Average Down [Zeiierman]AVERAGING DOWN
Averaging down is an investment strategy that involves buying additional contracts of an asset when the price drops. This way, the investor increases the size of their position at discounted prices. The averaging down strategy is highly debated among traders and investors because it can either lead to huge losses or great returns. Nevertheless, averaging down is often used and favored by long-term investors and contrarian traders. With careful/proper risk management, averaging down can cover losses and magnify the returns when the asset rebounds. However, the main concern for a trader is that it can be hard to identify the difference between a pullback or the start of a new trend.
HOW DOES IT WORK
Averaging down is a method to lower the average price at which the investor buys an asset. A lower average price can help investors come back to break even quicker and, if the price continues to rise, get an even bigger upside and thus increase the total profit from the trade. For example, We buy 100 shares at $60 per share, a total investment of $6000, and then the asset drops to $40 per share; in order to come back to break even, the price has to go up 50%. (($60/$40) - 1)*100 = 50%.
The power of Averaging down comes into play if the investor buys additional shares at a lower price, like another 100 shares at $40 per share; the total investment is ($6000+$4000 = $10000). The average price for the investment is now $50. (($60 x 100) + ($40 x 100))/200; in order to get back to break even, the price has to rise 25% ($50/$40)-1)*100 = 25%, and if the price continues up to $60 per share, the investor can secure a profit at 16%. So by averaging down, investors and traders can cover the losses easier and potentially have more profit to secure at the end.
THE AVERAGE DOWN TRADINGVIEW TOOL
This script/indicator/trading tool helps traders and investors to get the average price of their position. The tool works for Long and Short and displays the entry price, average price, and the PnL in points.
HOW TO USE
Use the tool to calculate the average price of your long or short position in any market and timeframe.
Get the current PnL for the investment and keep track of your entry prices.
APPLY TO CHART
When you apply the tool on the chart, you have to select five entry points, and within the setting panel, you can choose how many of these five entry points are active and how many contracts each entry has. Then, the tool will display your average price based on the entries and the number of contracts used at each price level.
LONG
Set your entries and the number of contracts at each price level. The indicator will then display all your long entries and at what price you will break even. The entry line changes color based on if the entry is in profit or loss.
SHORT
Set your entries and the number of contracts at each price level. The indicator will then display all your short entries and at what price you will break even. The entry line changes color based on if the entry is in profit or loss.
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Disclaimer
Copyright by Zeiierman.
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
חפש סקריפטים עבור "algo"
Example: Monte Carlo SimulationExperimental:
Example execution of Monte Carlo Simulation applied to the markets(this is my interpretation of the algo so inconsistencys may appear).
note:
the algorithm is very demanding so performance is limited.
RAT Moving Average Crossover StrategyThis is based on general moving average crossovers but some modifications made to generate buy sell signals.
Weis pip zigzag jayyWhat you see here is the Weis pip zigzag wave plotted directly on the price chart. This script is the companion to the Weis pip wave ( ) which is plotted in the lower panel of the displayed chart and can be used as an alternate way of plotting the same results. The Weis pip zigzag wave shows how far in terms of price a Weis wave has traveled through the duration of a Weis wave. The Weis pip zigzag wave is used in combination with the Weis cumulative volume wave. The two waves must be set to the same "wave size".
To use this script you must set the wave size. Using the traditional Weis method simply enter the desired wave size in the box "Select Weis Wave Size" In this example, it is set to 5. Each wave for each security and each timeframe requires its own wave size. Although not the traditional method a more automatic way to set wave size would be to use ATR. This is not the true Weis method but it does give you similar waves and, importantly, without the hassle described above. Once the Weis wave size is set then the pip wave will be shown.
I have put a pip zigzag of a 5 point Weis wave on the bar chart - that is a different script. I have added it to allow your eye to see what a Weis wave looks like. You will notice that the wave is not in straight lines connecting wave tops to bottoms this is a function of the limitations of Pinescript version 1. This script would need to be in version 4 to allow straight lines. There are too many calculations within this script to allow conversion to Pinescript version 4 or even Version 3. I am in the process of rewriting this script to reduce the number of calculations and streamline the algorithm.
The numbers plotted on the chart are calculated to be relative numbers. The script is limited to showing only three numbers vertically. Only the highest three values of a number are shown. For example, if the highest recent pip value is 12,345 only the first 3 numerals would be displayed ie 123. But suppose there is a recent value of 691. It would not be helpful to display 691 if the other wave size is shown as 123. To give the appropriate relative value the script will show a value of 7 instead of 691. This informs you of the relative magnitude of the values. This is done automatically within the script. There is likely no need to manually override the automatically calculated value. I will create a video that demonstrates the manual override method.
What is a Weis wave? David Weis has been recognized as a Wyckoff method analyst he has written two books one of which, Trades About to Happen, describes the evolution of the now popular Weis wave. The method employed by Weis is to identify waves of price action and to compare the strength of the waves on characteristics of wave strength. Chief among the characteristics of strength is the cumulative volume of the wave. There are other markers that Weis uses as well for example how the actual price difference between the start of the Weis wave from start to finish. Weis also uses time, particularly when using a Renko chart. Weis specifically uses candle or bar closes to define all wave action ie a line chart.
David Weis did a futures io video which is a popular source of information about his method.
This is the identical script with the identical settings but without the offending links. If you want to see the pip Weis method in practice then search Weis pip wave. If you want to see Weis chart in pdf then message me and I will give a link or the Weis pdf. Why would you want to see the Weis chart for May 27, 2020? Merely to confirm the veracity of my algorithm. You could compare my Weis chart here () from the same period to the David Weis chart from May 27. Both waves are for the ES!1 4 hour chart and both for a wave size of 5.
NAND PerceptronExperimental NAND Perceptron based upon Python template that aims to predict NAND Gate Outputs. A Perceptron is one of the foundational building blocks of nearly all advanced Neural Network layers and models for Algo trading and Machine Learning.
The goal behind this script was threefold:
To prove and demonstrate that an ACTUAL working neural net can be implemented in Pine, even if incomplete.
To pave the way for other traders and coders to iterate on this script and push the boundaries of Tradingview strategies and indicators.
To see if a self-contained neural network component for parameter optimization within Pinescript was hypothetically possible.
NOTE: This is a highly experimental proof of concept - this is NOT a ready-made template to include or integrate into existing strategies and indicators, yet (emphasis YET - neural networks have a lot of potential utility and potential when utilized and implemented properly).
Hardcoded NAND Gate outputs with Bias column (X0):
// NAND Gate + X0 Bias and Y-true
// X0 // X1 // X2 // Y
// 1 // 0 // 0 // 1
// 1 // 0 // 1 // 1
// 1 // 1 // 0 // 1
// 1 // 1 // 1 // 0
Column X0 is bias feature/input
Column X1 and X2 are the NAND Gate
Column Y is the y-true values for the NAND gate
yhat is the prediction at that timestep
F0,F1,F2,F3 are the Dot products of the Weights (W0,W1,W2) and the input features (X0,X1,X2)
Learning rate and activation function threshold are enabled by default as input parameters
Uncomment sections for more training iterations/epochs:
Loop optimizations would be amazing to have for a selectable length for training iterations/epochs but I'm not sure if it's possible in Pine with how this script is structured.
Error metrics and loss have not been implemented due to difficulty with script length and iterations vs epochs - I haven't been able to configure the input parameters to successfully predict the right values for all four y-true values for the NAND gate (only been able to get 3/4; If you're able to get all four predictions to be correct, let me know, please).
// //---- REFERENCE for final output
// A3 := 1, y0 true
// B3 := 1, y1 true
// C3 := 1, y2 true
// D3 := 0, y3 true
PLEASE READ: Source article/template and main code reference:
towardsdatascience.com
towardsdatascience.com
towardsdatascience.com
Baseline-C [ID: AC-P]The "AC-P" version of jiehonglim's NNFX Baseline script is my personal customized version of the NNFX Baseline concept as part of the NNFX Algorithm stack/structure for 1D Trend Trading for Forex. Everget's JMA implementation is used for the baseline smoothing method, with optional ATR bands at 1.0x and 1.5x from the baseline.
NNFX = No Nonsense Forex
Baseline = Component of the NNFX Algorithm that consists of a single moving average
Baseline ---> Meant to be used in conjunction with ATR/C1/C2/Vol Indicator/Exit Indicator as per NNFX Algorithm setup/structure. C1 is 1st Confirmation Indicator, C2 is 2nd Confirmation Indicator.
JMA (Jurik Moving Average) is used for the baseline and slow baseline.
A slow baseline option is included, but disabled by default.
The faint orange/purple lines are 1.0x/1.5x ATR from the Baseline, and are what I use as potential TP/SL targets or to evaluate when to stay out of a trade (chop/missed entry/exit/other/ATR breach), depending on the trade setup (in conjunction with C1/C2/Vol Indicator/Exit Indicator)
This script is heavily based upon jiehonglim's NNFX Baseline script for signaling, barcoloring, and ATR.
SSL Channel option included but disabled by default (Erwinbeckers SSL component)
POC (Point of Control) from Volume Profile is included/enabled by default for both the current timeframe and 12HR timeframe
03.freeman's InfoPanel Divergence Indicator was used a reference to replace the current/previous ATR information infopanel/info draw from jiehonglim's script. I'm not sure whether I like the previous way ATR info was displayed vs how I have it currently, but it's something that is completely optional:
Specifically: I am tuning this baseline/indicator for 1D trading as part of the NNFX system, for Forex.
DO NOT USE THIS INDICATOR WITHOUT PROPER TUNING/ADJUSTMENT for your timeframe and asset class.
Note about lack of alerts:
Alerts for baseline crosses (and other crosses) have been purposefully omitted for this version upon initial publication. While getting alerts for baseline crosses under certain conditions/filtered conditions that eliminate low-importance signals and crossover whipsaw would be great, it's something I'm still looking into.
SPECIFICALLY: There are entry, exit, take profit, and continuation signal components in relation to the Baseline to the rest of the NNFX Algorithm stack (ATR/C1/C2/Vol Indicator/Exit Indicator), including but limited to the "1 candle rule" and the "7 candle rule" as per NNFX.
Implementing alerts that are significant that also factor in these rules while reducing alert spam/false signals would be ideal, but it's also the HTF/Daily chart - visually, entry/exit/continuation signal alignment is easy to spot when trading 1D - alerts may be redundant/a pursuit in diminishing returns (for now).
//-------------------------------------------------------------------
// Acknowledgements/Reference:
// jiehonglim, NNFX Baseline Script - Moving Averages
//
// Fractured, Many Moving Averages
//
// everget, Jurik Moving Average/JMA
//
// 03.freeman, InfoPanel Divergence Indicator
//
// Ggqmna Volume stops
//
// Libertus RSI Divs
//
// ChrisMoody, CM_Price-Action-Bars-Price Patterns That Work
//
// Erwinbeckers SSL Channel
//
Enhanced Market Analyzer with Adaptive Cognitive LearningThe "Enhanced Market Analyzer with Advanced Features and Adaptive Cognitive Learning" is an advanced, multi-dimensional trading indicator that leverages sophisticated algorithms to analyze market trends and generate predictive trading signals. This indicator is designed to merge traditional technical analysis with modern machine learning techniques, incorporating features such as adaptive learning, Monte Carlo simulations, and probabilistic modeling. It is ideal for traders who seek deeper market insights, adaptive strategies, and reliable buy/sell signals.
Key Features:
Adaptive Cognitive Learning:
Utilizes Monte Carlo simulations, reinforcement learning, and memory feedback to adapt to changing market conditions.
Adjusts the weighting and learning rate of signals dynamically to optimize predictions based on historical and real-time data.
Hybrid Technical Indicators:
Custom RSI Calculation: An RSI that adapts its length based on recursive learning and error adjustments, making it responsive to varying market conditions.
VIDYA with CMO Smoothing: An advanced moving average that incorporates Chander Momentum Oscillator for adaptive smoothing.
Hamming Windowed VWMA: A volume-weighted moving average that applies a Hamming window for smoother calculations.
FRAMA: A fractal adaptive moving average that responds dynamically to price movements.
Advanced Statistical Analysis:
Skewness and Kurtosis: Provides insights into the distribution and potential risk of market trends.
Z-Score Calculations: Identifies extreme market conditions and adjusts trading thresholds dynamically.
Probabilistic Monte Carlo Simulation:
Runs thousands of simulations to assess potential price movements based on momentum, volatility, and volume factors.
Integrates the results into a probabilistic signal that informs trading decisions.
Feature Extraction:
Calculates a variety of market metrics, including price change, momentum, volatility, volume change, and ATR.
Normalizes and adapts these features for use in machine learning algorithms, enhancing signal accuracy.
Ensemble Learning:
Combines signals from different technical indicators, such as RSI, MACD, Bollinger Bands, Stochastic Oscillator, and statistical features.
Weights each signal based on cumulative performance and learning feedback to create a robust ensemble signal.
Recursive Memory and Feedback:
Stores and averages past RSI calculations in a memory array to provide historical context and improve future predictions.
Adaptive memory factor adjusts the influence of past data based on current market conditions.
Multi-Factor Dynamic Length Calculation:
Determines the length of moving averages based on volume, volatility, momentum, and rate of change (ROC).
Adapts to various market conditions, ensuring that the indicator is responsive to both high and low volatility environments.
Adaptive Learning Rate:
The learning rate can be adjusted based on market volatility, allowing the system to adapt its speed of learning and sensitivity to changes.
Enhances the system's ability to react to different market regimes.
Monte Carlo Simulation Engine:
Simulates thousands of random outcomes to model potential future price movements.
Weights and aggregates these simulations to produce a final probabilistic signal, providing a comprehensive risk assessment.
RSI with Dynamic Adjustments:
The initial RSI length is adjusted recursively based on calculated errors between true RSI and predicted RSI.
The adaptive RSI calculation ensures that the indicator remains effective across various market phases.
Hybrid Moving Averages:
Short-Term and Long-Term Averages: Combines FRAMA, VIDYA, and Hamming VWMA with specific weights for a unique hybrid moving average.
Weighted Gradient: Applies a color gradient to indicate trend strength and direction, improving visual clarity.
Signal Generation:
Generates buy and sell signals based on the ensemble model and multi-factor analysis.
Uses percentile-based thresholds to determine overbought and oversold conditions, factoring in historical data for context.
Optional settings to enable adaptation to volume and volatility, ensuring the indicator remains effective under different market conditions.
Monte Carlo and Learning Parameters:
Users can customize the number of Monte Carlo simulations, learning rate, memory factor, and reward decay for tailored performance.
Applications:
Scalping and Day Trading:
The fast response of the adaptive RSI and ensemble learning model makes this indicator suitable for short-term trading strategies.
Swing Trading:
The combination of long-term moving averages and probabilistic models provides reliable signals for medium-term trends.
Volatility Analysis:
The ATR, Bollinger Bands, and adaptive moving averages offer insights into market volatility, helping traders adjust their strategies accordingly.
Bollinger Bands Mean Reversion by Kevin Davey Bollinger Bands Mean Reversion Strategy Description
The Bollinger Bands Mean Reversion Strategy is a popular trading approach based on the concept of volatility and market overreaction. The strategy leverages Bollinger Bands, which consist of an upper and lower band plotted around a central moving average, typically using standard deviations to measure volatility. When the price moves beyond these bands, it signals potential overbought or oversold conditions, and the strategy seeks to exploit a reversion back to the mean (the central band).
Strategy Components:
1. Bollinger Bands:
The bands are calculated using a 20-period Simple Moving Average (SMA) and a multiple (usually 2.0) of the standard deviation of the asset’s price over the same period. The upper band represents the SMA plus two standard deviations, while the lower band is the SMA minus two standard deviations. The distance between the bands increases with higher volatility and decreases with lower volatility.
2. Mean Reversion:
Mean reversion theory suggests that, over time, prices tend to move back toward their historical average. In this strategy, a buy signal is triggered when the price falls below the lower Bollinger Band, indicating a potential oversold condition. Conversely, the position is closed when the price rises back above the upper Bollinger Band, signaling an overbought condition.
Entry and Exit Logic:
Buy Condition: The strategy enters a long position when the price closes below the lower Bollinger Band, anticipating a mean reversion to the central band (SMA).
Sell Condition: The long position is exited when the price closes above the upper Bollinger Band, implying that the market is likely overbought and a reversal could occur.
This approach uses mean reversion principles, aiming to capitalize on short-term price extremes and volatility compression, often seen in sideways or non-trending markets. Scientific studies have shown that mean reversion strategies, particularly those based on volatility indicators like Bollinger Bands, can be effective in capturing small but frequent price reversals  .
Scientific Basis for Bollinger Bands:
Bollinger Bands, developed by John Bollinger, are widely regarded in both academic literature and practical trading as an essential tool for volatility analysis and mean reversion strategies. Research has shown that Bollinger Bands effectively identify relative price highs and lows, and can be used to forecast price volatility and detect potential breakouts . Studies in financial markets, such as those by Fernández-Rodríguez et al. (2003), highlight the efficacy of Bollinger Bands in detecting overbought or oversold conditions in various assets .
Who is Kevin Davey?
Kevin Davey is an award-winning algorithmic trader and highly regarded expert in developing and optimizing systematic trading strategies. With over 25 years of experience, Davey gained significant recognition after winning the prestigious World Cup Trading Championships multiple times, where he achieved triple-digit returns with minimal drawdown. His success has made him a key figure in algorithmic trading education, with a focus on disciplined and rule-based trading systems.
ICT Judas Swing | Flux Charts💎 GENERAL OVERVIEW
Introducing our new ICT Judas Swing Indicator! This indicator is built around the ICT's "Judas Swing" strategy. The strategy looks for a liquidity grab around NY 9:30 session and a Fair Value Gap for entry confirmation. For more information about the process, check the "HOW DOES IT WORK" section.
Features of the new ICT Judas Swing :
Implementation of ICT's Judas Swing Strategy
2 Different TP / SL Methods
Customizable Execution Settings
Customizable Backtesting Dashboard
Alerts for Buy, Sell, TP & SL Signals
📌 HOW DOES IT WORK ?
The strategy begins by identifying the New York session from 9:30 to 9:45 and marking recent liquidity zones. These liquidity zones are determined by locating high and low pivot points: buyside liquidity zones are identified using high pivots that haven't been invalidated, while sellside liquidity zones are found using low pivots. A break of either buyside or sellside liquidity must occur during the 9:30-9:45 session, which is interpreted as a liquidity grab by smart money. The strategy assumes that after this liquidity grab, the price will reverse and move in the opposite direction. For entry confirmation, a fair value gap (FVG) in the opposite direction of the liquidity grab is required. A buyside liquidity grab calls for a bearish FVG, while a sellside grab requires a bullish FVG. Based on the type of FVG—bullish for buys and bearish for sells—the indicator will then generate a Buy or Sell signal.
After the Buy or Sell signal, the indicator immediately draws the take-profit (TP) and stop-loss (SL) targets. The indicator has three different TP & SL modes, explained in the "Settings" section of this write-up.
You can set up alerts for entry and TP & SL signals, and also check the current performance of the indicator and adjust the settings accordingly to the current ticker using the backtesting dashboard.
🚩 UNIQUENESS
This indicator is an all-in-one suit for the ICT's Judas Swing concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. Different and customizable algorithm modes will help the trader fine-tune the indicator for the asset they are currently trading. Three different TP / SL modes are available to suit your needs. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers.
⚙️ SETTINGS
1. General Configuration
Swing Length -> The swing length for pivot detection. Higher settings will result in
FVG Detection Sensitivity -> You may select between Low, Normal, High or Extreme FVG detection sensitivity. This will essentially determine the size of the spotted FVGs, with lower sensitivies resulting in spotting bigger FVGs, and higher sensitivies resulting in spotting all sizes of FVGs.
2. TP / SL
TP / SL Method ->
a) Dynamic: The TP / SL zones will be auto-determined by the algorithm based on the Average True Range (ATR) of the current ticker.
b) Fixed : You can adjust the exact TP / SL ratios from the settings below.
Dynamic Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
pseudorenko█ CALCULATE PSEUDO-RENKO VALUE
Calculates and returns the Pseudo-Renko Stabilized value (or close price) based on a given input value, along with the direction of the current Renko brick. This function adapts the traditional Renko brick size dynamically based on the volatility of the input value using a combination of SMA and EMA calculations. The calculated price represents the closing price of the most recent Pseudo-Renko brick, while the direction indicates the trend ( 1 for uptrend, -1 for downtrend).
Parameters:
* `val` :
* Type: ` float `
* Description: The input value upon which the Pseudo-Renko calculations are performed. You can use any price series or custom value as input.
* `sensitivity` :
* Type: ` float `
* Default Value: ` 1.0 `
* Description: Controls the sensitivity of the brick size to the volatility of the `val`. Higher values lead to larger bricks, resulting in a smoother Renko chart. Lower values produce smaller bricks, leading to a more reactive chart.
* Possible Values: Any positive float.
* `length` :
* Type: ` int `
* Default Value: ` 7 `
* Description: The length used for calculating the EMA and SMA in the dynamic brick size calculation. It influences how quickly the brick size adapts to changing volatility of the `val`.
* Possible Values: Any positive integer.
Return Values:
* `lastRenkoClose` :
* Type: ` float `
* Description: The closing price of the last completed Pseudo-Renko brick based on the `val`.
* `renkoDirection` :
* Type: ` int `
* Description: The direction of the current Pseudo-Renko brick based on the `val`:
* ` 1 `: Uptrend
* ` -1 `: Downtrend
* ` 0 `: No change (initially, or no brick change since the previous bar)
Example Usage:
//@version=5
indicator("Pseudo-Renko Stabilized (Val)", overlay=true)
// Get user inputs
sensitivityInput = input.float(0.1, "Sensitivity",0.01,step=0.01)
lengthInput = input.int(5, "Length",2)
// Example usage with the 'close' price as the input value
= pseudo_renko(math.avg(close,open), sensitivityInput, lengthInput)
// Plot the Renko close price
plot(renkoClose, "Renko Close", renkoDirection>0?color.aqua:color.orange,2)
// You can also use other values as input, such as:
// = pseudo_renko(high, sensitivityInput, lengthInput)
// = pseudo_renko(low, sensitivityInput, lengthInput)
This example demonstrates how to use the `pseudo_renko` function within an indicator. It takes user inputs for `sensitivity` and `length`, then calculates the Pseudo-Renko values using the average of the `close` and `open` prices as the `val`. The resulting `renkoClose` price is plotted on the chart, with a color change based on the `renkoDirection`. It also illustrates how you can use other values, like `high` and `low`, as input to the function.
Note: The Pseudo-Renko algorithm is based on adapting the Renko brick size dynamically based on the input `val`. This provides more flexibility compared to the normal, but is experimental. The `sensitivity` and `length` parameters, along with the choice of the `val`, offer further customization to tune the algorithm's behavior to your preference and trading style.
Pseudo-Renko Stabilized (Val)█ CALCULATE PSEUDO-RENKO VALUE
Calculates and returns the Pseudo-Renko Stabilized value (or close price) based on a given input value, along with the direction of the current Renko brick. This function adapts the traditional Renko brick size dynamically based on the volatility of the input value using a combination of SMA and EMA calculations. The calculated price represents the closing price of the most recent Pseudo-Renko brick, while the direction indicates the trend ( 1 for uptrend, -1 for downtrend).
Parameters:
* `val` :
* Type: ` float `
* Description: The input value upon which the Pseudo-Renko calculations are performed. You can use any price series or custom value as input.
* `sensitivity` :
* Type: ` float `
* Default Value: ` 1.0 `
* Description: Controls the sensitivity of the brick size to the volatility of the `val`. Higher values lead to larger bricks, resulting in a smoother Renko chart. Lower values produce smaller bricks, leading to a more reactive chart.
* Possible Values: Any positive float.
* `length` :
* Type: ` int `
* Default Value: ` 7 `
* Description: The length used for calculating the EMA and SMA in the dynamic brick size calculation. It influences how quickly the brick size adapts to changing volatility of the `val`.
* Possible Values: Any positive integer.
Return Values:
* `lastRenkoClose` :
* Type: ` float `
* Description: The closing price of the last completed Pseudo-Renko brick based on the `val`.
* `renkoDirection` :
* Type: ` int `
* Description: The direction of the current Pseudo-Renko brick based on the `val`:
* ` 1 `: Uptrend
* ` -1 `: Downtrend
* ` 0 `: No change (initially, or no brick change since the previous bar)
Example Usage:
//@version=5
indicator("Pseudo-Renko Stabilized (Val)", overlay=true)
// Get user inputs
sensitivityInput = input.float(0.1, "Sensitivity",0.01,step=0.01)
lengthInput = input.int(5, "Length",2)
// Example usage with the 'close' price as the input value
= pseudo_renko(math.avg(close,open), sensitivityInput, lengthInput)
// Plot the Renko close price
plot(renkoClose, "Renko Close", renkoDirection>0?color.aqua:color.orange,2)
// You can also use other values as input, such as:
// = pseudo_renko(high, sensitivityInput, lengthInput)
// = pseudo_renko(low, sensitivityInput, lengthInput)
This example demonstrates how to use the `pseudo_renko` function within an indicator. It takes user inputs for `sensitivity` and `length`, then calculates the Pseudo-Renko values using the average of the `close` and `open` prices as the `val`. The resulting `renkoClose` price is plotted on the chart, with a color change based on the `renkoDirection`. It also illustrates how you can use other values, like `high` and `low`, as input to the function.
Note: The Pseudo-Renko algorithm is based on adapting the Renko brick size dynamically based on the input `val`. This provides more flexibility compared to the normal, but is experimental. The `sensitivity` and `length` parameters, along with the choice of the `val`, offer further customization to tune the algorithm's behavior to your preference and trading style.
ICT Unicorn | Flux Charts💎 GENERAL OVERVIEW
Introducing our new ICT Unicorn Indicator! This indicator is built around the ICT's "Unicorn" strategy. The strategy uses Breaker Blocks and Fair Value Gaps for entry confirmation. For more information about the process, check the "HOW DOES IT WORK" section.
Features of the new ICT Unicorn Indicator :
Implementation of ICT's Unicorn Strategy
Toggleable Retracement Entry Method
3 Different TP / SL Methods
Customizable Execution Settings
Customizable Backtesting Dashboard
Alerts for Buy, Sell, TP & SL Signals
📌 HOW DOES IT WORK ?
The ICT Unicorn entry model merges the concepts of Breaker Blocks and Fair Value Gaps (FVGs), offering a distinct method for identifying trade opportunities. By integrating these two elements, we can have a position entry with stop-loss and take-profit targets on the potential support & resistance zones. This model is particularly reliable for trade entry, as it combines two powerful entry techniques.
An ICT Unicorn Model consists of a FVG which is overlapping with a Breaker Block of the same type. Here is an example :
When a FVG overlaps with a Breaker Block of the same type, the indicator gives a Buy or Sell signal depending on the FVG type (Bullish & Bearish). If the "Require Retracement" option is enabled in the settings, the signals are not given immediately. Instead, the current price of the ticker will need to touch the FVG once more before the signals are given.
After the Buy or Sell signal, the indicator immediately draws the take-profit (TP) and stop-loss (SL) targets. The indicator has three different TP & SL modes, explained in the "Settings" section of this write-up.
You can set up alerts for entry and TP & SL signals, and also check the current performance of the indicator and adjust the settings accordingly to the current ticker using the backtesting dashboard.
🚩 UNIQUENESS
This indicator is an all-in-one suit for the ICT's Unicorn concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. Different and customizable algorithm modes will help the trader fine-tune the indicator for the asset they are currently trading. Three different TP / SL modes are available to suit your needs. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers.
⚙️ SETTINGS
1. General Configuration
FVG Detection Sensitivity -> You may select between Low, Normal, High or Extreme FVG detection sensitivity. This will essentially determine the size of the spotted FVGs, with lower sensitivies resulting in spotting bigger FVGs, and higher sensitivies resulting in spotting all sizes of FVGs.
Swing Length -> Swing length is used when finding order block formations. Smaller values will result in finding smaller order & breaker blocks.
Require Retracement ->
a) Disabled : The entry signal is given immediately once a FVG overlaps with a Breaker Block of the same type.
b) Enabled : The current price of the ticker will need to touch the FVG once more before the entry signal is given.
2. TP / SL
TP / SL Method ->
a) Unicorn : This is the default option. The SL will be set to the lowest low of the last 100 bars with an extra offset in a Buy signal. For Sell signals, the SL will be set to the highest high of the last 100 bars with an extra offset. The TP is then set to a value using the SL value and maintaining a risk-reward ratio.
b) Dynamic: The TP / SL zones will be auto-determined by the algorithm based on the Average True Range (ATR) of the current ticker.
c) Fixed : You can adjust the exact TP / SL ratios from the settings below.
Dynamic Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
ICT 9:30am First FVGThis indicator is designed based on ICT (Inner Circle Trader)'s algorithmic price action theory, specifically targeting the first fair value gap (FVG) that forms immediately after the New York Stock Exchange opens at 9:30am. The FVG represents an imbalance in the price delivery where a significant price action gap occurs, which can play a crucial role in future price movements.
Features:
Identification of First FVG: Automatically identifies and plots the first fair value gap that forms post the 9:30am NY open.
Customizable Visualization: Choose between block or line styles for visual representation, with customizable colors and border styles.
Date Labeling: Optionally displays date labels for each identified gap to track patterns over time.
Imbalance Extension: Options to extend the imbalances to the current bar, helping to visualize their influence on ongoing price action.
Purpose:
The first fair value gap formed after the market opens is an important algorithmic price range in ICT's price action theory. This indicator simplifies the identification of these critical gaps and helps in understanding their impact on future price action.
Uptrick: Trend SMA Oscillator### In-Depth Analysis of the "Uptrick: Trend SMA Oscillator" Indicator
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#### Introduction to the Indicator
The "Uptrick: Trend SMA Oscillator" is an advanced yet user-friendly technical analysis tool designed to help traders across all levels of experience identify and follow market trends with precision. This indicator builds upon the fundamental principles of the Simple Moving Average (SMA), a cornerstone of technical analysis, to deliver a clear, visually intuitive overlay on the price chart. Through its strategic use of color-coding and customizable parameters, the Uptrick: Trend SMA Oscillator provides traders with actionable insights into market dynamics, enhancing their ability to make informed trading decisions.
#### Core Concepts and Methodology
1. **Foundational Principle – Simple Moving Average (SMA):**
- The Simple Moving Average (SMA) is the heart of the Uptrick: Trend SMA Oscillator. The SMA is a widely-used technical indicator that calculates the average price of an asset over a specified number of periods. By smoothing out price data, the SMA helps to reduce the noise from short-term fluctuations, providing a clearer picture of the overall trend.
- In the Uptrick: Trend SMA Oscillator, two SMAs are employed:
- **Primary SMA (oscValue):** This is applied to the closing price of the asset over a user-defined period (default is 14 periods). This SMA tracks the price closely and is sensitive to changes in market direction.
- **Smoothing SMA (oscV):** This second SMA is applied to the primary SMA, further smoothing the data and helping to filter out minor price movements that might otherwise be mistaken for trend reversals. The default period for this smoothing is 50, but it can be adjusted to suit the trader's preference.
2. **Color-Coding for Trend Visualization:**
- One of the most distinctive features of this indicator is its use of color to represent market trends. The indicator’s line changes color based on the relationship between the primary SMA and the smoothing SMA:
- **Bullish (Green):** The line turns green when the primary SMA is equal to or greater than the smoothing SMA, indicating that the market is in an upward trend.
- **Bearish (Red):** Conversely, the line turns red when the primary SMA falls below the smoothing SMA, signaling a downward trend.
- This color-coded system provides traders with an immediate, easy-to-interpret visual cue about the market’s direction, allowing for quick decision-making.
#### Detailed Explanation of Inputs
1. **Bullish Color (Default: Green #00ff00):**
- This input allows traders to customize the color that represents bullish trends on the chart. The default setting is green, a color commonly associated with upward market movement. However, traders can adjust this to any color that suits their visual preferences or matches their overall chart theme.
2. **Bearish Color (Default: Red RGB: 245, 0, 0):**
- The bearish color input determines the color of the line when the market is trending downwards. The default setting is a vivid red, signaling caution or selling opportunities. Like the bullish color, this can be customized to fit the trader’s needs.
3. **Line Thickness (Default: 5):**
- This setting controls the thickness of the line plotted by the indicator. The default thickness of 5 makes the line prominent on the chart, ensuring that the trend is easily visible even in complex or crowded chart setups. Traders can adjust the thickness to make the line thinner or thicker, depending on their visual preferences.
4. **Primary SMA Period (Value 1 - Default: 14):**
- The primary SMA period defines how many periods (e.g., days, hours) are used to calculate the moving average based on the asset’s closing prices. The default period of 14 is a balanced setting that offers a good mix of responsiveness and stability, but traders can adjust this depending on their trading style:
- **Shorter Periods (e.g., 5-10):** These make the indicator more sensitive, capturing trends more quickly but also increasing the likelihood of reacting to short-term price fluctuations or "noise."
- **Longer Periods (e.g., 20-50):** These smooth the data more, providing a more stable trend line that is less prone to whipsaws but may be slower to respond to trend changes.
5. **Smoothing SMA Period (Value 2 - Default: 50):**
- The smoothing SMA period determines how much the primary SMA is smoothed. A longer smoothing period results in a more gradual, stable line that focuses on the broader trend. The default of 50 is designed to smooth out most of the short-term fluctuations while still being responsive enough to detect significant trend shifts.
- **Customization:**
- **Shorter Smoothing Periods (e.g., 20-30):** Make the indicator more responsive, better for fast-moving markets or for traders who want to capture quick trends.
- **Longer Smoothing Periods (e.g., 70-100):** Enhance stability, ideal for long-term traders looking to avoid reacting to minor price movements.
#### Unique Characteristics and Advantages
1. **Simplicity and Clarity:**
- The Uptrick: Trend SMA Oscillator’s design prioritizes simplicity without sacrificing effectiveness. By relying on the widely understood SMA, it avoids the complexity of more esoteric indicators while still providing reliable trend signals. This simplicity makes it accessible to traders of all levels, from novices who are just learning about technical analysis to experienced traders looking for a straightforward, dependable tool.
2. **Visual Feedback Mechanism:**
- The indicator’s use of color to signify market trends is a particularly powerful feature. This visual feedback mechanism allows traders to assess market conditions at a glance. The clarity of the green and red color scheme reduces the mental effort required to interpret the indicator, freeing the trader to focus on strategy execution.
3. **Adaptability Across Markets and Timeframes:**
- One of the strengths of the Uptrick: Trend SMA Oscillator is its versatility. The basic principles of moving averages apply equally well across different asset classes and timeframes. Whether trading stocks, forex, commodities, or cryptocurrencies, traders can use this indicator to gain insights into market trends.
- **Intraday Trading:** For day traders who operate on short timeframes (e.g., 1-minute, 5-minute charts), the oscillator can be adjusted to be more responsive, capturing quick shifts in momentum.
- **Swing Trading:** Swing traders, who typically hold positions for several days to weeks, will find the default settings or slightly adjusted periods ideal for identifying and riding medium-term trends.
- **Long-Term Trading:** Position traders and investors can adjust the indicator to focus on long-term trends by increasing the periods for both the primary and smoothing SMAs, filtering out minor fluctuations and highlighting sustained market movements.
4. **Minimal Lag:**
- One of the challenges with moving averages is lag—the delay between when the price changes and when the indicator reflects this change. The Uptrick: Trend SMA Oscillator addresses this by allowing traders to adjust the periods to find a balance between responsiveness and stability. While all SMAs inherently have some lag, the customizable nature of this indicator helps traders mitigate this effect to align with their specific trading goals.
5. **Customizable and Intuitive:**
- While many technical indicators come with a fixed set of parameters, the Uptrick: Trend SMA Oscillator is fully customizable, allowing traders to tailor it to their trading style, market conditions, and personal preferences. This makes it a highly flexible tool that can be adjusted as markets evolve or as a trader’s strategy changes over time.
#### Practical Applications for Different Trader Profiles
1. **Day Traders:**
- **Use Case:** Day traders can customize the SMA periods to create a faster, more responsive indicator. This allows them to capture short-term trends and make quick decisions. For example, reducing the primary SMA to 5 and the smoothing SMA to 20 can help day traders react promptly to intraday price movements.
- **Strategy Integration:** Day traders might use the Uptrick: Trend SMA Oscillator in conjunction with volume-based indicators to confirm the strength of a trend before entering or exiting trades.
2. **Swing Traders:**
- **Use Case:** Swing traders can use the default settings or slightly adjust them to smooth out minor price fluctuations while still capturing medium-term trends. This approach helps in identifying the optimal points to enter or exit trades based on the broader market direction.
- **Strategy Integration:** Swing traders can combine this indicator with oscillators like the Relative Strength Index (RSI) to confirm overbought or oversold conditions, thereby refining their entry and exit strategies.
3. **Position Traders:**
- **Use Case:** Position traders, who hold trades for extended periods, can extend the SMA periods to focus on long-term trends. By doing so, they minimize the impact of short-term market noise and focus on the underlying trend.
- **Strategy Integration:** Position traders might use the Uptrick: Trend SMA Oscillator in combination with fundamental analysis. The indicator can help confirm the timing of entries and exits based on broader economic or corporate developments.
4. **Algorithmic and Quantitative Traders:**
- **Use Case:** The simplicity and clear logic of the Uptrick: Trend SMA Oscillator make it an excellent candidate for algorithmic trading strategies. Its binary output—bullish or bearish—can be easily coded into automated trading systems.
- **Strategy Integration:** Quant traders might use the indicator as part of a larger trading system that incorporates multiple indicators and rules, optimizing the SMA periods based on historical backtesting to achieve the best results.
5. **Novice Traders:**
- **Use Case:** Beginners can use the Uptrick: Trend SMA Oscillator to learn the basics of trend-following strategies.
The visual simplicity of the color-coded line helps novice traders quickly understand market direction without the need to interpret complex data.
- **Educational Value:** The indicator serves as an excellent starting point for those new to technical analysis, providing a practical example of how moving averages work in a real-world trading environment.
#### Combining the Indicator with Other Tools
1. **Relative Strength Index (RSI):**
- The RSI is a momentum oscillator that measures the speed and change of price movements. When combined with the Uptrick: Trend SMA Oscillator, traders can look for instances where the RSI shows divergence from the price while the oscillator confirms the trend. This can be a powerful signal of an impending reversal or continuation.
2. **Moving Average Convergence Divergence (MACD):**
- The MACD is another popular trend-following momentum indicator. By using it alongside the Uptrick: Trend SMA Oscillator, traders can confirm the strength of a trend and identify potential entry and exit points with greater confidence. For example, a bullish crossover on the MACD that coincides with the Uptrick: Trend SMA Oscillator turning green can be a strong buy signal.
3. **Volume Indicators:**
- Volume is often considered the fuel behind price movements. Using volume indicators like the On-Balance Volume (OBV) or Volume Weighted Average Price (VWAP) in conjunction with the Uptrick: Trend SMA Oscillator can help traders confirm the validity of a trend. A trend identified by the oscillator that is supported by increasing volume is typically more reliable.
4. **Fibonacci Retracement:**
- Fibonacci retracement levels are used to identify potential reversal levels in a trending market. When the Uptrick: Trend SMA Oscillator indicates a trend, traders can use Fibonacci retracement levels to find potential entry points that align with the broader trend direction.
#### Implementation in Different Market Conditions
1. **Trending Markets:**
- The Uptrick: Trend SMA Oscillator excels in trending markets, where it provides clear signals on the direction of the trend. In a strong uptrend, the line will remain green, helping traders stay in the trade for longer periods. In a downtrend, the red line will signal the continuation of bearish conditions, prompting traders to stay short or avoid long positions.
2. **Sideways or Range-Bound Markets:**
- In range-bound markets, where price oscillates within a confined range without a clear trend, the Uptrick: Trend SMA Oscillator may produce more frequent changes in color. While this could indicate potential reversals at the range boundaries, traders should be cautious of false signals. It may be beneficial to pair the oscillator with a volatility indicator to better navigate such conditions.
3. **Volatile Markets:**
- In highly volatile markets, where prices can swing rapidly, the sensitivity of the Uptrick: Trend SMA Oscillator can be adjusted by modifying the SMA periods. A shorter SMA period might capture quick trends, but traders should be aware of the increased risk of whipsaws. Combining the oscillator with a volatility filter or using it in a higher time frame might help mitigate some of this risk.
#### Final Thoughts
The "Uptrick: Trend SMA Oscillator" is a versatile, easy-to-use indicator that stands out for its simplicity, visual clarity, and adaptability. It provides traders with a straightforward method to identify and follow market trends, using the well-established concept of moving averages. The indicator’s customizable nature makes it suitable for a wide range of trading styles, from day trading to long-term investing, and across various asset classes.
By offering immediate visual feedback through color-coded signals, the Uptrick: Trend SMA Oscillator simplifies the decision-making process, allowing traders to focus on execution rather than interpretation. Whether used on its own or as part of a broader technical analysis toolkit, this indicator has the potential to enhance trading strategies and improve overall performance.
Its accessibility and ease of use make it particularly appealing to novice traders, while its adaptability and reliability ensure that it remains a valuable tool for more experienced market participants. As markets continue to evolve, the Uptrick: Trend SMA Oscillator remains a timeless tool, rooted in the fundamental principles of technical analysis, yet flexible enough to meet the demands of modern trading.
Machine Learning Signal FilterIntroducing the "Machine Learning Signal Filter," an innovative trading indicator designed to leverage the power of machine learning to enhance trading strategies. This tool combines advanced data processing capabilities with user-friendly customization options, offering traders a sophisticated yet accessible means to optimize their market analysis and decision-making processes. Importantly, this indicator does not repaint, ensuring that signals remain consistent and reliable after they are generated.
Machine Learning Integration
The "Machine Learning Signal Filter" employs machine learning algorithms to analyze historical price data and identify patterns that may not be immediately apparent through traditional technical analysis. By utilizing techniques such as regression analysis and neural networks, the indicator continuously learns from new data, refining its predictive capabilities over time. This dynamic adaptability allows the indicator to adjust to changing market conditions, potentially improving the accuracy of trading signals.
Key Features and Benefits
Dynamic Signal Generation: The indicator uses machine learning to generate buy and sell signals based on complex data patterns. This approach enables it to adapt to evolving market trends, offering traders timely and relevant insights. Crucially, the indicator does not repaint, providing reliable signals that traders can trust.
Customizable Parameters: Users can fine-tune the indicator to suit their specific trading styles by adjusting settings such as the temporal synchronization and neural pulse rate. This flexibility ensures that the indicator can be tailored to different market environments.
Visual Clarity and Usability: The indicator provides clear visual cues on the chart, including color-coded signals and optional display of signal curves. Users can also customize the table's position and text size, enhancing readability and ease of use.
Comprehensive Performance Metrics: The indicator includes a detailed metrics table that displays key performance indicators such as return rates, trade counts, and win/loss ratios. This feature helps traders assess the effectiveness of their strategies and make data-driven decisions.
How It Works
The core of the "Machine Learning Signal Filter" is its ability to process and learn from large datasets. By applying machine learning models, the indicator identifies potential trading opportunities based on historical data patterns. It uses regression techniques to predict future price movements and neural networks to enhance pattern recognition. As new data is introduced, the indicator refines its algorithms, improving its accuracy and reliability over time.
Use Cases
Trend Following: Ideal for traders seeking to capitalize on market trends, the indicator helps identify the direction and strength of price movements.
Scalping: With its ability to provide quick signals, the indicator is suitable for scalpers aiming for rapid profits in volatile markets.
Risk Management: By offering insights into trade performance, the indicator aids in managing risk and optimizing trade setups.
In summary, the "Machine Learning Signal Filter" is a powerful tool that combines the analytical strength of machine learning with the practical needs of traders. Its ability to adapt and provide actionable insights makes it an invaluable asset for navigating the complexities of financial markets.
The "Machine Learning Signal Filter" is a tool designed to assist traders by providing insights based on historical data and machine learning techniques. It does not guarantee profitable trades and should be used as part of a comprehensive trading strategy. Users are encouraged to conduct their own research and consider their financial situation before making trading decisions. Trading involves significant risk, and it is possible to lose more than the initial investment. Always trade responsibly and be aware of the risks involved.
Uptrick: Adaptive Trend Strength Index (ATSI)### **Adaptive Trend Strength Index (ATSI): Trend Detection Tool**
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### Introduction
The **Adaptive Trend Strength Index (ATSI)** is a state-of-the-art indicator designed to offer traders an unparalleled view into market trends. By combining the principles of adaptive trend analysis with advanced volatility filtering, ATSI provides a powerful and visually intuitive method for identifying and following market trends. Its unique algorithm and customizable features make it an essential tool for traders across all markets—whether you're trading stocks, forex, commodities, or cryptocurrencies.
### The Purpose and Design Philosophy
At its core, the ATSI was built with the understanding that financial markets are dynamic, ever-changing entities influenced by a multitude of factors, including market sentiment, economic data, geopolitical events, and, critically, volatility. Traditional trend indicators often fall short by either over-smoothing price data (thus lagging behind the actual trend) or reacting too quickly to minor price fluctuations, resulting in false signals.
**ATSI solves this dilemma by adapting to market conditions in real-time.** It effectively filters out market noise while being sensitive enough to detect meaningful shifts in trend direction. The result is a trend line that is both responsive and smooth, providing traders with a clear, actionable view of the market's current trajectory.
### Key Features and Functionality
#### 1. **Adaptive Trend Calculation**
The heart of ATSI is its adaptive trend algorithm, which adjusts based on market conditions. It leverages a combination of price action analysis and volatility filtering to determine the strength and direction of the trend. Here’s how it works:
- **Volatility Sensitivity:** ATSI incorporates the Average True Range (ATR) to measure market volatility. This volatility measure is then adjusted by a user-defined sensitivity factor. This ensures that the indicator responds dynamically to different market environments—be it high-volatility breakouts or low-volatility consolidations.
- **Adaptive Smoothing:** The trend calculation is further enhanced by an exponential moving average (EMA) applied not just to the raw price data, but also to the resulting trend line itself. This dual-layer smoothing process helps to eliminate noise, resulting in a cleaner and more reliable trend line.
- **Real-Time Adaptation:** Unlike rigid indicators that require constant tweaking to stay relevant in changing market conditions, ATSI adapts in real-time. This adaptability makes it particularly valuable in fast-moving markets where conditions can change rapidly.
#### 2. **Visual Clarity**
In trading, visual clarity can make the difference between spotting a lucrative trend and missing out. ATSI excels in this regard by offering a clear, color-coded trend line that provides instant feedback on market conditions:
- **Thicker and Smoother Line:** ATSI’s trend line is designed to be visually prominent. By default, it is thicker than most standard indicators, making it easy to spot even in dense charts. Additionally, the smoothing applied to the line ensures that it flows smoothly, avoiding the jagged, noisy appearance that can plague other indicators.
- **Color-Coded Trends:** The trend line changes color based on the direction and strength of the trend:
- **Green Line**: Indicates a bullish trend, suggesting upward momentum in the market.
- **Red Line**: Indicates a bearish trend, signaling downward momentum.
- **Gold Line**: Represents a neutral or weak trend, where the market is consolidating or where there is no clear direction.
This color-coding is not just for aesthetics—it’s a critical feature that allows traders to quickly assess market conditions at a glance.
#### 3. **Customizable Parameters**
ATSI is built with the understanding that every trader’s strategy is unique. Whether you’re a day trader looking for short-term trends or a swing trader interested in catching longer moves, ATSI can be tailored to fit your needs:
- **Trend Length:** The length parameter controls how much historical data is considered in the trend calculation. A shorter length will make the indicator more sensitive to recent price changes, while a longer length will smooth out short-term fluctuations, focusing on the broader trend.
- **Smoothing Factor:** This parameter controls the level of smoothing applied to the trend line. A higher smoothing factor will result in a smoother, more stable trend line, while a lower factor will make the line more responsive to quick changes in price.
- **Volatility Sensitivity:** By adjusting the volatility sensitivity, you can control how reactive the indicator is to market volatility. A higher sensitivity makes the indicator more likely to detect trends in volatile markets, while a lower sensitivity helps to filter out noise in calmer markets.
- **Line Width:** ATSI allows you to adjust the thickness of the trend line, ensuring that it stands out on your chart. This is particularly useful when trading on charts with a lot of overlays or when you need a clear, bold line to guide your trading decisions.
- **Color Customization:** The colors for bullish, bearish, and neutral trends can be fully customized to match your personal preferences or to integrate seamlessly with your existing chart setup.
### Practical Applications
ATSI is a versatile indicator that can be applied to a wide range of trading strategies. Here’s how it can enhance your trading:
#### 1. **Trend Following**
For traders who thrive on catching and riding trends, ATSI is a game-changer. Its adaptive nature ensures that you stay in the trend for as long as possible without being shaken out by minor fluctuations. The clear color-coded line makes it easy to identify when a trend starts and ends, providing clear entry and exit signals.
#### 2. **Risk Management**
One of the biggest challenges in trading is managing risk, particularly in volatile markets. ATSI’s volatility sensitivity feature helps traders adjust their strategies based on current market conditions. For example, in a high-volatility environment, the indicator will become more sensitive, allowing you to tighten your stop losses or take profits earlier. Conversely, in a low-volatility market, the indicator will smooth out minor fluctuations, reducing the risk of being stopped out prematurely.
#### 3. **Trend Reversals and Consolidations**
ATSI is also highly effective in identifying trend reversals and periods of consolidation. The neutral (gold) line indicates periods where the market is undecided, which can often precede significant moves. Recognizing these periods can help you avoid getting caught in choppy markets and position yourself for the next big move.
#### 4. **Market Timing**
Timing the market is often seen as the holy grail of trading. While no indicator can predict the future with 100% accuracy, ATSI’s real-time adaptation gives you a significant edge. By responding to changes in market conditions as they happen, ATSI helps you make timely decisions, whether you’re entering a trade, exiting a position, or adjusting your risk parameters.
### Comparative Advantage
What sets ATSI apart from other trend indicators is its combination of adaptability, visual clarity, and ease of use:
- **Adaptability:** Most trend indicators are static—they apply the same calculations regardless of market conditions. ATSI, however, adapts to the market in real-time, ensuring that it remains relevant and reliable across different market environments.
- **Visual Clarity:** The thicker, smoother, color-coded line is not just aesthetically pleasing—it’s a functional design choice that helps you quickly interpret market conditions. Whether you’re glancing at your chart or conducting an in-depth analysis, the ATSI line stands out, providing immediate insight.
- **Ease of Use:** Despite its advanced features, ATSI is incredibly easy to use. The default settings are optimized for general use, but the indicator offers a high degree of customization for those who want to tailor it to their specific trading strategy.
### Conclusion
The **Adaptive Trend Strength Index (ATSI)** is more than just another trend indicator—it’s a comprehensive tool designed to give traders an edge in today’s fast-paced, volatile markets. By combining adaptive trend analysis with advanced volatility filtering, ATSI offers a unique blend of responsiveness and reliability. Its clear, color-coded visual representation of trends makes it easy to use, even for traders who are new to technical analysis, while its customizable parameters provide the flexibility that experienced traders demand.
Whether you’re looking to ride the next big trend, manage your risk more effectively, or simply get a clearer picture of the market’s current direction, ATSI is an invaluable addition to your trading toolkit. With its cutting-edge design and powerful functionality, ATSI is poised to become the go-to indicator for traders seeking to enhance their market analysis and improve their trading outcomes.
Correlation Clusters [LuxAlgo]The Correlation Clusters is a machine learning tool that allows traders to group sets of tickers with a similar correlation coefficient to a user-set reference ticker.
The tool calculates the correlation coefficients between 10 user-set tickers and a user-set reference ticker, with the possibility of forming up to 10 clusters.
🔶 USAGE
Applying clustering methods to correlation analysis allows traders to quickly identify which set of tickers are correlated with a reference ticker, rather than having to look at them one by one or using a more tedious approach such as correlation matrices.
Tickers belonging to a cluster may also be more likely to have a higher mutual correlation. The image above shows the detailed parts of the Correlation Clusters tool.
The correlation coefficient between two assets allows traders to see how these assets behave in relation to each other. It can take values between +1.0 and -1.0 with the following meaning
Value near +1.0: Both assets behave in a similar way, moving up or down at the same time
Value close to 0.0: No correlation, both assets behave independently
Value near -1.0: Both assets have opposite behavior when one moves up the other moves down, and vice versa
There is a wide range of trading strategies that make use of correlation coefficients between assets, some examples are:
Pair Trading: Traders may wish to take advantage of divergences in the price movements of highly positively correlated assets; even highly positively correlated assets do not always move in the same direction; when assets with a correlation close to +1.0 diverge in their behavior, traders may see this as an opportunity to buy one and sell the other in the expectation that the assets will return to the likely same price behavior.
Sector rotation: Traders may want to favor some sectors that are expected to perform in the next cycle, tracking the correlation between different sectors and between the sector and the overall market.
Diversification: Traders can aim to have a diversified portfolio of uncorrelated assets. From a risk management perspective, it is useful to know the correlation between the assets in your portfolio, if you hold equal positions in positively correlated assets, your risk is tilted in the same direction, so if the assets move against you, your risk is doubled. You can avoid this increased risk by choosing uncorrelated assets so that they move independently.
Hedging: Traders may want to hedge positions with correlated assets, from a hedging perspective, if you are long an asset, you can hedge going long a negatively correlated asset or going short a positively correlated asset.
Grouping different assets with similar behavior can be very helpful to traders to avoid over-exposure to those assets, traders may have multiple long positions on different assets as a way of minimizing overall risk when in reality if those assets are part of the same cluster traders are maximizing their risk by taking positions on assets with the same behavior.
As a rule of thumb, a trader can minimize risk via diversification by taking positions on assets with no correlations, the proposed tool can effectively show a set of uncorrelated candidates from the reference ticker if one or more clusters centroids are located near 0.
🔶 DETAILS
K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group.
The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized.
It's an unsupervised method because it starts without labels and then forms and labels groups itself.
🔹 Execution Window
In the image above we can see how different execution windows provide different correlation coefficients, informing traders of the different behavior of the same assets over different time periods.
Users can filter the data used to calculate correlations by number of bars, by time, or not at all, using all available data. For example, if the chart timeframe is 15m, traders may want to know how different assets behave over the last 7 days (one week), or for an hourly chart set an execution window of one month, or one year for a daily chart. The default setting is to use data from the last 50 bars.
🔹 Clusters
On this graph, we can see different clusters for the same data. The clusters are identified by different colors and the dotted lines show the centroids of each cluster.
Traders can select up to 10 clusters, however, do note that selecting 10 clusters can lead to only 4 or 5 returned clusters, this is caused by the machine learning algorithm not detecting any more data points deviating from already detected clusters.
Traders can fine-tune the algorithm by changing the 'Cluster Threshold' and 'Max Iterations' settings, but if you are not familiar with them we advise you not to change these settings, the defaults can work fine for the application of this tool.
🔹 Correlations
Different correlations mean different behaviors respecting the same asset, as we can see in the chart above.
All correlations are found against the same asset, traders can use the chart ticker or manually set one of their choices from the settings panel. Then they can select the 10 tickers to be used to find the correlation coefficients, which can be useful to analyze how different types of assets behave against the same asset.
🔶 SETTINGS
Execution Window Mode: Choose how the tool collects data, filter data by number of bars, time, or no filtering at all, using all available data.
Execute on Last X Bars: Number of bars for data collection when the 'Bars' execution window mode is active.
Execute on Last: Time window for data collection when the `Time` execution window mode is active. These are full periods, so `Day` means the last 24 hours, `Week` means the last 7 days, and so on.
🔹 Clusters
Number of Clusters: Number of clusters to detect up to 10. Only clusters with data points are displayed.
Cluster Threshold: Number used to compare a new centroid within the same cluster. The lower the number, the more accurate the centroid will be.
Max Iterations: Maximum number of calculations to detect a cluster. A high value may lead to a timeout runtime error (loop takes too long).
🔹 Ticker of Reference
Use Chart Ticker as Reference: Enable/disable the use of the current chart ticker to get the correlation against all other tickers selected by the user.
Custom Ticker: Custom ticker to get the correlation against all the other tickers selected by the user.
🔹 Correlation Tickers
Select the 10 tickers for which you wish to obtain the correlation against the reference ticker.
🔹 Style
Text Size: Select the size of the text to be displayed.
Display Size: Select the size of the correlation chart to be displayed, up to 500 bars.
Box Height: Select the height of the boxes to be displayed. A high height will cause overlapping if the boxes are close together.
Clusters Colors: Choose a custom colour for each cluster.
Custom Supertrend Multi-Timeframe Indicator [Pineify]Supertrend Multi-Timeframe Indicator
Introduction
The Supertrend Multi-Timeframe Indicator is an advanced trading tool designed to help traders identify trend directions and potential buy/sell signals by combining Supertrend indicators from multiple timeframes. This script is original in its approach to integrating Supertrend calculations across different timeframes, providing a more comprehensive view of market trends.
Concepts and Calculations
The indicator utilizes the Supertrend algorithm, which is based on the Average True Range (ATR). The Supertrend is a popular tool for trend-following strategies, and this script enhances its capabilities by incorporating data from a larger timeframe.
Supertrend Factor: Determines the sensitivity of the Supertrend line.
ATR Length: Defines the period for calculating the Average True Range.
Larger Supertrend Factor and ATR Length: Applied to the larger timeframe for a broader trend perspective.
Larger Timeframe: The higher timeframe from which the secondary Supertrend data is sourced.
How It Works
The script calculates the Supertrend for the current timeframe using the specified factor and ATR length.
Simultaneously, it requests Supertrend data from a larger timeframe.
Buy and sell signals are generated based on crossovers and crossunders of the Supertrend lines from both timeframes.
Visual cues (up and down arrows) are plotted on the chart to indicate buy and sell signals.
Background colors change to reflect the trend direction: green for an uptrend and red for a downtrend.
Usage
Add the indicator to your TradingView chart.
Customize the Supertrend factors, ATR lengths, and larger timeframe according to your trading strategy.
Enable or disable buy and sell alerts as needed.
Monitor the chart for visual signals and background color changes to make informed trading decisions.
Note: The indicator is best used in conjunction with other technical analysis tools and should not be relied upon as the sole basis for trading decisions.
Conclusion
The Supertrend Multi-Timeframe Indicator offers a unique and powerful way to analyze market trends by leveraging the strengths of the Supertrend algorithm across multiple timeframes. Its customizable settings and clear visual signals make it a valuable addition to any trader's toolkit.
HTF TriangleHTF Triangle by ZeroHeroTrading aims at detecting ascending and descending triangles using higher time frame data, without repainting nor misalignment issues.
It addresses user requests for combining Ascending Triangle and Descending Triangle into one indicator.
Ascending triangles are defined by an horizontal upper trend line and a rising lower trend line. It is a chart pattern used in technical analysis to predict the continuation of an uptrend.
Descending triangles are defined by a falling upper trend line and an horizontal lower trend line. It is a chart pattern used in technical analysis to predict the continuation of a downtrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected ascending and descending triangles on the chart.
It supports alerting when a detection occurs.
It allows for selecting ascending and/or descending triangle detection.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a high/low factor detection criteria to apply on higher time frame bars high/low as a proportion of the distance between the reference bar high/low and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Ascending checkbox: Turns on/off ascending triangle detection. Default is on.
Descending checkbox: Turns on/off descending triangle detection. Default is on.
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe. Default is 5 minutes.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria. Default is 3. Minimum is 1.
High/Low Factor checkbox: Turns on/off high/low factor detection criteria. Default is on.
High/Low Factor field: Sets high/low factor to apply on higher time frame bars high/low as a proportion of the distance between the reference bar high/low and open/close. Default is 0. Minimum is 0. Maximum is 1.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars. Default is on.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
Ascending Triangle
Low must be higher than previous bar.
Open/close max value must be lower than (or equal to) reference bar high.
When high/low factor criteria is turned on, high must be higher than (or equal to) reference bar open/close max value plus high/low factor proportion of the distance between reference bar high and open/close max value.
Descending Triangle
High must be lower than previous bar.
Open/close min value must be higher than (or equal to) reference bar low.
When high/low factor criteria is turned on, low must be lower than (or equal to) reference bar open/close min value minus high/low factor proportion of the distance between reference bar low and open/close min value.
HTF Descending TriangleHTF Descending Triangle aims at detecting descending triangles using higher time frame data, without repainting nor misalignment issues.
Descending triangles are defined by a falling upper trend line and an horizontal lower trend line. It is a chart pattern used in technical analysis to predict the continuation of a downtrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected descending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a low factor detection criteria to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
Low Factor checkbox: Turns on/off low factor detection criteria.
Low Factor field: Sets low factor to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
High must be lower than previous bar.
Open/close min value must be higher than reference bar low.
When low factor criteria is turned on, low must be lower than reference bar open/close min value minus low factor proportion of the distance between reference bar low and open/close min value.
HTF Ascending TriangleHTF Ascending Triangle aims at detecting ascending triangles using higher time frame data, without repainting nor misalignment issues.
Ascending triangles are defined by an horizontal upper trend line and a rising lower trend line. It is a chart pattern used in technical analysis to predict the continuation of an uptrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected ascending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a high factor detection criteria to apply on higher time frame bars high as a proportion of the distance between the reference bar high and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
High Factor checkbox: Turns on/off high factor detection criteria.
High Factor field: Sets high factor to apply on higher time frame bars high as a proportion of the distance between the reference bar high and close/open.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
Low must be higher than previous bar.
Open/close max value must be lower than reference bar high.
When high factor criteria is turned on, high must be higher than reference bar open/close max value plus high factor proportion of the distance between reference bar high and open/close max value.
TrendLine Toolkit w/ Breaks (Real-Time)The TrendLine Toolkit script introduces an innovating capability by extending the conventional use of trendlines beyond price action to include oscillators and other technical indicators. This tool allows traders to automatically detect and display trendlines on any TradingView built-in oscillator or community-built script, offering a versatile approach to trend analysis. With breakout detection and real-time alerts, this script enhances the way traders interpret trends in various indicators.
🔲 Methodology
Trendlines are a fundamental tool in technical analysis used to identify and visualize the direction and strength of a price trend. They are drawn by connecting two or more significant points on a price chart, typically the highs or lows of consecutive price movements (pivots).
Drawing Trendlines:
Uptrend Line - Connects a series of higher lows. It signals an upward price trend.
Downtrend Line - Connects a series of lower highs. It indicates a downward price trend.
Support and Resistance:
Support Line - A trendline drawn under rising prices, indicating a level where buying interest is historically strong.
Resistance Line - A trendline drawn above falling prices, showing a level where selling interest historically prevails.
Identification of Trends:
Uptrend - Prices making higher highs and higher lows.
Downtrend - Prices making lower highs and lower lows.
Sideways (or Range-bound) - Prices moving within a horizontal range.
A trendline helps confirm the existence and direction of a trend, providing guidance in aligning with the prevailing market sentiment. Additionally, they are usually paired with breakout analysis, a breakout occurs when the price breaches a trendline. This signals a potential change in trend direction or an acceleration of the existing trend.
The script adapts this methodology to oscillators and other indicators. Instead of relying on price pivots, which can only be detected in retrospect, the script utilizes a trailing stop on the oscillator to identify potential swings in real-time, you may find more info about it here (SuperTrend toolkit) . We detect swings or pivots simply by testing for crosses between the indicator and its trailing stop.
type oscillator
float o = Oscillator Value
float s = Trailing Stop Value
oscillator osc = oscillator.new()
bool l = ta.crossunder(osc.o, osc.s) => Utilized as a formed high
bool h = ta.crossover (osc.o, osc.s) => Utilized as a formed low
This approach enables the algorithm to detect trendlines between consecutive pivot highs or lows on the oscillator itself, providing a dynamic and immediate representation of trend dynamics.
🔲 Breakout Detection
The script goes beyond trendline creation by incorporating breakout detection directly within the oscillator. After identifying a trendline, the algorithm continuously monitors the oscillator for potential breakouts, signaling shifts in market sentiment.
🔲 Setup Guide
A simple example on one of my public scripts, Z-Score Heikin-Ashi Transformed
🔲 Settings
Source - Choose an oscillator source of which to base the Toolkit on.
Zeroing - The Mid-Line value of the oscillator, for example RSI & MFI use 50.
Sensitivity - Calibrates the Sensitivity of which TrendLines are detected, higher values result in more detections.
🔲 Alerts
Bearish TrendLine
Bullish TrendLine
Bearish Breakout
Bullish Breakout
As well as the option to trigger 'any alert' call.
By integrating trendline analysis into oscillators, this Toolkit enhances the capabilities of technical analysis, bringing a dynamic and comprehensive approach to identifying trends, support/resistance levels, and breakout signals across various indicators.
ZigZag Library [TradingFinder]🔵 Introduction
The "Zig Zag" indicator is an analytical tool that emerges from pricing changes. Essentially, it connects consecutive high and low points in an oscillatory manner. This method helps decipher price changes and can also be useful in identifying traditional patterns.
By sifting through partial price changes, "Zig Zag" can effectively pinpoint price fluctuations within defined time intervals.
🔵 Key Features
1. Drawing the Zig Zag based on Pivot points :
The algorithm is based on pivots that operate consecutively and alternately (switch between high and low swing). In this way, zigzag lines are connected from a swing high to a swing low and from a swing low to a swing high.
Also, with a very low probability, it is possible to have both low pivots and high pivots in one candle. In these cases, the algorithm tries to make the best decision to make the most suitable choice.
You can control what period these decisions are based on through the "PiPe" parameter.
2.Naming and labeling each pivot based on its position as "Higher High" (HH), "Lower Low" (LL), "Higher Low" (HL), and "Lower High" (LH).
Additionally, classic patterns such as HH, LH, LL, and HL can be recognized. All traders analyzing financial markets using classic patterns and Elliot Waves can benefit from the "zigzag" indicator to facilitate their analysis.
" HH ": When the price is higher than the previous peak (Higher High).
" HL ": When the price is higher than the previous low (Higher Low).
" LH ": When the price is lower than the previous peak (Lower High).
" LL ": When the price is lower than the previous low (Lower Low).
🔵 How to Use
First, you can add the library to your code as shown in the example below.
import TFlab/ZigZagLibrary_TradingFinder/1 as ZZ
Function "ZigZag" Parameters :
🟣 Logical Parameters
1. HIGH : You should place the "high" value here. High is a float variable.
2. LOW : You should place the "low" value here. Low is a float variable.
3. BAR_INDEX : You should place the "bar_index" value here. Bar_index is an integer variable.
4. PiPe : The desired pivot period for plotting Zig Zag is placed in this parameter. For example, if you intend to draw a Zig Zag with a Swing Period of 5, you should input 5.
PiPe is an integer variable.
Important :
Apart from the "PiPe" indicator, which is part of the customization capabilities of this indicator, you can create a multi-time frame mode for the indicator using 3 parameters "High", "Low" and "BAR_INDEX". In this way, instead of the data of the current time frame, use the data of other time frames.
Note that it is better to use the current time frame data, because using the multi-time frame mode is associated with challenges that may cause bugs in your code.
🟣 Setting Parameters
5. SHOW_LINE : It's a boolean variable. When true, the Zig Zag line is displayed, and when false, the Zig Zag line display is disabled.
6. STYLE_LINE : In this variable, you can determine the style of the Zig Zag line. You can input one of the 3 options: line.style_solid, line.style_dotted, line.style_dashed. STYLE_LINE is a constant string variable.
7. COLOR_LINE : This variable takes the input of the line color.
8. WIDTH_LINE : The input for this variable is a number from 1 to 3, which is used to adjust the thickness of the line that draws the Zig Zag. WIDTH_LINE is an integer variable.
9. SHOW_LABEL : It's a boolean variable. When true, labels are displayed, and when false, label display is disabled.
10. COLOR_LABEL : The color of the labels is set in this variable.
11. SIZE_LABEL : The size of the labels is set in this variable. You should input one of the following options: size.auto, size.tiny, size.small, size.normal, size.large, size.huge.
12. Show_Support : It's a boolean variable that, when true, plots the last support line, and when false, disables its plotting.
13. Show_Resistance : It's a boolean variable that, when true, plots the last resistance line, and when false, disables its plotting.
Suggestion :
You can use the following code snippet to import Zig Zag into your code for time efficiency.
//import Library
import TFlab/ZigZagLibrary_TradingFinder/1 as ZZ
// Input and Setting
// Zig Zag Line
ShZ = input.bool(true , 'Show Zig Zag Line', group = 'Zig Zag') //Show Zig Zag
PPZ = input.int(5 ,'Pivot Period Zig Zag Line' , group = 'Zig Zag') //Pivot Period Zig Zag
ZLS = input.string(line.style_dashed , 'Zig Zag Line Style' , options = , group = 'Zig Zag' )
//Zig Zag Line Style
ZLC = input.color(color.rgb(0, 0, 0) , 'Zig Zag Line Color' , group = 'Zig Zag') //Zig Zag Line Color
ZLW = input.int(1 , 'Zig Zag Line Width' , group = 'Zig Zag')//Zig Zag Line Width
// Label
ShL = input.bool(true , 'Label', group = 'Label') //Show Label
LC = input.color(color.rgb(0, 0, 0) , 'Label Color' , group = 'Label')//Label Color
LS = input.string(size.tiny , 'Label size' , options = , group = 'Label' )//Label size
Show_Support= input.bool(false, 'Show Last Support',
tooltip = 'Last Support' , group = 'Support and Resistance')
Show_Resistance = input.bool(false, 'Show Last Resistance',
tooltip = 'Last Resistance' , group = 'Support and Resistance')
//Call Function
ZZ.ZigZag(high ,low ,bar_index ,PPZ , ShZ ,ZLS , ZLC, ZLW ,ShL , LC , LS , Show_Support , Show_Resistance )
