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Pendiente EMA20This indicator measures the slope of the 20-period Exponential Moving Average (EMA20) to objectively determine whether it is rising, falling, or flat, removing the need for visual guesswork. The slope is calculated as the difference between the current EMA20 value and its value N bars ago. This provides a simple and reliable way to filter market conditions: Positive slope → EMA20 is rising (bullish bias) Negative slope → EMA20 is falling (bearish bias) Near zero → EMA20 is flat (range / no clear trend) The background color highlights these states for quick visual confirmation. Notes A zero threshold is included by default and may need adjustment depending on the asset and timeframe. For better robustness in ranging markets, consider normalizing the slope using ATR or ticks. This indicator is intended as a trend filter, not as a standalone trading system.
Indicador Pine Script®
por danavillanueva
AI Adaptive Trend Navigator Echo EditionAI Adaptive Trend Navigator This is an advanced trend-following system optimized for high-volatility index futures (TX). Built upon the LuxAlgo clustering framework, this version introduces several critical enhancements to meet professional trading standards: 1. State Consistency Iteration Enhanced the underlying logic for dynamic arrays and User-Defined Types (UDTs) to ensure stable "State Persistence." This fix eliminates logic gaps during real-time price fluctuations, ensuring that historical backtests perfectly align with live execution. 2. Adaptive Factor Tuning (K-means) The system simulates dozens of parameter paths in real-time, using K-means clustering to automatically select the optimal factor suited for the current market volatility. 3. Advanced Practical Filters Dynamic Buffer Strategy: Filters out market noise during consolidation and early session volatility. Confidence Threshold: Only triggers signals when the AI performance score meets the required quality. Cooldown Logic: Prevents rapid signal flipping in choppy markets. 🧠 開發理念：將 AI 自適應力帶入台指期實戰 針對台指期（TX）高波動特性開發，透過機器學習演算法動態尋優，解決傳統指標參數固定的滯後性。 ✨ Echo 版核心優化點 數據連續性迭代：底層邏輯優化，確保訊號在即時盤勢中穩定不跳斷，回測與實戰高度吻合。 自適應動態尋優：透過 K-means 聚類自動鎖定當前最佳 ATR 因子。 實戰多重濾網：包含空間緩衝區 (Buffer) 與信心門檻，大幅提升訊號品質。 📊 視覺說明 🚀 Rocket: AI confirms trend momentum. ⚡ Lightning: Trend exhaustion or reversal warning. ⚠️ Disclaimer: For educational and technical analysis purposes only.
Indicador Pine Script®
por EchoQuan
[Sumit Ingole] 200-EMA SUMIT INGOLE Indicator Name: 200 EMA Strategy Pro Overview The 200-period Exponential Moving Average (EMA) is widely regarded as the "Golden Line" by professional traders and institutional investors. This indicator is a powerful tool designed to identify the long-term market trend and filter out short-term market noise. By giving more weight to recent price data than a simple moving average, this EMA reacts more fluidly to market shifts while remaining a rock-solid trend confirmation tool. Key Features Trend Filter: Instantly distinguish between a Bull market and a Bear market. Price above 200 EMA: Bullish Bias Price below 200 EMA: Bearish Bias Dynamic Support & Resistance: Acts as a psychological floor or ceiling where major institutions often place buy or sell orders. Institutional Benchmark: Since many hedge funds and banks track this specific level, price reactions near the 200 EMA are often highly significant. Reduced Lag: Optimized exponential calculation ensures you stay ahead of the curve compared to traditional lagging indicators. How to Trade with 200 EMA Trend Confirmation: Only look for "Buy" setups when the price is trading above the 200 EMA to ensure you are trading with the primary trend. Mean Reversion: When the price stretches too far away from the 200 EMA, it often acts like a magnet, pulling the price back toward it. The "Death Cross" & "Golden Cross": Use this in conjunction with shorter EMAs (like the 50 EMA) to identify major trend reversals. Exit Strategy: Can be used as a trailing stop-loss for long-term positional trades. Best Used On: Timeframes: Daily (1D), 4-Hour (4H), and Weekly (1W) for maximum accuracy. Assets: Highly effective for Stocks, Forex (Major pairs), and Crypto (BTC/ETH). Disclaimer: This tool is for educational and analytical purposes only. Trading involves risk, and it is recommended to use this indicator alongside other technical analysis tools for better confirmation.
Indicador Pine Script®
por rahulingole4712
PineStats█ OVERVIEW PineStats is a comprehensive statistical analysis library for Pine Script v6, providing 104 functions across 6 modules. Built for quantitative traders, researchers, and indicator developers who need professional-grade statistics without reinventing the wheel. For building mean-reversion strategies, analyzing return distributions, measuring correlations, or testing for market regimes. █ MODULES CORE STATISTICS (20 functions) • Central tendency: mean, median, WMA, EMA • Dispersion: variance, stdev, MAD, range • Standardization: z-score, robust z-score, normalize, percentile • Distribution shape: skewness, kurtosis PROBABILITY DISTRIBUTIONS (17 functions) • Normal: PDF, CDF, inverse CDF (quantile function) • Power-law: Hill estimator, MLE alpha, survival function • Exponential: PDF, CDF, rate estimation • Normality testing: Jarque-Bera test ENTROPY (9 functions) • Shannon entropy (information theory) • Tsallis entropy (non-extensive, fat-tail sensitive) • Permutation entropy (ordinal patterns) • Approximate entropy (regularity measure) • Entropy-based regime detection PROBABILITY (21 functions) • Win rates and expected value • First passage time estimation • TP/SL probability analysis • Conditional probability and Bayes updates • Streak and drawdown probabilities REGRESSION (19 functions) • Linear regression: slope, intercept, forecast • Goodness of fit: R², adjusted R², standard error • Statistical tests: t-statistic, p-value, significance • Trend analysis: strength, angle, acceleration • Quadratic regression CORRELATION (18 functions) • Pearson, Spearman, Kendall correlation • Covariance, beta, alpha (Jensen's) • Rolling correlation analysis • Autocorrelation and cross-correlation • Information ratio, tracking error █ QUICK START import HenriqueCentieiro/PineStats/1 as stats // Z-score for mean reversion z = stats.zscore(close, 20) // Test if returns are normally distributed returns = (close - close ) / close isGaussian = stats.is_normal(returns, 100, 0.05) // Regression channel = stats.linreg_channel(close, 50, 2.0) // Correlation with benchmark spyReturns = request.security("SPY", timeframe.period, close/close - 1) beta = stats.beta(returns, spyReturns, 60) █ USE CASES ✓ Mean Reversion — z-scores, percentiles, Bollinger-style analysis ✓ Regime Detection — entropy measures, correlation regimes ✓ Risk Analysis — drawdown probability, VaR via quantiles ✓ Strategy Evaluation — expected value, win rates, R:R analysis ✓ Distribution Analysis — normality tests, fat-tail detection ✓ Multi-Asset — beta, alpha, correlation, relative strength █ NOTES • All functions return `na` on invalid inputs • Designed for Pine Script v6 • Fully documented in the library header • Part of the Pine ecosystem: PineStats, PineQuant, PineCriticality, PineWavelet █ REFERENCES • Abramowitz & Stegun — Normal CDF approximation • Acklam's algorithm — Inverse normal CDF • Hill estimator — Power-law tail estimation • Tsallis statistics — Non-extensive entropy Full documentation in the library header. mean(src, length)   Calculates the arithmetic mean (simple moving average) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Arithmetic mean of the last `length` values, or `na` if inputs invalid wma_custom(src, length)   Calculates weighted moving average with linearly decreasing weights   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Weighted moving average, or `na` if inputs invalid ema_custom(src, length)   Calculates exponential moving average   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Exponential moving average, or `na` if inputs invalid median(src, length)   Calculates the median value over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Median value, or `na` if inputs invalid variance(src, length)   Calculates population variance over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population variance, or `na` if inputs invalid stdev(src, length)   Calculates population standard deviation over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population standard deviation, or `na` if inputs invalid mad(src, length)   Calculates Median Absolute Deviation (MAD) - robust dispersion measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: MAD value, or `na` if inputs invalid data_range(src, length)   Calculates the range (highest - lowest) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Range value, or `na` if inputs invalid zscore(src, length)   Calculates z-score (number of standard deviations from mean)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for mean and stdev calculation (must be >= 2)   Returns: Z-score, or `na` if inputs invalid or stdev is zero zscore_robust(src, length)   Calculates robust z-score using median and MAD (resistant to outliers)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Robust z-score, or `na` if inputs invalid or MAD is zero normalize(src, length)   Normalizes value to range using min-max scaling   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Normalized value in , or `na` if inputs invalid or range is zero percentile(src, length)   Calculates percentile rank of current value within lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Percentile rank (0 to 100), or `na` if inputs invalid winsorize(src, length, lower_pct, upper_pct)   Winsorizes values by clamping to percentile bounds (reduces outlier impact)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)      lower_pct (simple float) : Lower percentile bound (0-100, e.g., 5 for 5th percentile)      upper_pct (simple float) : Upper percentile bound (0-100, e.g., 95 for 95th percentile)   Returns: Winsorized value clamped to bounds skewness(src, length)   Calculates sample skewness (measure of distribution asymmetry)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 3)   Returns: Skewness value (negative = left tail, positive = right tail), or `na` if invalid kurtosis(src, length)   Calculates excess kurtosis (measure of distribution tail heaviness)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Excess kurtosis (>0 = heavy tails, <0 = light tails), or `na` if invalid count_valid(src, length)   Counts non-na values in lookback window (useful for data quality checks)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Count of valid (non-na) values sum(src, length)   Calculates sum over lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Sum of values, or `na` if inputs invalid cumsum(src)   Calculates cumulative sum (running total from first bar)   Parameters:      src (float) : Source series   Returns: Cumulative sum change(src, length)   Returns the change (difference) from n bars ago   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Current value minus value from `length` bars ago roc(src, length)   Calculates Rate of Change (percentage change from n bars ago)   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Percentage change as decimal (0.05 = 5%), or `na` if invalid normal_pdf_standard(x)   Calculates the standard normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate   Returns: PDF value at x for standard normal N(0,1) normal_pdf(x, mu, sigma)   Calculates the normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: PDF value at x for normal N(mu, sigma²) normal_cdf_standard(x)   Calculates the standard normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate   Returns: Probability P(X <= x) for standard normal N(0,1) @description Uses Abramowitz & Stegun approximation (formula 7.1.26), accurate to ~1.5e-7 normal_cdf(x, mu, sigma)   Calculates the normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: Probability P(X <= x) for normal N(mu, sigma²) normal_inv_standard(p)   Calculates the inverse standard normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))   Returns: x such that P(X <= x) = p for standard normal N(0,1) @description Uses Acklam's algorithm, accurate to ~1.15e-9 normal_inv(p, mu, sigma)   Calculates the inverse normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))      mu (float) : Mean of the distribution      sigma (float) : Standard deviation (must be > 0)   Returns: x such that P(X <= x) = p for normal N(mu, sigma²) power_law_alpha(src, length, tail_pct)   Estimates power-law exponent (alpha) using Hill estimator   Parameters:      src (float) : Source series (typically absolute returns or drawdowns)      length (simple int) : Lookback period (must be >= 10 for reliable estimates)      tail_pct (simple float) : Percentage of data to use for tail estimation (default: 0.1 = top 10%)   Returns: Estimated alpha (tail index), typically 2-4 for financial data @description Alpha < 2 indicates infinite variance (very heavy tails) @description Alpha < 3 indicates infinite kurtosis @description Alpha > 4 suggests near-Gaussian behavior power_law_alpha_mle(src, length, x_min)   Estimates power-law alpha using maximum likelihood (Clauset method)   Parameters:      src (float) : Source series (positive values expected)      length (simple int) : Lookback period (must be >= 20)      x_min (float) : Minimum threshold for power-law behavior   Returns: Estimated alpha using MLE power_law_pdf(x, alpha, x_min)   Calculates power-law probability density (Pareto Type I)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: PDF value power_law_survival(x, alpha, x_min)   Calculates power-law survival function P(X > x)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: Probability of exceeding x power_law_ks(src, length, alpha, x_min)   Tests if data follows power-law using simplified Kolmogorov-Smirnov   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (float) : Estimated alpha from power_law_alpha()      x_min (float) : Threshold value   Returns: KS statistic (lower = better fit, typically < 0.1 for good fit) is_power_law(src, length, tail_pct, ks_threshold)   Simple test if distribution appears to follow power-law   Parameters:      src (float) : Source series      length (simple int) : Lookback period      tail_pct (simple float) : Tail percentage for alpha estimation      ks_threshold (simple float) : Maximum KS statistic for acceptance (default: 0.1)   Returns: true if KS test suggests power-law fit exp_pdf(x, lambda)   Calculates exponential probability density function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: PDF value exp_cdf(x, lambda)   Calculates exponential cumulative distribution function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: Probability P(X <= x) exp_lambda(src, length)   Estimates exponential rate parameter (lambda) using MLE   Parameters:      src (float) : Source series (positive values)      length (simple int) : Lookback period   Returns: Estimated lambda (1/mean) jarque_bera(src, length)   Calculates Jarque-Bera test statistic for normality   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)   Returns: JB statistic (higher = more deviation from normality) @description Under normality, JB ~ chi-squared(2). JB > 6 suggests non-normality at 5% level is_normal(src, length, significance)   Tests if distribution is approximately normal   Parameters:      src (float) : Source series      length (simple int) : Lookback period      significance (simple float) : Significance level (default: 0.05)   Returns: true if Jarque-Bera test does not reject normality shannon_entropy(src, length, n_bins)   Calculates Shannon entropy from a probability distribution   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      n_bins (simple int) : Number of histogram bins for discretization (default: 10)   Returns: Shannon entropy in bits (log base 2) @description Higher entropy = more randomness/uncertainty, lower = more predictability shannon_entropy_norm(src, length, n_bins)   Calculates normalized Shannon entropy   Parameters:      src (float) : Source series      length (simple int) : Lookback period      n_bins (simple int) : Number of histogram bins   Returns: Normalized entropy where 0 = perfectly predictable, 1 = maximum randomness tsallis_entropy(src, length, q, n_bins)   Calculates Tsallis entropy with q-parameter   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      q (float) : Entropic index (q=1 recovers Shannon entropy)      n_bins (simple int) : Number of histogram bins   Returns: Tsallis entropy value @description q < 1: emphasizes rare events (fat tails) @description q = 1: equivalent to Shannon entropy @description q > 1: emphasizes common events optimal_q(src, length)   Estimates optimal q parameter from kurtosis   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Estimated q value that best captures the distribution's tail behavior @description Uses relationship: q ≈ (5 + kurtosis) / (3 + kurtosis) for kurtosis > 0 tsallis_q_gaussian(x, q, beta)   Calculates Tsallis q-Gaussian probability density   Parameters:      x (float) : Value to evaluate      q (float) : Tsallis q parameter (must be < 3)      beta (float) : Width parameter (inverse temperature, must be > 0)   Returns: q-Gaussian PDF value @description q=1 recovers standard Gaussian permutation_entropy(src, length, order)   Calculates permutation entropy (ordinal pattern complexity)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 20)      order (simple int) : Embedding dimension / pattern length (2-5, default: 3)   Returns: Normalized permutation entropy @description Measures complexity of temporal ordering patterns @description 0 = perfectly predictable sequence, 1 = random approx_entropy(src, length, m, r)   Calculates Approximate Entropy (ApEn) - regularity measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 50)      m (simple int) : Embedding dimension (default: 2)      r (simple float) : Tolerance as fraction of stdev (default: 0.2)   Returns: Approximate entropy value (higher = more irregular/complex) @description Lower ApEn indicates more self-similarity and predictability entropy_regime(src, length, q, n_bins)   Detects market regime based on entropy level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      q (float) : Tsallis q parameter (use optimal_q() or default 1.5)      n_bins (simple int) : Number of histogram bins   Returns: Regime indicator: -1 = trending (low entropy), 0 = transition, 1 = ranging (high entropy) entropy_risk(src, length)   Calculates entropy-based risk indicator   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period   Returns: Risk score where 1 = maximum divergence from Gaussian 1 hit_rate(src, length)   Calculates hit rate (probability of positive outcome) over lookback   Parameters:      src (float) : Source series (positive values count as hits)      length (simple int) : Lookback period   Returns: Hit rate as decimal hit_rate_cond(condition, length)   Calculates hit rate for custom condition over lookback   Parameters:      condition (bool) : Boolean series (true = hit)      length (simple int) : Lookback period   Returns: Hit rate as decimal expected_value(src, length)   Calculates expected value of a series   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Expected value (mean) expected_value_trade(win_prob, take_profit, stop_loss)   Calculates expected value for a trade with TP and SL levels   Parameters:      win_prob (float) : Probability of hitting TP (0-1)      take_profit (float) : Take profit in price units or %      stop_loss (float) : Stop loss in price units or % (positive value)   Returns: Expected value per trade @description EV = (win_prob * TP) - ((1 - win_prob) * SL) breakeven_winrate(take_profit, stop_loss)   Calculates breakeven win rate for given TP/SL ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: Required win rate for breakeven (EV = 0) reward_risk_ratio(take_profit, stop_loss)   Calculates the reward-to-risk ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: R:R ratio fpt_probability(src, length, target, max_bars)   Estimates probability of price reaching target within N bars   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move (in same units as src, e.g., % return)      max_bars (simple int) : Maximum bars to consider   Returns: Probability of reaching target within max_bars @description Based on random walk with drift approximation fpt_mean(src, length, target)   Estimates mean first passage time to target level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move   Returns: Expected number of bars to reach target (can be infinite) fpt_historical(src, length, target)   Counts historical bars to reach target from each point   Parameters:      src (float) : Source series (typically price or returns)      length (simple int) : Lookback period      target (float) : Target move from each starting point   Returns: Array of first passage times (na if target not reached within lookback) tp_probability(src, length, tp_distance, sl_distance)   Estimates probability of hitting TP before SL   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for estimation      tp_distance (float) : Take profit distance (positive)      sl_distance (float) : Stop loss distance (positive)   Returns: Probability of TP being hit first trade_probability(src, length, tp_pct, sl_pct)   Calculates complete trade probability and EV analysis   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      tp_pct (float) : Take profit percentage      sl_pct (float) : Stop loss percentage   Returns: Tuple: cond_prob(condition_a, condition_b, length)   Calculates conditional probability P(B|A) from historical data   Parameters:      condition_a (bool) : Condition A (the given condition)      condition_b (bool) : Condition B (the outcome)      length (simple int) : Lookback period   Returns: P(B|A) = P(A and B) / P(A) bayes_update(prior, likelihood, false_positive)   Updates probability using Bayes' theorem   Parameters:      prior (float) : Prior probability P(H)      likelihood (float) : P(E|H) - probability of evidence given hypothesis      false_positive (float) : P(E|~H) - probability of evidence given hypothesis is false   Returns: Posterior probability P(H|E) streak_prob(win_rate, streak_length)   Calculates probability of N consecutive wins given win rate   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive wins   Returns: Probability of streak losing_streak_prob(win_rate, streak_length)   Calculates probability of experiencing N consecutive losses   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive losses   Returns: Probability of losing streak drawdown_prob(src, length, dd_threshold)   Estimates probability of drawdown exceeding threshold   Parameters:      src (float) : Source series (returns)      length (simple int) : Lookback period      dd_threshold (float) : Drawdown threshold (as positive decimal, e.g., 0.10 = 10%)   Returns: Historical probability of exceeding drawdown threshold prob_to_odds(prob)   Calculates odds from probability   Parameters:      prob (float) : Probability (0-1)   Returns: Odds (prob / (1 - prob)) odds_to_prob(odds)   Calculates probability from odds   Parameters:      odds (float) : Odds ratio   Returns: Probability (0-1) implied_prob(decimal_odds)   Calculates implied probability from decimal odds (betting)   Parameters:      decimal_odds (float) : Decimal odds (e.g., 2.5 means $2.50 return per $1 bet)   Returns: Implied probability logit(prob)   Calculates log-odds (logit) from probability   Parameters:      prob (float) : Probability (must be in (0, 1))   Returns: Log-odds inv_logit(log_odds)   Calculates probability from log-odds (inverse logit / sigmoid)   Parameters:      log_odds (float) : Log-odds value   Returns: Probability (0-1) linreg_slope(src, length)   Calculates linear regression slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Slope coefficient (change per bar) linreg_intercept(src, length)   Calculates linear regression intercept   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Intercept (predicted value at oldest bar in window) linreg_value(src, length)   Calculates predicted value at current bar using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value at current bar (end of regression line) linreg_forecast(src, length, offset)   Forecasts value N bars ahead using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period for regression      offset (simple int) : Bars ahead to forecast (positive = future)   Returns: Forecasted value linreg_channel(src, length, mult)   Calculates linear regression channel with bands   Parameters:      src (float) : Source series      length (simple int) : Lookback period      mult (simple float) : Standard deviation multiplier for bands   Returns: Tuple: r_squared(src, length)   Calculates R-squared (coefficient of determination)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: R² value where 1 = perfect linear fit adj_r_squared(src, length)   Calculates adjusted R-squared (accounts for sample size)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Adjusted R² value std_error(src, length)   Calculates standard error of estimate (residual standard deviation)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Standard error residual(src, length)   Calculates residual at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Residual (actual - predicted) residuals(src, length)   Returns array of all residuals in lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Array of residuals t_statistic(src, length)   Calculates t-statistic for slope coefficient   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: T-statistic (slope / standard error of slope) slope_pvalue(src, length)   Approximates p-value for slope t-test (two-tailed)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Approximate p-value is_significant(src, length, alpha)   Tests if regression slope is statistically significant   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (simple float) : Significance level (default: 0.05)   Returns: true if slope is significant at alpha level trend_strength(src, length)   Calculates normalized trend strength based on R² and slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Trend strength where sign indicates direction trend_angle(src, length)   Calculates trend angle in degrees   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Angle in degrees (positive = uptrend, negative = downtrend) linreg_acceleration(src, length)   Calculates trend acceleration (second derivative)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for each regression   Returns: Acceleration (change in slope) linreg_deviation(src, length)   Calculates deviation from regression line in standard error units   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Deviation in standard error units (like z-score) quadreg_coefficients(src, length)   Fits quadratic regression and returns coefficients   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Tuple: for y = a*x² + b*x + c quadreg_value(src, length)   Calculates quadratic regression value at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value from quadratic fit correlation(x, y, length)   Calculates Pearson correlation coefficient between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Correlation coefficient covariance(x, y, length)   Calculates sample covariance between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 2)   Returns: Covariance value beta(asset, benchmark, length)   Calculates beta coefficient (slope of regression of y on x)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period   Returns: Beta coefficient @description Beta = Cov(asset, benchmark) / Var(benchmark) alpha(asset, benchmark, length, risk_free)   Calculates alpha (Jensen's alpha / intercept)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period      risk_free (float) : Risk-free rate (default: 0)   Returns: Alpha value (excess return not explained by beta) spearman(x, y, length)   Calculates Spearman rank correlation coefficient   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Spearman correlation @description More robust to outliers than Pearson correlation kendall_tau(x, y, length)   Calculates Kendall's tau rank correlation (simplified)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Kendall's tau correlation_change(x, y, length, change_period)   Calculates change in correlation over time   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      change_period (simple int) : Period over which to measure change   Returns: Change in correlation correlation_regime(x, y, length, ma_length)   Detects correlation regime based on level and stability   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      ma_length (simple int) : Moving average length for smoothing   Returns: Regime: -1 = negative, 0 = uncorrelated, 1 = positive correlation_stability(x, y, length, stability_length)   Calculates correlation stability (inverse of volatility)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback for correlation      stability_length (simple int) : Lookback for stability calculation   Returns: Stability score where 1 = perfectly stable relative_strength(asset, benchmark, length)   Calculates relative strength of asset vs benchmark   Parameters:      asset (float) : Asset price series      benchmark (float) : Benchmark price series      length (simple int) : Smoothing period   Returns: Relative strength ratio (normalized) tracking_error(asset, benchmark, length)   Calculates tracking error (standard deviation of excess returns)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Tracking error (annualize by multiplying by sqrt(252) for daily data) information_ratio(asset, benchmark, length)   Calculates information ratio (risk-adjusted excess return)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Information ratio capture_ratio(asset, benchmark, length, up_capture)   Calculates up/down capture ratio   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period      up_capture (simple bool) : If true, calculate up capture; if false, down capture   Returns: Capture ratio autocorrelation(src, length, lag)   Calculates autocorrelation at specified lag   Parameters:      src (float) : Source series      length (simple int) : Lookback period      lag (simple int) : Lag for autocorrelation (default: 1)   Returns: Autocorrelation at specified lag partial_autocorr(src, length)   Calculates partial autocorrelation at lag 1   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: PACF at lag 1 (equals ACF at lag 1) autocorr_test(src, length, max_lag)   Tests for significant autocorrelation (Ljung-Box inspired)   Parameters:      src (float) : Source series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to test   Returns: Sum of squared autocorrelations (higher = more autocorrelation) cross_correlation(x, y, length, lag)   Calculates cross-correlation at specified lag   Parameters:      x (float) : First series      y (float) : Second series (lagged)      length (simple int) : Lookback period      lag (simple int) : Lag to apply to y (positive = y leads x)   Returns: Cross-correlation at specified lag cross_correlation_peak(x, y, length, max_lag)   Finds lag with maximum cross-correlation   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to search (both directions)   Returns: Tuple:
Biblioteca Pine Script®
por HenriqueCentieiro
1
Bias Daily 3.0Bias Daily Indicator with Breakout Alerts This indicator plots bullish, bearish, and consolidation bias levels based on previous daily candles. It draws horizontal lines at prior candle highs and lows and lets you track momentum shifts visually. It also includes flexible alerts: Break previous candle high Break previous candle low Break either high or low Perfect for spotting key breakout levels and identifying market bias across all intraday and higher timeframes. Fully customizable line colors, styles, and number of previous levels displayed.
Indicador Pine Script®
por Munnymike
Multi-Timeframe EMA50 Structure + ATR Sniper SystemThis indicator is a comprehensive Trend-Following and Risk Management system designed for swing traders who focus on high-probability structural entries. It combines three core concepts into one visual tool: Multi-Timeframe Structure: Tracks the EMA50 across key swing timeframes (2D, 3D, 1W, 2W, 1M). Momentum Health: Detects MACD Divergences on these timeframes to warn of potential reversals. ATR Sniper Zone (Risk Control): Visually defines the "Buy Zone" and "Hard Stop Level" based on volatility (Daily ATR), preventing FOMO and ensuring consistent Risk/Reward ratios.
Indicador Pine Script®
por geese1028
US 3H HARDCORE SCALPING ALGO (FINAL) utc+9English: User Manual1. OverviewThis indicator is a high-intensity scalping tool designed to capture volatility during the first 3 hours of the US market session. It combines trend filtering, value-based entries, and volume validation to identify high-probability trade setups.2. Key ComponentsTrend Filter (EMA 200): Determines the long-term market direction. Only buy signals are generated above the EMA 200, and only sell signals below it.Value Area (VWAP): Represents the Volume Weighted Average Price. It acts as a "magnet" for price and a baseline for fair value.Session Focus (KST 23:00 - 02:00): Highlights the US session opening hours in Korea Standard Time (Red background). It automatically calculates the 3-hour window regardless of the chart timeframe.Volume Filter: Ensures that signals are only generated when trading activity is higher than the 20-period average, filtering out "fake" breakouts.3. Entry ConditionsLong (Buy) Signal:Time: Must be within the Red Focus Zone.Trend: Price is above EMA 200 ($Close > EMA_{200}$).Value: Price is above VWAP.Reaction: The bar's low touches or dips below VWAP, but the bar closes back above it (Pullback recovery).Volume: Current volume is higher than the 20-period Volume SMA.Short (Sell) Signal:Time: Must be within the Red Focus Zone.Trend: Price is below EMA 200 ($Close < EMA_{200}$).Value: Price is below VWAP.Reaction: The bar's high touches or rises above VWAP, but the bar closes back below it (Rejection recovery).Volume: Current volume is higher than the 20-period Volume SMA.4. Visual ElementsYellow Line: EMA 200.Aqua Line: VWAP.Red Background: US 3-hour focus window.Information Label (Top Right): Real-time display of current trend, VWAP position, and session status.
Indicador Pine Script®
por minovsajh
Phantom Support & Resistance Auto [PT-IND-SR.001]Overview Phantom Support & Resistance Auto is a context-focused support and resistance indicator designed to visualize price interaction zones derived from multiple market behaviors. The script does not generate buy or sell signals. Instead, it provides a structured map of potential reaction areas, allowing traders to better understand where price has historically reacted, consolidated, or extended liquidity. This indicator is intended to be used as a decision-support and contextual analysis tool, not as a standalone trading system. How the Script Works The indicator combines several independent but complementary methods of identifying support and resistance. Each method captures a different type of market behavior, and all components can be enabled or disabled independently. 1) High / Low Zones (Range Extremes) This module tracks the highest high and lowest low over a configurable lookback period. These levels represent recent range boundaries, which often act as reaction zones during consolidation or pullbacks. They are visualized as extended horizontal levels to preserve historical context. 2) Pivot Zones (Filtered & Merged Levels) Pivot zones are derived from confirmed pivot highs and lows. To avoid excessive and overlapping levels, the script applies a merge tolerance based on either: ATR distance, or Percentage distance from price Nearby pivot levels are merged into a single zone, and each zone tracks how many times price has interacted with it. This interaction count adjusts visual strength, creating a relative importance hierarchy rather than treating all levels equally. An optional higher-timeframe source can be used to project structurally significant levels onto lower timeframes. 3) Wick Liquidity Zones This module detects candles with disproportionately large wicks relative to their bodies. Such candles often indicate liquidity grabs, stop runs, or rejection areas. Detected wick levels are extended forward to highlight areas where liquidity was previously absorbed. This component focuses on price rejection behavior, not trend direction. 4) PR Levels (Volatility-Adjusted Predicted Ranges) PR levels are derived from a volatility-adjusted average price model. Using ATR as a normalization factor, the script calculates a central average along with upper and lower projected zones. These levels are adaptive, expanding and contracting with volatility, and are intended to represent probabilistic price ranges, not fixed targets. 5) MACD-Based Support & Resistance (Heikin Ashi Source) This module derives dynamic support and resistance levels based on MACD momentum shifts, calculated from Heikin Ashi price data to reduce noise. When MACD momentum transitions occur, recent highs and lows are captured and projected as potential reaction zones. This component focuses on momentum-driven structural changes, rather than static price levels. Why These Components Are Combined Each component captures a different dimension of market behavior: High / Low zones → Range extremes Pivot zones → Structural reaction points Wick zones → Liquidity and rejection behavior PR levels → Volatility-normalized price ranges MACD S&R → Momentum-based structural shifts By combining these sources, the indicator provides a layered view of support and resistance, allowing traders to evaluate confluence, alignment, or divergence between different types of levels instead of relying on a single method. The script does not assume all levels are equal; visual weighting helps distinguish structural levels from situational ones. Visualization & Outputs Color-coded horizontal zones with strength-based opacity Optional glow effects for visual clarity Independent toggles for each S&R source A table showing percentage distances between projected PR levels, helping users contextualize price location within its current range All visual components are configurable and can be selectively disabled to reduce chart clutter. How to Use Use this indicator as a context and mapping tool Observe areas where multiple zone types align for higher contextual significance Combine with your own entry logic, confirmations, and trade management rules Suitable for multi-timeframe analysis and market structure studies Risk Management Notice This indicator should always be used as part of a well-defined risk management plan. Support and resistance zones represent areas of potential interaction, not guaranteed reactions. Users are responsible for applying appropriate: Position sizing Stop placement Risk-to-reward rules The indicator does not manage risk automatically and should not replace proper risk management practices. What This Script Is NOT It is not a buy/sell signal generator It does not predict future price direction It does not guarantee reactions at every level It should not be used as a standalone trading strategy Originality & Purpose The originality of this script lies in its structured integration of multiple support and resistance methodologies, each preserved as a distinct analytical layer rather than blended into a single opaque output. The purpose is to help traders understand where price has interacted with liquidity, structure, and volatility, not to automate trade decisions.
Indicador Pine Script®
por Phantom_Trader_Global
Futures Trend Signaler Final VersionFutures Trend Signaler is a compact, multi-timeframe EMA “trend dashboard” built for intraday futures/index trading. It displays a clean table (1m + two lower timeframes you choose, e.g., 15s and 1s) that shows: EMA 9 vs EMA 21 (short-term momentum / immediate trend direction) EMA 21 vs EMA 50 (trend “sustainability” / broader continuation bias) Price vs 1m EMA 9 (LTF/Ultra price position relative to the 1-minute momentum line) Each cell is color-coded (green = bullish, red = bearish, gray = neutral/na) so you can read bias at a glance. When a new EMA crossover occurs, the table also flags it (and tracks the most recent bull/bear cross) so you can quickly see if momentum just flipped—without cluttering the chart with overlapping markers. Fully customizable table position and text size. Designed to stay lightweight by using minimal higher/lower timeframe requests. Disclaimer: This indicator is for informational/educational purposes only and is not financial advice. Always use proper risk management.
Indicador Pine Script®
por babaflargslugdook
Pivot Points with Support/ResistanceA) Pivot Resistance Levels (R1, R2, R3…) Resistance pivots are projected upside levels where price often pauses, rejects, or reverses. They are commonly used as profit targets for long trades and areas to watch for short setups when buyers show weakness. B) Pivot Support Levels (S1, S2, S3…) Support pivots are projected downside levels where price often stabilizes or bounces. They are commonly used as profit targets for short trades and areas to watch for long setups when sellers lose momentum. C) Role in Market Structure S/R pivots map out probable intraday supply and demand zones based on the prior session’s price action. They help define the day’s trading range and highlight high-probability reaction areas. D) Trading Interpretation Acceptance above resistance → bullish continuation Rejection at resistance → potential pullback or reversal Acceptance below support → bearish continuation Rejection at support → potential bounce Best used with trend context, volume, and confluence (CPR, VAH/VAL, Camarilla)
Indicador Pine Script®
por isaackirby2004
1
Pivot Points (PP/BC/TC)A) Central Pivot (CP) The Central Pivot is the main equilibrium level for the session. It represents fair value where buyers and sellers are balanced. Price above CP shows bullish bias; price below CP shows bearish bias. B) Top Central (TC) The Top Central is the upper boundary of the CPR. It acts as short-term resistance in normal conditions and support in strong bullish trends. Acceptance above TC suggests upside continuation. C) Bottom Central (BC) The Bottom Central is the lower boundary of the CPR. It acts as short-term support in normal conditions and resistance in strong bearish trends. Acceptance below BC suggests downside continuation.
Indicador Pine Script®
por isaackirby2004