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!
חפש סקריפטים עבור "backtest"
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.
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.
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features.
Core Functionality
1. Signal Generation Logic
Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes).
Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273).
RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active.
Buy Signal: A buy is triggered if, after setup:
Price rises back above the level,
The RSI rate of change (ROC) indicates exhaustion of the drop,
The current bar shows positive momentum.
2. Trade Management
Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically.
Exit Signals: The script signals exit based on price action relative to these risk management levels.
3. Filters & Enhancements
Parabolic Move Filter: Prevents entries during extreme price moves.
Dead Cat Bounce Filter: Avoids false signals after sharp reversals.
Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts).
Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement.
User Inputs & Customization
Trade Direction: Toggle between LONG and SHORT signal generation.
Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements.
Trade Settings: Set take profit, stop loss, and trailing stop behavior.
Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis.
Backtesting: Integrated backtester with summary and detailed statistics tables.
Technical Features
Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics.
Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations.
Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart.
Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry).
Intended Use
Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD).
Timeframe: 30-second chart.
Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support.
Notable Customization & Extension Points
Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum".
Displacement Logic: Future update to use "FVG concept" for displacement.
High-Contrast RSI: Optional visual enhancements for RSI extremes.
Time-based Stop: Consideration for adding a time-based stop mechanism.
This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
MACD Enhanced Strategy MTF with Stop Loss [LTB]Test strategy for MACD
This strategy, named "MACD Enhanced Strategy MTF with Stop Loss ," is a modified Moving Average Convergence Divergence (MACD) strategy with enhancements such as multi-timeframe (MTF) analysis, custom scoring, and a dynamic stop loss mechanism. Let’s break down how to effectively use it:
Key Elements of the Strategy
MACD Indicator with Modifications:
The strategy uses MACD, a well-known momentum indicator, with customizable parameters:
fastLength, slowLength, and signalLength represent the standard MACD settings.
Instead of relying solely on MACD crossovers, it introduces scoring parameters for histogram direction (histside), indicator direction (indiside), and signal cross (crossscore). This allows for a more nuanced decision-making process when determining buy and sell signals.
Multi-Timeframe Analysis (MTF):
The strategy compares the current timeframe's MACD score with that of a higher timeframe (HTF). It dynamically selects the higher timeframe based on the current timeframe. For example, if the current chart period is 1, it will select 5 as the higher timeframe.
This MTF approach aims to align trades with broader trends, filtering out false signals that could be present when analyzing only a single timeframe.
Scoring System:
A custom scoring system (count() function) is used to evaluate buy and sell signals. This includes calculations based on the direction and momentum of MACD (indi) and the histogram. The score is used to determine the strength of signals.
Positive scores indicate bullish sentiment, while negative scores indicate bearish sentiment.
This scoring mechanism aims to reduce the influence of noise and provide more reliable entries.
Entry Conditions:
Long Condition: When the Result value (a combination of MTF and current MACD analysis) changes and becomes positive, a long entry is triggered.
Short Condition: When the Result changes and becomes negative, a short entry is initiated.
Stop Loss Mechanism:
The countstop() function calculates dynamic stop loss values for both long and short trades. It is based on the Average True Range (ATR) multiplied by a factor (Mult), providing adaptive stop loss levels depending on market volatility.
The stop loss is plotted on the chart to show potential risk levels for open trades, with the line appearing only if shotsl is enabled.
How to Use the Strategy
To properly use the strategy, follow these steps:
Parameter Optimization:
Adjust the input parameters such as fastLength, slowLength, and signalLength to tune the MACD indicator to the specific asset you’re trading. The values provided are typical defaults, but optimizing these values based on backtesting can help improve performance.
Customize the scoring parameters (crossscore, indiside, histside) to balance how much weight you want to put on the direction, histogram, and cross events of the MACD indicator.
Select Appropriate Timeframes:
This strategy employs a multi-timeframe (MTF) approach, so it's important to understand how the higher timeframe (HTF) is selected based on the current timeframe. For instance, if you are trading on a 5-minute chart, the higher timeframe will be 15 minutes, which helps filter out lower timeframe noise.
Ensure you understand the relationship between the timeframe you’re using and the HTF it automatically selects. The strategy’s effectiveness can vary depending on how these timeframes align with the asset’s overall volatility.
Run Backtests:
Always backtest the strategy over historical data to determine its reliability for the asset and timeframes you’re interested in. Note that the MTF approach may require substantial data to capture how different timeframes interact.
Use the backtest results to adjust the scoring parameters or the Stop Loss Factor (Mult) for better risk management.
Stop Loss Usage:
The stop loss is calculated dynamically using ATR, which means that it adjusts with changing volatility. This can be useful to avoid being stopped out too often during periods of increased volatility.
The shotsl parameter can be set to true to visualize the stop loss line on the chart. This helps to monitor the protection level and make better decisions regarding holding or closing a trade manually.
Entry Signals and Trade Execution:
Look for changes in the Result value to determine entry points. For a long position, the Result needs to become positive, and for a short position, it must be negative.
Note that the strategy's entries are more conservative because it waits for the Result to confirm the direction using multiple factors, which helps filter out false breakouts.
Risk Management:
The adaptive stop loss mechanism reduces the risk by basing the stop level on market volatility. However, you must still consider additional risk management practices such as position sizing and profit targets.
Given the scoring mechanism, it might not enter trades frequently, which means using this strategy may result in fewer but potentially more accurate trades. It’s important to be patient and not force trades that don’t align with the calculated results.
Real-Time Monitoring:
Make sure to monitor trades actively. Since the strategy recalculates the score on each bar, real-time changes in the Result value could provide exit opportunities even if the stop loss isn't triggered.
Summary
The "MACD Enhanced Strategy MTF with Stop Loss " is a sophisticated version of the MACD strategy, enhanced with multi-timeframe analysis and adaptive stop loss. Properly using it involves optimizing MACD and scoring parameters, selecting suitable timeframes, and actively managing entries and exits based on a combination of scoring and volatility-based stop losses. Always conduct thorough backtesting before applying it in a live environment to ensure the strategy performs well on the asset you're trading.
Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro:
Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable.
Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have.
Why Traders Need This Strategy
Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering:
Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts.
Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades.
Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES.
Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos.
DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art.
Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code.
Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust.
Strategy Components
1. Market Regime Detection
The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment.
How It Works:
Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear).
Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend.
Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold.
Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg.
Other (Regime 5): Default for unclear conditions.
Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9).
Why It’s Brilliant:
Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions.
HTF RSI/MACD add a big-picture filter, rare in basic scripts.
Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other).
2. Multi-Factor Signal Scoring
Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals.
Candlestick Patterns:
Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg).
Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike.
Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile).
Additional Factors:
Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear.
RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15).
MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear.
Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1).
HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2).
Scoring:
bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0.
Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long.
Why It’s Brilliant:
Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives.
Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets).
HTF confirmation aligns with the big picture, a quant edge over simplistic scripts.
3. Futures-Tuned Risk Management
The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs.
Position Sizing:
Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES).
Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1.
Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES.
Stops and Take-Profits:
Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk).
ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops.
Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry.
Impact: ATR stops adapt to volatility, while fixed stops suit stable markets.
Trailing Stops:
Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5).
Impact: Locks in profits during trends, a game-changer in volatile sessions.
Circuit Breaker:
Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity).
Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage).
Why It’s Brilliant:
Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES.
Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts.
Flexible stops (ATR or fixed) suit different trading styles.
4. Trade Entry and Exit Logic
Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks.
Entry Conditions:
Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance.
Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance.
Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering.
Exit Conditions:
Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit).
Trailing Stop: Activates at 50% TP, trails by ATR * 1.5.
Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price).
Reset: Clears stop/TP prices when flat (position_size = 0).
Why It’s Brilliant:
Score-based entries ensure multi-factor confirmation, filtering out weak signals.
Trailing stops maximize profits in trends, unlike static exits in basic scripts.
Emergency exits add an extra safety layer, critical for futures volatility.
5. DAFE Visuals
The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning.
Shimmering Bollinger Band Fill:
Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower).
Purpose: Highlights volatility and key levels with a futuristic glow.
Visuals make complex regimes and signals instantly clear, even for newbies.
Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts.
BB glow emphasizes tradeable levels, enhancing decision-making.
Chart Background (Regime Heatmap):
Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through.
Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups.
Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution.
Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies.
Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable.
Bollinger Bands Glow (Dynamic Fill):
Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals.
Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal.
Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum.
Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating.
Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes.
Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states.
Moving Averages:
Display: Blue fast EMA (20), red slow EMA (50), 2px.
Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread.
Dynamic SL/TP Lines:
Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer.
Purpose: Tracks stops/TPs in real-time, color-coded by regime.
6. Dual Dashboards
Two dashboards deliver real-time insights, making the strat a quant command center.
Bottom-Left Metrics Dashboard (2x13):
Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score.
Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish).
Purpose: Consolidates market context and signal strength.
Top-Right Position Dashboard (2x7):
Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($).
Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short).
Purpose: Tracks live trades and profitability.
Why It’s Brilliant:
Dual dashboards cover market context and trade status, a rare feature.
Color-coding and concise metrics guide beginners (e.g., green “Buy” = go).
Real-time PNL and SL/TP visibility empower disciplined trading.
7. Performance Tracking
Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed).
Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance).
Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts.
Key Features
Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets.
Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs.
Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries.
Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits.
Safe and Smart: 5% drawdown breaker and emergency exits protect capital.
DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
How to Use
Add to Chart: Load on a 5min ES/MES chart in TradingView.
Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility.
Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL.
Backtest: Run in strategy tester to compare regimes.
Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Slippage: 3
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Dskyz (DAFE) Aurora Divergence – Quant Master Dskyz (DAFE) Aurora Divergence – Quant Master
Introducing the Dskyz (DAFE) Aurora Divergence – Quant Master , a strategy that’s your secret weapon for mastering futures markets like MNQ, NQ, MES, and ES. Born from the legendary Aurora Divergence indicator, this fully automated system transforms raw divergence signals into a quant-grade trading machine, blending precision, risk management, and cyberpunk DAFE visuals that make your charts glow like a neon skyline. Crafted with care and driven by community passion, this strategy stands out in a sea of generic scripts, offering traders a unique edge to outsmart institutional traps and navigate volatile markets.
The Aurora Divergence indicator was a cult favorite for spotting price-OBV divergences with its aqua and fuchsia orbs, but traders craved a system to act on those signals with discipline and automation. This strategy delivers, layering advanced filters (z-score, ATR, multi-timeframe, session), dynamic risk controls (kill switches, adaptive stops/TPs), and a real-time dashboard to turn insights into profits. Whether you’re a newbie dipping into futures or a pro hunting reversals, this strat’s got your back with a beginner guide, alerts, and visuals that make trading feel like a sci-fi mission. Let’s dive into every detail and see why this original DAFE creation is a must-have.
Why Traders Need This Strategy
Futures markets are a battlefield—fast-paced, volatile, and riddled with institutional games that can wipe out undisciplined traders. From the April 28, 2025 NQ 1k-point drop to sneaky ES slippage, the stakes are high. Meanwhile, platforms are flooded with unoriginal, low-effort scripts that promise the moon but deliver noise. The Aurora Divergence – Quant Master rises above, offering:
Unmatched Originality: A bespoke system built from the ground up, with custom divergence logic, DAFE visuals, and quant filters that set it apart from copycat clutter.
Automation with Precision: Executes trades on divergence signals, eliminating emotional slip-ups and ensuring consistency, even in chaotic sessions.
Quant-Grade Filters: Z-score, ATR, multi-timeframe, and session checks filter out noise, targeting high-probability reversals.
Robust Risk Management: Daily loss and rolling drawdown kill switches, plus ATR-based stops/TPs, protect your capital like a fortress.
Stunning DAFE Visuals: Aqua/fuchsia orbs, aurora bands, and a glowing dashboard make signals intuitive and charts a work of art.
Community-Driven: Evolved from trader feedback, this strat’s a labor of love, not a recycled knockoff.
Traders need this because it’s a complete, original system that blends accessibility, sophistication, and style. It’s your edge to trade smarter, not harder, in a market full of traps and imitators.
1. Divergence Detection (Core Signal Logic)
The strategy’s core is its ability to detect bullish and bearish divergences between price and On-Balance Volume (OBV), pinpointing reversals with surgical accuracy.
How It Works:
Price Slope: Uses linear regression over a lookback (default: 9 bars) to measure price momentum (priceSlope).
OBV Slope: OBV tracks volume flow (+volume if price rises, -volume if falls), with its slope calculated similarly (obvSlope).
Bullish Divergence: Price slope negative (falling), OBV slope positive (rising), and price above 50-bar SMA (trend_ma).
Bearish Divergence: Price slope positive (rising), OBV slope negative (falling), and price below 50-bar SMA.
Smoothing: Requires two consecutive divergence bars (bullDiv2, bearDiv2) to confirm signals, reducing false positives.
Strength: Divergence intensity (divStrength = |priceSlope * obvSlope| * sensitivity) is normalized (0–1, divStrengthNorm) for visuals.
Why It’s Brilliant:
- Divergences catch hidden momentum shifts, often exploited by institutions, giving you an edge on reversals.
- The 50-bar SMA filter aligns signals with the broader trend, avoiding choppy markets.
- Adjustable lookback (min: 3) and sensitivity (default: 1.0) let you tune for different instruments or timeframes.
2. Filters for Precision
Four advanced filters ensure signals are high-probability and market-aligned, cutting through the noise of volatile futures.
Z-Score Filter:
Logic: Calculates z-score ((close - SMA) / stdev) over a lookback (default: 50 bars). Blocks entries if |z-score| > threshold (default: 1.5) unless disabled (useZFilter = false).
Impact: Avoids trades during extreme price moves (e.g., blow-off tops), keeping you in statistically safe zones.
ATR Percentile Volatility Filter:
Logic: Tracks 14-bar ATR in a 100-bar window (default). Requires current ATR > 80th percentile (percATR) to trade (tradeOk).
Impact: Ensures sufficient volatility for meaningful moves, filtering out low-volume chop.
Multi-Timeframe (HTF) Trend Filter:
Logic: Uses a 50-bar SMA on a higher timeframe (default: 60min). Longs require price > HTF MA (bullTrendOK), shorts < HTF MA (bearTrendOK).
Impact: Aligns trades with the bigger trend, reducing counter-trend losses.
US Session Filter:
Logic: Restricts trading to 9:30am–4:00pm ET (default: enabled, useSession = true) using America/New_York timezone.
Impact: Focuses on high-liquidity hours, avoiding overnight spreads and erratic moves.
Evolution:
- These filters create a robust signal pipeline, ensuring trades are timed for optimal conditions.
- Customizable inputs (e.g., zThreshold, atrPercentile) let traders adapt to their style without compromising quality.
3. Risk Management
The strategy’s risk controls are a masterclass in balancing aggression and safety, protecting capital in volatile markets.
Daily Loss Kill Switch:
Logic: Tracks daily loss (dayStartEquity - strategy.equity). Halts trading if loss ≥ $300 (default) and enabled (killSwitch = true, killSwitchActive).
Impact: Caps daily downside, crucial during events like April 27, 2025 ES slippage.
Rolling Drawdown Kill Switch:
Logic: Monitors drawdown (rollingPeak - strategy.equity) over 100 bars (default). Stops trading if > $1000 (rollingKill).
Impact: Prevents prolonged losing streaks, preserving capital for better setups.
Dynamic Stop-Loss and Take-Profit:
Logic: Stops = entry ± ATR * multiplier (default: 1.0x, stopDist). TPs = entry ± ATR * 1.5x (profitDist). Longs: stop below, TP above; shorts: vice versa.
Impact: Adapts to volatility, keeping stops tight but realistic, with TPs targeting 1.5:1 reward/risk.
Max Bars in Trade:
Logic: Closes trades after 8 bars (default) if not already exited.
Impact: Frees capital from stagnant trades, maintaining efficiency.
Kill Switch Buffer Dashboard:
Logic: Shows smallest buffer ($300 - daily loss or $1000 - rolling DD). Displays 0 (red) if kill switch active, else buffer (green).
Impact: Real-time risk visibility, letting traders adjust dynamically.
Why It’s Brilliant:
- Kill switches and ATR-based exits create a safety net, rare in generic scripts.
- Customizable risk inputs (maxDailyLoss, dynamicStopMult) suit different account sizes.
- Buffer metric empowers disciplined trading, a DAFE signature.
4. Trade Entry and Exit Logic
The entry/exit rules are precise, filtered, and adaptive, ensuring trades are deliberate and profitable.
Entry Conditions:
Long Entry: bullDiv2, cooldown passed (canSignal), ATR filter passed (tradeOk), in US session (inSession), no kill switches (not killSwitchActive, not rollingKill), z-score OK (zOk), HTF trend bullish (bullTrendOK), no existing long (lastDirection != 1, position_size <= 0). Closes shorts first.
Short Entry: Same, but for bearDiv2, bearTrendOK, no long (lastDirection != -1, position_size >= 0). Closes longs first.
Adaptive Cooldown: Default 2 bars (cooldownBars). Doubles (up to 10) after a losing trade, resets after wins (dynamicCooldown).
Exit Conditions:
Stop-Loss/Take-Profit: Set per trade (ATR-based). Exits on stop/TP hits.
Other Exits: Closes if maxBarsInTrade reached, ATR filter fails, or kill switch activates.
Position Management: Ensures no conflicting positions, closing opposites before new entries.
Built To Be Reliable and Consistent:
- Multi-filtered entries minimize false signals, a stark contrast to basic scripts.
- Adaptive cooldown prevents overtrading, especially after losses.
- Clean position handling ensures smooth execution, even in fast markets.
5. DAFE Visuals
The visuals are a DAFE hallmark, blending function with clean flair to make signals intuitive and charts stunning.
Aurora Bands:
Display: Bands around price during divergences (bullish: below low, bearish: above high), sized by ATR * bandwidth (default: 0.5).
Colors: Aqua (bullish), fuchsia (bearish), with transparency tied to divStrengthNorm.
Purpose: Highlights divergence zones with a glowing, futuristic vibe.
Divergence Orbs:
Display: Large/small circles (aqua below for bullish, fuchsia above for bearish) when bullDiv2/bearDiv2 and canSignal. Labels show strength (0–1).
Purpose: Pinpoints entries with eye-catching clarity.
Gradient Background:
Display: Green (bullish), red (bearish), or gray (neutral), 90–95% transparent.
Purpose: Sets the market mood without clutter.
Strategy Plots:
- Stop/TP Lines: Red (stops), green (TPs) for active trades.
- HTF MA: Yellow line for trend context.
- Z-Score: Blue step-line (if enabled).
- Kill Switch Warning: Red background flash when active.
What Makes This Next-Level?:
- Visuals make complex signals (divergences, filters) instantly clear, even for beginners.
- DAFE’s unique aesthetic (orbs, bands) sets it apart from generic scripts, reinforcing originality.
- Functional plots (stops, TPs) enhance trade management.
6. Metrics Dashboard
The top-right dashboard (2x8 table) is your command center, delivering real-time insights.
Metrics:
Daily Loss ($): Current loss vs. day’s start, red if > $300.
Rolling DD ($): Drawdown vs. 100-bar peak, red if > $1000.
ATR Threshold: Current percATR, green if ATR exceeds, red if not.
Z-Score: Current value, green if within threshold, red if not.
Signal: “Bullish Div” (aqua), “Bearish Div” (fuchsia), or “None” (gray).
Action: “Consider Buying”/“Consider Selling” (signal color) or “Wait” (gray).
Kill Switch Buffer ($): Smallest buffer to kill switch, green if > 0, red if 0.
Why This Is Important?:
- Consolidates critical data, making decisions effortless.
- Color-coded metrics guide beginners (e.g., green action = go).
- Buffer metric adds transparency, rare in off-the-shelf scripts.
7. Beginner Guide
Beginner Guide: Middle-right table (shown once on chart load), explains aqua orbs (bullish, buy) and fuchsia orbs (bearish, sell).
Key Features:
Futures-Optimized: Tailored for MNQ, NQ, MES, ES with point-value adjustments.
Highly Customizable: Inputs for lookback, sensitivity, filters, and risk settings.
Real-Time Insights: Dashboard and visuals update every bar.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
User-Friendly: Guide, visuals, and dashboard make it accessible yet powerful.
Original Design: DAFE’s unique logic and visuals stand out from generic scripts.
How to Use
Add to Chart: Load on a 5min MNQ/ES chart in TradingView.
Configure Inputs: Adjust instrument, filters, or risk (defaults optimized for MNQ).
Monitor Dashboard: Watch signals, actions, and risk metrics (top-right).
Backtest: Run in strategy tester to evaluate performance.
Live Trade: Connect to a broker (e.g., Tradovate) for automation. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Use bar replay (e.g., April 28, 2025 NQ drop) to test volatility handling.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Backtest results may not reflect live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Aurora Divergence – Quant Master isn’t just a strategy—it’s a movement. Crafted with originality and driven by community passion, it rises above the flood of generic scripts to deliver a system that’s as powerful as it is beautiful. With its quant-grade logic, DAFE visuals, and robust risk controls, it empowers traders to tackle futures with confidence and style. Join the DAFE crew, light up your charts, and let’s outsmart the markets together!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade fast, trade bold.
[Sextan] Your Indicator Source PINE v5 MTFLevel: 1
NOTE1: As requested, this is a multiple time frame(MTF) version of input signal source, which enable you to backtest any indicator/strategy MTF with "{Sextan} PINEv4 Sextans Backtest Framework". Courtesy of cheatcountry for his request.security() wrapper in PINE v5 to avoid repainting caused by request.security() function.
NOTE2: Many request this indicator template to support PINE v5. Now, here it is .This is ONLY an PINE v5 EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" (for bactest framework it does not need to be written by PINE v5)intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error.
I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example.
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
[Sextan] Your Indicator Source PINE v4 MTFLevel: 1
NOTE1: As requested, this is a multiple time frame(MTF) version of input signal source, which enable you to backtest any indicator/strategy MTF with "{Sextan} PINEv4 Sextans Backtest Framework". Courtesy of cheatcountry for his security() wrapper to avoid repainting caused by security() function.
NOTE2: This is ONLY an EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error.
I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example.
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Trading The Loonie (CADUSD)A port of the trading strategy described at technical.traders.com
"In “Trading The Loonie,” which appeared in the December 2015 issue of STOCKS & COMMODITIES, author Markos Katsanos
explains the heavy correlation between the Canadian dollar and crude oil. He then goes on to describe how one could
trade this correlation. Using similar logic as that employed in Bollinger Bands, Katsanos has built a study to
provide buy and sell signals for trading the Canadian dollar future."
See Also:
- Backtesting and forwardtesting (of TradingView Strategies)
- 9 Mistakes Quants Make that Cause Backtests to Lie (blog.quantopian.com)
- When Backtests Meet Reality (financial-hacker.com)
- Why MT4 backtesting does not work (www.stevehopwoodforex.com)
Ichimoku Cloud StrategyBased on the trading strategy described at
stockcharts.com
See Also:
- Backtesting and forwardtesting (of TradingView Strategies)
- 9 Mistakes Quants Make that Cause Backtests to Lie
- When Backtests Meet Reality
- Why MT4 backtesting does not work
Liquid Pulse Liquid Pulse by Dskyz (DAFE) Trading Systems
Liquid Pulse is a trading algo built by Dskyz (DAFE) Trading Systems for futures markets like NQ1!, designed to snag high-probability trades with tight risk control. it fuses a confluence system—VWAP, MACD, ADX, volume, and liquidity sweeps—with a trade scoring setup, daily limits, and VIX pauses to dodge wild volatility. visuals include simple signals, VWAP bands, and a dashboard with stats.
Core Components for Liquid Pulse
Volume Sensitivity (volumeSensitivity) controls how much volume spikes matter for entries. options: 'Low', 'Medium', 'High' default: 'High' (catches small spikes, good for active markets) tweak it: 'Low' for calm markets, 'High' for chaos.
MACD Speed (macdSpeed) sets the MACD’s pace for momentum. options: 'Fast', 'Medium', 'Slow' default: 'Medium' (solid balance) tweak it: 'Fast' for scalping, 'Slow' for swings.
Daily Trade Limit (dailyTradeLimit) caps trades per day to keep risk in check. range: 1 to 30 default: 20 tweak it: 5-10 for safety, 20-30 for action.
Number of Contracts (numContracts) sets position size. range: 1 to 20 default: 4 tweak it: up for big accounts, down for small.
VIX Pause Level (vixPauseLevel) stops trading if VIX gets too hot. range: 10 to 80 default: 39.0 tweak it: 30 to avoid volatility, 50 to ride it.
Min Confluence Conditions (minConditions) sets how many signals must align. range: 1 to 5 default: 2 tweak it: 3-4 for strict, 1-2 for more trades.
Min Trade Score (Longs/Shorts) (minTradeScoreLongs/minTradeScoreShorts) filters trade quality. longs range: 0 to 100 default: 73 shorts range: 0 to 100 default: 75 tweak it: 80-90 for quality, 60-70 for volume.
Liquidity Sweep Strength (sweepStrength) gauges breakouts. range: 0.1 to 1.0 default: 0.5 tweak it: 0.7-1.0 for strong moves, 0.3-0.5 for small.
ADX Trend Threshold (adxTrendThreshold) confirms trends. range: 10 to 100 default: 41 tweak it: 40-50 for trends, 30-35 for weak ones.
ADX Chop Threshold (adxChopThreshold) avoids chop. range: 5 to 50 default: 20 tweak it: 15-20 to dodge chop, 25-30 to loosen.
VWAP Timeframe (vwapTimeframe) sets VWAP period. options: '15', '30', '60', '240', 'D' default: '60' (1-hour) tweak it: 60 for day, 240 for swing, D for long.
Take Profit Ticks (Longs/Shorts) (takeProfitTicksLongs/takeProfitTicksShorts) sets profit targets. longs range: 5 to 100 default: 25.0 shorts range: 5 to 100 default: 20.0 tweak it: 30-50 for trends, 10-20 for chop.
Max Profit Ticks (maxProfitTicks) caps max gain. range: 10 to 200 default: 60.0 tweak it: 80-100 for big moves, 40-60 for tight.
Min Profit Ticks to Trail (minProfitTicksTrail) triggers trailing. range: 1 to 50 default: 7.0 tweak it: 10-15 for big gains, 5-7 for quick locks.
Trailing Stop Ticks (trailTicks) sets trail distance. range: 1 to 50 default: 5.0 tweak it: 8-10 for room, 3-5 for fast locks.
Trailing Offset Ticks (trailOffsetTicks) sets trail offset. range: 1 to 20 default: 2.0 tweak it: 1-2 for tight, 5-10 for loose.
ATR Period (atrPeriod) measures volatility. range: 5 to 50 default: 9 tweak it: 14-20 for smooth, 5-9 for reactive.
Hardcoded Settings volLookback: 30 ('Low'), 20 ('Medium'), 11 ('High') volThreshold: 1.5 ('Low'), 1.8 ('Medium'), 2 ('High') swingLen: 5
Execution Logic Overview trades trigger when confluence conditions align, entering long or short with set position sizes. exits use dynamic take-profits, trailing stops after a profit threshold, hard stops via ATR, and a time stop after 100 bars.
Features Multi-Signal Confluence: needs VWAP, MACD, volume, sweeps, and ADX to line up.
Risk Control: ATR-based stops (capped 15 ticks), take-profits (scaled by volatility), and trails.
Market Filters: VIX pause, ADX trend/chop checks, volatility gates. Dashboard: shows scores, VIX, ADX, P/L, win %, streak.
Visuals Simple signals (green up triangles for longs, red down for shorts) and VWAP bands with glow. info table (bottom right) with MACD momentum. dashboard (top right) with stats.
Chart and Backtest:
NQ1! futures, 5-minute chart. works best in trending, volatile conditions. tweak inputs for other markets—test thoroughly.
Backtesting: NQ1! Frame: Jan 19, 2025, 09:00 — May 02, 2025, 16:00 Slippage: 3 Commission: $4.60
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Disclaimer this is for education only. past results don’t predict future wins. trading’s risky—only use money you can lose. backtest and validate before going live. (expect moderators to nitpick some random chart symbol rule—i’ll fix and repost if they pull it.)
About the Author Dskyz (DAFE) Trading Systems crafts killer trading algos. Liquid Pulse is pure research and grit, built for smart, bold trading. Use it with discipline. Use it with clarity. Trade smarter. I’ll keep dropping badass strategies ‘til i build a brand or someone signs me up.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Supertrend Advance Pullback StrategyHandbook for the Supertrend Advance Strategy
1. Introduction
Purpose of the Handbook:
The main purpose of this handbook is to serve as a comprehensive guide for traders and investors who are looking to explore and harness the potential of the Supertrend Advance Strategy. In the rapidly changing financial market, having the right tools and strategies at one's disposal is crucial. Whether you're a beginner hoping to dive into the world of trading or a seasoned investor aiming to optimize and diversify your portfolio, this handbook offers the insights and methodologies you need. By the end of this guide, readers should have a clear understanding of how the Supertrend Advance Strategy works, its benefits, potential pitfalls, and practical application in various trading scenarios.
Overview of the Supertrend Advance Pullback Strategy:
At its core, the Supertrend Advance Strategy is an evolution of the popular Supertrend Indicator. Designed to generate buy and sell signals in trending markets, the Supertrend Indicator has been a favorite tool for many traders around the world. The Advance Strategy, however, builds upon this foundation by introducing enhanced mechanisms, filters, and methodologies to increase precision and reduce false signals.
1. Basic Concept:
The Supertrend Advance Strategy relies on a combination of price action and volatility to determine the potential trend direction. By assessing the average true range (ATR) in conjunction with specific price points, this strategy aims to highlight the potential starting and ending points of market trends.
2. Methodology:
Unlike the traditional Supertrend Indicator, which primarily focuses on closing prices and ATR, the Advance Strategy integrates other critical market variables, such as volume, momentum oscillators, and perhaps even fundamental data, to validate its signals. This multidimensional approach ensures that the generated signals are more reliable and are less prone to market noise.
3. Benefits:
One of the main benefits of the Supertrend Advance Strategy is its ability to filter out false breakouts and minor price fluctuations, which can often lead to premature exits or entries in the market. By waiting for a confluence of factors to align, traders using this advanced strategy can increase their chances of entering or exiting trades at optimal points.
4. Practical Applications:
The Supertrend Advance Strategy can be applied across various timeframes, from intraday trading to swing trading and even long-term investment scenarios. Furthermore, its flexible nature allows it to be tailored to different asset classes, be it stocks, commodities, forex, or cryptocurrencies.
In the subsequent sections of this handbook, we will delve deeper into the intricacies of this strategy, offering step-by-step guidelines on its application, case studies, and tips for maximizing its efficacy in the volatile world of trading.
As you journey through this handbook, we encourage you to approach the Supertrend Advance Strategy with an open mind, testing and tweaking it as per your personal trading style and risk appetite. The ultimate goal is not just to provide you with a new tool but to empower you with a holistic strategy that can enhance your trading endeavors.
2. Getting Started
Navigating the financial markets can be a daunting task without the right tools. This section is dedicated to helping you set up the Supertrend Advance Strategy on one of the most popular charting platforms, TradingView. By following the steps below, you'll be able to integrate this strategy into your charts and start leveraging its insights in no time.
Setting up on TradingView:
TradingView is a web-based platform that offers a wide range of charting tools, social networking, and market data. Before you can apply the Supertrend Advance Strategy, you'll first need a TradingView account. If you haven't set one up yet, here's how:
1. Account Creation:
• Visit TradingView's official website.
• Click on the "Join for free" or "Sign up" button.
• Follow the registration process, providing the necessary details and setting up your login credentials.
2. Navigating the Dashboard:
• Once logged in, you'll be taken to your dashboard. Here, you'll see a variety of tools, including watchlists, alerts, and the main charting window.
• To begin charting, type in the name or ticker of the asset you're interested in the search bar at the top.
3. Configuring Chart Settings:
• Before integrating the Supertrend Advance Strategy, familiarize yourself with the chart settings. This can be accessed by clicking the 'gear' icon on the top right of the chart window.
• Adjust the chart type, time intervals, and other display settings to your preference.
Integrating the Strategy into a Chart:
Now that you're set up on TradingView, it's time to integrate the Supertrend Advance Strategy.
1. Accessing the Pine Script Editor:
• Located at the top-center of your screen, you'll find the "Pine Editor" tab. Click on it.
• This is where custom strategies and indicators are scripted or imported.
2. Loading the Supertrend Advance Strategy Script:
• Depending on whether you have the script or need to find it, there are two paths:
• If you have the script: Copy the Supertrend Advance Strategy script, and then paste it into the Pine Editor.
• If searching for the script: Click on the “Indicators” icon (looks like a flame) at the top of your screen, and then type “Supertrend Advance Strategy” in the search bar. If available, it will show up in the list. Simply click to add it to your chart.
3. Applying the Strategy:
• After pasting or selecting the Supertrend Advance Strategy in the Pine Editor, click on the “Add to Chart” button located at the top of the editor. This will overlay the strategy onto your main chart window.
4. Configuring Strategy Settings:
• Once the strategy is on your chart, you'll notice a small settings ('gear') icon next to its name in the top-left of the chart window. Click on this to access settings.
• Here, you can adjust various parameters of the Supertrend Advance Strategy to better fit your trading style or the specific asset you're analyzing.
5. Interpreting Signals:
• With the strategy applied, you'll now see buy/sell signals represented on your chart. Take time to familiarize yourself with how these look and behave over various timeframes and market conditions.
3. Strategy Overview
What is the Supertrend Advance Strategy?
The Supertrend Advance Strategy is a refined version of the classic Supertrend Indicator, which was developed to aid traders in spotting market trends. The strategy utilizes a combination of data points, including average true range (ATR) and price momentum, to generate buy and sell signals.
In essence, the Supertrend Advance Strategy can be visualized as a line that moves with the price. When the price is above the Supertrend line, it indicates an uptrend and suggests a potential buy position. Conversely, when the price is below the Supertrend line, it hints at a downtrend, suggesting a potential selling point.
Strategy Goals and Objectives:
1. Trend Identification: At the core of the Supertrend Advance Strategy is the goal to efficiently and consistently identify prevailing market trends. By recognizing these trends, traders can position themselves to capitalize on price movements in their favor.
2. Reducing Noise: Financial markets are often inundated with 'noise' - short-term price fluctuations that can mislead traders. The Supertrend Advance Strategy aims to filter out this noise, allowing for clearer decision-making.
3. Enhancing Risk Management: With clear buy and sell signals, traders can set more precise stop-loss and take-profit points. This leads to better risk management and potentially improved profitability.
4. Versatility: While primarily used for trend identification, the strategy can be integrated with other technical tools and indicators to create a comprehensive trading system.
Type of Assets/Markets to Apply the Strategy:
1. Equities: The Supertrend Advance Strategy is highly popular among stock traders. Its ability to capture long-term trends makes it particularly useful for those trading individual stocks or equity indices.
2. Forex: Given the 24-hour nature of the Forex market and its propensity for trends, the Supertrend Advance Strategy is a valuable tool for currency traders.
3. Commodities: Whether it's gold, oil, or agricultural products, commodities often move in extended trends. The strategy can help in identifying and capitalizing on these movements.
4. Cryptocurrencies: The volatile nature of cryptocurrencies means they can have pronounced trends. The Supertrend Advance Strategy can aid crypto traders in navigating these often tumultuous waters.
5. Futures & Options: Traders and investors in derivative markets can utilize the strategy to make more informed decisions about contract entries and exits.
It's important to note that while the Supertrend Advance Strategy can be applied across various assets and markets, its effectiveness might vary based on market conditions, timeframe, and the specific characteristics of the asset in question. As always, it's recommended to use the strategy in conjunction with other analytical tools and to backtest its effectiveness in specific scenarios before committing to trades.
4. Input Settings
Understanding and correctly configuring input settings is crucial for optimizing the Supertrend Advance Strategy for any specific market or asset. These settings, when tweaked correctly, can drastically impact the strategy's performance.
Grouping Inputs:
Before diving into individual input settings, it's important to group similar inputs. Grouping can simplify the user interface, making it easier to adjust settings related to a specific function or indicator.
Strategy Choice:
This input allows traders to select from various strategies that incorporate the Supertrend indicator. Options might include "Supertrend with RSI," "Supertrend with MACD," etc. By choosing a strategy, the associated input settings for that strategy become available.
Supertrend Settings:
1. Multiplier: Typically, a default value of 3 is used. This multiplier is used in the ATR calculation. Increasing it makes the Supertrend line further from prices, while decreasing it brings the line closer.
2. Period: The number of bars used in the ATR calculation. A common default is 7.
EMA Settings (Exponential Moving Average):
1. Period: Defines the number of previous bars used to calculate the EMA. Common periods are 9, 21, 50, and 200.
2. Source: Allows traders to choose which price (Open, Close, High, Low) to use in the EMA calculation.
RSI Settings (Relative Strength Index):
1. Length: Determines how many periods are used for RSI calculation. The standard setting is 14.
2. Overbought Level: The threshold at which the asset is considered overbought, typically set at 70.
3. Oversold Level: The threshold at which the asset is considered oversold, often at 30.
MACD Settings (Moving Average Convergence Divergence):
1. Short Period: The shorter EMA, usually set to 12.
2. Long Period: The longer EMA, commonly set to 26.
3. Signal Period: Defines the EMA of the MACD line, typically set at 9.
CCI Settings (Commodity Channel Index):
1. Period: The number of bars used in the CCI calculation, often set to 20.
2. Overbought Level: Typically set at +100, denoting overbought conditions.
3. Oversold Level: Usually set at -100, indicating oversold conditions.
SL/TP Settings (Stop Loss/Take Profit):
1. SL Multiplier: Defines the multiplier for the average true range (ATR) to set the stop loss.
2. TP Multiplier: Defines the multiplier for the average true range (ATR) to set the take profit.
Filtering Conditions:
This section allows traders to set conditions to filter out certain signals. For example, one might only want to take buy signals when the RSI is below 30, ensuring they buy during oversold conditions.
Trade Direction and Backtest Period:
1. Trade Direction: Allows traders to specify whether they want to take long trades, short trades, or both.
2. Backtest Period: Specifies the time range for backtesting the strategy. Traders can choose from options like 'Last 6 months,' 'Last 1 year,' etc.
It's essential to remember that while default settings are provided for many of these tools, optimal settings can vary based on the market, timeframe, and trading style. Always backtest new settings on historical data to gauge their potential efficacy.
5. Understanding Strategy Conditions
Developing an understanding of the conditions set within a trading strategy is essential for traders to maximize its potential. Here, we delve deep into the logic behind these conditions, using the Supertrend Advance Strategy as our focal point.
Basic Logic Behind Conditions:
Every strategy is built around a set of conditions that provide buy or sell signals. The conditions are based on mathematical or statistical methods and are rooted in the study of historical price data. The fundamental idea is to recognize patterns or behaviors that have been profitable in the past and might be profitable in the future.
Buy and Sell Conditions:
1. Buy Conditions: Usually formulated around bullish signals or indicators suggesting upward price momentum.
2. Sell Conditions: Centered on bearish signals or indicators indicating downward price momentum.
Simple Strategy:
The simple strategy could involve using just the Supertrend indicator. Here:
• Buy: When price closes above the Supertrend line.
• Sell: When price closes below the Supertrend line.
Pullback Strategy:
This strategy capitalizes on price retracements:
• Buy: When the price retraces to the Supertrend line after a bullish signal and is supported by another bullish indicator.
• Sell: When the price retraces to the Supertrend line after a bearish signal and is confirmed by another bearish indicator.
Indicators Used:
EMA (Exponential Moving Average):
• Logic: EMA gives more weight to recent prices, making it more responsive to current price movements. A shorter-period EMA crossing above a longer-period EMA can be a bullish sign, while the opposite is bearish.
RSI (Relative Strength Index):
• Logic: RSI measures the magnitude of recent price changes to analyze overbought or oversold conditions. Values above 70 are typically considered overbought, and values below 30 are considered oversold.
MACD (Moving Average Convergence Divergence):
• Logic: MACD assesses the relationship between two EMAs of a security’s price. The MACD line crossing above the signal line can be a bullish signal, while crossing below can be bearish.
CCI (Commodity Channel Index):
• Logic: CCI compares a security's average price change with its average price variation. A CCI value above +100 may mean the price is overbought, while below -100 might signify an oversold condition.
And others...
As the strategy expands or contracts, more indicators might be added or removed. The crucial point is to understand the core logic behind each, ensuring they align with the strategy's objectives.
Logic Behind Each Indicator:
1. EMA: Emphasizes recent price movements; provides dynamic support and resistance levels.
2. RSI: Indicates overbought and oversold conditions based on recent price changes.
3. MACD: Showcases momentum and direction of a trend by comparing two EMAs.
4. CCI: Measures the difference between a security's price change and its average price change.
Understanding strategy conditions is not just about knowing when to buy or sell but also about comprehending the underlying market dynamics that those conditions represent. As you familiarize yourself with each condition and indicator, you'll be better prepared to adapt and evolve with the ever-changing financial markets.
6. Trade Execution and Management
Trade execution and management are crucial aspects of any trading strategy. Efficient execution can significantly impact profitability, while effective management can preserve capital during adverse market conditions. In this section, we'll explore the nuances of position entry, exit strategies, and various Stop Loss (SL) and Take Profit (TP) methodologies within the Supertrend Advance Strategy.
Position Entry:
Effective trade entry revolves around:
1. Timing: Enter at a point where the risk-reward ratio is favorable. This often corresponds to confirmatory signals from multiple indicators.
2. Volume Analysis: Ensure there's adequate volume to support the movement. Volume can validate the strength of a signal.
3. Confirmation: Use multiple indicators or chart patterns to confirm the entry point. For instance, a buy signal from the Supertrend indicator can be confirmed with a bullish MACD crossover.
Position Exit Strategies:
A successful exit strategy will lock in profits and minimize losses. Here are some strategies:
1. Fixed Time Exit: Exiting after a predetermined period.
2. Percentage-based Profit Target: Exiting after a certain percentage gain.
3. Indicator-based Exit: Exiting when an indicator gives an opposing signal.
Percentage-based SL/TP:
• Stop Loss (SL): Set a fixed percentage below the entry price to limit potential losses.
• Example: A 2% SL on an entry at $100 would trigger a sell at $98.
• Take Profit (TP): Set a fixed percentage above the entry price to lock in gains.
• Example: A 5% TP on an entry at $100 would trigger a sell at $105.
Supertrend-based SL/TP:
• Stop Loss (SL): Position the SL at the Supertrend line. If the price breaches this line, it could indicate a trend reversal.
• Take Profit (TP): One could set the TP at a point where the Supertrend line flattens or turns, indicating a possible slowdown in momentum.
Swing high/low-based SL/TP:
• Stop Loss (SL): For a long position, set the SL just below the recent swing low. For a short position, set it just above the recent swing high.
• Take Profit (TP): For a long position, set the TP near a recent swing high or resistance. For a short position, near a swing low or support.
And other methods...
1. Trailing Stop Loss: This dynamic SL adjusts with the price movement, locking in profits as the trade moves in your favor.
2. Multiple Take Profits: Divide the position into segments and set multiple TP levels, securing profits in stages.
3. Opposite Signal Exit: Exit when another reliable indicator gives an opposite signal.
Trade execution and management are as much an art as they are a science. They require a blend of analytical skill, discipline, and intuition. Regularly reviewing and refining your strategies, especially in light of changing market conditions, is crucial to maintaining consistent trading performance.
7. Visual Representations
Visual tools are essential for traders, as they simplify complex data into an easily interpretable format. Properly analyzing and understanding the plots on a chart can provide actionable insights and a more intuitive grasp of market conditions. In this section, we’ll delve into various visual representations used in the Supertrend Advance Strategy and their significance.
Understanding Plots on the Chart:
Charts are the primary visual aids for traders. The arrangement of data points, lines, and colors on them tell a story about the market's past, present, and potential future moves.
1. Data Points: These represent individual price actions over a specific timeframe. For instance, a daily chart will have data points showing the opening, closing, high, and low prices for each day.
2. Colors: Used to indicate the nature of price movement. Commonly, green is used for bullish (upward) moves and red for bearish (downward) moves.
Trend Lines:
Trend lines are straight lines drawn on a chart that connect a series of price points. Their significance:
1. Uptrend Line: Drawn along the lows, representing support. A break below might indicate a trend reversal.
2. Downtrend Line: Drawn along the highs, indicating resistance. A break above might suggest the start of a bullish trend.
Filled Areas:
These represent a range between two values on a chart, usually shaded or colored. For instance:
1. Bollinger Bands: The area between the upper and lower band is filled, giving a visual representation of volatility.
2. Volume Profile: Can show a filled area representing the amount of trading activity at different price levels.
Stop Loss and Take Profit Lines:
These are horizontal lines representing pre-determined exit points for trades.
1. Stop Loss Line: Indicates the level at which a trade will be automatically closed to limit losses. Positioned according to the trader's risk tolerance.
2. Take Profit Line: Denotes the target level to lock in profits. Set according to potential resistance (for long trades) or support (for short trades) or other technical factors.
Trailing Stop Lines:
A trailing stop is a dynamic form of stop loss that moves with the price. On a chart:
1. For Long Trades: Starts below the entry price and moves up with the price but remains static if the price falls, ensuring profits are locked in.
2. For Short Trades: Starts above the entry price and moves down with the price but remains static if the price rises.
Visual representations offer traders a clear, organized view of market dynamics. Familiarity with these tools ensures that traders can quickly and accurately interpret chart data, leading to more informed decision-making. Always ensure that the visual aids used resonate with your trading style and strategy for the best results.
8. Backtesting
Backtesting is a fundamental process in strategy development, enabling traders to evaluate the efficacy of their strategy using historical data. It provides a snapshot of how the strategy would have performed in past market conditions, offering insights into its potential strengths and vulnerabilities. In this section, we'll explore the intricacies of setting up and analyzing backtest results and the caveats one must be aware of.
Setting Up Backtest Period:
1. Duration: Determine the timeframe for the backtest. It should be long enough to capture various market conditions (bullish, bearish, sideways). For instance, if you're testing a daily strategy, consider a period of several years.
2. Data Quality: Ensure the data source is reliable, offering high-resolution and clean data. This is vital to get accurate backtest results.
3. Segmentation: Instead of a continuous period, sometimes it's helpful to backtest over distinct market phases, like a particular bear or bull market, to see how the strategy holds up in different environments.
Analyzing Backtest Results:
1. Performance Metrics: Examine metrics like the total return, annualized return, maximum drawdown, Sharpe ratio, and others to gauge the strategy's efficiency.
2. Win Rate: It's the ratio of winning trades to total trades. A high win rate doesn't always signify a good strategy; it should be evaluated in conjunction with other metrics.
3. Risk/Reward: Understand the average profit versus the average loss per trade. A strategy might have a low win rate but still be profitable if the average gain far exceeds the average loss.
4. Drawdown Analysis: Review the periods of losses the strategy could incur and how long it takes, on average, to recover.
9. Tips and Best Practices
Successful trading requires more than just knowing how a strategy works. It necessitates an understanding of when to apply it, how to adjust it to varying market conditions, and the wisdom to recognize and avoid common pitfalls. This section offers insightful tips and best practices to enhance the application of the Supertrend Advance Strategy.
When to Use the Strategy:
1. Market Conditions: Ideally, employ the Supertrend Advance Strategy during trending market conditions. This strategy thrives when there are clear upward or downward trends. It might be less effective during consolidative or sideways markets.
2. News Events: Be cautious around significant news events, as they can cause extreme volatility. It might be wise to avoid trading immediately before and after high-impact news.
3. Liquidity: Ensure you are trading in assets/markets with sufficient liquidity. High liquidity ensures that the price movements are more reflective of genuine market sentiment and not due to thin volume.
Adjusting Settings for Different Markets/Timeframes:
1. Markets: Each market (stocks, forex, commodities) has its own characteristics. It's essential to adjust the strategy's parameters to align with the market's volatility and liquidity.
2. Timeframes: Shorter timeframes (like 1-minute or 5-minute charts) tend to have more noise. You might need to adjust the settings to filter out false signals. Conversely, for longer timeframes (like daily or weekly charts), you might need to be more responsive to genuine trend changes.
3. Customization: Regularly review and tweak the strategy's settings. Periodic adjustments can ensure the strategy remains optimized for the current market conditions.
10. Frequently Asked Questions (FAQs)
Given the complexities and nuances of the Supertrend Advance Strategy, it's only natural for traders, both new and seasoned, to have questions. This section addresses some of the most commonly asked questions regarding the strategy.
1. What exactly is the Supertrend Advance Strategy?
The Supertrend Advance Strategy is an evolved version of the traditional Supertrend indicator. It's designed to provide clearer buy and sell signals by incorporating additional indicators like EMA, RSI, MACD, CCI, etc. The strategy aims to capitalize on market trends while minimizing false signals.
2. Can I use the Supertrend Advance Strategy for all asset types?
Yes, the strategy can be applied to various asset types like stocks, forex, commodities, and cryptocurrencies. However, it's crucial to adjust the settings accordingly to suit the specific characteristics and volatility of each asset type.
3. Is this strategy suitable for day trading?
Absolutely! The Supertrend Advance Strategy can be adjusted to suit various timeframes, making it versatile for both day trading and long-term trading. Remember to fine-tune the settings to align with the timeframe you're trading on.
4. How do I deal with false signals?
No strategy is immune to false signals. However, by combining the Supertrend with other indicators and adhering to strict risk management protocols, you can minimize the impact of false signals. Always use stop-loss orders and consider filtering trades with additional confirmation signals.
5. Do I need any prior trading experience to use this strategy?
While the Supertrend Advance Strategy is designed to be user-friendly, having a foundational understanding of trading and market analysis can greatly enhance your ability to employ the strategy effectively. If you're a beginner, consider pairing the strategy with further education and practice on demo accounts.
6. How often should I review and adjust the strategy settings?
There's no one-size-fits-all answer. Some traders adjust settings weekly, while others might do it monthly. The key is to remain responsive to changing market conditions. Regular backtesting can give insights into potential required adjustments.
7. Can the Supertrend Advance Strategy be automated?
Yes, many traders use algorithmic trading platforms to automate their strategies, including the Supertrend Advance Strategy. However, always monitor automated systems regularly to ensure they're operating as intended.
8. Are there any markets or conditions where the strategy shouldn't be used?
The strategy might generate more false signals in markets that are consolidative or range-bound. During significant news events or times of unexpected high volatility, it's advisable to tread with caution or stay out of the market.
9. How important is backtesting with this strategy?
Backtesting is crucial as it allows traders to understand how the strategy would have performed in the past, offering insights into potential profitability and areas of improvement. Always backtest any new setting or tweak before applying it to live trades.
10. What if the strategy isn't working for me?
No strategy guarantees consistent profits. If it's not working for you, consider reviewing your settings, seeking expert advice, or complementing the Supertrend Advance Strategy with other analysis methods. Remember, continuous learning and adaptation are the keys to trading success.
Other comments
Value of combining several indicators in this script and how they work together
Diversification of Signals: Just as diversifying an investment portfolio can reduce risk, using multiple indicators can offer varied perspectives on potential price movements. Each indicator can capture a different facet of the market, ensuring that traders are not overly reliant on a single data point.
Confirmation & Reduced False Signals: A common challenge with many indicators is the potential for false signals. By requiring confirmation from multiple indicators before acting, the chances of acting on a false signal can be significantly reduced.
Flexibility Across Market Conditions: Different indicators might perform better under different market conditions. For example, while moving averages might excel in trending markets, oscillators like RSI might be more useful during sideways or range-bound conditions. A mashup strategy can potentially adapt better to varying market scenarios.
Comprehensive Analysis: With multiple indicators, traders can gauge trend strength, momentum, volatility, and potential market reversals all at once, providing a holistic view of the market.
How do the different indicators in the Supertrend Advance Strategy work together?
Supertrend: This is primarily a trend-following indicator. It provides traders with buy and sell signals based on the volatility of the price. When combined with other indicators, it can filter out noise and give more weight to strong, confirmed trends.
EMA (Exponential Moving Average): EMA gives more weight to recent price data. It can be used to identify the direction and strength of a trend. When the price is above the EMA, it's generally considered bullish, and vice versa.
RSI (Relative Strength Index): An oscillator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions. By cross-referencing with other indicators like EMA or MACD, traders can spot potential reversals or confirmations of a trend.
MACD (Moving Average Convergence Divergence): This indicator identifies changes in the strength, direction, momentum, and duration of a trend in a stock's price. When the MACD line crosses above the signal line, it can be a bullish sign, and when it crosses below, it can be bearish. Pairing MACD with Supertrend can provide dual confirmation of a trend.
CCI (Commodity Channel Index): Initially developed for commodities, CCI can indicate overbought or oversold conditions. It can be used in conjunction with other indicators to determine entry and exit points.
In essence, the synergy of these indicators provides a balanced, comprehensive approach to trading. Each indicator offers its unique lens into market conditions, and when they align, it can be a powerful indication of a trading opportunity. This combination not only reduces the potential drawbacks of each individual indicator but leverages their strengths, aiming for more consistent and informed trading decisions.
Backtesting and Default Settings
• This indicator has been optimized to be applied for 1 hour-charts. However, the underlying principles of this strategy are supply and demand in the financial markets and the strategy can be applied to all timeframes. Daytraders can use the 1min- or 5min charts, swing-traders can use the daily charts.
• This strategy has been designed to identify the most promising, highest probability entries and trades for each stock or other financial security.
• The combination of the qualifiers results in a highly selective strategy which only considers the most promising swing-trading entries. As a result, you will normally only find a low number of trades for each stock or other financial security per year in case you apply this strategy for the daily charts. Shorter timeframes will result in a higher number of trades / year.
• Consequently, traders need to apply this strategy for a full watchlist rather than just one financial security.
• Default properties: RSI on (length 14, RSI buy level 50, sell level 50), EMA, RSI, MACD on, type of strategy pullback, SL/TP type: ATR (length 10, factor 3), trade direction both, quantity 5, take profit swing hl 5.1, highest / lowest lookback 2, enable ATR trail (ATR length 10, SL ATR multiplier 1.4, TP multiplier 2.1, lookback = 4, trade direction = both).
[Sextan] Your Indicator Source for PINE v5Level: 1
NOTE: Many request this indicator template to support PINE v5. Now, here it is .This is ONLY an PINE v5 EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" (for bactest framework it does not need to be written by PINE v5)intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error.
I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example.
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Intraday Session Levels: Pre-Mkt, 5m, 15m (Replay/Toggle/Labels)Intraday Session Levels: Pre-Mkt, 5m, 15m (Replay/Toggle/Labels)
Version v1.0
Live session levels for every trader!
This indicator automatically tracks and draws the most actionable intraday levels as they develop—live in real-time and fully compatible with TradingView’s bar replay and backtesting.
How it works:
Pre-Market High & Low:
Levels appear and update live as soon as the pre-market session starts (4:00am ET), then “freeze” at the official open (9:30am ET) and remain visible for the rest of the day.
First 5-Minute Candle High/Low:
Drawn instantly after the first 5-minute candle (9:30–9:35am ET) completes.
First 15-Minute Candle High/Low:
Drawn right after the first 15-minute candle (9:30–9:45am ET) completes.
Labels on every line
Each level is clearly labeled on your chart (“PreMkt High”, “5m Low”, “15m High”, etc).
Perfect for backtesting:
All levels display exactly as they would have appeared in real time, making this indicator fully bar replay and historical test compatible.
Flexible ON/OFF toggles:
Instantly show or hide Pre-Mkt, 5m, and 15m levels via the settings panel.
Why use it?
Identify support/resistance and key reaction zones intraday
Fade or break the opening range with confidence
Backtest your strategies with accurate historical context
Reduce chart clutter with customizable, minimal visuals
Whether you’re a scalper, day trader, or backtest enthusiast, this tool keeps your charts focused and your edge sharp.
Developed by
Breakout Trend Trading Strategy - V1Strategy in nutshell:
This strategy is made to be used in daily time-frames. Works better on trending instruments where volume is available. Hence, this is more suitable for trending shares rather than currencies, commodities and indexes where volume data is either not present or not reliable.
Breakout signifies the continuation of trend. Hence, trade in the direction of breakouts. Breakouts are calculated based on high volume and price movement in a day. This will be combined with few other conditions to generate buy and sell signals along with stop and compound targets. Supertrend is used for trend bias. Our buy and sell targets do not directly depend on the bias. But, entry criteria in opposite trend is made much difficult than that of trend direction. Further explanation of method and input parameters are explained below.
Backtesting parameters :
Capital and position sizing : Capital and position sizing parameters are set to test investing 2000 wholly on certain stock without compounding.
Initial Capital : 2000
Order Size : 100% of equity
Pyramiding : 1
ExitOnSignal : If unchecked exit is triggered solely on trailing stop
Trade Direction : Long, Short or All. Short condition is riskier than long conditions and often results in losses as per my observation. On most of the stocks trending up, strategy will not generate any short signals. This is achieved by comparing yearly high lows to previous two years to decide whether to allow short or long entries.
allowImmediateCompound : Applicable only if compounding/pyramiding is enabled in trade. If checked allows to place compounding orders immediately. If unchecked, it waits for stopline to cross order price before placing next compound.
Display Mode :
Targets : Whenever breakout happens, show marker for upTarget and downTarget
TargetChannel : Show up target and downtarget as a channel
Target With Stop : Along with targets, show also stop levels for breakouts
Up Channel : Channel created from UpTarget and respective stops
Down Channel : Channel created from DownTarget and respective stops
ShowTrailingStop : Shows trailing stop and compound lines when there is a trading position.
ShowTargetLevels : Shows Buy Sell target levels along with stop and compound lines. Trades are done as market orders. Hence, target levels are displayed after strategy makes the trade. Since only one order allowed per side without compounding, target, stop and compound levels are shown sometimes even without trade being made. These can be considered as entry levels if there is no existing position.
ShowPreviousLevels : Shows previous buy/sell target levels. When enabled, layout can look messy.
StopMultiplyer: To Set trailing stop loss.
BacktestYears: Number of years to include in backtest
So far my test cases are:
Positive : AAPL, AMZN, TSLA, RUN, VRT, ASX:APT
Negative Test Cases: WPL, WHC, NHC, WOW, COL, NAB (All ASX stocks)
Special test case: WDI
Negative test cases still show losses in backtesting. I have attempted including many conditions to eliminate or reduce the loss. But, further efforts has resulted in reduction in profits in positive cases as well. Still experimenting. Will update whenever I find improvements. Comments and suggestions welcome :)
Volatility Momentum Breakout StrategyDescription:
Overview:
The Volatility Momentum Breakout Strategy is designed to capture significant price moves by combining a volatility breakout approach with trend and momentum filters. This strategy dynamically calculates breakout levels based on market volatility and uses these levels along with trend and momentum conditions to identify trade opportunities.
How It Works:
1. Volatility Breakout:
• Methodology:
The strategy computes the highest high and lowest low over a defined lookback period (excluding the current bar to avoid look-ahead bias). A multiple of the Average True Range (ATR) is then added to (or subtracted from) these levels to form dynamic breakout thresholds.
• Purpose:
This method helps capture significant price movements (breakouts) while ensuring that only past data is used, thereby maintaining realistic signal generation.
2. Trend Filtering:
• Methodology:
A short-term Exponential Moving Average (EMA) is applied to determine the prevailing trend.
• Purpose:
Long trades are considered only when the current price is above the EMA, indicating an uptrend, while short trades are taken only when the price is below the EMA, indicating a downtrend.
3. Momentum Confirmation:
• Methodology:
The Relative Strength Index (RSI) is used to gauge market momentum.
• Purpose:
For long entries, the RSI must be above a mid-level (e.g., above 50) to confirm upward momentum, and for short entries, it must be below a similar threshold. This helps filter out signals during overextended conditions.
Entry Conditions:
• Long Entry:
A long position is triggered when the current closing price exceeds the calculated long breakout level, the price is above the short-term EMA, and the RSI confirms momentum (e.g., above 50).
• Short Entry:
A short position is triggered when the closing price falls below the calculated short breakout level, the price is below the EMA, and the RSI confirms momentum (e.g., below 50).
Risk Management:
• Position Sizing:
Trades are sized to risk a fixed percentage of account equity (set here to 5% per trade in the code, with each trade’s stop loss defined so that risk is limited to approximately 2% of the entry price).
• Stop Loss & Take Profit:
A stop loss is placed a fixed ATR multiple away from the entry price, and a take profit target is set to achieve a 1:2 risk-reward ratio.
• Realistic Backtesting:
The strategy is backtested using an initial capital of $10,000, with a commission of 0.1% per trade and slippage of 1 tick per bar—parameters chosen to reflect conditions faced by the average trader.
Important Disclaimers:
• No Look-Ahead Bias:
All breakout levels are calculated using only past data (excluding the current bar) to ensure that the strategy does not “peek” into future data.
• Educational Purpose:
This strategy is experimental and provided solely for educational purposes. Past performance is not indicative of future results.
• User Responsibility:
Traders should thoroughly backtest and paper trade the strategy under various market conditions and adjust parameters to fit their own risk tolerance and trading style before live deployment.
Conclusion:
By integrating volatility-based breakout signals with trend and momentum filters, the Volatility Momentum Breakout Strategy offers a unique method to capture significant price moves in a disciplined manner. This publication provides a transparent explanation of the strategy’s components and realistic backtesting parameters, making it a useful tool for educational purposes and further customization by the TradingView community.
EMA Crossover Strategy with Take Profit and Candle HighlightingStrategy Overview:
This strategy is based on the Exponential Moving Averages (EMA), specifically the EMA 20 and EMA 50. It takes advantage of EMA crossovers to identify potential trend reversals and uses multiple take-profit levels and a stop-loss for risk management.
Key Components:
EMA Crossover Signals:
Buy Signal (Uptrend): A buy signal is generated when the EMA 20 crosses above the EMA 50, signaling the start of a potential uptrend.
Sell Signal (Downtrend): A sell signal is generated when the EMA 20 crosses below the EMA 50, signaling the start of a potential downtrend.
Take Profit Levels:
Once a buy or sell signal is triggered, the strategy calculates multiple take-profit levels based on the range of the previous candle. The user can define multipliers for each take-profit level.
Take Profit 1 (TP1): 50% of the previous candle's range above or below the entry price.
Take Profit 2 (TP2): 100% of the previous candle's range above or below the entry price.
Take Profit 3 (TP3): 150% of the previous candle's range above or below the entry price.
Take Profit 4 (TP4): 200% of the previous candle's range above or below the entry price.
These levels are adjusted dynamically based on the previous candle's high and low, so they adapt to changing market conditions.
Stop Loss:
A stop-loss is set to manage risk. The default stop-loss is 3% from the entry price, but this can be adjusted in the settings. The stop-loss is triggered if the price moves against the position by this amount.
Trend Direction Highlighting:
The strategy highlights the bars (candles) with colors:
Green bars indicate an uptrend (when EMA 20 crosses above EMA 50).
Red bars indicate a downtrend (when EMA 20 crosses below EMA 50).
These visual cues help users easily identify the market direction.
Strategy Entries and Exits:
Entries: The strategy enters a long (buy) position when the EMA 20 crosses above the EMA 50 and a short (sell) position when the EMA 20 crosses below the EMA 50.
Exits: The strategy exits the positions at any of the defined take-profit levels or the stop-loss. Multiple exit levels provide opportunities to take profit progressively as the price moves in the favorable direction.
Entry and Exit Conditions in Detail:
Buy Entry Condition (Uptrend):
A buy position is opened when EMA 20 crosses above EMA 50, signaling the start of an uptrend.
The strategy calculates take-profit levels above the entry price based on the previous bar's range (high-low) and the multipliers for TP1, TP2, TP3, and TP4.
Sell Entry Condition (Downtrend):
A sell position is opened when EMA 20 crosses below EMA 50, signaling the start of a downtrend.
The strategy calculates take-profit levels below the entry price, similarly based on the previous bar's range.
Exit Conditions:
Take Profit: The strategy attempts to exit the position at one of the take-profit levels (TP1, TP2, TP3, or TP4). If the price reaches any of these levels, the position is closed.
Stop Loss: The strategy also has a stop-loss set at a default value (3% below the entry for long trades, and 3% above for short trades). The stop-loss helps to protect the position from significant losses.
Backtesting and Performance Metrics:
The strategy can be backtested using TradingView's Strategy Tester. The results will show how the strategy would have performed historically, including key metrics like:
Net Profit
Max Drawdown
Win Rate
Profit Factor
Average Trade Duration
These performance metrics can help users assess the strategy's effectiveness over historical periods and optimize the input parameters (e.g., multipliers, stop-loss level).
Customization:
The strategy allows for the adjustment of several key input values via the settings panel:
Take Profit Multipliers: Users can customize the multipliers for each take-profit level (TP1, TP2, TP3, TP4).
Stop Loss Percentage: The user can also adjust the stop-loss percentage to a custom value.
EMA Periods: The default periods for the EMA 50 and EMA 20 are fixed, but they can be adjusted for different market conditions.
Pros of the Strategy:
EMA Crossover Strategy: A classic and well-known strategy used by traders to identify the start of new trends.
Multiple Take Profit Levels: By taking profits progressively at different levels, the strategy locks in gains as the price moves in favor of the position.
Clear Trend Identification: The use of green and red bars makes it visually easier to follow the market's direction.
Risk Management: The stop-loss and take-profit features help to manage risk and optimize profit-taking.
Cons of the Strategy:
Lagging Indicators: The strategy relies on EMAs, which are lagging indicators. This means that the strategy might enter trades after the trend has already started, leading to missed opportunities or less-than-ideal entry prices.
No Confirmation Indicators: The strategy purely depends on the crossover of two EMAs and does not use other confirming indicators (e.g., RSI, MACD), which might lead to false signals in volatile markets.
How to Use in Real-Time Trading:
Use for Backtesting: Initially, use this strategy in backtest mode to understand how it would have performed historically with your preferred settings.
Paper Trading: Once comfortable, you can use paper trading to test the strategy in real-time market conditions without risking real money.
Live Trading: After testing and optimizing the strategy, you can consider using it for live trading with proper risk management in place (e.g., starting with a small position size and adjusting parameters as needed).
Summary:
This strategy is designed to identify trend reversals using EMA crossovers, with customizable take-profit levels and a stop-loss to manage risk. It's well-suited for traders looking for a systematic way to enter and exit trades based on clear market signals, while also providing flexibility to adjust for different risk profiles and trading styles.
Absorption DetectorABSORPTION DETECTOR -
The Absorption Detector identifies institutional order flow by detecting "absorption" patterns where smart money quietly accumulates or distributes positions by absorbing retail order flow. This creates high-probability support and resistance zones for trading. This is an approximation only and does not read any footprint data.
WHAT IS ABSORPTION?
Absorption occurs when institutions take the opposite side of retail trades, creating specific candlestick patterns with high volume and significant wicks. The indicator identifies two main patterns:
SELLING ABSORPTION (P-Pattern): Red zones above candles where institutions sell into retail buying pressure, creating resistance levels. Look for high volume candles with large upper wicks that close in the lower half.
BUYING ABSORPTION (B-Pattern): Green zones below candles where institutions buy from retail selling pressure, creating support levels. Look for high volume candles with large lower wicks that close in the upper half.
KEY FEATURES
- Automatic detection of institutional absorption patterns
- Dynamic support and resistance zone creation
- Customizable styling for all visual elements
- Historic zone display for backtesting analysis
- Strength-based filtering to show only high-probability setups
- Real-time alerts for new absorption patterns
- Professional info panel with key statistics
- Multi-timeframe compatibility
MAIN SETTINGS
Volume Threshold (1.2): Minimum volume surge required compared to average. Higher values = fewer but stronger signals.
Minimum Volume (2500): Absolute volume floor to prevent signals during low-volume periods.
Min Wick Size (0.2): Minimum wick size as ATR multiple. Ensures significant rejection occurred.
Minimum Strength (1.5): Combined volume and wick strength filter. Higher values = higher quality signals.
Show Historic Zones (OFF): Enable to see all historical zones for backtesting. Disable for better performance.
Zone Extension (20): How many bars to project zones forward for anticipating future reactions.
TRADING APPROACH
ZONE REACTION STRATEGY: Wait for price to approach absorption zones and trade the bounce or rejection. Use the zones as dynamic support and resistance levels.
BREAKOUT STRATEGY: Trade decisive breaks of strong absorption zones with proper risk management. Failed zones often lead to strong moves.
CONFLUENCE TRADING: Combine absorption zones with other technical analysis for highest probability setups. Look for alignment with trend lines, Fibonacci levels, and key support/resistance.
RISK MANAGEMENT: Always use stop losses beyond the absorption zones. Target minimum 1:2 risk-reward ratios. Position size appropriately based on zone strength.
OPTIMIZATION GUIDE
For Conservative Trading (fewer, higher quality signals):
- Volume Threshold: 1.5
- Minimum Strength: 2.0
- Min Wick Size: 0.3
For Aggressive Trading (more signals, requires careful filtering):
- Volume Threshold: 1.1
- Minimum Strength: 1.0
- Min Wick Size: 0.15
BEST PRACTICES
Markets: Works best on liquid instruments with good volume - major forex pairs, popular stocks, liquid futures, and established cryptocurrencies.
Timeframes: Effective on all timeframes from 1-minute scalping to daily swing trading. Adjust settings based on your timeframe and trading style.
Confirmation: Never trade absorption signals in isolation. Always combine with trend analysis, market structure, and proper risk management.
Session Timing: Be aware of market sessions and avoid trading during low liquidity periods or major news events.
Backtesting: Use the historic zones feature to validate performance on your chosen market and timeframe before live trading.
CUSTOMIZATION
The indicator offers complete visual customization including zone colors, border styles, label appearances, and info panel positioning. All colors can be adapted to match your chart theme and personal preferences.
Alert system provides both basic and custom message alerts for real-time notifications of new absorption patterns.
PERFORMANCE NOTES
Default settings are optimized for most markets and timeframes. For best performance on older charts, keep "Show Historic Zones" disabled unless specifically backtesting.
The indicator maintains excellent performance even with extensive historical analysis enabled, handling up to 500 zones and 100 labels for comprehensive backtesting.
Divergence IQ [TradingIQ]Hello Traders!
Introducing "Divergence IQ"
Divergence IQ lets traders identify divergences between price action and almost ANY TradingView technical indicator. This tool is designed to help you spot potential trend reversals and continuation patterns with a range of configurable features.
Features
Divergence Detection
Detects both regular and hidden divergences for bullish and bearish setups by comparing price movements with changes in the indicator.
Offers two detection methods: one based on classic pivot point analysis and another that provides immediate divergence signals.
Option to use closing prices for divergence detection, allowing you to choose the data that best fits your strategy.
Normalization Options:
Includes multiple normalization techniques such as robust scaling, rolling Z-score, rolling min-max, or no normalization at all.
Adjustable normalization window lets you customize the indicator to suit various market conditions.
Option to display the normalized indicator on the chart for clearer visual comparison.
Allows traders to take indicators that aren't oscillators, and convert them into an oscillator - allowing for better divergence detection.
Simulated Trade Management:
Integrates simulated trade entries and exits based on divergence signals to demonstrate potential trading outcomes.
Customizable exit strategies with options for ATR-based or percentage-based stop loss and profit target settings.
Automatically calculates key trade metrics such as profit percentage, win rate, profit factor, and total trade count.
Visual Enhancements and On-Chart Displays:
Color-coded signals differentiate between bullish, bearish, hidden bullish, and hidden bearish divergence setups.
On-chart labels, lines, and gradient flow visualizations clearly mark divergence signals, entry points, and exit levels.
Configurable settings let you choose whether to display divergence signals on the price chart or in a separate pane.
Performance Metrics Table:
A performance table dynamically displays important statistics like profit, win rate, profit factor, and number of trades.
This feature offers an at-a-glance assessment of how the divergence-based strategy is performing.
The image above shows Divergence IQ successfully identifying and trading a bullish divergence between an indicator and price action!
The image above shows Divergence IQ successfully identifying and trading a bearish divergence between an indicator and price action!
The image above shows Divergence IQ successfully identifying and trading a hidden bullish divergence between an indicator and price action!
The image above shows Divergence IQ successfully identifying and trading a hidden bearish divergence between an indicator and price action!
The performance table is designed to provide a clear summary of simulated trade results based on divergence setups. You can easily review key metrics to assess the strategy’s effectiveness over different time periods.
Customization and Adaptability
Divergence IQ offers a wide range of configurable settings to tailor the indicator to your personal trading approach. You can adjust the lookback and lookahead periods for pivot detection, select your preferred method for normalization, and modify trade exit parameters to manage risk according to your strategy. The tool’s clear visual elements and comprehensive performance metrics make it a useful addition to your technical analysis toolbox.
The image above shows Divergence IQ identifying divergences between price action and OBV with no normalization technique applied.
While traders can look for divergences between OBV and price, OBV doesn't naturally behave like an oscillator, with no definable upper and lower threshold, OBV can infinitely increase or decrease.
With Divergence IQ's ability to normalize any indicator, traders can normalize non-oscillator technical indicators such as OBV, CVD, MACD, or even a moving average.
In the image above, the "Robust Scaling" normalization technique is selected. Consequently, the output of OBV has changed and is now behaving similar to an oscillator-like technical indicator. This makes spotting divergences between the indicator and price easier and more appropriate.
The three normalization techniques included will change the indicator's final output to be more compatible with divergence detection.
This feature can be used with almost any technical indicator.
Stop Type
Traders can select between ATR based profit targets and stop losses, or percentage based profit targets and stop losses.
The image above shows options for the feature.
Divergence Detection Method
A natural pitfall of divergence trading is that it generally takes several bars to "confirm" a divergence. This makes trading the divergence complicated, because the entry at time of the divergence might look great; however, the divergence wasn't actually signaled until several bars later.
To circumvent this issue, Divergence IQ offers two divergence detection mechanisms.
Pivot Detection
Pivot detection mode is the same as almost every divergence indicator on TradingView. The Pivots High Low indicator is used to detect market/indicator highs and lows and, consequently, divergences.
This method generally finds the "best looking" divergences, but will always take additional time to confirm the divergence.
Immediate Detection
Immediate detection mode attempts to reduce lag between the divergence and its confirmation to as little as possible while avoiding repainting.
Immediate detection mode still uses the Pivots Detection model to find the first high/low of a divergence. However, the most recent high/low does not utilize the Pivot Detection model, and instead immediately looks for a divergence between price and an indicator.
Immediate Detection Mode will always signal a divergence one bar after it's occurred, and traders can set alerts in this mode to be alerted as soon as the divergence occurs.
TradingView Backtester Integration
Divergence IQ is fully compatible with the TradingView backtester!
Divergence IQ isn’t designed to be a “profitable strategy” for users to trade. Instead, the intention of including the backtester is to let users backtest divergence-based trading strategies between the asset on their chart and almost any technical indicator, and to see if divergences have any predictive utility in that market.
So while the backtester is available in Divergence IQ, it’s for users to personally figure out if they should consider a divergence an actionable insight, and not a solicitation that Divergence IQ is a profitable trading strategy. Divergence IQ should be thought of as a Divergence backtesting toolkit, not a full-feature trading strategy.
Strategy Properties Used For Backtest
Initial Capital: $1000 - a realistic amount of starting capital that will resonate with many traders
Amount Per Trade: 5% of equity - a realistic amount of capital to invest relative to portfolio size
Commission: 0.02% - a conservative amount of commission to pay for trade that is standard in crypto trading, and very high for other markets.
Slippage: 1 tick - appropriate for liquid markets, but must be increased in markets with low activity.
Once more, the backtester is meant for traders to personally figure out if divergences are actionable trading signals on the market they wish to trade with the indicator they wish to use.
And that's all!
If you have any cool features you think can benefit Divergence IQ - please feel free to share them!
Thank you so much TradingView community!
Slight Swing Momentum Strategy.Introduction:
The Swing Momentum Strategy is a quantitative trading strategy designed to capture mid-term opportunities in the financial markets by combining swing trading principles with momentum indicators. It utilizes a combination of technical indicators, including moving averages, crossover signals, and volume analysis, to generate buy and sell signals. The strategy aims to identify market trends and capitalize on price momentum for profit generation.
Highlights:
The strategy offers several key highlights that make it unique and potentially attractive to traders:
Swing Trading with Momentum: The strategy combines the principles of swing trading, which aim to capture short-to-medium-term price swings, with momentum indicators that help identify strong price trends and potential breakout opportunities.
Technical Indicator Optimization: The strategy utilizes a selection of optimized technical indicators, including moving averages and crossover signals, to filter out the noise and focus on high-probability trading setups. This optimization enhances the strategy's ability to identify favourable entry and exit points.
Risk Management: The strategy incorporates risk management techniques, such as position sizing based on equity and dynamic stop loss levels, to manage risk exposure and protect capital. This helps to minimize drawdowns and preserve profits.
Buy Condition:
The buy condition in the strategy is determined by a combination of factors, including A1, A2, A3, XG, and weeklySlope. Let's break it down:
A1 Condition: The A1 condition checks for specific price relationships. It verifies that the ratio of the highest price to the closing price is less than 1.03, the ratio of the opening price to the lowest price is less than 1.03, and the ratio of the highest price to the previous day's closing price is greater than 1.06. This condition looks for a specific pattern indicating potential bullish momentum.
A2 Condition: The A2 condition checks for price relationships related to the closing price. It verifies that the ratio of the closing price to the opening price is greater than 1.05 or that the ratio of the closing price to the previous day's closing price is greater than 1.05. This condition looks for signs of upward price movement and momentum.
A3 Condition: The A3 condition focuses on volume. It checks if the current volume crosses above the highest volume over the last 60 periods. This condition aims to identify increased buying interest and potentially confirms the strength of the potential upward price movement.
XG Condition: The XG condition combines the A1 and A2 conditions and checks if they are true for both the current and previous bars. It also verifies that the ratio of the closing price to the 5-period EMA crosses above the 9-period SMA of the same ratio. This condition helps identify potential buy signals when multiple factors align, indicating a strong bullish momentum and potential entry point.
Weekly Trend Factor: The weekly slope condition calculates the slope of the 50-period SMA over a weekly timeframe. It checks if the slope is positive, indicating an overall upward trend on a weekly basis. This condition provides additional confirmation that the stock is in an upward trend.
When all of these conditions align, the buy condition is triggered, indicating a favourable time to enter a long position.
Sell Condition:
The sell condition is relatively straightforward in the strategy:
Sell Signal: The sell condition simply checks if the closing price crosses below the 10-period EMA. When this condition is met, it indicates a potential reversal or weakening of the upward price momentum, and a sell signal is generated.
Backtest Outcome:
The strategy was backtested over the period from January 22nd, 1999 to May 3rd, 2023, using daily candlestick charts for the NASDAQ: NVDA. The strategy used an initial capital of 1,000,000 USD, The order quantity is defined as 10% of the equity. The strategy allows for pyramiding with 1 order, and the transaction fee is set at 0.03% per trade. Here are the key outcomes of the backtest:
Net Profit: 539,595.84 USD, representing a return of 53.96%.
Percent Profitable: 48.82%
Total Closed Trades: 127
Profit Factor: 2.331
Max Drawdown: 68,422.70 USD
Average Trade: 4,248.79 USD
Average Number of Bars in Trades: 11, indicating the average duration of the trades.
Conclusion:
In conclusion, the Swing Momentum Strategy is a quantitative trading approach that combines swing trading principles with momentum indicators to identify and capture mid term trading opportunities. The strategy has demonstrated promising results during backtesting, including a significant net profit and a favourable profit factor.
Lorentzian Classification Strategy Based in the model of Machine learning: Lorentzian Classification by @jdehorty, you will be able to get into trending moves and get interesting entries in the market with this strategy. I also put some new features for better backtesting results!
Backtesting context: 2022-07-19 to 2023-04-14 of US500 1H by PEPPERSTONE. Commissions: 0.03% for each entry, 0.03% for each exit. Risk per trade: 2.5% of the total account
For this strategy, 3 indicators are used:
Machine learning: Lorentzian Classification by @jdehorty
One Ema of 200 periods for identifying the trend
Supertrend indicator as a filter for some exits
Atr stop loss from Gatherio
Trade conditions:
For longs:
Close price is above 200 Ema
Lorentzian Classification indicates a buying signal
This gives us our long signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as buy.
The other half will be closed when the model indicates a selling signal or Supertrend indicator gives a bearish signal. This will be showed as cl buy.
For shorts:
Close price is under 200 Ema
Lorentzian Classification indicates a selling signal
This gives us our short signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as sell.
The other half will be closed when the model indicates a buying signal or Supertrend indicator gives a bullish signal. This will be showed as cl sell.
Risk management
To calculate the amount of the position you will use just a small percent of your initial capital for the strategy and you will use the atr stop loss or last swing for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a buy signal at price of 4,000 usd. The stop loss price from atr stop loss or last swing is 3,900. You calculate the distance in percent between 4,000 and 3,900. In this case, that distance would be of 2.50%. Then, you calculate your position by this way: (initial or current capital * risk per trade of your account) / (stop loss distance).
Using these values on the formula: (1000*2,5%)/(2,5%) = 1000usd. It means, you have to use 1000 usd for risking 2.5% of your account.
We will use this risk management for applying compound interest.
> In settings, with position amount calculator, you can enter the amount in usd of your account and the amount in percentage for risking per trade of the account. You will see this value in green color in the upper left corner that shows the amount in usd to use for risking the specific percentage of your account.
> You can also choose a fixed amount, so you will have to activate fixed amount in risk management for trades and set the fixed amount for backtesting.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, break evens, positions, signals, indicators, a table of some stats from backtesting, etc.
You will find the settings for risk management at the end of the script if you want to change something or trying new values for other assets for backtesting.
If you want to change the initial capital for backtest the strategy, go to properties, and also enter the commisions of your exchange and slippage for more realistic results.
In risk managment you can find an option called "Use leverage ?", activate this if you want to backtest using leverage, which means that in case of not having enough money for risking the % determined by you of your account using your initial capital, you will use leverage for using the enough amount for risking that % of your acount in a buy position. Otherwise, the amount will be limited by your initial/current capital
I also added a function for backtesting if you had added or withdrawn money frequently:
Adding money: You can choose how often you want to add money (Monthly, yearly, daily or weekly). Then a fixed amount of money and activate or deactivate this function
Withdraw money: You can choose if you want to withdraw a fixed amount or a percentage of earnings. Then you can choose a fixed amount of money, the period of time and activate or deactivate this function. Also, the percentage of earnings if you choosed this option.
Some other assets where strategy has worked
BTCUSD 4H, 1D
ETHUSD 4H, 1D
BNBUSD 4H
SPX 1D
BANKNIFTY 4H, 15 min
Some things to consider
USE UNDER YOUR OWN RISK. PAST RESULTS DO NOT REPRESENT THE FUTURE.
DEPENDING OF % ACCOUNT RISK PER TRADE, YOU COULD REQUIRE LEVERAGE FOR OPEN SOME POSITIONS, SO PLEASE, BE CAREFULL AND USE CORRECTLY THE RISK MANAGEMENT
Do not forget to change commissions and other parameters related with back testing results!. If you have problems loading the script reduce max bars back number in general settings
Strategies for trending markets use to have more looses than wins and it takes a long time to get profits, so do not forget to be patient and consistent !
Please, visit the post from @jdehorty called Machine Learning: Lorentzian Classification for a better understanding of his script!
Any support and boosts will be well received. If you have any question, do not doubt to ask!
