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Neural Weight Oscillator (Zeiierman)█ Overview The Neural Weight Oscillator (Zeiierman) is an adaptive multi-factor oscillator that combines structured decision-making with dynamic market learning. The script analyzes three core market behaviors: Trend, Mean Reversion, and Momentum. Instead of treating these components equally, the oscillator uses the Best-Worst Method (BWM) to determine which market behavior should have the greatest influence under current market conditions. An adaptive training layer then studies historical market reactions and gradually amplifies the features that have recently produced the strongest directional behavior. The result is a hybrid oscillator that blends: Human-defined market logic Adaptive feature weighting Multi-factor momentum analysis Dynamic market learning Unlike traditional oscillators that rely on static formulas, the Neural Weight Oscillator continuously adjusts its internal structure based on both trader-defined weighting preferences and changing market behavior. █ How It Works ⚪ Market Structure Engine The oscillator builds its analysis from three independent behavioral models: Trend, Mean Reversion, and Momentum. The Trend component measures structural direction by comparing the fast EMA against the slow EMA, then adds the EMA slope to capture acceleration. trendSpread = (emaFast - emaSlow) / atr trendSlope = (emaFast - emaFast ) / atr trendScore = normalize(trendSpread + trendSlope, -2.5, 2.5) The Mean Reversion component measures stretched conditions using RSI exhaustion and statistical deviation from the market mean. zScore = dev == 0 ? 0 : (close - basis) / dev meanScore = (100 - rsi) * 0.5 + normalize(-zScore, -2.5, 2.5) * 0.5 The Momentum component measures directional acceleration using ROC, RSI momentum, and EMA velocity. rocNorm = normalize(close / close - 1.0, -0.05, 0.05) momentumScore = rocNorm * 0.45 + rsi * 0.35 + emaMomentum * 0.20 Each component produces its own normalized score before being blended into the final oscillator. ⚪ Best-Worst Method (BWM) The core weighting system in the oscillator is based on the Best-Worst Method (BWM), a structured decision-making framework that creates balanced weighting relationships among multiple factors. bestIdx = criterionIndex(bestCriterion) worstIdx = criterionIndex(worstCriterion) array.set(bo, bestIdx, 1.0) array.set(ow, worstIdx, 1.0) Instead of assigning arbitrary percentages manually, BWM allows the trader to define which market behavior matters most and which matters least. The script then automatically calculates balanced internal weights. The process begins by selecting: The “Best” factor → the market behavior trusted most The “Worst” factor → the market behavior trusted least relWeight = math.sqrt((aBW / boVal) * owVal) The oscillator then compares all remaining factors relative to those two extremes and converts those relationships into normalized internal weights. ⚪ How To Think About The BWM Weights The easiest way to think about BWM is: “What type of market behavior do I trust most in the current environment?” Different market conditions naturally favor different behaviors. In strong directional trends , traders often prioritize Trend because structural continuation becomes the dominant force. In choppy or range-bound markets , Mean Reversion may become more important because the market repeatedly returns back toward equilibrium. During aggressive breakout environments , Momentum may deserve the highest weighting because acceleration becomes the primary driver. The goal is not to find a “perfect” weight configuration, but rather to align the oscillator with the type of behavior currently dominating the market. ⚪ Adaptive Neural Training Layer The oscillator includes an adaptive learning layer that learns how the market has recently reacted to the model’s internal features. The script looks back at prior Trend, Mean Reversion, and Momentum feature values, then compares them to the future price reaction. target = close / close - 1.0 targetDirection = target > 0 ? 1.0 : target < 0 ? -1.0 : 0.0 High-quality samples are ranked by how strong the move was relative to volatility. sampleScore = math.abs(target) / qualityVol The model then compares its internal prediction against the actual market direction and adjusts the learned feature weights over time. pred = twTrend * s.trend + twMean * s.mean + twMomentum * s.momentum + tbias err = pred - s.target This allows the oscillator to gradually learn which features are producing the strongest directional behavior. ⚪ Adaptive Feature Amplification The learned weights are converted into feature amplifiers. trendAmplifier = 1.0 + learnTrend * blend meanAmplifier = 1.0 + learnMean * blend momentumAmplifier = 1.0 + learnMomentum * blend This allows stronger features to gain more influence, while weaker features receive less influence. █ How to Use ⚪ Reading the Oscillator The oscillator operates between 0 and 100. Values above 50 suggest bullish pressure dominates the market, while values below 50 suggest bearish pressure dominates. As the oscillator moves farther away from the neutral 50 level, directional imbalance becomes stronger. Readings above 70 typically indicate strong bullish expansion, while readings below 30 indicate strong bearish pressure. Extreme zones above 80 or below 20 may signal exhaustion conditions where reversals become more likely. ⚪ Using the BWM Weighting System The BWM system allows traders to align the oscillator with current market behavior by controlling how much influence Trend, Mean Reversion, and Momentum should have inside the model. Imagine the market is trending strongly upward. You may believe: Trend is the dominant market behavior. Mean Reversion still matters during pullbacks. Momentum should have the least influence. In this case, you could choose: Best = Trend Worst = Momentum You then control how strongly Trend dominates the other factors through the comparison inputs. For example: Best-to-Others: Trend = 1 Mean = 3 Mom = 6 Relative-to-Worst: Trend = 4 Mean = 2 Mom = 1 This tells the oscillator: Trend is selected as the strongest market behavior. Momentum is selected as the weakest market behavior. Trend is 3x more important than Mean Reversion. Trend is 6x more important than Momentum. Mean Reversion is 2x more important than Momentum. The script automatically converts these relationships into balanced internal weights. As a result, the oscillator becomes more trend-sensitive while reducing the influence of short-term momentum fluctuations and weak counter-trend behavior. If the market becomes highly rotational or range-bound, traders may instead increase the importance of Mean Reversion so the oscillator becomes more responsive to exhaustion and reversal conditions. During aggressive breakout environments, increasing Momentum weighting can help the oscillator react faster to acceleration phases. The weighting system is designed to adapt the oscillator’s personality to different market environments rather than forcing one static interpretation onto every condition. █ Settings Fast EMA: controls the responsiveness of the Trend and Momentum calculations. Slow EMA: controls the structural trend baseline used throughout the oscillator. Smoothing: controls the smoothness of the final oscillator line. The Best and Worst: determine how the BWM weighting model prioritizes market behaviors. Best-to-Others: define how strongly the selected Best factor dominates the remaining components. Relative-to-Worst: define how much stronger each component is compared to the selected Worst factor. Use Training: enables the adaptive learning layer. Influence: controls how strongly the learned model amplifies features. Line Impact: controls how much the adaptive model can directly influence the oscillator line itself. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Pine Script® indicator
by Zeiierman
5
SuperTrend Logistic Regression | Flux ChartsGENERAL OVERVIEW The SuperTrend Logistic Regression indicator combines the classic SuperTrend trend-following tool with a self-training logistic regression model that assigns a probability percentage to every SuperTrend flip. Each time the SuperTrend changes direction, the indicator evaluates the market conditions at the flip across 14 features spanning candle structure, trend context, and technical indicators, then outputs a probability score between 0% and 100% representing how similar the current setup is to past flips that resolved profitably on the same chart. The model trains itself in real time using past SuperTrend signals as labeled examples. Each historical flip becomes a training example: the features captured at the flip are paired with the outcome at the next opposite flip, producing a continuously growing dataset that the model uses to refine its weights. The probability displayed on each new flip reflects what the model has learned about this specific instrument and timeframe, not a universal assumption about what makes a good signal. screenshot: Full chart showing SuperTrend line with multiple flips and probability labels 🔹What is the purpose of the indicator? The indicator addresses a common problem with SuperTrend: not every flip leads to a sustained trend. Many flips occur during choppy or transitional conditions and reverse quickly, producing losing signals. By scoring each flip with a probability, traders get a quantitative sense of how much confidence the model has in that specific signal based on similar past setups. The indicator does not change the SuperTrend calculation itself, so every flip is still detected and visualized. The probability score adds a filtering layer that helps traders separate high-confidence flips from low-confidence ones. 🔹What is the theory behind the indicator? The indicator is built on the idea that market behavior around trend reversals is not random. Certain combinations of candle structure, volatility conditions, volume patterns, and prior trend context tend to precede trends that follow through, while different combinations tend to precede trends that reverse quickly. Logistic regression is a statistical method well-suited to learning these relationships. Given a set of input features and historical outcomes, it produces a set of weights that map feature combinations to probability estimates. In this indicator, each SuperTrend flip becomes a data point. The features at the flip are recorded, and the outcome is determined at the next opposite flip. If price moved in the signal's direction (higher for bull, lower for bear), the signal is labeled as a win. Otherwise, it is labeled as a loss. The model continuously retrains on this growing dataset, adjusting weights so that feature patterns historically associated with wins produce higher probabilities, and feature patterns associated with losses produce lower probabilities. SUPERTREND LOGISTIC REGRESSION FEATURES SuperTrend Core Logistic Regression Model Candle Features Trend Context Features Technical Features Separate Bull and Bear Models Exponential Decay Weighting Minimum Sample Gating Probability Labels and Filtering Gradient-Colored SuperTrend Visualization Optional Momentum Dots Alerts SUPERTREND CORE The SuperTrend is the foundation of every signal. All detection, training, and prediction is tied to SuperTrend flips. The core SuperTrend calculation in this indicator is standard: ATR multiplied by a user-defined factor produces the trailing line, and a flip occurs when price crosses through the line, changing the direction state. 🔹How SuperTrend is calculated The SuperTrend is derived from Average True Range (ATR) with a user-configurable period and factor. When the calculated line is below price, the indicator is in an uptrend state and the line acts as dynamic support. When the line is above price, the indicator is in a downtrend state and the line acts as dynamic resistance. A flip occurs at the exact bar where price crosses through the line, changing the direction from bullish to bearish or vice versa. 🔹Why SuperTrend was chosen SuperTrend is one of the most widely used trend-following tools on TradingView. It produces clean, deterministic signals with well-understood behavior. Rather than reinventing or modifying the underlying calculation, this indicator treats SuperTrend as a signal source and adds a separate statistical layer on top. This keeps the core behavior familiar to traders already using SuperTrend and ensures that every flip is still detected, regardless of the probability score. 🔹SuperTrend Inputs Two inputs control the SuperTrend calculation: Factor (the ATR multiplier, default 3.0) and ATR Period (the number of bars in the ATR calculation, default 10). These correspond to the standard SuperTrend parameters. Changing these values affects where flips occur, which in turn affects what signals the model trains on. LOGISTIC REGRESSION MODEL Logistic regression is a statistical model that predicts binary outcomes from a set of input features. It takes each feature, multiplies it by a learned weight, sums the results, adds a bias term, and passes the total through a sigmoid function that maps the output to a probability between 0 and 1. The model learns the weights by comparing its predictions against actual outcomes and adjusting through gradient descent. 🔹What is Logistic Regression? Logistic regression works by finding the set of weights that best separates winning signals from losing signals in feature space. A large positive weight on a feature means that higher values of that feature are associated with winning outcomes. A large negative weight means higher values are associated with losing outcomes. A weight near zero means the feature does not discriminate between winners and losers on this chart. The sigmoid function is what turns the weighted sum into a probability. It outputs values near 0 when the weighted sum is very negative, values near 1 when the weighted sum is very positive, and values near 0.5 when the weighted sum is near zero. This gives the output a natural probability interpretation. 🔹How the model is trained Training happens every time a new SuperTrend flip occurs. The indicator looks at all past resolved signals (signals that have already seen their next opposite flip and therefore have a known outcome) and runs gradient descent over them. Gradient descent calculates how much each weight should change to reduce the model's prediction error, then applies those changes iteratively. The indicator runs multiple training epochs on each update to ensure the weights converge reasonably well to the current data. L2 regularization is applied to prevent any single weight from becoming extreme. Weight clipping further constrains weights to a reasonable range, avoiding instability when training data is thin or features are noisy. Together, these make the model more robust on small sample sizes. 🔹Outcome evaluation For training to work, every historical signal needs a win/loss label. The indicator uses flip-to-flip evaluation: a bull signal is considered a win if the close at the next bear flip is higher than the close at the bull flip. A bear signal is considered a win if the close at the next bull flip is lower than the close at the bear flip. This matches the natural lifecycle of a SuperTrend trade, where entry and exit are both on flips. 🔹A Winning Signal Example A winning bull signal forms when a bull flip occurs and, by the time the next bear flip occurs, the close at the bear flip is higher than the close at the bull flip. The probability label on the bull flip reflects the model's confidence at entry, while the outcome is confirmed at the next flip. 🔹A Losing Signal Example A losing signal forms when price fails to move in the signal's direction by the next opposite flip. For example, a bull flip where price immediately reverses and ends lower than entry when the next bear flip occurs. CANDLE FEATURES Candle features describe the shape and volume of the flip candle itself, compared against recent averages. These are the most direct, local features the model uses. All candle features are toggleable in the "Candle Features" input group and are normalized to a range centered at zero, where zero means the feature value matches the recent average. 🔹Body Body measures the size of the flip candle's body (the absolute distance from open to close) relative to the average body size over the recent lookback window. A value above zero means the flip candle had a larger body than recent candles, suggesting stronger conviction. A value below zero means the body was smaller than usual, suggesting indecision. The model learns whether strong-bodied flips tend to produce winning trends on this chart. 🔹Upper Wick Upper Wick measures the length of the upper wick (the distance from the body top to the high) relative to recent average upper wicks. Long upper wicks can indicate rejection at highs, while short upper wicks suggest price accepted the top of the candle cleanly. The model learns how upper wick behavior at flips correlates with outcomes. 🔹Lower Wick Lower Wick measures the length of the lower wick (the distance from the body bottom to the low) relative to recent average lower wicks. Long lower wicks can indicate rejection at lows, while short lower wicks suggest clean acceptance. This feature is disabled by default because it historically showed the weakest predictive signal across tested instruments. 🔹Range Range measures the total high-to-low range of the flip candle relative to the recent average range. A wide-range flip candle indicates volatility expansion, while a tight-range candle suggests contraction. The model learns whether flips during volatility expansion tend to perform differently from flips during contraction. 🔹Volume Volume compares the flip candle's volume to the average volume over the recent lookback window. Higher than average volume at a flip generally indicates stronger participation, while lower than average volume suggests weak conviction. The model learns how volume conviction at flips relates to trend outcomes. 🔹Delta Volume Delta Volume estimates the net buying versus selling pressure within the flip candle by sampling 1-minute lower timeframe bars. The indicator distributes each 1-minute bar's volume to either the bullish or bearish side based on whether that bar closed up or down, then outputs the net direction as a feature value from fully bearish to fully bullish. This gives the model a finer-grained view of what happened inside the flip candle beyond the aggregate close. TREND CONTEXT FEATURES Trend context features describe the market conditions leading into the flip, not just the flip candle itself. These are calculated from the lookback window before the flip. All trend context features are toggleable in the "Trend Features" input group. 🔹Momentum Momentum counts consecutive candle direction leading into the flip. Each bullish candle increments the streak in the positive direction, and each bearish candle increments it in the negative direction. The value is capped so very long streaks do not dominate. A strongly positive momentum value before a bull flip indicates buying was already building; a strongly negative value before a bull flip indicates a sharp reversal from recent selling. The model learns which regime tends to produce better bull outcomes. 🔹Pre-Flip Trend Pre-Flip Trend averages the signed candle bodies (close minus open) over the recent lookback window, normalized by the average range. This gives a broader picture of whether the market was drifting up, drifting down, or chopping sideways before the flip. Unlike momentum, which only counts direction, pre-flip trend captures the magnitude of the directional bias. 🔹ATR Slope ATR Slope compares the current ATR value to the ATR value from the lookback period ago. A positive slope means volatility is expanding into the flip, often associated with stronger follow-through. A negative slope means volatility is contracting, often associated with weaker signals. The model learns whether volatility expansion at the flip is a positive or negative factor for the specific instrument. 🔹Volume Trend Volume Trend compares the average volume of recent bars to the average volume of bars further back. A positive value means participation is increasing; a negative value means it is fading. This feature is disabled by default because it historically showed weak signal across tested instruments. 🔹ST Distance ST Distance measures how far price was from the SuperTrend line before the flip, normalized by ATR. A value near zero means the flip was a tight cross; a larger absolute value means price was well above or below the line and had to travel significantly to trigger the flip. The model learns whether tight crosses or aggressive breaks produce better outcomes. 🔹Trend Duration Trend Duration measures how many bars the previous trend lasted before flipping. Short previous trends might indicate choppy conditions where flips come and go quickly. Longer previous trends might signal genuine exhaustion at reversal. The model learns how previous trend length relates to the success of the current flip. TECHNICAL FEATURES Technical features bring in classic technical indicator readings at the flip point. These are toggleable in the "Technical Features" input group. 🔹RSI RSI (Relative Strength Index) is calculated with the same lookback as the other features and then centered around the 50 level. Values near the oversold end push the feature toward -1, values near the overbought end push it toward +1, and values near 50 are near zero. The model learns whether overbought or oversold RSI readings at flips correlate with different outcomes on the specific chart. On some instruments, flips at extreme RSI readings perform well; on others, they perform poorly. The model discovers this from the data. 🔹BB Position BB Position measures where price sits within Bollinger Bands at the flip. Price at the lower band gives a value of -1, price at the basis gives 0, and price at the upper band gives +1. The Bollinger Bands are calculated with the same lookback as the other features. This feature captures mean-reversion versus breakout context: a flip near the lower band is different from a flip near the upper band, and the model learns which band positions tend to precede successful trends. SEPARATE BULL AND BEAR MODELS The indicator maintains completely independent models for bull signals and bear signals. Each has its own set of weights, its own training dataset, and its own prediction logic. 🔹Why separate models? A single model that assumes features mean opposite things for opposite directions would be an oversimplification. For example, a long lower wick on a bull flip might indicate strong buyer defense, while a long lower wick on a bear flip might indicate weak sellers. These are different setups with different implications, and forcing them into one model with flipped signs would blur the signal. By training separate bull and bear models, the indicator gives each direction room to learn its own relationships. A feature that strongly predicts bull wins might be irrelevant or even negatively correlated for bear wins, and the separate models can capture this without interference. 🔹Implementation Each direction has its own array of signals and its own weights array. When a bull flip occurs, the bull signals array is updated and the bull model is retrained. When a bear flip occurs, the bear signals array and bear model are updated separately. The two models never share state, and their predictions are based only on their own training data. EXPONENTIAL DECAY WEIGHTING Not all training examples are equally relevant. Recent signals reflect current market conditions, while older signals may reflect regimes that no longer apply. The indicator addresses this with exponential decay weighting during training. 🔹How decay works Each resolved signal is assigned a weight based on its age. The newest resolved signal gets the highest weight (1.0 after normalization), and older signals get progressively smaller weights based on a decay factor. A decay factor close to 1 means old and new signals are weighted roughly equally. A decay factor close to 0 means recent signals dominate and old signals are effectively ignored. 🔹Why decay is important Markets change. A feature that strongly predicted wins six months ago might have weak or reversed predictive power now. Without decay weighting, the model would be slow to adapt to new conditions because old data would dilute the influence of recent outcomes. With decay weighting, the model naturally updates its weights to reflect the most recent dynamics while still using historical data to maintain stability. PROBABILITY LABELS AND FILTERING When a new flip passes the minimum sample gate, the indicator calculates the probability from the current feature values and the trained weights. This is displayed as a colored triangle label at the flip bar. 🔹Label Appearance Bull flip labels appear below the bar as upward-pointing green triangles with the probability percentage inside. Bear flip labels appear above the bar as downward-pointing red triangles with the probability percentage inside. The colors are partially transparent so the labels do not obscure price action. 🔹High Probability Example A high probability label means the model found the current flip's features similar to past flips that resolved as wins. For example, a bull flip with strong pre-flip momentum, expanding volatility, positive delta volume, and price well above the SuperTrend line might receive a probability above 65% if those conditions have historically preceded successful bull trends. 🔹Low Probability Example A low probability label means the model found the current flip's features similar to past flips that resolved as losses. Low probability signals might share characteristics with flips that occurred during choppy conditions or weak momentum regimes. 🔹Min Probability Filter A Min Probability input lets users hide labels below a confidence threshold. Setting it to 0 shows every signal. Setting it to 60 only shows labels where the model estimates at least 60% probability of a profitable outcome. This filter only affects visual display and alerts. The SuperTrend line and color change still appear on every flip, and the model still trains on every signal regardless of the filter. 🔹Interpreting the probability The probability represents the model's estimate that the current flip will resolve profitably by the next opposite flip, based on how similar past flips performed. A 70% label does not guarantee a win; it means the model finds this flip's conditions more similar to past winners than past losers. Probabilities should be interpreted relative to the base rate (the overall percentage of past flips that won) rather than as absolute guarantees. VISUAL CUSTOMIZATION The indicator includes several visual elements that help traders see the SuperTrend state and the probability labels clearly. 🔹Gradient-Colored SuperTrend The SuperTrend line is plotted with a color gradient based on how far current price has moved from the line since the most recent flip. The color intensifies as price extends further in the trend direction, giving a visual indication of how developed the trend is at any point. Fills between the body midpoint and the SuperTrend line reinforce this gradient effect. 🔹Momentum Dots An optional momentum dots display overlays circles on the SuperTrend line with colors that shift between yellow and the trend color (green or red) based on price position. This provides a secondary visual cue for trend strength. The Enable Momentum Dots input toggles this display on or off. INPUTS 🔹SuperTrend Factor: ATR multiplier used in the SuperTrend calculation. Default is 3.0. Increasing this value makes the SuperTrend less sensitive and produces fewer, wider flips. Decreasing it makes the SuperTrend more sensitive and produces more frequent flips. ATR Period: Number of bars used in the ATR calculation. Default is 10. Larger periods smooth the ATR, while smaller periods make it more reactive. 🔹Display Enable Momentum Dots: Toggles the momentum dots overlay on the SuperTrend line. Default is on. 🔹Filters Min Probability %: Minimum probability required for a signal label to appear on the chart. Default is 0 (show all signals). Setting this to a higher value hides lower-confidence signals. 🔹Candle Features Body: Enable body size feature. Default is on. Upper Wick: Enable upper wick feature. Default is on. Lower Wick: Enable lower wick feature. Default is off. Range: Enable candle range feature. Default is on. Volume: Enable volume feature. Default is on. Delta Vol: Enable delta volume feature using 1-minute LTF data. Default is on. 🔹Trend Features Momentum: Enable consecutive candle direction feature. Default is on. Pre-Flip Trend: Enable average directional body feature. Default is on. ATR Slope: Enable volatility expansion feature. Default is on. Volume Trend: Enable volume building feature. Default is off. ST Distance: Enable distance from SuperTrend line feature. Default is on. Trend Duration: Enable previous trend length feature. Default is on. 🔹Technical Features RSI: Enable RSI feature. Default is on. BB Position: Enable Bollinger Band position feature. Default is on. ALERTS The indicator includes alert conditions for the following events: Bull Flip: Fires on a confirmed bullish SuperTrend flip that passes the Min Probability filter. Bear Flip: Fires on a confirmed bearish SuperTrend flip that passes the Min Probability filter. Users can configure alerts from the TradingView alerts menu and choose which condition to subscribe to. IMPORTANT NOTES 🔹Non-Repainting Behavior Signals are detected and probability labels are calculated on the bar where the SuperTrend flip confirms. The probability value is based only on feature values at the time of the flip and the model weights as of that bar. The outcome label used for training is only recorded after the next opposite flip has occurred, so the current bar's prediction never uses future data. 🔹Probability Interpretation The probability represents the model's learned estimate from past data on the current chart. It is not a guaranteed win rate. Markets can shift in ways the model has not yet seen, and sample size is always a limitation. Probabilities should be used as one input among many in a trading decision, not as a standalone signal. UNIQUENESS The SuperTrend Logistic Regression indicator takes a distinct approach to SuperTrend enhancement. Rather than altering the SuperTrend calculation itself, it preserves the classic SuperTrend behavior and adds a statistical layer on top that scores each flip independently. Every flip is still detected, so users never miss signals; the probability only determines what gets visually emphasized. The logistic regression implementation uses proper gradient descent with L2 regularization, weight clipping, and exponential decay weighting, making the training process stable and adaptive. Weights persist across bars and are refined with each new resolved signal rather than being recalculated from scratch. Separate bull and bear models learn direction-specific feature relationships independently, avoiding the oversimplification of assuming features have opposite meanings for opposite directions. All 14 features are toggleable, spanning candle structure, trend context, and technical indicators, so users can customize which market characteristics feed into the model. The probability is backed by a concrete, testable definition: the likelihood that the current flip will resolve profitably by the time the next opposite flip occurs, based on how similar past flips performed. This matches how SuperTrend is naturally traded on flip-to-flip cycles. Minimum sample gating ensures that probability labels only appear once the model has enough training data to make meaningful predictions, preventing misleading early signals. Together, these choices make the indicator a disciplined, data-driven extension of SuperTrend that adapts to each instrument and timeframe it runs on, rather than applying a one-size-fits-all scoring system.
Pine Script® indicator
by fluxchart
Opening Range + ICT Killzones + Volume Profile [WL] 📊 Opening Range, ICT Killzones, and a session Volume Profile 📊 in one tool. Built for intraday traders who want clean structure around the session open without cluttering the chart. See more indicators at wealthlearn.ca/indicators 🔎 WHAT THIS IS 🔎 A session-aware terminal that maps the 9:30 to 9:45 Opening Range , plots Asia, London, NY AM, NY Lunch, and NY PM killzones, and builds a live Volume Profile with POC, VAH, VAL, HVNs, and LVNs for the current and previous session. A z-score filtered Volume Impulse layer prints only stronger displacement events, so weak prints are not painted. ⚙️ HOW IT WORKS ⚙️ 🌅 Opening Range 🌅 i) Tracks the configured opening range window, default 0930 to 0945 ii) Draws the OR box live, then locks it once the window closes iii) Plots an extension ladder at 0.5x, 1x, 1.5x, and 2x of the OR size on both sides 📊 Volume Profile 📊 i) Bar weighted distribution across 12 to 72 rows, default 32 ii) POC, VAH, VAL based on a configurable Value Area percent, default 70 iii) Up to 8 HVNs and 8 LVNs per session with adjustable spacing and threshold iv) Current and previous session profiles render side by side for context ⚔️ Session Killzones ⚔️ i) Asia, London, NY AM, NY Lunch, NY PM with their own session strings ii) Box transparency, border, and label styling are all controllable iii) Draws up to N days back so prior-day killzones stay on the chart ⚡ Volume Impulse Filter ⚡ i) Session standard deviation z-score plus a relative volume gate ii) Only prints when both pass the threshold and wick share is contained iii) Spacing limit prevents back-to-back labels stacking 📈 Trend and Regime 📈 i) KER style efficiency ratio, fast and slow EMA separation, structure lookback ii) Classifies session as Bullish, Bearish, or Chop with a 0 to 100 score iii) Previous-day classification: Strong Uptrend, Uptrend, Chop / Mean Reverting, Downtrend, Strong Downtrend , based on percent rank of true range and efficiency ratio 🎛️ HUD and Pressure Meter 🎛️ i) Top-right HUD: state, trend, pressure, magnet bias, range size, POC, VAH, VAL, distance to live and prior nodes, impulse counts ii) Bottom-right Pressure Meter: 12-segment session-volume gauge from -3 sigma to +3 sigma 👤 WHO THIS IS FOR 👤 ES, NQ, RTY, YM, GC, CL futures traders. SPY, QQQ, IWM index traders. Anyone running an opening-range or ICT killzone playbook on the 1m to 15m timeframes. Pairs cleanly with prop-firm rule sets where the first 15 minutes of the session matter. Volume Profile, Order Flow, ORB and/or ICT traders. 🎚️ INPUTS 🎚️ Session window, opening range window, timezone. Volume profile rows, width, value area percent, HVN and LVN thresholds and spacing. Volume impulse z-score, wick max, relative volume length and trigger. KER length, EMA lengths, structure lookback. Killzone enable toggles and session strings. Visual toggles for HUD, pressure meter, node labels, OR labels, OR extension ladder, impulse labels. 📝 NOTES 📝 This indicator does not generate buy or sell signals , only volume extremes w/ standard deviation. It maps structure, session context, and displacement so you can apply your own read, plan, and execution rules. 🏆 CREDITS 🏆 Volume profile math is original to this script. Killzone session conventions follow the standard ICT framework. KER math follows the public Kaufman efficiency ratio. If this script is useful, boost it. Comment what timeframe and instrument you trade and I will tune defaults for it. Built as part of the WealthLearn workflow at wealthlearn.ca . See more indicators at wealthlearn.ca/indicators Disclaimer: This indicator and its accompanying content are provided for educational, technical analysis, and informational purposes only. This is not financial, investment, or trading advice. Past performance is not indicative of future results. Trading in financial markets involves a high degree of risk, and you should only trade with capital you can afford to lose. The author assumes no responsibility for any trading losses incurred as a result of using this tool.
Pine Script® indicator
by WealthLearn
4
Sigma Structure [RWCS]What it is: Sigma Structure is a confluence-based trading indicator that unifies three distinct analytical layers into a single, cohesive view: a Z-Score oscillator measuring price deviation from its 20 EMA, a normalized MACD histogram for momentum context, and an Order Block detection engine that identifies structural demand and supply zones directly on the price chart. The result is an indicator that tells you not just when price is statistically extended, but where that extension is occurring relative to meaningful price structure — giving every signal a location and every location a statistical weight. How it works: 1. Z-Score layer: Price is measured as the number of standard deviations it sits above or below its 20-period EMA. This produces an oscillator that reads consistently across any asset or timeframe — a reading of +2 on Bitcoin means the same thing structurally as +2 on the S&P or EURUSD. The line color intensifies from faded to full aqua as it moves above zero, and faded to full fuchsia below, so the degree of extension is immediately legible at a glance. Fixed bands at ±1, ±2, and ±3 sigma define the statistical landscape. 2. MACD layer: A standard MACD histogram is computed normally, then linearly scaled so its rolling peak aligns with the ±3σ band. No calculation is modified — only the display axis is shared with the Z-Score. This means crossovers, divergences, and momentum shifts read identically to a standard MACD, but now live in the same visual space as the bands, letting you see momentum and mean-reversion context simultaneously. 3. Order Block layer: The indicator scans for order blocks using a sequential candle method — a bearish candle followed by a configurable number of consecutive bullish candles (demand), or a bullish candle followed by consecutive bearish candles (supply). Detected zones are drawn directly on the price chart as shaded regions with solid top boundaries and dashed bottom boundaries, color-coded aqua for demand and fuchsia for supply. Zones extend rightward bar by bar and self-invalidate the moment price closes through them, so what you see on the chart is always live and relevant. 4. Confluence signals: Two signal types fire when the Z-Score and Order Block layers align. An OB Reversal label appears when price is inside an Order Block while the Z-Score is at or beyond ±2σ — the statistical extension and the structural level are confirming each other as a fade opportunity. An OB Continuation label appears when price pulls back into an Order Block and the Z-Score reclaims zero — the trend is reasserting after a mean-reversion dip into demand or supply. 5. Volatility divergence: A background highlight layer compares price's rolling highs and lows against the rolling highs and lows of realized volatility (standard deviation of log returns). When price makes a new low without a corresponding expansion in realized volatility, a bullish divergence is flagged. The inverse flags bearish divergence. These are not entry signals on their own — they indicate moments where price action and volatility are telling different stories and warrant closer attention. Possible ways to use it: 1. Reversal setups: When the Z-Score reaches ±2σ or beyond and price simultaneously tags an active Order Block zone, the statistical extension and structural level are aligned. The OB Reversal label marks these bars. Look for MACD histogram compression or a zero cross in the same window for additional confirmation before acting. 2. Trend continuation entries: In trending markets, price frequently pulls back into demand or supply zones and finds support exactly where it should. When the Z-Score crosses back through zero inside an active zone, the OB Continuation label fires — this is your structural retest with momentum confirmation. 3. Divergence as a filter: The volatility divergence highlights flag potential exhaustion in price moves that lack volatility confirmation. Use these as a reason to tighten risk or wait for the OB/Z-Score confluence before entering, rather than chasing the move. 4. EMA trend bias: The fast and slow EMA overlay on the price chart provides a quick structural read. Aligning your OB Reversal or Continuation signals in the direction of the EMA cross adds a higher-timeframe trend filter without requiring a second indicator. 5. Alert-driven scanning: Three configurable alerts cover the ±2σ Trade Zone cross, OB Reversal confluence, and OB Continuation setup. Set these across a watchlist to surface actionable conditions without manual chart monitoring. Settings guide: 1. EMA / Std Dev Length: Both default to 20, matching a standard Bollinger Band configuration. Increase for smoother, slower signals on higher timeframes. 2. MACD Norm Lookback: Controls how far back the indicator looks to find the MACD histogram's peak for scaling. Higher values produce more stable scaling; lower values make the histogram more reactive to recent momentum. 3. Sequential Candles for OB: The number of consecutive candles required after the origin candle to confirm a block. Higher values produce fewer, higher-quality zones. 4. Max Active Zones: How many demand and supply zones can coexist on each side. Older zones are removed when the limit is reached. 5. Divergence Lookback: The rolling window for comparing price extremes against volatility extremes. Shorter values produce more frequent signals; longer values are more selective. Disclaimer: This indicator is published for educational and informational purposes only. Nothing presented here constitutes financial advice, a solicitation, or a recommendation to buy or sell any financial instrument. All trading involves risk, including the possible loss of principal. Past performance of any indicator or methodology is not indicative of future results. You are solely responsible for your own trading decisions. Always conduct your own research and consult a qualified financial professional before making any investment decisions.
Pine Script® indicator
by RWCS_LTD
Hawkes Flow Ignite [forexobroker]Hawkes Flow Ignite models order-flow clustering with a self-exciting Poisson (Hawkes) process. Each volume burst raises the intensity lambda(t), which then decays exponentially with user-set half-life. Sustained activity = sustained intensity. Signals fire on strong directional bars within a cluster regime. 🔶 ALGORITHM 1. Burst event: volume > sma(volume, N) + k * stdev(volume, N). 2. Intensity update: lambda = decay * lambda + (burst ? 1 : 0), where decay = exp(-ln(2) / halflife). 3. Cluster regime active when lambda > sma(lambda, 100) + k * stdev(lambda, 100). 4. Strong bar detection: |close - open| > k * ATR, with sign giving direction. 🔶 SIGNAL LOGIC - Buy: cluster active AND (close - open) > k * ATR AND not already long AND cooldown elapsed AND barstate.isconfirmed. - Sell: cluster active AND (close - open) < -k * ATR. - Position-lock state machine. 🔶 INPUTS - Volume Window (default 20) - Burst k (default 1.5 sigma) - Decay Half-Life (default 15 bars) - Intensity Threshold k (default 1.0) - Bar Strength x ATR (default 0.30) - Cooldown Bars (default 4) - Visual: dashboard, glow, buy / sell colors 🔶 ALERTS HFI Buy, HFI Sell, HFI Any Signal, HFI Cluster Start, HFI Cluster End, HFI Burst, HFI Strong Up Bar, HFI Strong Dn Bar, HFI Webhook JSON. 🔶 LIMITATIONS - Forex tick volume is broker-aggregated and noisy; performance is best on instruments with reliable volume (futures, crypto, equities). - Half-life is a fixed parameter; real Hawkes processes use a learned kernel that varies by event type. - Cluster gate plus strong-bar gate is conservative; for higher cadence reduce bar-strength k. - Burst threshold uses fixed sigma; some sessions naturally have higher z-scores than others.
Pine Script® indicator
by forexobroker
Earnings Metrics TableUsefull table for a quick review of past earnings excluding EPS which I find pointless in my system. All I care about is generated revenue and profits (Net Profit Margin). Additionally I show FCF / Free Cash Flow. Number of past quarters rows is customizable, I keep it small and tight, 3 to four past quarters to support my buy/sell thesis. Thanks Claude as always for the help since I am not a coder. :) Other indicator in this example is "Performance" , usefull for quick past performance review. Be safe. Marty
Pine Script® indicator
by Marty_Bird92
PO3 Manipulation Detector [OS] PO3 MANIPULATION DETECTOR Open Source — License: Mozilla Public License 2.0 ▌ WHAT IT DOES Identifies MANIPULATION moves (liquidity sweeps + false breakouts) within the ICT/PO3 framework, and proposes trade setups in the OPPOSITE direction of the sweep. It does NOT require a formal accumulation phase: manipulation can occur on key levels without prior accumulation (PDH/PDL, historical swings, session H/L). ▌ THE PO3 CONCEPT (Power of Three) Markets cycle through three phases: • ACCUMULATION — consolidation, position building • MANIPULATION — liquidity sweep (false breakout) • DISTRIBUTION — true directional move This indicator specializes in detecting the second phase: it identifies when a significant level is "violated and rejected", quantifies the statistical quality of the rejection, and only fires a signal when a strict confirmation pipeline is satisfied. HOW IT WORKS — 4-LAYER PIPELINE 1) SWEEP DETECTION Tracks multiple liquidity sources simultaneously: • Swing highs/lows (configurable pivot length, default 5) • Prior session high/low (Asia / London / NY) • Prior Day High / Prior Day Low (PDH/PDL) Detection = high pierces the level + (optional) close back inside the level (true false-breakout). 2) CONFIRMATION SCORE (multi-criteria) Each sweep is scored against 5 "soft" criteria (toggleable): • Wick dominance — wick toward the level ≥ X% of range • Reversal candle — close in the opposite direction • Volume spike — volume ≥ 1.2× SMA(20) • Z-score extreme — |z-score| ≥ threshold (default 2.0σ) • Killzone active — Asia / London / New York Plus one "hard" gate: close must return inside the violated level. Signal fires only if score ≥ threshold (default 4/5). 3) BIAS GUARD (strong filters against counter-trend trades) Every candidate signal is gated by: • Strict mode — wick AND z-score BOTH mandatory • HTF EMA bias — close vs EMA50 on H4 (configurable) • Daily bias — close vs prior day midpoint • Bias alignment — optional: HTF + Daily must agree • Noise filter — exclude bars smaller than X×ATR • Cooldown — block new signals for N bars • Confirmation candle — optional: wait for the next bar to close in the rejection direction 4) PO3 PHASE FILTER Computes the live PO3 phase (ACC / MAN↑ / MAN↓ / DIS↑ / DIS↓ / NEUTRAL) on the HTF candle (default Daily) and blocks counter-phase signals: • DIS↑ → LONG only • DIS↓ → SHORT only • MAN↑ → SHORT only • MAN↓ → LONG only • ACC and NEUTRAL → both directions allowed HOW TO USE 1) Add the indicator to your chart (M5–H1 recommended). 2) Start with default settings — they are tuned conservatively for intraday workflow. 3) When a "MANIP CONFIRMED ↑" (green) or "↓" (red) label appears, read: • Source — which level was violated (Swing-H, Sess-H, PDH...) • Score — confirmation quality (e.g. 4/4) • z-score, Wick% — margins above thresholds • PoM — Point of Manipulation (sweep extreme = SL ref) • SL — suggested stop loss (PoM ± 0.4×ATR) • TP1/TP2/TP3 — projected targets at 1σ / 2σ / 3σ 4) The signal candle is colored (lime/red). Optional: enable "follow-through coloring" to visualize the duration of the rejection (semi-transparent until invalidation). 5) The info table (top-right) shows in real time: killzone, live score, wick%, vol/avg, z-score, HTF/Daily/PO3 bias, cooldown, and the LAST reason a candidate was blocked (e.g. "PO3 phase (DIS↓)", "Strict (wick/z)", "Cooldown") — useful for debugging why an obvious sweep didn't trigger a signal. KEY PARAMETERS LIQUIDITY SOURCES swingLen — pivot length (default 5; use 8 on H1) useSession — track session H/L usePDHL — track Prior Day H/L CONFIRMATION CRITERIA wickPctMin — minimum wick % of candle range (default 60) volMult — volume multiplier (default 1.2) zMin — |z-score| threshold (default 2.0) minScore — required score (default 4/5) BIAS GUARD useStrictCriticals — wick AND z mandatory (HARD gate) useHTFBias / htfTF / htfEMALen useDailyBias / requireBothBiases cooldownBars — bars to block after a signal (default 10) useConfirmCandle — wait for next-bar confirmation PO3 PHASE usePO3Filter / po3TF po3AccBodyMax / po3DisBodyMin RISK PROJECTION (visual only — no orders are sent) slBuffATR — SL buffer beyond PoM showSigmaTPs — project TPs at 1σ/2σ/3σ BAR COLORING useBarColor — color the confirmed reversal candle useFollowThru — also color follow-through bars ALERTS • PO3 MDET — MANIP confirmed BEAR (sell rally) • PO3 MDET — MANIP confirmed BULL (buy dip) RECOMMENDED TIMEFRAMES • M15 — sweet spot for intraday in killzones • M5 — scalping (raise minScore to 5, cooldown to 20) • H1 — cleaner setups (disable useKZ, raise swingLen) • H4+ — not recommended (killzones lose meaning) LIMITATIONS & NOTES • Pure indicator: places no orders, contains no strategy logic. • The projected σ levels are STATISTICAL mean-reversion targets, not price predictions. • Volume on spot forex is notoriously unreliable: the "volume spike" criterion carries less weight on FX than on futures or crypto. Consider disabling it on spot forex. • For instruments with restricted sessions (futures), adjust the killzone windows in the inputs. • The PO3 phase is computed locally with simplified body/range rules; it is a quantitative proxy and may differ from advanced discretionary implementations. • Repainting: NO on swing levels (pivots lag by N bars and are confirmed). Sessions and PDH/PDL are fixed once closed. Bias filters use lookahead_off on HTF requests. OPEN SOURCE Source code is fully readable and modifiable. License: Mozilla Public License 2.0 (MPL-2.0). If you fork or adapt this indicator to your own needs, please keep the header attribution and license notice intact. DISCLAIMER For educational purposes only. Past performance does not guarantee future results. This is not financial advice. Always validate in demo before any live use, and apply your own appropriate risk management. Marghe_fx
Pine Script® indicator
by Marghe_fx
Strategy Sensitivity MatrixThe Strategy Sensitivity Matrix is an institutional-grade backtesting tool designed to evaluate the robustness and parameter sensitivity of trend-following strategies. It enables users to compare the historical performance of a broad range of parameter combinations across multiple metrics to assess the overall stability of the selected strategy. The model displays the complete backtest landscape in a structured, color-coded matrix that allows investors to quickly identify robust parameter regions and evaluate historical performance stability across parameter combinations. At its core, the matrix systematically evaluates a wide range of parameter combinations, where every individual cell represents the backtest result for one unique parameter configuration. Users can switch between volatility-based strategies and moving-average strategies. In volatility mode, the matrix rows represent volatility lengths and the matrix columns represent volatility factors. In crossover mode, the rows represent fast moving-average lengths and the columns represent slow moving-average lengths. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported display metrics include: CAGR = Compounded Annual Growth Rate. Sharpe = CAGR per unit of standard deviation. Sortino = CAGR per unit of downside deviation. Martin = CAGR relative to the Ulcer Index (UI). Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Alpha (α) = Excess annualized risk-adjusted returns. Expectancy = Average expected return per trade. Profit Factor = Total gross profit per unit of losses. Win Rate = Ratio of profitable trades to total trades. Trades/Year = Average number of trades per year. The matrix follows an intuitive percentile-based coloring framework that dynamically compares the relative performance and stability of all parameter combinations. Stronger values above or equal to the matrix median are highlighted in green, with bright green representing the top 10% of all parameter combinations. Weaker values below the matrix median are highlighted in orange, while red represents objectively weak performance based on the selected metric. Broad clusters of consistently strong results generally suggest lower parameter sensitivity and potentially greater robustness, while isolated peaks generally suggest elevated parameter sensitivity. The summary table displayed above the matrix provides a broader distribution-level statistical overview of results across all parameter combinations. This structure allows investors to evaluate whether strong historical performance appears statistically widespread or narrowly concentrated across the parameter landscape. Stable parameter landscapes generally exhibit lower standard deviation, similar median and average values, and smaller performance gaps between the best and top 10% parameter combinations. The summary table includes the following sections: Start = Start month and year of the selected backtest period. End = End month and year of the selected backtest period. Metric = Performance metric currently displayed in the matrix. B&H = Buy-and-hold performance for the selected metric. Best = Best-performing parameter combination in the matrix. Top 10% = Average value of the top 10% parameter combinations. Median = Median value across all parameter combinations. Average = Average value across all parameter combinations. Std Dev = Standard deviation of all parameter combinations. ≥ B&H = Percentage of combinations equal or better than B&H. In summary, the Strategy Sensitivity Matrix is a powerful robustness analysis tool designed to help investors make data-driven decisions when evaluating parameter combinations across trend-following strategies. By evaluating the full parameter landscape, investors can quickly determine whether strong historical performance appears broadly distributed across stable parameter regions or narrowly concentrated within isolated parameter combinations. While historical robustness can provide valuable insight into past market behavior over the selected backtest period, users should remain mindful that market structures evolve over time and that historically stable parameter regions may not necessarily persist in future market conditions.
Pine Script® indicator
by QuantitativeAlpha
Squeeze Box About This Script The Squeeze Box indicator is designed to detect periods of price compression where volatility contracts and price begins to coil into a tight range. These areas often precede expansion moves, breakouts, trend continuation, or volatility events. Unlike traditional squeeze indicators that rely mainly on Bollinger Bands or Keltner Channels, this script focuses directly on the structure of consolidation itself. The indicator automatically identifies tight trading ranges and draws visual “squeeze boxes” around them, helping traders spot: Volatility contraction Quiet accumulation/distribution VCP-style tightening Darvas-like consolidation zones Pre-breakout pressure buildup Pause-and-go continuation patterns The script includes two optional squeeze-detection engines: ATR Range Compression Measures whether the total height of the consolidation zone is small relative to ATR. Regression Residual Compression Measures how tightly price hugs its regression line, helping identify orderly compression rather than random chop. These methods can be used individually or together to filter for higher-quality setups. Features Automatic squeeze box detection Dynamic box extension while compression remains active Optional midline display Adjustable sensitivity settings ATR-based compression filtering Regression-based “tightness” filtering Customizable colors and transparency Control over how many historical boxes remain visible Optional seed markers for early squeeze detection Best Uses This indicator works especially well for: Momentum continuation setups VCP (Volatility Contraction Pattern) style trading Breakout traders Episodic Pivot follow-through Swing trading Identifying low-risk consolidation areas before expansion General Interpretation Tight, narrow boxes often represent decreasing volatility and reduced selling pressure. A breakout above the box may signal renewed momentum or institutional accumulation. Failure to break out or rejection from the upper boundary may indicate unresolved supply. The script is intended as a structure and pressure visualization tool rather than a standalone buy/sell system. Suggested Settings ATR Multiplier: ~0.6 to 1.2 Regression Multiplier: ~0.20 to 0.50 Minimum Bars in Box: 3+ Lower values create more selective squeezes, while higher values identify broader consolidation zones. Seed Dots Optional dots beneath price indicate early compression conditions before a full squeeze box is confirmed. These dots act as an early warning that volatility and price movement are beginning to tighten. In many cases: Seed Dot → Squeeze Box → Expansion They are intended to help traders monitor developing consolidation structures and potential breakout setups. Philosophy Behind the Indicator Markets often move from: Expansion → Contraction → Expansion This script focuses on the contraction phase — the “quiet zone” where pressure builds before movement expands again. The goal is not simply to find volatility squeezes, but to locate areas where price becomes compressed, orderly, and structurally tight before a potential directional move.
Pine Script® indicator
by SpreadEagle71
S&P 500 Weighted Advance/ DeclineThis indicator reads the internal health of the S&P 500 by tracking all 11 State Street sector ETFs (XLK, XLF, XLV, etc.) in real time. Instead of just watching price, it tells you who is driving the market and how broad the move really is. The Two Lines Histogram (teal/red bars) = "Big Companies that drive the market" This is a cap-weighted view of all 11 sectors. Each sector is weighted by how big it is in the S&P 500 — so Tech (XLK) at ~31% carries far more influence than Materials (XLB) at ~2%. When the bars are teal, the cap-weighted market is positive from today's open. When red, it's negative. This tells you what the index itself is doing. White Line = "All sectors get equal vote" This is a simple average of all 11 sectors with equal weight — every sector gets one vote. This tells you what the average sector is doing, regardless of size. This is your breadth reading. Divergence Line (aqua/orange thin line) = The Gap This plots the difference between weighted and Unweighted. When it's positive, the weighted are outpacing the unweighted — the index is being carried by a few large sectors. When it's near zero, everything is moving together. How To Read It ✅ Healthy rally — Histogram is teal AND white line is also above zero, both close together. Broad participation. Safe to be long and trade in the direction of the move. ⚠️ Narrow rally (warning sign) — Histogram is teal but the white line is lagging below it. Only the big sectors (usually Tech or Financials) are carrying the index. The rally lacks breadth and is fragile. Avoid chasing breakouts. 🔄 Rotation signal — Divergence line flips from positive to negative. Money is likely rotating from growth/tech into smaller or defensive sectors. Watch for a trend change. 🔴 Healthy selloff — Histogram is red AND white line is also below zero, both close together. Everything is selling off uniformly. High conviction bearish environment. Avoid longs. Important Notes This is an intraday tool. All readings are based on % change from today's open, so the slate resets every session. Apply it to any chart (SPY, SPX, ES). The indicator ignores the chart symbol — it always pulls data from the 11 sector ETFs. Use it as confirmation, not a trigger. A long setup from your own strategy is stronger when both generals and soldiers are positive. A setup where only generals are positive deserves caution. On big gap days, the histogram will open far from zero. Focus on whether it's expanding or contracting as the day progresses, not the absolute level. The single most important thing to watch is the relationship between the histogram and the white line. When they agree, trust the move. When they diverge, be skeptical. Teal Above zero, close Strong broad rally — trust it Teal Below zero or lagging Narrow, fragile rally — be careful Red Below zero, close Broad selloff — stay defensive Red Above zero Only large caps selling — possible rotation Either Divergence line widening Breadth breaking down — watch for reversal
Pine Script® indicator
by mspdx
MFE / MAE TableThe MFE/MAE Table is a drop-in Pine Script v5 indicator for TradingView that tracks and displays Maximum Favorable Excursion (MFE) and Maximum Adverse Excursion (MAE) statistics across all long and short trades generated by any entry signal you plug in. It calculates average MFE/MAE, single-trade bests and worsts, last-trade values, and a live updating row for your currently open position — all presented in a color-coded on-chart table with fully configurable position (six corners/centers), text size (six levels), and value display format (percentage of entry price, raw ticks, or both).
Pine Script® indicator
by averagejoe_1919
WaveTrend XWaveTrend X (WVTX) A WaveTrend rebuild from first principles. Where the classic WaveTrend ( LazyBear ) normalizes its Channel Index by an arbitrary 0.015 constant inherited from CCI and smooths the result with double exponential moving averages, WVTX replaces both with components that adapt to the data: The CI normalization uses rolling standard deviation instead of 0.015. The smoothing uses a 1-D Kalman filter with ATR-adaptive process noise instead of double-EMA. The fixed ±60 / ±53 OB/OS bands become adaptive rolling-percentile bands. Signals are gated by a Hurst regime proxy (mean-reversion vs trending) and a Shannon entropy filter (noise suppression). Liquidity quality scales line opacity so low-volume readings visually fade. All markers fire only on confirmed bars — explicitly no-repaint. The default channel and signal periods are tuned for slower, more structural use than classic WaveTrend — see the Notes section for how to revert to classic intraday WT speeds. How it works The oscillator is built in five layers: 1. Channel Index (std-normalized). The standard WaveTrend CI is price's deviation from a moving average, normalized by a hard-coded constant. WVTX uses: CI = (src − anchor) / stdev × 50 The anchor is VWMA (default) or EMA over the channel period. The stdev is computed over the same window. The constant disappears — the oscillator's amplitude now reflects actual recent volatility, not a one-size-fits-all multiplier. 2. Kalman smoothing. A 1-D Kalman filter replaces the double-EMA that produces the WT1 line in classic WaveTrend. The filter balances two tunable noise terms: Process noise Q — how much you trust the CI to change between bars. Higher = faster, less smooth. Measurement noise R — how much measurement error you assume. Higher = smoother, more lag. With ATR-adaptive Q enabled (default), Q scales with normalized ATR — so the filter responds faster in volatile conditions and smooths harder in calm ones. This addresses classic WaveTrend's weakness of being either too slow during impulsive moves or too noisy during chop, never both right at once. WT2 is then a longer EMA of WT1 (the signal line, classic WaveTrend convention). 3. Hurst regime proxy. A two-window standard-deviation ratio approximates the Hurst exponent: H ≈ log₂(stdev(src, n) / stdev(src, n/2)) H > 0.5 = trending (volatility grows faster than √n); H < 0.5 = mean-reverting; H ≈ 0.5 = random walk. The configurable dead-band around 0.5 prevents constant regime-flipping near the boundary. Signals are gated accordingly: in trending regimes, both cross directions are allowed; in mean-reverting regimes, signals only fire from the extremes (WT1 in the bottom or top 30%) where reversion probability is structurally elevated. 4. Shannon entropy noise gate. The recent return distribution is binned into 8 equal-width bins, and Shannon entropy computed: H = −Σ pᵢ · log(pᵢ) / log(N_bins) Bounded . Low entropy = concentrated return distribution (structured market); high entropy = uniform distribution (random/noise). When entropy exceeds the configured maximum (default 0.72), signals are suppressed — the oscillator stops emitting markers in conditions where the return statistics suggest no exploitable structure. 5. Adaptive OB/OS. Instead of fixed thresholds, OB and OS bands are the configurable percentiles (default 82% and 18%) of WT1's distribution over a rolling window. The bands move as the oscillator's range shifts — useful for instruments whose volatility regime changes substantially over time. How to read it Three lines and an area: WT1 (main oscillator) — colored by its rolling percentile rank. Cool teal at the high extreme, slate near the middle, coral at the low extreme. WT2 (signal line) — light slate, follows WT1 with EMA lag. Diff = WT1 − WT2 (area) — teal area above zero, coral below. Visualizes the momentum between WT1 and the signal line. Adaptive OB and OS bands are colored by direction (coral above, teal below), with tinted fills extending to the ±50 boundaries. Background regime tint: Teal — trending regime (Hurst above the band) Purple — mean-reverting regime (Hurst below) No tint — neutral / random walk Entropy trace (amber, optional) — recentered around zero and scaled for visibility in the same pane. Above zero = recent returns more random than baseline; below zero = more structured. Signal markers: Teal triangle below pane — confirmed bullish cross (WT1 crosses up WT2, entropy structured, regime allows, WT1 in lower half) Coral triangle above pane — confirmed bearish cross (same gates, opposite direction, WT1 in upper half) Markers fire only on closed bars. There is no intra-bar signal display, by design. Inputs Price source — default hlc3. Channel period — lookback for the VWMA/EMA anchor and stdev normalization. Default 40. Signal period — EMA period for the WT2 signal line. Default 16. VWMA anchor — use VWMA instead of EMA as the price anchor. Default on. Kalman Q / R — process and measurement noise. Defaults 0.02 / 0.8. ATR-adaptive Q — scale Q with normalized ATR. Default on. Hurst regime gate / window / band — toggle, window, dead-band around 0.5. Defaults on / 60 / ±0.05. Entropy noise gate / window / max — toggle, window, entropy ceiling. Defaults on / 40 / 0.72. Liquidity quality scale / window — fade line opacity by recent volume rank. Defaults on / 50. OB/OS rank window / overbought / oversold percentile — sets the adaptive bands. Defaults 100 / 82% / 18%. Regime background / entropy trace — display toggles. Built-in alerts Bull cross — confirmed bullish crossover, gates passed Bear cross — confirmed bearish crossover, gates passed Bull cross, deep OS — bull cross from below the 20th percentile (high-quality setup) Bear cross, deep OB — bear cross from above the 80th percentile (high-quality setup) Trending regime — Hurst transitions into the trending zone Mean-rev regime — Hurst transitions into the mean-reverting zone The two "deep" alerts are the most actionable — a confirmed cross from the extremes of the rolling distribution is structurally more interesting than a cross near the middle. Notes The classic LazyBear WaveTrend uses channel period 10 and signal period 21 — fast, oscillator-style behavior suited to intraday momentum trading. WVTX's defaults are 40 and 16, four times slower. This is deliberate: with adaptive smoothing (Kalman + ATR), liquidity scaling, and entropy/regime gating layered on top, fast periods produce too many marginal signals for the gates to filter cleanly. Slower periods produce fewer but more structural crosses, which is where the gates earn their keep. If you want classic intraday WaveTrend behavior, set channel period to 10 and signal period to 21 — the math still works, just with more frequent (and noisier) output. The Kalman smoother is the most consequential of the rebuilds. Classic WaveTrend's double-EMA introduces predictable lag that traders learn to anticipate; the Kalman filter's adaptive smoothing means the lag isn't constant — it tightens in fast moves and relaxes in chop. For mean-reversion traders, this means signal markers are closer to the actual reversal pivot. For trend-followers, it means the oscillator follows directional moves more responsively. The Hurst regime gate makes the indicator behave differently in different markets. In a trending instrument (most major indices in their secular uptrends), the gate is permissive — crosses fire freely. In a mean-reverting instrument (ranging FX pairs, sideways equities), the gate restricts crosses to the extremes of the rolling distribution, where reversion probability is highest. The dead-band around 0.5 prevents flickering when the regime is ambiguous. The entropy gate is the most novel filter. It asks a different question from regime detection: not "what kind of market is this" but "is there any structure in the recent return distribution?" When entropy is high (close to 1.0), the distribution looks essentially uniform — no recurring pattern for an oscillator to exploit. The gate stops signals from firing in those conditions, which historically catches a lot of false WaveTrend crosses during news-driven chop. The adaptive OB/OS bands are tuned for traders who switch between instruments. Fixed ±60/±53 levels work fine on a single instrument with stable volatility; they're systematically wrong on instruments whose volatility regime changes substantially. Percentile bands self-calibrate, so the same indicator settings work across SPY, BTC, and EURUSD without manual retuning. The non-repaint design is enforced by barstate.isconfirmed — signals fire only when the bar has closed and won't be revised. Intra-bar, you may see WT1 approaching a cross; the marker only appears once the bar closes with the cross confirmed. The conservative choice: signals you can rely on for backtesting and live execution, at the cost of waiting until the bar ends. This is a signal generator with multiple filtering layers — but a signal is information, not an instruction. Five years of work on a trading system left me with dozens of indicators that ultimately didn't earn a place in the final build. They're not failures — they're tools that solved problems I no longer needed solved. So instead of shelving them, I'm publishing the majority of them open-source. If you're a discretionary trader, take what's useful. If you're a systems builder, the source is yours to dissect, modify, and improve. The best return on five years of work is for it to keep working — for someone. If you use this script — or part of it — in your own work, please credit the original with a link back to my profile. Note: these indicators have been updated to Pine Script v6 — some manually, some with AI assistance.
Pine Script® indicator
by TradeSymbiotic
True Seasonal Probability MapTrue Seasonal Probability Map is a weekly seasonal analysis tool for 1W charts. The script combines two related seasonal views: 1. True Seasonal Curve The curve groups historical weekly price changes by calendar week and builds a normalized seasonal path. Users can choose the lookback period, smoothing, detrending, and normalization mode. The curve helps visualize how the selected market has tended to move through the calendar year. 2. Bernstein Weekly Probability Map The weekly boxes show the historical percentage of positive closes for each calendar week. The Bernstein lookback can be set independently from the seasonal curve, using maximum available data, 30 years, 15 years, or a custom value. This allows users to compare a shorter seasonal regime with a broader long-term probability sample. The weekly boxes are aligned with the chart timeline. Boxes for the current year align with the corresponding weekly candles, while future boxes extend to the right of the current bar to show upcoming calendar weeks. Green indicates a positive historical bias, red indicates a negative historical bias, and neutral colors indicate a more balanced probability zone. A configurable average window highlights the current week plus the next selected number of weeks. By default, the box shows a 6-week average including the current week. The average value is displayed inside the highlighted period. Main settings: - True Seasonal Years: lookback used for the seasonal curve. - Bernstein Lookback: lookback used for weekly probability calculations. - Line Color Source: choose whether the seasonal curve colors follow Bernstein probabilities or the True Seasonal probability calculation. - Bernstein Future Weeks: number of future weekly probability boxes to display. - Current Avg Weeks: number of weeks included in the highlighted average window. - Reliability %: threshold used to classify stronger bullish or bearish probability zones. This indicator does not generate buy or sell signals. It is intended as a seasonal context tool. Historical seasonal tendencies and weekly positive-close probabilities do not predict future results. Use this tool together with market structure, trend, volatility, liquidity, macro context, and risk management.
Pine Script® indicator
by ComJoTrader
Candle Box SystemThis indicator is a hybrid market structure tool based on a 3-candle triangle formation combined with EMA trend filtering and mirror-based noise reduction. It detects short-term momentum shifts using structured price movement and validates signals with ATR-based volatility, strength comparison between candles, and EMA trend direction. The system generates BUY and SELL signals only when multiple conditions align, ensuring higher-quality setups and reducing market noise. 🔺 Triangle Logic Each signal is formed from a 3-candle structure that represents micro market flow. The triangle reflects short-term direction, momentum strength, and confirmation behavior. 📊 Reference Lines (Important Feature) On every valid signal, the indicator draws reference lines on the box zone. These lines act as dynamic support and resistance levels. If price reacts to these levels and cannot break or hold them properly, the signal weakens or becomes invalid. When price respects these lines as support or resistance and holds direction → the signal is considered strong BUY or SELL. 🪞 Mirror Filter Concept The indicator uses a mirror-based EMA distance filter to eliminate fake signals caused by price noise around the trend line. ⚠️ Usage Note This tool is designed for decision support only and should be used with proper risk management. 🇹🇷 TÜRKÇE AÇIKLAMA Mirror EMA Triangle System V2 Bu gösterge, 3 mumluk üçgen formasyonu, EMA trend filtresi ve mirror (ayna) tabanlı gürültü azaltma mantığını birleştiren hibrit bir piyasa yapısı aracıdır. Kısa vadeli momentum değişimlerini fiyat yapısı üzerinden tespit eder ve sinyalleri ATR volatilite filtresi, mumlar arası güç karşılaştırması ve EMA trend yönü ile doğrular. Sistem, yalnızca birden fazla koşul aynı anda oluştuğunda BUY ve SELL sinyali üretir, böylece daha kaliteli işlem fırsatları sunar. 🔺 Üçgen Mantığı Her sinyal 3 mumdan oluşan bir yapıdan gelir. Bu yapı piyasanın mikro hareket akışını temsil eder. 📊 Referans Çizgileri (ÖNEMLİ) Sinyal oluştuğunda kutu bölgesi üzerinde referans çizgileri çizilir. Bu çizgiler dinamik destek ve direnç seviyeleri gibi çalışır. Eğer fiyat bu çizgileri destek veya direnç olarak test edip kıramazsa ve yönünü korursa → sinyal güçlü kabul edilir (güçlü AL / SAT). 🪞 Mirror Filtresi EMA etrafındaki fiyat gürültüsünü filtrelemek için mirror mesafe mantığı kullanılır. ⚠️ Kullanım Notu Bu gösterge sadece karar destek aracıdır ve risk yönetimi ile birlikte kullanılmalıdır.
Pine Script® indicator
by onderarslantas107
Index Futures Position Size Calculator V2A simple, free position size calculator for CME index futures traders. Click Entry, click Stop Loss, pick your asset, get your contract size instantly. Built for fast NY session execution — no spreadsheets, no manual maths, no noise. This is the updated version. The first release was a bare-bones calculator showing contract size only with a fixed SL buffer. This version is a full rebuild — every feature below came from real trading feedback, not theory. ═══════════════════════════════════ ✦ SUPPORTED INSTRUMENTS MNQ · MES · NQ · ES — all CME tick values hardcoded. No manual lookup, no mistakes. ═══════════════════════════════════ ✦ WHAT IS NEW IN THIS VERSION → SL buffer is now a toggle — switch the auto ±1 handle offset on or off anytime from settings. When on, the SL label shows +1H so you always know what was calculated → Panel now shows four live values — asset, contract size, real USD risk after rounding, and full stop distance in points → Direction arrow — ▲ Buy or ▼ Sell auto-detected from your Entry and SL position → Panel size control — choose Small, Medium, Large or XL to fit any screen or preference → Lines now start exactly at your click point and extend right — no more lines appearing from far left → Price labels on both lines — see your exact Entry and adjusted SL price at a glance → Ghost line bug fixed — no more phantom line appearing at the bottom when switching timeframes → Dark TradingView-native panel design — colour-coded values, clean two-column layout, easy to read at a glance during live sessions ═══════════════════════════════════ ✦ FEATURES → One-click Entry and Stop Loss directly on the chart → Optional auto ±1 handle SL buffer — structural protection against wick hunts built in → Asset dropdown — MNQ, MES, NQ, ES with correct tick value loading automatically → Smart rounding — fractional contracts of 0.75 or higher round up, otherwise round down → Green Entry line and Red SL line from your exact click, both extending right → Live panel updates instantly when you move Entry or SL ═══════════════════════════════════ ✦ HOW TO USE Add to chart → click Entry → click Stop Loss → pick asset → set Account Size and Risk %. Read your size from the top-right panel. Three clicks and you are sized. ═══════════════════════════════════ ✦ PROP FIRM CHALLENGE SIZING Two clean methods to work within your max drawdown limit. Method 1 — Loss budget split Divide your max loss by how many consecutive losses you can afford. Enter the result as Account Size with Risk at 100%. Example: $2,000 max loss ÷ 5 losses → Account Size $400 · Risk 100% Method 2 — Direct percentage Enter your full max loss as Account Size and set your per-trade percentage. Example: $2,000 max loss, 20% per trade → Account Size $2,000 · Risk 20% Both give the same result — use whichever feels natural. ═══════════════════════════════════ ✦ A NOTE FROM THE AUTHOR Built together with Claude AI through real iterative development — every single feature in this indicator exists because a real trade needed it. This is 100% free and open source. No Discord, no course, no affiliate links, nothing to buy — ever. You are completely free to copy this, modify it, rename it, improve it and republish it as your own. Seriously — go ahead. If you build something better on top of this, that is exactly the point. Clean tools should be free and open to everyone. If it helps even one trader size their positions properly and protect their capital, it was worth sharing. ═══════════════════════════════════ ✦ DISCLAIMER Educational tool only. I do not take any responsibility when you use this indicator in your trading — always check the calculations before use. Futures trading carries substantial risk of loss. Not financial advice. Built with ❤️ by REDz and Claude
Pine Script® indicator
by Green_Room
Session Sweeps Pro | GainzAlgoWhat are Liquidity Sweeps? In modern algorithmic markets, price does not move randomly; it moves from one pocket of liquidity to the next. Liquidity Sweeps (also known as stop runs or "grabs" ) occur when price briefly breaches a well-defined technical level (like a swing high or low) to trigger the stop-loss orders resting there. Once this liquidity is "tapped," large institutional players often use these orders to fill their own counter-positions, leading to a sharp reversal . Identifying where these sweeps have occurred and where they are likely to occur next, is the cornerstone of institutional-grade supply and demand trading . Introducing: Session Sweeps by GainzAlgo This indicator is a premium technical suite designed to visualize the "gravity" of market liquidity. Unlike 90% of liquidity indicators that simply label old highs and lows, the Session Sweeps uses a probabilistic model to map out exactly where the "business" of the market is being conducted. It combines real-time sweep detection with a Cumulative Distribution Function (CDF) to provide a heads-up display (HUD) of the session's liquidity health. How It Works: The engine monitors price action for "breach and reclaim" signatures . Detection: When a pivot high or low is swept and price closes back within the previous range, the script identifies a confirmed sweep . Ghost S/R Zones: Upon detection, the script anchors a "Ghost Box" at the sweep level. These zones extend through the current session, acting as dynamic support and resistance levels that represent "already tapped" liquidity. Conflict Resolution: To keep your charts clean, the script includes a built-in "De-overlapping" logic . If multiple sweeps occur in the same tight price area, it prioritizes the most significant level , preventing visual clutter. The Liquidity Sweep CDF Profile The centerpiece of this indicator is the HUD Profile on the right margin. While standard Volume Profiles show you where volume was traded, our Sweep CDF shows you the distribution of liquidity grabs . Weighted Gravity: Every bar in the profile is weighted by the frequency of sweeps . A "thick" area in the profile indicates a price zone where the market has repeatedly reached for stops. Color-Coded Probability: The profile uses a manual RGB interpolation (shifting from Neon Cyan to Magenta). As the bars widen and change color, they represent the cumulative probability (0% to 100%) of the session's total liquidity being found at or below that price. Sweep POC (Point of Control): The yellow dashed line represents the Sweep POC , the specific price level with the highest density of liquidity sweeps in the current session. This is the market's true "Center of Gravity." Session Anchoring & Menu Inputs The indicator is built for the professional intraday and swing trader , offering full control over the data's scope: Session Period: Select between Daily, Weekly, or Monthly anchors. The CDF profile and S/R zones will automatically reset at the start of each new period, ensuring your data is fresh and relevant . Profile Resolution: Adjust the number of "Rows" to fine-tune the granularity of the CDF heatmap . Risk Threshold: Customize the sensitivity of the Sweep Risk Index (top right), which monitors volatility compression to warn you when a sweep is statistically imminent . How to Trade with Session Sweeps Mean Reversion: Watch for price to approach the Sweep POC during low-volatility periods. These often act as magnets where price stabilizes. Exhaustion Signals: Use the CDF percentages (85%, 70%, etc.). If price is trading at the 90% CDF level , it means the majority of the session's liquidity has already been "cleared" below. Pursuing further moves in that direction carries higher risk . S/R Flips: Use the Ghost S/R Boxes as high-probability entry/exit zones. A Bearish Sweep zone that was once resistance will often act as a "re-entry" point if price returns to test the liquidity remaining in that pocket. The Risk Index: Monitor the Sweep Risk % in the dashboard. When this index spikes ( Red ), be wary of placing stops at obvious swing points, as the "Stop Hunt" probability is at its peak.
Pine Script® indicator
by GainzAlgo
IBD Distribution TrackerIndicator Summary: IBD Distribution & Stalling Tracker The Goal: This custom TradingView indicator is designed to automatically detect and track "Distribution Days"—a core concept from William O'Neil and Investor's Business Daily (IBD). A distribution day signals that large institutions (mutual funds, hedge funds, banks) are actively selling and locking in profits, which often precedes a market correction. What Triggers a Warning Day? The script monitors the daily price action and volume, flagging a day if it meets any of the following three conditions: Standard Distribution: The price drops by 0.2% or more, and the trading volume is higher than the previous day. Churning: Heavy volume pushes into the market, but the price stalls and closes in the lower 50% of its daily range (this catches hidden selling on days that might technically close "green"). Stalling: Trading volume surges above the 25-day moving average, but the price barely moves, gaining less than 0.2% (indicating heavy overhead supply capping the advance). The "Smart" 25-Day Memory (The 5% Rule): Instead of just counting every bad day forever, the script utilizes a dynamic 25-day rolling window with an intelligent invalidation rule. It keeps a running tally of valid distribution days over the last 5 weeks. Invalidation: If the market manages to rally and close 5% higher than the closing price of a previous distribution day, that specific day is considered "defeated" by the bulls. The script automatically wipes it from the running count, ensuring the data is never skewed by old, irrelevant sell-offs. Visual Outputs: Chart Markers: Any day that triggers one of the three conditions has its candle colored purple and is marked with a purple downward triangle for quick visual scanning. Live Dashboard: A clean, on-screen table sits in the top right corner of the chart, displaying the current tally of valid distribution days. If the count hits 5 or more (the traditional IBD danger zone), the text flashes red to warn of elevated market risk.
Pine Script® indicator
by danabourque26
CandlePressure_UtilitiesCandlePressure_Utilities is a lightweight Pine library for converting raw OHLC candle structure into a normalized candle-pressure score, buy/sell percentage estimates, oscillator output, and compact display helpers. The library is designed for scripts that want a reusable candle-pressure layer without rebuilding the same CLV/body/wick math every time. It centralizes the pieces that commonly repeat across pressure-based scripts: • close-location value / CLV calculation • candle body dominance • upper-vs-lower wick imbalance • deadzone-filtered wick pressure • normalized pressure output from -1 to +1 • buy/sell percentage conversion • pressure oscillator conversion from -100 to +100 • alternate body/wick buy-sell allocation • compact volume and relative-volume formatting • table/label size and table-position helpers • small percent and black/white text helpers On the example chart, the pressure candles, pressure oscillator, buy/sell split, CLV/body/wick breakdown, alternate body/wick comparison, and compact table values are all materially driven by this library. This library is intentionally focused on pure candle structure. It does not confirm trend, detect pivots, calculate RSI/DMI/ATR context, decide trade direction, or choose final signal logic for the calling script. Those layers remain script-level decisions. ➖Quick Start➖ Import the library near the top of your script in global scope, alongside any other imports, before calling its helpers. Typical placement: //@version=6 indicator(...) or strategy(...) import MYNAMEISBRANDON/CandlePressure_Utilities/1 as cp Replace /1 with the latest published version if a newer version is available. The main helper for most scripts is candlePressureMetrics(), which returns: • pressure • buyPct • sellPct Example: = cp.candlePressureMetrics( open, high, low, close, volume) string splitText = cp.fmtBuySellSplit( buyPct, sellPct, volume) float pressureOsc = cp.pressureOsc( pressure) The library uses standard OHLCV argument order: open, high, low, close, volume ➖What The Library Measures➖ The default candle-pressure model uses: • Wick Deadzone = 0.02 • CLV Weight = 0.55 • Body Weight = 0.30 • Wick Weight = 0.15 CLV measures where the close finished inside the candle range. Body contribution measures open-to-close directional dominance. Wick contribution measures lower-wick vs upper-wick imbalance. The final pressure score is a weighted blend of those components, normalized from -1 to +1. That pressure score can then be converted into buy/sell percentage estimates, a -100 to +100 pressure oscillator, candle-overlay colors, table values, labels, or dashboard outputs. ➖Function Reference➖ These helpers are grouped by purpose. Most scripts will only need: • candlePressureMetrics() • fmtBuySellSplit() • pressureOsc() More advanced scripts can use the full component helpers for tables, tooltips, debug output, or custom pressure models. ➖Model + Math Helpers➖ modelDefaults() Returns the default candle-pressure model values used by this library. Returns: Wick deadzone, CLV weight, body weight, wick weight clamp(v, lo, hi) Restricts a value between a lower and upper bound. Parameters: v (float): Input value lo (float): Lower bound hi (float): Upper bound Returns: Clamped value safeDiv(numerator, denominator, fallback) Safely divides two values and returns the fallback when division is not valid. Parameters: numerator (float): Numerator value denominator (float): Denominator value fallback (float): Value returned when division is unsafe Returns: numerator / denominator, or fallback when unsafe ➖Display + UI Helpers➖ fmtCompact(val, sigFigs, naText) Formats large values into compact display text such as 1.5k, 2.4m, or 1.2b. Parameters: val (float): Value to format sigFigs (simple int): Significant figures to keep naText (simple string): Text returned when val is na Returns: Compact formatted string fmtBuySellSplit(buyPct, sellPct, volumeValue) Formats buy/sell percentages into rounded split text such as 62/38. Parameters: buyPct (float): Buy percentage sellPct (float): Sell percentage volumeValue (float): Volume value used to handle missing or no-volume bars Returns: Formatted buy/sell split text contrastText(bg) Chooses black or white text based on background brightness. Parameters: bg (color): Background color Returns: Readable contrast text color stripLeadingZero(txt) Removes the leading zero from decimal text. Parameters: txt (string): Input text Returns: Adjusted text, such as 0.25 -> .25 or -0.25 -> -.25 fmtRelVol(val, naText) Formats relative volume with two decimals and strips the leading zero. Parameters: val (float): Relative volume value naText (string): Text returned when val is na Returns: Formatted relative-volume text pctChange(currentValue, baseValue) Returns the percent change from a base value. Parameters: currentValue (float): Current or projected value baseValue (float): Comparison baseline Returns: Percent change fmtPctWhole(val, naText) Formats a percent value as rounded whole-percent text. Parameters: val (float): Percent value naText (string): Text returned when val is na Returns: Rounded percent string pctInt(pct) Rounds and clamps a percentage into 0–100 integer form. Parameters: pct (float): Percent value Returns: Integer percent from 0 to 100 pctIntVol(pct, volumeValue) Rounds and clamps a percentage into 0–100 integer form, returning 0 on no-volume bars. Parameters: pct (float): Percent value volumeValue (float): Volume value Returns: Integer percent from 0 to 100 tableTextSize(sizeText) Converts user-facing table-size text into Pine table text-size enums. Parameters: sizeText (string): Size text. Expected values: "Tiny", "Small", "Normal", or "Large" Returns: Pine table text-size enum labelSize(sizeText) Converts user-facing label-size text into Pine label-size enums. Parameters: sizeText (string): Size text. Expected values: "Tiny", "Small", "Normal", "Large", or "Huge" Returns: Pine label-size enum tablePos(posText) Converts user-facing table-position text into Pine table position enums. Parameters: posText (string): Table position text Returns: Pine table position enum bw(useBlack) Returns black text when the condition is true, otherwise white. Parameters: useBlack (bool): Whether black text should be used Returns: Black or white text color ➖Candle Pressure Helpers➖ candlePressurePartsFull(openValue, highValue, lowValue, closeValue, wickDeadzone, weightClv, weightBody, weightWick) Converts OHLC candle structure into the full normalized pressure component set. Parameters: openValue (float): Candle open highValue (float): Candle high lowValue (float): Candle low closeValue (float): Candle close wickDeadzone (float): Wick imbalance threshold below which wick contribution is forced to 0 weightClv (float): Weight assigned to the CLV component weightBody (float): Weight assigned to the body component weightWick (float): Weight assigned to the wick component Returns: CLV, body % of range, signed body term, raw wick imbalance, deadzoned wick imbalance, final pressure Note: wickDeadzone, weightClv, weightBody, and weightWick are optional. If omitted, the library uses its default model: Wick Deadzone 0.02 / CLV 0.55 / Body 0.30 / Wick 0.15 candlePressureParts(openValue, highValue, lowValue, closeValue, wickDeadzone, weightClv, weightBody, weightWick) Converts OHLC candle structure into the compact pressure component set. Parameters: openValue (float): Candle open highValue (float): Candle high lowValue (float): Candle low closeValue (float): Candle close wickDeadzone (float): Wick imbalance threshold below which wick contribution is forced to 0 weightClv (float): Weight assigned to the CLV component weightBody (float): Weight assigned to the body component weightWick (float): Weight assigned to the wick component Returns: CLV, body % of range, raw wick imbalance, deadzoned wick imbalance, final pressure Note: wickDeadzone, weightClv, weightBody, and weightWick are optional. If omitted, the library uses its default model: Wick Deadzone 0.02 / CLV 0.55 / Body 0.30 / Wick 0.15 pressureToBuySell(pressure, volumeValue) Converts normalized pressure into buy/sell percentages. Parameters: pressure (float): Candle pressure in the -1..+1 range volumeValue (float): Volume value used to handle missing or no-volume bars Returns: Buy %, Sell % pressureOsc(pressure) Converts normalized pressure into a -100..+100 oscillator value. Parameters: pressure (float): Candle pressure in the -1..+1 range Returns: Pressure oscillator value candlePressureMetrics(openValue, highValue, lowValue, closeValue, volumeValue, wickDeadzone, weightClv, weightBody, weightWick) One-call convenience wrapper for scripts that need final pressure, buy %, and sell %. Parameters: openValue (float): Candle open highValue (float): Candle high lowValue (float): Candle low closeValue (float): Candle close volumeValue (float): Volume value used to handle missing or no-volume bars wickDeadzone (float): Wick imbalance threshold below which wick contribution is forced to 0 weightClv (float): Weight assigned to the CLV component weightBody (float): Weight assigned to the body component weightWick (float): Weight assigned to the wick component Returns: Pressure, Buy %, Sell % Note: wickDeadzone, weightClv, weightBody, and weightWick are optional. If omitted, the library uses its default model: Wick Deadzone 0.02 / CLV 0.55 / Body 0.30 / Wick 0.15 bodyWickRateBuyPct(openValue, highValue, lowValue, closeValue) Returns an alternate buy percentage using body/wick structure only. Parameters: openValue (float): Candle open highValue (float): Candle high lowValue (float): Candle low closeValue (float): Candle close Returns: Buy percentage bodyWickRateBuySell(openValue, highValue, lowValue, closeValue, volumeValue) Returns alternate body/wick buy and sell percentages. Parameters: openValue (float): Candle open highValue (float): Candle high lowValue (float): Candle low closeValue (float): Candle close volumeValue (float): Volume value used to handle missing or no-volume bars Returns: Buy %, Sell % ➖Important Notes➖ Candle Pressure is not order flow. The buy/sell split produced by this library is an estimate derived from candle structure. It is not true bid/ask volume, footprint data, or exchange-level order flow. The pressure model is intentionally pure OHLC structure: • CLV measures where the close finished inside the candle range. • Body contribution measures open-to-close directional dominance. • Wick contribution measures lower-wick vs upper-wick imbalance. • Final pressure is a weighted blend of those components. Momentum filters such as RSI, DMI, ATR, trend state, relative volume, or multi-timeframe context should be added by the calling script when needed. This library provides the reusable candle-pressure foundation only. ➖Release Notes➖ v1 Initial release of CandlePressure_Utilities. This release provides a focused candle-pressure utility layer for Pine scripts that need reusable OHLC pressure calculations, buy/sell percentage estimates, pressure oscillator output, compact display formatting, and small table/label helper functions. Included in this release: • default candle-pressure model values • safe math helpers • compact number formatting • buy/sell split formatting • relative-volume formatting • table/label size and table-position helpers • percent and bias display helpers • full candle-pressure component output • compact candle-pressure component output • pressure-to-buy/sell conversion • pressure oscillator conversion • alternate body/wick buy-sell allocation The library is designed to stay focused on reusable candle-pressure mechanics. It does not decide trend, trade direction, signal confirmation, pivot structure, RSI/DMI filters, ATR filters, or final color logic. Calling scripts remain responsible for their own signal model and visual interpretation.
Pine Script® library
by MYNAMEISBRANDON
Takas Laguerre + TRIX +/- (Onder Edition Final)Takas Laguerre + TRIX +/- (Onder Edition Final) is a multi-factor momentum and volatility-based oscillator designed to analyze trend strength, directional pressure, and short-term reversal conditions. The indicator combines four independent analytical components: 🔹 Volatility Pressure (WVF-style Fear Model) A volatility-weighted structure derived from the relationship between price highs and lows over a defined lookback period. It is used to approximate short-term market stress and expansion phases. 🔹 TRIX Momentum Core A triple-smoothed exponential moving average system is used to generate a momentum oscillator. The difference between the final smoothed value and its prior value defines directional momentum (TRIX momentum). An additional strength filter compares momentum magnitude against its historical average to identify stronger trend phases. 🔹 Stochastic Reversal Signals A smoothed stochastic oscillator is used to detect short-term reversal conditions. Crossovers between %K and %D are used as timing triggers for potential directional shifts. 🔹 ADX Trend Strength Filter A directional movement-based ADX implementation measures trend strength and filters out weak or non-trending market conditions. Only signals occurring in higher trend-strength environments are considered valid. 📊 Signal Logic Bullish (+ Signal) occurs when: Stochastic %K crosses above %D ADX indicates sufficient trend strength TRIX momentum is positive Momentum strength exceeds its adaptive threshold Bearish (- Signal) occurs when: Stochastic %K crosses below %D ADX confirms trend presence TRIX momentum is negative Momentum strength confirms directional expansion 📈 Visualization The histogram represents TRIX momentum around a zero baseline, highlighting directional expansion and contraction phases. Color intensity reflects momentum strength conditions. Signal markers are plotted at the zero level to indicate potential directional shifts under confirmed trend conditions. ⚙️ Purpose This indicator is designed to combine momentum structure, volatility pressure, and trend confirmation into a single analytical framework for improved market phase recognition and timing precision.
Pine Script® indicator
by onderarslantas107