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ma_libraryTitle: Library: Advanced Moving Average Collection Description: This library provides a comprehensive set of Moving Average algorithms, ranging from standard filters (SMA, EMA) to adaptive trendlines (KAMA, FRAMA) and experimental smoothers (ALMA, JMA). It has been fully optimized for Pine Script v6, ensuring efficient execution and strict robustness against na (missing) values. Unlike standard implementations that propagate na values, these functions dynamically recalculate weights to maintain continuity in disjointed datasets. 🧩 Library Features Robustness: Non-recursive filters ignore na values within the lookback window. Recursive filters maintain state to prevent calculation breaks. Optimization: Logic updated to v6 standards, utilizing efficient loops and var persistence. Standardization: All functions utilize a consistent f_ prefix and standardized parameters for easy integration. Scope: Contains over 35 different smoothing algorithms. 📊 Input Requirements Source (src): The data series to smooth (usually close, hl2, etc.). Length (length): The lookback period (must be a simple int). Specifics: Some adaptive MAs (like f_evwma) require volume data, while others (like f_alma) require offset/sigma settings. 🛠️ Integration Example You can import the library and call functions directly, or use the built-in f_selector to create dynamic inputs for your users. code Pine download content_copy expand_less //@version=6 indicator("MA Library Demo", overlay=true) // Import the library import YourUsername/ma_/1 as ma // --- Example 1: Direct Function Call --- // calculating Jurik Moving Average (JMA) float jma_val = ma.f_jma(close, 14) plot(jma_val, "JMA", color=color.yellow, linewidth=2) // --- Example 2: User Selector --- // Allowing the user to choose the MA type via settings string selected_type = input.string("ALMA", "MA Type", options= ) int length = input.int(20, "Length") // Using the generic selector function float dynamic_ma = ma.f_selector(close, length, selected_type) plot(dynamic_ma, "Dynamic MA", color=color.aqua) 📋 Included Algorithms The following methods are available (prefixed with f_): Standard: SMA, EMA, WMA, VWMA, RMA Adaptive: KAMA (Kaufman), FRAMA (Fractal), VIDYA (Chande/VARMA), VAMA (Vol. Adjusted) Low Lag: ZLEMA (Zero Lag), HMA (Hull), JMA (Jurik), DEMA, TEMA Statistical/Math: LSMA (Least Squares), GMMA (Geometric Mean), FLSMA (Fisher Least Squares) Advanced/Exotic: ALMA (Arnaud Legoux) EIT (Ehlers Instantaneous Trend) ESD (Ehlers Simple Decycler) AHMA (Ahrens) BMF (Blackman Filter) CMA (Corrective) DSWF (Damped Sine Wave) EVWMA (Elastic Vol. Weighted) HCF (Hybrid Convolution) LMA (Leo) MD (McGinley Dynamic) MF (Modular Filter) MM (Moving Median) QMA (Quick) RPMA (Repulsion) RSRMA (Right Sided Ricker) SMMA (Smoothed) SSMA (Shapeshifting) SWMA (Sine Weighted) TMA (Triangular) TSF (True Strength Force) VBMA (Variable Band)
Biblioteca Pine Script®
por Nodial
Jags_LibLibrary "Jags_Lib" TODO: add library description here ComplexRejBear(_can)   Returns whether candle is a rejection based on complex rules within library   Parameters:      _can (int)   Returns: true/false ComplexRejBull(_can)   Returns whether candle is a rejection based on complex rules within library   Parameters:      _can (int)   Returns: true/false bullHCS(_can, _lookback)   Returns whether current candle is creating a bull HCS   Parameters:      _can (int)      _lookback (int)   Returns: integer value representing the first candle in the HCS. 0 = no HCS bearHCS(_can, _lookback)   Returns whether current candle is creating a bear HCS   Parameters:      _can (int)      _lookback (int)   Returns: integer value representing the first candle in the HCS. 0 = no HCS bullIBLQtake(_can, _lookback)   Returns whether current candle is is a BullRej which has taken IB liquidiy in the last _lookback candles   Parameters:      _can (int)      _lookback (int)   Returns: integer value representing the first candle in the HCS. 0 = no HCS bearIBLQtake(_can, _lookback)   Returns whether current candle is is a BearRej which has taken IB liquidiy in the last _lookback candles   Parameters:      _can (int)      _lookback (int)   Returns: integer value representing the first candle in the HCS. 0 = no HCS
Biblioteca Pine Script®
por Reginald22
Vantage_NewsVantage News is a Pine Script library that provides pre-market economic event filtering defaults intended for strategies that trade on YM futures. It determines a default for whether trading should be blocked, delayed, or allowed on any given day. Core Concept News events are pre-compiled into Pine Script data libraries organized by half-year (LO1_News2025H1, LO1_News2025H2, etc.), updated weekly on Sundays. There are no API calls — events are baked into arrays of dates, times, type IDs, and severities. Severity System Can be configured to define or override three default severity tiers: - Sev 3 (CPI, NFP, FOMC) — defaults to blocks the entire day or delays, depending on policy - Sev 2 (ISM PMI, claims) — defaults to delay trading until the event time + a configurable post-delay window - Sev 1 (secondary indicators) — defaults to no delays Blocking vs Delaying - Block: No trading for the full session. WillTradeToday() returns false. - Delay: Trading allowed after eventTime + delayMinutes. IsDelayed(currentTimeMs) returns true until the release time passes. Provides a per-event-type policy mechanism so overrides can force any event to block, delay, or be ignored regardless of its base severity. Next Trading Window Calculation FindNextTradingWindow() scans forward up to 14 days, skipping weekends and blocked days based on the provided configuration. If the next tradeable day has a delay, it returns the delayed start time — so an info panel can show e.g. "Mon 7:35 AM" to indicate the next trading opening Exception Mappings Each half-year library can ship per-event-type overrides (different severity, custom delay minutes, tags). When the applyLibExceptionMappings configuration is enabled, these override the base severity — allowing the data to carry date-specific adjustments. Special Handling CME early close days are encoded as a special event type. CheckCmeEarlyClose() returns a halt timestamp so a strategy can truncate the session. Caching Evaluation is lazy and memoized by date string — EvaluateForDate() only recomputes when the date changes. The event cache is built once at initialization via a day index for fast date lookups.
Biblioteca Pine Script®
por Vantage-Stack
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