to be a helper for manual trading rather than to automate a bot. Users are encouraged to take small profits and re-enter positions if favorable conditions persist. Be mindful that past performance does not guarantee future results.
For the default SPY setup the losses will mostly be 4-10% for ITM options. Be mindful of extreme volatile conditions where losses may reach 30% quickly, especially when trading ATM/OTM options.
The following settings can be changed:
8 pivot timeframes with left/right bars and days rendered
Here you can configure the timeframes for the pivots, which are crucial. The strategy wants that a crossover has happened recently (so it might enter after a crossunder if the crossover was recent) or the price is still above the crossed pivot.
When you decide to use a pivot timeframe higher than your chart, make sure it aligns the same starting point as the chart timeframe. As stated in the 43000478429 docs, there is a dependency between the resolution and the alignment of a starting point:
1–14 minutes — aligns to the beginning of a week
15–29 minutes — aligns to the beginning of a month
from 30 minutes and higher — aligns to the beginning of a year
This alignment also affects the setting of rendered days. I recommend a max value of 5 days for 1-14 minutes timeframes.
Also make sure a higher pivot timeframe can be divided by the lower. For instance I had repaint issues using 3m pivots on a 2m chart. But 4m pivots work fine.
Please look up docs 43000478429 to make sure this information is still up to date.
Pivot rounding
The pivot rounding option is used to add pivots based on a rounded price and limit the number of pivots. While this feature is disabled by default it can be useful with tweaking strategy variations, because many orders are placed at rounded levels and tend to act as strong price barriers.
There are multiple rounding methods: round, ceil/floor, roundn (decimal) and rounding to the minimal tick.
The next feature is a powerful extension called "Cooldown rounding":
Pivot cooldown rounding
This rounds new pivot levels for a cooldown period to keep the previous pivot line instead of adding a new line when they match the rounded value within the cooldown period. The existing line will be extended. This feature is useful because it makes sure the initial line is added to the exact high/low pivot level but any future lines within the rounding will just extend the existing line. This limits the number of pivots while still having precise levels (which normal rounding lacks) and allows more precise pivot trading.
This feature also helps ensure that the number of rendered lines will not exceed 500 too much, which is the render limit on TradingView.
You can set a maximum minutes for the cooldown. The default is 3 years which will enable the cooldown rounding permanently on the intraday (due to the max bar limit).
Pivot always added when new higher/lower pivot
When using cooldown rounding, one may find it useful to override this behavior when a new lower or higher pivot level has been reached. When enabled the new level will be added despite the fact that they may be rounded the same in the cooldown check. This is a good balance between limiting pivots but also allowing preciser trading.
VWAP bands multiplier
This is used to tweak the inner VWAP working for the upper and lower band. The default VWAP multiplier (0.9) is set based on backtesting since it performed better on historic data (the strategy does not trade below the lowerband). When you add the VWAP indicator from the TradingView library to the chart, make sure it uses the same multiplier setting as within this strategy so you have a correct view of the conditions the strategy acts on.
ATR EMA smoothing length
Used to tweak the ATR EMA smoothing. By default it is set up to 4 based on deep backtesting historic data.
EMA lengths
Changing the EMA length allows you to fine tune the EMA crossing behavior. By default the strategy is set up to EMA 9 and 20 which are considered commonly used values on the 2-minute chart.
Trading intraday time restrictions
For intraday charts you can configure when the strategy starts trading after market open and when it stops, including a hard sell. This makes sure there are no open positions left for the day during backtesting and can also aid in your trading style. For example some scalpers will not trade in the first two hours. Having no signals during this time can be beneficial. It is possible to configure these settings based on the number of bars or minutes.
Not trading on days the market closes earlier
By default the strategy does not trade on days the market closes earlier in the US. This makes sure there are no open positions left open during backtesting. Make sure to change it when using it on such a day. The days are: day before independence day, day after thanksgiving, Christmas eve and new years eve.
Not trading below VWAP lowerband
Backtesting has shown poor performance when trading below the VWAP lowerband but you are free to allow it to trade in such conditions. Past performance does not guarantee future results.
Minimum volume
A minimum volume can be set up. The current value is based on better deep backtest results for SPY using real-time data (48000). When you do not have a data plan for SPY, please set it to 0 and tweak based on backtests.
Minimum ATRP
The strategy has shown during my trading that it is sensitive to higher ATRP values and more volatile market conditions. There is more chance the index moves and we can profit from this during option scalping (if it moves in your favor). The default is based on SPY backtesting (0.04%), as a balance to have a lot of trades but also capture minimal movement.
RSI range
A RSI range can be set using a minimum and maximum value so we can limit trading during overbought/oversold conditions. Backtesting for SPY has shown the strategy performs better on historic data within a tighter range, so a default range has been set to 40-65.
Allow orders on every tick (no effect on stop/profit/trail)
This setting is used to allow orders on every tick. The strategy has been developed without trading on every tick but you can change this, for example when you have configured a setup different than the default configuration that you know works well with this. The default setup will not work well with it due to too many constant signals.
Stop percentage + ATRP threshold
One of the most important settings for managing the risk. I recommend setting a stop percentage first and later the ATRP threshold where the stop is calculated based on the current ATRP value. The calculated value will only be in effect when it is greater than the normal stop--the normal stop acts as baseline. The default stop is low (0.03). With a default ATRP threshold stop of 1.12, the calculated value overrules the normal stop when the value is greater. 0.03 acts as a minimum value but in reality the stop will most likely be higher on average for SPY with the default ATRP threshold.
For the default SPY setup the losses will be around 4-10% for ITM options. Be mindful of extreme volatile conditions where losses may reach 30% quickly, especially when trading ATM/OTM options.
Profit taker percentage + ATRP threshold
Same principles as the stop percentage above, but for profit taking. There is a very high ATRP threshold of 4 set by default. Backtests showed that trailing stops perform better on historic data.
Trailing stop
Used to set up a trailing stop. A useful feature to secure profit after a run-up, or get out with a small loss after initial activation. It is important to not use too tight values because they will give unrealistic backtest results and trigger too fast in real-time. Both the trail activation level and trail stop itself can be configured with a percentage value and ATRP value. I recommend setting up the ATRP last. By default the values are 0.05 for activation and 0.03 for the stop based on SPY real-time behavior.
Always sell on pivot crossunder confirmation
The strategy includes pivot crossunder confirmations as sell condition. By default it will not sell on every crossunder confirmation but checks for different conditions (explained in detail earlier in this description). You can change this behavior.
Always sell below first EMA when position has been above
The strategy sells below the first EMA when the position has been above it. By default it will not always sell but checks for different conditions (mentioned earlier in this description). You can change this behavior.
Buy modes pivot
By default the strategy buys between pivots as long as there has been a pivot crossover and EMAs crossover recently or price is still above it. You can change the behavior so it only buys on pivot crossovers or pivot crossover confirmations. Backtesting on the default setup shows decreased performance but for other strategy variations and pivot setups this feature can be useful since many scalpers do not buy between pivots.
Strict mode
There is a strict mode that adds extra checks such as not trading when there is no next low or high pivot, requiring a VWAP uptrend only and minimum candle percentages. This mode is for analyzing history and seeing performance during these conditions. It is worth it to create a separate alert for strict mode so you are aware of these conditions during trading. The deep backtests improved with these setting but past performance does not guarantee future results.
In the strict mode section you can override the stop, minimum ATRP, set up a minimum percentage, only trade VWAP uptrends and to not trade candles without a wick.
A summary and some extra detail
At the time of release only long trades are supported
The strategy is meant for quick scalping but one might find other uses for it
Enable extended hours on intraday charts so it captures more pivots
It does not trade extended hours (pre and post market) since options do not trade during those times
real-time data is recommended and required if a symbol has delayed data by default
You can configure that it trades minutes after market open and hard sells minutes after market open
The entries have a specific label text, example: "833 LE1 / 569.71 / P:569.8". This means: / / . The condition number is only for development/debug purposes for me when you have an issue.
The strategy cannot be tweaked to work on multiple symbols and timeframes with a single config. So you will have to make a config for every timeframe and symbol. I recommend using the Indicator Templates feature of TradingView. This way you can save the settings per timeframe and symbol
The strategy is per default config very dependent on (trailing) stops because it trades between pivots too. It wants that a pivot and EMA crossover has happened more recently than a crossunder. But you can change this behavior to always force crossover buys and crossunder sells.
It’s recommended to set up alerts to notify you of entry and exit signals. Watching the chart alone might cause you to miss trades, especially in fast-moving markets.
Only a max of 500 lines can be rendered on the chart, but the strategy will function with more under the hood. When you exceed 500 you will notice the beginning of the chart has no pivots, but beneath everything functions for backtesting.
Changing settings
Changing the settings for a different symbol and/or timeframe can be a challenging task. Here's a how-to you could use the first time to help you get going:
Set commission and slippage to 0. I prefer to do this so it is more clear whether you are balancing on break-even trades
Enable the pivot timeframe equal or above your chart timeframe. Avoid repainting as discussed earlier by choosing timeframes that align with the same timeframe
Set all volume, ATR, stop, profit takers and trail values to 0
Make sure strict mode is disabled at the bottom of the settings
You now have a clean state and you should see the backtest results purely based on pivot and EMA conditions
Tweak the stop and profit taker, beginning with the simple values and then ATRP threshold
At the last moment tweak the trailing stops. Tight trailing stops create an unrealistic backtest so you will need to tweak them based on real-time behavior of the symbol you're using which you will have to monitor during signals while the market is open. The default values are low (2m intraday SPY). Only with the bar magnifier feature it is somewhat possible to tweak realistic with history data. The tighter they are, the more unrealistic your backtest results. As a starting point, set the trailing stop low and find the highest activation level that doesn't change the results drastically, then increase the stop to the value you think reflects real-time behavior.
Keep refining by testing it during real-time behavior. Does it exit too early according to your own judgment? You need to increase the stop and maybe the activation level.
I hope you will find this useful!
DISCLAIMER
Trading is risky & most day traders lose money. This indicator is purely for informational & educational purposes only. Past performance does not guarantee future results.
חפש סקריפטים עבור "backtest"
MACD Optimizer Pro [Kioseff Trading]Massive update! This script now includes 12 different moving averages and 30+ built-in technical indicators to enhance your trading strategy optimization! (:
This script (MACD Optimizer Pro) allows the user to optimize and test hundreds of MACD strategies, simultaneously, in under 40 seconds. Of course, theoretically, an unlimited number of trading strategies can be tested with the MACD Optimizer Pro. After the optimization period - the MACD Optimizer Pro will show the most profitable MACD strategy or, should you choose, the highest win-rate MACD strategy or the most-efficient MACD strategy!
Optimization results can be backtested and verified using the native TradingView backtester - which is included in the MACD Optimizer Pro - and made easy to use! This feature makes settings alerts a simple practice!
Features
Test hundreds of MACD strategies, simultaneously, in under 40 seconds.
Optimize long MACD strategies and short MACD strategies.
12 different built-in moving averages included to improve your MACD strategy.
30+ built-in technical indicators to improve your MACD strategy.
Runs as a strategy script - profit factor, PnL , win-rate, number of trades, max drawdown, equity curve and other pertinent statistics shown.
Alerts
Optimize any MACD setting
Profit targets, trailing stops, fixed stop losses, and a binary MACD strategy can all be tested.
Strategies can be optimized for highest win rate, highest net profit, most efficient profit.
Limit orders can be simulated.
External indicators can be used for optimization i.e. your own, custom-built indicator, an indicator from your favorite author, or almost any publicly available
TradingView indicator.
Date range for optimization and backtesting are configurable.
Explanation
The image above shows a list of configurations for the optimizer. You can
You can test hundreds of different MACD settings in under 40 seconds on any timeframe, asset, etc.
The image above shows additional settings to filter the outcome of your optimization testing. Additionally, you can test an unlimited number of profit targets and stop losses!
You can add one of several built-in TradingView indicators to filter trade entries.
The image above shows all built-in moving averages and TradingView indicators that can be incorporated into your MACD strategy.
Additionally, you can add your own, custom indicator to the optimization test, your favorite indicator by your favorite author or almost any publicly available indicator on TradingView.
The image above shows the settings section in which you can implement this feature.
The image above shows an example of the custom indicator feature! In this instance, I am using the public indicator titled "Self-Optimizing" RSI and requiring it to measure below a level prior to entry! Almost any custom indicator, your favorite indicator, etc. is compatible with this feature!
The MACD Optimizer has improved user friendliness over previous versions. The optimizer can be as simple or complex as you'd like - capable of handling both "easy" and "difficult" tasks at your discretion.
Additionally, you can configure the optimizer to prioritize MACD strategies that earn profit most efficiently!
The image above shows this feature in action.
You can also configure the optimizer to prioritize MACD strategies that achieve the highest win rate!
The image above shows this feature in action.
Instructions
The instructions below show a rudimentary approach to using the optimizer.
1. Build your strategy in the settings.
You should also disable the "Run a Backtest" feature to improve load times during optimization.
The image above shows my custom strategy settings.
Now that you've got some data on your chart - you should try "Freezing" the "Smoothing" setting for MACD . When doing this, the optimizer will test hundreds of MACD settings with a fixed "Smoothing" setting. Try using the best "Smoothing" setting you were able to find for your initial testing.
2. Take the best "Smoothing" setting and test various MACD and Signal Lengths.
The image above shows me configuring the MACD Optimizer to test different MACD line lengths and Signal line lengths with a fixed "smoothing" setting.
From the results, we can see that there are better MACD settings than what was shown in our initial test!
With this information we can execute a TradingView backtest.
3. Execute a TradingView Backtest.
You must enable the "Run a Backtest" feature to perform a TradingView backtest. Additionally, it's advised to enable the "STOP OPTIMIZATION" feature when performing a TradingView backtest. Enabling this feature will improve load times for the backtest to only a few seconds (since the optimizer won't look for the best setting when this feature is enabled).
The image above shows completion of the process!
From here, you can perform further testing, set alerts, etc.
Backtest Settings Shown
Initial Capital: The initial capital used for the shown backtests is $3,500 USD. Set the initial capital to replicate your true starting capital (: PnL for the MACD strategies (listed in table) is calculated using a starting capital of $10,000 USD.
Slippage: The slippage settings for the displayed backtest was set to 2 ticks.
Commission: Commission was adjusted to 0.1%.
Verify Price for Limit Orders was set to 2 ticks.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Thanks for checking this out!
סקריפט בתשלום
A Physicist's Bitcoin Trading Strategy
1. Summary
This strategy and indicator were designed for, and intended to be used to guide trading activity in, crypto markets, particularly Bitcoin. This strategy uses a custom indicator to determine the state of the market (bullish vs bearish) and allocates funds accordingly. This particular variation also uses the custom indicator to determine when the market is significantly oversold and takes advantage of the opportunity (it buys the dip). The specific mathematical formula that is used to calculate the underlying custom indicator allows the trader to get in before, or near the start of, the bull trends, and get out before the bear trends. The strategy's properties dialogue box includes many display settings and parameters for optimization and customization to meet the user's needs and risk tolerance; this is both a tool to gauge the market, as well as a trading strategy to beat the market. Guidelines for parameter settings are provided. A sample dataset of backtest results using randomized parameters, both within the guidelines and outside the guidelines, is available upon request; notably, all trials outperformed the intended market (Bitcoin) during the 9-year backtest period.
2. The Indicator and Strategy
2.1. The Indicator
A mathematical formula is used to determine the state of the market according to three different "frequencies", which, for lack of better terminology, are called fast, moderate, and slow indicators. There are two parameters for each of the three indicators, one called response time and the other is a simple look-back period. Finally, four exponential moving averages are used to smooth each indicator. In total, there are 18 different levels of bullishness/bearishness. The purpose of using three indicators, rather than one, is to capture the full character of the market, from a macro/global scope down to a micro/local scope. I.e. the full indicator looks at the forest, the trees, and the branches, simultaneously.
2.2. The Strategy
The trend-trading strategy is very simple; there are only four types of orders: 1) The entire position (e.g. all bitcoins held) is sold (if it hasn't already been totally sold) when the indicator becomes maximally bearish, 2) When the movement of the indicator is in the bullish direction, the strategy dollar-cost-average (DCA) buys at an exponentially decreasing rate, i.e. it buys more in the early stages of the transition from bear->bull. 3) When the indicator is maximally bullish, it goes "all-in" † (if it hasn't already gone all-in), i.e. it converts all available cash into the underlying security/token. And, 4) when the movement of the indicator is in the bearish direction, the strategy DCA sells (again, exponentially decreasing) to get out quickly. No leverage is used in this strategy. The strategy never takes a short position.
A second "buy-the-dip" strategy is also used, and it is the synergy of these two strategies, together, that is responsible for most of the outperformance in the backtests (this strategy handily beats the non-dip-buying variation in backtests). To do this, the custom indicator is used to determine when the market is significantly oversold on a short-term basis, and the strategy responds by DCA buying. However, unlike the DCA buying during bull/bear transitions, the buy-the-dip DCA buying increases with time. Specifically, within each candle that is short-term oversold, the strategy converts 10% x # of candles since becoming oversold (up to a max of 6 candles) of available cash into the underlying security/token. I.e. the first buy is 10% of available cash and occurs in the first oversold candle, the second buy is 20% of available cash and occurs in the second oversold candle, etc. up to six consecutive oversold candles. Lastly, to ensure no conflicting orders and no leverage (buying more than what is affordable with the available cash in the fund), buy-the-dip orders take precedence over the trend-trading orders enumerated in the previous paragraph.
† Technically the strategy goes 99.5% in when it goes "all-in". This is to ensure no leverage is used given that there may be a commission of 0.5%.
3. Backtest Results
Backtest results demonstrate significant outperformance over buy-and-hold. The default parameters of the strategy/indicator have been set by the author to achieve maximum (or, close to maximum) outperformance on backtests executed on the BTCUSD (Bitcoin) chart. However, significant outperformance over buy-and-hold is still easily achievable using non-default parameters. Basically, as long as the parameters are set to adequately capture the full character of the market, significant outperformance on backtests is achievable and is quite easy. In fact, after some experimentation, it seems as if underperformance hardly achievable and requires deliberately setting the parameters illogically (e.g. setting one parameter of the slow indicator faster than the fast indicator). In the interest of providing a quality product to the user, suggestions and guidelines for parameter settings are provided in section (6). Finally, some metrics of the strategy's outperformance on the BTCUSD chart are listed below, both for the default (optimal) parameters as well as for a random sample of parameter settings that adhere to the guidelines set forth in section (6).
Using the default parameters, relative to buy-and-hold strategy, backtested from August 2011 to August 2020,
Total cumulative outperformance (total return of strategy minus total return of buy-n-hold): 13,000,000%.
Rolling 1-year outperformance: mean 318%, median 84%, 1st quartile 55%, 3rd quartile, 430%.
Rolling 1-month outperformance: mean 2.8% (annualized, 39%), median -2.1%, 1st quartile -7.7%, 3rd quartile 13.2%, 10th percentile -13.9%, 90th percentile 24.5%.
Using the default parameters, relative to buy-and-hold strategy, during specific periods,
Cumulative outperformance during the past year (August 2019-August 2020): 37%.
12/17/2016 - 12/17/2017 (2017 bull market) absolute performance of 2563% vs buy-n-hold absolute performance of 2385%
11/29/2012 - 11/29/2013 (2013 bull market) absolute performance of 14033% vs buy-n-hold absolute performance of 9247%
Using a random sample (n=20) of combinations of parameter settings that adhere to the guidelines outlined in section (6), relative to buy-and-hold strategy, backtested from August 2011 to August 2020,
Average total cumulative outperformance, from August 2011 to August 2020: 2,000,000%.
Median total cumulative outperformance, from August 2011 to August 2020: 1,000,000%.
4. Limitations
This strategy is basically a DCA-swing trading strategy, and as such it is intended to be used on the 6-hr chart. Similar performance is expected on daily chart, 12-hr chart, and 4-hr chart, but performance is likely to be limited when used on charts of shorter time-frames. However, due to the flexibility afforded by the large quantity of parameters, as well as the tools included, it may be possible to tweak the indicator settings to get some outperformance on smaller time-frames. Admittedly, the author did not spend much time investigating this.
As is apparent in the backtests, this strategy has very limited absolute performance during large bear markets, such as Bitcoin's 2018 bear market. As described, it does outperform the underlying security by a large amount in backtests, but a large absolute return is unlikely during large and prolonged declines (unless, of course, your unit of account is the underlying token, in which case an outperformance of the underlying is, by definition, an absolute positive return).
This strategy is likely to underperform if used to trade ETFs of broad equity markets. This strategy may produce a small amount of outperformance when used to trade precious metals ETFs, given that the parameters are set optimally by the user.
5. Use
The default parameters have already been set for highly optimal backtest results on the chart of BTCUSD (Bitcoin / US Dollar BITSTAMP), (although, a different combination of parameter settings may yet produce better results). Still, there is a great number of combinations that can be explored, so the user is free to tweak the settings to meet his/her/their needs. Some display options are provided to give the user a visual aid while tweaking the parameters. These include a blue/red background display of the custom indicator, a calibration system, and options to display information about the backtest results. The background pattern represents the various levels of bullishness/bearishness as semi-transparent layers of blue and red, with blue corresponding with bullish and red corresponding with bearish.
The parameters that affect the indicator are the response times, the periods, and some EMA lengths. The parameters that affect the quantity of contracts (tokens/shares/bitcoins/etc) to be bought/sold are the transitionary buy/sell rates. There are also two sets of date parameters.
The response time and period parameters are direct inputs into the underlying math formula and are used to create the base-level indicators (fast, moderate, and slow). The response times control the speed of each of the three indicators (shorter is fast, longer is slower) and the period controls how much historical data is used in computation. Information about how these should be set are included in section (6). Another set of parameters control EMA look-back periods that serve to smooth the base-level indicators. Increasing these EMA lengths makes the overall indicator less sensitive to short-term price action, while reducing them does the opposite. The effect of these parameters are obvious when the background blue/red visualization is displayed. Another EMA length is an EMA for the entire indicator. Increasing this parameter reduces the responsiveness of the trading strategy (buy/sell orders) to quick/small changes of the overall level of the indicator, so as to avoid unnecessary buying and selling in times of relatively small and balanced price perturbations. Note, changing this parameter does not have an effect on the overall indicator itself, and thus will not affect the blue/red background representation.
The transitionary buy/sell rates control the portion of the available asset to be converted to the other. E.g. if the buy rate is set to 90%, then 90% of the available cash will be used to buy contracts/tokens/shares/bitcoins during transitions bullish transitions, e.g. if the available cash at the start of the bullish transition is $10,000 and the parameter is set to 90%, then $9,000 will be used to buy in the first candle during which the transition is bullish, then $900 will be used to buy in the second candle, then $90 in the third candle, etc.
There are two dates that can be set. The first is the date at which the strategy goes all in. This is included because the buy-and-hold strategy is the benchmark against which this strategy is compared, so setting this date to some time before the strategy starts to make trades will show, very clearly, the outperformance of the strategy, especially when the initial capital parameter in the Properties tab is equal to the price of one unit of the underlying security on the date that is set, e.g. all-in on Bitcoin on 8/20/2011 and set initial capital to the BTCUSD price on that date, which was $11.70. The second date is a date to control when the strategy can begin to place trades.
Finally (actually, firstly in the Inputs dialogue box), a set of checkbox inputs controls whether or not the backtest is on or off, and what is displayed. The display options are the blue/red (bull/bear) background layers †, a set of calibrators, a plot of the total strategy equity, a plot of the cash position of the strategy, a plot of the size of the position of the strategy in contracts/shares/units (labeled as BTC position), and a plot of the rolling 1-year performances of buy-and-hold and the strategy.
About the calibrators: The calibration system allows the user to quickly assess and calibrate how well the indicator... indicates. Quite simply, the system has two parts: one plot that is the cumulative sum of the product of the indicator level and the change in the underlying price, i.e. sum of ‡, over all candles. The second part is a similar plot that is reduced according to the quickness with which the indicator changes, i.e. sum of . Maximizing the first plot at the expense of the second will cause the indicator to match the price action very well but therefore it will change very rapidly, from bullish to bearish, which is visualized by a background pattern that changes frequently from blue to red to blue. Ignoring the first plot and maximizing the second will also cause the indicator to more closely match the price action, but the transitions will be slower and less frequent, and will therefore focus on identifying the major trends of the market.
† The blue/red background has many layers and will make the chart lag as the user interacts with it.
‡ Bearish states are coded as negative quantities, so a bearish state x negative price action = positive number, and bullish state x positive price action = positive number.
6. Suggestions and Guidelines
As described in section (2.1), the indicator used in this strategy was designed to determine the state of the market--whether it is bullish or bearish--as well as the change in the state of the market--whether it is increasingly bullish or increasingly bearish. As such, the following suggestions are provided based on the principles of the indicator's design,
1. Response Time 1 should be less than (<) Response Time 2 which should be < Response Time 3
2. Fast Period < Moderate Period < Slow Period
3. In terms of the period of a full market cycle (e.g. ~ 4 years for BTC, ~ 5.5 years for equities, etc.), response times 1, 2, and 3 should be about 0.3% to 1%, 3% to 20%, and 20% to 50% of a full market cycle period, respectively. However, this is a loose guideline.
4. In terms of the period of a full market cycle, periods 1, 2, and 3 should all be about 25% to 75% of a full cycle period. Again, this is a loose guideline.
4. EMA 1 Length < EMA 2 Length < EMA 3 Length < EMA 4 Length
5. EMA Lengths 1, 2, 3, and 4 should be limited to about 1/4th the length of a full market cycle. Note, EMA lengths are measured in bars (candles), not in days. 1/4th of 1000 days is 250 days which is 250 x 4 = 1000 6-hr candles.
The following guidelines are provided based on results of over 100 backtests on the BTCUSD chart using randomized parameters †,
1. 9 days < Response Time 1 < 14 days
2. 5 days < EMA 1 Length < 100 days
3. 600 days < EMA 4 length < 1000 days
4. The ratio of the EMA range (EMA 4 len - EMA 1 len) to the sum of EMA lengths (EMA 1 len + EMA 2 len + ...) be greater than 0.4
5. The ratio of the sum of EMA 1 and EMA 2 lengths to the sum of EMA 3 and EMA 4 lengths be less than 0.3.
A suggestion from the author: Given that backtests show a high degree of outperformance using the guidelines enumerated above, a good trading strategy may be to not rely on any one particular combination of parameters. Rather, a random set of combinations of parameter settings that adhere to the guidelines above could be used to create multiple instances of the strategy in a TradingView chart, each of which varies by a small amount due to their unique parameter settings. The proportion of the entire set of strategy instances that agree about the current state of the market could indicate to the trader the level of confidence of the indicator, in aggregate.
† A sample dataset of backtest results using randomized parameters is available upon request; notably, all trials outperformed the intended market (Bitcoin).
7. General Remarks About the Indicator
Other than some exponential moving averages, no traditional technical indicators or technical analysis tools are employed in this strategy. No MACD, no RSI, no CMF, no Bollinger bands, parabolic SARs, Ichimoku clouds, hoosawatsits, XYZs, ABCs, whatarethese. No tea leaves can be found in this strategy, only mathematics. It is in the nature of the underlying math formula, from which the indicator is produced, to quickly identify trend changes.
8. Remarks About Expectations of Future Results and About Backtesting
8.1. In General
As it's been stated in many prospectuses and marketing literature, "past performance is no guarantee of future results." Backtest results are retrospective, and hindsight is 20/20. Therefore, no guarantee can, nor should, be expressed by me or anybody else who is selling a financial product (unless you have a money printer, like the Federal Reserve does).
8.2. Regarding This Strategy
No guarantee of future results using this strategy is expressed by the author, not now nor at any time in the future.
With that written, the author is free to express his own expectations and opinions based on his intimate knowledge of how the indicator works, and the author will take that liberty by writing the following: As described in section (7), this trading strategy does not include any traditional technical indicators or TA tools (other than smoothing EMAs). Instead, this strategy is based on a principle that does not change, it employs a complex indicator that is based on a math formula that does not change, and it places trades based on five simple rules that do not change. And, as described in section (2.1), the indicator is designed to capture the full character of the market, from a macro/global scope down to a micro/local scope. Additionally, as described in section (3), outperformance of the market for which this strategy was intended during backtesting does not depend on luckily setting the parameters "just right." In fact, all random combinations of parameter settings that followed the guidelines outperformed the intended market in backtests. Additionally, no parameters are included within the underlying math formula from which the indicator is produced; it is not as if the formula contains a "5" and future outperformance would depend on that "5" being a "6" instead. And, again as described, it is in the nature of the formula to quickly identify trend changes. Therefore, it is the opinion of the author that the outperformance of this strategy in backtesting is directly attributable to the fundamental nature of the math formula from which the indicator is produced. As such, it is also the opinion of the author that continued outperformance by using this strategy, applied to the crypto (Bitcoin) market, is likely, given that the parameter settings are set reasonably and in accordance with the guidelines. The author does not, however, expect future outperformance of this strategy to match or exceed the outperformance observed in backtests using the default parameters, i.e. it probably won't outperform by anything close to 13,000,000% during the next 9 years.
Additionally, based on the rolling 1-month outperformance data listed in section (3), expectations of short-term outperformance should be kept low; the median 1-month outperformance was -2%, so it's basically a 50/50 chance that any significant outperformance is seen in any given month. The true strength of this strategy is to be out of the market during large, sharp declines and capitalizing on the opportunities presented at the bottom of those declines by buying the dip. Given that such price action does not happen every month, outperformance in the initial months of use is approximately as likely as underperformance.
9. Access
Those who are interested in using this strategy may send a personal message to inquire about how to gain access. Those who are interested in acquiring the sample dataset of backtest results may send a personal message to request a copy of the data.
Self Optimizing PSAR [Starbots]Self Optimizing Parabolic SAR Strategy (non-repainting)
Strategy constantly backtest 169 different combinations of Parabolic SAR indicator for maximum profitability and trades based on the best performing combination at that time.
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# Parabolic SAR (PSAR)
Parabolic SAR is a time and price technical analysis tool created by J. Welles Wilder and it's primarily used to identify points of potential stops and reverses. In fact, the SAR in Parabolic SAR stands for "Stop and Reverse". The indicator's calculations create a parabola which is located below price during a Bullish Trend and above Price during a Bearish Trend.
You can read more about this indicator here:
www.tradingview.com
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The logic of self - optimizing:
This script is always backtesting 169 different combinations of Parabolic SAR settings in the background and saves the net. profit gained for every single one of them, then strategy selects and use the best performing combination of settings currently available for you to trade.
It's recalculating on every bar close - if one of the parameters starts performing better than others - have a higher net profit gain (it's literally like running 169 backtests with different settings) strategy switches to that parameter and continues trading like that until one of the other indicator parameters starts performing better again and switches to that settings.
We are optimizing our strategy based on 13 different 'Increment' factors of PSAR. We keep the 'Start' factor (default 0.02) and 'Max Value' factor (default 0.2) at default for all of them.
According to creator of this indicator J. Welles Wilder, we usually want to change only 'Increment' factors of PSAR in the calculation and leave the rest at default and that's what we do, we are changing only 'Increment' input.
Inputs : (you don't need to change them at all, it's a good balance for fast and slow detection of trends on PSAR)
Start = 0.02
Max value = 0.2
Increment1 = 0.005, Increment2 = 0.01, Increment3 = 0.015
Increment4 = 0.02, Increment5 = 0.025, Increment6 = 0.03
Increment7 = 0.035, Increment8 = 0.04, Increment9 = 0.045
Increment10 = 0.05, Increment11 = 0.055, Increment12 = 0.06
Increment13 = 0.065
PSAR buy / sell conditions looks like this:
PSAR1 = start 0.02, max value 0.2, increment1 0.005
PSAR2 = start 0.02, max value 0.2, increment2 0.01
PSAR3 = start 0.02, max value 0.2, increment3 0.015
PSAR4 = start 0.02, max value 0.2, increment3 0.02
...
PSAR13 = start 0.02, max value 0.2, increment13 0.065
Backtester in the background works like this:
backtest buying PSAR1 settings with selling PSAR1 settings => save net. profit
backtest buy PSAR1 with sell PSAR2 ;
backtest buy PSAR1 with sell PSAR3 ;
backtest buy PSAR1 with sell PSAR4 ;
..........
backtest buy PSAR1 with sell PSAR13 ;
..........
backtest buy PSAR13 with sell PSAR1 ;
backtest buy PSAR13 with sell PSAR2 ;
......
backtest buy PSAR13 with sell PSAR13 ;
=>
It will backtest 16x16=169 different PSAR settings and save their profits.
Your strategy then trades based on the best performing (highest net.profit) PSAR Setting currently available. It will check the calculations and backtest them on every new bar close - it's like running 169 strategies at time, and manually selecting the best performing one.
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If you wish to use it as INDICATOR - turn on 'Recalculate after every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
Strategy example is backtested on Daily chart of SHIBUSDT Binance
All settings at default. (1000 capital, 100 order size, 0.1% fee, 1 tick slippage)
Settings:
-Start = default Parabolic SAR setting is 0.02
-Max Value = default Parabolic SAR setting is 0.2
--Recommended PSAR Increment settings:
0.02 is default, higher timeframes usually performs good on the faster Increment factors 0.03-0.05+, smaller timeframes on slow Increment factors 0.005-0.02. I recommend you the most common and logical 13 different Increment factors for optimizing in the strategy as default already (from 0.005 to 0.065 - strategy will then optimize and trade based on the most profitable combination).
- Noise-Intensity Filter 🐎0.00-0.20%🐢
This will punish the tiny trades made by certain combinations and give more advantage to big average trades. It's basically like fee calculation, it will deduct 0.xx% fee from every trade when optimizing on their backtests.
You will usually want to have it around 0.05-0.10% like your fees on exchange.
-> 🐎Less than <0.10% allows strategy to be VERY SENSITIVE to market. (a lot of trades - quick buy-sell changes)
-> 🐢More than >0.10% will slow down the strategy, it will be LESS SENSITIVE to market volatility. (less trades - slowly switches the trend direction from buy to sell)
Close Trades on Neutral
After a lot of Trades, Algo starts developing self-intelligence. It can also have a neutral score. (Grey Plots). Sell when the strategy is neutral.
Other settings:
-Take Profit, Multiple Take Profit, Trailing Take Profit, Stop Loss, Trailing Stop Loss with functional alerts.
-Backtesting Range - backtest within your desired time window. Example: 'from 01 / 01 /2020 to 01 / 01 /2023'.
- Strategy is trading on the bar close without repaint. You can trade Long-Sell/Short Sell or Long-Short both directions. Alerts available, insert webhook messages in the inputs.
- Turn on Profit Calendar for better overview of how your strategy performs monthly/annualy
- Notes window : add your custom comments in here or save your webhook message text inside here for later use. I find this helpful to save texts inside.
Recommended TF : 4h, 8h, 1d (Trend Indicators are good at detecting directions of the market, but we can have a lot of noise and false movements on charts, you want to avoid that and ride the long term movements)
This script is fairly simple to use. It's self-optimizing and adjusting to the markets on the go.
Range Oscillator Strategy + Stoch Confirm🔹 Short summary
This is a free, educational long-only strategy built on top of the public “Range Oscillator” by Zeiierman (used under CC BY-NC-SA 4.0), combined with a Stochastic timing filter, an EMA-based exit filter and an optional risk-management layer (SL/TP and R-multiple exits). It is NOT financial advice and it is NOT a magic money machine. It’s a structured framework to study how range-expansion + momentum + trend slope can be combined into one rule-based system, often with intentionally RARE trades.
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0. Legal / risk disclaimer
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• This script is FREE and public. I do not charge any fee for it.
• It is for EDUCATIONAL PURPOSES ONLY.
• It is NOT financial advice and does NOT guarantee profits.
• Backtest results can be very different from live results.
• Markets change over time; past performance is NOT indicative of future performance.
• You are fully responsible for your own trades and risk.
Please DO NOT use this script with money you cannot afford to lose. Always start in a demo / paper trading environment and make sure you understand what the logic does before you risk any capital.
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1. About default settings and risk (very important)
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The script is configured with the following defaults in the `strategy()` declaration:
• `initial_capital = 10000`
→ This is only an EXAMPLE account size.
• `default_qty_type = strategy.percent_of_equity`
• `default_qty_value = 100`
→ This means 100% of equity per trade in the default properties.
→ This is AGGRESSIVE and should be treated as a STRESS TEST of the logic, not as a realistic way to trade.
TradingView’s House Rules recommend risking only a small part of equity per trade (often 1–2%, max 5–10% in most cases). To align with these recommendations and to get more realistic backtest results, I STRONGLY RECOMMEND you to:
1. Open **Strategy Settings → Properties**.
2. Set:
• Order size: **Percent of equity**
• Order size (percent): e.g. **1–2%** per trade
3. Make sure **commission** and **slippage** match your own broker conditions.
• By default this script uses `commission_value = 0.1` (0.1%) and `slippage = 3`, which are reasonable example values for many crypto markets.
If you choose to run the strategy with 100% of equity per trade, please treat it ONLY as a stress-test of the logic. It is NOT a sustainable risk model for live trading.
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2. What this strategy tries to do (conceptual overview)
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This is a LONG-ONLY strategy designed to explore the combination of:
1. **Range Oscillator (Zeiierman-based)**
- Measures how far price has moved away from an adaptive mean.
- Uses an ATR-based range to normalize deviation.
- High positive oscillator values indicate strong price expansion away from the mean in a bullish direction.
2. **Stochastic as a timing filter**
- A classic Stochastic (%K and %D) is used.
- The logic requires %K to be below a user-defined level and then crossing above %D.
- This is intended to catch moments when momentum turns up again, rather than chasing every extreme.
3. **EMA Exit Filter (trend slope)**
- An EMA with configurable length (default 70) is calculated.
- The slope of the EMA is monitored: when the slope turns negative while in a long position, and the filter is enabled, it triggers an exit condition.
- This acts as a trend-protection exit: if the medium-term trend starts to weaken, the strategy exits even if the oscillator has not yet fully reverted.
4. **Optional risk-management layer**
- Percentage-based Stop Loss and Take Profit (SL/TP).
- Risk/Reward (R-multiple) exit based on the distance from entry to SL.
- Implemented as OCO orders that work *on top* of the logical exits.
The goal is not to create a “holy grail” system but to serve as a transparent, configurable framework for studying how these concepts behave together on different markets and timeframes.
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3. Components and how they work together
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(1) Range Oscillator (based on “Range Oscillator (Zeiierman)”)
• The script computes a weighted mean price and then measures how far price deviates from that mean.
• Deviation is normalized by an ATR-based range and expressed as an oscillator.
• When the oscillator is above the **entry threshold** (default 100), it signals a strong move away from the mean in the bullish direction.
• When it later drops below the **exit threshold** (default 30), it can trigger an exit (if enabled).
(2) Stochastic confirmation
• Classic Stochastic (%K and %D) is calculated.
• An entry requires:
- %K to be below a user-defined “Cross Level”, and
- then %K to cross above %D.
• This is a momentum confirmation: the strategy tries to enter when momentum turns up from a pullback rather than at any random point.
(3) EMA Exit Filter
• The EMA length is configurable via `emaLength` (default 70).
• The script monitors the EMA slope: it computes the relative change between the current EMA and the previous EMA.
• If the slope turns negative while the strategy holds a long position and the filter is enabled, it triggers an exit condition.
• This is meant to help protect profits or cut losses when the medium-term trend starts to roll over, even if the oscillator conditions are not (yet) signalling exit.
(4) Risk management (optional)
• Stop Loss (SL) and Take Profit (TP):
- Defined as percentages relative to average entry price.
- Both are disabled by default, but you can enable them in the Inputs.
• Risk/Reward Exit:
- Uses the distance from entry to SL to project a profit target at a configurable R-multiple.
- Also optional and disabled by default.
These exits are implemented as `strategy.exit()` OCO orders and can close trades independently of oscillator/EMA conditions if hit first.
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4. Entry & Exit logic (high level)
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A) Time filter
• You can choose a **Start Year** in the Inputs.
• Only candles between the selected start date and 31 Dec 2069 are used for backtesting (`timeCondition`).
• This prevents accidental use of tiny cherry-picked windows and makes tests more honest.
B) Entry condition (long-only)
A long entry is allowed when ALL the following are true:
1. `timeCondition` is true (inside the backtest window).
2. If `useOscEntry` is true:
- Range Oscillator value must be above `entryLevel`.
3. If `useStochEntry` is true:
- Stochastic condition (`stochCondition`) must be true:
- %K < `crossLevel`, then %K crosses above %D.
If these filters agree, the strategy calls `strategy.entry("Long", strategy.long)`.
C) Exit condition (logical exits)
A position can be closed when:
1. `timeCondition` is true AND a long position is open, AND
2. At least one of the following is true:
- If `useOscExit` is true: Oscillator is below `exitLevel`.
- If `useMagicExit` (EMA Exit Filter) is true: EMA slope is negative (`isDown = true`).
In that case, `strategy.close("Long")` is called.
D) Risk-management exits
While a position is open:
• If SL or TP is enabled:
- `strategy.exit("Long Risk", ...)` places an OCO stop/limit order based on the SL/TP percentages.
• If Risk/Reward exit is enabled:
- `strategy.exit("RR Exit", ...)` places an OCO order using a projected R-multiple (`rrMult`) of the SL distance.
These risk-based exits can trigger before the logical oscillator/EMA exits if price hits those levels.
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5. Recommended backtest configuration (to avoid misleading results)
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To align with TradingView House Rules and avoid misleading backtests:
1. **Initial capital**
- 10 000 (or any value you personally want to work with).
2. **Order size**
- Type: **Percent of equity**
- Size: **1–2%** per trade is a reasonable starting point.
- Avoid risking more than 5–10% per trade if you want results that could be sustainable in practice.
3. **Commission & slippage**
- Commission: around 0.1% if that matches your broker.
- Slippage: a few ticks (e.g. 3) to account for real fills.
4. **Timeframe & markets**
- Volatile symbols (e.g. crypto like BTCUSDT, or major indices).
- Timeframes: 1H / 4H / **1D (Daily)** are typical starting points.
- I strongly recommend trying the strategy on **different timeframes**, for example 1D, to see how the behaviour changes between intraday and higher timeframes.
5. **No “caution warning”**
- Make sure your chosen symbol + timeframe + settings do not trigger TradingView’s caution messages.
- If you see warnings (e.g. “too few trades”), adjust timeframe/symbol or the backtest period.
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5a. About low trade count and rare signals
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This strategy is intentionally designed to trade RARELY:
• It is **long-only**.
• It uses strict filters (Range Oscillator threshold + Stochastic confirmation + optional EMA Exit Filter).
• On higher timeframes (especially **1D / Daily**) this can result in a **low total number of trades**, sometimes WELL BELOW 100 trades over the whole backtest.
TradingView’s House Rules mention 100+ trades as a guideline for more robust statistics. In this specific case:
• The **low trade count is a conscious design choice**, not an attempt to cherry-pick a tiny, ultra-profitable window.
• The goal is to study a **small number of high-conviction long entries** on higher timeframes, not to generate frequent intraday signals.
• Because of the low trade count, results should NOT be interpreted as statistically strong or “proven” – they are only one sample of how this logic would have behaved on past data.
Please keep this in mind when you look at the equity curve and performance metrics. A beautiful curve with only a handful of trades is still just a small sample.
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6. How to use this strategy (step-by-step)
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1. Add the script to your chart.
2. Open the **Inputs** tab:
- Set the backtest start year.
- Decide whether to use Oscillator-based entry/exit, Stochastic confirmation, and EMA Exit Filter.
- Optionally enable SL, TP, and Risk/Reward exits.
3. Open the **Properties** tab:
- Set a realistic account size if you want.
- Set order size to a realistic % of equity (e.g. 1–2%).
- Confirm that commission and slippage are realistic for your broker.
4. Run the backtest:
- Look at Net Profit, Max Drawdown, number of trades, and equity curve.
- Remember that a low trade count means the statistics are not very strong.
5. Experiment:
- Tweak thresholds (`entryLevel`, `exitLevel`), Stochastic settings, EMA length, and risk params.
- See how the metrics and trade frequency change.
6. Forward-test:
- Before using any idea in live trading, forward-test on a demo account and observe behaviour in real time.
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7. Originality and usefulness (why this is more than a mashup)
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This script is not intended to be a random visual mashup of indicators. It is designed as a coherent, testable strategy with clear roles for each component:
• Range Oscillator:
- Handles mean vs. range-expansion states via an adaptive, ATR-normalized metric.
• Stochastic:
- Acts as a timing filter to avoid entering purely on extremes and instead waits for momentum to turn.
• EMA Exit Filter:
- Trend-slope-based safety net to exit when the medium-term direction changes against the position.
• Risk module:
- Provides practical, rule-based exits: SL, TP, and R-multiple exit, which are useful for structuring risk even if you modify the core logic.
It aims to give traders a ready-made **framework to study and modify**, not a black box or “signals” product.
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8. Limitations and good practices
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• No single strategy works on all markets or in all regimes.
• This script is long-only; it does not short the market.
• Performance can degrade when market structure changes.
• Overfitting (curve fitting) is a real risk if you endlessly tweak parameters to maximise historical profit.
Good practices:
- Test on multiple symbols and timeframes.
- Focus on stability and drawdown, not only on how high the profit line goes.
- View this as a learning tool and a basis for your own research.
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9. Licensing and credits
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• Core oscillator idea & base code:
- “Range Oscillator (Zeiierman)”
- © Zeiierman, licensed under CC BY-NC-SA 4.0.
• Strategy logic, Stochastic confirmation, EMA Exit Filter, and risk-management layer:
- Modifications by jokiniemi.
Please respect both the original license and TradingView House Rules if you fork or republish any part of this script.
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10. No payments / no vendor pitch
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• This script is completely FREE to use on TradingView.
• There is no paid subscription, no external payment link, and no private signals group attached to it.
• If you have questions, please use TradingView’s comment system or private messages instead of expecting financial advice.
Use this script as a tool to learn, experiment, and build your own understanding of markets.
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11. Example backtest settings used in screenshots
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To avoid any confusion about how the results shown in screenshots were produced, here is one concrete example configuration:
• Symbol: BTCUSDT (or similar major BTC pair)
• Timeframe: 1D (Daily)
• Backtest period: from 2018 to the most recent data
• Initial capital: 10 000
• Order size type: Percent of equity
• Order size: 2% per trade
• Commission: 0.1%
• Slippage: 3 ticks
• Risk settings: Stop Loss and Take Profit disabled by default, Risk/Reward exit disabled by default
• Filters: Range Oscillator entry/exit enabled, Stochastic confirmation enabled, EMA Exit Filter enabled
If you change any of these settings (symbol, timeframe, risk per trade, commission, slippage, filters, etc.), your results will look different. Please always adapt the configuration to your own risk tolerance, market, and trading style.
Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4hInvestment Strategy (Quantitative Trading)
| 🛑 | Watch "LIVE" and 'COPY' this strategy in real time:
🔗 Link: www.tradingview.com
Hello, welcome, feel free 🌹💐
Since the stone age to the most technological age, one thing has not changed, that which continues impress human beings the most, is the other human being!
Deep down, it's all very simple or very complicated, depends on how you look at it.
I believe that everyone was born to do something very well in life.
But few are those who have, let's use the word 'luck' .
Few are those who have the 'luck' to discover this thing.
That is why few are happy and successful in their jobs and professions.
Thank God I had this 'luck' , and discovered what I was born to do well.
And I was born to program. 👨💻
📋 Summary : Project Titan
0️⃣ : 🦄 Project Titan
1️⃣ : ⚖️ Quantitative THEMIS
2️⃣ : 🏛️ Titan Community
3️⃣ : 👨💻 Who am I ❔
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
8️⃣ : ❓ What is Backtest ❓
9️⃣ : ❓ How to build a Consistent system ❓
🔟 : ❓ What is a Quantitative Trading system ❓
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
1️⃣4️⃣ : 🔧 Fixed Technical
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
1️⃣6️⃣ : ⚠️ Risk Profile
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
1️⃣8️⃣ : 💸 Initial Capital
1️⃣9️⃣ : ⚙️ Entry Options
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
3️⃣0️⃣ : 🛠️ Roadmap
3️⃣1️⃣ : 🧻 Notes ❕
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
3️⃣3️⃣ : ♻️ ® No Repaint
3️⃣4️⃣ : 🔒 Copyright ©️
3️⃣5️⃣ : 👏 Acknowledgments
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
0️⃣ : 🦄 Project Titan
This is the first real, 100% automated Quantitative Strategy made available to the public and the pinescript community for TradingView.
You will be able to automate all signals of this strategy for your broker , centralized or decentralized and also for messaging services : Discord, Telegram or Twitter .
This is the first strategy of a larger project, in 2023, I will provide a total of 6 100% automated 'Quantitative' strategies to the pinescript community for TradingView.
The future strategies to be shared here will also be unique , never before seen, real 'Quantitative' bots with real, validated results in real operation.
Just like the 'Quantitative THEMIS' strategy, it will be something out of the loop throughout the pinescript/tradingview community, truly unique tools for building mutual wealth consistently and continuously for our community.
1️⃣ : ⚖️ Quantitative THEMIS : Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
This is a truly unique and out of the curve strategy for BTC /USD .
A truly real strategy, with real, validated results and in real operation.
A unique tool for building mutual wealth, consistently and continuously for the members of the Titan community.
Initially we will operate on a monthly, quarterly, annual or biennial subscription service.
Our goal here is to build a great community, in exchange for an extremely fair value for the use of our truly unique tools, which bring and will bring real results to our community members.
With this business model it will be possible to provide all Titan users and community members with the purest and highest degree of sophistication in the market with pinescript for tradingview, providing unique and truly profitable strategies.
My goal here is to offer the best to our members!
The best 'pinescript' tradingview service in the world!
We are the only Start-Up in the world that will decentralize real and full access to truly real 'quantitative' tools that bring and will bring real results for mutual and ongoing wealth building for our community.
2️⃣ : 🏛️ Titan Community : 👽 Pro 🔁 Aff 🛸
Become a Titan Pro 👽
To get access to the strategy: "Quantitative THEMIS" , and future Titan strategies in a 100% automated way, along with all tutorials for automation.
Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months.
👽 Pro 🅼 Monthly
👽 Pro 🆀 Quarterly
👽 Pro🅰 Annual
👽 Pro👾Two Years
You will have access to a truly unique system that is out of the curve .
A 100% real, 100% automated, tested, validated, profitable, and in real operation strategy.
Become a Titan Affiliate 🛸
By becoming a Titan Affiliate 🛸, you will automatically receive 50% of the value of each new subscription you refer .
You will receive 50% for any of the above plans that you refer .
This way we will encourage our community to grow in a fair and healthy way, because we know what we have in our hands and what we deliver real value to our users.
We are at the highest level of sophistication in the market, the consistency here and the results here speak for themselves.
So growing our community means growing mutual wealth and raising collective conscience.
Wealth must be created not divided.
And here we are creating mutual wealth on all ends and in all ways.
A non-zero sum system, where everybody wins.
3️⃣ : 👨💻 Who am I ❔
My name is FilipeSoh I am 26 years old, Technical Analyst, Trader, Computer Engineer, pinescript Specialist, with extensive experience in several languages and technologies.
For the last 4 years I have been focusing on developing, editing and creating pinescript indicators and strategies for Tradingview for people and myself.
Full-time passionate workaholic pinescript developer with over 10,000 hours of pinescript development.
• Pinescript expert ▬Tradingview.
• Specialist in Automated Trading
• Specialist in Quantitative Trading.
• Statistical/Probabilistic Trading Specialist - Mark Douglas Scholl.
• Inventor of the 'Classic Forecast' Indicators.
• Inventor of the 'Backtest Table'.
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
Statistical/probabilistic trading is the only way to get a positive mathematical expectation regarding the market and consequently that is the only way to make money consistently from it.
I will present below some more details about the Quantitative THEMIS strategy, it is a real strategy, tested, validated and in real operation, 'Skin in the Game' , a consistent way to make money with statistical/probabilistic trading in a 100% automated.
I am a Technical Analyst , I used to be a Discretionary Trader , today I am 100% a Statistical Trader .
I've gotten rich and made a lot of money, and I've also lost a lot with 'leverage'.
That was a few years ago.
The book that changed everything for me was "Trading in The Zone" by Mark Douglas.
That's when I understood that the market is just a game of statistics and probability, like a casino!
It was then that I understood that the human brain is not prepared for trading, because it involves triggers and mental emotions.
And emotions in trading and in making trading decisions do not go well together, not in the long run, because you always have the burden of being wrong with the outcome of that particular position.
But remembering that the market is just a statistical game!
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
Let's use a 'coin' as an example:
If we toss a 'coin' up 10 times.
Do you agree that it is impossible for us to know exactly the result of the 'plays' before they actually happen?
As in the example above, would you agree, that we cannot "guess" the outcome of a position before it actually happens?
As much as we cannot "guess" whether the coin will drop heads or tails on each flip.
We can analyze the "backtest" of the 10 moves made with that coin:
If we analyze the 10 moves and count the number of times the coin fell heads or tails in a specific sequence, we then have a percentage of times the coin fell heads or tails, so we have a 'backtest' of those moves.
Then on the next flip we can now assume a point or a favorable position for one side, the side with the highest probability .
In a nutshell, this is more or less how probabilistic statistical trading works.
As Statistical Traders we can never say whether such a Trader/Position we take will be a winner or a loser.
But still we can have a positive and consistent result in a "sequence" of trades, because before we even open a position, backtests have already been performed so we identify an anomaly and build a system that will have a positive statistical advantage in our favor over the market.
The advantage will not be in one trade itself, but in the "sequence" of trades as a whole!
Because our system will work like a casino, having a positive mathematical expectation relative to the players/market.
Design, develop, test models and systems that can take advantage of market anomalies, until they change.
Be the casino! - Mark Douglas
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
In recent years I have focused and specialized in developing 100% automated trading systems, essentially for the cryptocurrency market.
I have developed many extremely robust and efficient systems, with positive mathematical expectation towards the market.
These are not complex systems per se , because here we want to avoid 'over-optimization' as much as possible.
As Da Vinci said: "Simplicity is the highest degree of sophistication".
I say this because I have tested, tried and developed hundreds of systems/strategies.
I believe I have programmed more than 10,000 unique indicators/strategies, because this is my passion and purpose in life.
I am passionate about what I do, completely!
I love statistical trading because it is the only way to get consistency in the long run!
This is why I have studied, applied, developed, and specialized in 100% automated cryptocurrency trading systems.
The reason why our systems are extremely "simple" is because, as I mentioned before, in statistical trading we want to exploit the market anomaly to the maximum, that is, this anomaly will change from time to time, usually we can exploit a trading system efficiently for about 6 to 12 months, or for a few years, that is; for fixed 'scalpers' systems.
Because at some point these anomalies will be identified , and from the moment they are identified they will be exploited and will stop being anomalies .
With the system presented here; you can even copy the indicators and input values shared here;
However; what I have to offer you is: it is me , our team , and our community !
That is, we will constantly monitor this system, for life , because our goal here is to create a unique , perpetual , profitable , and consistent system for our community.
Myself , our team and our community will keep this script periodically updated , to ensure the positive mathematical expectation of it.
So we don't mind sharing the current parameters and values , because the real value is also in the future updates that this system will receive from me and our team , guided by our culture and our community of real users !
As we are hosted on 'tradingview', all future updates for this strategy, will be implemented and updated automatically on your tradingview account.
What we want here is: to make sure you get gains from our system, because if you get gains , our ecosystem will grow as a whole in a healthy and scalable way, so we will be generating continuous mutual wealth and raising the collective consciousness .
People Need People: 3️⃣🅿
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
Today my greatest skill is to develop statistically profitable and 100% automated strategies for 'pinescript' tradingview.
Note that I said: 'profitable' because in fact statistical trading is the only way to make money in a 'consistent' way from the market.
And consequently have a positive wealth curve every cycle, because we will be based on mathematics, not on feelings and news.
Because the human brain is not prepared to do trading.
Because trading is connected to the decision making of the cerebral cortex.
And the decision making is automatically linked to emotions, and emotions don't match with trading decision making, because in those moments, we can feel the best and also the worst sensations and emotions, and this certainly affects us and makes us commit grotesque mistakes!
That's why the human brain is not prepared to do trading.
If you want to participate in a fully automated, profitable and consistent trading system; be a Titan Pro 👽
I believe we are walking an extremely enriching path here, not only in terms of financial returns for our community, but also in terms of knowledge about probabilistic and automated statistical trading.
You will have access to an extremely robust system, which was built upon very strong concepts and foundations, and upon the world's main asset in a few years: Bitcoin .
We are the tip of the best that exists in the cryptocurrency market when it comes to probabilistic and automated statistical trading.
Result is result! Me being dressed or naked.
This is just the beginning!
But there is a way to consistently make money from the market.
Being the Casino! - Mark Douglas
8️⃣ : ❓ What is Backtest ❓
Imagine the market as a purely random system, but even in 'randomness' there are patterns.
So now imagine the market and statistical trading as follows:
Repeating the above 'coin' example, let's think of it as follows:
If we toss a coin up 10 times again.
It is impossible to know which flips will have heads or tails, correct?
But if we analyze these 10 tosses, then we will have a mathematical statistic of the past result, for example, 70 % of the tosses fell 'heads'.
That is:
7 moves fell on "heads" .
3 moves fell on "tails" .
So based on these conditions and on the generic backtest presented here, we could adopt " heads " as our system of moves, to have a statistical and probabilistic advantage in relation to the next move to be performed.
That is, if you define a system, based on backtests , that has a robust positive mathematical expectation in relation to the market you will have a profitable system.
For every move you make you will have a positive statistical advantage in your favor over the market before you even make the move.
Like a casino in relation to all its players!
The casino does not have an advantage over one specific player, but over all players, because it has a positive mathematical expectation about all the moves that night.
The casino will always have a positive statistical advantage over its players.
Note that there will always be real players who will make real, million-dollar bankrolls that night, but this condition is already built into the casino's 'strategy', which has a pre-determined positive statistical advantage of that night as a whole.
Statistical trading is the same thing, as long as you don't understand this you will keep losing money and consistently.
9️⃣ : ❓ How to build a Consistent system ❓
See most traders around the world perform trades believing that that specific position taken will make them filthy rich, because they simply believe faithfully that the position taken will be an undoubted winner, based on a trader's methodology: 'trading a trade' without analyzing the whole context, just using 'empirical' aspects in their system.
But if you think of trading, as a sequence of moves.
You see, 'a sequence' !
When we think statistically, it doesn't matter your result for this , or for the next specific trade , but the final sequence of trades as a whole.
As the market has a random system of results distribution , if your system has a positive statistical advantage in relation to the market, at the end of that sequence you'll have the biggest probability of having a winning bank.
That's how you do real trading!
And with consistency!
Trading is a long term game, but when you change the key you realize that it is a simple game to make money in a consistent way from the market, all you need is patience.
Even more when we are based on Bitcoin, which has its 'Halving' effect where, in theory, we will never lose money in 3 to 4 years intervals, due to its scarcity and the fact that Bitcoin is the 'discovery of digital scarcity' which makes it the digital gold, we believe in this thesis and we follow Satoshi's legacy.
So align Bitcoin with a probabilistic statistical trading system with a positive mathematical expectation of the market and 100% automated with the long term, and all you need is patience, and you will become rich.
In fact Bitcoin by itself is already a path, buy, wait for each halving and your wealth will be maintained.
No inflation, unlike fiat currencies.
This is a complete and extremely robust strategy, with the most current possible and 'not possible' techniques involved and applied here.
Today I am at another level in developing 100% automated 'quantitative' strategies.
I was born for this!
🔟 : ❓ What is a Quantitative Trading system ❓
In addition to having access to a revolutionary strategy you will have access to disruptive 100% multifunctional tables with the ability to perform 'backtests' for better tracking and monitoring of your system on a customized basis.
I would like to emphasize one thing, and that is that you keep this in mind.
Today my greatest skill in 'pinescript' is to build indicators, but mainly strategies, based on statistical and probabilistic trading, with a postive mathematical expectation in relation to the market, in a 100% automated way.
This with the goal of building a consistent and continuous positive equity curve through mathematics using data, converting it into statistical / probabilistic parameters and applying them to a Quantitative model.
Before becoming a Quantitative Trader , I was a Technical Analyst and a Discretionary Trader .
First as a position trader and then as a day trader.
Before becoming a Trader, I trained myself as a Technical Analyst , to masterly understand the shape and workings of the market in theory.
But everything changed when I met 'Mark Douglas' , when I got to know his works, that's when my head exploded 🤯, and I started to understand the market for good!
The market is nothing more than a 'random' system of distributing results.
See that I said: 'random' .
Do yourself a mental exercise.
Is there really such a thing as random ?
I believe not, as far as we know maybe the 'singularity'.
So thinking this way, to translate, the market is nothing more than a game of probability, statistics and pure mathematics.
Like a casino!
What happens is that most traders, whenever they take a position, take it with all the empirical certainty that such position will win or lose, and do not take into consideration the total sequence of results to understand their place in the market.
Understanding your place in the market gives you the ability to create and design systems that can exploit the present market anomaly, and thus make money statistically, consistently, and 100% automated.
Thinking of it this way, it is easy to make money from the market.
There are many ways to make money from the market, but the only consistent way I know of is through 'probabilistic and automated statistical trading'.
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
There are some fundamental points that must be addressed here in order to understand what makes up a system based on statistics and probability applied to a quantitative model.
When we talk about 'discretionary' trading, it is a trading system based on human decisions after the defined 'empirical' conditions are met.
It is quite another thing to build a fully automated system without any human interference/interaction .
That said:
Building a statistically profitable system is perfectly possible, but this is a high level task , but with possible high rewards and consistent gains.
Here you will find a real "Skin In The Game" strategy.
With all due respect, but the vast majority of traders who post strategies on TradingView do not understand what they are doing.
Most of them do not understand the minimum complexity involved in the main variable for the construction of a real strategy, the mother variable: "strategy".
I say this by my own experience, because I have analyzed practically all the existing publications of TradingView + 200,000 indicators and strategies.
I breathe pinescript, I eat pinescript, I sleep pinescript, I bathe pinescript, I live TradingView.
But the main advantage for the TradingView users, is that all entry and exit orders made by this strategy can be checked and analyzed thoroughly, to validate and prove the veracity of this strategy, because this is a 100% real strategy.
Here there is a huge world of possibilities, but only one way to build a 'pinescript strategy' that will work correctly aligned to the real world with real results .
There are some fundamental points to take into consideration when building a profitable trading system:
The most important of these for me is: 'DrawDown' .
Followed by: 'Hit Rate' .
And only after that we use the parameter: 'Profit'.
See, this is because here, we are dealing with the 'imponderable' , and anything can happen in this scenario.
But there is one thing that makes us sleep peacefully at night, and that is: controlling losses .
That is, in other words: controlling the DrawDown .
The amateur is concerned with 'winning', the professional is concerned with conserving capital.
If we have the losses under control, then we can move on to the other two parameters: hit rate and profit.
See, the second most important factor in building a system is the hit rate.
I say this from my own experience.
I have worked with many systems with a 'low hit rate', but extremely profitable.
For example: systems with hit rates of 40 to 50%.
But as much as statistically and mathematically the profit is rewarding, operating systems with a low hit rate is always very stressful psychologically.
That's why there are two big reasons why when I build an automated trading system, I focus on the high hit rate of the system, they are
1 - To reduce psychological damage as much as possible .
2 - And more important , when we create a system with a 'high hit rate' , there is a huge intrinsic advantage here, that most statistic traders don't take in consideration.
That is: knowing more quickly when the system stops being functional.
The main advantage of a system with a high hit rate is: to identify when the system stops being functional and stop exploiting the market's anomaly.
Look: When we are talking about trading and random distribution of results on the market, do you agree that when we create a trading system, we are focused on exploring some anomaly of that market?
When that anomaly is verified by the market, it will stop being functional with time.
That's why trading systems, 'scalpers', especially for cryptocurrencies, need constant monitoring, quarterly, semi-annually or annually.
Because market movements change from time to time.
Because we go through different cycles from time to time, such as congestion cycles, accumulation , distribution , volatility , uptrends and downtrends .
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
You see there is a very important point that must be stressed here.
As we are always trying to exploit an 'anomaly' in the market.
So the 'number' of indicators/tools that will integrate the system is of paramount importance.
But most traders do not take this into consideration.
To build a professional, robust, consistent, and profitable system, you don't need to use hundreds of indicators to build your setup.
This will actually make it harder to read when the setup stops working and needs some adjustment.
So focusing on a high hit rate is very important here, this is a fundamental principle that is widely ignored , and with a high hit rate, we can know much more accurately when the system is no longer functional much faster.
As Darwin said: "It is not the strongest or the most intelligent that wins the game of life, it is the most adapted.
So simple systems, as contradictory as it may seem, are more efficient, because they help to identify inflection points in the market much more quickly.
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
See I have built, hundreds of thousands of indicators and 'pinescript' strategies, hundreds of thousands.
This is an extremely professional, robust and profitable system.
Based on the currency pairs: BTC /USDT
There are many ways and avenues to build a profitable trading setup/system.
And actually this is not a difficult task, taking in consideration, as the main factor here, that our trading and investment plan is for the long term, so consequently we will face scenarios with less noise.
He who is in a hurry eats raw.
As mentioned before.
Defining trends in pinescript is technically a simple task, the hardest task is to determine congestion zones with low volume and volatility, it's in these moments that many false signals are generated, and consequently is where most setups face their maximum DrawDown.
That's why this strategy was strictly and thoroughly planned, built on a very solid foundation, to avoid as much noise as possible, for a positive and consistent equity curve in each market cycle, 'Consistency' is our 'Mantra' around here.
1️⃣4️⃣ : 🔧 Fixed Technical
• Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
• Pair: BTC/USDTP
• Time Frame: 4 hours
• Broker: Binance (Recommended)
For a more conservative scenario, we have built the Quantitative THEMIS for the 4h time frame, with the main focus on consistency.
So we can avoid noise as much as possible!
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
In order to build a 'perpetual' system specific to BTC/USDT, it took a lot of testing, and more testing, and a lot of investment and research.
There is one initial and fundamental point that we can address to justify the incredible consistency presented here.
That fundamental point is our exit via Take Profit or Stop Loss percentage (%).
🎯 Take Profit (%)
🛑 Stop Loss (%)
See, today I have been testing some more advanced backtesting models for some cryptocurrency systems.
In which I perform 'backtest of backtest', i.e. we use a set of strategies each focused on a principle, operating individually, but they are part of something unique, i.e. we do 'backtests' of 'backtests' together.
What I mean is that we do a lot of backtesting around here.
I can assure you, that always the best output for a trading system is to set fixed output values!
In other words:
🎯 Take Profit (%)
🛑 Stop Loss (%)
This happens because statistically setting fixed exit structures in the vast majority of times, presents a superior result on the capital/equity curve, throughout history and for the vast majority of setups compared to other exit methods.
This is due to a mathematical principle of simplicity, 'avoiding more noise'.
Thus whenever the Quantitative THEMIS strategy takes a position it has a target and a defined maximum stop percentage.
1️⃣6️⃣ : ⚠️ Risk Profile
The strategy, currently has 3 risk profiles ⚠️ patterns for 'fixed percentage exits': Take Profit (%) and Stop Loss (%) .
They are: ⚠️ Rich's Profiles
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
You will be able to select and switch between the above options and profiles through the 'input' menu of the strategy by navigating to the "⚠️ Risk Profile" menu.
You can then select, test and apply the Risk Profile above that best suits your risk management, expectations and reality , as well as customize all the 'fixed exit' values through the TP and SL menus below.
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
The strategy currently also has 'Moving Exits' based on indicator signals.
These are Moving Exits (Indicators)
📈 LONG : (EXIT)
🧃 (MAO) Short : true
📉 SHORT : (EXIT)
🧃 (MAO) Long: false
You can select and toggle between the above options through the 'input' menu of the strategy by navigating to the "LONG : Exit" and "SHORT : Exit" menu.
1️⃣8️⃣ : 💸 Initial Capital
By default the "Initial Capital" set for entries and backtests of this strategy is: 10000 $
You can set another value for the 'Starting Capital' through the tradingview menu under "properties" , and edit the value of the "Initial Capital" field.
This way you can set and test other 'Entry Values' for your trades, tests and backtests.
1️⃣9️⃣ : ⚙️ Entry Options
By default the 'order size' set for this strategy is 100 % of the 'initial capital' on each new trade.
You can set and test other entry options like : contracts , cash , % of equity
You should make these changes directly in the input menu of the strategy by navigating to the menu "⚙️ Properties : TradingView" below.
⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100
Leverage: 1
So you can define and test other 'Entry Options' for your trades, tests and backtests.
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
It is possible to automate the signals of this strategy for any centralized or decentralized broker, as well as for messaging services: Discord, Telegram and Twitter.
All in an extremely simple and uncomplicated way through the tutorials available in PDF /VIDEO for our Titan Pro 👽 subscriber community.
With our tutorials in PDF and Video it will be possible to automate the signals of this strategy for the chosen service in an extremely simple way with less than 10 steps only.
Tradingview naturally doesn't count with native integration between brokers and tradingview.
But it is possible to use 'third party services' to do the integration and automation between Tradingview and your centralized or decentralized broker.
Here are the standard, available and recommended 'third party services' to automate the signals from the 'Quantitative THEMIS' strategy on the tradingview for your broker:
1) Wundertrading (Recommended):
2) 3commas:
3) Zignaly:
4) Aleeert.com (Recommended):
5) Alertatron:
Note! 'Third party services' cannot perform 'withdrawals' via their key 'API', they can only open positions, so your funds will always be 'safe' in your brokerage firm, being traded via the 'API', when they receive an entry and exit signal from this strategy.
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
You can automate this strategy for any of the brokers below, through your broker's 'API' by connecting it to the 'third party automation services' for tradingview available and mentioned in the menu above:
1) Binance (Recommended)
2) Bitmex
3) Bybit
4) KuCoin
5) Deribit
6) OKX
7) Coinbase
8) Huobi
9) Bitfinex
10) Bitget
11) Bittrex
12) Bitstamp
13) Gate. io
14) Kraken
15) Gemini
16) Ascendex
17) VCCE
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
You can also automate and monitor the signals of this strategy much more efficiently by sending them to the following popular messaging services:
1) Discord
2) Telegram
3) Twitter
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
It will also be possible to copy/replicate the entries and exits of this strategy to your broker in an extremely simple and agile way, through the available copy-trader services.
This way it will be possible to replicate the signals of this strategy at each entry and exit to your broker through the API connecting it to the integrated copy-trader services available through the tradingview automation services below:
1) Wundetrading:
2) Zignaly:
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
I believe that today I am at another level in 'pinescript' development.
I consider myself today a true unicorn as a pinescript developer, someone unique and very rare.
If you choose another tool or another pinescript service, this tool will be just another one, with no real results.
But if you join our Titan community, you will have access to a unique tool! And you will get real results!
I already earn money consistently with statistical and automated trading and as an expert pinescript developer.
I am here to evolve my skills as much as possible, and one day become a pinescript 'Wizard'.
So excellence, quality and professionalism will always be my north here.
You will never find a developer like me, and who will take so seriously such a revolutionary project as this one. A Maverick! ▬ The man never stops!
Here you will find the highest degree of sophistication and development in the market for 'pinescript'.
You will get the best of me and the best of pinescript possible.
Let me show you how a professional in my field does it.
Become a Titan Pro Member 👽 and get Full Access to this strategy and all the Automation Tutorials.
Be the Titan in your life!
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
Get financial return for your referrals, Decentralize the World, and raise the collective consciousness.
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
® Titan Investimentos | Quantitative THEMIS ⚖️ | Pro 👽 2.6 | Dev: © FilipeSoh 🧙 | 🤖 100% Automated : Discord, Telegram, Twitter, Wundertrading, 3commas, Zignaly, Aleeert, Alertatron, Uniswap-v3 | BINANCE:BTCUSDTPERP 4h
🛒 Subscribe this strategy ❗️ Be a Titan Member 🏛️
🛒 Titan Pro 👽 🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🛒 Titan Affiliate 🛸 🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
📋 Summary : QT THEMIS ⚖️
🕵️♂️ Check This Strategy..................................................................0
🦄 ® Titan Investimentos...............................................................1
👨💻 © Developer..........................................................................2
📚 Signal Automation Tutorials : (PDF/VIDEO).......................................3
👨🔧 Revision...............................................................................4
📊 Table : (BACKTEST)..................................................................5
📊 Table : (INFORMATIONS).............................................................6
⚙️ Properties : (TRADINGVIEW)........................................................7
📆 Backtest : (TRADINGVIEW)..........................................................8
⚠️ Risk Profile...........................................................................9
🟢 On 🔴 Off : (LONG/SHORT).......................................................10
📈 LONG : (ENTRY)....................................................................11
📉 SHORT : (ENTRY)...................................................................12
📈 LONG : (EXIT).......................................................................13
📉 SHORT : (EXIT)......................................................................14
🧩 (EI) External Indicator.............................................................15
📡 (QT) Quantitative...................................................................16
🎠 (FF) Forecast......................................................................17
🅱 (BB) Bollinger Bands................................................................18
🧃 (MAP) Moving Average Primary......................................................19
🧃 (MAP) Labels.........................................................................20
🍔 (MAQ) Moving Average Quaternary.................................................21
🍟 (MACD) Moving Average Convergence Divergence...............................22
📣 (VWAP) Volume Weighted Average Price........................................23
🪀 (HL) HILO..........................................................................24
🅾 (OBV) On Balance Volume.........................................................25
🥊 (SAR) Stop and Reverse...........................................................26
🛡️ (DSR) Dynamic Support and Resistance..........................................27
🔊 (VD) Volume Directional..........................................................28
🧰 (RSI) Relative Momentum Index.................................................29
🎯 (TP) Take Profit %..................................................................30
🛑 (SL) Stop Loss %....................................................................31
🤖 Automation Selected...............................................................32
📱💻 Discord............................................................................33
📱💻 Telegram..........................................................................34
📱💻 Twitter...........................................................................35
🤖 Wundertrading......................................................................36
🤖 3commas............................................................................37
🤖 Zignaly...............................................................................38
🤖 Aleeert...............................................................................39
🤖 Alertatron...........................................................................40
🤖 Uniswap-v3..........................................................................41
🧲🤖 Copy-Trading....................................................................42
♻️ ® No Repaint........................................................................43
🔒 Copyright ©️..........................................................................44
🏛️ Be a Titan Member..................................................................45
Nº Active Users..........................................................................46
⏱ Time Left............................................................................47
| 0 | 🕵️♂️ Check This Strategy
🕵️♂️ Version Demo: 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
🕵️♂️ Version Pro: 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
| 1 | 🦄 ® Titan Investimentos
Decentralizing the World 🗺
Raising the Collective Conscience 🗺
🦄Site:
🦄TradingView: www.tradingview.com
🦄Discord:
🦄Telegram:
🦄Youtube:
🦄Twitter:
🦄Instagram:
🦄TikTok:
🦄Linkedin:
🦄E-mail:
| 2 | 👨💻 © Developer
🧠 Developer: @FilipeSoh🧙
📺 TradingView: www.tradingview.com
☑️ Linkedin:
✅ Fiverr:
✅ Upwork:
🎥 YouTube:
🐤 Twitter:
🤳 Instagram:
| 3 | 📚 Signal Automation Tutorials : (PDF/VIDEO)
📚 Discord: 🔗 Link: 🔒Titan Pro👽
📚 Telegram: 🔗 Link: 🔒Titan Pro👽
📚 Twitter: 🔗 Link: 🔒Titan Pro👽
📚 Wundertrading: 🔗 Link: 🔒Titan Pro👽
📚 3comnas: 🔗 Link: 🔒Titan Pro👽
📚 Zignaly: 🔗 Link: 🔒Titan Pro👽
📚 Aleeert: 🔗 Link: 🔒Titan Pro👽
📚 Alertatron: 🔗 Link: 🔒Titan Pro👽
📚 Uniswap-v3: 🔗 Link: 🔒Titan Pro👽
📚 Copy-Trading: 🔗 Link: 🔒Titan Pro👽
| 4 | 👨🔧 Revision
👨🔧 Start Of Operations: 01 Jan 2019 21:00 -0300 💡 Start Of Operations (Skin in the game) : Revision 1.0
👨🔧 Previous Review: 01 Jan 2022 21:00 -0300 💡 Previous Review : Revision 2.0
👨🔧 Current Revision: 01 Jan 2023 21:00 -0300 💡 Current Revision : Revision 2.6
👨🔧 Next Revision: 28 May 2023 21:00 -0300 💡 Next Revision : Revision 2.7
| 5 | 📊 Table : (BACKTEST)
📊 Table: true
🖌️ Style: label.style_label_left
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 6 | 📊 Table : (INFORMATIONS)
📊 Table: false
🖌️ Style: label.style_label_right
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 7 | ⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100 %
🚀 Leverage: 1
| 8 | 📆 Backtest : (TradingView)
🗓️ Mon: true
🗓️ Tue: true
🗓️ Wed: true
🗓️ Thu: true
🗓️ Fri: true
🗓️ Sat: true
🗓️ Sun: true
📆 Range: custom
📆 Start: UTC 31 Oct 2008 00:00
📆 End: UTC 31 Oct 2030 23:45
📆 Session: 0000-0000
📆 UTC: UTC
| 9 | ⚠️ Risk Profile
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
| 10 | 🟢 On 🔴 Off : (LONG/SHORT)
🟢📈 LONG: true
🟢📉 SHORT: true
| 11 | 📈 LONG : (ENTRY)
📡 (QT) Long: true
🧃 (MAP) Long: false
🅱 (BB) Long: false
🍟 (MACD) Long: false
🅾 (OBV) Long: false
| 12 | 📉 SHORT : (ENTRY)
📡 (QT) Short: true
🧃 (MAP) Short: false
🅱 (BB) Short: false
🍟 (MACD) Short: false
🅾 (OBV) Short: false
| 13 | 📈 LONG : (EXIT)
🧃 (MAP) Short: true
| 14 | 📉 SHORT : (EXIT)
🧃 (MAP) Long: false
| 15 | 🧩 (EI) External Indicator
🧩 (EI) Connect your external indicator/filter: false
🧩 (EI) Connect your indicator here (Study mode only): close
🧩 (EI) Connect your indicator here (Study mode only): close
| 16 | 📡 (QT) Quantitative
📡 (QT) Quantitative: true
📡 (QT) Market: BINANCE:BTCUSDTPERP
📡 (QT) Dice: openai
| 17 | 🎠 (FF) Forecast
🎠 (FF) Include current unclosed current candle: true
🎠 (FF) Forecast Type: flat
🎠 (FF) Nº of candles to use in linear regression: 3
| 18 | 🅱 (BB) Bollinger Bands
🅱 (BB) Bollinger Bands: true
🅱 (BB) Type: EMA
🅱 (BB) Period: 20
🅱 (BB) Source: close
🅱 (BB) Multiplier: 2
🅱 (BB) Linewidth: 0
🅱 (BB) Color: #131722
| 19 | 🧃 (MAP) Moving Average Primary
🧃 (MAP) Moving Average Primary: true
🧃 (MAP) BarColor: false
🧃 (MAP) Background: false
🧃 (MAP) Type: SMA
🧃 (MAP) Source: open
🧃 (MAP) Period: 100
🧃 (MAP) Multiplier: 2.0
🧃 (MAP) Linewidth: 2
🧃 (MAP) Color P: #42bda8
🧃 (MAP) Color N: #801922
| 20 | 🧃 (MAP) Labels
🧃 (MAP) Labels: true
🧃 (MAP) Style BUY ZONE: shape.labelup
🧃 (MAP) Color BUY ZONE: #42bda8
🧃 (MAP) Style SELL ZONE: shape.labeldown
🧃 (MAP) Color SELL ZONE: #801922
| 21 | 🍔 (MAQ) Moving Average Quaternary
🍔 (MAQ) Moving Average Quaternary: true
🍔 (MAQ) BarColor: false
🍔 (MAQ) Background: false
🍔 (MAQ) Type: SMA
🍔 (MAQ) Source: close
🍔 (MAQ) Primary: 14
🍔 (MAQ) Secondary: 22
🍔 (MAQ) Tertiary: 44
🍔 (MAQ) Quaternary: 16
🍔 (MAQ) Linewidth: 0
🍔 (MAQ) Color P: #42bda8
🍔 (MAQ) Color N: #801922
| 22 | 🍟 (MACD) Moving Average Convergence Divergence
🍟 (MACD) Macd Type: EMA
🍟 (MACD) Signal Type: EMA
🍟 (MACD) Source: close
🍟 (MACD) Fast: 12
🍟 (MACD) Slow: 26
🍟 (MACD) Smoothing: 9
| 23 | 📣 (VWAP) Volume Weighted Average Price
📣 (VWAP) Source: close
📣 (VWAP) Period: 340
📣 (VWAP) Momentum A: 84
📣 (VWAP) Momentum B: 150
📣 (VWAP) Average Volume: 1
📣 (VWAP) Multiplier: 1
📣 (VWAP) Diviser: 2
| 24 | 🪀 (HL) HILO
🪀 (HL) Type: SMA
🪀 (HL) Function: Maverick🧙
🪀 (HL) Source H: high
🪀 (HL) Source L: low
🪀 (HL) Period: 20
🪀 (HL) Momentum: 26
🪀 (HL) Diviser: 2
🪀 (HL) Multiplier: 1
| 25 | 🅾 (OBV) On Balance Volume
🅾 (OBV) Type: EMA
🅾 (OBV) Source: close
🅾 (OBV) Period: 16
🅾 (OBV) Diviser: 2
🅾 (OBV) Multiplier: 1
| 26 | 🥊 (SAR) Stop and Reverse
🥊 (SAR) Source: close
🥊 (SAR) High: 1.8
🥊 (SAR) Mid: 1.6
🥊 (SAR) Low: 1.6
🥊 (SAR) Diviser: 2
🥊 (SAR) Multiplier: 1
| 27 | 🛡️ (DSR) Dynamic Support and Resistance
🛡️ (DSR) Source D: close
🛡️ (DSR) Source R: high
🛡️ (DSR) Source S: low
🛡️ (DSR) Momentum R: 0
🛡️ (DSR) Momentum S: 2
🛡️ (DSR) Diviser: 2
🛡️ (DSR) Multiplier: 1
| 28 | 🔊 (VD) Volume Directional
🔊 (VD) Type: SMA
🔊 (VD) Period: 68
🔊 (VD) Momentum: 3.8
🔊 (VD) Diviser: 2
🔊 (VD) Multiplier: 1
| 29 | 🧰 (RSI) Relative Momentum Index
🧰 (RSI) Type UP: EMA
🧰 (RSI) Type DOWN: EMA
🧰 (RSI) Source: close
🧰 (RSI) Period: 29
🧰 (RSI) Smoothing: 22
🧰 (RSI) Momentum R: 64
🧰 (RSI) Momentum S: 142
🧰 (RSI) Diviser: 2
🧰 (RSI) Multiplier: 1
| 30 | 🎯 (TP) Take Profit %
🎯 (TP) Take Profit: false
🎯 (TP) %: 2.2
🎯 (TP) Color: #42bda8
🎯 (TP) Linewidth: 1
| 31 | 🛑 (SL) Stop Loss %
🛑 (SL) Stop Loss: false
🛑 (SL) %: 2.7
🛑 (SL) Color: #801922
🛑 (SL) Linewidth: 1
| 32 | 🤖 Automation : Discord | Telegram | Twitter | Wundertrading | 3commas | Zignaly | Aleeert | Alertatron | Uniswap-v3
🤖 Automation Selected : Discord
| 33 | 🤖 Discord
🔗 Link Discord: discord.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Long: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Long: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Short: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Short: 🔒Titan Pro👽
| 34 | 🤖 Telegram
🔗 Link Telegram: telegram.org
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Short: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Short: 🔒Titan Pro👽
| 35 | 🤖 Twitter
🔗 Link Twitter: twitter.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Short: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Short: 🔒Titan Pro👽
| 36 | 🤖 Wundertrading : Binance | Bitmex | Bybit | KuCoin | Deribit | OKX | Coinbase | Huobi | Bitfinex | Bitget
🔗 Link Wundertrading: wundertrading.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Short: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Short: 🔒Titan Pro👽
| 37 | 🤖 3commas : Binance | Bybit | OKX | Bitfinex | Coinbase | Deribit | Bitmex | Bittrex | Bitstamp | Gate.io | Kraken | Gemini | Huobi | KuCoin
🔗 Link 3commas: 3commas.io
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Long: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Long: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Short: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Short: 🔒Titan Pro👽
| 38 | 🤖 Zignaly : Binance | Ascendex | Bitmex | Kucoin | VCCE
🔗 Link Zignaly: zignaly.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
🤖 Type Automation: Profit Sharing
🤖 Type Provider: Webook
🔑 Key: 🔒Titan Pro👽
🤖 pair: BTCUSDTP
🤖 exchange: binance
🤖 exchangeAccountType: futures
🤖 orderType: market
🚀 leverage: 1x
% positionSizePercentage: 100 %
💸 positionSizeQuote: 10000 $
🆔 signalId: @Signal1234
| 39 | 🤖 Aleeert : Binance
🔗 Link Aleeert: aleeert.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Short: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Short: 🔒Titan Pro👽
| 40 | 🤖 Alertatron : Binance | Bybit | Deribit | Bitmex
🔗 Link Alertatron: alertatron.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Short: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Short: 🔒Titan Pro👽
| 41 | 🤖 Uniswap-v3
🔗 Link Alertatron: uniswap.org
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Short: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Short: 🔒Titan Pro👽
| 42 | 🧲🤖 Copy-Trading : Zignaly | Wundertrading
🔗 Link 📚 Copy-Trading: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Zignaly: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Wundertrading: 🔒Titan Pro👽
| 43 | ♻️ ® Don't Repaint!
♻️ This Strategy does not Repaint!: ® Signs Do not repaint❕
♻️ This is a Real Strategy!: Quality : ® Titan Investimentos
📋️️ Get more information about Repainting here:
| 44 | 🔒 Copyright ©️
🔒 Copyright ©️: Copyright © 2023-2024 All rights reserved, ® Titan Investimentos
🔒 Copyright ©️: ® Titan Investimentos
🔒 Copyright ©️: Unique and Exclusive Strategy. All rights reserved
| 45 | 🏛️ Be a Titan Members
🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
| 46 | ⏱ Time Left
Time Left Titan Demo 🐄: ⏱♾ | ⏱ : ♾ Titan Demo 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
Time Left Titan Pro 👽: 🔒Titan Pro👽 | ⏱ : Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months. (👽 Pro 🅼 Monthly, 👽 Pro 🆀 Quarterly, 👽 Pro🅰 Annual, 👽 Pro👾Two Years)
| 47 | Nº Active Users
Nº Active Subscribers Titan Pro 👽: 5️⃣6️⃣ | 1✔️ 5✔️ 10✔️ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
Nº Active Affiliates Titan Aff 🛸: 6️⃣ | 1✔️ 5✔️ 10❌ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🆑 Conservative: 🎯 TP=2.7 % | 🛑 SL=2.7 %
• 📆All years: 🆑 Conservative: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1669.89 %
💲 + 166989.43 USD
Total Close Trades:
⚪️ 369
Percent Profitable:
🟡 64.77 %
Profit Factor:
🟢 2.314
DrawDrown Maximum:
🔴 -24.82 %
💲 -10221.43 USD
Avg Trade:
💲 + 452.55 USD
✔️ Trades Winning: 239
❌ Trades Losing: 130
✔️ Average Gross Win: + 12.31 %
❌ Average Gross Loss: - 9.78 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 499.33 %
% Average Gain Monthly: 41.61 %
% Average Gain Weekly: 9.6 %
% Average Gain Day: 1.37 %
💲 Average Gain Annual: 49933 $
💲 Average Gain Monthly: 4161 $
💲 Average Gain Weekly: 960 $
💲 Average Gain Day: 137 $
• 📆 Year: 2020: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🆑 Conservative: 🚀 Leverage 1️⃣x
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: Ⓜ️ Moderate: 🎯 TP=2.8 % | 🛑 SL=2.7 %
• 📆 All years: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1472.04 %
💲 + 147199.89 USD
Total Close Trades:
⚪️ 362
Percent Profitable:
🟡 63.26 %
Profit Factor:
🟢 2.192
DrawDrown Maximum:
🔴 -22.69 %
💲 -9269.33 USD
Avg Trade:
💲 + 406.63 USD
✔️ Trades Winning: 229
❌ Trades Losing : 133
✔️ Average Gross Win: + 11.82 %
❌ Average Gross Loss: - 9.29 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 8
% Average Gain Annual: 440.15 %
% Average Gain Monthly: 36.68 %
% Average Gain Weekly: 8.46 %
% Average Gain Day: 1.21 %
💲 Average Gain Annual: 44015 $
💲 Average Gain Monthly: 3668 $
💲 Average Gain Weekly: 846 $
💲 Average Gain Day: 121 $
• 📆 Year: 2020: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🅰 Aggressive: 🎯 TP=1.6 % | 🛑 SL=6.9 %
• 📆 All years: 🅰 Aggressive: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 989.38 %
💲 + 98938.38 USD
Total Close Trades:
⚪️ 380
Percent Profitable:
🟢 84.47 %
Profit Factor:
🟢 2.156
DrawDrown Maximum:
🔴 -17.88 %
💲 -9182.84 USD
Avg Trade:
💲 + 260.36 USD
✔️ Trades Winning: 321
❌ Trades Losing: 59
✔️ Average Gross Win: + 5.75 %
❌ Average Gross Loss: - 14.51 %
✔️ Maximum Consecutive Wins: 21
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 295.84 %
% Average Gain Monthly: 24.65 %
% Average Gain Weekly: 5.69 %
% Average Gain Day: 0.81 %
💲 Average Gain Annual: 29584 $
💲 Average Gain Monthly: 2465 $
💲 Average Gain Weekly: 569 $
💲 Average Gain Day: 81 $
• 📆 Year: 2020: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🅰 Aggressive: 🚀 Leverage 1️⃣x
3️⃣0️⃣ : 🛠️ Roadmap
🛠️• 14/ 01 /2023 : Titan THEMIS Launch
🛠️• Updates January/2023 :
• 📚 Tutorials for Automation 🤖 already Available : ✔️
• ✔️ Discord
• ✔️ Wundertrading
• ✔️ Zignaly
• 📚 Tutorials for Automation 🤖 In Preparation : ⭕
• ⭕ Telegram
• ⭕ Twitter
• ⭕ 3comnas
• ⭕ Aleeert
• ⭕ Alertatron
• ⭕ Uniswap-v3
• ⭕ Copy-Trading
🛠️• Updates February/2023 :
• 📰 Launch of advertising material for Titan Affiliates 🛸
• 🛍️🎥🖼️📊 (Sales Page/VSL/Videos/Creative/Infographics)
🛠️• 28/05/2023 : Titan THEMIS update ▬ Version 2.7
🛠️• 28/05/2023 : BOT BOB release ▬ Version 1.0
• (Native Titan THEMIS Automation - Through BOT BOB, a bot for automation of signals, indicators and strategies of TradingView, of own code ▬ in validation.
• BOT BOB
Automation/Connection :
• API - For Centralized Brokers.
• Smart Contracts - Wallet Web - For Decentralized Brokers.
• This way users can automate any indicator or strategy of TradingView and Titan in a decentralized, secure and simplified way.
• Without having the need to use 'third party services' for automating TradingView indicators and strategies like the ones available above.
🛠️• 28/05/2023 : Release ▬ Titan Culture Guide 📝
3️⃣1️⃣ : 🧻 Notes ❕
🧻 • Note ❕ The "Demo 🐄" version, ❌does not have 'integrated automation', to automate the signals of this strategy and enjoy a fully automated system, you need to have access to the Pro version with '100% integrated automation' and all the tutorials for automation available. Become a Titan Pro 👽
🧻 • Note ❕ You will also need to be a "Pro User or higher on Tradingview", to be able to use the webhook feature available only for 'paid' profiles on the platform.
With the webhook feature it is possible to send the signals of this strategy to almost anywhere, in our case to centralized or decentralized brokerages, also to popular messaging services such as: Discord, Telegram or Twiter.
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
🚨 • Disclaimer ❕❕ Past positive result and performance of a system does not guarantee its positive result and performance for the future!
🚨 • Disclaimer ❗❗❗ When using this strategy: Titan Investments is totally Exempt from any claim of liability for losses. The responsibility on the management of your funds is solely yours. This is a very high risk/volatility market! Understand your place in the market.
3️⃣3️⃣ : ♻️ ® No Repaint
This Strategy does not Repaint! This is a real strategy!
3️⃣4️⃣ : 🔒 Copyright ©️
Copyright © 2022-2023 All rights reserved, ® Titan Investimentos
3️⃣5️⃣ : 👏 Acknowledgments
I want to start this message in thanks to TradingView and all the Pinescript community for all the 'magic' created here, a unique ecosystem! rich and healthy, a fertile soil, a 'new world' of possibilities, for a complete deepening and improvement of our best personal skills.
I leave here my immense thanks to the whole community: Tradingview, Pinecoders, Wizards and Moderators.
I was not born Rich .
Thanks to TradingView and pinescript and all its transformation.
I could develop myself and the best of me and the best of my skills.
And consequently build wealth and patrimony.
Gratitude.
One more story for the infinite book !
If you were born poor you were born to be rich !
Raising🔼 the level and raising🔼 the ruler! 📏
My work is my 'debauchery'! Do better! 💐🌹
Soul of a first-timer! Creativity Exudes! 🦄
This is the manifestation of God's magic in me. This is the best of me. 🧙
You will copy me, I know. So you owe me. 💋
My mission here is to raise the consciousness and self-esteem of all Titans and Titanids! Welcome! 🧘 🏛️
The only way to accomplish great work is to do what you love ! Before I learned to program I was wasting my life!
Death is the best creation of life .
Now you are the new , but in the not so distant future you will gradually become the old . Here I stay forever!
Playing the game like an Athlete! 🖼️ Enjoy and Enjoy 🍷 🗿
In honor of: BOB ☆
1 name, 3 letters, 3 possibilities, and if read backwards it's the same thing, a palindrome. ☘
Gratitude to the oracles that have enabled me the 'luck' to get this far: Dal&Ni&Fer
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
House Rules : This publication and strategy follows all TradingView house guidelines and rules:
📺 TradingView House Rules: www.tradingview.com
📺 Script publication rules: www.tradingview.com
📺 Vendor requirements: www.tradingview.com
📺 Links/References rules: www.tradingview.com
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
🟩 Titan Pro 👽 🟩
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
🟥 Titan Affiliate 🛸 🟥
Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4hInvestment Strategy (Quantitative Trading)
| 🛑 | Watch "LIVE" and 'COPY' this strategy in real time:
🔗 Link: www.tradingview.com
Hello, welcome, feel free 🌹💐
Since the stone age to the most technological age, one thing has not changed, that which continues impress human beings the most, is the other human being!
Deep down, it's all very simple or very complicated, depends on how you look at it.
I believe that everyone was born to do something very well in life.
But few are those who have, let's use the word 'luck' .
Few are those who have the 'luck' to discover this thing.
That is why few are happy and successful in their jobs and professions.
Thank God I had this 'luck' , and discovered what I was born to do well.
And I was born to program. 👨💻
📋 Summary : Project Titan
0️⃣ : 🦄 Project Titan
1️⃣ : ⚖️ Quantitative THEMIS
2️⃣ : 🏛️ Titan Community
3️⃣ : 👨💻 Who am I ❔
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
8️⃣ : ❓ What is Backtest ❓
9️⃣ : ❓ How to build a Consistent system ❓
🔟 : ❓ What is a Quantitative Trading system ❓
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
1️⃣4️⃣ : 🔧 Fixed Technical
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
1️⃣6️⃣ : ⚠️ Risk Profile
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
1️⃣8️⃣ : 💸 Initial Capital
1️⃣9️⃣ : ⚙️ Entry Options
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
3️⃣0️⃣ : 🛠️ Roadmap
3️⃣1️⃣ : 🧻 Notes ❕
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
3️⃣3️⃣ : ♻️ ® No Repaint
3️⃣4️⃣ : 🔒 Copyright ©️
3️⃣5️⃣ : 👏 Acknowledgments
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
0️⃣ : 🦄 Project Titan
This is the first real, 100% automated Quantitative Strategy made available to the public and the pinescript community for TradingView.
You will be able to automate all signals of this strategy for your broker , centralized or decentralized and also for messaging services : Discord, Telegram or Twitter .
This is the first strategy of a larger project, in 2023, I will provide a total of 6 100% automated 'Quantitative' strategies to the pinescript community for TradingView.
The future strategies to be shared here will also be unique , never before seen, real 'Quantitative' bots with real, validated results in real operation.
Just like the 'Quantitative THEMIS' strategy, it will be something out of the loop throughout the pinescript/tradingview community, truly unique tools for building mutual wealth consistently and continuously for our community.
1️⃣ : ⚖️ Quantitative THEMIS : Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
This is a truly unique and out of the curve strategy for BTC /USD .
A truly real strategy, with real, validated results and in real operation.
A unique tool for building mutual wealth, consistently and continuously for the members of the Titan community.
Initially we will operate on a monthly, quarterly, annual or biennial subscription service.
Our goal here is to build a great community, in exchange for an extremely fair value for the use of our truly unique tools, which bring and will bring real results to our community members.
With this business model it will be possible to provide all Titan users and community members with the purest and highest degree of sophistication in the market with pinescript for tradingview, providing unique and truly profitable strategies.
My goal here is to offer the best to our members!
The best 'pinescript' tradingview service in the world!
We are the only Start-Up in the world that will decentralize real and full access to truly real 'quantitative' tools that bring and will bring real results for mutual and ongoing wealth building for our community.
2️⃣ : 🏛️ Titan Community : 👽 Pro 🔁 Aff 🛸
Become a Titan Pro 👽
To get access to the strategy: "Quantitative THEMIS" , and future Titan strategies in a 100% automated way, along with all tutorials for automation.
Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months.
👽 Pro 🅼 Monthly
👽 Pro 🆀 Quarterly
👽 Pro🅰 Annual
👽 Pro👾Two Years
You will have access to a truly unique system that is out of the curve .
A 100% real, 100% automated, tested, validated, profitable, and in real operation strategy.
Become a Titan Affiliate 🛸
By becoming a Titan Affiliate 🛸, you will automatically receive 50% of the value of each new subscription you refer .
You will receive 50% for any of the above plans that you refer .
This way we will encourage our community to grow in a fair and healthy way, because we know what we have in our hands and what we deliver real value to our users.
We are at the highest level of sophistication in the market, the consistency here and the results here speak for themselves.
So growing our community means growing mutual wealth and raising collective conscience.
Wealth must be created not divided.
And here we are creating mutual wealth on all ends and in all ways.
A non-zero sum system, where everybody wins.
3️⃣ : 👨💻 Who am I ❔
My name is FilipeSoh I am 26 years old, Technical Analyst, Trader, Computer Engineer, pinescript Specialist, with extensive experience in several languages and technologies.
For the last 4 years I have been focusing on developing, editing and creating pinescript indicators and strategies for Tradingview for people and myself.
Full-time passionate workaholic pinescript developer with over 10,000 hours of pinescript development.
• Pinescript expert ▬Tradingview.
• Specialist in Automated Trading
• Specialist in Quantitative Trading.
• Statistical/Probabilistic Trading Specialist - Mark Douglas Scholl.
• Inventor of the 'Classic Forecast' Indicators.
• Inventor of the 'Backtest Table'.
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
Statistical/probabilistic trading is the only way to get a positive mathematical expectation regarding the market and consequently that is the only way to make money consistently from it.
I will present below some more details about the Quantitative THEMIS strategy, it is a real strategy, tested, validated and in real operation, 'Skin in the Game' , a consistent way to make money with statistical/probabilistic trading in a 100% automated.
I am a Technical Analyst , I used to be a Discretionary Trader , today I am 100% a Statistical Trader .
I've gotten rich and made a lot of money, and I've also lost a lot with 'leverage'.
That was a few years ago.
The book that changed everything for me was "Trading in The Zone" by Mark Douglas.
That's when I understood that the market is just a game of statistics and probability, like a casino!
It was then that I understood that the human brain is not prepared for trading, because it involves triggers and mental emotions.
And emotions in trading and in making trading decisions do not go well together, not in the long run, because you always have the burden of being wrong with the outcome of that particular position.
But remembering that the market is just a statistical game!
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
Let's use a 'coin' as an example:
If we toss a 'coin' up 10 times.
Do you agree that it is impossible for us to know exactly the result of the 'plays' before they actually happen?
As in the example above, would you agree, that we cannot "guess" the outcome of a position before it actually happens?
As much as we cannot "guess" whether the coin will drop heads or tails on each flip.
We can analyze the "backtest" of the 10 moves made with that coin:
If we analyze the 10 moves and count the number of times the coin fell heads or tails in a specific sequence, we then have a percentage of times the coin fell heads or tails, so we have a 'backtest' of those moves.
Then on the next flip we can now assume a point or a favorable position for one side, the side with the highest probability .
In a nutshell, this is more or less how probabilistic statistical trading works.
As Statistical Traders we can never say whether such a Trader/Position we take will be a winner or a loser.
But still we can have a positive and consistent result in a "sequence" of trades, because before we even open a position, backtests have already been performed so we identify an anomaly and build a system that will have a positive statistical advantage in our favor over the market.
The advantage will not be in one trade itself, but in the "sequence" of trades as a whole!
Because our system will work like a casino, having a positive mathematical expectation relative to the players/market.
Design, develop, test models and systems that can take advantage of market anomalies, until they change.
Be the casino! - Mark Douglas
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
In recent years I have focused and specialized in developing 100% automated trading systems, essentially for the cryptocurrency market.
I have developed many extremely robust and efficient systems, with positive mathematical expectation towards the market.
These are not complex systems per se , because here we want to avoid 'over-optimization' as much as possible.
As Da Vinci said: "Simplicity is the highest degree of sophistication".
I say this because I have tested, tried and developed hundreds of systems/strategies.
I believe I have programmed more than 10,000 unique indicators/strategies, because this is my passion and purpose in life.
I am passionate about what I do, completely!
I love statistical trading because it is the only way to get consistency in the long run!
This is why I have studied, applied, developed, and specialized in 100% automated cryptocurrency trading systems.
The reason why our systems are extremely "simple" is because, as I mentioned before, in statistical trading we want to exploit the market anomaly to the maximum, that is, this anomaly will change from time to time, usually we can exploit a trading system efficiently for about 6 to 12 months, or for a few years, that is; for fixed 'scalpers' systems.
Because at some point these anomalies will be identified , and from the moment they are identified they will be exploited and will stop being anomalies .
With the system presented here; you can even copy the indicators and input values shared here;
However; what I have to offer you is: it is me , our team , and our community !
That is, we will constantly monitor this system, for life , because our goal here is to create a unique , perpetual , profitable , and consistent system for our community.
Myself , our team and our community will keep this script periodically updated , to ensure the positive mathematical expectation of it.
So we don't mind sharing the current parameters and values , because the real value is also in the future updates that this system will receive from me and our team , guided by our culture and our community of real users !
As we are hosted on 'tradingview', all future updates for this strategy, will be implemented and updated automatically on your tradingview account.
What we want here is: to make sure you get gains from our system, because if you get gains , our ecosystem will grow as a whole in a healthy and scalable way, so we will be generating continuous mutual wealth and raising the collective consciousness .
People Need People: 3️⃣🅿
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
Today my greatest skill is to develop statistically profitable and 100% automated strategies for 'pinescript' tradingview.
Note that I said: 'profitable' because in fact statistical trading is the only way to make money in a 'consistent' way from the market.
And consequently have a positive wealth curve every cycle, because we will be based on mathematics, not on feelings and news.
Because the human brain is not prepared to do trading.
Because trading is connected to the decision making of the cerebral cortex.
And the decision making is automatically linked to emotions, and emotions don't match with trading decision making, because in those moments, we can feel the best and also the worst sensations and emotions, and this certainly affects us and makes us commit grotesque mistakes!
That's why the human brain is not prepared to do trading.
If you want to participate in a fully automated, profitable and consistent trading system; be a Titan Pro 👽
I believe we are walking an extremely enriching path here, not only in terms of financial returns for our community, but also in terms of knowledge about probabilistic and automated statistical trading.
You will have access to an extremely robust system, which was built upon very strong concepts and foundations, and upon the world's main asset in a few years: Bitcoin .
We are the tip of the best that exists in the cryptocurrency market when it comes to probabilistic and automated statistical trading.
Result is result! Me being dressed or naked.
This is just the beginning!
But there is a way to consistently make money from the market.
Being the Casino! - Mark Douglas
8️⃣ : ❓ What is Backtest ❓
Imagine the market as a purely random system, but even in 'randomness' there are patterns.
So now imagine the market and statistical trading as follows:
Repeating the above 'coin' example, let's think of it as follows:
If we toss a coin up 10 times again.
It is impossible to know which flips will have heads or tails, correct?
But if we analyze these 10 tosses, then we will have a mathematical statistic of the past result, for example, 70 % of the tosses fell 'heads'.
That is:
7 moves fell on "heads" .
3 moves fell on "tails" .
So based on these conditions and on the generic backtest presented here, we could adopt " heads " as our system of moves, to have a statistical and probabilistic advantage in relation to the next move to be performed.
That is, if you define a system, based on backtests , that has a robust positive mathematical expectation in relation to the market you will have a profitable system.
For every move you make you will have a positive statistical advantage in your favor over the market before you even make the move.
Like a casino in relation to all its players!
The casino does not have an advantage over one specific player, but over all players, because it has a positive mathematical expectation about all the moves that night.
The casino will always have a positive statistical advantage over its players.
Note that there will always be real players who will make real, million-dollar bankrolls that night, but this condition is already built into the casino's 'strategy', which has a pre-determined positive statistical advantage of that night as a whole.
Statistical trading is the same thing, as long as you don't understand this you will keep losing money and consistently.
9️⃣ : ❓ How to build a Consistent system ❓
See most traders around the world perform trades believing that that specific position taken will make them filthy rich, because they simply believe faithfully that the position taken will be an undoubted winner, based on a trader's methodology: 'trading a trade' without analyzing the whole context, just using 'empirical' aspects in their system.
But if you think of trading, as a sequence of moves.
You see, 'a sequence' !
When we think statistically, it doesn't matter your result for this , or for the next specific trade , but the final sequence of trades as a whole.
As the market has a random system of results distribution , if your system has a positive statistical advantage in relation to the market, at the end of that sequence you'll have the biggest probability of having a winning bank.
That's how you do real trading!
And with consistency!
Trading is a long term game, but when you change the key you realize that it is a simple game to make money in a consistent way from the market, all you need is patience.
Even more when we are based on Bitcoin, which has its 'Halving' effect where, in theory, we will never lose money in 3 to 4 years intervals, due to its scarcity and the fact that Bitcoin is the 'discovery of digital scarcity' which makes it the digital gold, we believe in this thesis and we follow Satoshi's legacy.
So align Bitcoin with a probabilistic statistical trading system with a positive mathematical expectation of the market and 100% automated with the long term, and all you need is patience, and you will become rich.
In fact Bitcoin by itself is already a path, buy, wait for each halving and your wealth will be maintained.
No inflation, unlike fiat currencies.
This is a complete and extremely robust strategy, with the most current possible and 'not possible' techniques involved and applied here.
Today I am at another level in developing 100% automated 'quantitative' strategies.
I was born for this!
🔟 : ❓ What is a Quantitative Trading system ❓
In addition to having access to a revolutionary strategy you will have access to disruptive 100% multifunctional tables with the ability to perform 'backtests' for better tracking and monitoring of your system on a customized basis.
I would like to emphasize one thing, and that is that you keep this in mind.
Today my greatest skill in 'pinescript' is to build indicators, but mainly strategies, based on statistical and probabilistic trading, with a postive mathematical expectation in relation to the market, in a 100% automated way.
This with the goal of building a consistent and continuous positive equity curve through mathematics using data, converting it into statistical / probabilistic parameters and applying them to a Quantitative model.
Before becoming a Quantitative Trader , I was a Technical Analyst and a Discretionary Trader .
First as a position trader and then as a day trader.
Before becoming a Trader, I trained myself as a Technical Analyst , to masterly understand the shape and workings of the market in theory.
But everything changed when I met 'Mark Douglas' , when I got to know his works, that's when my head exploded 🤯, and I started to understand the market for good!
The market is nothing more than a 'random' system of distributing results.
See that I said: 'random' .
Do yourself a mental exercise.
Is there really such a thing as random ?
I believe not, as far as we know maybe the 'singularity'.
So thinking this way, to translate, the market is nothing more than a game of probability, statistics and pure mathematics.
Like a casino!
What happens is that most traders, whenever they take a position, take it with all the empirical certainty that such position will win or lose, and do not take into consideration the total sequence of results to understand their place in the market.
Understanding your place in the market gives you the ability to create and design systems that can exploit the present market anomaly, and thus make money statistically, consistently, and 100% automated.
Thinking of it this way, it is easy to make money from the market.
There are many ways to make money from the market, but the only consistent way I know of is through 'probabilistic and automated statistical trading'.
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
There are some fundamental points that must be addressed here in order to understand what makes up a system based on statistics and probability applied to a quantitative model.
When we talk about 'discretionary' trading, it is a trading system based on human decisions after the defined 'empirical' conditions are met.
It is quite another thing to build a fully automated system without any human interference/interaction .
That said:
Building a statistically profitable system is perfectly possible, but this is a high level task , but with possible high rewards and consistent gains.
Here you will find a real "Skin In The Game" strategy.
With all due respect, but the vast majority of traders who post strategies on TradingView do not understand what they are doing.
Most of them do not understand the minimum complexity involved in the main variable for the construction of a real strategy, the mother variable: "strategy".
I say this by my own experience, because I have analyzed practically all the existing publications of TradingView + 200,000 indicators and strategies.
I breathe pinescript, I eat pinescript, I sleep pinescript, I bathe pinescript, I live TradingView.
But the main advantage for the TradingView users, is that all entry and exit orders made by this strategy can be checked and analyzed thoroughly, to validate and prove the veracity of this strategy, because this is a 100% real strategy.
Here there is a huge world of possibilities, but only one way to build a 'pinescript strategy' that will work correctly aligned to the real world with real results .
There are some fundamental points to take into consideration when building a profitable trading system:
The most important of these for me is: 'DrawDown' .
Followed by: 'Hit Rate' .
And only after that we use the parameter: 'Profit'.
See, this is because here, we are dealing with the 'imponderable' , and anything can happen in this scenario.
But there is one thing that makes us sleep peacefully at night, and that is: controlling losses .
That is, in other words: controlling the DrawDown .
The amateur is concerned with 'winning', the professional is concerned with conserving capital.
If we have the losses under control, then we can move on to the other two parameters: hit rate and profit.
See, the second most important factor in building a system is the hit rate.
I say this from my own experience.
I have worked with many systems with a 'low hit rate', but extremely profitable.
For example: systems with hit rates of 40 to 50%.
But as much as statistically and mathematically the profit is rewarding, operating systems with a low hit rate is always very stressful psychologically.
That's why there are two big reasons why when I build an automated trading system, I focus on the high hit rate of the system, they are
1 - To reduce psychological damage as much as possible .
2 - And more important , when we create a system with a 'high hit rate' , there is a huge intrinsic advantage here, that most statistic traders don't take in consideration.
That is: knowing more quickly when the system stops being functional.
The main advantage of a system with a high hit rate is: to identify when the system stops being functional and stop exploiting the market's anomaly.
Look: When we are talking about trading and random distribution of results on the market, do you agree that when we create a trading system, we are focused on exploring some anomaly of that market?
When that anomaly is verified by the market, it will stop being functional with time.
That's why trading systems, 'scalpers', especially for cryptocurrencies, need constant monitoring, quarterly, semi-annually or annually.
Because market movements change from time to time.
Because we go through different cycles from time to time, such as congestion cycles, accumulation , distribution , volatility , uptrends and downtrends .
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
You see there is a very important point that must be stressed here.
As we are always trying to exploit an 'anomaly' in the market.
So the 'number' of indicators/tools that will integrate the system is of paramount importance.
But most traders do not take this into consideration.
To build a professional, robust, consistent, and profitable system, you don't need to use hundreds of indicators to build your setup.
This will actually make it harder to read when the setup stops working and needs some adjustment.
So focusing on a high hit rate is very important here, this is a fundamental principle that is widely ignored , and with a high hit rate, we can know much more accurately when the system is no longer functional much faster.
As Darwin said: "It is not the strongest or the most intelligent that wins the game of life, it is the most adapted.
So simple systems, as contradictory as it may seem, are more efficient, because they help to identify inflection points in the market much more quickly.
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
See I have built, hundreds of thousands of indicators and 'pinescript' strategies, hundreds of thousands.
This is an extremely professional, robust and profitable system.
Based on the currency pairs: BTC /USDT
There are many ways and avenues to build a profitable trading setup/system.
And actually this is not a difficult task, taking in consideration, as the main factor here, that our trading and investment plan is for the long term, so consequently we will face scenarios with less noise.
He who is in a hurry eats raw.
As mentioned before.
Defining trends in pinescript is technically a simple task, the hardest task is to determine congestion zones with low volume and volatility, it's in these moments that many false signals are generated, and consequently is where most setups face their maximum DrawDown.
That's why this strategy was strictly and thoroughly planned, built on a very solid foundation, to avoid as much noise as possible, for a positive and consistent equity curve in each market cycle, 'Consistency' is our 'Mantra' around here.
1️⃣4️⃣ : 🔧 Fixed Technical
• Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
• Pair: BTC/USDTP
• Time Frame: 4 hours
• Broker: Binance (Recommended)
For a more conservative scenario, we have built the Quantitative THEMIS for the 4h time frame, with the main focus on consistency.
So we can avoid noise as much as possible!
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
In order to build a 'perpetual' system specific to BTC/USDT, it took a lot of testing, and more testing, and a lot of investment and research.
There is one initial and fundamental point that we can address to justify the incredible consistency presented here.
That fundamental point is our exit via Take Profit or Stop Loss percentage (%).
🎯 Take Profit (%)
🛑 Stop Loss (%)
See, today I have been testing some more advanced backtesting models for some cryptocurrency systems.
In which I perform 'backtest of backtest', i.e. we use a set of strategies each focused on a principle, operating individually, but they are part of something unique, i.e. we do 'backtests' of 'backtests' together.
What I mean is that we do a lot of backtesting around here.
I can assure you, that always the best output for a trading system is to set fixed output values!
In other words:
🎯 Take Profit (%)
🛑 Stop Loss (%)
This happens because statistically setting fixed exit structures in the vast majority of times, presents a superior result on the capital/equity curve, throughout history and for the vast majority of setups compared to other exit methods.
This is due to a mathematical principle of simplicity, 'avoiding more noise'.
Thus whenever the Quantitative THEMIS strategy takes a position it has a target and a defined maximum stop percentage.
1️⃣6️⃣ : ⚠️ Risk Profile
The strategy, currently has 3 risk profiles ⚠️ patterns for 'fixed percentage exits': Take Profit (%) and Stop Loss (%) .
They are: ⚠️ Rich's Profiles
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
You will be able to select and switch between the above options and profiles through the 'input' menu of the strategy by navigating to the "⚠️ Risk Profile" menu.
You can then select, test and apply the Risk Profile above that best suits your risk management, expectations and reality , as well as customize all the 'fixed exit' values through the TP and SL menus below.
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
The strategy currently also has 'Moving Exits' based on indicator signals.
These are Moving Exits (Indicators)
📈 LONG : (EXIT)
🧃 (MAO) Short : true
📉 SHORT : (EXIT)
🧃 (MAO) Long: false
You can select and toggle between the above options through the 'input' menu of the strategy by navigating to the "LONG : Exit" and "SHORT : Exit" menu.
1️⃣8️⃣ : 💸 Initial Capital
By default the "Initial Capital" set for entries and backtests of this strategy is: 10000 $
You can set another value for the 'Starting Capital' through the tradingview menu under "properties" , and edit the value of the "Initial Capital" field.
This way you can set and test other 'Entry Values' for your trades, tests and backtests.
1️⃣9️⃣ : ⚙️ Entry Options
By default the 'order size' set for this strategy is 100 % of the 'initial capital' on each new trade.
You can set and test other entry options like : contracts , cash , % of equity
You should make these changes directly in the input menu of the strategy by navigating to the menu "⚙️ Properties : TradingView" below.
⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100
Leverage: 1
So you can define and test other 'Entry Options' for your trades, tests and backtests.
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
It is possible to automate the signals of this strategy for any centralized or decentralized broker, as well as for messaging services: Discord, Telegram and Twitter.
All in an extremely simple and uncomplicated way through the tutorials available in PDF /VIDEO for our Titan Pro 👽 subscriber community.
With our tutorials in PDF and Video it will be possible to automate the signals of this strategy for the chosen service in an extremely simple way with less than 10 steps only.
Tradingview naturally doesn't count with native integration between brokers and tradingview.
But it is possible to use 'third party services' to do the integration and automation between Tradingview and your centralized or decentralized broker.
Here are the standard, available and recommended 'third party services' to automate the signals from the 'Quantitative THEMIS' strategy on the tradingview for your broker:
1) Wundertrading (Recommended):
2) 3commas:
3) Zignaly:
4) Aleeert.com (Recommended):
5) Alertatron:
Note! 'Third party services' cannot perform 'withdrawals' via their key 'API', they can only open positions, so your funds will always be 'safe' in your brokerage firm, being traded via the 'API', when they receive an entry and exit signal from this strategy.
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
You can automate this strategy for any of the brokers below, through your broker's 'API' by connecting it to the 'third party automation services' for tradingview available and mentioned in the menu above:
1) Binance (Recommended)
2) Bitmex
3) Bybit
4) KuCoin
5) Deribit
6) OKX
7) Coinbase
8) Huobi
9) Bitfinex
10) Bitget
11) Bittrex
12) Bitstamp
13) Gate. io
14) Kraken
15) Gemini
16) Ascendex
17) VCCE
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
You can also automate and monitor the signals of this strategy much more efficiently by sending them to the following popular messaging services:
1) Discord
2) Telegram
3) Twitter
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
It will also be possible to copy/replicate the entries and exits of this strategy to your broker in an extremely simple and agile way, through the available copy-trader services.
This way it will be possible to replicate the signals of this strategy at each entry and exit to your broker through the API connecting it to the integrated copy-trader services available through the tradingview automation services below:
1) Wundetrading:
2) Zignaly:
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
I believe that today I am at another level in 'pinescript' development.
I consider myself today a true unicorn as a pinescript developer, someone unique and very rare.
If you choose another tool or another pinescript service, this tool will be just another one, with no real results.
But if you join our Titan community, you will have access to a unique tool! And you will get real results!
I already earn money consistently with statistical and automated trading and as an expert pinescript developer.
I am here to evolve my skills as much as possible, and one day become a pinescript 'Wizard'.
So excellence, quality and professionalism will always be my north here.
You will never find a developer like me, and who will take so seriously such a revolutionary project as this one. A Maverick! ▬ The man never stops!
Here you will find the highest degree of sophistication and development in the market for 'pinescript'.
You will get the best of me and the best of pinescript possible.
Let me show you how a professional in my field does it.
Become a Titan Pro Member 👽 and get Full Access to this strategy and all the Automation Tutorials.
Be the Titan in your life!
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
Get financial return for your referrals, Decentralize the World, and raise the collective consciousness.
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
® Titan Investimentos | Quantitative THEMIS ⚖️ | Demo 🐄 2.6 | Dev: © FilipeSoh 🧙 | 🤖 100% Automated : Discord, Telegram, Twitter, Wundertrading, 3commas, Zignaly, Aleeert, Alertatron, Uniswap-v3 | BINANCE:BTCUSDTPERP 4h
🛒 Subscribe this strategy ❗️ Be a Titan Member 🏛️
🛒 Titan Pro 👽 🔗 🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🛒 Titan Affiliate 🛸 🔗 🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
📋 Summary : QT THEMIS ⚖️
🕵️♂️ Check This Strategy..................................................................0
🦄 ® Titan Investimentos...............................................................1
👨💻 © Developer..........................................................................2
📚 Signal Automation Tutorials : (PDF/VIDEO).......................................3
👨🔧 Revision...............................................................................4
📊 Table : (BACKTEST)..................................................................5
📊 Table : (INFORMATIONS).............................................................6
⚙️ Properties : (TRADINGVIEW)........................................................7
📆 Backtest : (TRADINGVIEW)..........................................................8
⚠️ Risk Profile...........................................................................9
🟢 On 🔴 Off : (LONG/SHORT).......................................................10
📈 LONG : (ENTRY)....................................................................11
📉 SHORT : (ENTRY)...................................................................12
📈 LONG : (EXIT).......................................................................13
📉 SHORT : (EXIT)......................................................................14
🧩 (EI) External Indicator.............................................................15
📡 (QT) Quantitative...................................................................16
🎠 (FF) Forecast......................................................................17
🅱 (BB) Bollinger Bands................................................................18
🧃 (MAP) Moving Average Primary......................................................19
🧃 (MAP) Labels.........................................................................20
🍔 (MAQ) Moving Average Quaternary.................................................21
🍟 (MACD) Moving Average Convergence Divergence...............................22
📣 (VWAP) Volume Weighted Average Price........................................23
🪀 (HL) HILO..........................................................................24
🅾 (OBV) On Balance Volume.........................................................25
🥊 (SAR) Stop and Reverse...........................................................26
🛡️ (DSR) Dynamic Support and Resistance..........................................27
🔊 (VD) Volume Directional..........................................................28
🧰 (RSI) Relative Momentum Index.................................................29
🎯 (TP) Take Profit %..................................................................30
🛑 (SL) Stop Loss %....................................................................31
🤖 Automation Selected...............................................................32
📱💻 Discord............................................................................33
📱💻 Telegram..........................................................................34
📱💻 Twitter...........................................................................35
🤖 Wundertrading......................................................................36
🤖 3commas............................................................................37
🤖 Zignaly...............................................................................38
🤖 Aleeert...............................................................................39
🤖 Alertatron...........................................................................40
🤖 Uniswap-v3..........................................................................41
🧲🤖 Copy-Trading....................................................................42
♻️ ® No Repaint........................................................................43
🔒 Copyright ©️..........................................................................44
🏛️ Be a Titan Member..................................................................45
Nº Active Users..........................................................................46
⏱ Time Left............................................................................47
| 0 | 🕵️♂️ Check This Strategy
🕵️♂️ Version Demo: 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
🕵️♂️ Version Pro: 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
| 1 | 🦄 ® Titan Investimentos
Decentralizing the World 🗺
Raising the Collective Conscience 🗺
🦄Site:
🦄TradingView: www.tradingview.com
🦄Discord:
🦄Telegram:
🦄Youtube:
🦄Twitter:
🦄Instagram:
🦄TikTok:
🦄Linkedin:
🦄E-mail:
| 2 | 👨💻 © Developer
🧠 Developer: @FilipeSoh🧙
📺 TradingView: www.tradingview.com
☑️ Linkedin:
✅ Fiverr:
✅ Upwork:
🎥 YouTube:
🐤 Twitter:
🤳 Instagram:
| 3 | 📚 Signal Automation Tutorials : (PDF/VIDEO)
📚 Discord: 🔗 Link: 🔒Titan Pro👽
📚 Telegram: 🔗 Link: 🔒Titan Pro👽
📚 Twitter: 🔗 Link: 🔒Titan Pro👽
📚 Wundertrading: 🔗 Link: 🔒Titan Pro👽
📚 3comnas: 🔗 Link: 🔒Titan Pro👽
📚 Zignaly: 🔗 Link: 🔒Titan Pro👽
📚 Aleeert: 🔗 Link: 🔒Titan Pro👽
📚 Alertatron: 🔗 Link: 🔒Titan Pro👽
📚 Uniswap-v3: 🔗 Link: 🔒Titan Pro👽
📚 Copy-Trading: 🔗 Link: 🔒Titan Pro👽
| 4 | 👨🔧 Revision
👨🔧 Start Of Operations: 01 Jan 2019 21:00 -0300 💡 Start Of Operations (Skin in the game) : Revision 1.0
👨🔧 Previous Review: 01 Jan 2022 21:00 -0300 💡 Previous Review : Revision 2.0
👨🔧 Current Revision: 01 Jan 2023 21:00 -0300 💡 Current Revision : Revision 2.6
👨🔧 Next Revision: 28 May 2023 21:00 -0300 💡 Next Revision : Revision 2.7
| 5 | 📊 Table : (BACKTEST)
📊 Table: true
🖌️ Style: label.style_label_left
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 6 | 📊 Table : (INFORMATIONS)
📊 Table: false
🖌️ Style: label.style_label_right
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 7 | ⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100 %
🚀 Leverage: 1
| 8 | 📆 Backtest : (TradingView)
🗓️ Mon: true
🗓️ Tue: true
🗓️ Wed: true
🗓️ Thu: true
🗓️ Fri: true
🗓️ Sat: true
🗓️ Sun: true
📆 Range: custom
📆 Start: UTC 31 Oct 2008 00:00
📆 End: UTC 31 Oct 2030 23:45
📆 Session: 0000-0000
📆 UTC: UTC
| 9 | ⚠️ Risk Profile
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
| 10 | 🟢 On 🔴 Off : (LONG/SHORT)
🟢📈 LONG: true
🟢📉 SHORT: true
| 11 | 📈 LONG : (ENTRY)
📡 (QT) Long: true
🧃 (MAP) Long: false
🅱 (BB) Long: false
🍟 (MACD) Long: false
🅾 (OBV) Long: false
| 12 | 📉 SHORT : (ENTRY)
📡 (QT) Short: true
🧃 (MAP) Short: false
🅱 (BB) Short: false
🍟 (MACD) Short: false
🅾 (OBV) Short: false
| 13 | 📈 LONG : (EXIT)
🧃 (MAP) Short: true
| 14 | 📉 SHORT : (EXIT)
🧃 (MAP) Long: false
| 15 | 🧩 (EI) External Indicator
🧩 (EI) Connect your external indicator/filter: false
🧩 (EI) Connect your indicator here (Study mode only): close
🧩 (EI) Connect your indicator here (Study mode only): close
| 16 | 📡 (QT) Quantitative
📡 (QT) Quantitative: true
📡 (QT) Market: BINANCE:BTCUSDTPERP
📡 (QT) Dice: openai
| 17 | 🎠 (FF) Forecast
🎠 (FF) Include current unclosed current candle: true
🎠 (FF) Forecast Type: flat
🎠 (FF) Nº of candles to use in linear regression: 3
| 18 | 🅱 (BB) Bollinger Bands
🅱 (BB) Bollinger Bands: true
🅱 (BB) Type: EMA
🅱 (BB) Period: 20
🅱 (BB) Source: close
🅱 (BB) Multiplier: 2
🅱 (BB) Linewidth: 0
🅱 (BB) Color: #131722
| 19 | 🧃 (MAP) Moving Average Primary
🧃 (MAP) Moving Average Primary: true
🧃 (MAP) BarColor: false
🧃 (MAP) Background: false
🧃 (MAP) Type: SMA
🧃 (MAP) Source: open
🧃 (MAP) Period: 100
🧃 (MAP) Multiplier: 2.0
🧃 (MAP) Linewidth: 2
🧃 (MAP) Color P: #42bda8
🧃 (MAP) Color N: #801922
| 20 | 🧃 (MAP) Labels
🧃 (MAP) Labels: true
🧃 (MAP) Style BUY ZONE: shape.labelup
🧃 (MAP) Color BUY ZONE: #42bda8
🧃 (MAP) Style SELL ZONE: shape.labeldown
🧃 (MAP) Color SELL ZONE: #801922
| 21 | 🍔 (MAQ) Moving Average Quaternary
🍔 (MAQ) Moving Average Quaternary: true
🍔 (MAQ) BarColor: false
🍔 (MAQ) Background: false
🍔 (MAQ) Type: SMA
🍔 (MAQ) Source: close
🍔 (MAQ) Primary: 14
🍔 (MAQ) Secondary: 22
🍔 (MAQ) Tertiary: 44
🍔 (MAQ) Quaternary: 16
🍔 (MAQ) Linewidth: 0
🍔 (MAQ) Color P: #42bda8
🍔 (MAQ) Color N: #801922
| 22 | 🍟 (MACD) Moving Average Convergence Divergence
🍟 (MACD) Macd Type: EMA
🍟 (MACD) Signal Type: EMA
🍟 (MACD) Source: close
🍟 (MACD) Fast: 12
🍟 (MACD) Slow: 26
🍟 (MACD) Smoothing: 9
| 23 | 📣 (VWAP) Volume Weighted Average Price
📣 (VWAP) Source: close
📣 (VWAP) Period: 340
📣 (VWAP) Momentum A: 84
📣 (VWAP) Momentum B: 150
📣 (VWAP) Average Volume: 1
📣 (VWAP) Multiplier: 1
📣 (VWAP) Diviser: 2
| 24 | 🪀 (HL) HILO
🪀 (HL) Type: SMA
🪀 (HL) Function: Maverick🧙
🪀 (HL) Source H: high
🪀 (HL) Source L: low
🪀 (HL) Period: 20
🪀 (HL) Momentum: 26
🪀 (HL) Diviser: 2
🪀 (HL) Multiplier: 1
| 25 | 🅾 (OBV) On Balance Volume
🅾 (OBV) Type: EMA
🅾 (OBV) Source: close
🅾 (OBV) Period: 16
🅾 (OBV) Diviser: 2
🅾 (OBV) Multiplier: 1
| 26 | 🥊 (SAR) Stop and Reverse
🥊 (SAR) Source: close
🥊 (SAR) High: 1.8
🥊 (SAR) Mid: 1.6
🥊 (SAR) Low: 1.6
🥊 (SAR) Diviser: 2
🥊 (SAR) Multiplier: 1
| 27 | 🛡️ (DSR) Dynamic Support and Resistance
🛡️ (DSR) Source D: close
🛡️ (DSR) Source R: high
🛡️ (DSR) Source S: low
🛡️ (DSR) Momentum R: 0
🛡️ (DSR) Momentum S: 2
🛡️ (DSR) Diviser: 2
🛡️ (DSR) Multiplier: 1
| 28 | 🔊 (VD) Volume Directional
🔊 (VD) Type: SMA
🔊 (VD) Period: 68
🔊 (VD) Momentum: 3.8
🔊 (VD) Diviser: 2
🔊 (VD) Multiplier: 1
| 29 | 🧰 (RSI) Relative Momentum Index
🧰 (RSI) Type UP: EMA
🧰 (RSI) Type DOWN: EMA
🧰 (RSI) Source: close
🧰 (RSI) Period: 29
🧰 (RSI) Smoothing: 22
🧰 (RSI) Momentum R: 64
🧰 (RSI) Momentum S: 142
🧰 (RSI) Diviser: 2
🧰 (RSI) Multiplier: 1
| 30 | 🎯 (TP) Take Profit %
🎯 (TP) Take Profit: false
🎯 (TP) %: 2.2
🎯 (TP) Color: #42bda8
🎯 (TP) Linewidth: 1
| 31 | 🛑 (SL) Stop Loss %
🛑 (SL) Stop Loss: false
🛑 (SL) %: 2.7
🛑 (SL) Color: #801922
🛑 (SL) Linewidth: 1
| 32 | 🤖 Automation : Discord | Telegram | Twitter | Wundertrading | 3commas | Zignaly | Aleeert | Alertatron | Uniswap-v3
🤖 Automation Selected : Discord
| 33 | 🤖 Discord
🔗 Link Discord:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Long: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Long: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Short: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Short: 🔒Titan Pro👽
| 34 | 🤖 Telegram
🔗 Link Telegram:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Short: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Short: 🔒Titan Pro👽
| 35 | 🤖 Twitter
🔗 Link Twitter:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Short: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Short: 🔒Titan Pro👽
| 36 | 🤖 Wundertrading : Binance | Bitmex | Bybit | KuCoin | Deribit | OKX | Coinbase | Huobi | Bitfinex | Bitget
🔗 Link Wundertrading:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Short: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Short: 🔒Titan Pro👽
| 37 | 🤖 3commas : Binance | Bybit | OKX | Bitfinex | Coinbase | Deribit | Bitmex | Bittrex | Bitstamp | Gate.io | Kraken | Gemini | Huobi | KuCoin
🔗 Link 3commas:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Long: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Long: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Short: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Short: 🔒Titan Pro👽
| 38 | 🤖 Zignaly : Binance | Ascendex | Bitmex | Kucoin | VCCE
🔗 Link Zignaly:
🔗 Link 📚 Automation: 🔒Titan Pro👽
🤖 Type Automation: Profit Sharing
🤖 Type Provider: Webook
🔑 Key: 🔒Titan Pro👽
🤖 pair: BTCUSDTP
🤖 exchange: binance
🤖 exchangeAccountType: futures
🤖 orderType: market
🚀 leverage: 1x
% positionSizePercentage: 100 %
💸 positionSizeQuote: 10000 $
🆔 signalId: @Signal1234
| 39 | 🤖 Aleeert : Binance
🔗 Link Aleeert:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Short: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Short: 🔒Titan Pro👽
| 40 | 🤖 Alertatron : Binance | Bybit | Deribit | Bitmex
🔗 Link Alertatron:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Short: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Short: 🔒Titan Pro👽
| 41 | 🤖 Uniswap-v3
🔗 Link Alertatron:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Short: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Short: 🔒Titan Pro👽
| 42 | 🧲🤖 Copy-Trading : Zignaly | Wundertrading
🔗 Link 📚 Copy-Trading: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Zignaly: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Wundertrading: 🔒Titan Pro👽
| 43 | ♻️ ® Don't Repaint!
♻️ This Strategy does not Repaint!: ® Signs Do not repaint❕
♻️ This is a Real Strategy!: Quality : ® Titan Investimentos
📋️️ Get more information about Repainting here:
| 44 | 🔒 Copyright ©️
🔒 Copyright ©️: Copyright © 2023-2024 All rights reserved, ® Titan Investimentos
🔒 Copyright ©️: ® Titan Investimentos
🔒 Copyright ©️: Unique and Exclusive Strategy. All rights reserved
| 45 | 🏛️ Be a Titan Members
🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
| 46 | ⏱ Time Left
Time Left Titan Demo 🐄: ⏱♾ | ⏱ : ♾ Titan Demo 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
Time Left Titan Pro 👽: 🔒Titan Pro👽 | ⏱ : Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months. (👽 Pro 🅼 Monthly, 👽 Pro 🆀 Quarterly, 👽 Pro🅰 Annual, 👽 Pro👾Two Years)
| 47 | Nº Active Users
Nº Active Subscribers Titan Pro 👽: 5️⃣6️⃣ | 1✔️ 5✔️ 10✔️ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
Nº Active Affiliates Titan Aff 🛸: 6️⃣ | 1✔️ 5✔️ 10❌ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🆑 Conservative: 🎯 TP=2.7 % | 🛑 SL=2.7 %
• 📆All years: 🆑 Conservative: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1669.89 %
💲 + 166989.43 USD
Total Close Trades:
⚪️ 369
Percent Profitable:
🟡 64.77 %
Profit Factor:
🟢 2.314
DrawDrown Maximum:
🔴 -24.82 %
💲 -10221.43 USD
Avg Trade:
💲 + 452.55 USD
✔️ Trades Winning: 239
❌ Trades Losing: 130
✔️ Average Gross Win: + 12.31 %
❌ Average Gross Loss: - 9.78 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 499.33 %
% Average Gain Monthly: 41.61 %
% Average Gain Weekly: 9.6 %
% Average Gain Day: 1.37 %
💲 Average Gain Annual: 49933 $
💲 Average Gain Monthly: 4161 $
💲 Average Gain Weekly: 960 $
💲 Average Gain Day: 137 $
• 📆 Year: 2020: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🆑 Conservative: 🚀 Leverage 1️⃣x
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: Ⓜ️ Moderate: 🎯 TP=2.8 % | 🛑 SL=2.7 %
• 📆 All years: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1472.04 %
💲 + 147199.89 USD
Total Close Trades:
⚪️ 362
Percent Profitable:
🟡 63.26 %
Profit Factor:
🟢 2.192
DrawDrown Maximum:
🔴 -22.69 %
💲 -9269.33 USD
Avg Trade:
💲 + 406.63 USD
✔️ Trades Winning: 229
❌ Trades Losing : 133
✔️ Average Gross Win: + 11.82 %
❌ Average Gross Loss: - 9.29 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 8
% Average Gain Annual: 440.15 %
% Average Gain Monthly: 36.68 %
% Average Gain Weekly: 8.46 %
% Average Gain Day: 1.21 %
💲 Average Gain Annual: 44015 $
💲 Average Gain Monthly: 3668 $
💲 Average Gain Weekly: 846 $
💲 Average Gain Day: 121 $
• 📆 Year: 2020: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🅰 Aggressive: 🎯 TP=1.6 % | 🛑 SL=6.9 %
• 📆 All years: 🅰 Aggressive: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 989.38 %
💲 + 98938.38 USD
Total Close Trades:
⚪️ 380
Percent Profitable:
🟢 84.47 %
Profit Factor:
🟢 2.156
DrawDrown Maximum:
🔴 -17.88 %
💲 -9182.84 USD
Avg Trade:
💲 + 260.36 USD
✔️ Trades Winning: 321
❌ Trades Losing: 59
✔️ Average Gross Win: + 5.75 %
❌ Average Gross Loss: - 14.51 %
✔️ Maximum Consecutive Wins: 21
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 295.84 %
% Average Gain Monthly: 24.65 %
% Average Gain Weekly: 5.69 %
% Average Gain Day: 0.81 %
💲 Average Gain Annual: 29584 $
💲 Average Gain Monthly: 2465 $
💲 Average Gain Weekly: 569 $
💲 Average Gain Day: 81 $
• 📆 Year: 2020: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🅰 Aggressive: 🚀 Leverage 1️⃣x
3️⃣0️⃣ : 🛠️ Roadmap
🛠️• 14/ 01 /2023 : Titan THEMIS Launch
🛠️• Updates January/2023 :
• 📚 Tutorials for Automation 🤖 already Available : ✔️
• ✔️ Discord
• ✔️ Wundertrading
• ✔️ Zignaly
• 📚 Tutorials for Automation 🤖 In Preparation : ⭕
• ⭕ Telegram
• ⭕ Twitter
• ⭕ 3comnas
• ⭕ Aleeert
• ⭕ Alertatron
• ⭕ Uniswap-v3
• ⭕ Copy-Trading
🛠️• Updates February/2023 :
• 📰 Launch of advertising material for Titan Affiliates 🛸
• 🛍️🎥🖼️📊 (Sales Page/VSL/Videos/Creative/Infographics)
🛠️• 28/05/2023 : Titan THEMIS update ▬ Version 2.7
🛠️• 28/05/2023 : BOT BOB release ▬ Version 1.0
• (Native Titan THEMIS Automation - Through BOT BOB, a bot for automation of signals, indicators and strategies of TradingView, of own code ▬ in validation.
• BOT BOB
Automation/Connection :
• API - For Centralized Brokers.
• Smart Contracts - Wallet Web - For Decentralized Brokers.
• This way users can automate any indicator or strategy of TradingView and Titan in a decentralized, secure and simplified way.
• Without having the need to use 'third party services' for automating TradingView indicators and strategies like the ones available above.
🛠️• 28/05/2023 : Release ▬ Titan Culture Guide 📝
3️⃣1️⃣ : 🧻 Notes ❕
🧻 • Note ❕ The "Demo 🐄" version, ❌does not have 'integrated automation', to automate the signals of this strategy and enjoy a fully automated system, you need to have access to the Pro version with '100% integrated automation' and all the tutorials for automation available. Become a Titan Pro 👽
🧻 • Note ❕ You will also need to be a "Pro User or higher on Tradingview", to be able to use the webhook feature available only for 'paid' profiles on the platform.
With the webhook feature it is possible to send the signals of this strategy to almost anywhere, in our case to centralized or decentralized brokerages, also to popular messaging services such as: Discord, Telegram or Twiter.
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
🚨 • Disclaimer ❕❕ Past positive result and performance of a system does not guarantee its positive result and performance for the future!
🚨 • Disclaimer ❗❗❗ When using this strategy: Titan Investments is totally Exempt from any claim of liability for losses. The responsibility on the management of your funds is solely yours. This is a very high risk/volatility market! Understand your place in the market.
3️⃣3️⃣ : ♻️ ® No Repaint
This Strategy does not Repaint! This is a real strategy!
3️⃣4️⃣ : 🔒 Copyright ©️
Copyright © 2022-2023 All rights reserved, ® Titan Investimentos
3️⃣5️⃣ : 👏 Acknowledgments
I want to start this message in thanks to TradingView and all the Pinescript community for all the 'magic' created here, a unique ecosystem! rich and healthy, a fertile soil, a 'new world' of possibilities, for a complete deepening and improvement of our best personal skills.
I leave here my immense thanks to the whole community: Tradingview, Pinecoders, Wizards and Moderators.
I was not born Rich .
Thanks to TradingView and pinescript and all its transformation.
I could develop myself and the best of me and the best of my skills.
And consequently build wealth and patrimony.
Gratitude.
One more story for the infinite book !
If you were born poor you were born to be rich !
Raising🔼 the level and raising🔼 the ruler! 📏
My work is my 'debauchery'! Do better! 💐🌹
Soul of a first-timer! Creativity Exudes! 🦄
This is the manifestation of God's magic in me. This is the best of me. 🧙
You will copy me, I know. So you owe me. 💋
My mission here is to raise the consciousness and self-esteem of all Titans and Titanids! Welcome! 🧘 🏛️
The only way to accomplish great work is to do what you love ! Before I learned to program I was wasting my life!
Death is the best creation of life .
Now you are the new , but in the not so distant future you will gradually become the old . Here I stay forever!
Playing the game like an Athlete! 🖼️ Enjoy and Enjoy 🍷 🗿
In honor of: BOB ☆
1 name, 3 letters, 3 possibilities, and if read backwards it's the same thing, a palindrome. ☘
Gratitude to the oracles that have enabled me the 'luck' to get this far: Dal&Ni&Fer
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
House Rules : This publication and strategy follows all TradingView house guidelines and rules:
📺 TradingView House Rules: www.tradingview.com
📺 Script publication rules: www.tradingview.com
📺 Vendor requirements: www.tradingview.com
📺 Links/References rules: www.tradingview.com
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
🟩 Titan Pro 👽 🟩
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
🟥 Titan Affiliate 🛸 🟥
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (🐑):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (💧):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (🏦):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (⚖️):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (🛡️):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
AccumulationPro Money Flow StrategyAccumulationPro Money Flow Strategy identifies stock trading opportunities by analyzing money flow and potential long-only opportunities following periods of increased money inflow. It employs proprietary responsive indicators and oscillators to gauge the strength and momentum of the inflow relative to previous periods, detecting money inflow, buying/selling pressure, and potential continuation/reversals, while using trailing stop exits to maximize gains while minimizing losses, with careful consideration of risk management and position sizing.
Setup Instructions:
1. Configuring the Strategy Properties:
Click the "Settings" icon (the gear symbol) next to the strategy name.
Navigate to the "Properties" tab within the Settings window.
Initial Capital: This value sets the starting equity for the strategy backtesting. Keep in mind that you will need to specify your current account size in the "Inputs" settings for position sizing.
Base Currency: Leave this setting at its "Default" value.
Order Size: This setting, which determines the capital used for each trade during backtesting, is automatically calculated and updated by the script. You should leave it set to "1 Contract" and the script will calculate the appropriate number of contracts based on your risk per trade, account size, and stop-loss placement.
Pyramiding: Set this setting at 1 order to prevent the strategy from adding to existing positions.
Commission: Enter your broker's commission fee per trade as a percentage, some brokers might offer commission free trading. Verify Price for limit orders: Keep this value as 0 ticks.
Slippage: This value depends on the instrument you are trading, If you are trading liquid stocks on a 1D chart slippage might be neglected. You can Keep this value as 1 ticks if you want to be conservative.
Margin for long positions/short positions: Set both of these to 100% since this strategy does not employ leverage or margin trading.
Recalculate:
Select the "After order is filled" option.
Select the "On every tick" option.
Fill Orders: Keep “Using bar magnifier” unselected.
Select "On bar close". Select "Using standard OHLC"
2. Configuring the Strategy Inputs:
Click the "Inputs" tab in the Settings window.
From/Thru (Date Range): To effectively backtest the strategy, define a substantial period that includes various bullish and bearish cycles. This ensures the testing window captures a range of market conditions and provides an adequate number of trades. It is usually favorable to use a minimum of 8 years for backtesting. Ensure the "Show Date Range" box is checked.
Account Size: This is your actual current Account Size used in the position sizing table calculations.
Risk on Capital %: This setting allows you to specify the percentage of your capital you are willing to risk on each trade. A common value is 0.5%.
3. Configuring Strategy Style:
Select the "Style" tab.
Select the checkbox for “Stop Loss” and “Stop Loss Final” to display the black/red Average True Range Stop Loss step-lines
Make sure the checkboxes for "Upper Channel", "Middle Line", and "Lower Channel" are selected.
Select the "Plots Background" checkboxes for "Color 0" and "Color 1" so that the potential entry and exit zones become color-coded.
Having the checkbox for "Tables" selected allows you to see position sizing and other useful information within the chart.
Have the checkboxes for "Trades on chart" and "Signal Labels" selected for viewing entry and exit point labels and positions.
Uncheck* the "Quantity" checkbox.
Precision: select “Default”.
Check “Labels on price scale”
Check “Values in status line”
Strategy Application Guidelines:
Entry Conditions:
The strategy identifies long entry opportunities based on substantial money inflow, as detected by our proprietary indicators and oscillators. This assessment considers the strength and momentum of the inflow relative to previous periods, in conjunction with strong price momentum (indicated by our modified, less-lagging MACD) and/or a potential price reversal (indicated by our modified, less-noisy Stochastic). Additional confirmation criteria related to price action are also incorporated. Potential entry and exit zones are visually represented by bands on the chart.
A blue upward-pointing arrow, accompanied by the label 'Long' and green band fills, signifies a long entry opportunity. Conversely, a magenta downward-pointing arrow, labeled 'Close entry(s) order Long' with yellow band fills, indicates a potential exit.
Take Profit:
The strategy employs trailing stops, rather than fixed take-profit levels, to maximize gains while minimizing losses. Trailing stops adjust the stop-loss level as the stock price moves in a favorable direction. The strategy utilizes two types of trailing stop mechanisms: one based on the Average True Range (ATR), and another based on price action, which attempts to identify shifts in price momentum.
Stop Loss:
The strategy uses an Average True Range (ATR)-based stop-loss, represented by two lines on the chart. The black line indicates the primary ATR-based stop-loss level, set upon trade entry. The red line represents a secondary ATR stop-loss buffer, used in the position sizing calculation to account for potential slippage or price gaps.
To potentially reduce the risk of stop-hunting, discretionary traders might consider using a market sell order within the final 30 to 60 minutes of the main session, instead of automated stop-loss orders.
Order Types:
Market Orders are intended for use with this strategy, specifically when the candle and signal on the chart stabilize within the final 30 to 60 minutes of the main trading session.
Position Sizing:
A key aspect of this strategy is that its position size is calculated and displayed in a table on the chart. The position size is calculated based on stop-loss placement, including the stop-loss buffer, and the capital at risk per trade which is commonly set around 0.5% Risk on Capital per Trade.
Backtesting:
The backtesting results presented below the chart are for informational purposes only and are not intended to predict future performance. Instead, they serve as a tool for identifying instruments with which the strategy has historically performed well.
It's important to note that the backtester utilizes a tiny portion of the capital for each trade while our strategy relies on a diversified portfolio of multiple stocks or instruments being traded at once.
Important Considerations:
Volume data is crucial; the strategy will not load or function correctly without it. Ensure that your charts include volume data, preferably from a centralized exchange.
Our system is designed for trading a portfolio. Therefore, if you intend to use our system, you should employ appropriate position sizing, without leverage or margin, and seek out a variety of long opportunities, rather than opening a single trade with an excessively large position size.
If you are trading without automated signals, always allow the chart to stabilize. Refrain from taking action until the final 1 hour to 30 minutes before the end of the main trading session to minimize the risk of acting on false signals.
To align with the strategy's design, it's generally preferable to enter a trade during the same session that the signal appears, rather than waiting for a later session.
Disclaimer:
Trading in financial markets involves a substantial degree of risk. You should be aware of the potential for significant financial losses. It is imperative that you trade responsibly and avoid overtrading, as this can amplify losses. Remember that market conditions can change rapidly, and past performance is not indicative of future results. You could lose some or all of your initial investment. It is strongly recommended that you fully understand the risks involved in trading and seek independent financial advice from a qualified professional before using this strategy.
Canuck Trading Trader StrategyCanuck Trading Trader Strategy
Overview
The Canuck Trading Trader Strategy is a high-performance, trend-following trading system designed for NASDAQ:TSLA on a 15-minute timeframe. Optimized for precision and profitability, this strategy leverages short-term price trends to capture consistent gains while maintaining robust risk management. Ideal for traders seeking an automated, data-driven approach to trading Tesla’s volatile market, it delivers strong returns with controlled drawdowns.
Key Features
Trend-Based Entries: Identifies short-term trends using a 2-candle lookback period and a minimum trend strength of 0.2%, ensuring responsive trade signals.
Risk Management: Includes a configurable 3.0% stop-loss to cap losses and a 2.0% take-profit to lock in gains, balancing risk and reward.
High Precision: Utilizes bar magnification for accurate backtesting, reflecting realistic trade execution with 1-tick slippage and 0.1 commission.
Clean Interface: No on-chart indicators, providing a distraction-free trading experience focused on performance.
Flexible Sizing: Allocates 10% of equity per trade with support for up to 2 simultaneous positions (pyramiding).
Performance Highlights
Backtested from March 1, 2024, to June 20, 2025, on NASDAQ:TSLA (15-minute timeframe) with $1,000,000 initial capital:
Net Profit: $2,279,888.08 (227.99%)
Win Rate: 52.94% (3,039 winning trades out of 5,741)
Profit Factor: 3.495
Max Drawdown: 2.20%
Average Winning Trade: $1,050.91 (0.55%)
Average Losing Trade: $338.20 (0.18%)
Sharpe Ratio: 2.468
Note: Past performance is not indicative of future results. Always validate with your own backtesting and forward testing.
Usage Instructions
Setup:
Apply the strategy to a NASDAQ:TSLA 15-minute chart.
Ensure your TradingView account supports bar magnification for accurate results.
Configuration:
Lookback Candles: Default is 2 (recommended).
Min Trend Strength: Set to 0.2% for optimal trade frequency.
Stop Loss: Default 3.0% to cap losses.
Take Profit: Default 2.0% to secure gains.
Order Size: 10% of equity per trade.
Pyramiding: Allows up to 2 orders.
Commission: Set to 0.1.
Slippage: Set to 1 tick.
Enable "Recalculate After Order is Filled" and "Recalculate on Every Tick" in backtest settings.
Backtesting:
Run backtests over March 1, 2024, to June 20, 2025, to verify performance.
Adjust stop-loss (e.g., 2.5%) or take-profit (e.g., 1–3%) to suit your risk tolerance.
Live Trading:
Use with a compatible broker or TradingView alerts for automated execution.
Monitor execution for slippage or latency, especially given the high trade frequency (5,741 trades).
Validate in a demo account before deploying with real capital.
Risk Disclosure
Trading involves significant risk and may result in losses exceeding your initial capital. The Canuck Trading Trader Strategy is provided for educational and informational purposes only. Users are responsible for their own trading decisions and should conduct thorough testing before using in live markets. The strategy’s high trade frequency requires reliable execution infrastructure to minimize slippage and latency.
ICT Master Suite [Trading IQ]Hello Traders!
We’re excited to introduce the ICT Master Suite by TradingIQ, a new tool designed to bring together several ICT concepts and strategies in one place.
The Purpose Behind the ICT Master Suite
There are a few challenges traders often face when using ICT-related indicators:
Many available indicators focus on one or two ICT methods, which can limit traders who apply a broader range of ICT related techniques on their charts.
There aren't many indicators for ICT strategy models, and we couldn't find ICT indicators that allow for testing the strategy models and setting alerts.
Many ICT related concepts exist in the public domain as indicators, not strategies! This makes it difficult to verify that the ICT concept has some utility in the market you're trading and if it's worth trading - it's difficult to know if it's working!
Some users might not have enough chart space to apply numerous ICT related indicators, which can be restrictive for those wanting to use multiple ICT techniques simultaneously.
The ICT Master Suite is designed to offer a comprehensive option for traders who want to apply a variety of ICT methods. By combining several ICT techniques and strategy models into one indicator, it helps users maximize their chart space while accessing multiple tools in a single slot.
Additionally, the ICT Master Suite was developed as a strategy . This means users can backtest various ICT strategy models - including deep backtesting. A primary goal of this indicator is to let traders decide for themselves what markets to trade ICT concepts in and give them the capability to figure out if the strategy models are worth trading!
What Makes the ICT Master Suite Different
There are many ICT-related indicators available on TradingView, each offering valuable insights. What the ICT Master Suite aims to do is bring together a wider selection of these techniques into one tool. This includes both key ICT methods and strategy models, allowing traders to test and activate strategies all within one indicator.
Features
The ICT Master Suite offers:
Multiple ICT strategy models, including the 2022 Strategy Model and Unicorn Model, which can be built, tested, and used for live trading.
Calculation and display of key price areas like Breaker Blocks, Rejection Blocks, Order Blocks, Fair Value Gaps, Equal Levels, and more.
The ability to set alerts based on these ICT strategies and key price areas.
A comprehensive, yet practical, all-inclusive ICT indicator for traders.
Customizable Timeframe - Calculate ICT concepts on off-chart timeframes
Unicorn Strategy Model
2022 Strategy Model
Liquidity Raid Strategy Model
OTE (Optimal Trade Entry) Strategy Model
Silver Bullet Strategy Model
Order blocks
Breaker blocks
Rejection blocks
FVG
Strong highs and lows
Displacements
Liquidity sweeps
Power of 3
ICT Macros
HTF previous bar high and low
Break of Structure indications
Market Structure Shift indications
Equal highs and lows
Swings highs and swing lows
Fibonacci TPs and SLs
Swing level TPs and SLs
Previous day high and low TPs and SLs
And much more! An ongoing project!
How To Use
Many traders will already be familiar with the ICT related concepts listed above, and will find using the ICT Master Suite quite intuitive!
Despite this, let's go over the features of the tool in-depth and how to use the tool!
The image above shows the ICT Master Suite with almost all techniques activated.
ICT 2022 Strategy Model
The ICT Master suite provides the ability to test, set alerts for, and live trade the ICT 2022 Strategy Model.
The image above shows an example of a long position being entered following a complete setup for the 2022 ICT model.
A liquidity sweep occurs prior to an upside breakout. During the upside breakout the model looks for the FVG that is nearest 50% of the setup range. A limit order is placed at this FVG for entry.
The target entry percentage for the range is customizable in the settings. For instance, you can select to enter at an FVG nearest 33% of the range, 20%, 66%, etc.
The profit target for the model generally uses the highest high of the range (100%) for longs and the lowest low of the range (100%) for shorts. Stop losses are generally set at 0% of the range.
The image above shows the short model in action!
Whether you decide to follow the 2022 model diligently or not, you can still set alerts when the entry condition is met.
ICT Unicorn Model
The image above shows an example of a long position being entered following a complete setup for the ICT Unicorn model.
A lower swing low followed by a higher swing high precedes the overlap of an FVG and breaker block formed during the sequence.
During the upside breakout the model looks for an FVG and breaker block that formed during the sequence and overlap each other. A limit order is placed at the nearest overlap point to current price.
The profit target for this example trade is set at the swing high and the stop loss at the swing low. However, both the profit target and stop loss for this model are configurable in the settings.
For Longs, the selectable profit targets are:
Swing High
Fib -0.5
Fib -1
Fib -2
For Longs, the selectable stop losses are:
Swing Low
Bottom of FVG or breaker block
The image above shows the short version of the Unicorn Model in action!
For Shorts, the selectable profit targets are:
Swing Low
Fib -0.5
Fib -1
Fib -2
For Shorts, the selectable stop losses are:
Swing High
Top of FVG or breaker block
The image above shows the profit target and stop loss options in the settings for the Unicorn Model.
Optimal Trade Entry (OTE) Model
The image above shows an example of a long position being entered following a complete setup for the OTE model.
Price retraces either 0.62, 0.705, or 0.79 of an upside move and a trade is entered.
The profit target for this example trade is set at the -0.5 fib level. This is also adjustable in the settings.
For Longs, the selectable profit targets are:
Swing High
Fib -0.5
Fib -1
Fib -2
The image above shows the short version of the OTE Model in action!
For Shorts, the selectable profit targets are:
Swing Low
Fib -0.5
Fib -1
Fib -2
Liquidity Raid Model
The image above shows an example of a long position being entered following a complete setup for the Liquidity Raid Modell.
The user must define the session in the settings (for this example it is 13:30-16:00 NY time).
During the session, the indicator will calculate the session high and session low. Following a “raid” of either the session high or session low (after the session has completed) the script will look for an entry at a recently formed breaker block.
If the session high is raided the script will look for short entries at a bearish breaker block. If the session low is raided the script will look for long entries at a bullish breaker block.
For Longs, the profit target options are:
Swing high
User inputted Lib level
For Longs, the stop loss options are:
Swing low
User inputted Lib level
Breaker block bottom
The image above shows the short version of the Liquidity Raid Model in action!
For Shorts, the profit target options are:
Swing Low
User inputted Lib level
For Shorts, the stop loss options are:
Swing High
User inputted Lib level
Breaker block top
Silver Bullet Model
The image above shows an example of a long position being entered following a complete setup for the Silver Bullet Modell.
During the session, the indicator will determine the higher timeframe bias. If the higher timeframe bias is bullish the strategy will look to enter long at an FVG that forms during the session. If the higher timeframe bias is bearish the indicator will look to enter short at an FVG that forms during the session.
For Longs, the profit target options are:
Nearest Swing High Above Entry
Previous Day High
For Longs, the stop loss options are:
Nearest Swing Low
Previous Day Low
The image above shows the short version of the Silver Bullet Model in action!
For Shorts, the profit target options are:
Nearest Swing Low Below Entry
Previous Day Low
For Shorts, the stop loss options are:
Nearest Swing High
Previous Day High
Order blocks
The image above shows indicator identifying and labeling order blocks.
The color of the order blocks, and how many should be shown, are configurable in the settings!
Breaker Blocks
The image above shows indicator identifying and labeling order blocks.
The color of the breaker blocks, and how many should be shown, are configurable in the settings!
Rejection Blocks
The image above shows indicator identifying and labeling rejection blocks.
The color of the rejection blocks, and how many should be shown, are configurable in the settings!
Fair Value Gaps
The image above shows indicator identifying and labeling fair value gaps.
The color of the fair value gaps, and how many should be shown, are configurable in the settings!
Additionally, you can select to only show fair values gaps that form after a liquidity sweep. Doing so reduces "noisy" FVGs and focuses on identifying FVGs that form after a significant trading event.
The image above shows the feature enabled. A fair value gap that occurred after a liquidity sweep is shown.
Market Structure
The image above shows the ICT Master Suite calculating market structure shots and break of structures!
The color of MSS and BoS, and whether they should be displayed, are configurable in the settings.
Displacements
The images above show indicator identifying and labeling displacements.
The color of the displacements, and how many should be shown, are configurable in the settings!
Equal Price Points
The image above shows the indicator identifying and labeling equal highs and equal lows.
The color of the equal levels, and how many should be shown, are configurable in the settings!
Previous Custom TF High/Low
The image above shows the ICT Master Suite calculating the high and low price for a user-defined timeframe. In this case the previous day’s high and low are calculated.
To illustrate the customizable timeframe function, the image above shows the indicator calculating the previous 4 hour high and low.
Liquidity Sweeps
The image above shows the indicator identifying a liquidity sweep prior to an upside breakout.
The image above shows the indicator identifying a liquidity sweep prior to a downside breakout.
The color and aggressiveness of liquidity sweep identification are adjustable in the settings!
Power Of Three
The image above shows the indicator calculating Po3 for two user-defined higher timeframes!
Macros
The image above shows the ICT Master Suite identifying the ICT macros!
ICT Macros are only displayable on the 5 minute timeframe or less.
Strategy Performance Table
In addition to a full-fledged TradingView backtest for any of the ICT strategy models the indicator offers, a quick-and-easy strategy table exists for the indicator!
The image above shows the strategy performance table in action.
Keep in mind that, because the ICT Master Suite is a strategy script, you can perform fully automatic backtests, deep backtests, easily add commission and portfolio balance and look at pertinent metrics for the ICT strategies you are testing!
Lite Mode
Traders who want the cleanest chart possible can toggle on “Lite Mode”!
In Lite Mode, any neon or “glow” like effects are removed and key levels are marked as strict border boxes. You can also select to remove box borders if that’s what you prefer!
Settings Used For Backtest
For the displayed backtest, a starting balance of $1000 USD was used. A commission of 0.02%, slippage of 2 ticks, a verify price for limit orders of 2 ticks, and 5% of capital investment per order.
A commission of 0.02% was used due to the backtested asset being a perpetual future contract for a crypto currency. The highest commission (lowest-tier VIP) for maker orders on many exchanges is 0.02%. All entered positions take place as maker orders and so do profit target exits. Stop orders exist as stop-market orders.
A slippage of 2 ticks was used to simulate more realistic stop-market orders. A verify limit order settings of 2 ticks was also used. Even though BTCUSDT.P on Binance is liquid, we just want the backtest to be on the safe side. Additionally, the backtest traded 100+ trades over the period. The higher the sample size the better; however, this example test can serve as a starting point for traders interested in ICT concepts.
Community Assistance And Feedback
Given the complexity and idiosyncratic applications of ICT concepts amongst its proponents, the ICT Master Suite’s built-in strategies and level identification methods might not align with everyone's interpretation.
That said, the best we can do is precisely define ICT strategy rules and concepts to a repeatable process, test, and apply them! Whether or not an ICT strategy is trading precisely how you would trade it, seeing the model in action, taking trades, and with performance statistics is immensely helpful in assessing predictive utility.
If you think we missed something, you notice a bug, have an idea for strategy model improvement, please let us know! The ICT Master Suite is an ongoing project that will, ideally, be shaped by the community.
A big thank you to the @PineCoders for their Time Library!
Thank you!
FXC NQ Opening Range Breakout Strategy V2.4Mechanical Strategy that trades breakouts on NQ futures on the 15min timeframe during the NYSE session. It's designed to manage Apex and Top Step accounts with the lowest risk possible.
Risk Disclaimer:
Past results as well as strategy tester reports do not indicate future performance. Guarantees do not exist in trading. By using this strategy you risk losing all your money.
Important:
It only trades on Monday, Wednesday and Friday and takes usually only 1 trade per trading day.
It works on the 15min timeframe only.
The settings are optimised already for NQ but feel free to change them.
How it works:
Every selected trading day it measures the range of the first 15min candle after the NYSE open. As soon as price closes above on the 15min timeframe, it will trade the breakout targeting a set risk to reward ratio. SL on the opposite side of the range. It will trail the SL after a set amount of points and uses a buffer of the set amount of points to trail it.
Settings:
Opening Range Time : This is the time of the day in hours and minutes when the strategy starts looking for trades. It's in the EST/ NY Timezone and set to 9:30-09:45 by default
because that's the NYSE open.
Session Time : This is the time of the day in hours and minutes until the strategy trades. It's in the EST/ NY Timezone and set to 09:45-14:45 by default.
because that's what gave the best results in backtesting. Open trades will get closed automatically once the end of the session is reached. No matter if win or loss. This is just to prevent holding positions over night.
Session Border This setting is to select the border color in which the session box will be plotted.
Opening Range Box This setting is to select the fill color of the opening range box.
Opening Range Border This setting is to select the border color of the session box.
Trade Timeframe This setting determines on which timeframe candle has to close outside the opening range box in order to take a trade. It's set to 15min by default because this is what worked by far the best in backtests and live trading.
Stop Loss Buffer in Points: This is simply the buffer in points that is added to the SL for safety reasons. If you have it on 0, the SL will be at the exact price of the opposite side of the range. By default it's set to 0 pips because this is what delivered the best results in backtests.
Profit Target Factor: This is simply the total SL size in points multiplied by x.
Example: If you put 2, you get a 1:2 Risk to Reward Ratio. By Default it's set to 4 because this gave the best results in backtests, because trades always get closed either by trailing SL or because the end of the session is reached.
Use Trailing Stop Loss: This setting is to enable/ disable the trailing stop loss. It's enabled by default because this is a fundamental part of the strategy.
Trailing Stop Buffer: This setting determines after how many points in profit the trailing SL will be activated.
Risk Type: You can chose either between Fixed USD Amount, Risk per Trade in % or Fixed Contract Size. By default it's set to fixed contract size.
Risk Amount (USD or Contracts): This setting is to set how many USD or how many contracts you want to risk per trade. Make sure to check which risk type you have selected before you chose the risk amount.
Use Limit Orders If enabled, the strategy will place a pending order x points from the current price, instead of a market order. Limit orders are enabled by default for a better performance. Important: It doesn't actually place a limit order. The strategy will just wait for a pullback and then enter with a market order. It's more like a hidden limit order.
Limit Order Distance (points): If you have limit orders enabled, this setting determines how many points from the current price the limit order will be placed.
Trading Days: These checkboxes are to select on which week days the strategy has to trade. Thursday is disabled by default because backtests have shown that Thursday is the least profitable day
Backtest Settings:
For the backtest the commissions ere set to 0.35 USD per mini contract which is the highest amount Tradeovate charges. Margin was not accounted for because typically on Apex accounts you can use way more contracts than you need for the extremely low max drawdown. Margin would be important on personal accounts but even there typically it's not an issue at all especially because this strategy runs on the 15min timeframe so it won't use a lot of contracts anyways.
What makes it unique:
This script is unique because it's designed to be used on Apex and Top Step accounts with extremely strict drawdown rules.
The strategy is optimised to be traded with a fixed contract size instead of using % risk. The reason for that is that the drawdown rules of these Futures Prop Accounts are very strict and the fact that the smallest trade-able contract size is 1.
Why the source code is hidden:
The source code is hidden because I invested a lot of time and money into developing this strategy and optimising it with paid 3rd party software. Also since I use it myself on my Apex accounts and prop firms don't allow copy trading I don't want it to be used by too many traders.
Trailing Take Profit - Close Based📝 Description
This script demonstrates a new approach to the trailing take profit.
Trailing Take Profit is a price-following technique. When used, instead of setting a limit order for the take profit target exiting from your position at the specified price, a stop order is conditionally set when the take profit target is reached. Then, the stop price (a.k.a trailing price), is placed below the take profit target at a distance defined by the user percentagewise. On regular time intervals, the stop price gets updated by following the "Trail Barrier" price (high by default) upwards. When the current price hits the stop price you exit the trade. Check the chart for more details.
This script demonstrates how to implement the close-based Trailing Take Profit logic for long positions, but it can also be applied for short positions if the logic is "reversed".
📢 NOTE
To generate some entries and showcase the "Trailing Take Profit" technique, this script uses the crossing of two moving averages. Please keep in mind that you should not relate the Backtesting results you see in the "Strategy Tester" tab with the success of the technique itself.
This is not a complete strategy per se, and the backtest results are affected by many parameters that are outside of the scope of this publication. If you choose to use this new approach of the "Trailing Take Profit" in your logic you have to make sure that you are backtesting the whole strategy.
⚔️ Comparison
In contrast to my older "Trailing Take Profit" publication where the trailing take profit implementation was tick-based, this new approach is close-based, meaning that the update of the stop price occurs at the bar close instead of every tick.
While comparing the real-time results of the two implementations is like comparing apples to oranges, because they have different dynamic behavior, the new approach offers better consistency between the backtesting results and the real-time results.
By updating the stop price on every bar close, you do not rely on the backtester assumptions anymore (check the Reasoning section below for more info).
The new approach resembles the conditional "Trailing Exit" technique, where the condition is true when the current price crosses over the take profit target. Then, the stop order is placed at the trailing price and it gets updated on every bar close to "follow" the barrier price (high). On the other hand, the older tick-based approach had more "tight" dynamics since the trailing price gets updated on every tick leaving less room for price fluctuations by making it more probable to reach the trailing price.
🤔 Reasoning
This new close-based approach addresses several practical issues the older tick-based approach had. Those issues arise mainly from the technicalities of the TV Backtester. More specifically, due to the assumptions the Broker Emulator makes for the price action of the history bars, the backtesting results in the TV Backtester are exaggerated, and depending on the timeframe, the backtesting results look way better than they are in reality.
The effect above, and the inability to reason about the performance of a strategy separated people into two groups. Those who never use this feature, because they couldn't know for sure the actual effect it might have in their strategy, (even if it turned out to be more profitable) and those who abused this type of "repainting" behavior to show off, and hijack some boosts from the community by boasting about the "fake" results of their strategies.
Even if there are ways to evaluate the effectiveness of the tick-based approach that is applied in an existing strategy (this is out of the topic of this publication), it requires extra effort to do the analysis. Using this closed-based approach we can have more predictable results, without surprises.
⚠️ Caveats
Since this approach updates the trailing price on bar close, you must wait for at least one bar to close after the price crosses over the take profit target.
Liquidity Maxing [JOAT]Liquidity Maxing - Institutional Liquidity Matrix
Introduction
Liquidity Maxing is an open-source strategy for TradingView built around institutional market structure concepts. It identifies structural shifts, evaluates trades through multi-factor confluence, and implements layered risk controls.
The strategy is designed for swing trading on 4-hour timeframes, focusing on how institutional order flow manifests in price action through structure breaks, inducements, and liquidity sweeps.
Core Functionality
Liquidity Maxing performs three primary functions:
Tracks market structure to identify when control shifts between buyers and sellers
Scores potential trades using an eight-factor confluence system
Manages position sizing and risk exposure dynamically based on volatility and user-defined limits
The goal is selective trading when multiple conditions align, rather than frequent entries.
Market Structure Engine
The structure engine tracks three key events:
Break of Structure (BOS): Price pushes beyond a prior pivot in the direction of trend
Change of Character (CHoCH): Control flips from bullish to bearish or vice versa
Inducement Sweeps (IDM): Market briefly runs stops against trend before moving in the real direction
The structure module continuously updates strong highs and lows, labeling structural shifts visually. IDM markers are optional and disabled by default to maintain chart clarity.
The trade engine requires valid structure alignment before considering entries. No structure, no trade.
Eight-Factor Confluence System
Instead of relying on a single indicator, Liquidity Maxing uses an eight-factor scoring system:
Structure alignment with current trend
RSI within healthy bands (different ranges for up and down trends)
MACD momentum agreement with direction
Volume above adaptive baseline
Price relative to main trend EMA
Session and weekend filter (configurable)
Volatility expansion/contraction via ATR shifts
Higher-timeframe EMA confirmation
Each factor contributes one point to the confluence score. The default minimum confluence threshold is 6 out of 8, but you can adjust this from 1-8 based on your preference for trade frequency versus selectivity.
Only when structure and confluence agree does the strategy proceed to risk evaluation.
Dynamic Risk Management
Risk controls are implemented in multiple layers:
ATR-based stops and targets with configurable risk-to-reward ratio (default 2:1)
Volatility-adjusted position sizing to maintain consistent risk per trade as ranges expand or compress
Daily and weekly risk budgets that halt new entries once thresholds are reached
Correlation cooldown to prevent clustered trades in the same direction
Global circuit breaker with maximum drawdown limit and emergency kill switch
If any guardrail is breached, the strategy will not open new positions. The dashboard clearly displays risk state for transparency.
Market Presets
The strategy includes configuration presets optimized for different market types:
Crypto (BTC/ETH): RSI bands 70/30, volume multiplier 1.2, enhanced ATR scaling
Forex Majors: RSI bands 75/25, volume multiplier 1.5
Indices (SPY/QQQ): RSI bands 70/30, volume multiplier 1.3
Custom: Default values for user customization
For crypto assets, the strategy automatically applies ATR volatility scaling to account for higher volatility characteristics.
Monitoring and Dashboards
The strategy includes optional monitoring layers:
Risk Operations Dashboard (top-right):
Trend state
Confluence score
ATR value
Current position size percentage
Global drawdown
Daily and weekly risk consumption
Correlation guard state
Alert mode status
Performance Console (top-left):
Net profit
Current equity
Win rate percentage
Average trade value
Sharpe-style ratio (rolling 50-bar window)
Profit factor
Open trade count
Optional risk tint on chart background provides visual indication of "safe to trade" versus "halted" state.
All visualization elements can be toggled on/off from the inputs for clean chart viewing or full telemetry during parameter tuning.
Alerts and Automation
The strategy supports alert integration with two formats:
Standard alerts: Human-readable messages for long, short, and risk-halt conditions
Webhook format: JSON-formatted payloads ready for external execution systems (optional)
Alert messages are predictable and unambiguous, suitable for manual review or automated forwarding to execution engines.
Built-in Validation Suite
The strategy includes an optional validation layer that can be enabled from inputs. It checks:
Internal consistency of structure and confluence metrics
Sanity and ordering of risk parameters
Position sizing compliance with user-defined floors and caps
This validation is optional and not required for trading, but provides transparency into system operation during development or troubleshooting.
Strategy Parameters
Market Presets:
Configuration Preset: Choose between Crypto (BTC/ETH), Forex Majors, Indices (SPY/QQQ), or Custom
Market Structure Architecture:
Pivot Length: Default 5 bars
Filter by Inducement (IDM): Default enabled
Visualize Structure: Default enabled
Structure Lookback: Default 50 bars
Risk & Capital Preservation:
Risk:Reward Ratio: Default 2.0
ATR Period: Default 14
ATR Multiplier (Stop): Default 2.0
Max Drawdown Circuit Breaker: Default 10%
Risk per Trade (% Equity): Default 1.5%
Daily Risk Limit: Default 6%
Weekly Risk Limit: Default 12%
Min Position Size (% Equity): Default 0.25%
Max Position Size (% Equity): Default 5%
Correlation Cooldown (bars): Default 3
Emergency Kill Switch: Default disabled
Signal Confluence:
RSI Length: Default 14
Trend EMA: Default 200
HTF Confirmation TF: Default Daily
Allow Weekend Trading: Default enabled
Minimum Confluence Score (0-8): Default 6
Backtesting Considerations
When backtesting this strategy, consider the following:
Commission: Default 0.05% (adjustable in strategy settings)
Initial Capital: Default $100,000 (adjustable)
Position Sizing: Uses percentage of equity (default 2% per trade)
Timeframe: Optimized for 4-hour charts, though can be tested on other timeframes
Results will vary significantly based on:
Market conditions and volatility regimes
Parameter settings, especially confluence threshold
Risk limit configuration
Symbol characteristics (crypto vs forex vs equities)
Past performance does not guarantee future results. Win rate, profit factor, and other metrics should be evaluated in context of drawdown periods, trade frequency, and market conditions.
How to Use This Strategy
This is a framework that requires understanding and parameter tuning, not a one-size-fits-all solution.
Recommended workflow:
Start on 4-hour timeframe with default parameters and appropriate market preset
Run backtests and study performance console metrics: focus on drawdown behavior, win rate, profit factor, and trade frequency
Adjust confluence threshold to match your risk appetite—higher thresholds mean fewer but more selective trades
Set realistic daily and weekly risk budgets appropriate for your account size and risk tolerance
Consider ATR multiplier adjustments based on market volatility characteristics
Only connect alerts or automation after thorough testing and parameter validation
Treat this as a risk framework with an integrated entry engine, not merely an entry signal generator. The risk controls are as important as the trade signals.
Strategy Limitations
Designed for swing trading timeframes; may not perform optimally on very short timeframes
Requires sufficient market structure to identify pivots; may struggle in choppy or low-volatility environments
Crypto markets require different parameter tuning than traditional markets
Risk limits may prevent entries during favorable setups if daily/weekly budgets are exhausted
Correlation cooldown may delay entries that would otherwise be valid
Backtesting results depend on data quality and may not reflect live trading with slippage
Design Philosophy
Many indicators tell you when price crossed a moving average or RSI left oversold. This strategy addresses questions institutional traders ask:
Who is in control of the market right now?
Is this move structurally significant or just noise?
Do I want to add more risk given what I've already done today/week?
If I'm wrong, exactly how painful can this be?
The strategy provides disciplined, repeatable answers to these questions through systematic structure analysis, confluence filtering, and multi-layer risk management.
Technical Implementation
The strategy uses Pine Script v6 with:
Custom types for structure, confluence, and risk state management
Functional programming approach for reusable calculations
State management through persistent variables
Optional visual elements that can be toggled independently
The code is open-source and can be modified to suit individual needs. All important logic is visible in the source code.
Disclaimer
This script is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation. Trading involves substantial risk of loss and is not suitable for all investors. Past performance, whether real or indicated by historical tests of strategies, is not indicative of future results.
No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between backtested results and actual results subsequently achieved by any particular trading strategy.
The user should be aware of the risks involved in trading and should trade only with risk capital. The authors and publishers of this script are not responsible for any losses or damages, including without limitation, any loss of profit, which may arise directly or indirectly from use of or reliance on this script.
This strategy uses technical analysis methods and indicators that are not guaranteed to be accurate or profitable. Market conditions change, and strategies that worked in the past may not work in the future. Users should thoroughly test any strategy in a paper trading environment before risking real capital.
Commission and slippage settings in backtests may not accurately reflect live trading conditions. Real trading results will vary based on execution quality, market liquidity, and other factors not captured in backtesting.
The user assumes full responsibility for all trading decisions made using this script. Always consult with a qualified financial advisor before making investment decisions.
Enjoy - officialjackofalltrades
Quantum Reversal Engine [ApexLegion]Quantum Reversal Engine
STRATEGY OVERVIEW
This strategy is constructed using 5 custom analytical filters that analyze different market dimensions - trend structure, momentum expansion, volume confirmation, price action patterns, and reversal detection - with results processed through a multi-component scoring calculation that determines signal generation and position management decisions.
Why These Custom Filters Were Independently Developed:
This strategy employs five custom-developed analytical filters:
1. Apex Momentum Core (AMC) - Custom oscillator with volatility-scaled deviation calculation
Standard oscillators lag momentum shifts by 2-3 bars. Custom calculation designed for momentum analysis
2. Apex Wick Trap (AWT) - Wick dominance analysis for trap detection
Existing wick analysis tools don't quantify trap conditions. Uses specific ratios for wick dominance detection
3. Apex Volume Pulse (AVP) - Volume surge validation with participation confirmation
Volume indicators typically use simple averages. Uses surge multipliers with participation validation
4. Apex TrendGuard (ATG) - Angle-based trend detection with volatility band integration
EMA slope calculations often produce false signals. Uses angle analysis with volatility bands for confirmation
5. Quantum Composite Filter (QCF) - Multi-component scoring and signal generation system
Composite scoring designed to filter noise by requiring multiple confirmations before signal activation.
Each filter represents mathematical calculations designed to address specific analytical requirements.
Framework Operation: The strategy functions as a scoring framework where each filter contributes weighted points based on market conditions. Entry signals are generated when minimum threshold scores are met. Exit management operates through a three-tier system with continued signal strength evaluation determining position holds versus closures at each TP level.
Integration Challenge: The core difficulty was creating a scoring system where five independent filters could work together without generating conflicting signals. This required backtesting to determine effective weight distributions.
Custom Filter Development:
Each of the five filters represents analytical approaches developed through testing and validation:
Integration Validation: Each filter underwent individual testing before integration. The composite scoring system required validation to verify that filters complement rather than conflict with each other, resulting in a cohesive analytical framework that was tested during the development period.
These filters represent custom-developed components created specifically for this strategy, with each component addressing different analytical requirements through testing and parameter adjustment.
Programming Features:
Multi-timeframe data handling with backup systems
Performance optimization techniques
Error handling for live trading scenarios
Parameter adaptation based on market conditions
Strategy Features:
Uses multi-filter confirmation approach
Adapts position holding based on continued signal strength
Includes analysis tools for trade review and optimization
Ongoing Development: The strategy was developed through testing and validation processes during the creation period.
COMPONENT EXPLANATION
EMA System
Uses 8 exponential moving averages (7, 14, 21, 30, 50, 90, 120, 200 periods) for trend identification. Primary signals come from 8/21 EMA crossovers, while longer EMAs provide structural context. EMA 1-4 determine short-term structure, EMA 5-8 provide long-term trend confirmation.
Apex Momentum Core (AMC)
Built custom oscillator mathematics after testing dozens of momentum calculation methods. Final algorithm uses price deviation from EMA baseline with volatility scaling to reduce lag while maintaining accuracy across different market conditions.
Custom momentum oscillator using price deviation from EMA baseline:
apxCI = 100 * (source - emaBase) / (sensitivity * sqrt(deviation + 1))
fastLine = EMA(apxCI, smoothing)
signalLine = SMA(fastLine, 4)
Signals generate when fastLine crosses signalLine at +50/-50 thresholds.
This identifies momentum expansion before traditional oscillators.
Apex Volume Pulse (AVP)
Created volume surge analysis that goes beyond simple averages. Extensive testing determined 1.3x multiplier with participation validation provides reliable confirmation while filtering false volume spikes.
Compares current volume to 21-period moving average.
Requires 1.3x average volume for signal confirmation. This filters out low-volume moves during quiet periods and confirms breakouts with actual participation.
Apex Wick Trap (AWT)
Developed proprietary wick trap detection through analysis of failed breakout patterns. Tested various ratio combinations before settling on 60% wick dominance + 20% body limit as effective trap identification parameters.
Analyzes candle structure to identify failed breakouts:
candleRange = math.max(high - low, 0.00001)
candleBody = math.abs(close - open)
bodyRatio = candleBody / candleRange
upperWick = high - math.max(open, close)
lowerWick = math.min(open, close) - low
upperWickRatio = upperWick / candleRange
lowerWickRatio = lowerWick / candleRange
trapWickLong = showAWT and lowerWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close > open
trapWickShort = showAWT and upperWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close < open This catches reversals after fake breakouts.
Apex TrendGuard (ATG)
Built angle-based trend detection after standard EMA crossovers proved insufficient. Combined slope analysis with volatility bands through iterative testing to eliminate false trend signals.
EMA slope analysis with volatility bands:
Fast EMA (21) vs Slow EMA (55) for trend direction
Angle calculation: atan(fast - slow) * 180 / π
ATR bands (1.75x multiplier) for breakout confirmation
Minimum 25° angle for strong trend classification
Core Algorithm Framework
1. Composite Signal Generation
calculateCompositeSignals() =>
// Component Conditions
structSignalLong = trapWickLong
structSignalShort = trapWickShort
momentumLong = amcBuySignal
momentumShort = amcSellSignal
volumeSpike = volume > volAvg_AVP * volMult_AVP
priceStrength_Long = close > open and close > close
priceStrength_Short = close < open and close < close
rsiMfiComboValue = (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
reversalTrigger_Long = ta.crossover(rsiMfiComboValue, 50)
reversalTrigger_Short = ta.crossunder(rsiMfiComboValue, 50)
isEMACrossUp = ta.crossover(emaFast_ATG, emaSlow_ATG)
isEMACrossDown = ta.crossunder(emaFast_ATG, emaSlow_ATG)
// Enhanced Composite Score Calculation
scoreBuy = 0.0
scoreBuy += structSignalLong ? scoreStruct : 0.0
scoreBuy += momentumLong ? scoreMomentum : 0.0
scoreBuy += flashSignal ? weightFlash : 0.0
scoreBuy += blinkSignal ? weightBlink : 0.0
scoreBuy += volumeSpike_AVP ? scoreVolume : 0.0
scoreBuy += priceStrength_Long ? scorePriceAction : 0.0
scoreBuy += reversalTrigger_Long ? scoreReversal : 0.0
scoreBuy += emaAlignment_Bull ? weightTrendAlign : 0.0
scoreBuy += strongUpTrend ? weightTrendAlign : 0.0
scoreBuy += highRisk_Long ? -1.2 : 0.0
scoreBuy += signalGreenDot ? 1.0 : 0.0
scoreBuy += isAMCUp ? 0.8 : 0.0
scoreBuy += isVssBuy ? 1.5 : 0.0
scoreBuy += isEMACrossUp ? 1.0 : 0.0
scoreBuy += signalRedX ? -1.0 : 0.0
scoreSell = 0.0
scoreSell += structSignalShort ? scoreStruct : 0.0
scoreSell += momentumShort ? scoreMomentum : 0.0
scoreSell += flashSignal ? weightFlash : 0.0
scoreSell += blinkSignal ? weightBlink : 0.0
scoreSell += volumeSpike_AVP ? scoreVolume : 0.0
scoreSell += priceStrength_Short ? scorePriceAction : 0.0
scoreSell += reversalTrigger_Short ? scoreReversal : 0.0
scoreSell += emaAlignment_Bear ? weightTrendAlign : 0.0
scoreSell += strongDownTrend ? weightTrendAlign : 0.0
scoreSell += highRisk_Short ? -1.2 : 0.0
scoreSell += signalRedX ? 1.0 : 0.0
scoreSell += isAMCDown ? 0.8 : 0.0
scoreSell += isVssSell ? 1.5 : 0.0
scoreSell += isEMACrossDown ? 1.0 : 0.0
scoreSell += signalGreenDot ? -1.0 : 0.0
compositeBuySignal = enableComposite and scoreBuy >= thresholdCompositeBuy
compositeSellSignal = enableComposite and scoreSell >= thresholdCompositeSell
if compositeBuySignal and compositeSellSignal
compositeBuySignal := false
compositeSellSignal := false
= calculateCompositeSignals()
// Final Entry Signals
entryCompositeBuySignal = compositeBuySignal and ta.rising(emaFast_ATG, 2)
entryCompositeSellSignal = compositeSellSignal and ta.falling(emaFast_ATG, 2)
Calculates weighted scores from independent modules and activates signals only when threshold requirements are met.
2. Smart Exit Hold Evaluation System
evaluateSmartHold() =>
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
avgVolume = ta.sma(volume, 20)
volumeSpike = volume > avgVolume * volMultiplier
// MTF Bull/Bear conditions
mtf_bull = mtf_emaFast_final > mtf_emaSlow_final
mtf_bear = mtf_emaFast_final < mtf_emaSlow_final
emaBackupDivergence = math.abs(mtf_emaFast_backup - mtf_emaSlow_backup) / mtf_emaSlow_backup
emaBackupStrong = emaBackupDivergence > 0.008
mtfConflict_Long = inLong and mtf_bear and emaBackupStrong
mtfConflict_Short = inShort and mtf_bull and emaBackupStrong
// Layer 1: ATR-Based Dynamic Threshold (Market Volatility Intelligence)
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : (atrRatio > 0.01 ? 1.5 : 2.8)
// Layer 2: ROI-Conditional Time Intelligence (Selective Pressure)
timeMultiplier_Long = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Long <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Long <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
timeMultiplier_Short = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Short <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Short <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
// Dual-Layer Threshold Calculation
baseThreshold_Long = mtfConflict_Long ? dynamicThreshold + 1.0 : dynamicThreshold
baseThreshold_Short = mtfConflict_Short ? dynamicThreshold + 1.0 : dynamicThreshold
timeAdjustedThreshold_Long = baseThreshold_Long * timeMultiplier_Long
timeAdjustedThreshold_Short = baseThreshold_Short * timeMultiplier_Short
// Final Smart Hold Decision with Dual-Layer Intelligence
smartHold_Long = not mtfConflict_Long and smartScoreLong >= timeAdjustedThreshold_Long and compositeBuyRecentCount >= signalMinCount
smartHold_Short = not mtfConflict_Short and smartScoreShort >= timeAdjustedThreshold_Short and compositeSellRecentCount >= signalMinCount
= evaluateSmartHold()
Evaluates whether to hold positions past TP1/TP2/TP3 levels based on continued signal strength, volume confirmation, and multi-timeframe trend alignment
HOW TO USE THE STRATEGY
Step 1: Initial Setup
Apply strategy to your preferred timeframe (backtested on 15M)
Enable "Use Heikin-Ashi Base" for smoother signals in volatile markets
"Show EMA Lines" and "Show Ichimoku Cloud" are enabled for visual context
Set default quantities to match your risk management (5% equity default)
Step 2: Signal Recognition
Visual Signal Guide:
Visual Signal Guide - Complete Reference:
🔶 Red Diamond: Bearish momentum breakdown - short reversal signal
🔷 Blue Diamond: Strong bullish momentum - long reversal signal
🔵 Blue Dot: Volume-confirmed directional move - trend continuation
🟢 Green Dot: Bullish EMA crossover - trend reversal confirmation
🟠 Orange X: Oversold reversal setup - counter-trend opportunity
❌ Red X: Bearish EMA breakdown - trend reversal warning
✡ Star Uprising: Strong bullish convergence
💥 Ultra Entry: Ultra-rapid downward momentum acceleration
▲ VSS Long: Velocity-based bullish momentum confirmation
▼ VSS Short: Velocity-based bearish momentum confirmation
Step 3: Entry Execution
For Long Positions:
1. ✅ EMA1 crossed above EMA2 exactly 3 bars ago [ta.crossover(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 > EMA2 (maintained)
3. ✅ Composite score ≥ 5.0 points (6.5+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Volume spike confirmation (green dot/blue dot signals)
6. ✅ Bullish candle closes above EMA structure
For Short Positions:
1. ✅ EMA1 crossed below EMA2 exactly 3 bars ago [ta.crossunder(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 < EMA2 (maintained)
3. ✅ Composite score ≥ 5.4 points (7.0+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Momentum breakdown (red diamond/red X signals)
6. ✅ Bearish candle closes below EMA structure
🎯 Critical Timing Note: The strategy requires EMA crossover to have occurred 3 bars prior to entry, not at the current bar. This attempts to avoid premature entries and may improve signal reliability.
Step 4: Reading Market Context
EMA Ribbon Interpretation:
All EMAs ascending = Strong uptrend context
EMAs 1-3 above EMAs 4-8 = Bullish structure
Tight EMA spacing = Low volatility/consolidation
Wide EMA spacing = High volatility/trending
Ichimoku Cloud Context:
Price above cloud = Bullish environment
Price below cloud = Bearish environment
Cloud color intensity = Momentum strength
Thick cloud = Strong support/resistance
THE SMART EXIT GRID SYSTEM
Smart Exit Grid Approach:
The Smart Exit Grid uses dynamic hold evaluation that continuously analyzes market conditions after position entry. This differs from traditional fixed profit targets by adapting exit timing based on real-time signal strength.
How Smart Exit Grid System Works
The system operates through three evaluation phases:
Smart Score Calculation:
The smart score calculation aggregates 22 signal components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. MTF analysis provides additional confirmation as a separate validation layer.
Signal Stack Management:
The per-tick signal accumulation system monitors 22 active signal types with MTF providing trend validation and conflict detection as a separate confirmation layer.
Take Profit Progression:
Smart Exit Activation:
The QRE system activates Smart Exit Grid immediately upon position entry. When strategy.entry() executes, the system initializes monitoring systems designed to track position progress.
Upon position opening, holdTimer begins counting, establishing the foundation for subsequent decisions. The Smart Exit Grid starts accumulating signals from entry, with all 22 signal components beginning real-time tracking when the trade opens.
The system operates on continuous evaluation where smartScoreLong and smartScoreShort calculate from the first tick after entry. QRE's approach is designed to capture market structure changes, trend deteriorations, or signal pattern shifts that can trigger protective exits even before the first take profit level is reached.
This activation creates a proactive position management framework. The 8-candle sliding window starts from entry, meaning that if market conditions change rapidly after entry - due to news events, liquidity shifts, or technical changes - the system can respond within the configured lookback period.
TP Markers as Reference Points:
The TP1, TP2, and TP3 levels function as reference points rather than mandatory exit triggers. When longTP1Hit or shortTP1Hit conditions activate, they serve as profit confirmation markers that inform the Smart Exit algorithm about achieved reward levels, but don't automatically initiate position closure.
These TP markers enhance the Smart Exit decision matrix by providing profit context to ongoing signal evaluation. The system recognizes when positions have achieved target returns, but the actual exit decision remains governed by continuous smart score evaluation and signal stack analysis.
TP2 Reached: Enhanced Monitoring
TP2 represents significant profit capture with additional monitoring features:
This approach is designed to help avoid premature profit-taking during trending conditions. If TP2 is reached but smartScoreLong remains above the dynamic threshold and the 8-candle sliding window shows persistent signals, the position continues holding. If market structure deteriorates before reaching TP2, the Smart Exit can trigger closure based on signal analysis.
The visual TP circles that appear when levels are reached serve as performance tracking tools, allowing users to see how frequently entries achieve various profit levels while understanding that actual exit timing depends on market structure analysis.
Risk Management Systems:
Operating independently from the Smart Exit Grid are two risk management systems: the Trap Wick Detection Protocol and the Stop Loss Mechanism. These systems maintain override authority over other exit logic.
The Trap Wick System monitors for conditionBearTrapExit during long positions and conditionBullTrapExit during short positions. When detected, these conditions trigger position closure with state reset, bypassing Smart Exit evaluations. This system recognizes that certain candlestick patterns may indicate reversal risk.
Volatility Exit Monitoring: The strategy monitors for isStrongBearCandle combined with conditionBearTrapExit, recognizing when market structure may be shifting.
Volume Validation: Before exiting on volatility, the strategy requires volume confirmation: volume > ta.sma(volume, 20) * 1.8. This is designed to filter exits on weak, low-volume movements.
The Stop Loss Mechanism operates through multiple triggers including traditional price-based stops (longSLHit, shortSLHit) and early exit conditions based on smart score deterioration combined with negative ROI. The early exit logic activates when smartScoreLong < 1.0 or smartScoreShort < 1.0 while realROI < -0.9%.
These risk management systems are designed so that risk scenarios can trigger protective closure with state reset across all 22 signal counters, TP tracking variables, and smart exit states.
This architecture - Smart Exit activation, TP markers as navigation tools, and independent risk management - creates a position management system that adapts to market conditions while maintaining risk discipline through dedicated protection protocols.
TP3 Reached: Enhanced Protection
Once TP3 is hit, the strategy shifts into enhanced monitoring:
EMA Structure Monitoring: isEMAStructureDown becomes a primary exit trigger
MTF Alignment: The higher timeframe receives increased consideration
Wick Trap Priority: conditionBearTrapExit becomes an immediate exit signal
Approach Differences:
Traditional Fixed Exits:
Exit at predetermined levels regardless of market conditions
May exit during trend continuation
May exit before trend completion
Limited adaptation to changing volatility
Smart Exit Grid Approach:
Adaptive timing based on signal conditions
Exits when supporting signals weaken
Multi-timeframe validation for trend confirmation
Volume confirmation requirements for holds
Structural monitoring for trend analysis
Dynamic ATR-Based Smart Score Threshold System
Market Volatility Adaptive Scoring
// Real-time ATR Analysis
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
// Three-Tier Dynamic Threshold Matrix
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
The market volatility adaptive scoring calculates real-time ATR with a 2% fallback for new markets. The atrRatio represents the relationship between current volatility and price, creating a foundation for threshold adjustment.
The three-tier dynamic threshold matrix responds to market conditions by adjusting requirements based on volatility levels: lowering thresholds during high volatility periods above 2% ATR ratio to 1.0 points, maintaining standard requirements at 1.5 points for medium volatility between 1-2%, and raising standards to 2.8 points during low volatility periods below 1%.
Profit-Loss Adaptive Management:
The system applies different evaluation criteria based on position performance:
Winning Positions (realROI ≥ 0%):
→ timeMultiplier = 1.0 (No additional pressure)
→ Maintains base threshold requirements
→ Allows natural progression to TP2/TP3 levels
Losing Positions (realROI < 0%):
→ Progressive time pressure activated
→ Increasingly strict requirements over time
→ Faster decision-making on underperforming trades
ROI-Adaptive Smart Hold Decision Process:
The strategy uses a profit-loss adaptive system:
Winning Position Management (ROI ≥ 0%):
✅ Standard threshold requirements maintained
✅ No additional time-based pressure applied
✅ Allows positions to progress toward TP2/TP3 levels
✅ timeMultiplier remains at 1.0 regardless of hold duration
Losing Position Management (ROI < 0%):
⚠️ Time-based threshold adjustments activated
⚠️ Progressive increase in required signal strength over time
⚠️ Earlier exit evaluation on underperforming positions
⚠️ timeMultiplier increases from 1.0 → 1.1 → 1.3 based on hold duration
Real-Time Monitoring:
Monitor Analysis Table → "Smart" filter → "Score" vs "Dynamic Threshold"
Winning positions: Evaluation based on signal strength deterioration only
Losing positions: Evaluation considers both signal strength and progressive time adjustments
Breakeven positions (0% ROI): Treated as winning positions - no time adjustments
This approach differentiates between winning and losing positions in the hold evaluation process, requiring higher signal thresholds for extended holding of losing positions while maintaining standard requirements for winning ones.
ROI-Conditional Decision Matrix Examples:
Scenario 1 - Winning Position in Any Market:
Position ROI: +0.8% → timeMultiplier = 1.0 (regardless of hold time)
ATR Medium (1.2%) → dynamicThreshold = 1.5
Final Threshold = 1.5 × 1.0 = 1.5 points ✅ Position continues
Scenario 2 - Losing Position, Extended Hold:
Position ROI: -0.5% → Time pressure activated
Hold Time: 20 bars → timeMultiplier = 1.3
ATR Low (0.8%) → dynamicThreshold = 2.8
Final Threshold = 2.8 × 1.3 = 3.64 points ⚡ Enhanced requirements
Scenario 3 - Fresh Losing Position:
Position ROI: -0.3% → Time pressure activated
Hold Time: 5 bars → timeMultiplier = 1.0 (still early)
ATR High (2.1%) → dynamicThreshold = 1.0
Final Threshold = 1.0 × 1.0 = 1.0 points 📊 Recovery opportunity
Scenario 4 - Breakeven Position:
Position ROI: 0.0% → timeMultiplier = 1.0 (no pressure)
Hold Time: 15 bars → No time penalty applied
Final Threshold = dynamicThreshold only ⚖️ Neutral treatment
🔄8-Candle Sliding Window Signal Rotation System
Composite Signal Counting Mechanism
// Dynamic Lookback Window (configurable: default 8)
signalLookbackBars = input.int(8, "Composite Lookback Bars", minval=1, maxval=50)
// Rolling Signal Analysis
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
Candle Flow Example (8-bar window):
→
✓ ✓ ✗ ✓ ✗ ✓ ✗ ✓ 🗑️
New Signal Count = 5/8 signals in window
Threshold Check: 5 ≥ signalMinCount (2) = HOLD CONFIRMED
Signal Decay & Refresh Mechanism
// Signal Persistence Tracking
if compositeBuyRecentCount >= signalMinCount
smartHold_Long = true
else
smartHold_Long = false
The composite signal counting operates through a configurable sliding window. The system maintains rolling counters that scan backward through the specified number of candles.
During each evaluation cycle, the algorithm iterates through historical bars, incrementing counters when composite signals are detected. This creates a dynamic signal persistence measurement where recent signal density determines holding decisions.
The sliding window rotation functions like a moving conveyor belt where new signals enter while the oldest signals drop off. For example, in an 8-bar window, if 5 out of 8 recent candles showed composite buy signals, and the minimum required count is 2, the system confirms the hold condition. As new bars form, the window slides forward, potentially changing the signal count and triggering exit conditions when signal density falls below the threshold.
Signal decay and refresh occur continuously where smartHold_Long remains true only when compositeBuyRecentCount exceeds signalMinCount. When recent signal density drops below the minimum requirement, the system switches to exit mode.
Advanced Signal Stack Management - 22-Signal Real-Time Evaluation
// Long Position Signal Stacking (calc_on_every_tick=true)
if inLong
// Primary Reversal Signals
if signalRedDiamond: signalCountRedDiamond += 1 // -0.5 points
if signalStarUprising: signalCountStarUprising += 1 // +1.5 points
if entryUltraShort: signalCountUltra += 1 // -1.0 points
// Trend Confirmation Signals
if strongUpTrend: trendUpCount_Long += 1 // +1.5 points
if emaAlignment_Bull: bullAlignCount_Long += 1 // +1.0 points
// Risk Assessment Signals
if highRisk_Long: riskCount_Long += 1 // -1.5 points
if topZone: tzoneCount_Long += 1 // -0.5 points
The per-tick signal accumulation system operates with calc_on_every_tick=true for real-time responsiveness. During long positions, the system monitors primary reversal signals where Red Diamond signals subtract 0.5 points as reversal warnings, Star Uprising adds 1.5 points for continuation signals, and Ultra Short signals deduct 1.0 points as counter-trend warnings.
Trend confirmation signals provide weighted scoring where strongUpTrend adds 1.5 points for aligned momentum, emaAlignment_Bull contributes 1.0 point for structural support, and various EMA-based confirmations contribute to the overall score. Risk assessment signals apply negative weighting where highRisk_Long situations subtract 1.5 points, topZone conditions deduct 0.5 points, and other risk factors create defensive scoring adjustments.
The smart score calculation aggregates all 22 components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. This score updates continuously, providing the foundation for hold-or-exit decisions.
MULTI-TIMEFRAME (MTF) SYSTEM
MTF Data Collection
The strategy requests higher timeframe data (default 30-minute) for trend confirmation:
= request.security(syminfo.tickerid, mtfTimeframe, , lookahead=barmerge.lookahead_off, gaps=barmerge.gaps_off)
MTF Watchtower System - Implementation Logic
The system employs a timeframe discrimination protocol where currentTFInMinutes is compared against a 30-minute threshold. This creates different operational behavior between timeframes:
📊 Timeframe Testing Results:
30M+ charts: Full MTF confirmation → Tested with full features
15M charts: Local EMA + adjusted parameters → Standard testing baseline
5M charts: Local EMA only → Requires parameter adjustment
1M charts: High noise → Limited testing conducted
When the chart timeframe is 30 minutes or above, the strategy activates useMTF = true and requests external MTF data through request.security(). For timeframes below 30 minutes, including your 5-minute setup, the system deliberately uses local EMA calculations to avoid MTF lag and data inconsistencies.
The triple-layer data sourcing architecture works as follows: timeframes from 1 minute to 29 minutes rely on chart-based EMA calculations for immediate responsiveness. Timeframes of 30 minutes and above utilize MTF data through the security function, with a backup system that doubles the EMA length (emaLen * 2) if MTF data fails. When MTF data is unavailable or invalid, the system falls back to local EMA as the final safety net.
Data validation occurs through a pipeline where mtf_dataValid checks not only for non-null values but also verifies that EMA values are positive above zero. The system tracks data sources through mtf_dataSource which displays "MTF Data" for successful external requests, "Backup EMA" for failed MTF with backup system active, or "Chart EMA" for local calculations.
🔄 MTF Smart Score Caching & Recheck System
// Cache Update Decision Logic
mtfSmartIntervalSec = input.int(300, "Smart Grid Recheck Interval (sec)") // 5-minute cache
canRecheckSmartScore = na(timenow) ? false :
(na(lastCheckTime) or (timenow - lastCheckTime) > mtfSmartIntervalSec * 1000)
// Cache Management
if canRecheckSmartScore
lastCheckTime := timenow
cachedSmartScoreLong := smartScoreLong // Store current calculation
cachedSmartScoreShort := smartScoreShort
The performance-optimized caching system addresses the computational intensity of continuous MTF analysis through intelligent interval management. The mtfSmartIntervalSec parameter, defaulting to 300 seconds (5 minutes), determines cache refresh frequency. The system evaluates canRecheckSmartScore by comparing current time against lastCheckTime plus the configured interval.
When cache updates trigger, the system stores current calculations in cachedSmartScoreLong and cachedSmartScoreShort, creating stable reference points that reduce excessive MTF requests. This cache management balances computational efficiency with analytical accuracy.
The cache versus real-time hybrid system creates a multi-layered decision matrix where immediate signals update every tick for responsive market reaction, cached MTF scores refresh every 5 minutes for stability filtering, dynamic thresholds recalculate every bar for volatility adaptation, and sliding window analysis updates every bar for trend persistence validation.
This architecture balances real-time signal detection with multi-timeframe strategic validation, creating adaptive trading intelligence that responds immediately to market changes while maintaining strategic stability through cached analysis and volatility-adjusted decision thresholds.
⚡The Execution Section Deep Dive
The execution section represents the culmination of all previous systems – where analysis transforms into action.
🚪 Entry Execution: The Gateway Protocol
Primary Entry Validation:
Entry isn't just about seeing a signal – it's about passing through multiple security checkpoints, each designed to filter out low-quality opportunities.
Stage 1: Signal Confirmation
entryCompositeBuySignal must be TRUE for longs
entryCompositeSellSignal must be TRUE for shorts
Stage 2: Enhanced Entry Validation
The strategy employs an "OR" logic system that recognizes different types of market opportunities:
Path A - Trend Reversal Entry:
When emaTrendReversal_Long triggers, it indicates the market structure is shifting in favor of the trade direction. This isn't just about a single EMA crossing – it represents a change in market momentum that experienced traders recognize as potential high-probability setups.
Path B - Momentum Breakout Entry:
The strongBullMomentum condition is where QRE identifies accelerating market conditions:
Criteria:
EMA1 rising for 3+ candles AND
EMA2 rising for 2+ candles AND
Close > 10-period high
This combination captures those explosive moves where the market doesn't just trend – it accelerates, creating momentum-driven opportunities.
Path C - Recovery Entry:
When previous exit states are clean (no recent stop losses), the strategy permits entry based purely on signal strength. This pathway is designed to help avoid the strategy becoming overly cautious after successful trades.
🛡️ The Priority Exit Matrix: When Rules Collide
Not all exit signals are created equal. QRE uses a strict hierarchy that is designed to avoid conflicting signals from causing hesitation:
Priority Level 1 - Exception Exits (Immediate Action):
Condition: TP3 reached AND Wick Trap detected
Action: Immediate exit regardless of other signals
Rationale: Historical analysis suggests wick traps at TP3 may indicate potential reversals
Priority Level 2 - Structural Breakdown:
Condition: TP3 active AND EMA structure deteriorating AND Smart Score insufficient
Logic: isEMAStructureDown AND NOT smartHold_Long
This represents the strategy recognizing that the underlying market structure that justified the trade is failing. It's like a building inspector identifying structural issues – you don't wait for additional confirmation.
Priority Level 3 - Enhanced Volatility Exits:
Conditions: TP2 active AND Strong counter-candle AND Wick trap AND Volume spike
Logic: Multiple confirmation required to reduce false exits
Priority Level 4 - Standard Smart Score Exits:
Condition: Any TP level active AND smartHold evaluates to FALSE
This is the bread-and-butter exit logic where signal deterioration triggers exit
⚖️ Stop Loss Management: Risk Control Protocol
Dual Stop Loss System:
QRE provides two stop loss modes that users can select based on their preference:
Fixed Mode (Default - useAdaptiveSL = false):
Uses predetermined percentage levels regardless of market volatility:
- Long SL = entryPrice × (1 - fixedRiskP - slipBuffer)
- Short SL = entryPrice × (1 + fixedRiskP + slipBuffer)
- Default: 0.6% risk + 0.3% slippage buffer = 0.9% total stop
- Consistent and predictable stop loss levels
- Recommended for users who prefer stable risk parameters
Adaptive Mode (Optional - useAdaptiveSL = true):
Dynamic system that adjusts stop loss based on market volatility:
- Base Calculation uses ATR (Average True Range)
- Long SL = entryPrice × (1 - (ATR × atrMultSL) / entryPrice - slipBuffer)
- Short SL = entryPrice × (1 + (ATR × atrMultSL) / entryPrice + slipBuffer)
- Automatically widens stops during high volatility periods
- Tightens stops during low volatility periods
- Advanced users can enable for volatility-adaptive risk management
Trend Multiplier Enhancement (Both Modes):
When strongUpTrend is detected for long positions, the stop loss receives 1.5x breathing room. Strong trends often have deeper retracements before continuing. This is designed to help avoid the strategy being shaken out of active trades by normal market noise.
Mode Selection Guidance:
- New Users: Start with Fixed Mode for predictable risk levels
- Experienced Users: Consider Adaptive Mode for volatility-responsive stops
- Volatile Markets: Adaptive Mode may provide better stop placement
- Stable Markets: Fixed Mode often sufficient for consistent risk management
Early Exit Conditions:
Beyond traditional stop losses, QRE implements "smart stops" that trigger before price-based stops:
Early Long Exit: (smartScoreLong < 1.0 OR prev5BearCandles) AND realROI < -0.9%
🔄 State Management: The Memory System
Complete State Reset Protocol:
When a position closes, QRE doesn't just wipe the slate clean – it performs a methodical reset:
TP State Cleanup:
All Boolean flags: tp1/tp2/tp3HitBefore → FALSE
All Reached flags: tp1/tp2/tp3Reached → FALSE
All Active flags: tp1/tp2/tp3HoldActive → FALSE
Signal Counter Reset:
Every one of the 22 signal counters returns to zero.
This is designed to avoid signal "ghosting" where old signals influence new trades.
Memory Preservation:
While operational states reset, certain information is preserved for learning:
killReasonLong/Short: Why did this trade end?
lastExitWasTP1/TP2/TP3: What was the exit quality?
reEntryCount: How many consecutive re-entries have occurred?
🔄 Re-Entry Logic: The Comeback System
Re-Entry Conditions Matrix:
QRE implements a re-entry system that recognizes not all exits are created equal:
TP-Based Re-Entry (Enabled):
Criteria: Previous exit was TP1, TP2, or TP3
Cooldown: Minimal or bypassed entirely
Logic: Target-based exits indicate potentially viable market conditions
EMA-Based Re-Entry (Conditional):
Criteria: Previous exit was EMA-based (structural change)
Requirements: Must wait for EMA confirmation in new direction
Minimum Wait: 5 candles
Advanced Re-Entry Features:
When adjustReEntryTargets is enabled, the strategy becomes more aggressive with re-entries:
Target Adjustment: TP1 multiplied by reEntryTP1Mult (default 2.0)
Stop Adjustment: SL multiplied by reEntrySLMult (default 1.5)
Logic: If we're confident enough to re-enter, we should be confident enough to hold for bigger moves
Performance Tracking: Strategy tracks re-entry win rate, average ROI, and total performance separately from initial entries for optimization analysis.
📊 Exit Reason Analytics: Learning from Every Trade
Kill Reason Tracking:
Every exit is categorized and stored:
"TP3 Exit–Wick Trap": Exit at target level with wick pattern detection
"Smart Exit–EMA Down": Structural breakdown exit
"Smart Exit–Volatility": Volatility-based protection exit
"Exit Post-TP1/TP2/TP3": Standard smart exit progression
"Long SL Exit" / "Short SL Exit": Stop loss exits
Performance Differentiation:
The strategy tracks performance by exit type, allowing for continuous analysis:
TP-based exits: Achieved target levels, analyze for pattern improvement
EMA-based exits: Mixed results, analyze for pattern improvement
SL-based exits: Learning opportunities, adjust entry criteria
Volatility exits: Protective measures, monitor performance
🎛️ Trailing Stop Implementation:
Conditional Trailing Activation:
Activation Criteria: Position profitable beyond trailingStartPct AND
(TP hold active OR re-entry trade)
Dynamic Trailing Logic:
Unlike simple trailing stops, QRE's implementation considers market context:
Trending Markets: Wider trail offsets to avoid whipsaws
Volatile Markets: Tighter offsets to protect gains
Re-Entry Trades: Enhanced trailing to maximize second-chance opportunities
Return-to-Entry Protection:
When deactivateOnReturn is enabled, the strategy will close positions that return to entry level after being profitable. This is designed to help avoid the frustration of watching profitable trades turn into losers.
🧠 How It All Works Together
The beauty of QRE lies not in any single component, but in how everything integrates:
The Entry Decision: Multiple pathways are designed to help identify opportunities while maintaining filtering standards.
The Progression System: Each TP level unlocks new protection features, like achieving ranks in a video game.
The Exit Matrix: Prioritized decision-making aims to reduce analysis paralysis while providing appropriate responses to different market conditions.
The Memory System: Learning from each trade while preventing contamination between separate opportunities.
The Re-Entry Logic: Re-entry system that balances opportunity with risk management.
This creates a trading system where entry conditions filter for quality, progression systems adapt to changing market conditions, exit priorities handle conflicting signals intelligently, memory systems learn from each trade cycle, and re-entry logic maximizes opportunities while managing risk exposure.
📊 ANALYSIS TABLE INTERPRETATION -
⚙️ Enabling Analysis Mode
Navigate to strategy settings → "Testing & Analysis" → Enable "Show Analysis Table". The Analysis Table displays different information based on the selected test filter and provides real-time insight into all strategy components, helping users understand current market conditions, position status, and system decision-making processes.
📋 Filter Mode Interpretations
"All" Mode (Default View):
Composite Section:
Buy Score: Aggregated strength from all 22 bullish signals (threshold 5.0+ triggers entry consideration)
Sell Score: Aggregated strength from all 22 bearish signals (threshold 5.4+ triggers entry consideration)
APEX Filters:
ATG Trend: Shows current trend direction analysis
Indicates whether momentum filters are aligned for directional bias
ReEntry Section:
Most Recent Exit: Displays exit type and timeframe since last position closure
Status: Shows if ReEntry system is Ready/Waiting/Disabled
Count: Current re-entry attempts versus maximum allowed attempts
Position Section (When Active):
Status: Current position state (LONG/SHORT/FLAT)
ROI: Dual calculation showing Custom vs Real ROI percentages
Entry Price: Original position entry level
Current Price: Live market price for comparison
TP Tracking: Progress toward profit targets
"Smart" Filter (Critical for Active Positions):
Smart Exit Section:
Hold Timer: Time elapsed since position opened (bar-based counting)
Status: Whether Smart Exit Grid is Enabled/Disabled
Score: Current smart score calculation from 22-component matrix
Dynamic Threshold: ATR-based minimum score required for holding
Final Threshold: Time and ROI-adjusted threshold actually used for decisions
Score Check: Pass/Fail based on Score vs Final Threshold comparison
Smart Hold: Current hold decision status
Final Hold: Final recommendation based on all factors
🎯 Advanced Smart Exit Debugging - ROI & Time-Based Threshold System
Understanding the Multi-Layer Threshold System:
Layer 1: Dynamic Threshold (ATR-Based)
atrRatio = ATR / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
Layer 2: Time Multiplier (ROI & Duration-Based)
Winning Positions (ROI ≥ 0%):
→ timeMultiplier = 1.0 (No time pressure, regardless of hold duration)
Losing Positions (ROI < 0%):
→ holdTimer ≤ 8 bars: timeMultiplier = 1.0 (Early stage, standard requirements)
→ holdTimer 9-16 bars: timeMultiplier = 1.1 (10% stricter requirements)
→ holdTimer 17+ bars: timeMultiplier = 1.3 (30% stricter requirements)
Layer 3: Final Threshold Calculation
finalThreshold = dynamicThreshold × timeMultiplier
Examples:
- Winning Position: 2.8 × 1.0 = 2.8 (Always standard)
- Losing Position (Early): 2.8 × 1.0 = 2.8 (Same as winning initially)
- Losing Position (Extended): 2.8 × 1.3 = 3.64 (Much stricter)
Real-Time Debugging Display:
Smart Exit Section shows:
Score: 3.5 → Current smartScoreLong/Short value
Dynamic Threshold: 2.8 → Base ATR-calculated threshold
Final Threshold: 3.64 (ATR×1.3) → Actual threshold used for decisions
Score Check: FAIL (3.5 vs 3.64) → Pass/Fail based on final comparison
Final Hold: NO HOLD → Actual system decision
Position Status Indicators:
Winner + Early: ATR×1.0 (No pressure)
Winner + Extended: ATR×1.0 (No pressure - winners can run indefinitely)
Loser + Early: ATR×1.0 (Recovery opportunity)
Loser + Extended: ATR×1.1 or ATR×1.3 (Increasing pressure to exit)
MTF Section:
Data Source: Shows whether using MTF Data/EMA Backup/Local EMA
Timeframe: Configured watchtower timeframe setting
Data Valid: Confirms successful MTF data retrieval status
Trend Signal: Higher timeframe directional bias analysis
Close Price: MTF price data availability confirmation
"Composite" Filter:
Composite Section:
Buy Score: Real-time weighted scoring from multiple indicators
Sell Score: Opposing directional signal strength
Threshold: Minimum scores required for signal activation
Components:
Flash/Blink: Momentum acceleration indicators (F = Flash active, B = Blink active)
Individual filter contributions showing which specific signals are firing
"ReEntry" Filter:
ReEntry System:
System: Shows if re-entry feature is Enabled/Disabled
Eligibility: Conditions for new entries in each direction
Performance: Success metrics of re-entry attempts when enabled
🎯 Key Status Indicators
Status Column Symbols:
✓ = Condition met / System active / Signal valid
✗ = Condition not met / System inactive / No signal
⏳ = Cooldown active (waiting period)
✅ = Ready state / Good condition
🔄 = Processing / Transitioning state
🔍 Critical Reading Guidelines
For Active Positions - Smart Exit Priority Reading:
1. First Check Position Type:
ROI ≥ 0% = Winning Position (Standard requirements)
ROI < 0% = Losing Position (Progressive requirements)
2. Check Hold Duration:
Early Stage (≤8 bars): Standard multiplier regardless of ROI
Extended Stage (9-16 bars): Slight pressure on losing positions
Long Stage (17+ bars): Strong pressure on losing positions
3. Score vs Final Threshold Analysis:
Score ≥ Final Threshold = HOLD (Continue position)
Score < Final Threshold = EXIT (Close position)
Watch for timeMultiplier changes as position duration increases
4. Understanding "Why No Hold?"
Common scenarios when Score Check shows FAIL:
Losing position held too long (timeMultiplier increased to 1.1 or 1.3)
Low volatility period (dynamic threshold raised to 2.8)
Signal deterioration (smart score dropped below required level)
MTF conflict (higher timeframe opposing position direction)
For Entry Signal Analysis:
Composite Score Reading: Signal strength relative to threshold requirements
Component Analysis: Individual filter contributions to overall score
EMA Structure: Confirm 3-bar crossover requirement met
Cooldown Status: Ensure sufficient time passed since last exit
For ReEntry Opportunities (when enabled):
System Status: Availability and eligibility for re-engagement
Exit Type Analysis: TP-based exits enable immediate re-entry, SL-based exits require cooldown
Condition Monitoring: Requirements for potential re-entry signals
Debugging Common Issues:
Issue: "Score is high but no hold?"
→ Check Final Threshold vs Score (not Dynamic Threshold)
→ Losing position may have increased timeMultiplier
→ Extended hold duration applying pressure
Issue: "Why different thresholds for same score?"
→ Position ROI status affects multiplier
→ Time elapsed since entry affects multiplier
→ Market volatility affects base threshold
Issue: "MTF conflicts with local signals?"
→ Higher timeframe trend opposing position
→ System designed to exit on MTF conflicts
→ Check MTF Data Valid status
⚡ Performance Optimization Notes
For Better Performance:
Analysis table updates may impact performance on some devices
Use specific filters rather than "All" mode for focused monitoring
Consider disabling during live trading for optimal chart performance
Enable only when needed for debugging or analysis
Strategic Usage:
Monitor "Smart" filter when positions are active for exit timing decisions
Use "Composite" filter during setup phases for signal strength analysis
Reference "ReEntry" filter after position closures for re-engagement opportunities
Track Final Threshold changes to understand exit pressure evolution
Advanced Debugging Workflow:
Position Entry Analysis:
Check Composite score vs threshold
Verify EMA crossover timing (3 bars prior)
Confirm cooldown completion
Hold Decision Monitoring:
Track Score vs Final Threshold progression
Monitor timeMultiplier changes over time
Watch for MTF conflicts
Exit Timing Analysis:
Identify which threshold layer caused exit
Track performance by exit type
Analyze re-entry eligibility
This analysis system provides transparency into strategy decision-making processes, allowing users to understand how signals are generated and positions are managed according to the programmed logic during various market conditions and position states.
SIGNAL TYPES AND CHARACTERISTICS
🔥 Core Momentum Signals
Flash Signal
Calculation: ta.rma(math.abs(close - close ), 5) > ta.sma(math.abs(close - close ), 7)
Purpose: Detects sudden price acceleration using smoothed momentum comparison
Characteristics: Triggers when recent price movement exceeds historical average movement
Usage: Primary momentum confirmation across multiple composite calculations
Weight: 1.3 points in composite scoring
Blink Signal
Calculation: math.abs(ta.change(close, 1)) > ta.sma(math.abs(ta.change(close, 1)), 5)
Purpose: Identifies immediate price velocity spikes
Characteristics: More sensitive than Flash, captures single-bar momentum bursts
Usage: Secondary momentum confirmation, often paired with Flash
Weight: 1.3 points in composite scoring
⚡ Advanced Composite Signals
Apex Pulse Signal
Calculation: apexAngleValue > 30 or apexAngleValue < -30
Purpose: Detects extreme EMA angle momentum
Characteristics: Identifies when trend angle exceeds ±30 degrees
Usage: Confirms directional momentum strength in trend-following scenarios
Pressure Surge Signal
Calculation: volSpike_AVP and strongTrendUp_ATG
Purpose: Combines volume expansion with trend confirmation
Characteristics: Requires both volume spike and strong uptrend simultaneously
Usage: bullish signal for trend continuation
Shift Wick Signal
Calculation: ta.crossunder(ema1, ema2) and isWickTrapDetected and directionFlip
Purpose: Detects bearish reversal with wick trap confirmation
Characteristics: Combines EMA crossunder with upper wick dominance and directional flip
Usage: Reversal signal for trend change identification
🛡️ Trap Exit Protection Signals
Bear Trap Exit
Calculation: isUpperWickTrap and isBearEngulfNow
Conditions: Previous bullish candle with 80%+ upper wick, followed by current bearish engulfing
Purpose: Emergency exit signal for long positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
Bull Trap Exit
Calculation: isLowerWickTrap and isBullEngulfNow
Conditions: Previous bearish candle with 80%+ lower wick, followed by current bullish engulfing
Purpose: Emergency exit signal for short positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
📊 Technical Analysis Foundation Signals
RSI-MFI Hybrid System
Base Calculation: (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
Oversold Threshold: < 35
Overbought Threshold: > 65
Weak Condition: < 35 and declining
Strong Condition: > 65 and rising
Usage: Momentum confirmation and reversal identification
ADX-DMI Trend Classification
Strong Up Trend: (adx > 25 and diplus > diminus and (diplus - diminus) > 5) or (ema1 > ema2 and ema2 > ema3 and ta.rising(ema2, 3))
Strong Down Trend: (adx > 20 and diminus > diplus - 5) or (ema1 < ema2 and ta.falling(ema1, 3))
Trend Weakening: adx < adx and adx < adx
Usage: Primary trend direction confirmation
Bollinger Band Squeeze Detection
Calculation: bbWidth < ta.lowest(bbWidth, 20) * 1.2
Purpose: Identifies low volatility periods before breakouts
Usage: Entry filter - avoids trades during consolidation
🎨 Visual Signal Indicators
Red X Signal
Calculation: isBearCandle and ta.crossunder(ema1, ema2)
Visual: Red X above price
Purpose: Bearish EMA crossunder with confirming candle
Composite Weight: +1.0 for short positions, -1.0 for long positions
Characteristics: Simple but effective trend change indicator
Green Dot Signal
Calculation: isBullCandle and ta.crossover(ema1, ema2)
Visual: Green dot below price
Purpose: Bullish EMA crossover with confirming candle
Composite Weight: +1.0 for long positions, -1.0 for short positions
Characteristics: Entry confirmation for trend-following strategies
Blue Diamond Signal
Trigger Conditions: amcBuySignal and score >= 4
Scoring Components: 11 different technical conditions
Key Requirements: AMC bullish + momentum rise + EMA expansion + volume confirmation
Visual: Blue diamond below price
Purpose: Bullish reversal or continuation signal
Characteristics: Multi-factor confirmation requiring 4+ technical alignments
Red Diamond Signal
Trigger Conditions: amcSellSignal and score >= 5
Scoring Components: 11 different technical conditions (stricter than Blue Diamond)
Key Requirements: AMC bearish + momentum crash + EMA compression + volume decline
Visual: Red diamond above price
Purpose: Potential bearish reversal or continuation signal
Characteristics: Requires higher threshold (5 vs 4) for more selective triggering
🔵 Specialized Detection Signals
Blue Dot Signal
Calculation: volumePulse and isCandleStrong and volIsHigh
Requirements: Volume > 2.0x MA, strong candle body > 35% of range, volume MA > 55
Purpose: Volume-confirmed momentum signal
Visual: Blue dot above price
Characteristics: Volume-centric signal for high-liquidity environments
Orange X Signal
Calculation: Complex multi-factor oversold reversal detection
Requirements: AMC oversold + wick trap + flash/blink + RSI-MFI oversold + bullish flip
Purpose: Oversold bounce signal with multiple confirmations
Visual: Orange X below price
Characteristics: Reversal signal requiring 5+ simultaneous conditions
VSS (Velocity Signal System)
Components: Volume spike + EMA angle + trend direction
Buy Signal: vssTrigger and vssTrendDir == 1
Sell Signal: vssTrigger and vssTrendDir == -1
Visual: Green/Red triangles
Purpose: Velocity-based momentum detection
Characteristics: Fast-response signal for momentum trading
⭐ Elite Composite Signals
Star Uprising Signal
Base Requirements: entryCompositeBuySignal and echoBodyLong and strongUpTrend and isAMCUp
Additional Confirmations: RSI hybrid strong + not high risk
Special Conditions: At bottom zone OR RSI bottom bounce OR strong volume bounce
Visual: Star symbol below price
Purpose: Bullish reversal signal from oversold conditions
Characteristics: Most selective bullish signal requiring multiple confirmations
Ultra Short Signal
Scoring System: 7-component scoring requiring 4+ points
Key Components: EMA trap + volume decline + RSI weakness + composite confirmation
Additional Requirements: Falling EMA structure + volume spike + flash confirmation
Visual: Explosion emoji above price
Purpose: Aggressive short entry for trend reversal or continuation
Characteristics: Complex multi-layered signal for experienced short selling
🎯 Composite Signal Architecture
Enhanced Composite Scoring
Long Composite: 15+ weighted components including structure, momentum, flash/blink, volume, price action, reversal triggers, trend alignment
Short Composite: Mirror structure with bearish bias
Threshold: 5.0 points required for signal activation
Conflict Resolution: If both long and short signals trigger simultaneously, both are disabled
Final Validation: Requires EMA momentum confirmation (ta.rising(emaFast_ATG, 2) for longs, ta.falling(emaFast_ATG, 2) for shorts)
Risk Assessment Integration
High Risk Long: RSI > 70 OR close > upper Bollinger Band 80%
High Risk Short: RSI < 30 OR close < lower Bollinger Band 80%
Zone Analysis: Top zone (95% of 50-bar high) vs Bottom zone (105% of 50-bar low)
Risk Penalty: High risk conditions subtract 1.5 points from composite scores
This signal architecture creates a multi-layered detection system where simple momentum signals provide foundation, technical analysis adds structure, visual indicators offer clarity, specialized detectors capture different market conditions, and composite signals identify potential opportunities while integrated risk assessment is designed to filter risky entries.
VISUAL FEATURES SHOWCASE
Ichimoku Cloud Visualization
Dynamic Color Intensity: Cloud transparency adapts to momentum strength - darker colors indicate stronger directional moves, while lighter transparency shows weakening momentum phases.
Gradient Color Mapping: Bullish momentum renders blue-purple spectrum with increasing opacity, while bearish momentum displays corresponding color gradients with intensity-based transparency.
Real-time Momentum Feedback: Color saturation provides immediate visual feedback on market structure strength, allowing traders to assess levels at a glance without additional indicators.
EMA Ribbon Bands
The 8-level exponential moving average system creates a comprehensive trend structure map with gradient color coding.
Signal Type Visualization
STRATEGY PROPERTIES & BACKTESTING DISCLOSURE
📊 Default Strategy Configuration:
✅ Initial Capital: 100,000 USD (realistic for average traders)
✅ Commission: 0.075% per trade (realistic exchange fees)
✅ Slippage: 3 ticks (market impact consideration)
✅ Position Size: 5% equity per trade (sustainable risk level)
✅ Pyramiding: Disabled (single position management)
✅ Sample Size: 185 trades over 12-month backtesting period
✅ Risk Management: Adaptive stop loss with maximum 1% risk per trade
COMPREHENSIVE BACKTESTING RESULTS
Testing Period & Market Conditions:
Backtesting Period: June 25, 2024 - June 25, 2025 (12 months)
Timeframe: 15-minute charts (MTF system active)
Market: BTCUSDT (Bitcoin/Tether)
Market Conditions: Full market cycle including volatility periods
Deep Backtesting: Enabled for maximum accuracy
📈 Performance Summary:
Total Return: +2.19% (+2,193.59 USDT)
Total Trades Executed: 185 trades
Win Rate: 34.05% (63 winning trades out of 185)
Profit Factor: 1.295 (gross profit ÷ gross loss)
Maximum Drawdown: 0.65% (653.17 USDT)
Risk-Adjusted Returns: Consistent with conservative risk management approach
📊 Detailed Trade Analysis:
Position Distribution:
Long Positions: 109 trades (58.9%) | Win Rate: 36.70%
Short Positions: 76 trades (41.1%) | Win Rate: 30.26%
Average Trade Duration: Optimized for 15-minute timeframe efficiency
Profitability Metrics:
Average Profit per Trade: 11.74 USDT (0.23%)
Average Winning Trade: 151.17 USDT (3.00%)
Average Losing Trade: 60.27 USDT (1.20%)
Win/Loss Ratio: 2.508 (winners are 2.5x larger than losses)
Largest Single Win: 436.02 USDT (8.69%)
Largest Single Loss: 107.41 USDT (controlled risk management)
💰 Financial Performance Breakdown:
Gross Profit: 9,523.93 USDT (9.52% of capital)
Gross Loss: 7,352.48 USDT (7.35% of capital)
Net Profit After Costs: 2,171.44 USDT (2.17%)
Commission Costs: 1,402.47 USDT (realistic trading expenses)
Maximum Equity Run-up: 2,431.66 USDT (2.38%)
⚖️ Risk Management Validation:
Maximum Drawdown: 0.65% showing controlled risk management
Drawdown Recovery: Consistent equity curve progression
Risk per Trade: Successfully maintained below 1.5% per position
Position Sizing: 5% equity allocation proved sustainable throughout testing period
📋 Strategy Performance Characteristics:
✅ Strengths Demonstrated:
Controlled Risk: Maximum drawdown well below industry standards (< 1%)
Positive Expectancy: Win/loss ratio of 2.5+ creates profitable edge
Consistent Performance: Steady equity curve without extreme volatility
Realistic Costs: Includes actual commission and slippage impacts
Sample Size: 185 trades during testing period
⚠️ Performance Considerations:
Win Rate: 34% win rate requires discipline to follow system signals
Market Dependency: Performance may vary significantly in different market conditions
Timeframe Sensitivity: Optimized for 15-minute charts; other timeframes may show different results
Slippage Impact: Real trading conditions may affect actual performance
📊 Benchmark Comparison:
Strategy Return: +2.19% over 12 months
Buy & Hold Bitcoin: +71.12% over same period
Strategy Advantage: Significantly lower drawdown and volatility
Risk-Adjusted Performance: Different risk profile compared to holding cryptocurrency
🎯 Real-World Application Insights:
Expected Trading Frequency:
Average: 15.4 trades per month (185 trades ÷ 12 months)
Weekly Frequency: Approximately 3-4 trades per week
Active Management: Requires regular monitoring during market hours
Capital Requirements:
Minimum Used in Testing: $10,000 for sustainable position sizing
Tested Range: $50,000-$100,000 for comfortable risk management
Commission Impact: 0.075% per trade totaled 1.4% of capital over 12 months
⚠️ IMPORTANT BACKTESTING DISCLAIMERS:
📈 Performance Reality:
Past performance does not guarantee future results. Backtesting results represent hypothetical performance and may not reflect actual trading outcomes due to market changes, execution differences, and emotional factors.
🔄 Market Condition Dependency:
This strategy's performance during the tested period may not be representative of performance in different market conditions, volatility regimes, or trending vs. sideways markets.
💸 Cost Considerations:
Actual trading costs may vary based on broker selection, market conditions, and trade size. Commission rates and slippage assumptions may differ from real-world execution.
🎯 Realistic Expectations:
The 34% win rate requires psychological discipline to continue following signals during losing streaks. Risk management and position sizing are critical for replicating these results.
⚡ Technology Dependencies:
Strategy performance assumes reliable internet connection, platform stability, and timely signal execution. Technical failures may impact actual results.
CONFIGURATION OPTIMIZATION
5-Minute Timeframe Optimization (Advanced Users Only)
⚠️ Important Warning: 5-minute timeframes operate without MTF confirmation, resulting in reduced signal quality and higher false signal rates.
Example 5-Minute Parameters:
Composite Thresholds: Long 6.5, Short 7.0 (vs 15M default 5.0/5.4)
Signal Lookback Bars: 12 (vs 15M default 8)
Volume Multiplier: 2.2 (vs 15M default 1.8)
MTF Timeframe: Disabled (automatic below 30M)
Risk Management Adjustments:
Position Size: Reduce to 3% (vs 5% default)
TP1: 0.8%, TP2: 1.2%, TP3: 2.0% (tighter targets)
SL: 0.8% (tighter stop loss)
Cooldown Minutes: 8 (vs 5 default)
Usage Notes for 5-Minute Trading:
- Wait for higher composite scores before entry
- Require stronger volume confirmation
- Monitor EMA structure more closely
15-Minute Scalping Setup:
TP1: 1.0%, TP2: 1.5%, TP3: 2.5%
Composite Threshold: 5.0 (higher filtering)
TP ATR Multiplier: 7.0
SL ATR Multiplier: 2.5
Volume Multiplier: 1.8 (requires stronger confirmation)
Hold Time: 2 bars minimum
3-Hour Swing Setup:
TP1: 2.0%, TP2: 4.0%, TP3: 8.0%
Composite Threshold: 4.5 (more signals)
TP ATR Multiplier: 8.0
SL ATR Multiplier: 3.2
Volume Multiplier: 1.2
Hold Time: 6 bars minimum
Market-Specific Adjustments
High Volatility Periods:
Increase ATR multipliers (TP: 2.0x, SL: 1.2x)
Raise composite thresholds (+0.5 points)
Reduce position size
Enable cooldown periods
Low Volatility Periods:
Decrease ATR multipliers (TP: 1.2x, SL: 0.8x)
Lower composite thresholds (-0.3 points)
Standard position sizing
Disable extended cooldowns
News Events:
Temporarily disable strategy 30 minutes before major releases
Increase volume requirements (2.0x multiplier)
Reduce position sizes by 50%
Monitor for unusual price action
RISK MANAGEMENT
Dual ROI System: Adaptive vs Fixed Mode
Adaptive RR Mode:
Uses ATR (Average True Range) for automatic adjustment
TP1: 1.0x ATR from entry price
TP2: 1.5x ATR from entry price
TP3: 2.0x ATR from entry price
Stop Loss: 1.0x ATR from entry price
Automatically adjusts to market volatility
Fixed Percentage Mode:
Uses predetermined percentage levels
TP1: 1.0% (default)
TP2: 1.5% (default)
TP3: 2.5% (default)
Stop Loss: 0.9% total (0.6% risk tolerance + 0.3% slippage buffer)(default)
Consistent levels regardless of volatility
Mode Selection: Enable "Use Adaptive RR" for ATR-based targets, disable for fixed percentages. Adaptive mode works better in varying volatility conditions, while fixed mode provides predictable risk/reward ratios.
Stop Loss Management
In Adaptive SL Mode:
Automatically scales with market volatility
Tight stops during low volatility (smaller ATR)
Wider stops during high volatility (larger ATR)
Include 0.3% slippage buffer in both modes
In Fixed Mode:
Consistent percentage-based stops
2% for crypto, 1.5% for forex, 1% for stocks
Manual adjustment needed for different market conditions
Trailing Stop System
Configuration:
Enable Trailing: Activates dynamic stop loss adjustment
Start Trailing %: Profit level to begin trailing (default 1.0%)
Trailing Offset %: Distance from current price (default 0.5%)
Close if Return to Entry: Optional immediate exit if price returns to entry level
Operation: Once position reaches trailing start level, stop loss automatically adjusts upward (longs) or downward (shorts) maintaining the offset distance from favorable price movement.
Timeframe-Specific Risk Considerations
15-Minute and Above (Tested):
✅ Full MTF system active
✅ Standard risk parameters apply
✅ Backtested performance metrics valid
✅ Standard position sizing (5%)
5-Minute Timeframes (Advanced Only):
⚠️ MTF system inactive - local signals only
⚠️ Higher false signal rate expected
⚠️ Reduced position sizing preferred (3%)
⚠️ Tighter stop losses required (0.8% vs 1.2%)
⚠️ Requires parameter optimization
⚠️ Monitor performance closely
1-Minute Timeframes (Limited Testing):
❌ Excessive noise levels
❌ Strategy not optimized for this frequency
Risk Management Practices
Allocate no more than 5% of your total investment portfolio to high-risk trading
Never trade with funds you cannot afford to lose
Thoroughly backtest and validate the strategy with small amounts before full implementation
Always maintain proper risk management and stop-loss settings
IMPORTANT DISCLAIMERS
Performance Disclaimer
Past performance does not guarantee future results. All trading involves substantial risk of loss. This strategy is provided for informational purposes and does not constitute financial advice.
Market Risk
Cryptocurrency and forex markets are highly volatile. Prices can move rapidly against positions, resulting in significant losses. Users should never risk more than they can afford to lose.
Strategy Limitations
This strategy relies on technical analysis and may not perform well during fundamental market shifts, news events, or unprecedented market conditions. No trading strategy can guarantee 100% success or eliminate the risk of loss.
Legal Compliance
You are responsible for compliance with all applicable regulations and laws in your jurisdiction. Consult with licensed financial professionals when necessary.
User Responsibility
Users are responsible for their own trading decisions, risk management, and compliance with applicable regulations in their jurisdiction.
Self Optimizing ROC [Starbots]Self Optimizing Rate of Change (ROC) Strategy. (non-repainting)
Script constantly tests 15 different ROC parameter combinations for maximum profitability and trades based on the best performing combination.
You will notice that signal lines switch after a bar close sometimes, this is when the strategy optimizes to the better combination and change plots, strategy is dynamic.
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The Rate-of-Change (ROC) indicator, which is also referred to as Momentum, is a pure momentum oscillator that measures the percent change in price from one period to the next. The ROC calculation compares the current price with the price “n” periods ago. The plot forms an oscillator that fluctuates above and below the zero line as the rate of change moves from positive to negative. As a momentum oscillator, ROC signals include centerline crossovers, divergences, and overbought-oversold readings.
ROC = (Close - Close n periods ago) / (Close n periods ago) * 100
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The logic of self - optimizing:
This script is always backtesting 15 different combinations of ROC settings in the background and saves the net. profit gained for every single one of them, then strategy selects and use the best performing combination of settings currently available for you to trade.
It's recalculating on every bar close - if one of the parameters starts performing better than others - have a higher net profit gain (it's literally like running 15 backtests with different settings in the background) strategy switches to that parameter and continues trading like that until one of the other indicator parameters starts performing better again and switches to that settings.
We are optimizing our strategy based on 15 different 'lengths' or also called 'periods' of ROC.
Inputs (ROC period) : (you don't need to change them, you have a nice wide variety of periods)
🔴Roc (default=9) = 5
🟢Roc2 = 6
🔵Roc3 = 7
🟡Roc4 = 8
🟣Roc5 = 9
🟠Roc6 = 10
🔴Roc7 = 11
🟢Roc8 = 12
🔵Roc9 = 13
🟡Roc10 = 14
🟣Roc11 = 15
🟠Roc12 = 16
🟡Roc13 = 17
🟣Roc14 = 18
🟠Roc15 = 20
Backtester in the background works like this:
backtest ROC1 => save net. profit
backtest ROC2 => save net. profit ;
backtest ROC3 => save net. profit ;
..........
..........
backtest ROC15 => save net. profit ;
=>
It will backtest 15 different ROC parameters and save their profits.
Your strategy then trades based on the best performing (highest net.profit) ROC Setting currently available. It will check the calculations and backtest them on every new bar close - it's like running 15 strategies at time, and manually selecting the best performing one.
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If you wish to use it as INDICATOR - turn on 'Recalculate after every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
-- Noise Filter - This will punish the tiny trades made by certain parameters and give more advantage to big average trades. It's basically normal fee calculation, it will deduct 0.xx % fee from every trade when optimizing. You usually want it to have the same number as your fees on exchange. Large number will choose big long swing trades, small number will prioritize small scalping trades.
-- Turn on ROC Combination Profits and spot the worst/best performing combination. You can change periods to get the best performance after checking this table stats.
-- Backtesting Range - backtest within your desired time window. Example: 'from 01 / 01 /2020 to 01 / 01 /2023'.
-- Optimizing range - you can decrease the amount of bars/data for optimizing script. This way you can keep it up to date to more recent market by selecting optimizing range to optimize it just from the recent 3-6months of data for example. Strategy before this selected range will normally trade (backtest) based on the first ROC period ( 'Roc(default=9)' Input) parameter in your menu if you have Optimizing Range turned on.
**** I recommend 'Optimizing Range' to be turned off, use max amount of available bars in your history for optimization script.
-- Strategy is trading on the bar close without repaint. You can trade Long-Sell or Long- Short. Alerts available, insert webhook messages.
-- Turn on Profit Calendar for better overview of how your strategy performs monthly/annualy
-- Recommended ROC periods: from 5 to 24.
-- Recommended Sources : close, hlc3, hlcc4
-- Recommended Chart Timeframe : 4h +
-- Notes window : add your custom comments here or save your webhook messages inside here
-- Trading Session: in a session, you have to specify the time range for every day. It will trade only within this window and close trades when it's out. Session from 9am to 5pm will look like that: 0900-1700 or 7am to 4:30pm 0700-1630. After the colon, you can specify days of the week for your trading session. 1234567 trading all days, 23456 – Monday to Friday ('1 is Sunday here'). 0000-0000:1234567 by default will trade every day nonstop. 00.00am to 00.00pm and 1234567 every day of the week for example - Cryptocurrencies.
This script is simple to use for any trader as it saves a lot of time for searching good parameters on your own. It's self-optimizing and adjusting to the markets on the go.
Alert(), alertcondition() or strategy alerts?Variety of possibilities offered by PineScript, especially thanks to recent additions, created some confusion. Especially one question repeats quite often - which method to use to trigger alerts?
I'm posting this to clarify and give some syntax examples. I'll discuss these 3 methods in chronological order, meaning - in the order they were introduced to PineScript.
ALERTCONDITION() - it is a function call, which can be used only in study-type script. Since years ago, you could create 2 types of a script: strategy and study. First one enables creating a backtest of a strategy. Second was to develop scripts which didn't require backtesting and could trigger alerts. alertcondition() calls in strategy-type scripts were rejected by Pine compiler. On the other hand compiling study-type scripts rejected all strategy...() calls. That created difficulties, because once you had a nice and backtested strategy, you had to rip it off from all strategy...() function calls to convert your script to study-type so you could produce alerts. Maintenance of two versions of each script was necessary and it was painful.
"STRATEGY ALERTS" were introduced because of alertcondition() pains. To create strategy alert, you need to click "Add alert" button inside Strategy Tester (backtester) and only there. Alerts set-up this way are bound with the backtester - whenever backtester triggers an order, which is visible on the chart, alert is also fired. And you can customize alert message using some placeholders like {{strategy.order.contracts}} or {{ticker}}.
ALERT() was added last. This is an alerts-triggering function call, which can be run from strategy-type script. Finally it is doable! You can connect it to any event coded in PineScript and generate any alert message you want, thanks to concatenation of strings and wrapping variables into tostring() function.
Out of these three alertcondition() is obviously archaic and probably will be discontinued. There is a chance this makes strategy/study distinction not making sense anymore, so I wouldn't be surprised if "studies" are deprecated at some point.
But what are the differences between "Strategy alerts" and alert()? "Strategy alerts" seem easier to set-up with just a few clicks and probably easier to understand and verify, because they go in sync with the backtester and on-chart trade markers. It is especially important to understand how they work if you're building strategy based on pending orders (stop and limit) - events in your code might trigger placing pending order, but alert will be triggered only (and when) such order is executed.
But "Strategy Alerts" have some limitations - not every variable you'd like to include in alert message is available from PineScript. And maybe you don't need the alert fired when the trade hit a stop-loss or take-profit, because you have already forwarded info about closing conditions in entry alert to your broker/exchange.
Alert() was added to PineScript to fill all these gaps. Is allows concatenating any alert message you want, with any variable you want inside it and you can attach alert() function at any event in your PineScript code. For example - when placing orders, crossing variables, exiting trades, but not explicitly at pending orders execution.
The Verdict
"Strategy Alerts" might seem a better fit - easier to set-up and verify, flexible and they fire only when a trade really happens, not producing unnecessary mess when each pending order is placed. But these advantages are illusionary, because they don't give you the full-control which is needed when trading with real money. Especially when using pending orders. If an alert is fired when price actually hit a stop-order or limit-order level, and even if you are executing such alert within 1 second thanks to a tool like TradingConnector, you might already be late and you are making entry at a market price. Slippage will play a great role here. You need to send ordering alert when logical conditions are met - then it will be executed at the price you want. Even if you need to cancel all the pending orders which were not executed. Because of that I strongly recommend sticking to ALERT() when building your alerts system.
Below is an example strategy, showing syntax to manage placing the orders and cancelling them. Yes, this is another spin-off from my TradingView Alerts to MT4 MT5 . As usual, please don't pay attention to backtest results, as this is educational script only.
P.S. For the last time - farewell alertcondition(). You served us well.
Zero Lag Trend Signals (MTF) [Quant Trading] V7Overview
The Zero Lag Trend Signals (MTF) V7 is a comprehensive trend-following strategy that combines Zero Lag Exponential Moving Average (ZLEMA) with volatility-based bands to identify high-probability trade entries and exits. This strategy is designed to reduce lag inherent in traditional moving averages while incorporating dynamic risk management through ATR-based stops and multiple exit mechanisms.
This is a longer term horizon strategy that takes limited trades. It is not a high frequency trading and therefore will also have limited data and not > 100 trades.
How It Works
Core Signal Generation:
The strategy uses a Zero Lag EMA (ZLEMA) calculated by applying an EMA to price data that has been adjusted for lag:
Calculate lag period: floor((length - 1) / 2)
Apply lag correction: src + (src - src )
Calculate ZLEMA: EMA of lag-corrected price
Volatility bands are created using the highest ATR over a lookback period multiplied by a band multiplier. These bands are added to and subtracted from the ZLEMA line to create upper and lower boundaries.
Trend Detection:
The strategy maintains a trend variable that switches between bullish (1) and bearish (-1):
Long Signal: Triggers when price crosses above ZLEMA + volatility band
Short Signal: Triggers when price crosses below ZLEMA - volatility band
Optional ZLEMA Trend Confirmation:
When enabled, this filter requires ZLEMA to show directional momentum before entry:
Bullish Confirmation: ZLEMA must increase for 4 consecutive bars
Bearish Confirmation: ZLEMA must decrease for 4 consecutive bars
This additional filter helps avoid false signals in choppy or ranging markets.
Risk Management Features:
The strategy includes multiple stop-loss and take-profit mechanisms:
Volatility-Based Stops: Default stop-loss is placed at ZLEMA ± volatility band
ATR-Based Stops: Dynamic stop-loss calculated as entry price ± (ATR × multiplier)
ATR Trailing Stop: Ratcheting stop-loss that follows price but never moves against position
Risk-Reward Profit Target: Take-profit level set as a multiple of stop distance
Break-Even Stop: Moves stop to entry price after reaching specified R:R ratio
Trend-Based Exit: Closes position when price crosses EMA in opposite direction
Performance Tracking:
The strategy includes optional features for monitoring and analyzing trades:
Floating Statistics Table: Displays key metrics including win rate, GOA (Gain on Account), net P&L, and max drawdown
Trade Log Labels: Shows entry/exit prices, P&L, bars held, and exit reason for each closed trade
CSV Export Fields: Outputs trade data for external analysis
Default Strategy Settings
Commission & Slippage:
Commission: 0.1% per trade
Slippage: 3 ticks
Initial Capital: $1,000
Position Size: 100% of equity per trade
Main Calculation Parameters:
Length: 70 (range: 70-7000) - Controls ZLEMA calculation period
Band Multiplier: 1.2 - Adjusts width of volatility bands
Entry Conditions (All Disabled by Default):
Use ZLEMA Trend Confirmation: OFF - Requires ZLEMA directional momentum
Re-Enter on Long Trend: OFF - Allows multiple entries during sustained trends
Short Trades:
Allow Short Trades: OFF - Strategy is long-only by default
Performance Settings (All Disabled by Default):
Use Profit Target: OFF
Profit Target Risk-Reward Ratio: 2.0 (when enabled)
Dynamic TP/SL (All Disabled by Default):
Use ATR-Based Stop-Loss & Take-Profit: OFF
ATR Length: 14
Stop-Loss ATR Multiplier: 1.5
Profit Target ATR Multiplier: 2.5
Use ATR Trailing Stop: OFF
Trailing Stop ATR Multiplier: 1.5
Use Break-Even Stop-Loss: OFF
Move SL to Break-Even After RR: 1.5
Use Trend-Based Take Profit: OFF
EMA Exit Length: 9
Trade Data Display (All Disabled by Default):
Show Floating Stats Table: OFF
Show Trade Log Labels: OFF
Enable CSV Export: OFF
Trade Label Vertical Offset: 0.5
Backtesting Date Range:
Start Date: January 1, 2018
End Date: December 31, 2069
Important Usage Notes
Default Configuration: The strategy operates in its most basic form with default settings - using only ZLEMA crossovers with volatility bands and volatility-based stop-losses. All advanced features must be manually enabled.
Stop-Loss Priority: If multiple stop-loss methods are enabled simultaneously, the strategy will use whichever condition is hit first. ATR-based stops override volatility-based stops when enabled.
Long-Only by Default: Short trading is disabled by default. Enable "Allow Short Trades" to trade both directions.
Performance Monitoring: Enable the floating stats table and trade log labels to visualize strategy performance during backtesting.
Exit Mechanisms: The strategy can exit trades through multiple methods: stop-loss hit, take-profit reached, trend reversal, or trailing stop activation. The trade log identifies which exit method was used.
Re-Entry Logic: When "Re-Enter on Long Trend" is enabled with ZLEMA trend confirmation, the strategy can take multiple long positions during extended uptrends as long as all entry conditions remain valid.
Capital Efficiency: Default setting uses 100% of equity per trade. Adjust "default_qty_value" to manage position sizing based on risk tolerance.
Realistic Backtesting: Strategy includes commission (0.1%) and slippage (3 ticks) to provide realistic performance expectations. These values should be adjusted based on your broker and market conditions.
Recommended Use Cases
Trending Markets: Best suited for markets with clear directional moves where trend-following strategies excel
Medium to Long-Term Trading: The default length of 70 makes this strategy more appropriate for swing trading rather than scalping
Risk-Conscious Traders: Multiple stop-loss options allow traders to customize risk management to their comfort level
Backtesting & Optimization: Comprehensive performance tracking features make this strategy ideal for testing different parameter combinations
Limitations & Considerations
Like all trend-following strategies, performance may suffer in choppy or ranging markets
Default 100% position sizing means full capital exposure per trade - consider reducing for conservative risk management
Higher length values (70+) reduce signal frequency but may improve signal quality
Multiple simultaneous risk management features may create conflicting exit signals
Past performance shown in backtests does not guarantee future results
Customization Tips
For more aggressive trading:
Reduce length parameter (minimum 70)
Decrease band multiplier for tighter bands
Enable short trades
Use lower profit target R:R ratios
For more conservative trading:
Increase length parameter
Enable ZLEMA trend confirmation
Use wider ATR stop-loss multipliers
Enable break-even stop-loss
Reduce position size from 100% default
For optimal choppy market performance:
Enable ZLEMA trend confirmation
Increase band multiplier
Use tighter profit targets
Avoid re-entry on trend continuation
Visual Elements
The strategy plots several elements on the chart:
ZLEMA line (color-coded by trend direction)
Upper and lower volatility bands
Long entry markers (green triangles)
Short entry markers (red triangles, when enabled)
Stop-loss levels (when positions are open)
Take-profit levels (when enabled and positions are open)
Trailing stop lines (when enabled and positions are open)
Optional ZLEMA trend markers (triangles at highs/lows)
Optional trade log labels showing complete trade information
Exit Reason Codes (for CSV Export)
When CSV export is enabled, exit reasons are coded as:
0 = Manual/Other
1 = Trailing Stop-Loss
2 = Profit Target
3 = ATR Stop-Loss
4 = Trend Change
Conclusion
Zero Lag Trend Signals V7 provides a robust framework for trend-following with extensive customization options. The strategy balances simplicity in its core logic with sophisticated risk management features, making it suitable for both beginner and advanced traders. By reducing moving average lag while incorporating volatility-based signals, it aims to capture trends earlier while managing risk through multiple configurable exit mechanisms.
The modular design allows traders to start with basic trend-following and progressively add complexity through ZLEMA confirmation, multiple stop-loss methods, and advanced exit strategies. Comprehensive performance tracking and export capabilities make this strategy an excellent tool for systematic testing and optimization.
Note: This strategy is provided for educational and backtesting purposes. All trading involves risk. Past performance does not guarantee future results. Always test thoroughly with paper trading before risking real capital, and adjust position sizing and risk parameters according to your risk tolerance and account size.
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TAGS:
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trend following, ZLEMA, zero lag, volatility bands, ATR stops, risk management, swing trading, momentum, trend confirmation, backtesting
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CATEGORY:
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Strategies
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CHART SETUP RECOMMENDATIONS:
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For optimal visualization when publishing:
Use a clean chart with no other indicators overlaid
Select a timeframe that shows multiple trade signals (4H or Daily recommended)
Choose a trending asset (crypto, forex major pairs, or trending stocks work well)
Show at least 6-12 months of data to demonstrate strategy across different market conditions
Enable the floating stats table to display key performance metrics
Ensure all indicator lines (ZLEMA, bands, stops) are clearly visible
Use the default chart type (candlesticks) - avoid Heikin Ashi, Renko, etc.
Make sure symbol information and timeframe are clearly visible
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COMPLIANCE NOTES:
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✅ Open-source publication with complete code visibility
✅ English-only title and description
✅ Detailed explanation of methodology and calculations
✅ Realistic commission (0.1%) and slippage (3 ticks) included
✅ All default parameters clearly documented
✅ Performance limitations and risks disclosed
✅ No unrealistic claims about performance
✅ No guaranteed results promised
✅ Appropriate for public library (original trend-following implementation with ZLEMA)
✅ Educational disclaimers included
✅ All features explained in detail
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QuantJazz Turbine Trader BETA v1.17QuantJazz Turbine Trader BETA v1.17 - Strategy Introduction and User Guide
Strategy Introduction
Welcome to the QuantJazz Turbine Trader BETA v1.17, a comprehensive trading strategy designed for TradingView. This strategy is built upon oscillator principles, drawing inspiration from the Turbo Oscillator by RedRox, and incorporates multiple technical analysis concepts including RSI, MFI, Stochastic oscillators, divergence detection, and an optional FRAMA (Fractal Adaptive Moving Average) filter.
The Turbine Trader aims to provide traders with a flexible toolkit for identifying potential entry and exit points in the market. It presents information through a main signal line oscillator, a histogram, and various visual cues like dots, triangles, and divergence lines directly on the indicator panel. The strategy component allows users to define specific conditions based on these visual signals to trigger automated long or short trades within the TradingView environment.
This guide provides an overview of the strategy's components, settings, and usage. Please remember that this is a BETA version (v1.17). While developed with care, it may contain bugs or behave unexpectedly.
LEGAL DISCLAIMER: QuantJazz makes no claims about the fitness or profitability of this tool. Trading involves significant risk, and you may lose all of your invested capital. All trading decisions made using this strategy are solely at the user's discretion and responsibility. Past performance is not indicative of future results. Always conduct thorough backtesting and risk assessment before deploying any trading strategy with real capital.
This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.
Core Concepts and Visual Elements
The Turbine Trader indicator displays several components in its own panel below the main price chart:
1. Signal Line (Avg & Avg2): This is the primary oscillator. It's a composite indicator derived from RSI, MFI (Money Flow Index), and Stochastic calculations, smoothed using an EMA (Exponential Moving Average).
Avg: The faster smoothed signal line.
Avg2: The slower smoothed signal line.
Color Coding: The space between Avg and Avg2 is filled. The color (Neon Blue/gColor or Neon Purple/rColor) indicates the trend based on the relationship between Avg and Avg2. Blue suggests bullish momentum (Avg > Avg2), while Purple suggests bearish momentum (Avg2 > Avg).
Zero Line Crosses: Crossovers of the Avg line with the zero level can indicate shifts in momentum.
2. Histogram (resMfi): This histogram is based on smoothed and transformed MFI calculations (Fast MFI and Slow MFI).
Color Coding: Bars are colored Neon Blue (histColorUp) when above zero, suggesting bullish pressure, and Neon Purple (histColorDn) when below zero, suggesting bearish pressure. Transparency is applied.
Zero Line Crosses: Crossovers of the histogram with the zero level can signal potential shifts in money flow.
3. Reversal Points (Dots): Dots appear on the Signal Line (specifically on Avg2) when the color changes (i.e., Avg crosses Avg2).
Small Dots: Appear when a reversal occurs while the oscillator is in an "extreme" zone (below -60 for bullish reversals, above +60 for bearish reversals).
Large Dots: Appear when a reversal occurs outside of these extreme zones.
Colors: Blue (gRdColor) for bullish reversals (Avg crossing above Avg2), Purple (rRdColor) for bearish reversals (Avg crossing below Avg2).
4. Take Profit (TP) Signals (Triangles): Small triangles appear above (+120) or below (-120) the zero line.
Bearish Triangle (Down, Purple rTpColor): Suggests a potential exit point for long positions or an entry point for short positions, based on the oscillator losing upward momentum above the 50 level.
Bullish Triangle (Up, Blue gTpColor): Suggests a potential exit point for short positions or an entry point for long positions, based on the oscillator losing downward momentum below the -50 level.
5. Divergence Lines: The strategy automatically detects and draws potential regular and hidden divergences between the price action (highs/lows) and the Signal Line (Avg).
Regular Bullish Divergence (White bullDivColor line, ⊚︎ label): Price makes a lower low, but the oscillator makes a higher low. Suggests potential bottoming.
Regular Bearish Divergence (White bearDivColor line, ⊚︎ label): Price makes a higher high, but the oscillator makes a lower high. Suggests potential topping.
Hidden Bullish Divergence (bullHidDivColor line, ⊚︎ label): Price makes a higher low, but the oscillator makes a lower low. Suggests potential continuation of an uptrend.
Hidden Bearish Divergence (bearHidDivColor line, ⊚︎ label): Price makes a lower high, but the oscillator makes a higher high. Suggests potential continuation of a downtrend.
Delete Broken Divergence Lines: If enabled, newer divergence lines originating from a similar point will replace older ones.
6. Status Line: A visual bar at the top (95 to 105) and bottom (-95 to -105) of the indicator panel. Its color intensity reflects the confluence of signals:
Score Calculation: +1 if Avg > Avg2, +1 if Avg > 0, +1 if Histogram > 0.
Top Bar (Bullish): Bright Blue (gStatColor) if score is 3, Faded Blue if score is 2, Black otherwise.
Bottom Bar (Bearish): Bright Purple (rStatColor) if score is 0, Faded Purple if score is 1, Black otherwise.
Strategy Settings Explained
The strategy's behavior is controlled via the settings panel (gear icon).
1. Date Range:
Start Date, End Date: Define the period for backtesting. Trades will only occur within this range.
2. Optional Webhook Configuration: (For Automation)
3C Email Token, 3C Bot ID: Enter your 3Commas API credentials if you plan to automate trading using webhooks. The strategy generates JSON alert messages compatible with 3Commas. You can go ahead and just leave the text field as defaulted, "TOKEN HERE" / "BOT ID HERE" if not using any bot automations at this time. You can always come back later and automate it. More info can be made available from QuantJazz should you need automation assistance with custom indicators and trading strategies.
3. 🚀 Signal Line:
Turn On/Off: Show or hide the main signal lines (Avg, Avg2).
gColor, rColor: Set the colors for bullish and bearish signal line states.
Length (RSI): The lookback period for the internal RSI calculation. Default is 2.
Smooth (EMA): The smoothing period for the EMAs applied to the composite signal. Default is 9.
RSI Source: The price source used for RSI calculation (default: close).
4. 📊 Histogram:
Turn On/Off: Show or hide the histogram.
histColorUp, histColorDn: Set the colors for positive and negative histogram bars.
Length (MFI): The base lookback period for MFI calculations. Default is 5. Fast and Slow MFI lengths are derived from this.
Smooth: Smoothing period for the final histogram output. Default is 1 (minimal smoothing).
5.💡 Other:
Show Divergence Line: Toggle visibility of regular divergence lines.
bullDivColor, bearDivColor: Colors for regular divergence lines.
Show Hidden Divergence: Toggle visibility of hidden divergence lines.
bullHidDivColor, bearHidDivColor: Colors for hidden divergence lines.
Show Status Line: Toggle visibility of the top/bottom status bars.
gStatColor, rStatColor: Colors for the status line bars.
Show TP Signal: Toggle visibility of the TP triangles.
gTpColor, rTpColor: Colors for the TP triangles.
Show Reversal points: Toggle visibility of the small/large dots on the signal line.
gRdColor, rRdColor: Colors for the reversal dots.
Delete Broken Divergence Lines: Enable/disable automatic cleanup of older divergence lines.
6. ⚙️ Strategy Inputs: (CRITICAL for Trade Logic)
This section defines which visual signals trigger trades. Each signal (Small/Large Dots, TP Triangles, Bright Bars, Signal/Histogram Crosses, Signal/Histogram Max/Min, Divergences) has a dropdown menu:
Long: This signal can trigger a long entry.
Short: This signal can trigger a short entry.
Disabled: This signal will not trigger any entry.
Must Be True Checkbox: If checked for a specific signal, that signal's condition must be met for any trade (long or short, depending on the dropdown selection for that signal) to be considered. Multiple "Must Be True" conditions act as AND logic – all must be true simultaneously.
How it Works:
The strategy first checks if all conditions marked as "Must Be True" (for the relevant trade direction - long or short) are met.
If all "Must Be True" conditions are met, it then checks if at least one of the conditions not marked as "Must Be True" (and set to "Long" or "Short" respectively) is also met.
If both steps pass, and other filters (like Date Range, FRAMA) allow, an entry order is placed.
Example: If "Large Bullish Dot" is set to "Long" and "Must Be True" is checked, AND "Bullish Divergence" is set to "Long" but "Must Be True" is not checked: A long entry requires BOTH the Large Bullish Dot AND the Bullish Divergence to occur simultaneously. If "Large Bullish Dot" was "Long" but not "Must Be True", then EITHER a Large Bullish Dot OR a Bullish Divergence could trigger a long entry (assuming no other "Must Be True" conditions are active).
Note: By default, the strategy is configured for long-only trades (strategy.risk.allow_entry_in(strategy.direction.long)). To enable short trades, you would need to comment out or remove this line in the Pine Script code and configure the "Strategy Inputs" accordingly.
7. 💰 Take Profit Settings:
Take Profit 1/2/3 (%): The percentage above the entry price (for longs) or below (for shorts) where each TP level is set. (e.g., 1.0 means 1% profit).
TP1/2/3 Percentage: The percentage of the currently open position to close when the corresponding TP level is hit. The percentages should ideally sum to 100% if you intend to close the entire position across the TPs.
Trailing Stop (%): The percentage below the highest high (for longs) or above the lowest low (for shorts) reached after the activation threshold, where the stop loss will trail.
Trailing Stop Activation (%): The minimum profit percentage the trade must reach before the trailing stop becomes active.
Re-entry Delay (Bars): The minimum number of bars to wait after a TP is hit before considering a re-entry. Default is 1 (allows immediate re-entry on the next bar if conditions met).
Re-entry Price Offset (%): The percentage the price must move beyond the previous TP level before a re-entry is allowed. This prevents immediate re-entry if the price hovers around the TP level.
8. 📈 FRAMA Filter: (Optional Trend Filter)
Use FRAMA Filter: Enable or disable the filter.
FRAMA Source, FRAMA Period, FRAMA Fast MA, FRAMA Slow MA: Parameters for the FRAMA calculation. Defaults provided are common starting points.
FRAMA Filter Type:
FRAMA > previous bars: Allows trades only if FRAMA is significantly above its recent average (defined by FRAMA Percentage and FRAMA Lookback). Typically used to confirm strong upward trends for longs.
FRAMA < price: Allows trades only if FRAMA is below the current price (framaSource). Can act as a baseline trend filter.
FRAMA Percentage (X), FRAMA Lookback (Y): Used only for the FRAMA > previous bars filter type.
How it Affects Trades: If Use FRAMA Filter is enabled:
Long entries require the FRAMA filter condition to be true.
Short entries require the FRAMA filter condition to be false (as currently coded, this acts as an inverse filter for shorts if enabled).
How to Use the Strategy
1. Apply to Chart: Open your desired chart on TradingView. Click "Indicators", find "QuantJazz Turbine Trader BETA v1.17" (you might need to add it via Invite-only scripts or if published publicly), and add it to your chart. The oscillator appears below the price chart, and the strategy tester panel opens at the bottom.
2. Configure Strategy Properties: In the Pine Script code itself (or potentially via the UI if supported), adjust the strategy() function parameters like initial_capital, default_qty_value, commission_value, slippage, etc., to match your account, broker fees, and risk settings. The user preferences provided (e.g., 1000 initial capital, 0.1% commission) are examples. Remember use_bar_magnifier is false by default in v1.17.
3. Configure Inputs (Settings Panel):
Set the Date Range for backtesting.
Crucially, configure the ⚙️ Strategy Inputs. Decide which signals should trigger entries and whether they are mandatory ("Must Be True"). Start simply, perhaps enabling only one or two signals initially, and test thoroughly. Remember the default long-only setting unless you modify the code.
Set up your 💰 Take Profit Settings, including TP levels, position size percentages for each TP, and the trailing stop parameters. Decide if you want to use the re-entry feature.
Decide whether to use the 📈 FRAMA Filter and configure its parameters if enabled.
Adjust visual elements (🚀 Signal Line, 📊 Histogram, 💡 Other) as needed for clarity.
4. Backtest: Use the Strategy Tester panel in TradingView. Analyze the performance metrics (Net Profit, Max Drawdown, Profit Factor, Win Rate, Trade List) across different assets, timeframes, and setting configurations. Pay close attention to how different "Strategy Inputs" combinations perform.
5. Refine: Based on backtesting results, adjust the input settings, TP/SL strategy, and signal combinations to optimize performance for your chosen market and timeframe, while being mindful of overfitting.
6. Automation (Optional): If using 3Commas or a similar platform:
Enter your 3C Email Token and 3C Bot ID in the settings.
Create alerts in TradingView (right-click on the chart or use the Alert panel).
Set the Condition to "QuantJazz Turbine Trader BETA v1.17".
In the "Message" box, paste the corresponding placeholder, which will pass the message in JSON from our custom code to TradingView to pass through your webhook: {{strategy.order.alert_message}}.
In the next tab, configure the Webhook URL provided by your automation platform. Put a Whale sound, while you're at it! 🐳
When an alert triggers, TradingView will send the pre-formatted JSON message from the strategy code to your webhook URL.
Final Notes
The QuantJazz Turbine Trader BETA v1.17 offers a wide range of customizable signals and strategy logic. Its effectiveness heavily depends on proper configuration and thorough backtesting specific to the traded asset and timeframe. Start with the default settings, understand each component, and methodically test different combinations of signals and parameters. Remember the inherent risks of trading and never invest capital you cannot afford to lose.
Gabriel's Witcher Strategy [65 Minute Trading Bot]Strategy Description: Gabriel's Witcher Strategy
Author: Gabriel
Platform: TradingView Pine Script (Version 5)
Backtested Asset: Avalanche (Coinbase Brokage for Volume adjustment)
Timeframe: 65 Minutes
Strategy Type: Comprehensive Trend-Following and Momentum Strategy with Scalping and Risk Management Features
Overview
Gabriel's Witcher Strategy is an advanced trading bot designed for the Avalanche pair on a 65-minute timeframe. This strategy integrates a multitude of technical indicators to identify and execute high-probability trading opportunities. By combining trend-following, momentum, volume analysis, and range filtering, the strategy aims to capitalize on both long and short market movements. Additionally, it incorporates scalping mechanisms and robust risk management features, including take-profit (TP) levels and commission considerations, to optimize trade performance and profitability.
====Key Components====
Source Selection:
Custom Source Flexibility: Allows traders to select from a wide range of price and volume sources (e.g., Close, Open, High, Low, HL2, HLC3, OHLC4, VWAP, On-Balance Volume, etc.) for indicator calculations, enhancing adaptability to various trading styles.
Various curves of Volume Analysis are employed:
Tick Volume Calculation: Utilizes tick volume as a fallback when actual volume data is unavailable, ensuring consistency across different data feeds.
Volume Indicators: Incorporates multiple volume-based indicators such as On-Balance Volume (OBV), Accumulation/Distribution (AccDist), Negative Volume Index (NVI), Positive Volume Index (PVI), and Price Volume Trend (PVT) for comprehensive market analysis.
Trend Indicators:
ADX (Average Directional Index): Measures trend strength using either the Classic or Masanakamura method, with customizable length and threshold settings. It's used to open positions when the mesured trend is strong, or exit when its weak.
Jurik Moving Average (JMA): A smooth moving average that reduces lag, configurable with various parameters including source, resolution, and repainting options.
Parabolic SAR: Identifies potential reversals in market trends with adjustable start, increment, and maximum settings.
Custom Trend Indicator: Utilizes highest and lowest price points over a specified timeframe to determine current and previous trend bases, visually represented with color-filled areas.
Momentum Indicators:
Relative Strength Index (RSI): Evaluates the speed and change of price movements, smoothed with a custom length and source. It's used to not enter the market for shorts in oversold or longs for overbought conditions, and to enter for long in oversold or shorts for overboughts.
Momentum-Based Calculations: Employs both Double Exponential Moving Averages (DEMA) on a MACD-based RSI to enhance momentum signal accuracy which is then further accelerated by a Hull MA. This is the technical analysis tool that determines bearish or bullish momentum.
OBV-Based Momentum Conditions: Uses two exponential moving averages of OBV to determine bullish or bearish momentum shifts, anomalities, breakouts where banks flow their funds in or Smart Money Concepts trade.
Moving Averages (MA):
Multiple MA Types: Includes Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Hull Moving Average (HMA), and Volume-Weighted Moving Average (VWMA), selectable via input parameters.
MA Speed Calculation: Measures the percentage change in MA values to determine the direction and speed of the trend.
Range Filtering:
Variance-Based Filter: Utilizes variance and moving averages to filter out trades during low-volatility periods, enhancing trade quality.
Color-Coded Range Indicators: Visualizes range filtering with color changes on the chart for quick assessment.
Scalping Mechanism:
Heikin-Ashi Candles: Optionally uses Heikin-Ashi candles for smoother price action analysis.
EMA-Based Trend Detection: Employs fast, medium, and slow EMAs to determine trend direction and potential entry points.
Fractal-Based Filtering: Detects regular or BW (Black & White) fractals to confirm trade signals.
Take Profit (TP) Management:
Dynamic TP Levels: Calculates TP levels based on the number of consecutive long or short entries, adjusting targets to maximize profits.
TP Signals and Re-Entry: Plots TP signals on the chart and allows for automatic re-entry upon TP hit, maintaining continuous trade flow.
Risk Management:
Commission Integration: Accounts for trading commissions to ensure net profitability.
Position Sizing: Configured to use a percentage of equity for each trade, adjustable via input parameters.
Pyramiding: Allows up to one additional position per direction to enhance gains during strong trends.
Alerts and Visual Indicators:
Buy/Sell Signals: Plots visual indicators (triangles and flags) on the chart to signify entry and TP points.
Bar Coloring: Changes bar colors based on ADX and trend conditions for immediate visual cues.
Price Levels: Marks significant price levels related to TP and position entries with cross styles.
Input Parameters
Source Settings:
Custom Sources (srcinput): Choose from various price and volume sources to tailor indicator calculations.
ADX Settings:
ADX Type (ADX_options): Select between 'CLASSIC' and 'MASANAKAMURA' methods.
ADX Length (ADX_len): Defines the period for ADX calculation.
ADX Threshold (th): Sets the minimum ADX value to consider a strong trend.
RSI Settings:
RSI Length (len_3): Period for RSI calculation.
RSI Source (src_3): Source data for RSI.
Trend Strength Settings:
Channel Length (n1): Period for trend channel calculation.
Average Length (n2): Period for smoothing trend strength.
Jurik Moving Average (JMA) Settings:
JMA Source (inp): Source data for JMA.
JMA Resolution (reso): Timeframe for JMA calculation.
JMA Repainting (rep): Option to allow JMA to repaint.
JMA Length (lengths): Period for JMA.
Parabolic SAR Settings:
SAR Start (start): Initial acceleration factor.
SAR Increment (increment): Acceleration factor increment.
SAR Maximum (maximum): Maximum acceleration factor.
SAR Point Width (width): Visual width of SAR points.
Trend Indicator Settings:
Trend Timeframe (timeframe): Period for trend indicator calculations.
Momentum Settings:
Source Type (srcType): Select between 'Price' and 'VWAP'.
Momentum Source (srcPrice): Source data for momentum calculations.
RSI Length (rsiLen): Period for momentum RSI.
Smooth Length (sLen): Smoothing period for momentum RSI.
OBV Settings:
OBV Line 1 (e1): EMA period for OBV line 1.
OBV Line 2 (e2): EMA period for OBV line 2.
Moving Average (MA) Settings:
MA Length (length): Period for MA calculations.
MA Type (matype): Select MA type (1: SMA, 2: EMA, 3: HMA, 4: WMA, 5: VWMA).
Range Filter Settings:
Range Filter Length (length0): Period for range filtering.
Range Filter Multiplier (mult): Multiplier for range variance.
Take Profit (TP) Settings:
TP Long (tp_long0): Percentage for long TP.
TP Short (tp_short0): Percentage for short TP.
Scalping Settings:
Scalping Activation (ACT_SCLP): Enable or disable scalping.
Scalping Length (HiLoLen): Period for scalping indicators.
Fast EMA Length (fastEMAlength): Period for fast EMA in scalping.
Medium EMA Length (mediumEMAlength): Period for medium EMA in scalping.
Slow EMA Length (slowEMAlength): Period for slow EMA in scalping.
Filter (filterBW): Enable or disable additional fractal filtering.
Pullback Lookback (Lookback): Number of bars for pullback consideration.
Use Heikin-Ashi Candles (UseHAcandles): Option to use Heikin-Ashi candles for smoother trend analysis.
Strategy Logic
Indicator Calculations:
Volume and Source Selection: Determines the primary data source based on user input, ensuring flexibility and adaptability.
ADX Calculation: Computes ADX using either the Classic or Masanakamura method to assess trend strength.
RSI Calculation: Evaluates market momentum using RSI, further smoothed with custom periods.
Trend Strength Assessment: Utilizes trend channel and average lengths to gauge the robustness of current trends.
Jurik Moving Average (JMA): Smooths price data to reduce lag and enhance trend detection.
Parabolic SAR: Identifies potential trend reversals with adjustable parameters for sensitivity.
Momentum Analysis: Combines RSI with DEMA and OBV-based conditions to confirm bullish or bearish momentum.
Moving Averages: Employs multiple MA types to determine trend direction and speed.
Range Filtering: Filters out low-volatility periods to focus on high-probability trades.
Trade Conditions:
Long Entry Conditions:
ADX Confirmation: ADX must be above the threshold, indicating a strong uptrend.
RSI and Momentum: RSI below 70 and positive momentum signals.
JMA and SAR: JMA indicates an uptrend, and Parabolic SAR is below the price.
Trend Indicator: Confirms the current trend direction.
Range Filter: Ensures market is in an upward range.
Scalping Option: If enabled, additional scalping conditions must be met.
Short Entry Conditions:
ADX Confirmation: ADX must be above the threshold, indicating a strong downtrend.
RSI and Momentum: RSI above 30 and negative momentum signals.
JMA and SAR: JMA indicates a downtrend, and Parabolic SAR is above the price.
Trend Indicator: Confirms the current trend direction.
Range Filter: Ensures market is in a downward range.
Scalping Option: If enabled, additional scalping conditions must be met.
Position Management:
Entry Execution: Places long or short orders based on the identified conditions and user-selected position types (Longs, Shorts, or Both).
Take Profit (TP): Automatically sets TP levels based on predefined percentages, adjusting dynamically with consecutive trades.
Re-Entry Mechanism: Allows for automatic re-entry upon TP hit, maintaining active trading positions.
Exit Conditions: Closes positions when TP levels are reached or when opposing trend signals are detected.
Visual Indicators:
Bar Coloring: Highlights bars in green for bullish conditions, red for bearish, and orange for neutral.
Plotting Price Levels: Marks significant price levels related to TP and trade entries with cross symbols.
Signal Shapes: Displays triangle and flag shapes on the chart to indicate trade entries and TP hits.
Alerts:
Custom Alerts: Configured to notify traders of long entries, short entries, and TP hits, enabling timely trade management and execution.
Usage Instructions
Setup:
Apply the Strategy: Add the script to your TradingView chart set to BTCUSDT with a 65-minute timeframe.
Configure Inputs: Adjust the input parameters under their respective groups (e.g., Source Settings, ADX, RSI, Trend Strength, etc.) to match your trading preferences and risk tolerance.
Position Selection:
Choose Position Type: Use the Position input to select Longs, Shorts, or Both based on your market outlook.
Execution: The strategy will automatically execute and manage positions according to the selected type, ensuring targeted trading actions.
Signal Interpretation:
Buy Signals: Blue triangles below the bars indicate potential long entry points.
Sell Signals: Red triangles above the bars indicate potential short entry points.
Take Profit Signals: Flags above or below the bars signify TP hits for long and short positions, respectively.
Bar Colors: Green bars suggest bullish conditions, red bars indicate bearish conditions, and orange bars represent neutral or consolidating markets.
Risk Management:
Default Position Size: Set to 100% of equity. Adjust the default_qty_value as needed for your risk management strategy.
Commission: Accounts for a 0.1% commission per trade. Adjust the commission_value to match your broker's fees.
Pyramiding: Allows up to one additional position per direction to enhance gains during strong trends.
Backtesting and Optimization:
Historical Testing: Utilize TradingView's backtesting features to evaluate the strategy's performance over historical data.
Parameter Tuning: Optimize input parameters to align the strategy with current market dynamics and personal trading objectives.
Alerts Configuration:
Set Up Alerts: Enable and configure alerts based on the predefined alertcondition statements to receive real-time notifications of trade signals and TP hits.
Additional Features
Comprehensive Indicator Integration: Combines multiple technical indicators to provide a holistic view of market conditions, enhancing trade signal accuracy.
Scalping Options: Offers an optional scalping mechanism to capitalize on short-term price movements, increasing trading flexibility.
Dynamic Take Profit Levels: Adjusts TP targets based on the number of consecutive trades, maximizing profit potential during favorable trends.
Advanced Volume Analysis: Utilizes various volume indicators to confirm trend strength and validate trade signals.
Customizable Range Filtering: Filters trades based on market volatility, ensuring trades are taken during optimal conditions.
Heikin-Ashi Candle Support: Optionally uses Heikin-Ashi candles for smoother price action analysis and reduced noise.
====Recommendations====
Thorough Backtesting:
Historical Performance: Before deploying the strategy in a live trading environment, perform comprehensive backtesting to understand its performance under various market conditions. These are the premium settings for Avalanche Coinbase.
Optimization: Regularly review and adjust input parameters to ensure the strategy remains effective amidst changing market volatility and trends. Backtest the strategy for each crypto and make sure you are in the right brokage when using the volume sources as it will affect the overall outcome of the trading strategy.
Risk Management:
Position Sizing: Adjust the default_qty_value to align with your risk tolerance and account size.
Stop-Loss Implementation: Although the strategy includes TP levels, they're also consided to be a stop-loss mechanisms to protect against adverse market movements.
Commission Adjustment: Ensure the commission_value accurately reflects your broker's fees to maintain realistic backtesting results. Generally, 0.1~0.3% are most of the average broker's comission fees.
Slipage: The slip comssion is 1 Tick, since the strategy is adjusted to only enter/exit on bar close where most positions are available.
Continuous Monitoring:
Strategy Performance: Regularly monitor the strategy's performance to ensure it operates as expected and make adjustments as needed. A max-drawndown hit has been added to operate in case the premium Avalanche settings go wrong, but you can turn it off an adjust the equity percentage to 50% if you are confortable with the high volatile max-drown or even 100% if your account allows you to borrow cash.
Customization:
Indicator Parameters: Tailor indicator settings (e.g., ADX length, RSI period, MA types) to better fit your specific trading style and market conditions.
Scalping Options: Enable or disable scalping based on your trading preferences and risk appetite.
Conclusion
Gabriel's Witcher Strategy is a robust and versatile trading solution designed to navigate the complexities of the Crypto market. By integrating a wide array of technical indicators and providing extensive customization options, this strategy empowers traders to execute informed and strategic trades. Its comprehensive approach, combining trend analysis, momentum detection, volume evaluation, and range filtering, ensures that trades are taken during optimal market conditions. Additionally, the inclusion of scalping features and dynamic take-profit management enhances the strategy's adaptability and profitability potential. Unlike any trading strategy, with both diligent testing and continuous monitoring under the strategy tester, it's possible to achieve sustained success by adjusting the settings to the individual Crypto that need it, for example this one is preset for Avalanche Coinbase 65 Miinutes but it can be adjust for BTCUSD or Etherium if you backtest and search for the right settings.
AlgoBuilder [Trend-Following] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely on and trade based on historical and backtested data using automation. The main goal is to build profitable trend-following strategies that outperform the underlying asset in terms of returns while minimizing drawdown. For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based trailing stop-loss mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability and sentiment function for traders who want to implement probabilities and market sentiment right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, compound annual growth rate (CAGR), profit factor, average trade, average risk-reward ratio (RR), and more. This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading (1x):
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on trend-following and risk management.
- (1x) This mode ensures no stacking of positions, allowing for only one running position or trade at a time.
◓: Mode | %: Risk percentage per trade
2. Trading (2x):
Similar to the 1x mode but allows for two pyramiding entries.
This approach enables traders to increase their position size as the trade moves in their favor, potentially enhancing profits during strong bullish trends.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes 100% of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 100% of equity to buy the asset)
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>/<) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
MA #1: Fast MA | MA #2: Medium MA | MA #3: Slow MA
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 1.5
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (14) * 1.5
⍺: ADR period | Σ: ADR Multiplier
Application in Strategy:
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detec buyside and sellside liquidity levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
There are two built-in trailing stop-loss (SL) options you can choose from while in a trade:
1. External Trailing Stop-Loss:
- Uses sell-side liquidity to trail your stop-loss, allowing price to consolidate before continuation. This method is less aggressive and provides more room for price fluctuations.
Example - External - Wick below the trailing SL - 12H trailing timeframe
⍺: Exit type | Σ: Trailing stop-loss timeframe
2. Internal Trailing Stop-Loss:
- Uses the most recent swing low with a period of 2 to trail your stop-loss. This method is more aggressive compared to the external trailing stop-loss, as it tightens the stop-loss closer to the current price action.
Example - Internal - Close below the trailing SL - 6H trailing timeframe
⍺: Exit type | Σ: Trailing stop-loss timeframe
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
- You can choose to set a break-even level at which your initial stop-loss moves to the entry price as soon as it hits, and your trailing stop-loss gets activated (if enabled).
- You can select either a percentage (%) or risk-to-reward (RR) based break-even, allowing you to set your break-even level as a percentage amount above the entry price or based on RR.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
The underlying calculations involve determining the price levels at which these actions are triggered. For break-even, it moves the initial stop-loss to the entry price and activate the trailing stop-loss once the break-even level is reached.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 50%
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What's the sentiment Filter? What are the underlying calculations?
Sentiment filter aims to calculate the percentage level of bullish or bearish fluctuations within equally divided price sections, in the latest price range.
Calculations:
This filter calculates the current sentiment by identifying the highest swing high and the lowest swing low, then evenly dividing the distance between them into percentage amounts. If the price is above the 50% mark, it indicates bullishness, whereas if it's below 50%, it suggests bearishness.
Sentiment Bias Identification:
Bullish Bias: The current price is trading above the 50% daily range.
Bearish Bias: The current price is trading below the 50% daily range.
Example - Sentiment Enabled | Bullish degree above 50% | Bullish sentimental bias
>: Minimum required sentiment for entry | %: Current sentimental degree in a (Bullish/Bearish) sentimental bias
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 5% | Price must be in a bearish range
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades, Compound Annual Growth Rate (CAGR), MAR and more.
CAGR: It calculates the 'Compound Annual Growth Rate' first and last taken trades on your chart. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two strategies. Since it annualizes values, it requires a minimum 4H timeframe to display the CAGR value. annualizing returns over smaller periods of times doesn't produce very meaningful figures.
MAR: Measure of return adjusted for risk: CAGR divided by Max Drawdown. Indicates how comfortable the system might be to trade. Higher than 0.5 is ideal, 1.0 and above is very good, and anything above 3.0 should be considered suspicious and you need to make sure the total number of trades are high enough by running a Deep Backtest in strategy tester. (available for TradingView Premium users.)
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most trend-following successful strategies have a percent profitability of 15-40% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Labels:
- OFF: Hides labels in the performance table.
- PnL: Shows the profit and loss of each trade individually, providing detailed insights into the performance of each trade.
- Range: Shows the range length and Average Day Range (ADR), offering additional context about market conditions during each trade.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, MAR (Mar Ratio), CAGR (Compound Annual Growth Rate), and net profit with minimum drawdown. Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Leveraging market sentiment to construct a profitable approach.
3. Utilizing built-in market structure-based trailing stop-loss mechanisms across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Strategy Properties
This script backtest is done on 4H COINBASE:BTCUSD , using the following backtesting properties:
Balance: $5000
Order Size: 10% of the equity
Risk % per trade: 1%
Commission: 0.04% (Default commission percentage according to TradingView competitions rules)
Slippage: 75 ticks
Pyramiding: 2
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Dankland Playground Moneymaker - V2“version 2” of my playground bot script. Its essentially a powerhouse suite of strategies. Although it is similar to the previous script, it nets different results as sections have been changed. Such as the somewhat reluctant removal of the Chande Momentum... The RSIs have also been updated, this was one of the main changes. RSIS now include a Moving Average cross of RSI to generate signals above and below the given thresholds instead of simply on crossing a threshold. This should give greater functionality overall. Most functions including Moving Averages have been updated to include a wider range of kinds of moving averages. This includes not just the moving average cross, but MACD and RSIs as well. I tried to perform the same upgrade on the %B, Stochastics and SMI, but hit the unpacked code limit of 60,000 lines... So, more “versions” will have to come for future “upgrades”, with the recognition that there will be cases where the old, “downgraded” versions may perform better and that some people (like myself) may continue to use them on some markets until I/we devise superior settings on the new ones for said markets. For instance, instead of replacing my 1 hr BTCUSD bot (where I used the now deleted Chande to pretty pleasing affect...) I made a new one for LINKUSD 10 min so I can have both running for now and work on replacing the BTCUSD later.
How it works basically is this... you have 16 oscillators which can all be used as independently as you wish. They can be split up into different groups or ran all together.
When in separate groups they should not be able to sell eachothers positions without triggering a full stop loss by turning the Independence/Stop All switches on. Every single oscillator has its own entry and exit position sizing which can be stated as either a percent of balance or a flat amount of contracts (or both combined). Each oscillator has a minimum amount of profit you can tell it to sell it, which is calculated from the average cost of your current position, which does include all groups. This works out to help you average out better entry and exit prices, essentially a method of DCAing.
You can set the minimum sale amount, which is to keep it from placing orders below your exchanges minimum dollar trade cost.
All this functionality combined also ensures more accurate back tests by ensuring that the script simply cannot spend money it doesn't see as in the balance, whereas other scripts will use a percentage of equity, and once 100% of your equity is in BTC for instance, it will keep buying more BTC for free and thus spoof up backtest numbers. If you look through the strategies here, many people claim to have amazing scripts and then you look into it and this is happening and skewing their numbers. These people are either very ignorant or what they made or scam artists and trolls in my opinion.
This version also includes On Bar Close switches for each oscillator. When switched on, signals are only allowed to generate on Bar Close. This helps to prevent retriggering from live signals, which when you are running this many oscillators, will become a problem! However, in most cases, you do not need to generate signals intrabar, as backtests will show, ignoring intrabar buys and sells (intrabar stop losses can still be very important though!) won't exactly keep you from high profitability strategies, but rather, allowing elements of chaos from live indicators moving up and down intrabar will, in fact, drift your actual results further and further from the backtest. You want an accurate backtest though. So choose wisely when you turn these off and you will do better.
The included oscillators are as follows:
NO MORE Chande Momentum cross – REMOVED – I was hitting PINE code limits here so I had to make choices and this one simply had to go. Begone!
Moving Average Cross
MACD cross
%B Bollinger cross
Stochastic cross + region filter
Stochastic RSI cross + region filter
SMII cross and region filter
Three RMIs
Know-Sure-Thing line-cross
Coppock Curve line-cross
TRIX line-cross
RSI of MA w/ MA cross
RSI of MA of KST w/ MA cross
RSI of MA of Coppock Curve w/ MA cross
RSI of MA of Trix w/ MA cross
So the idea is that this is essentially multiple strategies combined into one backtestable house. Balance is calculated for all position sizes in order to try to prevent false entries that plague so many scripts (IE, you set pyramiding to 2, each buy $1000, initial balance $1000, and yet it buys two orders off the bat for $2000 total and nets 400% profit because the second was considered free, happens on 90+% of scripts on Tradingview if you aren't very very careful!)
You tune each indicator and position size them so that they work together as well as you can and in doing so you are able to create a single backtest that is capable of running a bot, essentially, between multiple strategies - you can run a slower Moving Average cross, a faster SMI cross or MACD , or Bollinger that grabs big moves only, all the while having MACD trade small bonuses along the way. This way you can weight the Risk to Reward of each against eachother.
I will not try to claim this is something you can open and with no work have the best bot on the planet. This scripts intention is to take a lot of relatively common trading strategies and combine them under on roof with some risk management and the ability to weigh each against eachother.
If you are looking for a super advanced singular algorithm that tries to capture every peak and valley exactly on the dot, this is not for you. If you are looking for a tool with a high level of customizability, with a publisher who intends to update it to the best of his ability in accordance to seeking to make the best product that I personally can make for both myself and the community (because I will be using this myself of course!) that was specifically designed with the intention of performing well in spot markets by averaging low entry costs and high exit costs, this is for you! That is the exact intention here. It can certainly work with margin, but you will have to take extra care in setting your stop losses. I intend to make a version capable of going short which will be included as part of the package. It may take some work to keep all of the risk management working as well for shorts though. There will be more scripts added to the “package” as I hit the limit on this one a few times and have had to keep some ideas out already.
The current backtest shown is hand-optimized by myself for Link /USD 10min market (Binance US – shouldn't need much work to fit to other exchange markets) with multiple stop losses.
LETF Leveraged Edge Strategy v1.5Overview
The strategy is based on Stochastics to detect trends and then makes Buys and Sell based on custom entry and exit criteria as described below in the Execution Logic Rules section. It will NOT work with standard Stochastics.
This is not a standard Stochastics implementation. It has been customized and modified, and does not match any widely known Stochastics variations (like Fast, Slow, or Full Stochastics) in its smoothing and iterative calculation process with:
• A unique smoothing mechanism.
• Iterative calculations.
• Additional conditional logic for strategy execution.
This strategy is designed to focus on volatile, liquid leveraged ETFs to capture gains equal to or better than Buy and Hold, and mitigate the risk of trading with a goal of reducing drawdown to a lot less than Buy and Hold. It has had successful backtest performance to varying degrees with TQQQ, SOXL, FNGU, TECL, FAS, UPRO, NAIL and SPXL. Results have not been good on other LETFs that have been backtested.
Performance
In this backtest the Net Profit shows to be $4,561 or 45.61%. Considering the initial order size was $1,000 I have to wonder if the Strategy Tester is calculating this correctly. The Strategy Tester Performance Summary shows the Buy and Hold Return at $61,165 or 611.7%. Based on calculating the price of the last shares sold, less the price paid, times the number of initial shares purchased, my math shows the Buy and Hold Gain at $4,572 or about equal with the strategy performance in this case. The Performance Summary also states the strategy had a Max DD of 3.46% which I believe is incorrect. Based on other backtests I’ve done, I believe the strategy drawdown here was closer to 28.4% and the Buy and Hold Drawdown at 82.7%. I manually calculated the Buy and Hold drawdown.
How it Works
The author provides training and support resource materials for this at his website. The strategy execution logic is driven by these rules:
Execution Logic Rules
Buy the LETF When:
BR #1a) The Daily Fast Line (FL) crosses above the Daily Slow Line (SL) and the FL is between the Low (L*) and High (H*) Range set (often referred to as Oversold and Overbought Lines). This can execute (Buy) any trading day of the week.
BR #1b) Re-Buy the next day after any Stop or Take Profit Sell if the Buy Rule condition is true (FL is above SL), if not, remain in cash and wait for the next Buy Signal.
Sell the LETF When:
SR #1a) The Daily Fast Line (FL) crosses below Daily Slow Line (SL) within the Low (L*) and High (H*) Range (often referred to as Oversold and Overbought Lines). “Crossunder Range Exit” This can execute (Sell) any trading day of the week.
SR #1b) If the (FL) crosses Below the SL above the Exit Level*, wait. Only Sell if the FL drops down below the Exit Level* “Crossunder Level Exit” This can execute (Sell) any trading day of the week.
SR #2a) Sell at the open any day the gap-down price is at or below the 1-Day Stop%*, based on previous day’s closing price (Execute on the day it happens.)
SR #2b) Sell intraday any day the price is at or below the 1-Day Stop %*, based on previous day’s closing price (Execute on the day it happens.)
SR #3a) Sell at the open any day the price is at or below the Trailing Stop %*, based on highest intraday price since Buy date (Execute on the day it happens.)
SR #3b) Sell intraday any day the price is at or below the Trailing Stop%*, based on highest intraday price since Buy date (Execute on the day it happens.)
SR #4) Sell any day when the opening price exceeds, or intraday price meets the Profit Target % price* (Execute on the day it happens.)
SR #5) After each Sell go to Rule BR #1b to determine if a Re-Buy should occur the next day, or stay in cash until next Buy Signal
Settings:
Properties Tab – Initial Capital has been set to $10,000 and order size 10% of Equity, 0.1% commission and 3 Ticks for slippage. Net order size is $1,000
Input Tab:
Stochastic
Timeframe is selected to Daily or Weekly based on preference. Daily has more trades, but on average higher profitability.
Type: Proprietary (best selection for most LETFs, but a few will work better with the Full selection
%k Length 20, %K Smoothing 14, %D Smoothing (many LETFs work better with a specific Stoch setting, often each different) A List of these is provided for your starting point.
Trade Settings
Direction: Longs (This strategy only works on the Long side)
Stop Type: Trailing is recommended, but Fixed is an option.
Stop % (based on user risk tolerance)
PD Stop % (Suggest start at 5%. Based on volatility of LETF and is a stop percentage from prior day’s close. Designed to protect against sudden market volatility. Will need to balance between strategy performance and user risk tolerance)
Profit Target: User preference. (I can help with suggestions based on historical performance)
Entry/Exit Conditions
Enter on Tie: Default Checked – if a Fast line crosses a Slow line for a Buy signal, but doesn’t do so in the range set, this will trigger if it crosses at a tie.
Renter – Default Checked – If stopped out of a position, this tells the strategy to re-buy the position the next day if the conditions are still positive.
Exit Level: This is a exit level for a Fast cross below a Slow line that takes place above the Sell Range, but only happens if the Fast continues down to the level set. These usually don’t happen often, but can have a significant impact on performance. Unfortunately, it’s a trial and error process starting with 90 and working down to see if there’s any positive impact.
Trade Range
Buy Range: Start at typical 20 to 80. Expand the low end down first to check on performance impact. Normally a wide buying range is better for performance.
Sell Range: Start at 20 to 80 and tighten gradually to see performance impact. In some cases a very tight sell range does better. I have worked on our primary LETFs for many months to determine ranges for each that typically produce better results.
External Indicator: Some additional indicators have a positive impact on the strategy performance by increasing P/l, reducing drawdown and reducing the number of trades. This is not always the case and each LETF and time period for the LETF will have a bearing on whether the secondary indicator will help or not. Two that have helped are the MACD Histogram, and the Sloe-Velocity Indicator by Kamleshkumar43. Sometimes a couple of different indicators will have a positive impact, then it’s a personal preference which you pick to use with the strategy.
Since this strategy is focused on a very narrow selection of liquid LETFs, I have a lot of experience experimenting with the settings for the primary ones and can suggest things that will help. Additional training on the rules, working with the settings, and mitigating some of the negative trades during choppy markets is available at the website.
Chart
The strategy can be selected to use either a Daily or Weekly version of stochastic. This is important because the characteristics are different while still generating very good gains and minimal drawdowns. Generally, the daily stochastic will have a greater number of, and certainly more frequent, trades than the weekly stochastic. However, on average the daily version of the stochastic will generates greater profitability.
The Settings tabs have tooltip icons that will assist in inputting values that correspond to the written rules for the strategy, and some include specific rule detail.
Buying
The strategy generates Buy signals with the Fast line crossing over the Slow line within a “Buy Range” which is adjusted based on volatility of the leveraged ETF. This is unique in that a default is set for these entries to occur if the values are tied and doesn’t need to be within the high and low range if that occurs. The trader can select in the strategy for this to occur the same day, if he’s selected a Daily Stochastic timeframe, or at the end of the trading week if he’s selected a Weekly stochastic timeframe. The volatility of a leveraged ETF will sometimes cause a shake-out exit, a trailing stop can be hit, or there can be an exit based on taking a profit. A big part of the timing challenge was how to handle these. The strategy normally (set as a default) will immediately re-buy the next day only if the original buy conditions are still true. This helps capture gains when conditions are still favorable but keeps the trader out when they’re not.
Selling
Exits are handled in several ways. The strategy will exit if there is a fast line cross below a slow line within the “range”. The range is adjusted based on volatility of the leveraged ETF. The exit occurs at the close of the day if the trader has selected to use a Daily stochastic setting. The exit will occur at the end of the trading week if the trader has chosen a weekly stochastic strategy. The trader will set a level based on the instrument and volatility for another exit type. The level will sometimes coincide with the range exit high level but does not need to. If a fast line crosses down through a slow line above the level set, and then comes down to that level, the strategy will exit the position.
Another unique aspect of the strategy is the PD Stop setting. This is short for “Prior Day”, Rather than a normal stop based on the price paid for a position, the PD Stop is based on a percentage drop from the previous day’s closing price. This helps account for the volatility of the leveraged ETF and will cause an exit quickly if there’s a market, or index moving event. This helps capture gains and reduce risk should there be continued pullback.
Exits will also occur based on setting a trailing stop level and profit taking level. These are adjusted based on the leveraged ETFs volatility and historical performance.
Limitations
Choppy, or sideways markets are the most prone to poor performance and potential for being stopped out multiple times. If stopped out two consecutive times, make sure you’re monitoring market health and there are clear signs of a new uptrend such as a 10D and 21D MA in proper alignment and moving up. If you get a Buy signal from the strategy and you’re not confident yet about market and price direction then it’s fine to wait a day, or several days, to enter after the Buy signal when you have greater confidence about market direction. The author can help with a short list of tactical rules developed for these sideways or choppy markets.
This strategy has proven successful backtest results with a very limited set of LETFs as discussed earlier. The author does not know if it will prove successful with any others, or other types of ETFs such as 2X or plain ETFs. A lot more testing needs to be done.
The strategy buys and sells , excluding stops or take profit, at the market close. It can be very challenging to enter an order at market close.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script do not provide any financial advice and are for educational and entertainment purposes only.
