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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)
Biblioteca Pine Script®
por 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()
Biblioteca Pine Script®
por 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.
Biblioteca Pine Script®
por 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)
Biblioteca Pine Script®
por 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.
Biblioteca Pine Script®
por 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)
Biblioteca Pine Script®
por 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
Biblioteca Pine Script®
por 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)
Biblioteca Pine Script®
por 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
Biblioteca Pine Script®
por 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)
Biblioteca Pine Script®
por TadLopes