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Indikatoren und Strategien

Value Area PRO (TPO/Volume Session VAH/VAL/POC) 📌 AP Capital Value Area PRO (TPO / Volume) AP Capital Value Area PRO is a session-based value area indicator designed for Gold (XAUUSD), NASDAQ (NAS100), and other CFD instruments. It focuses on where the market has accepted price during the current session and highlights high-probability interaction zones used by professional traders. Unlike rolling lookback volume profiles, this indicator builds a true session value area and provides actionable signals around VAH, VAL, and POC. 🔹 Core Features Session-Anchored Value Area Value Area is built only during the selected session Resets cleanly at session start Levels develop during the session and can be extended forward No repainting or shifting due to lookback changes TPO or Volume Mode TPO (Time-at-Price) mode – ideal for CFDs and tick-volume data Volume mode – uses broker volume if preferred Same logic, different weighting method Fixed Price Bin Size Uses a fixed bin size (e.g. 0.10 for Gold, 0.25–0.50 for NAS100) Produces cleaner, more realistic VAH/VAL levels Avoids distorted profiles caused by dynamic bin scaling VAH / VAL / POC Levels VAH (Value Area High) VAL (Value Area Low) POC (Point of Control) (optional) Lines can be extended to act as forward reference levels 🔹 Trading Signals & Alerts Value Re-Entry Identifies false breakouts where price: Trades outside value Then closes back inside Often seen before strong mean-reversion or continuation moves. Acceptance Detects initiative activity using: Multiple consecutive closes outside value Filters out weak single-candle breaks Rejection Flags strong rejection candles: Large candle body Wick outside value Close back inside the value area These conditions are especially effective on Gold intraday. 🔹 Optional Profile Histogram Right-side volume/TPO histogram Buy/sell imbalance visualization Fully optional to reduce chart clutter and improve performance 🔹 Best Use Cases Recommended markets XAUUSD (Gold) NAS100 / US100 Other index or metal CFDs Recommended timeframes 5m, 15m, 30m Suggested settings Mode: TPO Value Area: 70% Bin size: Gold: 0.10 NAS100: 0.25 or 0.50 🔹 How Traders Use It Trade rejections at VAH / VAL Look for acceptance to confirm trend days Use re-entries to fade failed breakouts Combine with trend filters, EMA structure, or session context ⚠️ Disclaimer This indicator is provided for educational and analytical purposes only and does not constitute financial advice. Always manage risk appropriately.
Pine Script® Indikator
von APCapitalTrading
Session VWAP Cumulative BiasThe Session VWAP Cumulative Bias indicator is designed to differentiate between "choppy" price action and true "institutional" trend days. Unlike standard VWAP indicators that only show where price is now, this tool tracks the cumulative sentiment of the entire session. Core Functions: Cumulative Z-Score Logic: It calculates the distance between price and VWAP (in Standard Deviations) and sums it up over the course of the day. This reveals the "weight" of the market bias—the longer price stays pinned away from the VWAP, the more extreme the histogram becomes. Scale Protection: It includes a "Capping" mechanism that prevents morning gaps or low-volume outliers from distorting the scale, ensuring the histogram remains readable from open to close. Momentum vs. Regime Toggles: Users can switch between VWAP Slope (measuring the speed of the average's movement) and Cumulative Bias (measuring total session dominance). Visual price Overlay: It automatically colors the price candles and plots a session-anchored VWAP line on the main chart, providing a clear visual of when price is "fair" versus "overextended." How to read it: Trend Confirmation: A steadily growing "mountain" in the histogram confirms an institutional trend day where dips are being bought (or rips sold). Mean Reversion: When price hits a new high but the Cumulative Histogram begins to round off or diverge, it signals that the "elastic band" is stretched and price is likely to return to the orange VWAP line. Regime Shifts: A cross of the zero-line on the histogram indicates a total shift in session control from buyers to sellers (or vice versa).
Pine Script® Indikator
von rothenbergws
4 Period Momentum Composite IndicatorThe 4‑Period Momentum Indicator blends four lookback windows (1m, 3m, 6m, 12m) into a single zero‑centered momentum line. The value recalculates from whatever candle you anchor on, giving you full control when scrolling through historical price action. Positive readings reflect upward momentum, negative readings show weakness, and zero‑line crossovers highlight potential trend shifts. Designed for multi‑timeframe use and ETF relative‑strength comparison.
Pine Script® Indikator
von winj78
Auto Fib Prev-Week Only for [4H+ Swing]Maps the previous week Fib levels: Captures real supply & demand. Defines where price was accepted or rejected. Creates levels that current price must respect. This indicator locks those levels in place and extends them forward. What the levels represent: - Previous Week High / Low - Major boundaries. Breaks require momentum. - 50% Level - Balance point. Chop and indecision are common here. - 61.8% Levels (Bull & Bear) - Primary mean-reversion zones. - Most reliable reaction levels. - 78.6% Levels - Last defense before trend failure or expansion. - Extensions (1.214 → 2.618 / negatives) - Exhaustion and target zones. Working.... Dashboard (bottom-right) - Nearest Sup / Res – Closest actionable level - On Level? – Price is currently reacting at a level - UpBreak% / DnBreak% – Probability of breaking vs rejecting - Bias – Market posture (UP / DOWN / NEUTRAL) - Tol – Sensitivity used for level detection BLUF: Maps last week’s structure forward to identify high-probability reaction zones and whether price is more likely to revert or break.
Pine Script® Indikator
von Warner_Bear
Relative Strength SpreadSPY vs IWM Relative Strength Spread Indicator The SPY vs IWM Relative Strength Spread indicator measures leadership between large-cap and small-cap equities by comparing the percent performance of SPY (S&P 500) against IWM (Russell 2000) over a user-defined lookback period. The indicator plots a zero-centered histogram in a separate pane, making relative strength shifts immediately visible. How It Works The indicator calculates the percent change of SPY and IWM over the same lookback window. It then subtracts IWM’s percent change from SPY’s percent change. The result is plotted as a histogram pinned to the 0% line. This design removes long-term drift and ensures that: Positive values indicate SPY is outperforming IWM Negative values indicate IWM is outperforming SPY How to Read the Histogram Above Zero (Green Bars) Large-cap stocks are leading → typically associated with risk-on stability and institutional flow into SPY-weighted names. Below Zero (Red Bars) Small-cap stocks are leading → often signals risk appetite expansion and speculative participation. Crosses of the Zero Line Mark potential leadership transitions between large caps and small caps. Why This Indicator Is Useful Identifies market regime shifts (risk-on vs risk-off behavior) Confirms or filters trend strength in equities Helps time rotations between large-cap and small-cap exposure Works consistently across all timeframes Because the calculation is based on percent change, the histogram remains normalized and comparable regardless of price level or timeframe. Best Use Cases As a market internals / breadth confirmation tool As a bias filter for SPY, IWM, or index futures To spot early leadership changes before price trends fully develop
Pine Script® Indikator
von Es-Sniper
John Trade AlertsImagine you are watching a ball bounce up and down on a graph. This script is like a set of rules that says: When to start playing When to stop playing When you got some prize levels and it yells to you (alerts) when those things happen. The main ideas Breakout Buy (ball jumps high) There is a line drawn high on the chart called the breakout level. If the price (the ball) closes above that line, and some extra “good conditions” are true (enough volume, uptrend, etc.), the script says: “We entered a Breakout trade now.” Pullback Buy (ball dips into a box) There is a zone (a small box) between a low line and a high line: the pullback zone. If the price closes inside that zone, and the pullback looks “healthy” (not too much volume, still above a moving average, etc.), the script says: “We entered a Pullback trade now.” Stops (when to get out if it goes wrong) For each entry type (Breakout or Pullback), there is a red stop line under the price. If the price falls below that stop line, the script says: “Stop hit, we’re out of the trade.” Hard Support / Invalidation (big no‑no level) There is a special hard support line. The script also looks at the 1‑hour chart in the background. If a 1‑hour candle closes below that hard support, it says: “Hard invalidation – idea is broken, get out.” Targets (prize levels) Above the current price there are several orange lines: Target 1, 2, 3A, 3B, 4A, 4B. If the price goes up and crosses one of these lines, the script says: “Target X reached!” Trend and Volume “health checks” It checks if the short‑term average price (SMA20) is going up → “uptrend.” It can check if price is above a long‑term average (SMA200). For breakouts, it checks if volume is stronger than usual (good push). For pullbacks, it prefers quieter than usual volume (calm dip). It can also check an Anchored VWAP line (a special average price from a chosen starting time) and only trade if price is above that too. Remembering if you are “in a trade” The script keeps a little memory: Are we currently in a position (inPos) or not? Was it a Breakout or a Pullback entry? What is our entry price and active stop? When it gets a new entry signal, it turns inPos to true, picks the right stop, and draws that stop line. When a stop or hard invalidation happens, it sets inPos to false again. It can also “forget” and reset at the start of a new trading day if you want. Alerts When: you get a Breakout entry or a Pullback entry or a Stop is hit or the hard support is broken on 1‑hour or a Target is reached the script sends a message you can use in TradingView alerts (pop‑ups, email, webhook, etc.). Things you see on the chart Teal line: Breakout level Green lines: Pullback zone low & high Red line: Active stop (only when you’re “in” a trade) Orange lines: Targets 1, 2, 3A, 3B, 4A, 4B Blue line: Anchored VWAP (if you turn it on) Purple faint line: SMA20 (short‑term trend) Gray faint line: SMA200 (long‑term trend) Little label near the last bar that says: if you’re IN or Flat which type of entry (Breakout/Pullback) what your current stop is So in kid words: It draws important lines on the chart. It watches the price move like a ball. When the ball does something special (jump above, fall below, hit a prize line), it shouts to you with alerts. It remembers if you’re in the game or not, and where your safety line (stop) is.
Pine Script® Indikator
von JmandersonBM
Daily Inputs - The Prometheus InitiativeDaily ES inputs from the Prometheus Initiative is a clean, customizable overlay indicator designed specifically for ES (S&P 500 E-mini futures) day traders who rely on manually selected key price levels each session. Instead of spending time manually drawing horizontal lines every day, this tool lets you quickly input the daily price levels directly in the settings and instantly see them plotted as horizontal lines across your chart. Key Features: • 15 fully editable price inputs with customizable settings. Why this indicator was created: Manually drawing 10–15 lines each morning is time-consuming. This indicator was developed to eliminate that friction — allowing fast, accurate plotting of levels so you can focus on execution rather than drawing tools. The largest benefit is that you can toggle the indicator on/off to keep a clean chart as to not interfere with your existing visual levels. Perfect for: - ES / NQ futures traders - Anyone who wants a clean, no-nonsense way to visualize custom horizontal levels How to use: 1. Add to your chart 2. Open Settings → Enter the daily levels provided 3. Watch price interact with the levels! Note: This is a manual input tool. Levels do NOT auto-calculate. It's meant to reflect the exact levels posted each day. Happy trading! 📈 Feel free to leave feedback or suggestions in the comments. Disclaimer: This indicator is for educational/visual purposes only. Trading futures involves substantial risk of loss and is not suitable for all investors.
Pine Script® Indikator
von Athlio
Dynamic Zone TraderDynamic Zone Trader - MACD-based trading system with adaptive stop loss and take profit zones. This indicator generates buy/sell signals from MACD histogram crossovers and automatically adjusts position sizing based on market conditions. Key Features: Detects breakout trades and expands targets to capture larger moves Identifies choppy/ranging conditions and tightens stops to reduce risk Shows supply and demand zones based on pivot highs/lows Displays three take profit levels (TP1, TP2, TP3) that scale with trade quality Entry signals filtered by 50 EMA to trade with the trend Signal strength score displayed on each entry marker How It Works: The indicator analyzes recent price structure and movement to classify each trade: Breakout trades (breaking recent highs/lows) get 1.6x larger zones Normal trades get standard 1.0x sizing Choppy weak signals get 0.75x smaller zones This allows you to take bigger positions on high-conviction setups while limiting risk during low-quality trades. Settings: MACD parameters (default 8/21/5) Base stop loss: 60 ticks Base take profit: 80 ticks EMA filter: 50 period Optional ADX trend filter Adjustable breakout detection sensitivity Works on any timeframe and instrument, but optimized for index futures like NQ/MNQ.
Pine Script® Indikator
von zackzackzackw
RSI Swing Indicator// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org // // DESCRIPTION: // This is an improved version of the original RSI Swing Indicator created by BalintDavid. // It highlights swing moves between RSI overbought/oversold extremes and updates swing labels // as price pushes to new highs or lows inside the same RSI regime. // // HOW TO USE: // 1) Set the RSI source, length, and overbought/oversold levels in Inputs. // 2) Watch the swing lines connect the last oversold to overbought (and vice-versa). // 3) Labels show structure: HH (higher high), LH (lower high), HL (higher low), LL (lower low). // 4) Enable "Show only last connecting line" to keep just the most recent connection. // // CONTACT: // ronbelson@gmail.com //
Pine Script® Indikator
von ronibelson
Week High/LowThis indicator plots the Previous Week High and Low as two horizontal dashed lines. It is designed to appear only on the Daily (D) and Weekly (W) timeframes, ensuring a clean higher-timeframe context without lower-timeframe noise. The levels are calculated from the completed weekly candle and automatically update at the start of each new week. These levels serve as weekly liquidity references, commonly used to assess premium/discount zones, potential stop-run areas, and higher-timeframe market reactions.
Pine Script® Indikator
von bacle8899
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.
Pine Script® Bibliothek
von ArunaReborn
Performance Table: Standard DCA | Last 6-12-24-48MThis indicator visualizes Standard Dollar-Cost Averaging (DCA) performance across multiple time horizons (6M, 12M, 24M, 48M). It summarizes invested capital, current portfolio value, net profit, and return percentage in a compact table, allowing quick comparison of short- and long-term DCA outcomes. Designed for long-term investors, it helps evaluate how consistent periodic investments perform over time without relying on market timing. The indicator is asset-agnostic and works on any symbol supported by TradingView. Key use cases: Long-term portfolio tracking DCA strategy validation Performance comparison across periods Educational and analytical purposes This tool focuses on clarity and realism, avoiding over-optimization and short-term noise. -- I hope this table helps investors better understand long-term DCA performance. Feedback and suggestions for improvement are always welcome.
Pine Script® Indikator
von sedatonat
ULTIMATE Multi-TF Previous CloseULTIMATE Multi-TF Previous Close displays previous close levels across multiple timeframes in one simple, non-repainting indicator. These levels often act as key decision points, providing natural support, resistance, and directional bias. Ideal for day traders, swing traders, and scalpers who want higher-timeframe context without clutter. Features include: Yearly → 5-minute timeframe coverage Right-extended horizontal levels Optional labels with exact prices Tick-accurate rounding Designed for clarity. Built for precision.
Pine Script® Indikator
von sunflowermom