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Edge-Preserving FilterIntroduction Edge-preserving smoothing is often used in image processing in order to preserve edge information while filtering the remaining signal. I introduce two concepts in this indicator, edge preservation and an adaptive cumulative average allowing for fast edge-signal transition with period increase over time. This filter have nothing to do with classic filters for image processing, those filters use kernels convolution and are most of the time in a spatial domain. Edge Detection Method We want to minimize smoothing when an edge is detected, so our first goal is to detect an edge. An edge will be considered as being a peak or a valley, if you recall there is one of my indicator who aim to detect peaks and valley (reference at the bottom of the post) , since this estimation return binary outputs we will use it to tell our filter when to stop filtering. Filtering Increase By Using Multi Steps Cumulative Average The edge detection is a binary output, using a exponential smoothing could be possible and certainly more efficient but i wanted instead to try using a cumulative average approach because it smooth more and is a bit more original to use an adaptive architecture using something else than exponential averaging. A cumulative average is defined as the sum of the price and the previous value of the cumulative average and then this result is divided by n with n = number of data points. You could say that a cumulative average is a moving average with a linear increasing period. So lets call CMA our cumulative average and n our divisor. When an edge is detected CMA = close price and n = 1 , else n is equal to previous n+1 and the CMA act as a normal cumulative average by summing its previous values with the price and dividing the sum by n until a new edge is detected, so there is a "no filtering state" and a "filtering state" with linear period increase transition, this is why its multi-steps. The Filter The filter have two parameters, a length parameter and a smooth parameter, length refer to the edge detection sensitivity, small values will detect short terms edges while higher values will detect more long terms edges. Smooth is directly related to the edge detection method, high values of smooth can avoid the detection of some edges. smooth = 200 smooth = 50 smooth = 3 Conclusion Preserving the price edges can be useful when it come to allow for reactivity during important price points, such filter can help with moving average crossover methods or can be used as a source for other indicators making those directly dependent of the edge detection. Rsi with a period of 200 and our filter as source, will cross triggers line when an edge is detected Feel free to share suggestions ! Thanks for reading ! References Peak/Valley estimator used for the detection of edges in price.
Pine Script® Indikator
von alexgrover
18
Heiken Ashi zero lag EMA v1.1 by JustUncleLI originally wrote this script earlier this year for my own use. This released version is an updated version of my original idea based on more recent script ideas. As always with my Alert scripts please do not trade the CALL/PUT indicators blindly, always analyse each position carefully. Always test indicator in DEMO mode first to see if it profitable for your trading style. DESCRIPTION: This Alert indicator utilizes the Heiken Ashi with non lag EMA was a scalping and intraday trading system that has been adapted also for trading with binary options high/low. There is also included filtering on MACD direction and trend direction as indicated by two MA: smoothed MA(11) and EMA(89). The the Heiken Ashi candles are great as price action trending indicator, they shows smooth strong and clear price fluctuations. Financial Markets: any. Optimsed settings for 1 min, 5 min and 15 min Time Frame; Expiry time for Binary options High/Low 3-6 candles. Indicators used in calculations: - Exponential moving average, period 89 - Smoothed moving average, period 11 - Non lag EMA, period 20 - MACD 2 colour (13,26,9) Generate Alerts use the following Trading Rules Heiken Ashi with non lag dot Trade only in direction of the trend. UP trend moving average 11 period is above Exponential moving average 89 period, Doun trend moving average 11 period is below Exponential moving average 89 period, CALL Arrow appears when: Trend UP SMA11>EMA89 (optionally disabled), Non lag MA blue dot and blue background. Heike ashi green color. MACD 2 Colour histogram green bars (optional disabled). PUT Arrow appears when: Trend UP SMA11
Pine Script® Indikator
von JustUncleL
32
MLActivationFunctionsLibrary "MLActivationFunctions" Activation functions for Neural networks. binary_step(value) Basic threshold output classifier to activate/deactivate neuron.   Parameters:      value : float, value to process.   Returns: float linear(value) Input is the same as output.   Parameters:      value : float, value to process.   Returns: float sigmoid(value) Sigmoid or logistic function.   Parameters:      value : float, value to process.   Returns: float sigmoid_derivative(value) Derivative of sigmoid function.   Parameters:      value : float, value to process.   Returns: float tanh(value) Hyperbolic tangent function.   Parameters:      value : float, value to process.   Returns: float tanh_derivative(value) Hyperbolic tangent function derivative.   Parameters:      value : float, value to process.   Returns: float relu(value) Rectified linear unit (RELU) function.   Parameters:      value : float, value to process.   Returns: float relu_derivative(value) RELU function derivative.   Parameters:      value : float, value to process.   Returns: float leaky_relu(value) Leaky RELU function.   Parameters:      value : float, value to process.   Returns: float leaky_relu_derivative(value) Leaky RELU function derivative.   Parameters:      value : float, value to process.   Returns: float relu6(value) RELU-6 function.   Parameters:      value : float, value to process.   Returns: float softmax(value) Softmax function.   Parameters:      value : float array, values to process.   Returns: float softplus(value) Softplus function.   Parameters:      value : float, value to process.   Returns: float softsign(value) Softsign function.   Parameters:      value : float, value to process.   Returns: float elu(value, alpha) Exponential Linear Unit (ELU) function.   Parameters:      value : float, value to process.      alpha : float, default=1.0, predefined constant, controls the value to which an ELU saturates for negative net inputs. .   Returns: float selu(value, alpha, scale) Scaled Exponential Linear Unit (SELU) function.   Parameters:      value : float, value to process.      alpha : float, default=1.67326324, predefined constant, controls the value to which an SELU saturates for negative net inputs. .      scale : float, default=1.05070098, predefined constant.   Returns: float exponential(value) Pointer to math.exp() function.   Parameters:      value : float, value to process.   Returns: float function(name, value, alpha, scale) Activation function.   Parameters:      name : string, name of activation function.      value : float, value to process.      alpha : float, default=na, if required.      scale : float, default=na, if required.   Returns: float derivative(name, value, alpha, scale) Derivative Activation function.   Parameters:      name : string, name of activation function.      value : float, value to process.      alpha : float, default=na, if required.      scale : float, default=na, if required.   Returns: float
Pine Script® Bibliothek
von RicardoSantos
2
Stochastic with Outlier Labels/MTFTL;DR This indicator is an update to a simple stochastic ('Stoch_MTF' by binarytrader666) that provides a novel outlier highlighting feature Improvements on stochastic: 1. Novel outlier highlighting that points out crosses that are the Nth consecutive cross or greater. 2. Allowing for multiple timeframes to be shown on the same chart 3. Highlighting/Labelling crosses and providing labels for alerts A cross of the stochastics in the high or low zones establishes a trend. Successive crosses in the same region seem to indicate a continuation of that trend. The outlier functionality here provides a signal for when X number of crosses have been in the same trend, signaling further strength of that signal. I also provided the necessary code for converting this to a strategy if you so wish at the bottom.
Pine Script® Indikator
von on2valhalla
1
Linear Regression Trend Channel with Entries & AlertsPlease Use this Indicator If you understand the risk posed by linear regression trend channel Features Provides trend channel (best value for period is 40 on 5 minute timeframe Provides BUY/SELL entries based on current channel Provides custom color for channel Best used with MattyPips strategy indicators Risks : Please note, this script is the likes of Bollinger bands and poses a risk of falling in a trend range. Entries may keep running on the same direction while the market is moving.
Pine Script® Indikator
von MrTuanDoan
23
Adaptive Trend Envelope [BackQuant]Adaptive Trend Envelope Overview Adaptive Trend Envelope is a volatility-aware trend-following overlay designed to stay responsive in fast markets while remaining stable during slower conditions. It builds a dynamic trend spine from two exponential moving averages and surrounds it with an adaptive envelope whose width expands and contracts based on realized return volatility. The result is a clean, self-adjusting trend structure that reacts to market conditions instead of relying on fixed parameters. This indicator is built to answer three core questions directly on the chart: Is the market trending or neutral? If trending, in which direction is the dominant pressure? Where is the dynamic trend boundary that price should respect? Core trend spine At the heart of the indicator is a blended trend spine: A fast EMA captures short-term responsiveness. A slow EMA captures structural direction. A volatility-based blend weight dynamically shifts influence between the two. When short-term volatility is low relative to long-term volatility, the fast EMA has more influence, keeping the trend responsive. When volatility rises, the blend shifts toward the slow EMA, reducing noise and preventing overreaction. This blended output is then smoothed again to form the final trend spine, which acts as the structural backbone of the system. Volatility-adaptive envelope The envelope surrounding the trend spine is not based on ATR or fixed percentages. Instead, it is derived from: Log returns of price. An exponentially weighted variance estimate. A configurable multiplier that scales envelope width. This creates bands that automatically widen during volatile expansions and tighten during compression. The envelope therefore reflects the true statistical behavior of price rather than an arbitrary distance. Inner hysteresis band Inside the main envelope, an inner band is constructed using a hysteresis fraction. This inner zone is used to stabilize regime transitions: It prevents rapid flipping between bullish and bearish states. It allows trends to persist unless price meaningfully invalidates them. It reduces whipsaws in sideways conditions. Trend regime logic The indicator operates with three regime states: Bullish Bearish Neutral Regime changes are confirmed using a configurable number of bars outside the adaptive envelope: A bullish regime is confirmed when price closes above the upper envelope for the required number of bars. A bearish regime is confirmed when price closes below the lower envelope for the required number of bars. A trend exits back to neutral when price reverts through the trend spine. This structure ensures that trends are confirmed by sustained pressure rather than single-bar spikes. Active trend line Once a regime is active, the indicator plots a single dominant trend line: In a bullish regime, the lower envelope becomes the active trend support. In a bearish regime, the upper envelope becomes the active trend resistance. In neutral conditions, price itself is used as a placeholder. This creates a simple, actionable visual reference for trend-following decisions. Directional energy visualization The indicator uses layered fills to visualize directional pressure: Bullish energy fills appear when price holds above the active trend line. Bearish energy fills appear when price holds below the active trend line. Opacity gradients communicate strength and persistence rather than binary states. A subtle “rim” effect is added using ATR-based offsets to give depth and reinforce the active side of the trend without cluttering the chart. Signals and trend starts Discrete signals are generated only when a new trend regime begins: Buy signals appear at the first confirmed transition into a bullish regime. Sell signals appear at the first confirmed transition into a bearish regime. Signals are intentionally sparse. They are designed to mark regime shifts, not every pullback or continuation, making them suitable for higher-quality trend entries rather than frequent trading. Candle coloring Optional candle coloring reinforces regime context: Bullish regimes tint candles toward the bullish color. Bearish regimes tint candles toward the bearish color. Neutral states remain visually muted. This allows the chart to communicate trend state even when the envelope itself is partially hidden or de-emphasized. Alerts Built-in alerts are provided for key trend events: Bull trend start. Bear trend start. Transition from trend to neutral. Price crossing the trend spine. These alerts support hands-off trend monitoring across multiple instruments and timeframes. How to use it for trend following Trend identification Only trade in the direction of the active regime. Ignore counter-trend signals during confirmed trends. Entry alignment Use the first regime signal as a structural entry. Use pullbacks toward the active trend line as continuation opportunities. Trend management As long as price respects the active envelope boundary, the trend remains valid. A move back through the spine signals loss of trend structure. Market filtering Periods where the indicator remains neutral highlight non-trending environments. This helps avoid forcing trades during chop or compression. Adaptive Trend Envelope is designed to behave like a living trend structure. Instead of forcing price into static rules, it adapts to volatility, confirms direction through sustained pressure, and presents trend information in a clean, readable form that supports disciplined trend-following workflows.
Pine Script® Indikator
von BackQuant
6
DeeptestDeeptest: Quantitative Backtesting Library for Pine Script ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ OVERVIEW Deeptest is a Pine Script library that provides quantitative analysis tools for strategy backtesting. It calculates over 100 statistical metrics including risk-adjusted return ratios (Sharpe, Sortino, Calmar), drawdown analysis, Value at Risk (VaR), Conditional VaR, and performs Monte Carlo simulation and Walk-Forward Analysis. █ WHY THIS LIBRARY MATTERS Pine Script is a simple yet effective coding language for algorithmic and quantitative trading. Its accessibility enables traders to quickly prototype and test ideas directly within TradingView. However, the built-in strategy tester provides only basic metrics (net profit, win rate, drawdown), which is often insufficient for serious strategy evaluation. Due to this limitation, many traders migrate to alternative backtesting platforms that offer comprehensive analytics. These platforms require other language programming knowledge, environment setup, and significant time investment—often just to test a simple trading idea. Deeptest bridges this gap by bringing institutional-level quantitative analytics directly to Pine Script. Traders can now perform sophisticated analysis without leaving TradingView or learning complex external platforms. All calculations are derived from strategy.closedtrades.* , ensuring compatibility with any existing Pine Script strategy. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ ORIGINALITY AND USEFULNESS This library is original work that adds value to the TradingView community in the following ways: 1. Comprehensive Metric Suite: Implements 112+ statistical calculations in a single library, including advanced metrics not available in TradingView's built-in tester (p-value, Z-score, Skewness, Kurtosis, Risk of Ruin). 2. Monte Carlo Simulation: Implements trade-sequence randomization to stress-test strategy robustness by simulating 1000+ alternative equity curves. 3. Walk-Forward Analysis: Divides historical data into rolling in-sample and out-of-sample windows to detect overfitting by comparing training vs. testing performance. 4. Rolling Window Statistics: Calculates time-varying Sharpe, Sortino, and Expectancy to analyze metric consistency throughout the backtest period. 5. Interactive Table Display: Renders professional-grade tables with color-coded thresholds, tooltips explaining each metric, and period analysis cards for drawdowns/trades. 6. Benchmark Comparison: Automatically fetches S&P 500 data to calculate Alpha, Beta, and R-squared, enabling objective assessment of strategy skill vs. passive investing. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ KEY FEATURES Performance Metrics Net Profit, CAGR, Monthly Return, Expectancy Profit Factor, Payoff Ratio, Sample Size Compounding Effect Analysis Risk Metrics Sharpe Ratio, Sortino Ratio, Calmar Ratio (MAR) Martin Ratio, Ulcer Index Max Drawdown, Average Drawdown, Drawdown Duration Risk of Ruin, R-squared (equity curve linearity) Statistical Distribution Value at Risk (VaR 95%), Conditional VaR Skewness (return asymmetry) Kurtosis (tail fatness) Z-Score, p-value (statistical significance testing) Trade Analysis Win Rate, Breakeven Rate, Loss Rate Average Trade Duration, Time in Market Consecutive Win/Loss Streaks with Expected values Top/Worst Trades with R-multiple tracking Advanced Analytics Monte Carlo Simulation (1000+ iterations) Walk-Forward Analysis (rolling windows) Rolling Statistics (time-varying metrics) Out-of-Sample Testing Benchmark Comparison Alpha (excess return vs. benchmark) Beta (systematic risk correlation) Buy & Hold comparison R-squared vs. benchmark ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ QUICK START Basic Usage //@version=6 strategy("My Strategy", overlay=true) // Import the library import Fractalyst/Deeptest/1 as * // Your strategy logic fastMA = ta.sma(close, 10) slowMA = ta.sma(close, 30) if ta.crossover(fastMA, slowMA) strategy.entry("Long", strategy.long) if ta.crossunder(fastMA, slowMA) strategy.close("Long") // Run the analysis DT.runDeeptest() ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ METRIC EXPLANATIONS The Deeptest table displays 23 metrics across the main row, with 23 additional metrics in the complementary row. Each metric includes detailed tooltips accessible by hovering over the value. Main Row — Performance Metrics (Columns 0-6) Net Profit — (Final Equity - Initial Capital) / Initial Capital × 100 — >20%: Excellent, >0%: Profitable, <0%: Loss — Total return percentage over entire backtest period Payoff Ratio — Average Win / Average Loss — >1.5: Excellent, >1.0: Good, <1.0: Losses exceed wins — Average winning trade size relative to average losing trade. Breakeven win rate = 100% / (1 + Payoff) Sample Size — Count of closed trades — >=30: Statistically valid, <30: Insufficient data — Number of completed trades. Includes 95% confidence interval for win rate in tooltip Profit Factor — Gross Profit / Gross Loss — >=1.5: Excellent, >1.0: Profitable, <1.0: Losing — Ratio of total winnings to total losses. Uses absolute values unlike payoff ratio CAGR — (Final / Initial)^(365.25 / Days) - 1 — >=10%: Excellent, >0%: Positive growth — Compound Annual Growth Rate - annualized return accounting for compounding Expectancy — Sum of all returns / Trade count — >0.20%: Excellent, >0%: Positive edge — Average return per trade as percentage. Positive expectancy indicates profitable edge Monthly Return — Net Profit / (Months in test) — >0%: Profitable month average — Average monthly return. Geometric monthly also shown in tooltip Main Row — Trade Statistics (Columns 7-14) Avg Duration — Average time in position per trade — Mean holding period from entry to exit. Influenced by timeframe and trading style Max CW — Longest consecutive winning streak — Maximum consecutive wins. Expected value = ln(trades) / ln(1/winRate) Max CL — Longest consecutive losing streak — Maximum consecutive losses. Important for psychological risk tolerance Win Rate — Wins / Total Trades — Higher is better — Percentage of profitable trades. Breakeven win rate shown in tooltip BE Rate — Breakeven Trades / Total Trades — Lower is better — Percentage of trades that broke even (neither profit nor loss) Loss Rate — Losses / Total Trades — Lower is better — Percentage of unprofitable trades. Together with win rate and BE rate, sums to 100% Frequency — Trades per month — Trading activity level. Displays intelligently (e.g., "12/mo", "1.5/wk", "3/day") Exposure — Time in market / Total time × 100 — Lower = less risk — Percentage of time the strategy had open positions Main Row — Risk Metrics (Columns 15-22) Sharpe Ratio — (Return - Rf) / StdDev × sqrt(Periods) — >=3: Excellent, >=2: Good, >=1: Fair, <1: Poor — Measures risk-adjusted return using total volatility. Annualized using sqrt(252) for daily Sortino Ratio — (Return - Rf) / DownsideDev × sqrt(Periods) — >=2: Excellent, >=1: Good, <1: Needs improvement — Similar to Sharpe but only penalizes downside volatility. Can be higher than Sharpe Max DD — (Peak - Trough) / Peak × 100 — <5%: Excellent, 5-15%: Moderate, 15-30%: High, >30%: Severe — Largest peak-to-trough decline in equity. Critical for risk tolerance and position sizing RoR — Risk of Ruin probability — <1%: Excellent, 1-5%: Acceptable, 5-10%: Elevated, >10%: Dangerous — Probability of losing entire trading account based on win rate and payoff ratio R² — R-squared of equity curve vs. time — >=0.95: Excellent, 0.90-0.95: Good, 0.80-0.90: Moderate, <0.80: Erratic — Coefficient of determination measuring linearity of equity growth MAR — CAGR / |Max Drawdown| — Higher is better, negative = bad — Calmar Ratio. Reward relative to worst-case loss. Negative if max DD exceeds CAGR CVaR — Average of returns below VaR threshold — Lower absolute is better — Conditional Value at Risk (Expected Shortfall). Average loss in worst 5% of outcomes p-value — Binomial test probability — <0.05: Significant, 0.05-0.10: Marginal, >0.10: Likely random — Probability that observed results are due to chance. Low p-value means statistically significant edge Complementary Row — Extended Metrics Compounding — (Compounded Return / Total Return) × 100 — Percentage of total profit attributable to compounding (position sizing) Avg Win — Sum of wins / Win count — Average profitable trade return in percentage Avg Trade — Sum of all returns / Total trades — Same as Expectancy (Column 5). Displayed here for convenience Avg Loss — Sum of losses / Loss count — Average unprofitable trade return in percentage (negative value) Martin Ratio — CAGR / Ulcer Index — Similar to Calmar but uses Ulcer Index instead of Max DD Rolling Expectancy — Mean of rolling window expectancies — Average expectancy calculated across rolling windows. Shows consistency of edge Avg W Dur — Avg duration of winning trades — Average time from entry to exit for winning trades only Max Eq — Highest equity value reached — Peak equity achieved during backtest Min Eq — Lowest equity value reached — Trough equity point. Important for understanding worst-case absolute loss Buy & Hold — (Close_last / Close_first - 1) × 100 — >0%: Passive profit — Return of simply buying and holding the asset from backtest start to end Alpha — Strategy CAGR - Benchmark CAGR — >0: Has skill (beats benchmark) — Excess return above passive benchmark. Positive alpha indicates genuine value-added skill Beta — Covariance(Strategy, Benchmark) / Variance(Benchmark) — <1: Less volatile than market, >1: More volatile — Systematic risk correlation with benchmark Avg L Dur — Avg duration of losing trades — Average time from entry to exit for losing trades only Rolling Sharpe/Sortino — Dynamic based on win rate — >2: Good consistency — Rolling metric across sliding windows. Shows Sharpe if win rate >50%, Sortino if <=50% Curr DD — Current drawdown from peak — Lower is better — Present drawdown percentage. Zero means at new equity high DAR — CAGR adjusted for target DD — Higher is better — Drawdown-Adjusted Return. DAR^5 = CAGR if max DD = 5% Kurtosis — Fourth moment / StdDev^4 - 3 — ~0: Normal, >0: Fat tails, <0: Thin tails — Measures "tailedness" of return distribution (excess kurtosis) Skewness — Third moment / StdDev^3 — >0: Positive skew (big wins), <0: Negative skew (big losses) — Return distribution asymmetry VaR — 5th percentile of returns — Lower absolute is better — Value at Risk at 95% confidence. Maximum expected loss in worst 5% of outcomes Ulcer — sqrt(mean(drawdown^2)) — Lower is better — Ulcer Index - root mean square of drawdowns. Penalizes both depth AND duration ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ MONTE CARLO SIMULATION Purpose Monte Carlo simulation tests strategy robustness by randomizing the order of trades while keeping trade returns unchanged. This simulates alternative equity curves to assess outcome variability. Method Extract all historical trade returns Randomly shuffle the sequence (1000+ iterations) Calculate cumulative equity for each shuffle Build distribution of final outcomes Output The stress test table shows: Median Outcome: 50th percentile result 5th Percentile: Worst 5% of outcomes 95th Percentile: Best 95% of outcomes Success Rate: Percentage of simulations that were profitable Interpretation If 95% of simulations are profitable: Strategy is robust If median is far from actual result: High variance/unreliability If 5th percentile shows large loss: High tail risk ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ WALK-FORWARD ANALYSIS Purpose Walk-Forward Analysis (WFA) is the gold standard for detecting strategy overfitting. It simulates real-world trading by dividing historical data into rolling "training" (in-sample) and "validation" (out-of-sample) periods. A strategy that performs well on unseen data is more likely to succeed in live trading. Method The implementation uses a non-overlapping window approach following AmiBroker's gold standard methodology: Segment Calculation: Total trades divided into N windows (default: 12), IS = ~75%, OOS = ~25%, Step = OOS length Window Structure: Each window has IS (training) followed by OOS (validation). Each OOS becomes the next window's IS (rolling forward) Metrics Calculated: CAGR, Sharpe, Sortino, MaxDD, Win Rate, Expectancy, Profit Factor, Payoff Aggregation: IS metrics averaged across all IS periods, OOS metrics averaged across all OOS periods Output IS CAGR: In-sample annualized return OOS CAGR: Out-of-sample annualized return ( THE key metric ) IS/OOS Sharpe: In/out-of-sample risk-adjusted return Success Rate: % of OOS windows that were profitable Interpretation Robust: IS/OOS CAGR gap <20%, OOS Success Rate >80% Some Overfitting: CAGR gap 20-50%, Success Rate 50-80% Severe Overfitting: CAGR gap >50%, Success Rate <50% Key Principles: OOS is what matters — Only OOS predicts live performance Consistency > Magnitude — 10% IS / 9% OOS beats 30% IS / 5% OOS Window count — More windows = more reliable validation Non-overlapping OOS — Prevents data leakage ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ TABLE DISPLAY Main Table — Organized into three sections: Performance Metrics (Cols 0-6): Net Profit, Payoff, Sample Size, Profit Factor, CAGR, Expectancy, Monthly Trade Statistics (Cols 7-14): Avg Duration, Max CW, Max CL, Win, BE, Loss, Frequency, Exposure Risk Metrics (Cols 15-22): Sharpe, Sortino, Max DD, RoR, R², MAR, CVaR, p-value Color Coding 🟢 Green: Excellent performance 🟠 Orange: Acceptable performance ⚪ Gray: Neutral / Fair 🔴 Red: Poor performance ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ IMPLEMENTATION NOTES Data Source: All metrics calculated from strategy.closedtrades , ensuring compatibility with any Pine Script strategy Calculation Timing: All calculations occur on barstate.islastconfirmedhistory to optimize performance Limitations: Requires at least 1 closed trade for basic metrics, 30+ trades for reliable statistical analysis ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ QUICK NOTES ➙ This library has been developed and refined over two years of real-world strategy testing. Every calculation has been validated against industry-standard quantitative finance references. ➙ The entire codebase is thoroughly documented inline. If you are curious about how a metric is calculated or want to understand the implementation details, dive into the source code -- it is written to be read and learned from. ➙ This description focuses on usage and concepts rather than exhaustively listing every exported type and function. The library source code is thoroughly documented inline -- explore it to understand implementation details and internal logic. ➙ All calculations execute on barstate.islastconfirmedhistory to minimize runtime overhead. The library is designed for efficiency without sacrificing accuracy. ➙ Beyond analysis, this library serves as a learning resource. Study the source code to understand quantitative finance concepts, Pine Script advanced techniques, and proper statistical methodology. ➙ Metrics are their own not binary good/bad indicators. A high Sharpe ratio with low sample size is misleading. A deep drawdown during a market crash may be acceptable. Study each function and metric individually -- evaluate your strategy contextually, not by threshold alone. ➙ All strategies face alpha decay over time. Instead of over-optimizing a single strategy on one timeframe and market, build a diversified portfolio across multiple markets and timeframes. Deeptest helps you validate each component so you can combine robust strategies into a trading portfolio. ➙ Screenshots shown in the documentation are solely for visual representation to demonstrate how the tables and metrics will be displayed. Please do not compare your strategy's performance with the metrics shown in these screenshots -- they are illustrative examples only, not performance targets or benchmarks. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ HOW-TO Using Deeptest is intentionally straightforward. Just import the library and call DT.runDeeptest() at the end of your strategy code in main scope. . //@version=6 strategy("My Strategy", overlay=true) // Import the library import Fractalyst/Deeptest/1 as DT // Your strategy logic fastMA = ta.sma(close, 10) slowMA = ta.sma(close, 30) if ta.crossover(fastMA, slowMA) strategy.entry("Long", strategy.long) if ta.crossunder(fastMA, slowMA) strategy.close("Long") // Run the analysis DT.runDeeptest() And yes... it's compatible with any TradingView Strategy! 🪄 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ CREDITS Author: @Fractalyst Font Library: by @fikira - @kaigouthro - @Duyck Community: Inspired by the @PineCoders community initiative, encouraging developers to contribute open-source libraries and continuously enhance the Pine Script ecosystem for all traders. if you find Deeptest valuable in your trading journey, feel free to use it in your strategies and give a shoutout to @Fractalyst -- Your recognition directly supports ongoing development and open-source contributions to Pine Script. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ DISCLAIMER This library is provided for educational and research purposes. Past performance does not guarantee future results. Always test thoroughly and use proper risk management. The author is not responsible for any trading losses incurred through the use of this code.
Pine Script® Bibliothek
von Fractalyst
3
Institutional Intermarket Score PRO V3.3 (Presets)This indicator is built on an unusual, non-traditional intermarket concept and is designed to provide market context rather than trading signals. Institutional Intermarket Score – Indicator Description Overview The Institutional Intermarket Score is a contextual market indicator designed to provide a macro and intermarket perspective on the current market environment. It aggregates information from multiple user-selected correlated and inversely correlated assets to determine whether the broader market context favors risk-on, risk-off, or neutral conditions. This indicator is not a buy or sell signal. It does not attempt to predict short-term price movements, entries, or exits. Its sole purpose is to help the trader understand the broader market context before making any trading decisions. Core Concept Markets do not move in isolation. Institutional participants continuously monitor multiple related markets to assess risk, liquidity, and conviction before deploying capital. This indicator replicates that process by: Monitoring several correlated assets (assets that tend to move in the same direction) Monitoring several inversely correlated assets (assets that typically move in the opposite direction) Combining their behavior into a single, normalized intermarket score The result is a context filter, not a trading system. Asset Groups The indicator supports up to: 5 correlated assets 5 inversely correlated assets All assets are fully configurable by the user and can be enabled or disabled individually. Only active assets are included in all calculations. Market State Evaluation Each asset is evaluated using a Price vs VWAP relationship: Price above VWAP → bullish state Price below VWAP → bearish state This binary state is used consistently across all assets to maintain clarity and robustness. Intermarket Score ---------------------- The Intermarket Score represents the average directional alignment of all active assets and is normalized between -1 and +1. Positive values indicate a risk-on environment Negative values indicate a risk-off environment Values near zero indicate balance, rotation, or uncertainty The score is smoothed to reduce noise and highlight regime persistence rather than short-term fluctuations. Confirmation Metric (X / Y) ---------------------------------- In addition to the score, the indicator calculates a confirmation ratio: Y = total number of active assets X = number of assets aligned with the current regime Alignment is evaluated relative to the current regime: In bullish regimes, assets above VWAP confirm In bearish regimes, assets below VWAP confirm This metric reflects the quality and conviction of the intermarket consensus. High confirmation indicates broad agreement across markets. Low confirmation indicates divergence, uncertainty, or fragile conditions. Heatmap ----------- A compact heatmap visually displays the state of each individual asset: Green indicates alignment with the regime Red indicates opposition Neutral indicates inactive assets This allows immediate identification of: Which markets are confirming Which markets are diverging Whether consensus is broad or fragmented Intended Use ---------------- This indicator is designed to be used: Before evaluating trade setups As a filter, not a trigger In combination with price action, structure, and risk management Typical applications include: Avoiding trades against the broader market context Distinguishing strong trends from fragile moves Identifying periods of institutional alignment or hesitation What This Indicator Is Not It is not a buy or sell indicator It does not provide entry or exit signals It does not predict price direction on its own It does not guarantee profitable trades Any trading decisions remain entirely the responsibility of the user. Summary The Institutional Intermarket Score provides a high-level market image based on assets selected by the user. It reflects context, alignment, and conviction, not timing. Used correctly, it helps traders avoid low-quality trades, understand when markets are aligned or fragmented, and make decisions with greater awareness of the broader environment. It is a decision support tool, not a trading system. This indicator, is still evolving and its structure will continue to develop as new insights are tested...
Pine Script® Indikator
von ueuito
BTC - RVPM: Run Velocity & Probability MapBTC – RVPM: Run Velocity & Probability Map | RM Strategic Context: Understanding Price Runs A "Price Run" (also known as a streak or consecutive sessions) is a foundational concept in time-series analysis that measures the duration of a price movement without a significant counter-signal. While common indicators like RSI or MACD measure magnitude or momentum, they often ignore the Persistence of the trend. Historically, markets move through cycles of expansion and mean-reversion. A Price Run represents a period of "Unidirectional Flow" — a fingerprint of institutional accumulation or systematic distribution. However, standard "run-counting" is often too simplistic for the volatile crypto markets. What Makes RVPM Special? Most community run-counters are binary; they simply tell you if X days were green or red. The RVPM distinguishes itself through three proprietary layers: • The Intensity Filter: It doesnt just count days; it counts effort . By ignoring "flat" days through a percentage-return threshold, it filters out noise that would otherwise skew the statistical probability. • Dynamic Benchmarking: Instead of using an arbitrary number (like "7 days"), the RVPM looks back at 200 bars of history to find the local "Persistence Ceiling." It adapts to the current volatility regime of Bitcoin. • The Velocity Score: It transform simple counts into a -100 to +100 histogram, allowing traders to see momentum "decaying" (e.g., dropping from 90 to 70) even if the price continues to rise. The 3 Pillars of the Engine 1. Velocity Mapping (Persistence Histogram) The histogram calculates the density of directional effort within a defined window. It functions as the "Pulse" of the trend, mapping market behavior into three distinct zones: • High Velocity Zone (> 80 or < -80): Institutional Expansion. This identifies a "clean" move where one side of the market possesses total structural control. In this zone, the trend is efficient, and counter-signals are immediately absorbed. • The Neutral Zone (Near Zero): Momentum Equilibrium. When the histogram fluctuates near the zero line, the market is in a "Recharge Phase." Neither bulls nor bears are achieving persistent dominance. Tactically, this is the "Waiting Room" where range-bound chop is likely, and traders should wait for a new "Expansion" spike before committing. • Velocity Decay: The Exhaustion Warning. Velocity Decay occurs when the indicator moves from an extreme (e.g., +95) back toward the zero line (e.g., +50) while the price is still rising. This is a "Persistence Divergence." It tells you that while the trend is still moving, the consistency of the bars is fragmenting. The "fuel" is being depleted, and the trend is transitioning from an "Institutional Expansion" into a "Speculative Exhaustion." 2. n-of-m Consistency (The Pips) The "Pips" (Circles) mark when a specific consistency threshold is met (e.g., 5 out of 7 bars in one direction). This identifies "Leaky Trends" that are still statistically dominated by one side of the ledger. 3. Statistical Exhaustion (The Arrows) The Dark Red (Top) and Dark Green (Bottom) triangles represent the engine's "Mean-Reversion Signal." The calculation is based on a Relative Maximum Streak (RMS) logic: the script tracks the current linear, consecutive bar count (ignoring bars that fail the Intensity Filter) and continuously benchmarks this against the highest streak recorded over the last 200 bars ( ta.highest(streak, 200) ). The triangles are triggered specifically when the current run reaches 80% of this historical record (the "Anomaly Threshold"). Mathematically, this identifies a move that is statistically pushing against its half-year limit. By using this dynamic threshold rather than a fixed number, the "Extreme" signal automatically tightens during low-volatility regimes and expands during high-volatility expansions, ensuring the signal only appears when the "statistical rubber band" is at a true breaking point. Operational Interface: The RVPM Dashboard The Status Dashboard (Top Right) serves as a real-time monitor for momentum health, providing a clean summary of the underlying persistence data: • Current STREAK: The active, consecutive count of bars meeting the Intensity Filter. It is dynamically color-coded (Cyan/Bullish or Red/Bearish) to provide an instant read on trend seniority. • WINDOW Consistency: Measures the Momentum Density (the n-of-m value). A value of "6" in a "7-bar" window indicates a high-conviction regime that is successfully absorbing pullbacks without losing its primary trajectory. Tactical Playbook: The Mean-Reversion Rule Price action typically follows a "Rubber Band" effect. The further it is stretched without a break, the more "unstable" the trend becomes as the pool of available buyers or sellers is depleted. • The Setup: Wait for the Triangle Arrows to appear. • The Logic: The move has reached a 200-day anomaly. A "Liquidity Vacuum" is forming on the opposite side. • The Action: This is a high-probability Mean-Reversion signal. It is a tactical time to take profits or look for a sharp snap-back move toward the 20-period moving average or the "Institutional Mean." Settings & Parameters • Window Length (m): The lookback window used to calculate the Velocity Score. • Required Days (n): The minimum number of directional bars needed within the window to trigger a "Consistency Pip." • Intensity Filter (%): The minimum % change required for a bar to be counted toward a run. • Lookback Period: The historical window (Default: 200 bars) used to calculate the "Maximum Streak" records for exhaustion alerts. Timeframe Recommendation The RVPM is best viewed on the Daily (1D) timeframe. This filters out intraday noise and provides the most reliable statistical mapping for macro exhaustion points. Credits & Verification The RVPM logic aligns with institutional "Persistence" models and Glassnode's Price Stretch benchmarks. By benchmarking against a rolling 200-day window, the indicator automatically adapts to changing market volatility. Risk Disclaimer & No Financial Advice The information, data, and analytical models provided in this publication are for educational and informational purposes only. This script does not constitute financial, investment, or trading advice. Trading cryptocurrencies and other financial instruments carries a high degree of risk, and statistical anomalies or "Extreme Runs" do not guarantee future price action. Past performance is never indicative of future results. Every trader is responsible for their own due diligence and risk management. Rob Maths and the associated entities are not liable for any financial losses incurred through the use of this tool. Always consult with a certified financial professional before making significant investment decisions. Tags: bitcoin, btc, persistence, streaks, price-runs, momentum, mean-reversion, exhaustion, Rob Maths
Pine Script® Indikator
von Rob_Maths
Moving Average ExponentialThe EMA 50 Trend Filter At the heart of the Sniper system lies the 50-period Exponential Moving Average. Unlike simple moving averages, the EMA applies a weighting factor to recent price data, significantly reducing lag. Role in Strategy: Trend Identification: Serves as the binary divider between Long and Short bias. Dynamic Structure: Acts as dynamic support in uptrends and resistance in downtrends. Signal Filtering: The algorithm automatically suppresses any 'Buy' signals below the line and 'Sell' signals above it, ensuring you never trade against the institutional momentum.
Pine Script® Indikator
von GiladKatz
4
Impulse Reactor RSI-SMA Trend Indicator [ApexLegion]Impulse Reactor RSI-SMA Trend Indicator Introduction and Theoretical Background Design Rationale Standard indicators frequently generate binary 'BUY' or 'SELL' signals without accounting for the broader market context. This often results in erratic "Flip-Flop" behavior, where signals are triggered indiscriminately regardless of the prevailing volatility regime. Impulse Reactor was engineered to address this limitation by unifying two critical requirements: Quantitative Rigor and Execution Flexibility. The Solution Composite Analytical Framework This script is not a simple visual overlay of existing indicators. It is an algorithmic synthesis designed to function as a unified decision-making engine. The primary objective was to implement rigorous quantitative analysis (Volatility Normalization, Structural Filtering) directly within an alert-enabled framework. This architecture is designed to process signals through strict, multi-factor validation protocols before generating real-time notifications, allowing users to focus on structurally validated setups without manual monitoring. How It Works This is not a simple visual mashup. It utilizes a cross-validation algorithm where the Trend Structure acts as a gatekeeper for Momentum signals: Logic over Lag: Unlike simple moving average crossovers, this script uses a 15-layer Gradient Ribbon to detect "Laminar Flow." If the ribbon is knotted (Compression), the system mathematically suppresses all signals. Volatility Normalization: The core calculation adapts to ATR (Average True Range). This means the indicator automatically expands in volatile markets and contracts in quiet ones, maintaining accuracy without constant manual tweaking. Adaptive Signal Thresholding: It incorporates an 'Anti-Greed' algorithm (Dynamic Thresholding) that automatically adjusts entry criteria based on trend duration. This logic aims to mitigate the risk of entering positions during periods of statistical trend exhaustion. Why Use It? Market State Decoding: The gradient Ribbon visualizes the underlying trend phase in real-time. ◦ Cyan/Blue Flow: Strong Bullish Trend (Laminar Flow). ◦ Magenta/Pink Flow: Strong Bearish Trend. ◦ Compressed/Knotted: When the ribbon lines are tightly squeezed or overlapping, it signals Consolidation. The system filters signals here to avoid chop. Noise Reduction: The goal is not to catch every pivot, but to isolate high-confidence setups. The logic explicitly filters out minor fluctuations to help maintain position alignment with the broader trend. ⚖️ Chapter 1: System Architecture Introduction: Composite Analytical Framework System Overview Impulse Reactor serves as a comprehensive technical analysis engine designed to synthesize three distinct market dimensions—Momentum, Volatility, and Trend Structure—into a unified decision-making framework. Unlike traditional methods that analyze these metrics in isolation, this system functions as a central processing unit that integrates disparate data streams to construct a coherent model of market behavior. Operational Objective The primary objective is to transition from single-dimensional signal generation to a multi-factor assessment model. By fusing data from the Impulse Core (Volatility), Gradient Oscillator (Momentum), and Structural Baseline (Trend), the system aims to filter out stochastic noise and identify high-probability trade setups grounded in quantitative confluence. Market Microstructure Analysis: Limitations of Conventional Models Extensive backtesting and quantitative analysis have identified three critical inefficiencies in standard oscillator-based strategies: • Bounded Oscillator Limitations (The "Oscillation Trap"): Traditional indicators such as RSI or Stochastics are mathematically constrained between fixed values (0 to 100). In strong trending environments, these metrics often saturate in "overbought" or "oversold" zones. Consequently, traders relying on static thresholds frequently exit structurally valid positions prematurely or initiate counter-trend trades against prevailing momentum, resulting in suboptimal performance. • Quantitative Blindness to Quality: Standard moving averages and trend indicators often fail to distinguish the qualitative nature of price movement. They treat low-volume drift and high-velocity expansion identically. This inability to account for "Volatility Quality" leads to delayed responsiveness during critical market events. • Fractal Dissonance (Timeframe Disconnect): Financial markets exhibit fractal characteristics where trends on lower timeframes may contradict higher timeframe structures. Manual integration of multi-timeframe analysis increases cognitive load and susceptibility to human error, often resulting in conflicting biases at the point of execution. Core Design Principles To mitigate the aforementioned systemic inefficiencies, Impulse Reactor employs a modular architecture governed by three foundational principles: Principle A: Volatility Precursor Analysis Market mechanics demonstrate that volatility expansion often functions as a leading indicator for directional price movement. The system is engineered to detect "Volatility Deviation" — specifically, the divergence between short-term and long-term volatility baselines—prior to its manifestation in price action. This allows for entry timing aligned with the expansion phase of market volatility. Principle B: Momentum Density Visualization The system replaces singular momentum lines with a "Momentum Density" model utilizing a 15-layer Simple Moving Average (SMA) Ribbon. • Concept: This visualization represents the aggregate strength and consistency of the trend. • Application: A fully aligned and expanded ribbon indicates a robust trend structure ("Laminar Flow") capable of withstanding minor counter-trend noise, whereas a compressed ribbon signals consolidation or structural weakness. Principle C: Adaptive Confluence Protocols Signal validity is strictly governed by a multi-dimensional confluence logic. The system suppresses signal generation unless there is synchronized confirmation across all three analytical vectors: 1. Volatility: Confirmed expansion via the Impulse Core. 2. Momentum: Directional alignment via the Hybrid Oscillator. 3. Structure: Trend validation via the Baseline. This strict filtering mechanism significantly reduces false positives in non-trending (choppy) environments while maintaining sensitivity to genuine breakouts. 🔍 Chapter 2: Core Modules & Algorithmic Logic Module A: Impulse Core (Normalized Volatility Deviation) Operational Logic The Impulse Core functions as a volatility-normalized momentum gauge rather than a standard oscillator. It is designed to identify "Volatility Contraction" (Squeeze) and "Volatility Expansion" phases by quantifying the divergence between short-term and long-term volatility states. Volatility Z-Score Normalization The formula implements a custom normalization algorithm. Unlike standard oscillators that rely on absolute price changes, this logic calculates the Z-Score of the Volatility Spread. ◦ Numerator: (atr_f - atr_s) captures the raw momentum of volatility expansion. ◦ Denominator: (std_f + 1e-6) standardizes this value against historical variance. ◦ Result: This allows the indicator scales consistently across assets (e.g., Bitcoin vs. Euro) without manual recalibration. f_impulse() => atr_f = ta.atr(fastLen) // Fast Volatility Baseline atr_s = ta.atr(slowLen) // Slow Volatility Baseline std_f = ta.stdev(atr_f, devLen) // Volatility Standard Deviation (atr_f - atr_s) / (std_f + 1e-6) // Normalized Differential Calculation Algorithmic Framework • Differential Calculation: The system computes the spread between a Fast Volatility Baseline (ATR-10) and a Slow Volatility Baseline (ATR-30). • Normalization Protocol: To standardize consistency across diverse asset classes (e.g., Forex vs. Crypto), the raw differential is divided by the standard deviation of the volatility itself over a 30-period lookback. • Signal Generation: ◦ Contraction (Squeeze): When the Fast ATR compresses below the Slow ATR, it registers a potential volatility buildup phase. ◦ Expansion (Release): A rapid divergence of the Fast ATR above the Slow ATR signals a confirmed volatility expansion, validating the strength of the move. Module B: Gradient Oscillator (RSI-SMA Hybrid) Design Rationale To mitigate the "noise" and "false reversal" signals common in single-line oscillators (like standard RSI), this module utilizes a 15-Layer Gradient Ribbon to visualize momentum density and persistence. Technical Architecture • Ribbon Array: The system generates 15 sequential Simple Moving Averages (SMA) applied to a volatility-adjusted RSI source. The length of each layer increases incrementally. • State Analysis: Momentum Alignment (Laminar Flow): When all 15 layers are expanded and parallel, it indicates a robust trend where buying/selling pressure is distributed evenly across multiple timeframes. This state helps filter out premature "overbought/oversold" signals. • Consolidation (Compression): When the distance between the fastest layer (Layer 1) and the slowest layer (Layer 15) approaches zero or the layers intersect, the system identifies a "Non-Tradable Zone," preventing entries during choppy market conditions. // Laminar Flow Validation f_validate_trend() => // Calculate spread between Ribbon layers ribbon_spread = ta.stdev(ribbon_array, 15) // Only allow signals if Ribbon is expanded (Laminar Flow) is_flowing = ribbon_spread > min_expansion_threshold // If compressed (Knotted), force signal to false is_flowing ? signal : na Module C: Adaptive Signal Filtering (Behavioral Bias Mitigation) This subsystem, operating as an algorithmic "Anti-Greed" Mechanism, addresses the statistical tendency for signal degradation following prolonged trends. Dynamic Threshold Adjustment • Win Streak Detection: The algorithm internally tracks the outcome of closed trade cycles. • Sensitivity Multiplier: Upon detecting consecutive successful signals in the same direction, a Penalty_Factor is applied to the entry logic. • Operational Impact: This effectively raises the Required_Slope threshold for subsequent signals. For example, after three consecutive bullish signals, the system requires a 30% steeper trend angle to validate a fourth entry. This enforces stricter discipline during extended trends to reduce the probability of entering at the point of trend exhaustion. Anti-Greed Logic: Dynamic Threshold Calculation f_adjust_threshold(base_slope, win_streak) => // Adds a 10% penalty to the difficulty for every consecutive win penalty_factor = 0.10 risk_scaler = 1 + (win_streak * penalty_factor) // Returns the new, harder-to-reach threshold base_slope * risk_scaler Module D: Trend Baseline (Triple-Smoothed Structure) The Trend Baseline serves as the structural filter for all signals. It employs a Triple-Smoothed Hybrid Algorithm designed to balance lag reduction with noise filtration. Smoothing Stages 1. Volatility Banding: Utilizes a SuperTrend-based calculation to establish the upper and lower boundaries of price action. 2. Weighted Filter: Applies a Weighted Moving Average (WMA) to prioritize recent price data. 3. Exponential Smoothing: A final Exponential Moving Average (EMA) pass is applied to create a seamless baseline curve. Functionality This "Heavy" baseline resists minor intraday volatility spikes while remaining responsive to sustained structural shifts. A signal is only considered valid if the price action maintains structural integrity relative to this baseline 🚦 Chapter 3: Risk Management & Exit Protocols Quantitative Risk Management (TP/SL & Trailing) Foundational Architecture: Volatility-Adjusted Geometry Unlike strategies relying on static nominal values, Impulse Reactor establishes dynamic risk boundaries derived from quantitative volatility metrics. This design aligns trade invalidation levels mathematically with the current market regime. • ATR-Based Dynamic Bracketing: The protocol calculates Stop-Loss and Take-Profit levels by applying Fibonacci coefficients (Default: 0.786 for SL / 1.618 for TP) to the Average True Range (ATR). ◦ High Volatility Environments: The risk bands automatically expand to accommodate wider variance, preventing premature exits caused by standard market noise. ◦ Low Volatility Environments: The bands contract to tighten risk parameters, thereby dynamically adjusting the Risk-to-Reward (R:R) geometry. • Close-Validation Protocol ("Soft Stop"): Institutional algorithms frequently execute liquidity sweeps—driving prices briefly below key support levels to accumulate inventory. ◦ Mechanism: When the "Soft Stop" feature is enabled, the system filters out intraday volatility spikes. The stop-loss is conditional; execution is triggered only if the candle closes beyond the invalidation threshold. ◦ Strategic Advantage: This logic distinguishes between momentary price wicks and genuine structural breakdowns, preserving positions during transient volatility. • Step-Function Trailing Mechanism: To protect unrealized PnL while allowing for normal price breathing, a two-phase trailing methodology is employed: ◦ Phase 1 (Activation): The trailing function remains dormant until the price advances by a pre-defined percentage threshold. ◦ Phase 2 (Dynamic Floor): Once armed, the stop level creates a moving floor, adjusting relative to price action while maintaining a volatility-based (ATR) buffer to systematically protect unrealized PnL. • Algorithmic Exit Protocols (Dynamic Liquidity Analysis) ◦ Rationale: Inefficiencies of Static Targets Static "Take Profit" levels often result in suboptimal exits. They compel traders to close positions based on arbitrary figures rather than evolving market structure, potentially capping upside during significant trends or retaining positions while the underlying trend structure deteriorates. ◦ Solution: Structural Integrity Assessment The system utilizes a Dynamic Liquidity Engine to continuously audit the validity of the position. Instead of targeting a specific price point, the algorithm evaluates whether the trend remains statistically robust. Multi-Factor Exit Logic (The Tri-Vector System) The Smart Exit protocol executes only when specific algorithmic invalidation criteria are met: • 1. Momentum Exhaustion (Confluence Decay): The system monitors a 168-hour rolling average of the Confluence Score. A significant deviation below this historical baseline indicates momentum exhaustion, signaling that the driving force behind the trend has dissipated prior to a price reversal. This enables preemptive exits before a potential drawdown. • 2. Statistical Over-Extension (Mean Reversion): Utilizing the core volatility logic, the system identifies instances where price deviates beyond 2.0 standard deviations from the mean. While the trend may be technically bullish, this statistical anomaly suggests a high probability of mean reversion (elastic snap-back), triggering a defensive exit to capitalize on peak valuation. • 3. Oscillator Rejection (Immediate Pivot): To manage sudden V-shaped volatility, the system monitors RSI pivots. If a sharp "Pivot High" or divergence is detected, the protocol triggers an immediate "Peak Exit," bypassing standard trend filters to secure liquidity during high-velocity reversals. 🎨 Chapter 4: Visualization Guide Gradient Oscillator Ribbon The 15-layer SMA ribbon visualized via plot(r1...r15) represents the "Momentum Density" of the market. • Visuals: ◦ Cyan/Blue Ribbon: Indicates Bullish Momentum. ◦ Pink/Magenta Ribbon: Indicates Bearish Momentum. • Interpretation: ◦ Laminar Flow: When the ribbon expands widely and flows in parallel, it signifies a robust trend where momentum is distributed evenly across timeframes. This is the ideal state for trend-following. ◦ Compression (Consolidation): If the ribbon becomes narrow, twisted, or knotted, it indicates a "Non-Tradable Zone" where the market lacks a unified direction. Traders are advised to wait for clarity. ◦ Over-Extension: If the top layer crosses the Overbought (85) or Oversold (15) lines, it visually warns of potential market overheating. Trend Baseline The thick, color-changing line plotted via plot(baseline) represents the Structural Backbone of the market. • Visuals: Changes color based on the trend direction (Blue for Bullish, Pink for Bearish). • Interpretation: Structural Filter: Long positions are statistically favored only when price action sustains above this baseline, while short positions are favored below it. Dynamic Support/Resistance: The baseline acts as a dynamic support level during uptrends and resistance during downtrends. Entry Signals & Labels Text labels ("Long Entry", "Short Entry") appear when the system detects high-probability setups grounded in quantitative confluence. • Visuals: Labeled signals appear above/below specific candles. • Interpretation: These signals represent moments where Volatility (Expansion), Momentum (Alignment), and Structure (Trend) are synchronized. Smart Exit: Labels such as "Smart Exit" or "Peak Exit" appear when the system detects momentum exhaustion or structural decay, prompting a defensive exit to preserve capital. Dynamic TP/SL Boxes The semi-transparent colored zones drawn via fill() represent the risk management geometry. • Visuals: Colored boxes extending from the entry point to the Take Profit (TP) and Stop Loss (SL) levels. • Function: Volatility-Adjusted Geometry: Unlike static price targets, these boxes expand during high volatility (to prevent wicks from stopping you out) and contract during low volatility (to optimize Risk-to-Reward ratios). SAR + MACD Glow Small glowing shapes appearing above or below candles. • Visuals: Triangle or circle glows near the price bars. • Interpretation: This visual indicates a secondary confirmation where Parabolic SAR and MACD align with the main trend direction. It serves as an additional confluence factor to increase confidence in the trade setup. Support/Resistance Table A small table located at the bottom-right of the chart. • Function: Automatically identifies and displays recent Pivot Highs (Resistance) and Pivot Lows (Support). • Interpretation: These levels can be used as potential targets for Take Profit or invalidation points for manual Stop Loss adjustments. 🖥️ Chapter 5: Dashboard & Operational Guide Integrated Analytics Panel (Dashboard Overview) To facilitate rapid decision-making without manual calculation, the system aggregates critical market dimensions into a unified "Heads-Up Display" (HUD). This panel monitors real-time metrics across multiple timeframes and analytical vectors. A. Intermediate Structure (12H Trend) • Function: Anchors the intraday analysis to the broader market structure using a 12-hour rolling window. • Interpretation: ◦ Bullish (> +0.5%): Indicates a positive structural bias. Long setups align with the macro flow. ◦ Bearish (< -0.5%): Indicates structural weakness. Short setups are statistically favored. ◦ Neutral: Represents a ranging environment where the Confluence Score becomes the primary weighting factor. B. Composite Confluence Score (Signal Confidence) • Definition: A probability metric derived from the synchronization of Volatility (Impulse Core), Momentum (Ribbon), and Trend (Baseline). • Grading Scale: Strong Buy/Sell (> 7.0 / < 3.0): Indicates full alignment across all three vectors. Represents a "Prime Setup" eligible for standard position sizing. Buy/Sell (5.0–7.0 / 3.0–5.0): Indicates a valid trend but with moderate volatility confirmation. Neutral: Signals conflicting data (e.g., Bullish Momentum vs. Bearish Structure). Trading is not recommended ("No-Trade Zone"). C. Statistical Deviation Status (Mean Reversion) • Logic: Utilizes Bollinger Band deviation principles to quantify how far price has stretched from the statistical mean (20 SMA). • Alert States: Over-Extended (> 2.0 SD): Warning that price is statistically likely to revert to the mean (Elastic Snap-back), even if the trend remains technically valid. New entries are discouraged in this zone. Normal: Price is within standard distribution limits, suitable for trend-following entries. D. Volatility Regime Classification • Metric: Compares current ATR against a 100-period historical baseline to categorize the market state. • Regimes: Low Volatility (Lvl < 1.0): Market Compression. Often precedes volatility expansion events. Mid Volatility (Lvl 1.0 - 1.5): Standard operating environment. High Volatility (Lvl > 1.5): Elevated market stress. Risk parameters should be adjusted (e.g., reduced position size) to account for increased variance. E. Performance Telemetry • Function: Displays the historical reliability of the Trend Baseline for the current asset and timeframe. • Operational Threshold: If the displayed Win Rate falls below 40%, it suggests the current market behavior is incoherent (choppy) and does not respect trend logic. In such cases, switching assets or timeframes is recommended. Operational Protocols & Signal Decoding Visual Interpretation Standards • Laminar Flow (Trade Confirmation): A valid trend is visually confirmed when the 15-layer SMA Ribbon is fully expanded and parallel. This indicates distributed momentum across timeframes. • Consolidation (No-Trade): If the ribbon appears twisted, knotted, or compressed, the market lacks a unified directional vector. • Baseline Interaction: The Triple-Smoothed Baseline acts as a dynamic support/resistance filter. Long positions remain valid only while price sustains above this structure. System Calibration (Settings) • Adaptive Signal Filtering (Prev. Anti-Greed): Enabled by default. This logic automatically raises the required trend slope threshold following consecutive wins to mitigate behavioral bias. • Impulse Sensitivity: Controls the reactivity of the Volatility Core. Higher settings capture faster moves but may introduce more noise. ⚙️ Chapter 6: System Configuration & Alert Guide This section provides a complete breakdown of every adjustable setting within Impulse Reactor to assist you in tailoring the engine to your specific needs. 🌐 LANGUAGE SETTINGS (Localization) ◦ Select Language (Default: English): Function: Instantly translates all chart labels, dashboard texts into your preferred language. Supported: English, Korean, Chinese, Spanish ⚡ IMPULSE CORE SETTINGS (Volatility Engine) ◦ Deviation Lookback (Default: 30): The period used to calculate the standard deviation of volatility. Role: Sets the baseline for normalizing momentum. Higher values make the core smoother but slower to react. ◦ Fast Pulse Length (Default: 10): The short-term ATR period. Role: Detects rapid volatility expansion. ◦ Slow Pulse Length (Default: 30): The long-term ATR baseline. Role: Establishes the background volatility level. The core signal is derived from the divergence between Fast and Slow pulses. 🎯 TP/SL SETTINGS (Risk Management) ◦ SL/TP Fibonacci (Default: 0.786 / 1.618): Selects the Fibonacci ratio used for risk calculation. ◦ SL/TP Multiplier (Default: 1.5 / 2): Applies a multiplier to the ATR-based bands. Role: Expands or contracts the Take Profit and Stop Loss boxes. Increase these values for higher volatility assets (like Altcoins) to avoid premature stop-outs. ◦ ATR Length (Default: 14): The lookback period for calculating the Average True Range used in risk geometry. ◦ Use Soft Stop (Close Basis): Role: If enabled, Stop Loss alerts only trigger if a candle closes beyond the invalidation level. This prevents being stopped out by wick manipulations. 🔊 RIBBON SETTINGS (Momentum Visualization) ◦ Show SMA Ribbon: Toggles the visibility of the 15-layer gradient ribbon. ◦ Ribbon Line Count (Default: 15): The number of SMA lines in the ribbon array. ◦ Ribbon Start Length (Default: 2) & Step (Default: 1): Defines the spread of the ribbon. Role: Controls the "thickness" of the momentum density visualization. A wider step creates a broader ribbon, useful for higher timeframes. 📎 DISPLAY OPTIONS ◦ Show Entry Lines / TP/SL Box / Position Labels / S/R Levels / Dashboard: Toggles individual visual elements on the chart to reduce clutter. ◦ Show SAR+MACD Glow: Enables the secondary confirmation shapes (triangles/circles) above/below candles. 📈 TREND BASELINE (Structural Filter) ◦ Supertrend Factor (Default: 12) & ATR Period (Default: 90): Controls the sensitivity of the underlying Supertrend algorithm used for the baseline calculation. ◦ WMA Length (40) & EMA Length (14): The smoothing periods for the Triple-Smoothed Baseline. ◦ Min Trend Duration (Default: 10): The minimum number of bars the trend must be established before a signal is considered valid. 🧠 SMART EXIT (Dynamic Liquidity) ◦ Use Smart Exit: Enables the momentum exhaustion logic. ◦ Exit Threshold Score (Default: 3): The sensitivity level for triggering a Smart Exit. Lower values trigger earlier exits. ◦ Average Period (168) & Min Hold Bars (5): Defines the rolling window for momentum decay analysis and the minimum duration a trade must be held before Smart Exit logic activates. 🛡️ TRAILING STOP (Step) ◦ Use Trailing Stop: Activates the step-function trailing mechanism. ◦ Step 1 Activation % (0.5) & Offset % (0.5): The price must move 0.5% in your favor to arm the first trail level, which sets a stop 0.5% behind price. ◦ Step 2 Activation % (1) & Offset % (0.2): Once price moves 1%, the trail tightens to 0.2%, securing the position. 🌀 SAR & MACD SETTINGS (Secondary Confirmation) ◦ SAR Start/Increment/Max: Standard Parabolic SAR parameters. ◦ SAR Score Scaling (ATR): Adjusts how much weight the SAR signal has in the overall confluence score. ◦ MACD Fast/Slow/Signal: Standard MACD parameters used for the "Glow" signals. 🔄 ANTI-GREED LOGIC (Behavioral Bias) ◦ Strict Entry after Win: Enables the negative feedback loop. ◦ Strict Multiplier (Default: 1.1): Increases the entry difficulty by 10% after each win. Role: Prevents overtrading and entering at the top of an extended trend. 🌍 HTF FILTER (Multi-Timeframe) ◦ Use Auto-Adaptive HTF Filter: Automatically selects a higher timeframe (e.g., 1H -> 4H) to filter signals. ◦ Bypass HTF on Steep Trigger: Allows an entry even against the HTF trend if the local momentum slope is exceptionally steep (catch powerful reversals). 📉 RSI PEAK & CHOPPINESS ◦ RSI Peak Exit (Instant): Triggers an immediate exit if a sharp RSI pivot (V-shape) is detected. ◦ Choppiness Filter: Suppresses signals if the Choppiness Index is above the threshold (Default: 60), indicating a flat market. 📐 SLOPE TRIGGER LOGIC ◦ Force Entry on Steep Slope: Overrides other filters if the price angle is extremely vertical (high velocity). ◦ Slope Sensitivity (1.5): The angle required to trigger this override. ⛔ FLAT MARKET FILTER (ADX & ATR) ◦ Use ADX Filter: Blocks signals if ADX is below the threshold (Default: 20), indicating no trend. ◦ Use ATR Flat Filter: Blocks signals if volatility drops below a critical level (dead market). 🔔 Alert Configuration Guide Impulse Reactor is designed with a comprehensive suite of alert conditions, allowing you to automate your trading or receive real-time notifications for specific market events. How to Set Up: Click the "Alert" (Clock) icon in the TradingView toolbar. Select "Impulse Reactor " from the Condition dropdown. Choose one of the specific trigger conditions below: 🚀 Entry Signals (Trend Initiation) Long Entry: Trigger: Fires when a confirmed Bullish Setup is detected (Momentum + Volatility + Structure align). Usage: Use this to enter new Long positions. Short Entry: Trigger: Fires when a confirmed Bearish Setup is detected. Usage: Use this to enter new Short positions. 🎯 Profit Taking (Target Levels) Long TP: Trigger: Fires when price hits the calculated Take Profit level for a Long trade. Usage: Automate partial or full profit taking. Short TP: Trigger: Fires when price hits the calculated Take Profit level for a Short trade. Usage: Automate partial or full profit taking. 🛡️ Defensive Exits (Risk Management) Smart Exit: Trigger: Fires when the system detects momentum decay or statistical exhaustion (even if the trend hasn't fully reversed). Usage: Recommended for tightening stops or closing positions early to preserve gains. Overbought / Oversold: Trigger: Fires when the ribbon extends into extreme zones. Usage: Warning signal to prepare for a potential reversal or pullback. 💡 Secondary Confirmation (Confluence) SAR+MACD Bullish: Trigger: Fires when Parabolic SAR and MACD align bullishly with the main trend. Usage: Ideal for Pyramiding (adding to an existing winning position). SAR+MACD Bearish: Trigger: Fires when Parabolic SAR and MACD align bearishly. Usage: Ideal for adding to short positions. ⚠️ Chapter 7: Conclusion & Risk Disclosure Methodological Synthesis Impulse Reactor represents a shift from reactive price tracking to proactive energy analysis. By decomposing market activity into its atomic components — Volatility, Momentum, and Structure — and reconstructing them into a coherent decision model, the system aims to provide a quantitative framework for market engagement. It is designed not to predict the future, but to identify high-probability conditions where kinetic energy and trend structure align. Disclaimer & Risk Warnings ◦ Educational Purpose Only This indicator, including all associated code, documentation, and visual outputs, is provided strictly for educational and informational purposes. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. ◦ No Guarantee of Performance Past performance is not indicative of future results. All metrics displayed on the dashboard (including "Win Rate" and "P&L") are theoretical calculations based on historical data. These figures do not account for real-world trading factors such as slippage, liquidity gaps, spread costs, or broker commissions. ◦ High-Risk Warning Trading cryptocurrencies, futures, and leveraged financial products involves a substantial risk of loss. The use of leverage can amplify both gains and losses. Users acknowledge that they are solely responsible for their trading decisions and should conduct independent due diligence before executing any trades. ◦ Software Limitations The software is provided "as is" without warranty. Users should be aware that market data feeds on analysis platforms may experience latency or outages, which can affect signal generation accuracy.
Pine Script® Indikator
von ApexLegion
15
Dark VectorThe Dark Vector is a professional-grade trend-following system designed to solve the two most common causes of trading losses: over-trading during chop and exiting trends too early. Unlike standard indicators that continuously recalculate based on every price tick, this system operates on a strict "State Machine" logic. This means it tracks the current market phase and refuses to issue conflicting signals. If the system is Long, it mathematically cannot issue another Long signal until the previous trend has concluded. The system relies on three core engines: 1. The Trend Architecture (Modified SuperTrend) The backbone of the system is an ATR-based trailing stop mechanism. It creates a dynamic trend line that adjusts to volatility. When volatility expands, the line widens to prevent premature stop-outs during market noise. When volatility contracts, the line tightens to protect profits. 2. The Noise Gate (Choppiness Index) This is the system's safety filter. It measures the fractal efficiency of the market—essentially determining if price is moving in a clear direction or moving sideways. When the market enters a consolidation phase (sideways chop), the Noise Gate activates, turning the candles gray and physically blocking all new entry signals. This prevents the user from entering trades in low-probability environments. 3. The Singularity State Machine This internal logic enforces trading discipline. It treats the trend as a binary state (Bullish or Bearish). It forces an alternating signal pattern, ensuring that you are only alerted to the specific moment a major trend reversal occurs, rather than being bombarded with repetitive signals during a long run. Best Way to Use This System To maximize profitability and minimize false positives, it is recommended to use the "Regime & Alignment" methodology outlined below. 1. The Traffic Light Rule Before placing any trade, observe the color of the candlesticks on the chart: Green Candles: The market is in a confirmed Bullish Impulse. You should only look for Long entries or hold existing positions. Shorting is statistically dangerous here. Red Candles: The market is in a confirmed Bearish Impulse. You should only look for Short entries or hold cash. Buying the dip here is high-risk. Gray Candles: The market is in a Chop/Squeeze regime. The Noise Gate is active. Do not open new positions. This indicates indecision, and the market is likely to destroy option premiums or stop out tight leverage. Wait for the candles to return to Green or Red before acting. 2. The Entry Trigger Enter a trade only when a text label (LONG or SHORT) appears. Long Signal: Occurs when price closes above the Trend Line AND the market is not in a Chop zone. Short Signal: Occurs when price closes below the Trend Line AND the market is not in a Chop zone. 3. The Exit Strategy There are two ways to manage the trade once active: The Trend Follower (Conservative): Hold the position until the Trend Line flips color. This captures the maximum duration of the move but may give back some profit at the very end. The Stop Loss (Active): The Trend Line (the white value in your dashboard) acts as your Trailing Stop. If a candle closes beyond this line, the trend is technically invalidated. You should exit immediately. 4. Multi-Timeframe Alignment (The Golden Rule) The highest win rates are achieved when your trading timeframe aligns with the higher-order trend. Step 1: Check the 4-Hour chart. Is the Trend Line Green? Step 2: Switch to the 15-Minute chart. Step 3: Only take the LONG signals on the 15-Minute chart. Ignore all Short signals. Reasoning: Counter-trend trades often fail. By trading only in the direction of the higher timeframe, you are swimming with the current, not against it. Recommended Settings by Style Swing Trading (Daily/4H): Keep the Trend Factor at 4.0. This ignores daily noise and keeps you in the trade for weeks or months. Day Trading (1H/15m): Lower the Trend Factor to 3.0. This makes the system more reactive to intraday reversals. Scalping (5m): Lower the Trend Factor to 2.0 and the ATR Length to 7. This is aggressive and requires strict adherence to the Stop Loss. Disclaimer This indicator is for educational and informational purposes only. It does not constitute financial advice, investment advice, or a recommendation to buy or sell any asset. Trading cryptocurrencies, stocks, and futures involves a high degree of risk and the potential for significant financial loss. The user assumes all responsibility for their trading decisions. Past performance of any system or indicator is not indicative of future results. Always practice risk management and never trade with money you cannot afford to lose.
Pine Script® Indikator
von DarkPoolCrypto
5