IPDA Standard Deviations [DexterLab x TFO x toodegrees]> Introduction and Acknowledgements
The IPDA Standard Deviations tool encompasses the Time and price relationship as studied by @TraderDext3r .
I am not the creator of this Theory, and I do not hold the answers to all the questions you may have; I suggest you to study it from Dexter's tweets, videos, and material.
This tool was born from a collaboration between @TraderDext3r, @tradeforopp and I, with the objective of bringing a comprehensive IPDA Standard Deviations tool to Tradingview.
> Tool Description
This is purely a graphical aid for traders to be able to quickly determine Fractal IPDA Time Windows, and trace the potential Standard Deviations of the moves at their respective high and low extremes.
The disruptive value of this tool is that it allows traders to save Time by automatically adapting the Time Windows based on the current chart's Timeframe, as well as providing customizations to filter and focus on the appropriate Standard Deviations.
> IPDA Standard Deviations by TraderDext3r
The underlying idea is based on the Interbank Price Delivery Algorithm's lookback windows on the daily chart as taught by the Inner Circle Trader:
IPDA looks at the past three months of price action to determine how to deliver price in the future.
Additionally, the ICT concept of projecting specific manipulation moves prior to large displacement upwards/downwards is used to navigate and interpret the priorly mentioned displacement move. We pay attention to specific Standard Deviations based on the current environment and overall narrative.
Dexter being one of the most prominent Inner Circle Trader students, harnessed the fractal nature of price to derive fractal IPDA Lookback Time Windows for lower Timeframes, and studied the behaviour of price at specific Deviations.
For Example:
The -1 to -2 area can initiate an algorithmic retracement before continuation.
The -2 to -2.5 area can initiate an algorithmic retracement before continuation, or a Smart Money Reversal.
The -4 area should be seen as the ultimate objective, or the level at which the displacement will slow down.
Given that these ideas stem from ICT's concepts themselves, they are to be used hand in hand with all other ICT Concepts (PD Array Matrix, PO3, Institutional Price Levels, ...).
> Fractal IPDA Time Windows
The IPDA Lookbacks Types identified by Dexter are as follows:
Monthly – 1D Chart: one widow per Month, highlighting the past three Months.
Weekly – 4H to 8H Chart: one window per Week, highlighting the past three Weeks.
Daily – 15m to 1H Chart: one window per Day, highlighting the past three Days.
Intraday – 1m to 5m Chart: one window per 4 Hours highlighting the past 12 Hours.
Inside these three respective Time Windows, the extreme High and Low will be identified, as well as the prior opposing short term market structure point. These represent the anchors for the Standard Deviation Projections.
> Tool Settings
The User is able to plot any type of Standard Deviation they want by inputting them in the settings, in their own line of the text box. They will always be plotted from the Time Windows extremes.
As previously mentioned, the User is also able to define their own Timeframe intervals for the respective IPDA Lookback Types. The specific Timeframes on which the different Lookback Types are plotted are edge-inclusive. In case of an overlap, the higher Timeframe Lookback will be prioritized.
Finally the User is able to filter and remove Standard Deviations in two ways:
"Remove Once Invalidated" will automatically delete a Deviation once its outer anchor extreme is traded through.
Manual Toggles will allow to remove the Upward or Downward Deviation of each Time Window at the discretion of the User.
Major shoutout to Dexter and TFO for their Time, it was a pleasure to collaborate and create this tool with them.
GLGT!
ค้นหาในสคริปต์สำหรับ "如何用wind搜索股票的发行价和份数"
Filtered, N-Order Power-of-Cosine, Sinc FIR Filter [Loxx]Filtered, N-Order Power-of-Cosine, Sinc FIR Filter is a Discrete-Time, FIR Digital Filter that uses Power-of-Cosine Family of FIR filters. This is an N-order algorithm that allows up to 50 values for alpha, orders, of depth. This one differs from previous Power-of-Cosine filters I've published in that it this uses Windowed-Sinc filtering. I've also included a Dual Element Lag Reducer using Kalman velocity, a standard deviation filter, and a clutter filter. You can read about each of these below.
Impulse Response
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What is a Standard Deviation Filter?
If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Whats a Windowed-Sinc Filter?
Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the step response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute.
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
For our purposes here, we are used a normalized Sinc function
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Related indicators
Variety, Low-Pass, FIR Filter Impulse Response Explorer
STD-Filtered, Variety FIR Digital Filters w/ ATR Bands
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter
STD/C-Filtered, Truncated Taylor Family FIR Filter
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
BTC/USD Breakout Hours – IST (Hyderabad)This indicator highlights the most volatile BTC/USD trading hours based on Hyderabad (IST) time.
It marks three key breakout windows:
London–US Overlap (17:30–20:30 IST) – Highest liquidity & volatility
US Market Open Momentum (19:00–23:30 IST) – Strong trend moves
Early London Session (12:30–15:30 IST) – Pre-US setup moves
The script automatically converts chart time to IST, shades each breakout window, and includes optional alerts for:
Window start
15 minutes before start
Ideal for traders who want to align entries with high-probability market moves while avoiding low-volume hours.
Closed Market / Back-Test Filter x 'Bull_Trap_9'Hello TradingView Traders!
This is a very valuable tool that I believe all traders will find useful.
This indicator / filter is '1 of 2'. I prefer it as a filter because it is not meant for live trade analysis. It is designed to make a trader aware of their individual trade sessions and to help aid in static chart candlestick back-testing.
Also, look for my indicator / filter, '2 of 2': 'Red Report Filter'
There are two functions to this filter.
Primary use: It allows a trader to set a session window: Open / Close.
During a trade session, like YM, I only trade 9:30 - 15:00. Without the filter, many times I have traded past my cutoff because I was focused on the chart and not the time.
With this filter on as close nears with an open trade and the filter starts to apply, I know I am at session close with no more trades upon exit. Otherwise, I know the session is done with no further trades.
It is also nice to have the filter on during the session open as a demarcation boundary.
Secondary use: It is used as a chart back-test tool.
When applied to a traders back-test chart, the trader can control their trade session envelopes for easier and more precise evaluation. The filter will allow only the candles per session that the trader wants to focus on and will filter all other non-session candles.
I can easily compare a whole week of 30m session data, concentrating solely on the filtered trade windows.
Please Note: The filter will be active as far back as the historic data prints.
Thanks for viewing!
Hurst Exponent Oscillator [PhenLabs]📊 Hurst Exponent Oscillator -
Version: PineScript™ v5
📌 Description
The Hurst Exponent Oscillator (HEO) by PhenLabs is a powerful tool developed for traders who want to distinguish between trending, mean-reverting, and random market behaviors with clarity and precision. By estimating the Hurst Exponent—a statistical measure of long-term memory in financial time series—this indicator helps users make sense of underlying market dynamics that are often not visible through traditional moving averages or oscillators.
Traders can quickly know if the market is likely to continue its current direction (trending), revert to the mean, or behave randomly, allowing for more strategic timing of entries and exits. With customizable smoothing and clear visual cues, the HEO enhances decision-making in a wide range of trading environments.
🚀 Points of Innovation
Integrates advanced Hurst Exponent calculation via Rescaled Range (R/S) analysis, providing unique market character insights.
Offers real-time visual cues for trending, mean-reverting, or random price action zones.
User-controllable EMA smoothing reduces noise for clearer interpretation.
Dynamic coloring and fill for immediate visual categorization of market regime.
Configurable visual thresholds for critical Hurst levels (e.g., 0.4, 0.5, 0.6).
Fully customizable appearance settings to fit different charting preferences.
🔧 Core Components
Log Returns Calculation: Computes log returns of the selected price source to feed into the Hurst calculation, ensuring robust and scale-independent analysis.
Rescaled Range (R/S) Analysis: Assesses the dispersion and cumulative deviation over a rolling window, forming the core statistical basis for the Hurst exponent estimate.
Smoothing Engine: Applies Exponential Moving Average (EMA) smoothing to the raw Hurst value for enhanced clarity.
Dynamic Rolling Windows: Utilizes arrays to maintain efficient, real-time calculations over user-defined lengths.
Adaptive Color Logic: Assigns different highlight and fill colors based on the current Hurst value zone.
🔥 Key Features
Visually differentiates between trending, mean-reverting, and random market modes.
User-adjustable lookback and smoothing periods for tailored sensitivity.
Distinct fill and line styles for each regime to avoid ambiguity.
On-chart reference lines for strong trending and mean-reverting thresholds.
Works with any price series (close, open, HL2, etc.) for versatile application.
🎨 Visualization
Hurst Exponent Curve: Primary plotted line (smoothed if EMA is used) reflects the ongoing estimate of the Hurst exponent.
Colored Zone Filling: The area between the Hurst line and the 0.5 reference line is filled, with color and opacity dynamically indicating the current market regime.
Reference Lines: Dash/dot lines mark standard Hurst thresholds (0.4, 0.5, 0.6) to contextualize the current regime.
All visual elements can be customized for thickness, color intensity, and opacity for user preference.
📖 Usage Guidelines
Data Settings
Hurst Calculation Length
Default: 100
Range: 10-300
Description: Number of bars used in Hurst calculation; higher values mean longer-term analysis, lower values for quicker reaction.
Data Source
Default: close
Description: Select which data series to analyze (e.g., Close, Open, HL2).
Smoothing Length (EMA)
Default: 5
Range: 1-50
Description: Length for smoothing the Hurst value; higher settings yield smoother but less responsive results.
Style Settings
Trending Color (Hurst > 0.5)
Default: Blue tone
Description: Color used when trending regime is detected.
Mean-Reverting Color (Hurst < 0.5)
Default: Orange tone
Description: Color used when mean-reverting regime is detected.
Neutral/Random Color
Default: Soft blue
Description: Color when market behavior is indeterminate or shifting.
Fill Opacity
Default: 70-80
Range: 0-100
Description: Transparency of area fills—higher opacity for stronger visual effect.
Line Width
Default: 2
Range: 1-5
Description: Thickness of the main indicator curve.
✅ Best Use Cases
Identifying if a market is regime-shifting from trending to mean-reverting (or vice versa).
Filtering signals in automated or systematic trading strategies.
Spotting periods of randomness where trading signals should be deprioritized.
Enhancing mean-reversion or trend-following models with regime-awareness.
⚠️ Limitations
Not predictive: Reflects current and recent market state, not future direction.
Sensitive to input parameters—overfitting may occur if settings are changed too frequently.
Smoothing can introduce lag in regime recognition.
May not work optimally in markets with structural breaks or extreme volatility.
💡 What Makes This Unique
Employs advanced statistical market analysis (Hurst exponent) rarely found in standard toolkits.
Offers immediate regime visualization through smart dynamic coloring and zone fills.
🔬 How It Works
Rolling Log Return Calculation:
Each new price creates a log return, forming the basis for robust, non-linear analysis. This ensures all price differences are treated proportionally.
Rescaled Range Analysis:
A rolling window maintains cumulative deviations and computes the statistical “range” (max-min of deviations). This is compared against the standard deviation to estimate “memory”.
Exponent Calculation & Smoothing:
The raw Hurst value is translated from the log of the rescaled range ratio, and then optionally smoothed via EMA to dampen noise and false signals.
Regime Detection Logic:
The smoothed value is checked against 0.5. Values above = trending; below = mean-reverting; near 0.5 = random. These control plot/fill color and zone display.
💡 Note:
Use longer calculation lengths for major market character study, and shorter ones for tactical, short-term adaptation. Smoothing balances noise vs. lag—find a best fit for your trading style. Always combine regime awareness with broader technical/fundamental context for best results.
MACD by Take and TradeImproved version of MACD with asymmetrical BUY and SELL approaches.
This indicator is based on popular MACD one, but with some "tricks" designed to make it more applicable to the rapidly changing crypto market.
Key benefits:
Dynamic auto-adjusted threshold to filter out weak signals
Highlighted BUY/SELL signals with divergence (if a signal is accompanied by divergence, for example, price makes a new high while macd has a second high below the first, this signal is considered stronger and will be highlighted in a darker color)
Boost BUY signals on very slow market in accumulation phase
Not symmetric! It uses 2 different signal lines, which allows to obtain SELL signals earlier comparing to classic MACD approach
Classic concept of MACD
Classic MACD, in its simplest case, consists of two lines - macd line and signal line. Macd line is a difference between so-called "fast" and "slow" EMA lines (there are just a Exponential Moving Average lines with different windows: "12" for fast and "26" for slow). Signal line is just a smoothed "macd" line.
When macd line crosses signal line from bottom to up and intersection point < 0, this is "BUY" signal. And vise versa, when macd line crosses signal line from top to bottom, and intersection point > 0, this is "SELL" signal.
Parameters used in default configuration of classic MACD indicator:
Fast line: EMA-12
Slow line: EMA-26
Signal line: EMA-9
Problem of classic concept
Classic MACD indicator usually gives not bad "BUY" signals, especially if using them not for operational trading but for "investment" strategy. But "SELL" signalls usually generated too late. Simply because the market tends to fall much faster than it rises.
Possible solution (the main feature of our version of MACD)
To make indicator react faster on SELL condition, while still keeping it reliable for BUY signals, we decided to use two signal lines . Faster than default signal line (with window=6) for BUY signals and much faster than default (with window=2) for SELL signals.
This approach allowed us to receive sell signals earlier and exit deals on more favorable prices. Trade off of this change - is the number of SELL signals - there were more of them. However, this does not matter, since we receive the very first sell signal with a "very fast signal line" much earlier than with classic indicator settings.
Parameters we use in our improved MACD indicator:
Fast line: EMA-12
Slow line: EMA-24
Faster signal line: EMA-6
Much faster signal line: EMA-2
Removing noise (false triggerings)
Other drawback of classic MACD - it generates a lot of "weak" (false) signals. This signals are generated when macd crosses signal line much close to zero-line. And usually there are a lot of such intersections.
To remove this kind of noise, we added a trigger threshold, which by default is equal to 2.5% of the average asset price over a long period of time. Due to the link to the average price, this threshold automatically takes a specific value for each trading pair. Threshold 2.5% works perfect for all trading pairs for 1D timeframe. For other timeframes user can (and maybe will want) change it.
Boost weak BUY signals in a prolonged bear market
Signals on bearish stage are usually very weak, because there is no volatility, and no price impulse. And such signals will be filtered out as "noise" - see above. But this time is perfect time to buy! Therefore, we further boost the buy signals in a prolonged bear market so that they can pass through the filter and appear on the chart. Bearish period is the best time to invest!
Developed by Take and Trade. Enjoy using it!
The Echo Forecast [LuxAlgo]This indicator uses a simple time series forecasting method derived from the similarity between recent prices and similar/dissimilar historical prices. We named this method "ECHO".
This method originally assumes that future prices can be estimated from a historical series of observations that are most similar to the most recent price variations. This similarity is quantified using the correlation coefficient. Such an assumption can prove to be relatively effective with the forecasting of a periodic time series. We later introduced the ability to select dissimilar series of observations for further experimentation.
This forecasting technique is closely inspired by the analogue method introduced by Lorenz for the prediction of atmospheric data.
1. Settings
Evaluation Window: Window size used for finding historical observations similar/dissimilar to recent observations. The total evaluation window is equal to "Forecast Window" + "Evaluation Window"
Forecast Window: Determines the forecasting horizon.
Forecast Mode: Determines whether to choose historical series similar or dissimilar to the recent price observations.
Forecast Construction: Determines how the forecast is constructed. See "Usage" below.
Src: Source input of the forecast
Other style settings are self-explanatory.
2. Usage
This tool can be used to forecast future trends but also to indicate which historical variations have the highest degree of similarity/dissimilarity between the observations in the orange zone.
The forecasting window determines the prices segment (in orange) to be used as a reference for the search of the most similar/dissimilar historical price segment (in green) within the gray area.
Most forecasting techniques highly benefit from a detrended series. Due to the nature of this method, we highly recommend applying it to a detrended and periodic series.
You can see above the method is applied on a smooth periodic oscillator and a momentum oscillator.
The construction of the forecast is made from the price changes obtained in the green area, denoted as w(t) . Using the "Cumulative" options we construct the forecast from the cumulative sum of w(t) . Finally, we add the most recent price value to this cumulated series.
Using the "Mean" options will add the series w(t) with the mean of the prices within the orange segment.
Finally the "Linreg" will add the series w(t) to an extrapolated linear regression fit to the prices within the orange segment.
Breakouts with timefilter [LuciTech]Here's the updated description with "colors" replaced by "colours" throughout, maintaining the original structure and content:
Breaking Point 2.0
This is a technical analysis overlay indicator designed to identify breakout levels based on pivot highs and lows, with a focus on price action during customizable time windows using London time (UK). It draws horizontal lines at pivot points and plots signals when price breaks above or below these levels, offering traders a tool to monitor potential bullish or bearish movements. The indicator includes options for time filtering and displaying only the most recent breakout.
Features
The Pivot Breakout Lines display horizontal lines at detected pivot highs (bullish) and pivot lows (bearish), coloured green and red by default. These lines extend from the pivot point to the breakout bar and can be set to show only the latest breakout.
The Breakout Signals mark bullish breakouts with an upward triangle below the bar and bearish breakouts with a downward triangle above the bar, using customizable colours.
The Time Filter restricts signals and lines to a specific window (default: 14:30–15:00 UK), which can be toggled on or off. A shaded background highlights this period when enabled.
How It Works
The indicator calculates pivot highs and lows using a user-defined lookback period (default: 5 bars). When price closes above a pivot high, it triggers a bullish signal and draws a line from the pivot to the breakout bar. When price closes below a pivot low, it triggers a bearish signal with a corresponding line.
If the time filter is active, signals and lines only appear within the specified window. Outside this period—or if the filter is disabled—they appear based solely on price action. The indicator maintains up to three recent pivots in memory, removing older ones as new pivots form.
Alerts are available for both bullish and bearish breakouts, triggered when signals occur.
Settings
Length controls the lookback period for pivot detection (default: 5).
Colours Bull/Bear sets the colours for bullish (default: green) and bearish (default: red) lines and signals.
Show Last Breakout toggles whether only the most recent breakout line and signal are displayed (default: false).
Time Filter enables or disables the time restriction (default: true).
Fill Background toggles a shaded area during the time window (default: true), with a customizable colour.
Time Settings define the start hour/minute and end hour/minute for the filter (default: 14:30–15:00).
Interpretation
The Pivot Breakout Lines highlight levels where price has previously reversed, potentially acting as support or resistance. A breakout above a pivot high may suggest bullish momentum, while a breakout below a pivot low may indicate bearish pressure.
The Breakout Signals provide visual cues for these events, useful for timing entries or exits. When "Show Last Breakout" is enabled, the chart focuses on the most recent signal, reducing clutter.
The Time Filter and background shading help traders concentrate on specific trading sessions, such as high-volatility periods. When disabled, the indicator tracks breakouts across all times.
Backtesting & Trading Engine [PineCoders]The PineCoders Backtesting and Trading Engine is a sophisticated framework with hybrid code that can run as a study to generate alerts for automated or discretionary trading while simultaneously providing backtest results. It can also easily be converted to a TradingView strategy in order to run TV backtesting. The Engine comes with many built-in strats for entries, filters, stops and exits, but you can also add you own.
If, like any self-respecting strategy modeler should, you spend a reasonable amount of time constantly researching new strategies and tinkering, our hope is that the Engine will become your inseparable go-to tool to test the validity of your creations, as once your tests are conclusive, you will be able to run this code as a study to generate the alerts required to put it in real-world use, whether for discretionary trading or to interface with an execution bot/app. You may also find the backtesting results the Engine produces in study mode enough for your needs and spend most of your time there, only occasionally converting to strategy mode in order to backtest using TV backtesting.
As you will quickly grasp when you bring up this script’s Settings, this is a complex tool. While you will be able to see results very quickly by just putting it on a chart and using its built-in strategies, in order to reap the full benefits of the PineCoders Engine, you will need to invest the time required to understand the subtleties involved in putting all its potential into play.
Disclaimer: use the Engine at your own risk.
Before we delve in more detail, here’s a bird’s eye view of the Engine’s features:
More than 40 built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
By combining your own strats to the built-in strats supplied with the Engine, and then tuning the numerous options and parameters in the Inputs dialog box, you will be able to play what-if scenarios from an infinite number of permutations.
USE CASES
You have written an indicator that provides an entry strat but it’s missing other components like a filter and a stop strategy. You add a plot in your indicator that respects the Engine’s External Signal Protocol, connect it to the Engine by simply selecting your indicator’s plot name in the Engine’s Settings/Inputs and then run tests on different combinations of entry stops, in-trade stops and profit taking strats to find out which one produces the best results with your entry strat.
You are building a complex strategy that you will want to run as an indicator generating alerts to be sent to a third-party execution bot. You insert your code in the Engine’s modules and leverage its trade management code to quickly move your strategy into production.
You have many different filters and want to explore results using them separately or in combination. Integrate the filter code in the Engine and run through different permutations or hook up your filtering through the external input and control your filter combos from your indicator.
You are tweaking the parameters of your entry, filter or stop strat. You integrate it in the Engine and evaluate its performance using the Engine’s statistics.
You always wondered what results a random entry strat would yield on your markets. You use the Engine’s built-in random entry strat and test it using different combinations of filters, stop and exit strats.
You want to evaluate the impact of fees and slippage on your strategy. You use the Engine’s inputs to play with different values and get immediate feedback in the detailed numbers provided in the Data Window.
You just want to inspect the individual trades your strategy generates. You include it in the Engine and then inspect trades visually on your charts, looking at the numbers in the Data Window as you move your cursor around.
You have never written a production-grade strategy and you want to learn how. Inspect the code in the Engine; you will find essential components typical of what is being used in actual trading systems.
You have run your system for a while and have compiled actual slippage information and your broker/exchange has updated his fees schedule. You enter the information in the Engine and run it on your markets to see the impact this has on your results.
FEATURES
Before going into the detail of the Inputs and the Data Window numbers, here’s a more detailed overview of the Engine’s features.
Built-in strats
The engine comes with more than 40 pre-coded strategies for the following standard system components:
Entries,
Filters,
Entry stops,
2 stage in-trade stops with kick-in rules,
Pyramiding rules,
Hard exits.
While some of the filter and stop strats provided may be useful in production-quality systems, you will not devise crazy profit-generating systems using only the entry strats supplied; that part is still up to you, as will be finding the elusive combination of components that makes winning systems. The Engine will, however, provide you with a solid foundation where all the trade management nitty-gritty is handled for you. By binding your custom strats to the Engine, you will be able to build reliable systems of the best quality currently allowed on the TV platform.
On-chart trade information
As you move over the bars in a trade, you will see trade numbers in the Data Window change at each bar. The engine calculates the P&L at every bar, including slippage and fees that would be incurred were the trade exited at that bar’s close. If the trade includes pyramided entries, those will be taken into account as well, although for those, final fees and slippage are only calculated at the trade’s exit.
You can also see on-chart markers for the entry level, stop positions, in-trade special events and entries/exits (you will want to disable these when using the Engine in strategy mode to see TV backtesting results).
Customization
You can couple your own strats to the Engine in two ways:
1. By inserting your own code in the Engine’s different modules. The modular design should enable you to do so with minimal effort by following the instructions in the code.
2. By linking an external indicator to the engine. After making the proper selections in the engine’s Settings and providing values respecting the engine’s protocol, your external indicator can, when the Engine is used in Indicator mode only:
Tell the engine when to enter long or short trades, but let the engine’s in-trade stop and exit strats manage the exits,
Signal both entries and exits,
Provide an entry stop along with your entry signal,
Filter other entry signals generated by any of the engine’s entry strats.
Conversion from strategy to study
TradingView strategies are required to backtest using the TradingView backtesting feature, but if you want to generate alerts with your script, whether for automated trading or just to trigger alerts that you will use in discretionary trading, your code has to run as a study since, for the time being, strategies can’t generate alerts. From hereon we will use indicator as a synonym for study.
Unless you want to maintain two code bases, you will need hybrid code that easily flips between strategy and indicator modes, and your code will need to restrict its use of strategy() calls and their arguments if it’s going to be able to run both as an indicator and a strategy using the same trade logic. That’s one of the benefits of using this Engine. Once you will have entered your own strats in the Engine, it will be a matter of commenting/uncommenting only four lines of code to flip between indicator and strategy modes in a matter of seconds.
Additionally, even when running in Indicator mode, the Engine will still provide you with precious numbers on your individual trades and global results, some of which are not available with normal TradingView backtesting.
Post-Exit Analysis for alternate outcomes (PEA)
While typical backtesting shows results of trade outcomes, PEA focuses on what could have happened after the exit. The intention is to help traders get an idea of the opportunity/risk in the bars following the trade in order to evaluate if their exit strategies are too aggressive or conservative.
After a trade is exited, the Engine’s PEA module continues analyzing outcomes for a user-defined quantity of bars. It identifies the maximum opportunity and risk available in that space, and calculates the drawdown required to reach the highest opportunity level post-exit, while recording the number of bars to that point.
Typically, if you can’t find opportunity greater than 1X past your trade using a few different reasonable lengths of PEA, your strategy is doing pretty good at capturing opportunity. Remember that 100% of opportunity is never capturable. If, however, PEA was finding post-trade maximum opportunity of 3 or 4X with average drawdowns of 0.3 to those areas, this could be a clue revealing your system is exiting trades prematurely. To analyze PEA numbers, you can uncomment complete sets of plots in the Plot module to reveal detailed global and individual PEA numbers.
Statistics
The Engine provides stats on your trades that TV backtesting does not provide, such as:
Average Profitability Per Trade (APPT), aka statistical expectancy, a crucial value.
APPT per bar,
Average stop size,
Traded volume .
It also shows you on a trade-by-trade basis, on-going individual trade results and data.
In-trade events
In-trade events can plot reminders and trigger alerts when they occur. The built-in events are:
Price approaching stop,
Possible tops/bottoms,
Large stop movement (for discretionary trading where stop is moved manually),
Large price movements.
Slippage and Fees
Even when running in indicator mode, the Engine allows for slippage and fees to be included in the logic and test results.
Alerts
The alert creation mechanism allows you to configure alerts on any combination of the normal or pyramided entries, exits and in-trade events.
Backtesting results
A few words on the numbers calculated in the Engine. Priority is given to numbers not shown in TV backtesting, as you can readily convert the script to a strategy if you need them.
We have chosen to focus on numbers expressing results relative to X (the trade’s risk) rather than in absolute currency numbers or in other more conventional but less useful ways. For example, most of the individual trade results are not shown in percentages, as this unit of measure is often less meaningful than those expressed in units of risk (X). A trade that closes with a +25% result, for example, is a poor outcome if it was entered with a -50% stop. Expressed in X, this trade’s P&L becomes 0.5, which provides much better insight into the trade’s outcome. A trade that closes with a P&L of +2X has earned twice the risk incurred upon entry, which would represent a pre-trade risk:reward ratio of 2.
The way to go about it when you think in X’s and that you adopt the sound risk management policy to risk a fixed percentage of your account on each trade is to equate a currency value to a unit of X. E.g. your account is 10K USD and you decide you will risk a maximum of 1% of it on each trade. That means your unit of X for each trade is worth 100 USD. If your APPT is 2X, this means every time you risk 100 USD in a trade, you can expect to make, on average, 200 USD.
By presenting results this way, we hope that the Engine’s statistics will appeal to those cognisant of sound risk management strategies, while gently leading traders who aren’t, towards them.
We trade to turn in tangible profits of course, so at some point currency must come into play. Accordingly, some values such as equity, P&L, slippage and fees are expressed in currency.
Many of the usual numbers shown in TV backtests are nonetheless available, but they have been commented out in the Engine’s Plot module.
Position sizing and risk management
All good system designers understand that optimal risk management is at the very heart of all winning strategies. The risk in a trade is defined by the fraction of current equity represented by the amplitude of the stop, so in order to manage risk optimally on each trade, position size should adjust to the stop’s amplitude. Systems that enter trades with a fixed stop amplitude can get away with calculating position size as a fixed percentage of current equity. In the context of a test run where equity varies, what represents a fixed amount of risk translates into different currency values.
Dynamically adjusting position size throughout a system’s life is optimal in many ways. First, as position sizing will vary with current equity, it reproduces a behavioral pattern common to experienced traders, who will dial down risk when confronted to poor performance and increase it when performance improves. Second, limiting risk confers more predictability to statistical test results. Third, position sizing isn’t just about managing risk, it’s also about maximizing opportunity. By using the maximum leverage (no reference to trading on margin here) into the trade that your risk management strategy allows, a dynamic position size allows you to capture maximal opportunity.
To calculate position sizes using the fixed risk method, we use the following formula: Position = Account * MaxRisk% / Stop% [, which calculates a position size taking into account the trade’s entry stop so that if the trade is stopped out, 100 USD will be lost. For someone who manages risk this way, common instructions to invest a certain percentage of your account in a position are simply worthless, as they do not take into account the risk incurred in the trade.
The Engine lets you select either the fixed risk or fixed percentage of equity position sizing methods. The closest thing to dynamic position sizing that can currently be done with alerts is to use a bot that allows syntax to specify position size as a percentage of equity which, while being dynamic in the sense that it will adapt to current equity when the trade is entered, does not allow us to modulate position size using the stop’s amplitude. Changes to alerts are on the way which should solve this problem.
In order for you to simulate performance with the constraint of fixed position sizing, the Engine also offers a third, less preferable option, where position size is defined as a fixed percentage of initial capital so that it is constant throughout the test and will thus represent a varying proportion of current equity.
Let’s recap. The three position sizing methods the Engine offers are:
1. By specifying the maximum percentage of risk to incur on your remaining equity, so the Engine will dynamically adjust position size for each trade so that, combining the stop’s amplitude with position size will yield a fixed percentage of risk incurred on current equity,
2. By specifying a fixed percentage of remaining equity. Note that unless your system has a fixed stop at entry, this method will not provide maximal risk control, as risk will vary with the amplitude of the stop for every trade. This method, as the first, does however have the advantage of automatically adjusting position size to equity. It is the Engine’s default method because it has an equivalent in TV backtesting, so when flipping between indicator and strategy mode, test results will more or less correspond.
3. By specifying a fixed percentage of the Initial Capital. While this is the least preferable method, it nonetheless reflects the reality confronted by most system designers on TradingView today. In this case, risk varies both because the fixed position size in initial capital currency represents a varying percentage of remaining equity, and because the trade’s stop amplitude may vary, adding another variability vector to risk.
Note that the Engine cannot display equity results for strategies entering trades for a fixed amount of shares/contracts at a variable price.
SETTINGS/INPUTS
Because the initial text first published with a script cannot be edited later and because there are just too many options, the Engine’s Inputs will not be covered in minute detail, as they will most certainly evolve. We will go over them with broad strokes; you should be able to figure the rest out. If you have questions, just ask them here or in the PineCoders Telegram group.
Display
The display header’s checkbox does nothing.
For the moment, only one exit strategy uses a take profit level, so only that one will show information when checking “Show Take Profit Level”.
Entries
You can activate two simultaneous entry strats, each selected from the same set of strats contained in the Engine. If you select two and they fire simultaneously, the main strat’s signal will be used.
The random strat in each list uses a different seed, so you will get different results from each.
The “Filter transitions” and “Filter states” strats delegate signal generation to the selected filter(s). “Filter transitions” signals will only fire when the filter transitions into bull/bear state, so after a trade is stopped out, the next entry may take some time to trigger if the filter’s state does not change quickly. When you choose “Filter states”, then a new trade will be entered immediately after an exit in the direction the filter allows.
If you select “External Indicator”, your indicator will need to generate a +2/-2 (or a positive/negative stop value) to enter a long/short position, providing the selected filters allow for it. If you wish to use the Engine’s capacity to also derive the entry stop level from your indicator’s signal, then you must explicitly choose this option in the Entry Stops section.
Filters
You can activate as many filters as you wish; they are additive. The “Maximum stop allowed on entry” is an important component of proper risk management. If your system has an average 3% stop size and you need to trade using fixed position sizes because of alert/execution bot limitations, you must use this filter because if your system was to enter a trade with a 15% stop, that trade would incur 5 times the normal risk, and its result would account for an abnormally high proportion in your system’s performance.
Remember that any filter can also be used as an entry signal, either when it changes states, or whenever no trade is active and the filter is in a bull or bear mode.
Entry Stops
An entry stop must be selected in the Engine, as it requires a stop level before the in-trade stop is calculated. Until the selected in-trade stop strat generates a stop that comes closer to price than the entry stop (or respects another one of the in-trade stops kick in strats), the entry stop level is used.
It is here that you must select “External Indicator” if your indicator supplies a +price/-price value to be used as the entry stop. A +price is expected for a long entry and a -price value will enter a short with a stop at price. Note that the price is the absolute price, not an offset to the current price level.
In-Trade Stops
The Engine comes with many built-in in-trade stop strats. Note that some of them share the “Length” and “Multiple” field, so when you swap between them, be sure that the length and multiple in use correspond to what you want for that stop strat. Suggested defaults appear with the name of each strat in the dropdown.
In addition to the strat you wish to use, you must also determine when it kicks in to replace the initial entry’s stop, which is determined using different strats. For strats where you can define a positive or negative multiple of X, percentage or fixed value for a kick-in strat, a positive value is above the trade’s entry fill and a negative one below. A value of zero represents breakeven.
Pyramiding
What you specify in this section are the rules that allow pyramiding to happen. By themselves, these rules will not generate pyramiding entries. For those to happen, entry signals must be issued by one of the active entry strats, and conform to the pyramiding rules which act as a filter for them. The “Filter must allow entry” selection must be chosen if you want the usual system’s filters to act as additional filtering criteria for your pyramided entries.
Hard Exits
You can choose from a variety of hard exit strats. Hard exits are exit strategies which signal trade exits on specific events, as opposed to price breaching a stop level in In-Trade Stops strategies. They are self-explanatory. The last one labelled When Take Profit Level (multiple of X) is reached is the only one that uses a level, but contrary to stops, it is above price and while it is relative because it is expressed as a multiple of X, it does not move during the trade. This is the level called Take Profit that is show when the “Show Take Profit Level” checkbox is checked in the Display section.
While stops focus on managing risk, hard exit strategies try to put the emphasis on capturing opportunity.
Slippage
You can define it as a percentage or a fixed value, with different settings for entries and exits. The entry and exit markers on the chart show the impact of slippage on the entry price (the fill).
Fees
Fees, whether expressed as a percentage of position size in and out of the trade or as a fixed value per in and out, are in the same units of currency as the capital defined in the Position Sizing section. Fees being deducted from your Capital, they do not have an impact on the chart marker positions.
In-Trade Events
These events will only trigger during trades. They can be helpful to act as reminders for traders using the Engine as assistance to discretionary trading.
Post-Exit Analysis
It is normally on. Some of its results will show in the Global Numbers section of the Data Window. Only a few of the statistics generated are shown; many more are available, but commented out in the Plot module.
Date Range Filtering
Note that you don’t have to change the dates to enable/diable filtering. When you are done with a specific date range, just uncheck “Date Range Filtering” to disable date filtering.
Alert Triggers
Each selection corresponds to one condition. Conditions can be combined into a single alert as you please. Just be sure you have selected the ones you want to trigger the alert before you create the alert. For example, if you trade in both directions and you want a single alert to trigger on both types of exits, you must select both “Long Exit” and “Short Exit” before creating your alert.
Once the alert is triggered, these settings no longer have relevance as they have been saved with the alert.
When viewing charts where an alert has just triggered, if your alert triggers on more than one condition, you will need the appropriate markers active on your chart to figure out which condition triggered the alert, since plotting of markers is independent of alert management.
Position sizing
You have 3 options to determine position size:
1. Proportional to Stop -> Variable, with a cap on size.
2. Percentage of equity -> Variable.
3. Percentage of Initial Capital -> Fixed.
External Indicator
This is where you connect your indicator’s plot that will generate the signals the Engine will act upon. Remember this only works in Indicator mode.
DATA WINDOW INFORMATION
The top part of the window contains global numbers while the individual trade information appears in the bottom part. The different types of units used to express values are:
curr: denotes the currency used in the Position Sizing section of Inputs for the Initial Capital value.
quote: denotes quote currency, i.e. the value the instrument is expressed in, or the right side of the market pair (USD in EURUSD ).
X: the stop’s amplitude, itself expressed in quote currency, which we use to express a trade’s P&L, so that a trade with P&L=2X has made twice the stop’s amplitude in profit. This is sometimes referred to as R, since it represents one unit of risk. It is also the unit of measure used in the APPT, which denotes expected reward per unit of risk.
X%: is also the stop’s amplitude, but expressed as a percentage of the Entry Fill.
The numbers appearing in the Data Window are all prefixed:
“ALL:” the number is the average for all first entries and pyramided entries.
”1ST:” the number is for first entries only.
”PYR:” the number is for pyramided entries only.
”PEA:” the number is for Post-Exit Analyses
Global Numbers
Numbers in this section represent the results of all trades up to the cursor on the chart.
Average Profitability Per Trade (X): This value is the most important gauge of your strat’s worthiness. It represents the returns that can be expected from your strat for each unit of risk incurred. E.g.: your APPT is 2.0, thus for every unit of currency you invest in a trade, you can on average expect to obtain 2 after the trade. APPT is also referred to as “statistical expectancy”. If it is negative, your strategy is losing, even if your win rate is very good (it means your winning trades aren’t winning enough, or your losing trades lose too much, or both). Its counterpart in currency is also shown, as is the APPT/bar, which can be a useful gauge in deciding between rivalling systems.
Profit Factor: Gross of winning trades/Gross of losing trades. Strategy is profitable when >1. Not as useful as the APPT because it doesn’t take into account the win rate and the average win/loss per trade. It is calculated from the total winning/losing results of this particular backtest and has less predictive value than the APPT. A good profit factor together with a poor APPT means you just found a chart where your system outperformed. Relying too much on the profit factor is a bit like a poker player who would think going all in with two’s against aces is optimal because he just won a hand that way.
Win Rate: Percentage of winning trades out of all trades. Taken alone, it doesn’t have much to do with strategy profitability. You can have a win rate of 99% but if that one trade in 100 ruins you because of poor risk management, 99% doesn’t look so good anymore. This number speaks more of the system’s profile than its worthiness. Still, it can be useful to gauge if the system fits your personality. It can also be useful to traders intending to sell their systems, as low win rate systems are more difficult to sell and require more handholding of worried customers.
Equity (curr): This the sum of initial capital and the P&L of your system’s trades, including fees and slippage.
Return on Capital is the equivalent of TV’s Net Profit figure, i.e. the variation on your initial capital.
Maximum drawdown is the maximal drawdown from the highest equity point until the drop . There is also a close to close (meaning it doesn’t take into account in-trade variations) maximum drawdown value commented out in the code.
The next values are self-explanatory, until:
PYR: Avg Profitability Per Entry (X): this is the APPT for all pyramided entries.
PEA: Avg Max Opp . Available (X): the average maximal opportunity found in the Post-Exit Analyses.
PEA: Avg Drawdown to Max Opp . (X): this represents the maximum drawdown (incurred from the close at the beginning of the PEA analysis) required to reach the maximal opportunity point.
Trade Information
Numbers in this section concern only the current trade under the cursor. Most of them are self-explanatory. Use the description’s prefix to determine what the values applies to.
PYR: Avg Profitability Per Entry (X): While this value includes the impact of all current pyramided entries (and only those) and updates when you move your cursor around, P&L only reflects fees at the trade’s last bar.
PEA: Max Opp . Available (X): It’s the most profitable close reached post-trade, measured from the trade’s Exit Fill, expressed in the X value of the trade the PEA follows.
PEA: Drawdown to Max Opp . (X): This is the maximum drawdown from the trade’s Exit Fill that needs to be sustained in order to reach the maximum opportunity point, also expressed in X. Note that PEA numbers do not include slippage and fees.
EXTERNAL SIGNAL PROTOCOL
Only one external indicator can be connected to a script; in order to leverage its use to the fullest, the engine provides options to use it as either an entry signal, an entry/exit signal or a filter. When used as an entry signal, you can also use the signal to provide the entry’s stop. Here’s how this works:
For filter state: supply +1 for bull (long entries allowed), -1 for bear (short entries allowed).
For entry signals: supply +2 for long, -2 for short.
For exit signals: supply +3 for exit from long, -3 for exit from short.
To send an entry stop level with an entry signal: Send positive stop level for long entry (e.g. 103.33 to enter a long with a stop at 103.33), negative stop level for short entry (e.g. -103.33 to enter a short with a stop at 103.33). If you use this feature, your indicator will have to check for exact stop levels of 1.0, 2.0 or 3.0 and their negative counterparts, and fudge them with a tick in order to avoid confusion with other signals in the protocol.
Remember that mere generation of the values by your indicator will have no effect until you explicitly allow their use in the appropriate sections of the Engine’s Settings/Inputs.
An example of a script issuing a signal for the Engine is published by PineCoders.
RECOMMENDATIONS TO ASPIRING SYSTEM DESIGNERS
Stick to higher timeframes. On progressively lower timeframes, margins decrease and fees and slippage take a proportionally larger portion of profits, to the point where they can very easily turn a profitable strategy into a losing one. Additionally, your margin for error shrinks as the equilibrium of your system’s profitability becomes more fragile with the tight numbers involved in the shorter time frames. Avoid <1H time frames.
Know and calculate fees and slippage. To avoid market shock, backtest using conservative fees and slippage parameters. Systems rarely show unexpectedly good returns when they are confronted to the markets, so put all chances on your side by being outrageously conservative—or a the very least, realistic. Test results that do not include fees and slippage are worthless. Slippage is there for a reason, and that’s because our interventions in the market change the market. It is easier to find alpha in illiquid markets such as cryptos because not many large players participate in them. If your backtesting results are based on moving large positions and you don’t also add the inevitable slippage that will occur when you enter/exit thin markets, your backtesting will produce unrealistic results. Even if you do include large slippage in your settings, the Engine can only do so much as it will not let slippage push fills past the high or low of the entry bar, but the gap may be much larger in illiquid markets.
Never test and optimize your system on the same dataset , as that is the perfect recipe for overfitting or data dredging, which is trying to find one precise set of rules/parameters that works only on one dataset. These setups are the most fragile and often get destroyed when they meet the real world.
Try to find datasets yielding more than 100 trades. Less than that and results are not as reliable.
Consider all backtesting results with suspicion. If you never entertained sceptic tendencies, now is the time to begin. If your backtest results look really good, assume they are flawed, either because of your methodology, the data you’re using or the software doing the testing. Always assume the worse and learn proper backtesting techniques such as monte carlo simulations and walk forward analysis to avoid the traps and biases that unchecked greed will set for you. If you are not familiar with concepts such as survivor bias, lookahead bias and confirmation bias, learn about them.
Stick to simple bars or candles when designing systems. Other types of bars often do not yield reliable results, whether by design (Heikin Ashi) or because of the way they are implemented on TV (Renko bars).
Know that you don’t know and use that knowledge to learn more about systems and how to properly test them, about your biases, and about yourself.
Manage risk first , then capture opportunity.
Respect the inherent uncertainty of the future. Cleanse yourself of the sad arrogance and unchecked greed common to newcomers to trading. Strive for rationality. Respect the fact that while backtest results may look promising, there is no guarantee they will repeat in the future (there is actually a high probability they won’t!), because the future is fundamentally unknowable. If you develop a system that looks promising, don’t oversell it to others whose greed may lead them to entertain unreasonable expectations.
Have a plan. Understand what king of trading system you are trying to build. Have a clear picture or where entries, exits and other important levels will be in the sort of trade you are trying to create with your system. This stated direction will help you discard more efficiently many of the inevitably useless ideas that will pop up during system design.
Be wary of complexity. Experienced systems engineers understand how rapidly complexity builds when you assemble components together—however simple each one may be. The more complex your system, the more difficult it will be to manage.
Play! . Allow yourself time to play around when you design your systems. While much comes about from working with a purpose, great ideas sometimes come out of just trying things with no set goal, when you are stuck and don’t know how to move ahead. Have fun!
@LucF
NOTES
While the engine’s code can supply multiple consecutive entries of longs or shorts in order to scale positions (pyramid), all exits currently assume the execution bot will exit the totality of the position. No partial exits are currently possible with the Engine.
Because the Engine is literally crippled by the limitations on the number of plots a script can output on TV; it can only show a fraction of all the information it calculates in the Data Window. You will find in the Plot Module vast amounts of commented out lines that you can activate if you also disable an equivalent number of other plots. This may be useful to explore certain characteristics of your system in more detail.
When backtesting using the TV backtesting feature, you will need to provide the strategy parameters you wish to use through either Settings/Properties or by changing the default values in the code’s header. These values are defined in variables and used not only in the strategy() statement, but also as defaults in the Engine’s relevant Inputs.
If you want to test using pyramiding, then both the strategy’s Setting/Properties and the Engine’s Settings/Inputs need to allow pyramiding.
If you find any bugs in the Engine, please let us know.
THANKS
To @glaz for allowing the use of his unpublished MA Squize in the filters.
To @everget for his Chandelier stop code, which is also used as a filter in the Engine.
To @RicardoSantos for his pseudo-random generator, and because it’s from him that I first read in the Pine chat about the idea of using an external indicator as input into another. In the PineCoders group, @theheirophant then mentioned the idea of using it as a buy/sell signal and @simpelyfe showed a piece of code implementing the idea. That’s the tortuous story behind the use of the external indicator in the Engine.
To @admin for the Volatility stop’s original code and for the donchian function lifted from Ichimoku .
To @BobHoward21 for the v3 version of Volatility Stop .
To @scarf and @midtownsk8rguy for the color tuning.
To many other scripters who provided encouragement and suggestions for improvement during the long process of writing and testing this piece of code.
To J. Welles Wilder Jr. for ATR, used extensively throughout the Engine.
To TradingView for graciously making an account available to PineCoders.
And finally, to all fellow PineCoders for the constant intellectual stimulation; it is a privilege to share ideas with you all. The Engine is for all TradingView PineCoders, of course—but especially for you.
Look first. Then leap.
SMART RSISimilar to RSI in concept, but with a few enhancements!
Improvements over the standard RSI indicator?
1. Adaptive Decision Boundaries:
Who says 70-30 are the best decision boundaries to use for trading off of the RSI indicator? Why not 80-20, or another combination? Is 70-30 still the best when you shorten or lengthen the RSI indicator's look-back window? What about when you change the time frame? I wondered this for a while too, and thats what inspired me to create this indicator! Instead of using fixed lines for the boundaries, the boundaries are calculated based off of a user specified percentile. What this means is that the reference lines are calculated by looking at the values the RSI indicator took over some look back window, and calculating an upper and lower bound where the RSI actually stayed n% of the time over that look-back window. The default parameter given for this argument is 90. What that means is over the last n days, the RSI indicator spent 90% of it's time between the upper and lower bound.
2. Smoothing The RSI Indicator:
The RSI indicator on smaller time windows tends to be very noisy. However a simple linear regression over a short time period on the RSI indicator helps to cancel out this noise without losing too much information. This makes cross-overs more meaningful as they are less likely to happen due to small deviations. In addition, it also paints a smoothed picture of the price momentum that is easy and pleasant to read. The reference lines are also smoothed.
3. Color Coding Crosses When They Happen!
Wouldn't it be great if your software highlights cross overs when they happen for you so you would not have to go back over your chart and identify it for yourself? Well this software does! It paints red behind the indicator when the RSI indicator goes above the upper reference line, and paints blue when the RSI goes below the lower reference line.
The default parameters were selected based on what I feel is useful for daily candles on BTCUSD. However you are free to change the parameters as you see fit for different securities and time frames.
MACD Overlay In main chart# MACD Overlay Indicator
## Overview
This indicator displays MACD (Moving Average Convergence Divergence) signals directly on the price chart without creating a separate window. It shows the momentum and trend changes through simple + and - symbols positioned relative to candlesticks.
## Features
- **Overlay Display**: Shows MACD information on the main price chart
- **Clean Interface**: Uses minimal + and - symbols instead of complex charts
- **Position Logic**: Symbol placement indicates MACD position relative to zero line
- **Energy Analysis**: Symbols represent histogram energy changes (absolute value)
- **Color Coding**: Different colors for golden cross and death cross signals
## Symbol Meaning
### Position Logic
- **Above Candlesticks**: MACD is above zero line (bullish territory)
- **Below Candlesticks**: MACD is below zero line (bearish territory)
### Symbol Meaning
- **+ Symbol**: MACD histogram absolute value is increasing (momentum strengthening)
- **- Symbol**: MACD histogram absolute value is decreasing (momentum weakening)
### Color Coding
- **Yellow**: Golden cross (MACD line above signal line)
- **Red**: Death cross (MACD line below signal line)
## Settings
- **Fast Length**: Default 12 (EMA fast period)
- **Slow Length**: Default 26 (EMA slow period)
- **Signal Smoothing**: Default 9 (Signal line period)
- **Oscillator MA Type**: EMA or SMA for MACD calculation
- **Signal Line MA Type**: EMA or SMA for signal line
## How to Use
1. **Trend Identification**:
- Symbols above candlesticks = Bullish MACD territory
- Symbols below candlesticks = Bearish MACD territory
2. **Momentum Analysis**:
- + symbols = Momentum is strengthening
- - symbols = Momentum is weakening
3. **Signal Confirmation**:
- Yellow symbols = MACD above signal line (bullish signal)
- Red symbols = MACD below signal line (bearish signal)
## Advantages
- **Space Efficient**: No separate indicator window required
- **Clean Chart**: Maintains price chart clarity
- **Quick Analysis**: Instant visual feedback on MACD status
- **Non-Intrusive**: Doesn't alter candlestick colors or backgrounds
## Best Practices
- Use in conjunction with price action analysis
- Combine with other technical indicators for confirmation
- Pay attention to symbol color changes for trend shifts
- Monitor symbol position changes for momentum shifts
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*This indicator provides a simplified way to monitor MACD signals without cluttering your chart with additional windows.*
Benford's Law Actual [Tagstrading]Benford’s Law Chart — First Digit Analysis of Percentage Price Drops
This script visualizes the distribution of the leading digit in the percentage change of price drops, and compares it to the theoretical distribution expected by Benford’s Law.
It helps traders, analysts, and quants to detect anomalies, unnatural behavior, or price manipulation in any asset or timeframe.
How to Use
Add to any chart or symbol (stocks, crypto, FX, etc.) and select the timeframe you wish to analyze.
Set the “Number of Bars to Analyze” input (default: 500) to control the length of the historical window.
The chart will display, for the latest window:
A blue line: the actual leading-digit distribution for percentage price changes between bars.
A red line: the expected distribution per Benford’s Law.
Labels below and above: digit markers and the expected (theoretical) percentages.
Summary panel on the right: frequency counts and actual vs. theoretical % for each digit.
Interpretation:
If your actual (blue) curve or digit counts are significantly different from the red Benford’s Law curve, it could indicate unnatural price action, fraud, bot activity, or structural anomalies.
Why is this useful for TradingView?
Financial forensics: Benford’s Law is a classic tool for detecting data manipulation and fraud in accounting. On charts, it can reveal if price movements are statistically “natural.”
Transparency and confidence: Helps communities audit markets, brokers, or exchanges for irregularities.
Adaptable: Works on any market, any timeframe.
What makes this script unique?
Focuses on % price changes, not raw prices.
This provides a fair comparison across assets, symbols, and timeframes.
Measures only the direction and magnitude of drops/rises — more suitable for detecting manipulation in active markets.
Clear and customizable visualization:
The Benford line, actual data, and summary are all visible and readable in one glance.
Optimized for speed and clarity (runs efficiently on all major charts).
How is it different from stg44’s Benford’s Law script?
This script analyzes the leading digit of percentage price changes (i.e., how much the price drops or rises in %),
while the original by stg44 analyzes the leading digit of price itself.
Results are less sensitive to price scale and more comparable across volatile and non-volatile assets.
The summary panel clearly shows ( ) for actual and for Benford theoretical values.
Full code is commented and open for the community.
Credits and Inspiration
This script was inspired by “Benford’s Law” by stg44:
Thanks to the TradingView community for sharing powerful visual ideas.
—
By tags trading
HSI1! First 30m Candle Strategy (15m Chart)## HSI1! First 30-Minute Candle Breakout Strategy (15m Chart) — Description
### Overview
This strategy is designed for trading **Hang Seng Index (HSI) Futures** on a 15-minute chart. It uses the price range established during the first 30 minutes of the Hong Kong main session (09:15–09:44:59) to define key breakout levels for a systematic trade entry each day.
### How the Strategy Works
#### 1. Reference Candle Period
- **Aggregation Window:** The strategy monitors the first two 15-minute bars of the session (09:15:00–09:44:59 HKT).
- **Range Capture:** It records the highest and lowest prices (the "reference high/low") during this window.
#### 2. Trade Setup
- After the 09:45 bar completes, the reference range is locked in.
- Throughout the rest of the trading day (within session hours), the strategy looks for breakouts beyond the reference range.
#### 3. Entry Rules
- **Long Entry (Buy):**
- Triggered if price rises to or above the reference high.
- Only entered if the user's settings permit "Buy Only" or "Both".
- **Short Entry (Sell):**
- Triggered if price falls to or below the reference low.
- Only entered if the user's settings permit "Sell Only" or "Both".
- **Single trade per day:**
- Once any trade executes, no additional trades are opened until the next session.
#### 4. Exit Rules
- **Take Profit (TP):**
- Target profit is set to a distance equal to the initial range added above the long entry (or subtracted below the short entry).
- Example: For a 100-point range, a long trade targets entry + 100 points.
- **Stop Loss (SL):**
- Longs are stopped out if price falls back to the session's reference low; shorts are stopped out if price rallies to the reference high.
#### 5. Session Control
- Active only within the regular day session (09:15–12:00 and 13:00–16:00 HKT).
- Trade tracking resets each new trading day.
#### 6. Trade Direction Manual Setting
- A user input allows restriction to "Buy Only", "Sell Only" or "Both" directions, providing discretion over daily bias.
### Example Workflow
| Step | Action |
|---------------------------|-------------------------------------------------------------------------|
| 09:15–09:44 | Aggregate first two 15m candles; record daily high/low |
| After 09:45 | Wait for a breakout (price crossing either the high or the low) |
| Long trade triggered | Enter at the reference high, target is "high + range", SL is at the low |
| Short trade triggered | Enter at the reference low, target is "low - range", SL at the high |
| Trade management | No more trades for the day, regardless of further breakouts |
| End of session (if open) | Trades may be closed per further logic or left to strategy to handle |
### Key Features and Benefits
- **Discipline:** Only one trade per day, minimizing overtrading.
- **Clarity:** Transparent entry/exit rules; no discretionary execution.
- **Flexibility:** User can bias system to buy-only, sell-only, or allow both, depending on trend or personal view.
- **Simple Risk Control:** Pre-defined stop loss and profit target for every trade.
- **Works best in:** Trending, breakout-prone markets with a history of impulsive moves early in the session.
This strategy is ideal for systematic traders looking to capture the Hang Seng's early session momentum, with robust rule-based management and minimal intervention.
Gold ORB Strategy (15-min Range, 5-min Entry)The Gold ORB (Opening Range Breakout) Strategy is designed for day traders looking to capitalize on the price action in the early part of the trading day, specifically using a 15-minute range for identifying the opening range and a 5-minute timeframe for breakout entries. The strategy trades the Gold market (XAU/USD) during the New York session.
Opening Range: The strategy defines the Opening Range (ORB) between 9:30 AM EST and 9:45 AM EST using the highest and lowest points during this 15-minute window.
Breakout Entries: The strategy enters trades when the price breaks above the ORB high for a long position or below the ORB low for a short position. It waits for a 5-minute candle close outside the range before entering a trade.
Stop Loss and Take Profit: The stop loss is placed at 50% of the ORB range, and the take profit is set at twice the ORB range (1:2 risk-reward ratio).
Time Window: The strategy only executes trades before 12:00 PM EST, avoiding late-day market fluctuations and consolidations.
Quantitative Breakout Bands (AIBitcoinTrend)Quantitative Breakout Bands (AIBitcoinTrend) is an advanced indicator designed to adapt to dynamic market conditions by utilizing a Kalman filter for real-time data analysis and trend detection. This innovative tool empowers traders to identify price breakouts, evaluate trends, and refine their trading strategies with precision.
👽 What Are Quantitative Breakout Bands, and Why Are They Unique?
Quantitative Breakout Bands combine advanced filtering techniques (Kalman Filters) with statistical measures such as mean absolute error (MAE) to create adaptive price bands. These bands adjust to market conditions dynamically, providing insights into volatility, trend strength, and breakout opportunities.
What sets this indicator apart is its ability to incorporate both position (price) and velocity (rate of price change) into its calculations, making it highly responsive yet smooth. This dual consideration ensures traders get reliable signals without excessive lag or noise.
👽 The Math Behind the Indicator
👾 Kalman Filter Estimation:
At the core of the indicator is the Kalman Filter, a recursive algorithm used to predict the next state of a system based on past observations. It incorporates two primary elements:
State Prediction: The indicator predicts future price (position) and velocity based on previous values.
Error Covariance Adjustment: The process and measurement noise parameters refine the prediction's accuracy by balancing smoothness and responsiveness.
👾 Breakout Bands Calculation:
The breakout bands are derived from the mean absolute error (MAE) of price deviations relative to the filtered trendline:
float upperBand = kalmanPrice + bandMultiplier * mae
float lowerBand = kalmanPrice - bandMultiplier * mae
The multiplier allows traders to adjust the sensitivity of the bands to market volatility.
👾 Slope-Based Trend Detection:
A weighted slope calculation measures the gradient of the filtered price over a configurable window. This slope determines whether the market is trending bullish, bearish, or neutral.
👾 Trailing Stop Mechanism:
The trailing stop employs the Average True Range (ATR) to calculate dynamic stop levels. This ensures positions are protected during volatile moves while minimizing premature exits.
👽 How It Adapts to Price Movements
Dynamic Noise Calibration: By adjusting process and measurement noise inputs, the indicator balances smoothness (to reduce noise) with responsiveness (to adapt to sharp price changes).
Trend Responsiveness: The Kalman Filter ensures that trend changes are quickly identified, while the slope calculation adds confirmation.
Volatility Sensitivity: The MAE-based bands expand and contract in response to changes in market volatility, making them ideal for breakout detection.
👽 How Traders Can Use the Indicator
👾 Breakout Detection:
Bullish Breakouts: When the price moves above the upper band, it signals a potential upward breakout.
Bearish Breakouts: When the price moves below the lower band, it signals a potential downward breakout.
The trailing stop feature offers a dynamic way to lock in profits or minimize losses during trending moves.
👾 Trend Confirmation:
The color-coded Kalman line and slope provide visual cues:
Bullish Trend: Positive slope, green line.
Bearish Trend: Negative slope, red line.
👽 Why It’s Useful for Traders
Dynamic and Adaptive: The indicator adjusts to changing market conditions, ensuring relevance across timeframes and asset classes.
Noise Reduction: The Kalman Filter smooths price data, eliminating false signals caused by short-term noise.
Comprehensive Insights: By combining breakout detection, trend analysis, and risk management, it offers a holistic trading tool.
👽 Indicator Settings
Process Noise (Position & Velocity): Adjusts filter responsiveness to price changes.
Measurement Noise: Defines expected price noise for smoother trend detection.
Slope Window: Configures the lookback for slope calculation.
Lookback Period for MAE: Defines the sensitivity of the bands to volatility.
Band Multiplier: Controls the band width.
ATR Multiplier: Adjusts the sensitivity of the trailing stop.
Line Width: Customizes the appearance of the trailing stop line.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Bollinger Bands Enhanced StrategyOverview
The common practice of using Bollinger bands is to use it for building mean reversion or squeeze momentum strategies. In the current script Bollinger Bands Enhanced Strategy we are trying to combine the strengths of both strategies types. It utilizes Bollinger Bands indicator to buy the local dip and activates trailing profit system after reaching the user given number of Average True Ranges (ATR). Also it uses 200 period EMA to filter trades only in the direction of a trend. Strategy can execute only long trades.
Unique Features
Trailing Profit System: Strategy uses user given number of ATR to activate trailing take profit. If price has already reached the trailing profit activation level, scrip will close long trade if price closes below Bollinger Bands middle line.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Major Trend Filter: Strategy utilizes 100 period EMA to take trades only in the direction of a trend.
Flexible Risk Management: Users can choose number of ATR as a stop loss (by default = 1.75) for trades. This is flexible approach because ATR is recalculated on every candle, therefore stop-loss readjusted to the current volatility.
Methodology
First of all, script checks if currently price is above the 200-period exponential moving average EMA. EMA is used to establish the current trend. Script will take long trades on if this filtering system showing us the uptrend. Then the strategy executes the long trade if candle’s low below the lower Bollinger band. To calculate the middle Bollinger line, we use the standard 20-period simple moving average (SMA), lower band is calculated by the substruction from middle line the standard deviation multiplied by user given value (by default = 2).
When long trade executed, script places stop-loss at the price level below the entry price by user defined number of ATR (by default = 1.75). This stop-loss level recalculates at every candle while trade is open according to the current candle ATR value. Also strategy set the trailing profit activation level at the price above the position average price by user given number of ATR (by default = 2.25). It is also recalculated every candle according to ATR value. When price hit this level script plotted the triangle with the label “Strong Uptrend” and start trail the price at the middle Bollinger line. It also started to be plotted as a green line.
When price close below this trailing level script closes the long trade and search for the next trade opportunity.
Risk Management
The strategy employs a combined and flexible approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined ATR stop loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 1.75*ATR drop from the entry point, but it can be adjusted according to the trader's preferences.
There is no fixed take profit, but strategy allows user to define user the ATR trailing profit activation parameter. By default, this stop-loss is set to a 2.25*ATR growth from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Bollinger bangs indicator to open long trades in the local dips. If price reached the lower band there is a high probability of bounce. Here is an issue: during the strong downtrend price can constantly goes down without any significant correction. That’s why we decided to use 200-period EMA as a trend filter to increase the probability of opening long trades during major uptrend only.
Usually, Bollinger Bands indicator is using for mean reversion or breakout strategies. Both of them have the disadvantages. The mean reversion buys the dip, but closes on the return to some mean value. Therefore, it usually misses the major trend moves. The breakout strategies usually have the issue with too high buy price because to have the breakout confirmation price shall break some price level. Therefore, in such strategies traders need to set the large stop-loss, which decreases potential reward to risk ratio.
In this strategy we are trying to combine the best features of both types of strategies. Script utilizes ate ATR to setup the stop-loss and trailing profit activation levels. ATR takes into account the current volatility. Therefore, when we setup stop-loss with the user-given number of ATR we increase the probability to decrease the number of false stop outs. The trailing profit concept is trying to add the beat feature from breakout strategies and increase probability to stay in trade while uptrend is developing. When price hit the trailing profit activation level, script started to trail the price with middle line if Bollinger bands indicator. Only when candle closes below the middle line script closes the long trade.
Backtest Results
Operating window: Date range of backtests is 2020.10.01 - 2024.07.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -9.78%
Maximum Single Profit: +25.62%
Net Profit: +6778.11 USDT (+67.78%)
Total Trades: 111 (48.65% win rate)
Profit Factor: 2.065
Maximum Accumulated Loss: 853.56 USDT (-6.60%)
Average Profit per Trade: 61.06 USDT (+1.62%)
Average Trade Duration: 76 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Fractal Breakout Trend Following StrategyOverview
The Fractal Breakout Trend Following Strategy is a trend-following system which utilizes the Willams Fractals and Alligator to execute the long trades on the fractal's breakouts which have a high probability to be the new uptrend phase beginning. This system also uses the normalized Average True Range indicator to filter trades after a large moves, because it's more likely to see the trend continuation after a consolidation period. Strategy can execute only long trades.
Unique Features
Trend and volatility filtering system: Strategy uses Williams Alligator to filter the counter-trend fractals breakouts and normalized Average True Range to avoid the trades after large moves, when volatility is high
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Flexible Risk Management: Users can choose the stop-loss percent (by default = 3%) for trades, but strategy also has the dynamic stop-loss level using down fractals.
Methodology
The strategy places stop order at the last valid fractal breakout level. Validity of this fractal is defined by the Williams Alligator indicator. If at the moment of time when price breaking the last fractal price is higher than Alligator's teeth line (8 period SMA shifted 5 bars in the future) this is a valid breakout. Moreover strategy has the additional volatility filtering system using normalized ATR. It calculates the average normalized ATR for last user-defined number of bars and if this value lower than the user-defined threshold value the long trade is executed.
When trade is opened, script places the stop loss at the price higher of two levels: user defined stop-loss from the position entry price or down fractal validation level. The down fractal is valid with the rule, opposite as the up fractal validation. Price shall break to the downside the last down fractal below the Willians Alligator's teeth line.
Strategy has no fixed take profit. Exit level changes with the down fractal validation level. If price is in strong uptrend trade is going to be active until last down fractal is not valid. Strategy closes trade when price hits the down fractal validation level.
Risk Management
The strategy employs a combined approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined stop-loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 3% drop from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Williams Fractals to open long trade when price has broken the key resistance level to the upside. This resistance level is the last up fractal and is shall be broken above the Williams Alligator's teeth line to be qualified as the valid breakout according to this strategy. The Alligator filtering increases the probability to avoid the false breakouts against the current trend.
Moreover strategy has an additional filter using Average True Range(ATR) indicator. If average value of ATR for the last user-defined number of bars is lower than user-defined threshold strategy can open the long trade according to open trade condition above. The logic here is following: we want to open trades after period of price consolidation inside the range because before and after a big move price is more likely to be in sideways, but we need a trend move to have a profit.
Another one important feature is how the exit condition is defined. On the one hand, strategy has the user-defined stop-loss (3% below the entry price by default). It's made to give users the opportunity to restrict their losses according to their risk-tolerance. On the other hand, strategy utilizes the dynamic exit level which is defined by down fractal activation. If we assume the breaking up fractal is the beginning of the uptrend, breaking down fractal can be the start of downtrend phase. We don't want to be in long trade if there is a high probability of reversal to the downside. This approach helps to not keep open trade if trend is not developing and hold it if price continues going up.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.19%
Maximum Single Profit: +24.97%
Net Profit: +3036.90 USDT (+30.37%)
Total Trades: 83 (28.92% win rate)
Profit Factor: 1.953
Maximum Accumulated Loss: 963.98 USDT (-8.29%)
Average Profit per Trade: 36.59 USDT (+1.12%)
Average Trade Duration: 72 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h and higher time frames and the BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Ichimoku Clouds Strategy Long and ShortOverview:
The Ichimoku Clouds Strategy leverages the Ichimoku Kinko Hyo technique to offer traders a range of innovative features, enhancing market analysis and trading efficiency. This strategy is distinct in its combination of standard methodology and advanced customization, making it suitable for both novice and experienced traders.
Unique Features:
Enhanced Interpretation: The strategy introduces weak, neutral, and strong bullish/bearish signals, enabling detailed interpretation of the Ichimoku cloud and direct chart plotting.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Dual Trading Modes: Long and Short modes are available, allowing alignment with market trends.
Flexible Risk Management: Offers three styles in each mode, combining fixed risk management with dynamic indicator states for versatile trade management.
Indicator Line Plotting: Enables plotting of Ichimoku indicator lines on the chart for visual decision-making support.
Methodology:
The strategy utilizes the standard Ichimoku Kinko Hyo model, interpreting indicator values with settings adjustable through a user-friendly menu. This approach is enhanced by TradingView's built-in strategy tester for customization and market selection.
Risk Management:
Our approach to risk management is dynamic and indicator-centric. With data from the last year, we focus on dynamic indicator states interpretations to mitigate manual setting causing human factor biases. Users still have the option to set a fixed stop loss and/or take profit per position using the corresponding parameters in settings, aligning with their risk tolerance.
Backtest Results:
Operating window: Date range of backtests is 2023.01.01 - 2024.01.04. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Maximum Single Position Loss: -6.29%
Maximum Single Profit: 22.32%
Net Profit: +10 901.95 USDT (+109.02%)
Total Trades: 119 (51.26% profitability)
Profit Factor: 1.775
Maximum Accumulated Loss: 4 185.37 USDT (-22.87%)
Average Profit per Trade: 91.67 USDT (+0.7%)
Average Trade Duration: 56 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters. Backtest is calculated using deep backtest option in TradingView built-in strategy tester
How to Use:
Add the script to favorites for easy access.
Apply to the desired chart and timeframe (optimal performance observed on the 1H chart, ForEx or cryptocurrency top-10 coins with quote asset USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Opening Range Gaps [TFO]This indicator displays Opening Range Gaps with an adjustable time window. Its intention is to capture the discrepancy between the close price of previous and new Real Trading Hours (RTH) sessions, i.e. yesterday's close compared to today's open. A gap will be drawn from this area with a solid line denoting its midpoint, and dashed lines denoting the upper and lower quartiles of its range. Its color is determined by whether the new session open price is above or below the previous session close.
The Gap Session parameter allows users to define the specific time window for which to capture the "gap" in price. Using U.S. index futures as an example, we can use 16:00 - 09:30 (EST) to capture the discrepancy between the previous day's close price and the current day's open price. However, this parameter is left as adjustable for users that may want to observe different markets or simply experiment with different time windows.
Show Session Delineations will draw vertical timestamps denoting the start and end times of the provided Gap Session. Track Start Price serves as a visual aid to track the initial price of the Gap Session until its end price is validated, for easy visual verification of a gap's upper and lower bounds. With both options turned off, the indicator will only display the gap boxes and lines, as shown here:
Extend Boxes will draw all gaps with an indefinite extension to the right. This can get messy with a large number of boxes, which is why we have a Keep Last parameter to limit how many sessions' drawings should be stored. Any drawings that were made beyond this number of sessions in the past will automatically be deleted.
The Timeframe Limit will dictate that the indicator as a whole will only draw objects on timeframes less than or equal to this timeframe, determined by the user. In some cases this may help users avoid resolution errors which may arise from using timeframes that are too large for a given session. For example, if a user wanted to track a Gap Session of 16:15-09:30, the Timeframe Limit should be set to 15 minutes because the close price at 16:15 cannot be observed on a 30 minute chart (or greater).
Fibonacci ClustersI was reading about Fibonacci Clusters on investopedia (www.investopedia.com) and couldn't find a script for it on tradingveiw. Apparently some people use it successfully but I found it a little chaotic. This script will mark the retracements in a window's length, and you can set this for six windows. This script isn't very pretty because it doesn't seem obviously useful and pinescript has far too many deficiencies to fully flesh this idea out. I was able to make more sense out of larger windowing times (500-4000 periods), than shorter ones (25-333). Try it out, see what it shows you. Happy trading
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter
A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts.
What this is
A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks.
Why Theil-Sen
Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns.
What it plots
Filtered baseline that advances by a fraction of the robust slope each bar.
Optional candle coloring by baseline slope sign for quick trend read.
Alerts when the baseline slope turns up or down.
How it behaves (high level)
Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes.
Takes the median of those slopes to get a robust estimate for the bar.
Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line.
Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive.
Inputs and what they do
Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother.
Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier.
Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves.
Slope Cap Mode — optional guardrail on each bar’s slope:
None — no cap.
ATR — cap scales with recent true range.
Percent — cap scales with price level.
Points — fixed absolute cap in price points.
ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size.
UI Settings — show or hide the line, paint candles by slope, choose long and short colors.
How to read it
Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction.
Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise.
Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets.
Use cases
Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups.
Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure.
Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles.
Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars.
Tuning guidance
Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves.
Position trading — longer window, moderate response. ATR cap tends to scale well across cycles.
Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities.
Alerts included
Theil-Sen Up Slope — baseline’s one-bar change crosses above zero.
Theil-Sen Down Slope — baseline’s one-bar change crosses below zero.
Strengths
Robust to outliers through median-based slope estimation.
Continuously advances with price rather than re-anchoring, which reduces lag at turns.
User-selectable slope caps to tame shock bars without over-smoothing everything.
Minimal visuals with optional candle painting for fast regime recognition.
Notes
This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
Momentum ProfileProfile market behavior in horizontal zones
Profile Sidebar
Buckets pointing rightward indicate upward security movement in the lookahead window at that level, and buckets pointing leftward indicate downward movement in the lookahead window.
Green profile buckets indicate the security's behavior following an uptrend in the lookbehind window. Conversely, Red profile buckets show security's behavior following a downtrend in the lookbehind window. Yellow profile buckets show behavior following sideways movement.
Buckets length corelates with the amount of movement measured in that direction at that level.
Inputs
Length determines how many bars back are considered for the calculation. On most securities, this can be increased to just above 4000 without issues.
Rows determines the number of buckets that the securities range is divided into.
You can increase or decrease the threshold for which moves are considered sideways with the sideways_filter input: higher means more moves are considered sideways.
The lookbehind input determines the lookbehind window. Specifically, how many bars back are considered when determining whether a data point is considered green (uptrend), red (downtrend), or yellow (no significant trend).
The lookahead input determines how many bars after the current bar are considered when determining the length and direction of each bucket (leftward for downward moves, rightward for upward moves).
Profile_width and Profile_spacing are cosmetic choices.
Intrabar support is not current supported.
Region Highlighting
Regions highlighted green saw an upward move in the lookahead window for both lookbehind downtrends and uptrends. In other words, both red and green profile buckets pointed rightward.
Regions highlighted red saw a downward move in the lookahead window both for lookbehind downtrends and uptrends.
Regions highlighted brown indicate a reversal region: uptrends were followed by downtrends, and vice versa. These regions often indicate a chop range or sometimes support/resistance levels. On the profile, this means that green buckets pointed left, and red buckets pointed right.
Regions highlighted purple indicate that whatever direction the security was moving, it continued that way. On the profile, this means that green buckets pointed right, and red buckets pointed left in that region.
Volume Forecasting [LuxAlgo]The Volume Forecasting indicator provides a forecast of volume by capturing and extrapolating periodic fluctuations. Historical forecasts are also provided to compare the method against volume at time t .
This script will not work on tickers that do not have volume data.
🔶 SETTINGS
Median Memory: Number of days used to compute the median and first/third quartiles.
Forecast Window: Number of bars forecasted in the future.
Auto Forecast Window: Set the forecast window so that the forecast length completes an interval.
🔶 USAGE
The periodic nature of volume on certain securities allows users to more easily forecast using historical volume. The forecast can highlight intervals where volume tends to be more important, that is where most trading activity takes place.
More pronounced periodicity will tend to return more accurate forecasts.
The historical forecast can also highlight intervals where high/low volume is not expected.
The interquartile range is also highlighted, giving an area where we can expect the volume to lie.
🔶 DETAILS
This forecasting method is similar to the time series decomposition method used to obtain the seasonal component.
We first segment the chart over equidistant intervals. Each interval is delimited by a change in the daily timeframe.
To forecast volume at time t+1 we see where the current bar lies in the interval, if the bar is the 78th in interval then the forecast on the next bar is made by taking the median of the 79th bar over N intervals, where N is the median memory.
This method ensures capturing the periodic fluctuation of volume.
