Регистрация

Индикаторы и стратегии

Smart Scalper Pro Template + VWAP 📌 Author Garry Evans Independent system developer focused on: Risk-first automation Market structure & liquidity behavior Discipline, consistency, and capital preservation “The edge isn’t the market — it’s the man who survives it.” ⚙️ Risk Management & Position Sizing The script is built around capital protection, not signal frequency. Risk logic includes: Fixed or dynamic risk per trade Market-adaptive position sizing Session-based trade limits Daily trade caps and auto-lockout protection Volatility-aware sizing (futures & crypto) ⚠️ Profit is pursued only after risk is controlled. 📊 Track Record Backtested across multiple market environments Forward-tested and actively used by the author Real-account trades are logged where platform rules allow Results vary by market, timeframe, and user-defined risk settings. 🌍 Supported Markets Designed to work across all liquid markets, including: Stocks Crypto (spot & futures) Options (signal-based framework) Futures (indices, metals, crypto futures) The system adapts to volatility and structure — it is not market-specific. ⚖️ Leverage Leverage is not required If used, leverage is fully user-controlled Risk logic scales exposure conservatively No martingale. No revenge sizing. No over-exposure logic. 🧪 Backtesting ✔ Yes Strategy logic has been backtested Filters reduce chop, noise, and forced trades Focus on drawdown control over curve-fitting 🛠 Support ✔ Yes Direct author support Ongoing improvements and updates Feature refinement based on real usage and feedback 👥 Community ✔ Yes Private user access High-quality feedback environment No public signal spam or hype-driven chat rooms ⏳ Trial Period ✔ Yes Limited trial access available Designed for evaluation only Trial users do not receive full feature access 🚫 Who This Script Is NOT For This system is not for: Traders looking for guaranteed profits Users expecting copy-paste “signal calls” Over-leveraged gamblers Those unwilling to follow risk rules Anyone seeking overnight results This is a discipline and automation tool, not a shortcut. 🧠 Final Positioning This is not a signal service. This is a risk-controlled execution framework designed to: Enforce discipline Reduce emotional trading Protect capital during bad market conditions Scale responsibly during favorable ones
Индикатор Pine Script®
от Garrythetrader954305
DayTradeMind Combined High Win Rate StrategyThe DayTradeMind Combined High Win Rate Strategy is a trend-following system that relies on confluence—the idea that a trade signal is stronger when multiple independent indicators agree. Instead of entering on a single indicator's whim, it uses a "voting" system to qualify entries and a strict risk-to-reward ratio to manage exits.Here is a breakdown of the three main layers of this strategy:1. The Voting Engine (Confluence Model)The strategy tracks four indicators and assigns a "point" for a bullish or bearish bias. It requires a minimum number of points (set by minConfirmations, usually 2/4) before it even considers a trade.IndicatorBullish Condition (1 point)Bearish Condition (1 point)PurposeMACDMACD Line > Signal LineMACD Line < Signal LineMeasures short-term momentum.DonchianPrice > 20-period MedianPrice < 20-period MedianIdentifies price relative to recent range.SuperTrendPrice above trend linePrice below trend lineFilters for the "Macro" trend direction.%B (Bollinger)Price in lower-mid range (0.2–0.5)Price in upper-mid range (0.5–0.8)Prevents buying when overextended.2. The Entry TriggerHaving enough "votes" (confirmations) isn't enough to enter. The strategy waits for a trigger event to ensure you aren't entering a stale trend. An entry only occurs if the minimum confirmations are met AND one of the following happens on the current bar:MACD Cross: The MACD line crosses over the signal line.Structural Break: The price crosses over the Donchian Middle (Median) line.This "Confirmation + Trigger" approach is designed to catch the start of a momentum push rather than buying a flat market.3. Mathematical Risk ManagementThe performance you see in your backtest (like the 46.86% return) is largely driven by the 2:1 Reward-to-Risk (RR) Ratio.Stop Loss (SL): Fixed at 2% below entry.Take Profit (TP): Fixed at 4% above entry.By aiming for a target twice as large as the risk, the strategy can remain profitable even with a win rate as low as 35%–40%. Mathematically, your winning trades compensate for more than two losing trades.Visualizing the SystemTriangles: Small green (up) and red (down) triangles appear on your chart only when the Votes + Trigger align perfectly.Background Shading: Faint green or red bands show you exactly when the "Confluence" is active. If the background is gray, the indicators are in conflict.Dashboard: The table in the top-right summarizes the current "score" for each indicator, letting you know how close you are to a potential trade signal.
Стратегия Pine Script®
от day_trade_mind
1
Auto Fibonacci Lines Depending on ZigZag %In the world of technical analysis, few tools are as powerful—or as misused—as Fibonacci Retracements. The Auto Fibonacci Lines Depending on ZigZag % is not just an indicator; it is a complete, automated trading system designed to eliminate subjectivity and bring institutional-grade precision to your charts. This script automates the identification of significant market structures using a ZigZag algorithm. Once a market swing is mathematically confirmed (based on your deviation settings), it instantly projects a complete suite of Retracement and Extension levels. This allows you to stop guessing where to draw your lines and start focusing on price action. 🧠 The Logic Behind the Indicator Understanding how your tools work is the first step to trusting them. This script operates on a three-step logic loop: ZigZag Identification: The script continuously monitors price action relative to the last known pivot point. It uses a user-defined Deviation % to filter out market noise. A new "Leg" is only confirmed when price reverses by this specific percentage. This ensures that the Fibonacci lines are only drawn on significant market moves, not random chop. Automated Anchor Points: Once a downward trend is confirmed (e.g., price drops 30% from the top), the script automatically anchors the Fibonacci tool to the Swing High (Start) and the Swing Low (End). It does this without you needing to click or drag anything. Dynamic Cleanup: Markets evolve. A key feature of this script is its self-cleaning mechanism. As soon as a new trend leg is confirmed, the script automatically deletes the old, invalidated Fibonacci lines and draws a fresh set for the new structure. This keeps your chart clean and focused on the now. 🎓 How to Trade This System This indicator is color-coded to simplify your decision-making process. It moves beyond standard "rainbow" charts by categorizing price levels into three distinct actionable zones. 1. The "Reload Zone" (White Lines: 0.618 - 0.786) ⚪ Role: High-Probability Support / Entry In institutional trading, the 0.618 (Golden Ratio) to 0.786 region is often where algorithms step in to defend a trend. Why it works : This is the "discount" area where smart money re-accumulates positions before the next leg up. 2. The "Decision Wall" (Blue Lines: 1.382 - 1.5) 🔵 Role: Strong Resistance / Trend Check This is a unique feature of this suite. The 1.382 and 1.5 levels often act as a "ceiling" for weak breakouts. Strategy : If you entered in the White Zone, the Blue Zone is your first major hurdle. If price stalls here, consider securing partial profits. Warning : A rejection from the Blue Lines often leads to a double-top formation. However, a clean break above the Blue Lines usually signals a parabolic move is beginning. 3. The "Extension Zone" (Yellow, Red, Purple > 1.618) 🟡🔴 Role : Take Profit / Exhaustion Levels above 1.5 (starting with the 1.618 Golden Extension) are statistical extremes. Strategy : These are Strict Take Profit levels. Do not FOMO (Fear Of Missing Out) into new long positions here. The probability of a reversal increases drastically as price climbs through these levels (2.618, 3.618, 4.618). 📐 The Mathematical Edge: Logarithmic vs. Linear One of the most critical features of this script is the ability to toggle between Logarithmic and Linear calculations. Why use Logarithmic? If you are trading Crypto (Bitcoin, Altcoins) or high-growth Tech Stocks, linear Fibonacci levels are mathematically incorrect over large moves. A 50% drop from $100 is different than a 50% drop from $10. This script calculates the percentage difference (Log Scale), ensuring your targets are accurate even during 100%+ parabolic runs. Why use Linear? For mature markets like Forex (EURUSD) or Indices (SPX500) where volatility is lower, Linear scaling is the industry standard. 🛠️ Configuration & Best Practices Deviation % : This is the heartbeat of the indicator. Swing Trading : Set to 20-30%. This filters out noise and only draws Fibs on major macro moves. Scalping : Set to 3-5%. This will catch smaller intraday waves. Text Place : Keeps your chart clean by pushing labels to the right, ensuring they don't overlap with the current price action. 👤 Who Is This Indicator For? The Disciplined Trader : Who wants to remove emotional bias from their charting. The Crypto Investor : Who needs accurate Logarithmic targets for long-term holding. The Confluence Trader : Who combines these automated levels with Order Blocks, RSI, or Volume to find the perfect entry. ⚠️ RISK DISCLAIMER & TERMS OF USE For Educational Purposes Only: This script and the strategies described herein are provided strictly for educational and informational purposes. They do not constitute financial, investment, or trading advice. The "Auto Fibonacci Lines" indicator is a tool for technical analysis and should not be used as the sole basis for any trading decision. No Guarantees: Past performance of any trading system or methodology is not necessarily indicative of future results. Financial markets are inherently volatile, and trading involves a high level of risk. You could lose some or all of your capital. User Responsibility: By using this script, you acknowledge that you are solely responsible for your own trading decisions and risk management. The author assumes no liability for any losses or damages resulting from the use of this tool or the information provided. Always consult with a qualified financial advisor before making investment decisions.
Индикатор Pine Script®
от abgthecoder
MLMatrixLibOverview MLMatrixLib is a comprehensive Pine Script v6 library implementing machine learning algorithms using native matrix operations. This library provides traders and developers with a toolkit of statistical and ML methods for building quantitative trading systems, performing data analysis, and creating adaptive indicators. How It Works The library leverages Pine Script's native matrix type to perform efficient linear algebra operations. Each algorithm is implemented from first principles, using matrix decomposition, iterative optimization, and statistical estimation techniques. All functions are designed for numerical stability with careful handling of edge cases. --- Library Contents (34 Sections) Section 1: Utility Functions & Matrix Operations Core building blocks including: • identity(n) - Creates n×n identity matrix • diagonal(values) - Creates diagonal matrix from array • ones(rows, cols) / zeros(rows, cols) - Matrix constructors • frobeniusNorm(m) / l1Norm(m) - Matrix norm calculations • hadamard(m1, m2) - Element-wise multiplication • columnMeans(m) / rowMeans(m) - Statistical aggregations • standardize(m) - Z-score normalization (zero mean, unit variance) • minMaxNormalize(m) - Scale values to range • fitStandardScaler(m) / fitMinMaxScaler(m) - Reusable scaler parameters • addBiasColumn(m) - Prepend column of ones for regression • arrayMedian(arr) / arrayPercentile(arr, p) - Array statistics Section 2: Activation Functions Numerically stable implementations: • sigmoid(x) / sigmoidMatrix(m) - Logistic function with overflow protection • tanhActivation(x) / tanhMatrix(m) - Hyperbolic tangent • relu(x) / reluMatrix(m) - Rectified Linear Unit • leakyRelu(x, alpha) - Leaky ReLU with configurable slope • elu(x, alpha) - Exponential Linear Unit • Derivatives for backpropagation: sigmoidDerivative, tanhDerivative, reluDerivative Section 3: Linear Regression (OLS) Ordinary Least Squares implementation using the normal equation (X'X)⁻¹X'y: • fitLinearRegression(X, y) - Fits model, returns coefficients, R², standard error • fitSimpleLinearRegression(x, y) - Single-variable regression • predictLinear(model, X) - Generate predictions • predictionInterval(model, X, confidence) - Confidence intervals using t-distribution • Model type stores: coefficients, R-squared, residuals, standard error Section 4: Weighted Linear Regression Generalized least squares with observation weights: • fitWeightedLinearRegression(X, y, weights) - Solves (X'WX)⁻¹X'Wy • Useful for downweighting outliers or emphasizing recent data Section 5: Polynomial Regression Fits polynomials of arbitrary degree: • fitPolynomialRegression(x, y, degree) - Constructs Vandermonde matrix • predictPolynomial(model, x) - Evaluate polynomial at points Section 6: Ridge Regression (L2 Regularization) Adds penalty term λ||β||² to prevent overfitting: • fitRidgeRegression(X, y, lambda) - Solves (X'X + λI)⁻¹X'y • Lambda parameter controls regularization strength Section 7: LASSO Regression (L1 Regularization) Coordinate descent algorithm for sparse solutions: • fitLassoRegression(X, y, lambda, maxIter, tolerance) - Iterative soft-thresholding • Produces sparse coefficients by driving some to exactly zero • softThreshold(x, lambda) - Core shrinkage operator Section 8: Elastic Net (L1 + L2 Regularization) Combines LASSO and Ridge penalties: • fitElasticNet(X, y, lambda, alpha, maxIter, tolerance) • Alpha balances L1 vs L2: alpha=1 is LASSO, alpha=0 is Ridge Section 9: Huber Robust Regression Iteratively Reweighted Least Squares (IRLS) for outlier resistance: • fitHuberRegression(X, y, delta, maxIter, tolerance) • Delta parameter defines transition between L1 and L2 loss • Downweights observations with large residuals Section 10: Quantile Regression Estimates conditional quantiles using linear programming approximation: • fitQuantileRegression(X, y, tau, maxIter, tolerance) • Tau specifies quantile (0.5 = median, 0.25 = lower quartile, etc.) Section 11: Logistic Regression (Binary Classification) Gradient descent optimization of cross-entropy loss: • fitLogisticRegression(X, y, learningRate, maxIter, tolerance) • predictProbability(model, X) - Returns probabilities • predictClass(model, X, threshold) - Returns binary predictions Section 12: Linear SVM (Support Vector Machine) Sub-gradient descent with hinge loss: • fitLinearSVM(X, y, C, learningRate, maxIter) • C parameter controls regularization (higher = harder margin) • predictSVM(model, X) - Returns class predictions Section 13: Recursive Least Squares (RLS) Online learning with exponential forgetting: • createRLSState(nFeatures, lambda, delta) - Initialize state • updateRLS(state, x, y) - Update with new observation • Lambda is forgetting factor (0.95-0.99 typical) • Useful for adaptive indicators that update incrementally Section 14: Covariance and Correlation Matrix statistics: • covarianceMatrix(m) - Sample covariance • correlationMatrix(m) - Pearson correlations • pearsonCorrelation(x, y) - Single correlation coefficient • spearmanCorrelation(x, y) - Rank-based correlation Section 15: Principal Component Analysis (PCA) Dimensionality reduction via eigendecomposition: • fitPCA(X, nComponents) - Power iteration method • transformPCA(X, model) - Project data onto principal components • Returns components, explained variance, and mean Section 16: K-Means Clustering Lloyd's algorithm with k-means++ initialization: • fitKMeans(X, k, maxIter, tolerance) - Cluster data points • predictCluster(model, X) - Assign new points to clusters • withinClusterVariance(model) - Measure cluster compactness Section 17: Gaussian Mixture Model (GMM) Expectation-Maximization algorithm: • fitGMM(X, k, maxIter, tolerance) - Soft clustering with probabilities • predictProbaGMM(model, X) - Returns membership probabilities • Models data as mixture of Gaussian distributions Section 18: Kalman Filter Linear state estimation: • createKalman1D(processNoise, measurementNoise, ...) - 1D filter • createKalman2D(processNoise, measurementNoise) - Position + velocity tracking • kalmanStep(state, measurement) - Predict-update cycle • Optimal filtering for noisy measurements Section 19: K-Nearest Neighbors (KNN) Instance-based learning: • fitKNN(X, y) - Store training data • predictKNN(model, X, k) - Classify by majority vote • predictKNNRegression(model, X, k) - Average of k neighbors • predictKNNWeighted(model, X, k) - Distance-weighted voting Section 20: Neural Network (Feedforward) Multi-layer perceptron: • createNeuralNetwork(architecture) - Define layer sizes • trainNeuralNetwork(nn, X, y, learningRate, epochs) - Backpropagation • predictNN(nn, X) - Forward pass • Supports configurable hidden layers Section 21: Naive Bayes Classifier Gaussian Naive Bayes: • fitNaiveBayes(X, y) - Estimate class-conditional distributions • predictNaiveBayes(model, X) - Maximum a posteriori classification • Assumes feature independence given class Section 22: Anomaly Detection Statistical outlier detection: • fitAnomalyDetector(X, contamination) - Mahalanobis distance-based • detectAnomalies(model, X) - Returns anomaly scores • isAnomaly(model, X, threshold) - Binary classification Section 23: Dynamic Time Warping (DTW) Time series similarity: • dtw(series1, series2) - Compute DTW distance • Handles sequences of different lengths • Useful for pattern matching Section 24: Markov Chain / Regime Detection Discrete state transitions: • fitMarkovChain(states, nStates) - Estimate transition matrix • predictNextState(transitionMatrix, currentState) - Most likely next state • stationaryDistribution(transitionMatrix) - Long-run probabilities Section 25: Hidden Markov Model (Simple) Baum-Welch algorithm: • fitHMM(observations, nStates, maxIter) - EM training • viterbi(model, observations) - Most likely state sequence • Useful for regime detection Section 26: Exponential Smoothing & Holt-Winters Time series smoothing: • exponentialSmooth(data, alpha) - Simple exponential smoothing • holtWinters(data, alpha, beta, gamma, seasonLength) - Triple smoothing • Captures trend and seasonality Section 27: Entropy and Information Theory Information measures: • entropy(probabilities) - Shannon entropy in bits • conditionalEntropy(jointProbs, marginalProbs) - H(X|Y) • mutualInformation(probsX, probsY, jointProbs) - I(X;Y) • kldivergence(p, q) - Kullback-Leibler divergence Section 28: Hurst Exponent Long-range dependence measure: • hurstExponent(data) - R/S analysis • H < 0.5: mean-reverting, H = 0.5: random walk, H > 0.5: trending Section 29: Change Detection (CUSUM) Cumulative sum control chart: • cusumChangeDetection(data, threshold, drift) - Detect regime changes • cusumOnline(value, prevCusumPos, prevCusumNeg, target, drift) - Streaming version Section 30: Autocorrelation Serial dependence analysis: • autocorrelation(data, maxLag) - ACF for all lags • partialAutocorrelation(data, maxLag) - PACF via Durbin-Levinson • Useful for time series model identification Section 31: Ensemble Methods Model combination: • baggingPredict(models, X) - Average predictions • votingClassify(models, X) - Majority vote • Improves robustness through aggregation Section 32: Model Evaluation Metrics Performance assessment: • mse(actual, predicted) / rmse / mae / mape - Regression metrics • accuracy(actual, predicted) - Classification accuracy • precision / recall / f1Score - Binary classification metrics • confusionMatrix(actual, predicted, nClasses) - Multi-class evaluation • rSquared(actual, predicted) / adjustedRSquared - Goodness of fit Section 33: Cross-Validation Model validation: • trainTestSplit(X, y, trainRatio) - Random split • Foundation for walk-forward validation Section 34: Trading Convenience Functions Trading-specific utilities: • priceMatrix(length) - OHLC data as matrix • logReturns(length) - Log return series • rollingSlope(src, length) - Linear trend strength • kalmanFilter(src, processNoise, measurementNoise) - Filtered price • kalmanFilter2D(src, ...) - Price with velocity estimate • adaptiveMA(src, sensitivity) - Kalman-based adaptive moving average • volAdjMomentum(src, length) - Volatility-normalized momentum • detectSRLevels(length, nLevels) - K-means based S/R detection • buildFeatures(src, lengths) - Multi-timeframe feature construction • technicalFeatures(length) - Standard indicator feature set (RSI, MACD, BB, ATR, etc.) • lagFeatures(src, lags) - Time-lagged features • sharpeRatio(returns) - Risk-adjusted return measure • sortinoRatio(returns) - Downside risk-adjusted return • maxDrawdown(equity) - Maximum peak-to-trough decline • calmarRatio(returns, equity) - Return/drawdown ratio • kellyCriterion(winRate, avgWin, avgLoss) - Optimal position sizing • fractionalKelly(...) - Conservative Kelly sizing • rollingBeta(assetReturns, benchmarkReturns) - Market exposure • fractalDimension(data) - Market complexity measure --- Usage Example ``` import YourUsername/MLMatrixLib/1 as ml // Create feature matrix matrix X = ml.priceMatrix(50) X := ml.standardize(X) // Fit linear regression ml.LinearRegressionModel model = ml.fitLinearRegression(X, y) float prediction = ml.predictLinear(model, X_new) // Kalman filter for smoothing float smoothedPrice = ml.kalmanFilter(close, 0.01, 1.0) // Detect support/resistance levels array levels = ml.detectSRLevels(100, 3) // K-means clustering for regime detection ml.KMeansModel km = ml.fitKMeans(features, 3) int cluster = ml.predictCluster(km, newFeature) ``` --- Technical Notes • All matrix operations use Pine Script's native matrix type • Numerical stability ensured through: - Clamping exponential arguments to prevent overflow - Division by zero protection with epsilon thresholds - Iterative algorithms with convergence tolerance • Designed for bar-by-bar execution in Pine Script's event-driven model • Compatible with Pine Script v6 --- Disclaimer This library provides mathematical tools for quantitative analysis. It does not constitute financial advice. Past performance of any algorithm does not guarantee future results. Users are responsible for validating models on their specific use cases and understanding the limitations of each method.
Библиотека Pine Script®
от ArunaReborn
Elite Risk-On/Risk-Off Oscillator (6 pairs) The Elite Risk-On / Risk-Off Oscillator is a market-regime indicator designed to determine whether conditions favor aggressive risk-taking or defensive capital preservation rather than to predict price direction. It combines six carefully selected relative-strength pairs that measure risk appetite across the most important parts of the market: IEI/HYG (credit stress, weighted most heavily because credit often leads equities) SPHB/SPLV (equity risk appetite via high-beta versus low-volatility stocks) IWM/SPY (liquidity and growth sensitivity through small-caps versus large-caps) MTUM/QUAL (trend durability versus balance-sheet quality) XLY/XLP (consumer cyclicality, wants versus needs) EEM/SPY (global risk and dollar-sensitive capital flows) Each pair is evaluated using relative performance against a moving-average and slope filter to classify it as risk-on (+1), neutral (0), or risk-off (-1), with defensive ratios inverted so that positive readings always indicate risk-on conditions; the weighted signals are then aggregated, normalized to a -100 to +100 scale, and smoothed into a single oscillator. Readings above approximately +40 indicate a supportive risk-on environment where trends are more likely to persist, readings between -40 and +40 reflect transitional or choppy conditions with lower conviction, and readings below -40 signal a risk-off regime where capital preservation and defense should be prioritized. The indicator is intended as a context and position-sizing tool, helping traders align strategy aggressiveness with underlying market conditions rather than relying on forecasts or narratives.
Индикатор Pine Script®
от RobertDun
XAUUSD Mean Reversion Strategy Gold (ATR and RSI)The XAUUSD Mean Reversion Strategy – Gold v6 is a non-repainting TradingView strategy designed specifically for Gold (XAUUSD). It capitalizes on price overextensions and statistically probable pullbacks toward the mean, a behavior Gold frequently exhibits during active market sessions. 🔍 Strategy Logic Uses EMA 50 as the mean price reference Detects overextended conditions with RSI (14) Trades are taken only when price deviates significantly from the mean Designed for both long and short positions 📈 Entry Conditions Long Trades Price below EMA 50 RSI below oversold level Short Trades Price above EMA 50 RSI above overbought level 📉 Exit & Risk Management ATR-based Stop Loss adapts to Gold’s volatility Take Profit Options Mean reversion back to EMA Fixed ATR-based risk-to-reward One trade at a time to control exposure ⚙️ Features Fully backtestable Non-repainting Optimized for XAUUSD volatility Adjustable inputs for optimization Works best on 5m–30m timeframes 📊 Recommended Use XAUUSD (Gold) London & New York sessions Intraday mean-reversion traders ⚠️ This strategy is for educational and research purposes only. Always perform your own testing and risk management before using it in live markets.
Стратегия Pine Script®
от JaxonJackFX
Absorption Pro V4This indicator detects absorption-style reversal setups and scores them with a multi-factor model. It builds key levels from ZigZag/Fibonacci and round numbers across multiple timeframes, then flags potential absorption candles using volume and a delta-proxy filter plus strict candle-structure rules. Signals are validated with trend context (MA/SMMA/EMA/ATR), VWAP positioning, and optional momentum/volatility filters (RSI, Stoch, CCI, MACD, ADX, Volume Profile). Only score-threshold crosses can trigger long/short markers and alerts (defaults tuned for NQ).
Индикатор Pine Script®
от Kara_Tools
NVentures Liquidity Radar ProInstitutional Liquidity Radar Pro OVERVIEW This indicator combines three institutional trading concepts into a unified confluence scoring system: Liquidity Zones (swing-based), Order Blocks, and Fair Value Gaps. The unique value lies not in these individual concepts, but in HOW they interact through the confluence scoring algorithm to filter high-probability zones. HOW THE CONFLUENCE SCORING WORKS The core innovation is the calcConfluence() function that assigns a numerical score to each detected level: 1. Base Score: Every swing pivot starts with score = 1 2. Zone Overlap Detection: The algorithm iterates through all active zones within confDist * ATR proximity. Each overlapping zone adds +1 to the score 3. Order Block Proximity: If an Order Block's midpoint (top + bottom) / 2 falls within the confluence distance, +1 is added 4. HTF Validation: Using request.security(), the indicator fetches higher timeframe swing pivots. If the current zone aligns with an HTF swing within 2 * confDist * ATR_htf, a +2 bonus is awarded Zones scoring 4+ are highlighted as high confluence - these represent areas where multiple institutional concepts converge. HOW LIQUIDITY ZONES ARE CALCULATED Detection: ta.pivothigh() and ta.pivotlow() with configurable lookback (default: 5 bars left/right) Zone Width - Three modes available: - ATR Dynamic: ATR(14) * multiplier (default 0.25) - Fixed %: close * (percentage / 100) - Wick Based: max(upperWick, lowerWick) * 1.5 Proximity Filter: isTooClose() prevents clustering by enforcing minimum ATR * minATRdist between zones HOW ORDER BLOCKS ARE DETECTED The detectBullishOB() / detectBearishOB() functions identify the last opposing candle before an impulse move: 1. Check if candle is opposing direction (bearish before bullish impulse, vice versa) 2. Validate consecutive candles in impulse direction (configurable, default: 3) 3. Volume confirmation: volume >= volMA * volMult (using 50-period SMA) 4. Minimum move validation: abs(close - close ) > ATR This filters out weak OBs and focuses on those with institutional volume footprints. HOW FAIR VALUE GAPS ARE DETECTED FVGs represent price imbalances: - Bullish FVG: low - high > ATR * fvgMinSize - Bearish FVG: low - high > ATR * fvgMinSize The ATR-relative sizing ensures gaps are significant relative to current volatility. HOW SWEEP DETECTION WORKS The checkSweep() function identifies false breakouts through wick analysis: 1. Calculate wick percentage: upperWick / totalRange or lowerWick / totalRange 2. Sweep conditions for resistance: high > zone.upper AND close < zone.price AND wickPct >= threshold 3. Sweep conditions for support: low < zone.lower AND close > zone.price AND wickPct >= threshold A sweep indicates liquidity was grabbed without genuine continuation - often preceding reversals. HOW FRESHNESS DECAY WORKS The calcFreshness() function implements linear decay: freshness = 1.0 - (age / decayBars) freshness = max(freshness, minFresh) This ensures old, tested zones fade visually while fresh zones remain prominent. WHY THESE COMPONENTS WORK TOGETHER The synergy is based on the principle that institutional activity leaves multiple footprints: - Swing Pivots = where retail stops cluster - Order Blocks = where institutions entered - FVGs = where aggressive institutional orders created imbalances - HTF Alignment = where higher timeframe participants are active When these footprints converge at the same price level (high confluence score), the probability of significant price reaction increases. CONFIGURATION - Swing Detection Length: 5-8 for intraday, 8-15 for swing trading - HTF Timeframe: One level above trading TF (e.g., D for H4) - Min Confluence to Display: 2 for comprehensive view, 3-4 for high-probability only - FVGs: Disabled by default for cleaner charts STATISTICS PANEL Displays: Active resistance/support zones, high confluence count, swept zones, active OBs, active FVGs, current ATR, selected HTF. ALERTS - Price approaching high confluence zone - Liquidity sweep detected - Bullish/Bearish Order Block formed - Bullish/Bearish FVG detected TECHNICAL NOTES - Uses User-Defined Types (UDTs) for clean data structure management - Respects Pine Script drawing limits (500 boxes/labels/lines) - All calculations are ATR-normalized for cross-market compatibility
Индикатор Pine Script®
от NVBIG
Forecast OscillatorGeneral Overview The Forecast Oscillator Plus (FOSC+) is not just another oscillator. It is an advanced quantitative analysis tool developed to bridge the gap left by traditional momentum indicators (like RSI or Stochastic) which often suffer from "lag" or remain pinned in extreme zones during strong trends. This "Plus" version has been specifically engineered and optimized for high-velocity scalping and day-trading on assets like NAS100 (Nasdaq) and XAUUSD (Gold) using ultra-short timeframes (1-min, 5-min). 🛡️ Why is FOSC+ Different? 1. Linear Regression Intelligence At the heart of this script is a powerful Linear Regression (LinReg) engine. Instead of comparing price to a simple average, FOSC+ calculates the percentage deviation between the current price and its predicted theoretical trajectory. This allows the indicator to identify not just if the price is "high" or "low," but if it is abnormally distanced from its current trend, signaling an imminent Mean Reversion. 2. Adaptive Dynamic Bands (Volatility-Adjusted) A major weakness of classic oscillators is the use of fixed levels (e.g., 80/20). FOSC+ utilizes Standard Deviation to generate overbought and oversold zones that "breathe" with the market. During high volatility, the bands expand to filter out noise and premature entries. During low volatility, they tighten to capture precise turning points. 3. Institutional Volume Filter (Anti-Fakeout) To succeed in the Nasdaq market, you must follow the "Smart Money." This script integrates a Volume Spike Filter. A signal (Buy/Sell) is only triggered if the current candle's volume is significantly higher than its moving average (adjustable multiplier). This ensures you only enter trades backed by real institutional strength. 4. Algo-Ready for PineConnector The code has been structured for seamless automation. With built-in EMA smoothing to reduce 1-minute "market chatter," the signals are clean and sharp, minimizing execution errors when sending orders to MetaTrader 5 via PineConnector. 📈 Technical Trading Guide Buy Signals (Green Triangle): Occur when the oscillator crosses above the dynamic oversold band OR crosses back above the zero line, provided that volume confirms the impulse. Sell Signals (Red Triangle): Occur when the oscillator crosses below the dynamic overbought band OR breaks below the zero line from above, with volume confirmation. Momentum Histogram: The colored columns indicate acceleration strength. Excellent for Trailing Stops: as long as the histogram is growing, the momentum is in your favor!. ⚙️ Recommended Parameters Length (14): The "Sweet Spot" for balancing reactivity and reliability. Smooth Len (4): Essential for 1-min charts to eliminate micro-fluctuations without adding lag. Volume Mult (1.15): Filters out the bottom 15% of volume to keep only significant candles. ⚠️ Stress-Tested for Real Conditions This script has been rigorously backtested with Slippage settings ranging from 10 to 25 points. Even under difficult market conditions with high spreads, the indicator maintains a positive expectancy, making it a premier tool for traders using Standard or Raw accounts.
Индикатор Pine Script®
от XtraKosack
Volume Variance SuppressionVolume Variance Suppression Indicator This indicator measures the variance of traded volume over a rolling window to detect periods of participation compression. When volume variance falls below a defined threshold, it signals: Reduced initiative order flow Dominance of passive liquidity Market balance / consolidation rather than trend These suppression phases often precede volatility expansion, failed auctions, or impulsive moves, as liquidity builds and positioning becomes crowded. The indicator is not directional and should be used as a market state filter, not a standalone signal. It helps distinguish balance vs expansion regimes and improves trade selection by aligning strategies with the current microstructural environment.
Индикатор Pine Script®
от ShadowBid
Institutional Volatility Expansion & Liquidity Thresholds (IVEL)Overview The IVEL Engine is an institutional-grade volatility modeling tool designed to identify the mathematical boundaries of price delivery. Unlike retail oscillators that use fixed scales, this script utilizes dynamic ATR-based multiples to map Institutional Premium and Discount zones in real-time. How to Use To maximize the effectiveness of the IVEL Engine, traders should focus on Price Delivery at the extreme thresholds: Identifying Institutional Premium (Short Setup) : When price expands into the Upper Red Zone, it has reached a mathematical exhaustion point. Seek short-side entries when price shows signs of rejection from this level back toward the Fair Value Baseline. Identifying Institutional Discount (Long Setup) : When price reaches the Lower Green Zone, it is considered "cheap" by institutional algorithms. Look for long-side absorption or accumulation patterns within this zone. Mean Reversion Targets: The Fair Value Baseline (Center Line) acts as the primary magnetic target. Successful trades taken at the outer thresholds should use the baseline as the first objective for profit-taking. Alerts & Execution Strategy The IVEL Engine is designed for automated monitoring so you don't have to watch the screen 24/7. To set up your execution workflow: Set the Alert : Right-click the indicator and select "Add Alert." Set the condition to "Price Crossing Institutional Premium" (Upper Red) or "Price Crossing Institutional Discount" (Lower Green). Wait for the Hit : Do not market-enter as soon as the alert fires. The alert tells you price has entered a High-Probability Liquidity Zone. Confirm the Rejection : Once alerted, drop down to a lower timeframe (e.g., 5m or 15m) and look for a "Shift in Market Structure" or an SMT Divergence. Execute : Enter once the rejection is confirmed, targeting the Fair Value Baseline as your primary TP1. Methodology The script anchors to an EMA-based baseline and projects expansion bands that adapt to current market conditions. Value Area : The blue inner region where the majority of trading volume occurs. Liquidity Exhaustion : The red and green outer regions where the probability of "Smart Money" reversal is highest.
Индикатор Pine Script®
от TJT_Pro