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HawkDoveScoreLibLibrary "HawkDoveScoreLib" hds_score(sym2y, sym10y, symBE10, symBS, symFCI, tfMacro, lenTrend, lenNorm, smoothScore, useRatePath, useCurve, useRealYield, useBalanceSh, useStressFCI, wRatePath, wCurve, wRealYield, wBS, wStress)   Parameters:      sym2y (string)      sym10y (string)      symBE10 (string)      symBS (string)      symFCI (string)      tfMacro (string)      lenTrend (int)      lenNorm (int)      smoothScore (simple int)      useRatePath (bool)      useCurve (bool)      useRealYield (bool)      useBalanceSh (bool)      useStressFCI (bool)      wRatePath (float)      wCurve (float)      wRealYield (float)      wBS (float)      wStress (float) hds_regime(score, thrDove, thrHawk)   Parameters:      score (float)      thrDove (int)      thrHawk (int) hds_regime_label(score, thrDove, thrHawk)   Parameters:      score (float)      thrDove (int)      thrHawk (int)
Perpustakaan Pine Script®
oleh tychester
pubLibCandlestickPatternsLibrary "pubLibCandlestickPatterns" candlestick pattern conditions for indicator and strategy development doji() bull_marubozu() bear_marubozu() spinning_top() bull_belt_hold_line() bear_belt_hold_line() bull_breakaway() bear_breakaway() concealing_baby_swallow() bull_counterattack() bear_counterattack() dark_cloud_cover() long_legged_doji() southern_doji() northern_doji() dumpling_top() bull_engulfing() bear_engulfing() frypan_bottom() hammer() hanging_man() bull_harami() bear_harami() bull_harami_cross() bear_harami_cross() high_wave() bull_hikkake() bear_hikkake() homing_pigeon() in_neck() bull_kicking() bear_kicking() matching_low() on_neck() piercing() bull_separating_lines() bear_separating_lines() upgap_side_by_side_white_lines() downgap_side_by_side_white_lines() stalled() bull_star() bear_star() bull_doji_star() bear_doji_star() morning_star() evening_star() morning_doji_star() evening_doji_star() abandoned_baby_bottom() abandoned_baby_top() inverted_hammer() shooting_star() dragonfly_doji() gravestone_doji() stick_sandwich() upward_gapping_tasuki() downward_gapping_tasuki() three_black_crows() advance_block() three_advancing_white_soldiers() bull_three_line_strike() bear_three_line_strike() rising_three_methods() falling_three_methods() three_stars_in_the_south() thrusting() tower_bottom() tower_top() tri_star_bottom() tri_star_top() tweezer_bottom() tweezer_top() upside_gap_two_crows()
Perpustakaan Pine Script®
oleh theEccentricTrader
NodialTreesLows2: ML Random Forest / Pivot Lows (Part 2 of 2)Title: `Library: ML Random Forest / Pivot Lows (Part 2 of 2)` Description: This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Lows (Long setups). It is a direct extension of NodialTreesL1 and cannot be used alone. Due to Pine Script's compilation limits on complexity and file size, the 12-tree ensemble model has been split into two separate libraries. ### 🧩 Library Contents This module exports the following methods representing the specific decision paths of the trained AI model: - `tree_6(array f)` - `tree_7(array f)` - `tree_8(array f)` - `tree_9(array f)` - `tree_10(array f)` - `tree_11(array f)` ### ⚠️ Implementation Guide To use this library, you must combine it with Part 1. Please refer to the NodialTreesLows1 library description for: 1. The full Integration Code Example (how to average the votes). 2. The exact Input Feature List (the 27 required metrics). 3. Detailed explanation of the Machine Learning logic. How to finish the integration: Import this library alongside Part 1 and add the results of `tree_6` through `tree_11` to your voting sum, as shown in the Part 1 documentation.
Perpustakaan Pine Script®
oleh Nodial
NodialTreesLows1: ML Random Forest / Pivot Lows (Part 1 of 2)Title: `Library: ML Random Forest / Pivot Lows (Part 1 of 2)` Description: This library contains the first half (Trees 0-5) of a Random Forest Classifier designed to validate Pivot Lows (Long setups). Due to Pine Script size constraints, the model is split into two libraries. You must use this library in conjunction with NodialTreesL2 to run the full ensemble. ### 🧩 System Architecture - Model: Random Forest (12 Trees total). - This Library: Contains `tree_0` to `tree_5`. - Logic: Each tree analyzes a feature array and outputs a probability score. - Target: Validating Swing Lows / Support Bounces. ### 📊 Input Requirements The methods expect an `array` of size 27 containing market features (Price Action, Momentum, Volatility, Volume, Structure). The exact order of features is critical for the model's accuracy. ### 🛠️ Integration Example Since this is a modular library, you need to import both parts and average their results to get the final prediction. ### 📋 Feature Mapping (Array Indexing) To get accurate predictions, the input array must contain exactly 27 floats in this specific order: 0. Timeframe (in seconds) 1. RSI (Raw Value) 2. MACD Histogram 3. Relative Volume 4. EMA Distance (%) 5. EMA Slope 6. ATR Ratio 7. ADX 8. Buying/Selling Pressure 9. Wick Ratio 10-16. Divergences & Pattern Flags (Boolean 0.0/1.0) 17-22. Proprietary Momentum Metrics ("Onion" Structure) 23-26. Derived Volatility/Volume Features *Note: For the advanced proprietary metrics (Indices 17-26), users must implement their own calculations or use compatible indicators.* //@version=6 indicator("My ML Long Strategy", overlay=true) // Import BOTH libraries import YourUsername/NodialTreesL1/1 as rf_part1 import YourUsername/NodialTreesL2/1 as rf_part2 // ... (Calculate your 27 features and fill the array) ... // var features = array.from(timeframe, rsi, macd, ...) // Calculate Ensemble Probability (Average of 12 Trees) float vote_sum = 0.0 // Trees from Part 1 vote_sum += rf_part1.tree_0(features) vote_sum += rf_part1.tree_1(features) vote_sum += rf_part1.tree_2(features) vote_sum += rf_part1.tree_3(features) vote_sum += rf_part1.tree_4(features) vote_sum += rf_part1.tree_5(features) // Trees from Part 2 (Trees 6-11) vote_sum += rf_part2.tree_6(features) vote_sum += rf_part2.tree_7(features) vote_sum += rf_part2.tree_8(features) vote_sum += rf_part2.tree_9(features) vote_sum += rf_part2.tree_10(features) vote_sum += rf_part2.tree_11(features) // Final Probability (0.0 to 1.0) float final_prob = vote_sum / 12.0 if final_prob > 0.60 label.new(bar_index, low, "Valid Low", color=color.green)
Perpustakaan Pine Script®
oleh Nodial
NodialTreesHighs2: ML Random Forest / Pivot Highs (Part 2 of 2)Title: `Library: ML Random Forest / Pivot Highs (Part 2 of 2)` Description: This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Highs (Short setups). It is a direct extension of NodialTreesH1 and cannot be used alone. Due to Pine Script's compilation limits on complexity and file size, the 12-tree ensemble model has been split into two separate libraries. ### 🧩 Library Contents This module exports the following methods representing the specific decision paths of the trained AI model: - `tree_6(array f)` - `tree_7(array f)` - `tree_8(array f)` - `tree_9(array f)` - `tree_10(array f)` - `tree_11(array f)` ### ⚠️ Implementation Guide To use this library, you must combine it with Part 1. Please refer to the NodialTreesH1 library description for: 1. The full Integration Code Example (how to average the votes). 2. The exact Input Feature List (the 27 required metrics). 3. Detailed explanation of the Machine Learning logic. How to finish the integration: Import this library alongside Part 1 and add the results of `tree_6` through `tree_11` to your voting sum, as shown in the Part 1 documentation.
Perpustakaan Pine Script®
oleh Nodial
NodialTreesHighs1: ML Random Forest / Pivot Highs (Part 1 of 2)Title: `Library: ML Random Forest / Pivot Highs (Part 1 of 2)` Description: This library contains the first half (Trees 0-5) of a Random Forest Classifier designed to validate Pivot Highs (Short setups). Due to Pine Script size constraints, the model is split into two libraries. You must use this library in conjunction with NodialTreesH2 to run the full ensemble. ### 🧩 System Architecture - Model: Random Forest (12 Trees total). - This Library: Contains `tree_0` to `tree_5`. - Logic: Each tree analyzes a feature array and outputs a probability score. - Target: Validating Swing Highs / Resistance Rejections. ### 📊 Input Requirements The methods expect an `array` of size 27 containing market features (Price Action, Momentum, Volatility, Volume, Structure). The exact order of features is critical for the model's accuracy. ### 🛠️ Integration Example Since this is a modular library, you need to import both parts and average their results to get the final prediction. ### 📋 Feature Mapping (Array Indexing) To get accurate predictions, the input array must contain exactly 27 floats in this specific order: 0. Timeframe (in seconds) 1. RSI (Raw Value) 2. MACD Histogram 3. Relative Volume 4. EMA Distance (%) 5. EMA Slope 6. ATR Ratio 7. ADX 8. Buying/Selling Pressure 9. Wick Ratio 10-16. Divergences & Pattern Flags (Boolean 0.0/1.0) 17-22. Proprietary Momentum Metrics ("Onion" Structure) 23-26. Derived Volatility/Volume Features *Note: For the advanced proprietary metrics (Indices 17-26), users must implement their own calculations or use compatible indicators.* //@version=6 indicator("My ML Short Strategy", overlay=true) // Import BOTH libraries import YourUsername/NodialTreesH1/1 as rf_part1 import YourUsername/NodialTreesH2/1 as rf_part2 // ... (Calculate your 27 features and fill the array) ... // var features = array.from(timeframe, rsi, macd, ...) // Calculate Ensemble Probability (Average of 12 Trees) float vote_sum = 0.0 // Trees from Part 1 vote_sum += rf_part1.tree_0(features) vote_sum += rf_part1.tree_1(features) vote_sum += rf_part1.tree_2(features) vote_sum += rf_part1.tree_3(features) vote_sum += rf_part1.tree_4(features) vote_sum += rf_part1.tree_5(features) // Trees from Part 2 (Trees 6-11) vote_sum += rf_part2.tree_6(features) vote_sum += rf_part2.tree_7(features) vote_sum += rf_part2.tree_8(features) vote_sum += rf_part2.tree_9(features) vote_sum += rf_part2.tree_10(features) vote_sum += rf_part2.tree_11(features) // Final Probability (0.0 to 1.0) float final_prob = vote_sum / 12.0 if final_prob > 0.60 label.new(bar_index, high, "Valid Short", color=color.red)
Perpustakaan Pine Script®
oleh Nodial
TPOSmartMoneyLibLibrary "TPOSmartMoneyLib" Library for TPO (Time Price Opportunity) and Smart Money concepts including session management, PDH/PDL detection, sweeping logic, and volume profile utilities f_price_to_tick(p)   Convert price to tick   Parameters:      p (float) : Price value   Returns: Tick value f_tick_to_row(t, row_ticks_in)   Convert tick to row   Parameters:      t (int) : Tick value      row_ticks_in (int) : Number of ticks per row   Returns: Row index f_row_to_price(row, row_ticks_in)   Convert row to price (midpoint)   Parameters:      row (int) : Row index      row_ticks_in (int) : Number of ticks per row   Returns: Price at row midpoint f_calc_row_ticks(natr_ref, row_gran_mult)   Calculate dynamic row size based on normalized ATR   Parameters:      natr_ref (float) : Daily normalized ATR reference value      row_gran_mult (float) : Row granularity multiplier   Returns: Number of ticks per row f_more_transp_pct(c, pct)   Increase color transparency by percentage   Parameters:      c (color) : Input color      pct (float) : Percentage to increase transparency (0.0 to 1.0)   Returns: Color with increased transparency f_dom_color(dom, buy_col, sell_col, gamma, transp_weak, transp_strong)   Calculate dominance color based on buy/sell ratio   Parameters:      dom (float) : Dominance ratio (-1 to 1, negative = sell, positive = buy)      buy_col (color) : Buy dominant color      sell_col (color) : Sell dominant color      gamma (float) : Gamma correction for color intensity      transp_weak (int) : Transparency for weak dominance      transp_strong (int) : Transparency for strong dominance   Returns: Blended color f_sess_part(sess_str, get_start)   Parse session string to get start or end time   Parameters:      sess_str (string) : Session string in format "HHMM-HHMM"      get_start (bool) : True to get start time, false to get end time   Returns: Time string in HHMM format f_hhmm_to_h(hhmm)   Convert HHMM string to hours   Parameters:      hhmm (string) : Time string in HHMM format   Returns: Hours (0-23) f_hhmm_to_m(hhmm)   Convert HHMM string to minutes   Parameters:      hhmm (string) : Time string in HHMM format   Returns: Minutes (0-59) f_prev_day_window_bounds(today_day_rth, win_start, win_end, session_tz)   Calculate previous day window bounds   Parameters:      today_day_rth (int) : Today's RTH start timestamp      win_start (string) : Window start time in HHMM format      win_end (string) : Window end time in HHMM format      session_tz (string) : Session timezone   Returns: Tuple of f_default_session_colors()   Get default session colors   Returns: Array of 4 colors f_session_names()   Get session names   Returns: Array of 4 session names f_process_hl(arr, rng, keep_bars, lock_to_live)   Process high/low lines with sweeping detection   Parameters:      arr (array) : Array of HLLine objects      rng (float) : Price range for visibility filtering      keep_bars (int) : Maximum bars to keep lines      lock_to_live (bool) : Whether to lock line end to current bar   Returns: 0 (for chaining) f_process_naked_lines(arr, calc_bars, bars_per_day, keep_to_day_end)   Process naked lines (POC/VAH/VAL) with sweeping detection   Parameters:      arr (array) : Array of NakedLine objects      calc_bars (int) : Maximum calculation bars      bars_per_day (int) : Bars per day for scope calculation      keep_to_day_end (bool) : Whether to extend to day end   Returns: 0 (for chaining) f_update_pdhl_lines(pd_hl, pdh, pdl, new_day, pd_rng, bars_per_day, pdh_color, pdl_color)   Detect and create PDH/PDL lines   Parameters:      pd_hl (array) : Array to store HLLine objects      pdh (float) : Previous day high      pdl (float) : Previous day low      new_day (bool) : Whether it's a new day      pd_rng (float) : Price range for visibility      bars_per_day (int) : Bars per day      pdh_color (color) : PDH line color      pdl_color (color) : PDL line color   Returns: 0 (for chaining) f_poc_from_vals(keys, vals)   Calculate POC from sorted keys and values   Parameters:      keys (array) : Sorted array of row keys      vals (array) : Array of volume values   Returns: POC row key f_value_area(keys, vals, poc_key, va_pct)   Calculate Value Area from volume distribution   Parameters:      keys (array) : Sorted array of row keys      vals (array) : Array of volume values      poc_key (int) : POC row key      va_pct (float) : Value Area percentage (typically 0.70)   Returns: Tuple of f_find_key_sorted(keys, target)   Find key in sorted array using binary search   Parameters:      keys (array) : Sorted array of keys      target (int) : Target key to find   Returns: Index of key, or -1 if not found f_zscore_safe(x, len)   Safe z-score calculation using built-in functions   Parameters:      x (float) : Input series      len (int) : Lookback length   Returns: Z-score HLLine   Represents a high/low line with sweeping detection   Fields:      ln (series line) : Line object      lb (series label) : Label object      lvl (series float) : Price level      startBar (series int) : Bar index where line starts      swept (series bool) : Whether the level has been swept      isHigh (series bool) : True if this is a high, false if low      col (series color) : Line color NakedLine   Represents a naked POC/VAH/VAL line   Fields:      ln (series line) : Line object      lb (series label) : Label object      lvl (series float) : Price level      startBar (series int) : Bar index where line starts      swept (series bool) : Whether the level has been swept      sweptBar (series int) : Bar index where swept occurred      endBar (series int) : Bar index where line should end
Perpustakaan Pine Script®
oleh cybermediaboy
TrinityCore1Library "TrinityCore1" default_config() calc_weights(sym, cp, cs, s1, s2, s3, s4, s5, cfg)   Parameters:      sym (string)      cp (string)      cs (string)      s1 (string)      s2 (string)      s3 (string)      s4 (string)      s5 (string)      cfg (TrinityConfig) calc_diagnostics(sym, cp, cs, s1, s2, s3, s4, s5, cfg)   Parameters:      sym (string)      cp (string)      cs (string)      s1 (string)      s2 (string)      s3 (string)      s4 (string)      s5 (string)      cfg (TrinityConfig) TrinityWeights   Fields:      stock (series float)      s1 (series float)      s2 (series float)      s3 (series float)      s4 (series float)      s5 (series float)      cash (series float) SafeDiag   Fields:      id (series string)      vol (series float)      corr (series float)      dd (series float)      score (series float)      final_w (series float) TrinityDiagLite   Fields:      ready_code (series int)      drop_code (series int)      is_ready (series bool)      risk_gate (series bool)      p_value (series float)      d_bar_index (series int) TrinityDiagFull   Fields:      base (TrinityDiagLite)      raw_vol (series float)      adj_vol (series float)      target_w (series float)      penalty_mult (series float)      sd1 (SafeDiag)      sd2 (SafeDiag)      sd3 (SafeDiag)      sd4 (SafeDiag)      sd5 (SafeDiag) TrinityConfig   Fields:      enabled (series bool)      len_20 (series int)      w_20 (series float)      len_63 (series int)      w_63 (series float)      len_126 (series int)      w_126 (series float)      len_252 (series int)      w_252 (series float)      risk_floor (series float)      risk_factor (series float)      target_vol_stock (series float)      target_vol_safe (series float)      gov_max_lev (series float)      gov_min_mult (series float)      top_k (series int)      sqs_base (series float)      sqs_w_corr (series float)      sqs_w_vol (series float)      sqs_w_dd (series float)      sqs_range_corr (series float)      sqs_range_dd (series float)      fall_thr (series float)      rate_fall (series float)      rate_accel (series float)      penalty_val (series float)      ready_bars (series int)
Perpustakaan Pine Script®
oleh excelar
RSMPatternLibLibrary "RSMPatternLib" RSM Pattern Library - All chart patterns from PATTERNS.md Implements: Candlestick patterns, Support/Resistance, Gaps, Triangles, Volume Divergence, and more ALL PATTERNS ARE OWN IMPLEMENTATION - No external dependencies EDGE CASES HANDLED: - Zero/tiny candle bodies - Missing volume data - Low bar count scenarios - Integer division issues - Price normalization for different instruments bullishEngulfing(minBodyRatio, minPrevBodyRatio)   Detects Bullish Engulfing pattern   Parameters:      minBodyRatio (float) : Minimum body size as ratio of total range (default 0.3)      minPrevBodyRatio (float) : Minimum previous candle body ratio to filter dojis (default 0.1)   Returns: bool True when bullish engulfing detected EDGE CASES: Handles doji previous candle, zero range, tiny bodies bearishEngulfing(minBodyRatio, minPrevBodyRatio)   Detects Bearish Engulfing pattern   Parameters:      minBodyRatio (float) : Minimum body size as ratio of total range (default 0.3)      minPrevBodyRatio (float) : Minimum previous candle body ratio to filter dojis (default 0.1)   Returns: bool True when bearish engulfing detected EDGE CASES: Handles doji previous candle, zero range, tiny bodies doji(maxBodyRatio, minRangeAtr)   Detects Doji candle (indecision)   Parameters:      maxBodyRatio (float) : Maximum body size as ratio of total range (default 0.1)      minRangeAtr (float) : Minimum range as multiple of ATR to filter flat candles (default 0.3)   Returns: bool True when doji detected EDGE CASES: Filters out no-movement bars, handles zero range shootingStar(wickMultiplier, maxLowerWickRatio, minBodyAtrRatio)   Detects Shooting Star (bearish reversal)   Parameters:      wickMultiplier (float) : Upper wick must be at least this times the body (default 2.0)      maxLowerWickRatio (float) : Lower wick max as ratio of body (default 0.5)      minBodyAtrRatio (float) : Minimum body size as ratio of ATR (default 0.1)   Returns: bool True when shooting star detected EDGE CASES: Handles zero body (uses range-based check), tiny bodies hammer(wickMultiplier, maxUpperWickRatio, minBodyAtrRatio)   Detects Hammer (bullish reversal)   Parameters:      wickMultiplier (float) : Lower wick must be at least this times the body (default 2.0)      maxUpperWickRatio (float) : Upper wick max as ratio of body (default 0.5)      minBodyAtrRatio (float) : Minimum body size as ratio of ATR (default 0.1)   Returns: bool True when hammer detected EDGE CASES: Handles zero body (uses range-based check), tiny bodies invertedHammer(wickMultiplier, maxLowerWickRatio)   Detects Inverted Hammer (bullish reversal after downtrend)   Parameters:      wickMultiplier (float) : Upper wick must be at least this times the body (default 2.0)      maxLowerWickRatio (float) : Lower wick max as ratio of body (default 0.5)   Returns: bool True when inverted hammer detected EDGE CASES: Same as shootingStar but requires bullish close hangingMan(wickMultiplier, maxUpperWickRatio)   Detects Hanging Man (bearish reversal after uptrend)   Parameters:      wickMultiplier (float) : Lower wick must be at least this times the body (default 2.0)      maxUpperWickRatio (float) : Upper wick max as ratio of body (default 0.5)   Returns: bool True when hanging man detected NOTE: Identical to hammer - context (uptrend) determines meaning morningStar(requireGap, minAvgBars)   Detects Morning Star (3-candle bullish reversal)   Parameters:      requireGap (bool) : Whether to require gap between candles (default false for crypto/forex)      minAvgBars (int) : Minimum bars for average body calculation (default 14)   Returns: bool True when morning star pattern detected EDGE CASES: Gap is optional, handles low bar count, uses shifted average eveningStar(requireGap, minAvgBars)   Detects Evening Star (3-candle bearish reversal)   Parameters:      requireGap (bool) : Whether to require gap between candles (default false for crypto/forex)      minAvgBars (int) : Minimum bars for average body calculation (default 14)   Returns: bool True when evening star pattern detected EDGE CASES: Gap is optional, handles low bar count gapUp()   Detects Gap Up   Returns: bool True when current bar opens above previous bar's high gapDown()   Detects Gap Down   Returns: bool True when current bar opens below previous bar's low gapSize()   Returns gap size in price   Returns: float Gap size (positive for gap up, negative for gap down, 0 for no gap) gapPercent()   Returns gap size as percentage   Returns: float Gap size as percentage of previous close gapType(volAvgLen, breakawayMinPct, highVolMult)   Classifies gap type based on volume   Parameters:      volAvgLen (int) : Length for volume average (default 20)      breakawayMinPct (float) : Minimum gap % for breakaway (default 1.0)      highVolMult (float) : Volume multiplier for high volume (default 1.5)   Returns: string Gap type: "Breakaway", "Common", "Continuation", or "None" EDGE CASES: Handles missing volume data, low bar count swingHigh(leftBars, rightBars)   Detects swing high using pivot   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)   Returns: float Swing high price or na swingLow(leftBars, rightBars)   Detects swing low using pivot   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)   Returns: float Swing low price or na higherHigh(leftBars, rightBars, lookback)   Checks if current swing high is higher than previous swing high   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when higher high pattern detected EDGE CASES: Searches backwards for pivots instead of using var (library-safe) higherLow(leftBars, rightBars, lookback)   Checks if current swing low is higher than previous swing low   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when higher low pattern detected lowerHigh(leftBars, rightBars, lookback)   Checks if current swing high is lower than previous swing high   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when lower high pattern detected lowerLow(leftBars, rightBars, lookback)   Checks if current swing low is lower than previous swing low   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when lower low pattern detected bullishTrend(leftBars, rightBars, lookback)   Detects Bullish Trend (HH + HL within lookback)   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : Lookback period (default 50)   Returns: bool True when making higher highs AND higher lows bearishTrend(leftBars, rightBars, lookback)   Detects Bearish Trend (LH + LL within lookback)   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : Lookback period (default 50)   Returns: bool True when making lower highs AND lower lows nearestResistance(lookback, leftBars, rightBars)   Finds nearest resistance level above current price   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: float Nearest resistance level or na EDGE CASES: Pre-computes pivots, handles bounds properly nearestSupport(lookback, leftBars, rightBars)   Finds nearest support level below current price   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: float Nearest support level or na resistanceBreakout(lookback, leftBars, rightBars)   Detects resistance breakout   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: bool True when price breaks above resistance EDGE CASES: Uses previous bar's resistance to avoid lookahead supportBreakdown(lookback, leftBars, rightBars)   Detects support breakdown   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: bool True when price breaks below support bullishVolumeDivergence(leftBars, rightBars, lookback)   Detects Bullish Volume Divergence (price makes lower low, volume decreases)   Parameters:      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)      lookback (int) : Bars to search for previous pivot (default 50)   Returns: bool True when bullish volume divergence detected EDGE CASES: Library-safe (no var), searches for previous pivot bearishVolumeDivergence(leftBars, rightBars, lookback)   Detects Bearish Volume Divergence (price makes higher high, volume decreases)   Parameters:      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)      lookback (int) : Bars to search for previous pivot (default 50)   Returns: bool True when bearish volume divergence detected rangeContracting(lookback)   Detects if price is in a contracting range (triangle formation)   Parameters:      lookback (int) : Bars to analyze (default 20)   Returns: bool True when range is contracting EDGE CASES: Uses safe integer division, checks minimum lookback ascendingTriangle(lookback, flatTolerance)   Detects Ascending Triangle (flat top, rising bottom)   Parameters:      lookback (int) : Bars to analyze (default 20)      flatTolerance (float) : Max normalized slope for "flat" line (default 0.002)   Returns: bool True when ascending triangle detected EDGE CASES: Safe division, normalized slope, minimum lookback descendingTriangle(lookback, flatTolerance)   Detects Descending Triangle (falling top, flat bottom)   Parameters:      lookback (int) : Bars to analyze (default 20)      flatTolerance (float) : Max normalized slope for "flat" line (default 0.002)   Returns: bool True when descending triangle detected symmetricalTriangle(lookback, minSlope)   Detects Symmetrical Triangle (converging trend lines)   Parameters:      lookback (int) : Bars to analyze (default 20)      minSlope (float) : Minimum normalized slope magnitude (default 0.0005)   Returns: bool True when symmetrical triangle detected doubleBottom(tolerance, minSpanBars, lookback)   Detects Double Bottom (W pattern) - OWN IMPLEMENTATION Two swing lows at similar price levels with a swing high between them   Parameters:      tolerance (float) : Max price difference between lows as % (default 3)      minSpanBars (int) : Minimum bars between the two lows (default 5)      lookback (int) : Max bars to search for pattern (default 100)   Returns: bool True when double bottom detected doubleTop(tolerance, minSpanBars, lookback)   Detects Double Top (M pattern) - OWN IMPLEMENTATION Two swing highs at similar price levels with a swing low between them   Parameters:      tolerance (float) : Max price difference between highs as % (default 3)      minSpanBars (int) : Minimum bars between the two highs (default 5)      lookback (int) : Max bars to search for pattern (default 100)   Returns: bool True when double top detected tripleBottom(tolerance, minSpanBars, lookback)   Detects Triple Bottom - OWN IMPLEMENTATION Three swing lows at similar price levels   Parameters:      tolerance (float) : Max price difference between lows as % (default 3)      minSpanBars (int) : Minimum total bars for pattern (default 10)      lookback (int) : Max bars to search for pattern (default 150)   Returns: bool True when triple bottom detected tripleTop(tolerance, minSpanBars, lookback)   Detects Triple Top - OWN IMPLEMENTATION Three swing highs at similar price levels   Parameters:      tolerance (float) : Max price difference between highs as % (default 3)      minSpanBars (int) : Minimum total bars for pattern (default 10)      lookback (int) : Max bars to search for pattern (default 150)   Returns: bool True when triple top detected bearHeadShoulders()   Detects Bearish Head and Shoulders (OWN IMPLEMENTATION) Head is higher than both shoulders, shoulders roughly equal, with valid neckline STRICT VERSION - requires proper structure, neckline, and minimum span   Returns: bool True when bearish H&S detected bullHeadShoulders()   Detects Bullish (Inverse) Head and Shoulders (OWN IMPLEMENTATION) Head is lower than both shoulders, shoulders roughly equal, with valid neckline STRICT VERSION - requires proper structure, neckline, and minimum span   Returns: bool True when bullish H&S detected bearAscHeadShoulders()   Detects Bearish Ascending Head and Shoulders (variant)   Returns: bool True when pattern detected bullAscHeadShoulders()   Detects Bullish Ascending Head and Shoulders (variant)   Returns: bool True when pattern detected bearDescHeadShoulders()   Detects Bearish Descending Head and Shoulders (variant)   Returns: bool True when pattern detected bullDescHeadShoulders()   Detects Bullish Descending Head and Shoulders (variant)   Returns: bool True when pattern detected isSwingLow()   Re-export: Detects swing low   Returns: bool True when swing low detected isSwingHigh()   Re-export: Detects swing high   Returns: bool True when swing high detected swingHighPrice(idx)   Re-export: Gets swing high price at index   Parameters:      idx (int) : Index (0 = most recent)   Returns: float Swing high price swingLowPrice(idx)   Re-export: Gets swing low price at index   Parameters:      idx (int) : Index (0 = most recent)   Returns: float Swing low price swingHighBarIndex(idx)   Re-export: Gets swing high bar index   Parameters:      idx (int) : Index (0 = most recent)   Returns: int Bar index of swing high swingLowBarIndex(idx)   Re-export: Gets swing low bar index   Parameters:      idx (int) : Index (0 = most recent)   Returns: int Bar index of swing low cupBottom(smoothLen, minDepthAtr, maxDepthAtr)   Detects Cup and Handle pattern formation Uses price acceleration and depth analysis   Parameters:      smoothLen (int) : Smoothing length for price (default 10)      minDepthAtr (float) : Minimum cup depth as ATR multiple (default 1.0)      maxDepthAtr (float) : Maximum cup depth as ATR multiple (default 5.0)   Returns: bool True when potential cup bottom detected EDGE CASES: Added depth filter, ATR validation cupHandle(lookback, maxHandleRetraceRatio)   Detects potential handle formation after cup   Parameters:      lookback (int) : Bars to look back for cup (default 30)      maxHandleRetraceRatio (float) : Maximum handle retracement of cup depth (default 0.5)   Returns: bool True when handle pattern detected bullishPatternCount()   Returns count of bullish patterns detected   Returns: int Number of bullish patterns currently active bearishPatternCount()   Returns count of bearish patterns detected   Returns: int Number of bearish patterns currently active detectedPatterns()   Returns string description of detected patterns   Returns: string Comma-separated list of detected patterns
Perpustakaan Pine Script®
oleh azgron
OverfittingMetricsLibrary "OverfittingMetrics" calculateMetrics(tradeResults, tradeTypes, minTrades, startCapital)   Parameters:      tradeResults (array)      tradeTypes (array)      minTrades (int)      startCapital (float) randomizeParameter(baseValue, variationPercent, seed)   Parameters:      baseValue (float)      variationPercent (float)      seed (int) classifyMarket(priceSeries, lookbackLength)   Parameters:      priceSeries (float)      lookbackLength (simple int) checkOverfittingWarnings(winRate, profitFactor, totalTrades)   Parameters:      winRate (float)      profitFactor (float)      totalTrades (int) calculateConsistency(tradeResults)   Parameters:      tradeResults (array) isOverfitDetected(winRate, profitFactor, totalTrades, minTrades)   Parameters:      winRate (float)      profitFactor (float)      totalTrades (int)      minTrades (int) getOverfitScore(winRate, profitFactor, totalTrades)   Parameters:      winRate (float)      profitFactor (float)      totalTrades (int)
Perpustakaan Pine Script®
oleh TadLopes
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:
Perpustakaan Pine Script®
oleh HenriqueCentieiro
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