Commencez

Statistic

RSI Return StudyEver wonder what happens after the RSI hits an extreme level on your favorite ticker? This indicator answers that question historically. What it does: Finds every instance where the RSI crossed a user-defined trigger level Measures the forward price return N trading days later Displays each signal date and return in a clean table Plots labels on the chart at each signal location Shows the average return across all historical signals How to use: Set "When RSI Touches..." to your oversold level (e.g. 25) or overbought level (e.g. 75) Set "Calculate Return Days Later" to how many trading days forward you want to measure Works on any timeframe — bar count is automatically calculated for you Adjust "Max Results/Labels to Display" to control how much shows on the chart Notes: All signals are included in the average regardless of the display limit Set "Min Bars Between Signals" to 0 to count every RSI cross, or increase it to reduce clustering during volatile periods
Indicateur Pine Script®
par DarkStarOptions
Candle Pattern Winrate Stats ## 📊 Candle Pattern Winrate Stats This indicator is designed to transform candlestick patterns from subjective, belief-based signals into objective, data-driven statistics. It automatically evaluates the historical performance (winrate) of multiple candlestick patterns directly on your chart. --- ## 🔍 Core Concept Instead of assuming that a pattern works, this script answers a more important question: "Does this pattern actually have a statistical edge?" The indicator: - Detects predefined candlestick patterns (e.g., Hammer, Doji, Engulfing) - Records each occurrence as a discrete event - Evaluates price movement after a fixed number of candles (lookforward window) - Classifies outcomes as Win or Loss - Aggregates results into a measurable winrate This allows traders to move from visual interpretation → to empirical validation. --- ## ⚙️ How It Works ### 1. Pattern Detection Supports multiple pattern categories: - Bullish Reversal (1 / 2 / 3 candles) - Bearish Reversal - Shadow-based patterns (Hammer, Shooting Star) - Neutral patterns (Doji, Spinning Top) Each pattern can be enabled or disabled independently. --- ### 2. Winrate Calculation Logic For every detected pattern: - A forward window (X candles) is defined - Price movement is evaluated after that window - If price moves in the expected direction → Win - Otherwise → Loss All results are stored and continuously updated to reflect real historical performance. --- ### 3. Filtering (Noise Reduction) Optional filters improve signal quality: - EMA Trend Filter Restricts signals based on trend direction (e.g., bullish patterns only above EMA) - Volume Filter Filters out low-volume conditions using a moving average of volume These filters help isolate higher-quality statistical samples. --- ### 4. Statistical Controls - Min Samples Displays only patterns with sufficient sample size to avoid unreliable conclusions - Percentile Lookback Normalizes performance relative to recent market conditions - Tolerance (%) Defines acceptable similarity between price values (e.g., open ≈ close) --- ## 🔬 Methodology & Statistical Framework This script treats each candlestick pattern as a discrete statistical event rather than a visual signal. For every detected pattern: - A fixed forward return horizon is applied (lookforward window) - Outcomes are simplified into binary classification (win / loss) - Empirical probabilities are derived from aggregated historical samples Unlike traditional candlestick indicators, this approach enables: - Objective measurement of pattern performance - Direct comparison across different pattern types - Reduction of subjective bias in decision-making --- ## 📋 Output - Winrate (%) for each pattern - Number of samples (data reliability) - On-chart summary table (customizable position) --- ## 🧠 Professional Usage ❌ Avoid: - Using patterns as direct entry signals ✅ Recommended: - Use winrate as a filtering tool - Focus on statistically significant patterns - Combine with: - Market Structure - Supply / Demand - Trend context --- ## 📊 Example Interpretation Pattern | Winrate | Sample | Insight ---|---|---|--- Hammer | 62% | 120 | Usable Doji | 48% | 300 | No statistical edge Engulfing | 68% | 80 | Strong signal --- ## ⚠️ Important Notes - Winrate ≠ Profit (risk/reward must be considered) - Market conditions evolve → statistics may change over time - Results vary across different timeframes and assets - Be cautious of overfitting when sample size is small --- ## 🎯 Who Is This For - System traders - Quantitative / data-driven traders - Developers building trading bots (MQL5, Pine Script, Python) - Traders seeking objective validation over subjective analysis --- ## 🚀 Key Insight "Does this pattern actually work — or does it just look like it does?"
Indicateur Pine Script®
par traderhomepro
ICT Weekly ProfilesOverview ICT Weekly Profiles is an advanced analytical tool designed to map, classify, and quantify recurring weekly price behavior based on Inner Circle Trader (ICT) concepts. This indicator transforms raw price action into a structured weekly profile by identifying where the market forms its high, low, and directional bias, while also providing a statistical ranking of recurring patterns. The goal is simple: to help traders understand how the market tends to behave throughout the week and use that information to anticipate future movements. ------------------------------------------------------------------------------------ Core Concept Markets often follow recurring behavioral patterns during the week, such as: Tuesday High or Low formations Wednesday reversals Thursday consolidations Friday expansions This indicator automatically detects and classifies these behaviors into well-defined weekly profiles, allowing traders to identify the dominant market structure. Weekly Profile Structure Each week is represented visually through: ------------------------------------------------------------------------------------ 1. Price Range Box A box is drawn from the weekly high to the weekly low. Bullish profiles are highlighted in green Bearish profiles are highlighted in red This provides a clear visual representation of the weekly range and directional bias. ------------------------------------------------------------------------------------ 2. Market Structure Lines The weekly movement is broken down into three segments: Open → High → Low → Close or Open → Low → High → Close This reveals the order of price events, which is critical in ICT-style analysis. ------------------------------------------------------------------------------------ 3. Profile Label Each week is labeled according to its behavior, for example: Classic Tuesday Low of the Week Wednesday High of the Week Consolidation Thursday Bullish Reversal These labels describe how the market formed its structure during the week. ------------------------------------------------------------------------------------ Profile Classification Logic The indicator classifies each week based on the day where the high and low occur. Bullish Profiles Tuesday Low of the Week Wednesday Low of the Week Thursday Bullish Reversal Midweek Rally Bearish Profiles Tuesday High of the Week Wednesday High of the Week Thursday Bearish Reversal Midweek Decline This classification reflects institutional accumulation and distribution behavior. ------------------------------------------------------------------------------------ Live Mode (Real-Time Analysis) While the week is still active, the indicator dynamically updates: Current weekly high and low Structure lines Active profile classification The label is displayed as (LIVE) until the week closes. ------------------------------------------------------------------------------------ Statistical Ranking System One of the most powerful features of this indicator is its built-in ranking system. It tracks: Total number of analyzed weeks Frequency of each profile Percentage occurrence Typical day where the high or low forms This transforms the indicator from a visual tool into a quantitative decision-making system. ------------------------------------------------------------------------------------ Timezone Flexibility The indicator allows you to select different timezones: London New York Tokyo This ensures accurate session alignment and correct weekly structure depending on the market being analyzed. ------------------------------------------------------------------------------------ Market Sessions Visualization Optional session tracking is included: Asia London New York Features: Session markers (dots) Session labels Optional background coloring This helps identify where liquidity and volatility are concentrated during the week. ------------------------------------------------------------------------------------ Market Open Filter The script automatically filters out periods when the market is closed: After Friday 17:00 New York time Before Sunday 17:00 New York time This prevents distorted data and improves accuracy. ------------------------------------------------------------------------------------ Customization Options Users can customize: Box colors (bullish / bearish) Text and line colors Ranking table position Session visualization settings Transparency levels ------------------------------------------------------------------------------------ Practical Usage This indicator can be used to: Identify dominant weekly patterns Anticipate where highs or lows are likely to form Establish directional bias early in the week Improve timing when combined with liquidity or structure-based strategies ------------------------------------------------------------------------------------ Strengths Based on institutional trading concepts (ICT) Combines structure and statistics Works across all markets (Forex, Crypto, Indices) Provides both real-time and historical analysis Offers probabilistic insights through ranking ------------------------------------------------------------------------------------ Limitations Does not incorporate volume analysis Focuses on structural behavior rather than momentum strength Relies on OHLC data only ------------------------------------------------------------------------------------ Conclusion ICT Weekly Profiles is more than a visual indicator. It is a structured framework for understanding how the market behaves on a weekly basis. By combining pattern recognition with statistical validation, it helps traders answer key questions: Where is the market likely to form its high or low? Which weekly patterns are most frequent? What is the current institutional bias?
Indicateur Pine Script®
par RicardoNFT
1
Liquidity Entry ZonesLiquidity Entry Zones Liquidity Entry Zones is an open-source liquidity-sweep entry framework built around one specific analytical idea: when price moves through a recently formed liquidity level, then reclaims back through that area with acceptable candle quality and directional context, that event can be treated as a structured entry opportunity rather than as a generic wick sweep or random rejection candle. This script is not designed to mark every swing high or swing low, and it is not intended to behave like a generic support/resistance overlay, a basic pivot script, or a simple “liquidity sweep detector” that treats every sweep in the same way. Its purpose is to store recent liquidity levels, detect meaningful sweeps through those levels, validate reclaim behavior, filter the candle using quality conditions, confirm the setup inside a limited time window, and optionally project a fixed-risk trade structure on the chart for review. :contentReference {index=1} The script also includes optional sweep guides, signal visuals, a compact status panel, and an internal trade simulation layer so users can inspect how the framework behaves under the chosen settings. Those review tools are included to support chart study and comparison, not to imply future performance. :contentReference {index=2} OPEN-SOURCE NOTE This script is published open-source so users can inspect the logic directly, verify what the script is doing, and adapt parts of the workflow for their own research if they wish. Even though the code is open, this description is intentionally detailed because many TradingView users do not read Pine Script. The goal is for a user to understand what the script does, how it works, why its parts belong together, and how it may be used in practice without needing to reverse-engineer the code line by line. OVERVIEW At a high level, the script does seven things: 1. It stores recent pivot highs and pivot lows as liquidity reference levels. 2. It checks whether price sweeps through one of those stored levels by a minimum pip distance. 3. It requires reclaim behavior after the sweep, using either a close-back-inside rule or a stronger reclaim condition. 4. It filters the sweep candle using wick percentage, body percentage, candle range, optional EMA context, and optional midline confirmation. 5. It allows a limited confirmation window after the sweep so entries are not forced to happen only on the exact sweep bar. 6. It can project a fixed take-profit / stop-loss model and track simulated outcomes. 7. It summarizes the current state and projected results in a status panel. The script is therefore meant to function as a complete liquidity-sweep reclaim framework rather than as a single-purpose pivot or line-drawing tool. :contentReference {index=3} CORE IDEA Many sweep-based tools detect only one event: price traded above a prior high or below a prior low. This script takes a narrower and more selective approach. Its central assumption is that a sweep alone is not enough. A useful entry event requires more than just temporary liquidity violation. The script therefore asks additional questions: - Was the swept level a recent stored liquidity level derived from pivot structure? - Did price move far enough beyond that level to qualify as a real sweep? - Did price reclaim back through that area in a meaningful way? - Was the sweep candle structurally acceptable in terms of wick, body, and range? - Did the confirmation happen within a defined number of bars? - Was the signal aligned with the optional EMA context? - Did the confirmation candle behave the way the selected setup requires? Because of that, the script does not treat all sweeps equally. It attempts to organize the process into a more selective sequence: first identify liquidity, then detect a sweep, then validate reclaim quality, then confirm within a limited window, then project the resulting setup into a standardized chart-review structure. That narrower focus is the main reason the script exists in its current form. :contentReference {index=4} WHY THIS SCRIPT IS NOT A SIMPLE MASHUP This script combines multiple components, but they are not included simply to place more features into one publication. Each component has a specific role inside the same analytical process: - Pivot storage defines where recent liquidity levels exist. - Sweep detection checks whether those levels have actually been taken by price. - Reclaim logic checks whether price closes back through the swept area in a meaningful way. - Candle-quality filters reduce weak or low-information sweeps. - The EMA filter provides optional directional context. - The confirmation window prevents stale sweeps from remaining valid indefinitely. - The cooldown logic reduces clustered signals. - The trade simulation layer maps the resulting setup into a consistent risk framework. - The panel organizes current state and projected review metrics into one readable output. These layers are interdependent. Without the pivot-based liquidity storage, there is no structured level to sweep. Without the minimum sweep distance, trivial overextensions would count too easily. Without reclaim validation, the script would mark many sweeps that never actually regained the level. Without wick/body/range filters, weak candles would be treated too similarly to stronger rejection candles. Without the confirmation window, old sweep conditions could stay alive for too long. Without the EMA filter, the framework would lose one of its optional directional context filters. Without the simulation layer, the user would still need to draw projected entry, stop, and target structure manually. Without the status panel, the user would have less organized feedback when monitoring current sweep state, bias, and simulated results. For that reason, the script is intended as a single liquidity-reclaim framework, not as a random bundle of unrelated features. :contentReference {index=5} WHAT THE SCRIPT DOES The script stores recent pivot highs and lows as liquidity levels, then watches for price to trade through those levels by at least the selected sweep distance in pip terms. Once a sweep occurs, the script can evaluate whether the candle reclaimed back through the level. That reclaim can be interpreted in one of two ways: - Close Back Inside - Strong Reclaim After that, the script can apply candle-quality filters based on: - wick percentage, - body percentage, - minimum candle range, - optional EMA alignment, - optional midline-break confirmation, - and candle-body direction for long or short confirmation. If the setup remains valid within the selected confirmation window, the script can confirm a BUY or SELL signal. When enabled, the simulation layer can then project: - entry, - fixed stop loss, - fixed take profit, - entry zone, - target box, - stop box, - entry and invalidation lines, - and active trade labels. The script can also show sweep guides, signal markers, bar coloring, and a status panel summarizing its current state and projected statistics. :contentReference {index=6} HOW THE SCRIPT WORKS 1) LIQUIDITY LEVEL STORAGE The script uses pivot highs and pivot lows to create recent liquidity reference points. A pivot high becomes a candidate buy-side liquidity reference. A pivot low becomes a candidate sell-side liquidity reference. The script stores a configurable number of recent levels so that sweep detection is based on previously identified structural points rather than arbitrary price movement. This makes the framework level-based rather than purely candle-based. :contentReference {index=7} 2) SWEEP DETECTION Once liquidity levels are stored, the script checks whether current price moves beyond a recent level by at least the configured minimum sweep distance. For a bearish sweep setup: price must move above a stored high. For a bullish sweep setup: price must move below a stored low. This distance is measured in pip terms using the selected pip-size logic. That means the same script can adapt to forex, JPY pairs, gold, or index-style symbols more consistently, assuming the pip mode is set correctly. :contentReference {index=8} 3) RECLAIM RULE A sweep alone is not enough. After the level is taken, the script requires reclaim behavior. It supports two reclaim interpretations: Close Back Inside: price must close back inside the swept level. Strong Reclaim: price must close back inside the swept level and also close beyond the candle midline in the reclaim direction. This is important because many sweep candles do not actually reclaim decisively. The reclaim rule exists to distinguish “level taken” from “level taken and then actively rejected back through.” :contentReference {index=9} 4) CANDLE QUALITY FILTERS The script evaluates sweep-candle quality using: - minimum wick percentage, - maximum body percentage, - minimum candle range in pips. This means the framework prefers sweeps where the wick expresses the actual sweep behavior and the body does not dominate too heavily relative to the total candle. The minimum-range filter helps avoid very small candles that technically sweep a level but do not carry enough information. The script also allows: - long confirmation must be bullish, - short confirmation must be bearish. These body-direction filters make the confirmation stricter and help align the final signal with the intended reclaim direction. :contentReference {index=10} 5) OPTIONAL EMA CONTEXT The script includes an optional EMA trend filter using a configurable EMA length. If enabled: - long-side confirmations require price above the EMA, - short-side confirmations require price below the EMA. This does not turn the script into a full trend-following system. Instead, it acts as a directional context filter designed to reduce signals that reclaim against the selected EMA bias. :contentReference {index=11} 6) CONFIRMATION WINDOW The script does not require the final entry to happen only on the exact sweep candle. Instead, when a valid sweep is detected, it can remain eligible for a limited number of bars. During that confirmation window, the script checks whether the final bullish or bearish confirmation conditions are met. This matters because some traders want the sweep candle itself to reclaim strongly, while others want to allow one or two bars for the actual confirmation to develop. The confirmation window makes that possible without letting very old sweep conditions remain valid indefinitely. :contentReference {index=12} 7) MIDLINE CONFIRMATION The script also supports an optional requirement that price close beyond the midpoint of the sweep candle. For bullish confirmation: price must close above the sweep candle midpoint. For bearish confirmation: price must close below the sweep candle midpoint. This adds an additional reclaim-strength condition and is intended to reduce weaker closes that technically qualify but do not show enough directional reclaim behavior. :contentReference {index=13} 8) SIGNAL COOLDOWN The script includes a cooldown period between signals. Once a signal fires, the framework waits the configured number of bars before allowing a new one. This reduces clustered signals and prevents the chart from rapidly stacking similar setups in a short space of time. :contentReference {index=14} 9) QUALITY SCORE The script computes an internal quality score for the sweep using a weighted combination of: - wick contribution, - body contribution, - candle-range contribution, - EMA alignment, - reclaim success, - and midline-break success. This score is used as an internal summary of signal quality and also appears in the panel or labels depending on the visual configuration. The score is not a guarantee of outcome. It is simply an internal ranking model that summarizes how well the current setup meets the script’s own filter conditions. :contentReference {index=15} 10) TRADE SIMULATION When enabled, the script can simulate a fixed-risk trade projection. For long signals: - entry is placed at close, - TP is placed above entry by the selected take-profit pip distance, - SL is placed below entry by the selected stop-loss pip distance. For short signals: - entry is placed at close, - TP is placed below entry by the selected take-profit pip distance, - SL is placed above entry by the selected stop-loss pip distance. The simulation can also: - block new signals while a trade is active, - keep or hide stopped trades, - draw entry zone, target zone, stop zone, entry line, invalidation line, and projection line, - track total trades, wins, losses, and net pips. This is a chart-review tool, not an execution engine. Its purpose is to make the framework easier to inspect after the signal appears. :contentReference {index=16} 11) SAME-BAR PRIORITY In the version you shared, same-bar TP/SL handling is conservative: if both target and stop appear to be touched on the same bar after entry, SL takes priority. This matters because bar data alone does not reveal exact intrabar order, and a strict rule prevents artificially optimistic results. :contentReference {index=17} 12) PANEL AND STATE MODEL The status panel summarizes the current internal state of the framework. It can display items such as: - whether simulation is on, - current EMA-based bias, - sweep state, - signal state, - quality score, - volatility state, - session state, - risk state, - total trades, - win rate, - net pips, - max drawdown in pips. This panel is meant to condense the script’s state into one readable location rather than force the user to infer everything visually from price bars and labels alone. :contentReference {index=18} WHAT MAKES THIS SCRIPT ORIGINAL This script uses familiar building blocks such as: - pivots, - liquidity sweeps, - EMA filtering, - candle wick/body analysis, - fixed TP/SL simulation, - status panels. Those building blocks are not original by themselves. The originality of this script is not in inventing a completely new primitive indicator. The originality lies in how those familiar elements are arranged into one selective workflow: pivot-based liquidity storage → minimum-distance sweep detection → reclaim validation → wick/body/range quality filtering → optional EMA alignment → limited-bar confirmation → cooldown control → fixed-risk trade projection → status-panel review That full sequence is the main reason this script exists as its own publication. It is not intended to be simply another pivot script, another stop-hunt detector, another EMA tool, or another TP/SL box script. It is specifically a liquidity-reclaim entry framework that combines structural level storage, sweep validation, candle-quality filtering, confirmation logic, and projected review in one workflow. :contentReference {index=19} WHAT APPEARS ON THE CHART Depending on settings, the chart may display: - EMA filter, - sweep guides, - signal markers, - BUY / SELL labels, - signal bar coloring, - entry zone, - target zone, - stop zone, - entry line, - invalidation line, - projection path, - status panel, - TP / SL hit labels. Users who want a cleaner chart can disable some visual components and keep only the layers most relevant to their workflow. :contentReference {index=20} HOW TO USE THE SCRIPT A practical workflow is: 1. Add the script to a standard candlestick chart. 2. Choose the correct pip mode for the instrument you are analyzing. 3. Set the pivot length and stored-level count to define how the script builds liquidity references. 4. Set the minimum sweep distance so trivial level violations are filtered out. 5. Choose the reclaim rule you want to use. 6. Configure candle-quality filters such as wick %, body %, and minimum range. 7. Decide whether to use the EMA trend filter. 8. Decide whether to require midline confirmation. 9. Choose the confirmation window and cooldown length. 10. If simulation is enabled, set TP and SL distances and decide whether new signals should be blocked while a trade is active. 11. Wait for a confirmed bullish or bearish liquidity reclaim. 12. Use the projected trade structure and panel as an analysis framework rather than as a blind instruction. 13. Review how the same rules behave over time and across symbols before relying on the framework in a live decision process. :contentReference {index=21} This script is best understood as a structured decision-support and chart-review tool, not as a fully self-sufficient trading system. SETTINGS REFERENCE Trend Filter - EMA Length: sets the EMA used for optional directional context. - Use EMA Trend Filter: enables or disables EMA filtering. Pip Settings - Pip Mode: controls how pip size is interpreted for the current instrument. Liquidity Sweep Detection - Swing Pivot Length: defines the pivot depth used to create liquidity levels. - Stored Liquidity Levels: controls how many recent levels remain in memory. - Minimum Sweep Distance (Pips): defines how far price must move beyond a level to count as a sweep. - Reclaim Rule: selects whether reclaim is based on close back inside or a stronger reclaim condition. Candle Quality Filters - Minimum Sweep Wick %: minimum wick contribution required from the sweep candle. - Maximum Body %: maximum body share allowed for the sweep candle. - Minimum Candle Range (Pips): minimum size required for the sweep candle. - Long Confirmation Must Be Bullish: requires bullish body for long confirmations. - Short Confirmation Must Be Bearish: requires bearish body for short confirmations. Entry Confirmation - Max Bars After Sweep For Confirmation: limits how many bars can pass before confirmation expires. - Require Sweep Candle Midline Break: adds midpoint-based reclaim confirmation. - Cooldown Bars Between Signals: prevents signals from clustering too closely. Trade Simulation - Enable Internal Trade Simulation: enables or disables projected trade logic. - Take Profit (Pips): projected target distance. - Stop Loss (Pips): projected stop distance. - Block New Signals While Trade Is Active: prevents overlapping simulated trades. - Show Stopped Trades: controls whether stopped-out trades remain visible. Visual Settings - Show Signal Labels: shows or hides BUY / SELL labels. - Show Entry Line: shows or hides the projected entry line. - Show Status Panel: enables or disables the panel. - Color Signal Bars: colors confirmed signal bars. - Show Sweep Guides: enables or disables sweep-guide visuals. - Guide Extension Bars: controls how far guides extend. - Signal Label Size: controls label size. - Panel Position: controls panel location. :contentReference {index=22} IMPORTANT PRACTICAL NOTE ON PIP MODE The script uses pip-based calculations for: - sweep distance, - minimum candle range, - take-profit distance, - stop-loss distance. Because of that, pip interpretation is critical. If sweeps appear too small, too large, too frequent, too rare, or if projected TP/SL distances look inconsistent for the instrument being analyzed, the first setting to verify is Pip Mode. This is especially important on gold, JPY pairs, index-style symbols, and broker-specific tick formats. :contentReference {index=23} LIMITATIONS AND SHORTCOMINGS This script has important limitations: - It is a liquidity-reclaim model, not a full market-structure engine. - It only evaluates sweeps of stored pivot-based levels. - It does not identify fair value gaps, order blocks, or discretionary structure beyond the stored liquidity levels. - Signal quality depends heavily on the selected wick/body/range filters. - EMA filtering is optional and only provides one form of directional context. - The confirmation window can materially change signal frequency and behavior. - The simulation layer uses simplified projected TP/SL logic and is not equivalent to real execution. - Same-bar TP/SL handling uses a rule-based priority rather than true intrabar reconstruction. - Historical projected outcomes should not be interpreted as guaranteed tradable results. - No sweep-based model can remove all false signals or all regime-dependent behavior. For those reasons, the script should be used as a structured analysis and review framework, not as a promise of future profitability. :contentReference {index=24} WHO THIS SCRIPT MAY BE USEFUL FOR This script may be useful for traders who: - focus on liquidity sweeps and reclaim behavior, - want a rules-based alternative to purely discretionary sweep reading, - want candle-quality filters in addition to simple level breaks, - want optional EMA context for directional alignment, - want projected entry/TP/SL structure on the chart, - want a compact state panel for monitoring the framework. It may be less suitable for traders who: - want a minimal pivot-only tool, - want a complete multi-concept smart-money framework, - want a full execution engine, - want historical projected results to be treated as live-performance evidence. DISCLAIMER This script is provided for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Market conditions change, historical behavior does not guarantee future results, and users should perform their own analysis, validation, and risk management before using the script in live decision-making.
Indicateur Pine Script®
par trade_w_samet
18
Rolling KPSS Statistic [LuxAlgo]The Rolling KPSS Statistic indicator evaluates the stationarity of price action within a rolling window using the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. This tool helps traders identify whether the current market environment is ranging, trending, or undergoing a structural regime shift by providing a continuous statistical measure of mean reversion. 🔶 USAGE The indicator is designed to distinguish between stationary price action (where price fluctuates around a mean or trend) and non-stationary price action (where price exhibits random walk or breakout behavior). By applying a rolling window calculation, it provides a real-time assessment of market stability. The script offers two primary modes of operation tailored to different market conditions: 🔹 Level Mode This mode tests for stationarity around a constant mean. It is the optimal setting for detecting ranging or mean-reverting environments. When the KPSS statistic is low in this mode, it suggests the market is staying within a defined horizontal corridor. 🔹 Trend Mode This mode tests for stationarity around a linear trend. It is used to identify if the market is moving consistently within a trending channel. A low KPSS value here indicates that while the price is moving up or down, it is doing so in a mathematically stable "stationary" trend rather than an erratic or parabolic one. 🔶 DETAILS The KPSS test operates on the null hypothesis that the series is stationary. Unlike other unit root tests (like the Augmented Dickey-Fuller test), a low value in KPSS indicates stationarity, while a high value suggests the presence of a unit root (non-stationarity). 🔹 Critical Levels The indicator plots three significance thresholds: 10%, 5%, and 1%. KPSS < 5% Level: The market is considered stationary. In "Level" mode, this implies a Range. In "Trend" mode, this implies a stable Trend. KPSS > 5% Level: The null hypothesis is rejected. The market is "Non-Stationary," suggesting a breakout, a trend reversal, or a transition into a random walk state. KPSS > 1% Level: This indicates a highly significant deviation from stationarity, often seen during impulsive market expansions. 🔹 Visual Feedback The indicator uses a Viridis gradient theme. Brighter colors (yellow/green) represent stationary, mean-reverting behavior, while darker colors (purple/blue) represent non-stationary or trending behavior. A real-time dashboard is also included to provide an immediate summary of the current market state. 🔶 SETTINGS 🔹 Main Settings Window Length: The number of bars used for the rolling window. The default of 100 provides a balanced statistical lookback for most timeframes. Source: The price data used for the calculation (default is Close). Test Type: Select between "Level" (for ranges) or "Trend" (for trending stationarity). Newey-West Bandwidth: A lag truncation parameter used to account for serial correlation in the residuals. 🔹 Visuals & Dashboard Show Critical Levels: Toggles the visibility of the 10%, 5%, and 1% dashed lines. Dashboard: Enables or disables the on-screen information table. Position/Size: Controls the location and scale of the dashboard on the chart.
Indicateur Pine Script®
par LuxAlgo
3
VolProfex Volatility Time Window AnalyzerVolProfex Volatility Time Window Analyzer (VPX VTWA) analyzes intraday volatility across user-defined time windows within a trading session. It displays a table ranking windows by average and median move size (price % or ATR multiples), using historical data over a lookback period, with optional trend quality filtering out choppy periods using Directional Efficiency Ratio (Kaufman ER). ​​ Session Session (HHMM-HHMM): Defines trading session in exchange local time (e.g., 0930-1600 for NYSE/NASDAQ). Session Timezone: Selects timezone handling DST automatically (e.g., America/New_York). Calculation Analysis Window (min): Time slot size for table display (options: 5,10,15,30,60,120,240). Intrabar Resolution (min): Lower TF for accurate intrabar data (options: 1,2,3,5,10,15,30); smaller = better precision, longer load. Lookback Days: Recent days analyzed (1-5000, default 1000). Move Method: High-Low Range (absolute) or Open-to-Close (directional). ATR Normalisation: ON shows moves as ATR multiples for regime-independent comparison. ATR Period: ATR lookback (1-200, default 14). Trend Quality Filter Enable Trend Quality Filter: Excludes choppy windows based on efficiency ratio. Min Trend Quality (%): Threshold (0-100, default 30); higher = stricter directional moves. Consolidation Enable Consolidation: Shows top N individually, groups rest. Show Top N Individually: Highest-vol windows at full detail (1-100, default 10). Group Remaining Into (min): Block size for lower-vol averages (options: 5,10,15,30,60,120). Display Highlight Top N Rows: Green highlight count (1-10, default 3). Table Position: Corner placement. Text Size: Table font (Tiny,Small,Normal,Large).
Indicateur Pine Script®
par VolProfex
4
Gap Analyzer[KG] Overview: Gap Analyzer is a statistical research tool that scans every bar on your chart, identifies significant gap-down and gap-up events beyond a user-defined threshold, and summarizes what historically happens inside those gap bars — from open to high, open to low, and open to close. What is a Gap? A gap occurs when a bar's open is meaningfully higher or lower than the prior bar's close, leaving an empty zone on the chart with no traded price. This indicator flags: Gap Down — when (Open − Prior Close) / Prior Close × 100 is at or below your threshold (default: −1%) Gap Up — when the same calculation is at or above your threshold (default: +1%) Visual Markers on the Chart: Every qualifying gap is marked directly on the price pane with three layers: Gap Zone Box — a semi-transparent dashed box fills the exact price area between the prior close and the current open, making the gap size immediately visible Bar Highlight — a subtle background tint on the gap bar itself (red for gap down, teal for gap up) Arrow + Label — a triangle arrow above/below the bar paired with the exact gap percentage printed inside the gap zone box (e.g. −6.43% or +5.91%) Statistical Tables: Two summary tables are rendered in the price pane — one for gap downs (bottom right by default) and one for gap ups (opposite corner). Each table contains 10 columns across 3 rows: Gap Down Table: Column Meaning Coun Total number of gap-down bars detectedΦGapAverage gap size across all qualifying bars O>C %% of gap-down bars that closed bearish (continuation) ΦO→HAverage % move from Open to High on continuation barsΦO→LAverage % move from Open to Low on continuation bars ΦO→CAverage % move from Open to Close on continuation bars O C) All percentage values are colour-coded — teal for positive, red for negative — for instant readability. The Φ Notation- The symbol Φ (Phi) denotes an average (mean) across all qualifying bars in that category. So ΦO→H means "the average percentage move from the Open to the High across all gap bars in this group" — a measure of how much intraday upside was available on average after the gap open. Inputs: Input Default Description Gap-Down Threshold−5%Minimum gap size to qualify as a gap down Gap-Up Threshold+5%Minimum gap size to qualify as a gap up Table Position Bottom Right Where to anchor the gap-down table (gap-up table auto-mirrors) Show Gap % Label On Toggles the arrow and % label on each gap bar Use Cases: Intraday traders — know statistically how far price tends to travel from open toward the high or low after a gap, helping size entries and targets Swing traders — quickly assess whether gaps on your instrument tend to fill (bounce) or continue, and by how much on average Back testing context — apply to any timeframe and ticker to build a historical gap behavior profile before trading a live gap event Notes: Works on any ticker and any timeframe All statistics are computed from the full visible history loaded on the chart; extending your chart history will give more robust sample sizes Past statistical behavior does not guarantee future results — use as context, not as a signal in isolation
Indicateur Pine Script®
par kingshukghosh71
Hidden Markov Model: Regime Probability [AlgoPoint]Hidden Markov Model: Regime Probability Traditional technical indicators are deterministic and lagging; they tell you what the price has already done. The Hidden Markov Model (HMM) Regime Probability system takes a completely different, quantitative approach. It uses probabilistic mathematics to estimate the unobservable "Hidden State" (Market Regime) the price is currently operating in. Inspired by the mathematical models used by institutional quantitative hedge funds, this script doesn't just look at price direction—it calculates the probability of the market being in a specific regime based on real-time observations of Momentum and Volatility. 1. The Three Hidden States (Regimes) The market is modeled as existing in one of three hidden states: ↗ Bullish Regime: High positive momentum with low or stable volatility. (Steady, grinding uptrends). ↘ Bearish Regime: High negative momentum with high volatility. (Aggressive sell-offs and panic). ↕ Chop / Chaos Regime: Zero/low momentum with high volatility. (Whipsaw, ranging, and unpredictable noise). 2. How It Works (The Quant Engine) Since Pine Script does not natively support complex matrix optimization, this script builds a robust Pseudo-HMM using a predefined Transition Matrix and Bayesian Updates. Observables (Emissions): The script calculates the Z-Scores of Smoothed Momentum (Rate of Change) and Volatility (ATR). Emission Probabilities (Gaussian PDF): It feeds these Z-Scores into a Gaussian Probability Density Function to see how well the current market matches the expected profile of a Bull, Bear, or Chop regime. Bayesian Update: Using a predefined Markov Transition Matrix (the statistical inertia of a trend), it updates the prior probabilities to give you a real-time percentage (0-100%) for each regime. 3. Advanced Visual Features & UI We built a custom UI/UX engine to make digesting complex probabilities instantaneous: Exponential Color Smoothing (Bar Colors): As the probability of a regime increases, the bar colors smoothly transition. We implemented an exponential color blending algorithm to prevent abrupt, distracting color changes and eliminate "muddy" colors during transitions. Pro Quant Dashboard: A built-in HUD (Heads-Up Display) provides a quick summary. It features a dominant state readout, an overall "Confidence Score", and ASCII-style mini progress bars (████░░░) for rapid visual processing of probabilities without needing to read the numbers. Stacked Area Oscillator: The bottom panel displays a 0-100 stacked area chart, showing the exact distribution of probabilities across Bull (Green), Chop (Purple), and Bear (Red) states. 4. How to Use This Tool This is not a standalone Buy/Sell signal indicator. It is a Strategy Filter and a Risk Manager. When Bull/Bear Probability is Dominant (>50%): The market is trending. Turn ON your trend-following indicators (like Moving Averages or Breakout systems) and ignore overbought/oversold signals. When Chop Probability is Dominant (>50%): The market is noisy. Turn OFF your trend-following systems. Either switch to Mean Reversion strategies (like RSI or Bollinger Bands) or stay in cash until a clear regime emerges. Watch the Confidence Score: If the Dashboard shows "LOW" confidence, it means the probabilities are split (e.g., 34% Bull, 33% Chop, 33% Bear). Wait for the model to gain confidence before committing capital. 5. Alerts The script includes non-repainting alerts that trigger only when the dominant regime changes: HMM Regime: BULLISH 🚀 * HMM Regime: BEARISH 🩸 * HMM Regime: CHOP ⚖️ 6. Settings Lookback Period: The window used to calculate the Z-scores for momentum and volatility. Transition Matrix: Allows advanced users to tweak the statistical likelihood of the market staying in its current state versus transitioning to a new one. Color Transition Speed: Adjusts the smoothness of the bar coloring. A lower value creates a buttery-smooth fade between regimes, while a value of 1.0 makes it instant.
Indicateur Pine Script®
par Iamalala
7
Market Regime AnalyzerStatistical regime detection with forward-looking transition probabilities. Combines drift testing, variance ratios, and volume delta to classify markets into 5 regimes and quantify transition probabilities. What Regime Are We In, and What's Likely Next? That's the question this indicator answers with statistical rigor and forward-looking probabilities. The Problem: Most traders classify regimes arbitrarily: "Bull if price > 200 MA" or "Bear if RSI < 30." These rules ignore statistical significance, volume confirmation, and mean reversion patterns. The result? Late entries, false signals, and confusion when markets transition. The Solution: Market Regime Analyzer combines drift detection, variance ratio testing, and volume delta analysis to classify markets into 5 distinct regimes. Then it calculates the probability of transitioning to each regime based on historical patterns. The Benefit: Know not just where you are, but where you're likely going - with probabilities, not guesses. The Five Market Regimes 🟢 Strong Bull (Regime 1) - Statistically significant upward drift (t-stat > 1.96) - Strong buying pressure (volume delta > 0.3) - No mean reversion detected - **Trade:** Trend-following strategies, ride the momentum 🟢 Weak Bull (Regime 2) - Upward drift present - BUT weak volume OR mean reversion detected - **Trade:** Reduce position size, tighten stops, prepare for consolidation ⚪ Consolidation (Regime 3) - No statistically significant drift - Mixed volume signals - Mean reversion likely present - **Trade:** Range-trading, avoid trend-following systems 🔴 Weak Bear (Regime 4) - Downward drift present - BUT weak volume pressure - **Trade:** Cautious shorts, reduce exposure, prepare for bounce 🔴 Strong Bear (Regime 5) - Statistically significant downward drift (t-stat < -1.96) - Strong selling pressure (volume delta < -0.3) - No mean reversion detected - **Trade:** Trend-following shorts, protective puts The Statistical Framework 1. Drift Detection with T-Statistics Instead of guessing if there's a trend, we test it statistically. How it works: - Calculates mean return over lookback period - Standardizes by volatility - Compares to significance threshold (default 1.96 = 95% confidence) What it tells you: - T-stat > 1.96: Statistically significant uptrend - T-stat < -1.96: Statistically significant downtrend - In between: No significant trend (consolidation) Why it matters: Only trades trends that are statistically validated, not just visually apparent. 2. Mean Reversion Testing (Variance Ratio) Based on Lo & MacKinlay (1988) research, this detects when markets are range-bound. How it works: - Compares variance at different time scales - Variance Ratio < 0.8 indicates mean reversion What it tells you: - Mean reversion = NO: Trends can continue - Mean reversion = YES: Expect price to return to mean, not breakout Why it matters: Prevents chasing breakouts in range-bound markets. 3. Volume Delta Analysis Total volume tells you HOW MUCH traded. Volume delta tells you WHO won. How it works: - Buying pressure - Selling pressure = Volume Delta - Normalized to show relative strength What it tells you: - Strong positive delta (>0.3): Buyers in control - Strong negative delta (<-0.3): Sellers in control - Weak delta: No clear winner Why it matters: Price can move up on weak buying or down on weak selling. Volume delta reveals the truth. 4. Transition Probability Matrix Historical regime changes predict future regime changes. How it works: - Tracks every regime transition over last 100 bars (configurable) - Builds probability distribution for next regime - Updates continuously Example: Current: Strong Bull Historical transitions from Strong Bull: - Stayed Strong Bull: 45% - Became Weak Bull: 30% - Became Consolidation: 20% - Became Weak Bear: 4% - Became Strong Bear: 1% What it tells you: Strong Bull has 75% chance of staying bullish (45% + 30%), only 5% chance of bearish turn. Why it matters: Adapts to your specific market's behavior patterns. How to Use This Indicator Strategy Adaptation In Strong Bull/Bear Regimes: - Use trend-following strategies - Wider stops, let winners run - Add to positions on pullbacks - High confidence in directional trades In Weak Bull/Bear Regimes: - Reduce position sizes by 50% - Tighter stops - Take profits earlier - Prepare for regime change In Consolidation: - Switch to range-trading strategies - Avoid trend-following systems - Sell resistance, buy support - Wait for regime change before trend trades Risk Management Position Sizing: - Strong regime + high continuation probability (>60%) = Normal size - Weak regime OR high transition probability = Half size - Consolidation = Quarter size or skip Stop Loss Placement: - Strong regime: Use wider stops (2x ATR) - Weak regime: Tighter stops (1x ATR) - Consolidation: Very tight stops (0.5x ATR) Entry Timing Best entries: - Regime just changed to Strong Bull/Bear - High probability (>50%) of staying in current regime - No divergence signals present - Drift and volume delta aligned Avoid entries: - High probability of regime change - Divergence signals appearing - Mean reversion detected in trending regime - Weak volume despite price movement Reading the Dashboard Current Regime Color-coded for instant recognition: - Dark Green = Strong Bull - Light Green = Weak Bull - Gray = Consolidation - Light Red = Weak Bear - Dark Red = Strong Bear Annualized Drift Expected annual return based on recent trend. - Positive = Upward bias - Negative = Downward bias - Near zero = No directional edge T-Statistic Measures statistical significance of drift. - > 1.96 = 95% confident in uptrend - < -1.96 = 95% confident in downtrend - Between = Not statistically significant Mean Reversion - Yes = Expect price to return to mean (range-bound) - No = Trends can continue (trending market) Volume Pressure Normalized volume delta strength. - > 0.3 = Strong buying - < -0.3 = Strong selling - Near 0 = Balanced Transition Probabilities Shows most likely next regime. - Highest probability = Most likely outcome - Evenly distributed = High uncertainty - Concentrated = High confidence in direction Practical Examples Example 1: Strong Bull with High Continuation Dashboard shows: Current Regime: Strong Bull Drift: +22% annualized T-Stat: 3.2 Mean Reversion: No Volume Pressure: +0.45 Probabilities: → Strong Bull: 50% → Weak Bull: 25% → Consolidation: 20% → Bears: 5% Interpretation: - Strong uptrend (t-stat 3.2 >> 1.96) - No mean reversion = trends can continue - Strong buying pressure (0.45 > 0.3) - 75% chance stays bullish (50% + 25%) Action: - Full position size on long setups - Use trend-following entries - Wider stops (2x ATR) - High conviction trades Example 2: Weak Bull Before Consolidation Dashboard shows: Current Regime: Weak Bull Drift: +8% annualized T-Stat: 1.2 Mean Reversion: Yes Volume Pressure: +0.15 Probabilities: → Strong Bull: 10% → Weak Bull: 30% → Consolidation: 50% → Weak Bear: 10% Interpretation: - Weak drift (t-stat 1.2 < 1.96) - Mean reversion detected = range-bound likely - Weak volume (0.15 < 0.3) - 50% chance of consolidation Action: - Reduce long positions - Tighten stops - Prepare for range-bound trading - Avoid new trend trades Example 3: Regime Transition Alert Previous: Weak Bull Current: Consolidation Volume divergence signal appeared: Price made new high, volume delta weakened Interpretation: - Trend exhausted - Buyers losing control - Regime confirmed the transition Action: - Exit trend-following longs - Switch to range-trading approach - Wait for new regime before new directional trades Settings Guide ### Regime Detection Period (50) Number of bars for statistical calculations. - **30-40:** More responsive, catches changes faster, more regime switches - **50 (default):** Balanced for daily/4H charts - **75-100:** More stable, fewer false regime changes, slower to adapt Transition History Depth (100) How much history to use for probabilities. - **50-75:** Adapts quickly to recent behavior - **100 (default):** Balanced robustness - **150-200:** More stable probabilities, slower to adapt Volume Delta Period (14) Period for volume calculations. - **7-10:** More sensitive to volume shifts - **14 (default):** Standard period - **20-30:** Smoother, less noise Significance Threshold (1.96) T-statistic required for trend classification. - **1.64:** 90% confidence, more trend regimes detected - **1.96 (default):** 95% confidence, balanced - **2.58:** 99% confidence, very conservative, mostly consolidation Best Practices Do: - Wait for regime confirmation (at least 3-5 bars in new regime) - Use probabilities to size positions appropriately - Combine with support/resistance for entries - Respect mean reversion signals - Adapt strategy to current regime Don't: - Trade every regime change immediately - Ignore high transition probabilities - Use trend strategies in consolidation - Override statistical signals with gut feel - Trade against Strong regimes without clear setup Timeframe Recommendations Daily Charts: - Default settings work well - Most reliable regime detection - Best for swing trading 4H Charts: - Use default or slightly higher lookback (60-75) - Good for active swing trading - More regime changes than daily 1H Charts: - Reduce lookback to 30-40 - More noise, use with caution - Better for intraday position trading 15M and below: - Not recommended - Too much noise for statistical validity - Regimes change too frequently Combining with Other Indicators Works Well With: Moving Averages - Use regime for directional bias - MAs for specific entry/exit points Support/Resistance - Regime shows context - S/R shows specific levels - High probability at confluence Volume Profile - Regime shows regime - Profile shows where volume is - Target high-volume nodes RSI/MACD - Regime provides context - Momentum shows entry timing - Combine for higher probability Example Combined Setup Regime: Strong Bull Price: Above 200 MA Level: Pullback to support RSI: Oversold (30) Volume Delta: Still positive Setup: Long entry Reason: Trend intact, healthy pullback, buyers still present Divergence Signals The indicator shows volume divergence warnings: Bearish Divergence (Red Triangle Down) - Price makes new high - Volume delta makes lower high - Warning: Buyers weakening, potential reversal Bullish Divergence (Green Triangle Up) - Price makes new low - Volume delta makes higher low - Warning: Sellers weakening, potential reversal How to use: - Divergence in Strong regime = early warning of regime change - Confirms when regime actually transitions - Don't trade divergence alone, wait for regime confirmation Limitations This Indicator Cannot: **Predict black swan events** - Unexpected news overrides all technical regimes **Work in all markets** - Needs liquid markets with reliable volume data **Guarantee profits** - Probabilities are not certainties **Replace fundamental analysis** - Technical regimes can diverge from fundamentals Works Best: - Liquid markets (major indices, forex, crypto, large-cap stocks) - Daily and 4H timeframes - Combined with other analysis - With proper risk management - In normal market conditions Common Questions "Why did the regime stay consolidation despite strong price move?" The indicator detected mean reversion (variance ratio < 0.8), indicating the move will likely reverse. Or the move wasn't statistically significant (t-stat < 1.96). Trust the statistics over visual appearance. "Probabilities show 30% for each regime. What does that mean?" High uncertainty. The market is at an inflection point. Reduce position sizes and wait for clearer regime formation. "Can I use this for day trading?" Not recommended on timeframes below 1H. Statistical tests need sufficient data. Better suited for swing trading. "Why does this show Strong Bull when my momentum indicators show weakness?" Momentum can weaken while the trend remains statistically significant. The indicator focuses on drift and volume, not momentum. Consider it a different perspective. Technical Notes Volume Delta Approximation Uses OHLCV data to approximate order flow: - Buy volume ≈ Volume on up-closes - Sell volume ≈ Volume on down-closes - Delta = Buy - Sell **Note:** Real order flow (from futures or Level 2) is more precise. This approximation works well on liquid markets. Statistical Tests Drift T-Test: - Null hypothesis: No drift (mean return = 0) - Reject if |t-stat| > threshold - Based on standard hypothesis testing Variance Ratio: - Compares 2-period variance to 1-period variance - Ratio = 1 for random walk - Ratio < 1 for mean reversion - Threshold of 0.8 based on empirical testing Transition Probability Implementation Due to Pine Script v5 limitations (no native 2D arrays), the 5×5 transition matrix is stored as a flat 1D array of 25 elements: - Position maps to index: `row × 5 + col` - Example: Transition from Regime 2 to Regime 4 is at index `1 × 5 + 3 = 8` - Laplace smoothing (0.1) prevents zero probabilities - Row sums normalized to calculate probabilities This approach is computationally efficient and maintains statistical accuracy. No Repainting All calculations confirmed on bar close. Regime changes appear when the bar closes, not during formation. Historical analysis is accurate. Alert Conditions Regime Change - Triggers when regime transitions to any new state - Message shows new regime number (1-5) Bearish Divergence - Triggers when price makes new high but volume delta doesn't confirm Bullish Divergence - Triggers when price makes new low but volume delta doesn't confirm Disclaimer FOR EDUCATIONAL PURPOSES ONLY This indicator uses statistical methods to analyze market regimes. It does not predict the future or guarantee trading success. Markets are probabilistic, not deterministic. A 70% probability of staying bullish means 30% chance of regime change. Always use proper risk management. Past regime transitions do not guarantee future transitions. Market structure can change. Statistical relationships can break down. Never risk more than you can afford to lose. Use stop losses on every trade. Test thoroughly before live trading. Consult a qualified financial advisor. © 2026 | Open Source Statistical rigor meets practical application
Indicateur Pine Script®
par DavidEH
Monte Carlo Simulation BandsMonte Carlo Simulation v2.4.2 Plots a one-bar-ahead price distribution band built from many simulated paths. The green band shows empirical percentiles of simulated final prices—these are distribution bounds, not a confidence interval of the mean. What It Does Simulates many one-bar price paths using a directional random walk with volatility scaling (uniform shocks, not Gaussian GBM). Plots Mean Forecast, Median Forecast, and configurable percentile bounds (default 5th/95th). Optional rolling HTF-days mean line (yellow) for trend context. Optional labels and forward projection lines. Alerts when the confirmed close breaks above or below the percentile band. Non-Repainting & HTF Behavior (Fail-Closed) All calculations are gated to confirmed bars only via explicit no_repaint_ok gate (barstate.isconfirmed). If you select an HTF Resolution, the script uses a strict request.security(..., lookahead_off, gaps_off) pipeline. If HTF data is unavailable, outputs are na—no silent fallback to chart timeframe. A separate "HTF Alignment (lagged)" plot shows the prior HTF close (htf_price ) as visual proof of no look-ahead. Volatility Source & Scaling If "Use Historical Volatility" is enabled, volatility is estimated from log returns on the selected resolution (HTF if set, otherwise chart). Annualization adapts to session type: Equities: 6.5 hours/day, 252 trading days/year Crypto: 24 hours/day, 365 days/year Substeps increase path smoothness within the same one-bar horizon—they do not extend the forecast to multiple bars. Key Inputs • Prob Up / Prob Down — Must satisfy Prob Up + Prob Down ≤ 1.0. If violated, simulation is skipped and table shows "✗ PROB>1". • # Simulations / # Substeps — Higher = smoother/more stable, but slower. Default 100×100 is a good balance. • Lower/Upper Percentile — Define the band width (e.g., 5 and 95 for a 90% distribution band). • Run On Last Bar Only — Performance mode (recommended). Skips historical computation; updates on each new confirmed bar. • Resolution (HTF) — Leave blank for chart timeframe, or set to Weekly/Monthly for HTF-aligned simulation. • Crypto 24/7 Session? — Enable for crypto markets to use correct annualization (365d, 24h). How to Use (Quickstart) Start with defaults and keep Run On Last Bar Only = true for speed. Set Prob Up and Prob Down so their sum ≤ 1.0 (e.g., 0.5 + 0.5 = 1.0 for neutral). Enable "Use Historical Volatility" and set a Volatility Lookback (e.g., 20 bars) for data-driven vol. Set Resolution (HTF) if you want the model to run on higher timeframe data (e.g., 1W). Expect updates only when a new HTF interval starts. Choose percentiles (e.g., 5 and 95) to define your distribution band width. Enable alerts for "Price Above Upper Percentile" or "Price Below Lower Percentile" to get notified of breakouts. Limitations & Disclosures Forecast horizon is one bar only. Substeps do not create a multi-bar forecast. Model uses uniform shocks with direction chosen from Prob Up/Down. This is not Geometric Brownian Motion (GBM) and is not calibrated to any option-implied distribution. Bounds are percentiles of final simulated prices, not a statistical confidence interval of the mean. HTF mode updates at the start of a new HTF interval (first chart bar where the HTF timestamp changes), so the band appears "step-like" in realtime. Historical volatility requires enough bars for the selected lookback; until then, values may be na. Performance depends on Sims × Substeps; extreme settings (e.g., 500×500) can be slow. This indicator does not predict direction—it shows a probabilistic range based on your inputs.
Indicateur Pine Script®
par actemplet
Historical Returns [BigBeluga]🔵 OVERVIEW The Historical Returns indicator visualizes daily and monthly return data to help traders assess seasonal performance and volatility behavior. It provides a clean and informative dashboard showing the current month’s daily return bubbles, monthly return curves, and a snapshot of the current month and year performance. This tool is ideal for spotting recurring return patterns and understanding the broader profitability context of a symbol. 🔵 CONCEPTS Daily Return Bubbles: Each trading day is analyzed for its return percentage, and plotted as a bubble with size proportional to the return magnitude. Monthly Performance Curves: Average or cumulative returns are calculated and plotted to show how the current month is performing relative to historical averages. Current Year Return: Current year performance as a single return value, giving traders context on long-term profitability. Current Month Average Return: Current month average performance as a single return value, giving traders context on short-term profitability. Extreme Return Labels: Optionally highlights daily returns above +4% or below -4% with labeled percentages for spike recognition. 🔵 FEATURES Shows daily return bubbles (1%–7%+), color-coded by direction. Labels monthly returns with the month name and percentage value. Displays a performance dashboard with: Daily return heatmap for the current month. Average return for the current month. Year-to-date return. Toggle between average and cumulative modes for monthly return curves. Clearly marks days with abnormal return spikes using optional labels. Clean fallback warning if not on a daily chart ("⚠️USE DAILY TIMEFRAME"). Custom color themes for bullish and bearish values. 🔵 HOW TO USE Use the monthly return curve to compare how the current month is performing against historical averages. Look for clusters of positive or negative bubbles as signals of strong directional weeks. Watch extreme return labels for volatility spikes or catalyst days. Use year-to-date return to assess how the asset is trending in the broader macro cycle. Combine with other BigBeluga tools to align trades with historically favorable periods. 🔵 CONCLUSION Historical Returns is your visual companion for return analytics — helping you identify profitable months, detect volatility surges, and understand historical seasonality at a glance. With a clean dashboard and insightful overlays, this tool supports better timing and improved statistical edge in both short- and long-term trades.
Indicateur Pine Script®
par BigBeluga
3
Z-Fusion Oscillator | Lyro RSThe Z-Fusion Oscillator converts five momentum indicators into Z-scores and blends them into one normalized signal that adapts across markets. By combining normalization, smoothing, and divergence detection, users can easily identify when momentum is accelerating, weakening, reversing, or entering extreme zones 🔶 USAGE The Z-Fusion Oscillator is designed to give traders a unified reading of market momentum—removing the noise of comparing tools that normally run on different scales. By transforming RSI, MACD histogram, Stochastic, Momentum, and Rate of Change into Z-scores, this tool standardizes all inputs, making trend strength and shifts easier to interpret. A dual-line system (fast Z-fusion line + slower baseline) highlights turning points, while overbought/oversold bands and “X-marks” help traders spot exhaustion and potential reversals. 🔹 Unified Momentum Structure The indicator’s core strength comes from combining five Z-scored signals into one average. Which makes momentum behavior more consistent across assets, reduces false extremes, and highlights true shifts in trend conviction. 🔹 Divergence Detection The tool includes fully integrated divergence detection: Regular Bullish Divergence: Price makes a lower low while Z-Fusion forms a higher low. Regular Bearish Divergence: Price makes a higher high while Z-Fusion forms a lower high Bullish and bearish divergences are marked directly on the oscillator with labels and colored pivot connections, making hidden momentum shifts obvious. 🔹 Visual Extremes Two sets of upper and lower Z-score thresholds help identify: Extreme overbought surges Extreme oversold drops Reversal zones Potential exhaustion conditions Background coloring reinforces when the oscillator moves beyond major levels, helping traders quickly assess momentum pressure. 🔹 Detecting Momentum Anomalies Z-scores allow the oscillator to highlight when market momentum behaves abnormally relative to its own recent history. For example: The oscillator reaching +1 or –1 after an extended trend may indicate a climax. A sharp Z-score reversal within an extreme zone can signal a trend exhaustion or a corrective move. Divergences often appear earlier due to normalization smoothing out indicator noise. This makes the Z-Fusion Oscillator particularly useful for spotting subtle shifts in trend direction that traditional indicators may miss. 🔶 DETAILS 🔹 Composite Z-Score Framework Each momentum tool is smoothed, normalized, and transformed: RSI → EMA-smoothed, Z-scored MACD histogram → Z-scored Stochastic → EMA + SMA smoothing, then Z-scored Momentum → EMA-smoothed, Z-scored Rate of Change → EMA-smoothed, Z-scored These are averaged into one composite Z-score to provide a consistent reading across assets and market conditions. 🔹 Fusion Trend Lines Two lines serve as the core signal: Fast Line (savg) – reacts quicker to trend changes Slow Line (savg2) – acts as a baseline filter Crossovers between these lines highlight momentum shifts, while their color reflects trend bias. 🔹 Overbought/Oversold Zones Two upper and two lower Z-score thresholds define “zones”: Upper zones highlight overheated momentum or potential bearish reversals Lower zones highlight depressed momentum or potential bullish reversals Filled regions and background colors help visually confirm extreme conditions. 🔹 Pivot-Based Divergence Engine The script includes filtered pivot detection with customizable look-backs and range limits to ensure divergences are meaningful, not noise-driven. 🔶 SETTINGS 🔹 Indicator Settings Source — Price series used for all calculations. Z-Score Length — Lookback period for Z-score normalization. Z-Score MA Length — Smoothing length for the fusion signal lines. Overbought/Oversold Levels — Four customizable threshold lines. Color Palette — Choose from preset themes or define custom colors. 🔹 RSI Length — RSI calculation period. EMA Smoothing Length — Smooths RSI before Z-score conversion. 🔹 MACD Fast Length — Fast EMA length. Slow Length — Slow EMA length. Signal Line Length — MACD signal smoothing. 🔹 Stochastic %K Length — Main stochastic length. EMA Smoothing — Smooths %K for stability. %D Length — Smoothing for the signal line. 🔹 Momentum Length — Momentum lookback. EMA Smoothing — Smooths momentum before Z-scoring. 🔹 Rate of Change Length — ROC lookback. EMA Smoothing — Smooths ROC values. 🔹 Divergence Enable/Disable Divergence Detection — Toggle divergence engine. Pivot Left/Right Lookback — Defines pivot detection sensitivity. Detection Range Limits — Controls allowable range for divergence. Bull/Bear Colors & Styling — Customize divergence visualization. 🔶 SUMMARY The Z-Fusion Oscillator combines multiple momentum signatures into a single normalized signal, enabling traders to: Identify reversals early Detect momentum exhaustion Spot bullish and bearish divergences Track overbought/oversold conditions Visualize trend strength with clarity Whether you're a swing trader, intraday analyst, or trend-reversal hunter, the Z-Fusion Oscillator provides a powerful and adaptive way to read momentum.
Indicateur Pine Script®
par LyroRS
2
Spot-Futures SpreadSpot-Futures Spread Indicator A comprehensive indicator that automatically calculates and visualizes the percentage spread between spot and perpetual futures prices across multiple exchanges. Key Features: Automatic Exchange Detection - Automatically detects your current exchange and finds the corresponding spot/futures pair Smart Fallback System - If the counterpart isn't available on your exchange, it automatically searches across 7+ major exchanges (Binance, Bybit, OKX, Gate.io, MEXC, KuCoin, HTX) and uses the first valid match Multi-Exchange Support - Works with 14 exchanges including Binance, Bybit, OKX, MEXC, BitGet, Gate.io, KuCoin, and more Clear Exchange Attribution - Shows exactly which exchanges are providing spot and futures data in the statistics table Configurable Moving Average - Track the average spread with customizable period Standard Deviation Bands - Identify unusual spread conditions with Bollinger-style bands Built-in Alerts - Get notified when spread crosses bands or zero (parity) Statistics Table - Real-time stats showing current spread, MA, std dev, and bands Manual Override Options - Advanced users can manually specify exchanges and symbols How It Works: The indicator calculates the spread as: (Futures Price - Spot Price) / Spot Price × 100 Positive spread = Futures trading at a premium (contango) Negative spread = Futures trading at a discount (backwardation) Zero = Parity between spot and futures Use Cases: Funding Rate Analysis - Correlates with perpetual funding rates Arbitrage Opportunities - Identify significant spot-futures divergences Market Sentiment - Premium/discount indicates bullish/bearish positioning Cross-Exchange Analysis - Compare spreads when spot and futures are on different exchanges Smart Features: Works whether you're viewing a spot or futures chart Automatically handles exchange-specific perpetual contract naming (.P, PERP, SWAP, etc.) Color-coded visualization (green for premium, red for discount) Customizable colors and display options Background shading based on spread direction Perfect For: Crypto traders monitoring funding rates, arbitrage traders, market makers, and anyone interested in spot-futures dynamics across multiple exchanges. Getting Started: Simply add the indicator to any spot or perpetual futures chart. It will automatically detect the exchange and find the corresponding pair. The statistics table shows which exchanges are being used for maximum transparency. Note: The indicator automatically ignores invalid symbols, so you'll never see errors even if a specific pair doesn't exist on a particular exchange. Kudos to @AlekMel that made the "Spot - Fut Spread v2" indicator that I enhance the Automatic detection feature which was not working in some case.
Indicateur Pine Script®
par sharkonet
LibWghtLibrary "LibWght" This is a library of mathematical and statistical functions designed for quantitative analysis in Pine Script. Its core principle is the integration of a custom weighting series (e.g., volume) into a wide array of standard technical analysis calculations. Key Capabilities: 1. **Universal Weighting:** All exported functions accept a `weight` parameter. This allows standard calculations (like moving averages, RSI, and standard deviation) to be influenced by an external data series, such as volume or tick count. 2. **Weighted Averages and Indicators:** Includes a comprehensive collection of weighted functions: - **Moving Averages:** `wSma`, `wEma`, `wWma`, `wRma` (Wilder's), `wHma` (Hull), and `wLSma` (Least Squares / Linear Regression). - **Oscillators & Ranges:** `wRsi`, `wAtr` (Average True Range), `wTr` (True Range), and `wR` (High-Low Range). 3. **Volatility Decomposition:** Provides functions to decompose total variance into distinct components for market analysis. - **Two-Way Decomposition (`wTotVar`):** Separates variance into **between-bar** (directional) and **within-bar** (noise) components. - **Three-Way Decomposition (`wLRTotVar`):** Decomposes variance relative to a linear regression into **Trend** (explained by the LR slope), **Residual** (mean-reversion around the LR line), and **Within-Bar** (noise) components. - **Local Volatility (`wLRLocTotStdDev`):** Measures the total "noise" (within-bar + residual) around the trend line. 4. **Weighted Statistics and Regression:** Provides a robust function for Weighted Linear Regression (`wLinReg`) and a full suite of related statistical measures: - **Between-Bar Stats:** `wBtwVar`, `wBtwStdDev`, `wBtwStdErr`. - **Residual Stats:** `wResVar`, `wResStdDev`, `wResStdErr`. 5. **Fallback Mechanism:** All functions are designed for reliability. If the total weight over the lookback period is zero (e.g., in a no-volume period), the algorithms automatically fall back to their unweighted, uniform-weight equivalents (e.g., `wSma` becomes a standard `ta.sma`), preventing errors and ensuring continuous calculation. --- **DISCLAIMER** This library is provided "AS IS" and for informational and educational purposes only. It does not constitute financial, investment, or trading advice. The author assumes no liability for any errors, inaccuracies, or omissions in the code. Using this library to build trading indicators or strategies is entirely at your own risk. As a developer using this library, you are solely responsible for the rigorous testing, validation, and performance of any scripts you create based on these functions. The author shall not be held liable for any financial losses incurred directly or indirectly from the use of this library or any scripts derived from it. wSma(source, weight, length)   Weighted Simple Moving Average (linear kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Linear-kernel weighted mean; falls back to the arithmetic mean if Σweight = 0. wEma(source, weight, length)   Weighted EMA (exponential kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Exponential-kernel weighted mean; falls back to classic EMA if Σweight = 0. wWma(source, weight, length)   Weighted WMA (linear kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Linear-kernel weighted mean; falls back to classic WMA if Σweight = 0. wRma(source, weight, length)   Weighted RMA (Wilder kernel, α = 1/len).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Wilder-kernel weighted mean; falls back to classic RMA if Σweight = 0. wHma(source, weight, length)   Weighted HMA (linear kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Linear-kernel weighted mean; falls back to classic HMA if Σweight = 0. wRsi(source, weight, length)   Weighted Relative Strength Index.   Parameters:      source (float) : series float Price series.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Weighted RSI; uniform if Σw = 0. wAtr(tr, weight, length)   Weighted ATR (Average True Range). Implemented as WRMA on *true range*.   Parameters:      tr (float) : series float True Range series.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Weighted ATR; uniform weights if Σw = 0. wTr(tr, weight, length)   Weighted True Range over a window.   Parameters:      tr (float) : series float True Range series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Weighted mean of TR; uniform if Σw = 0. wR(r, weight, length)   Weighted High-Low Range over a window.   Parameters:      r (float) : series float High-Low per bar.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Weighted mean of range; uniform if Σw = 0. wBtwVar(source, weight, length, biased)   Weighted Between Variance (biased/unbiased).   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: variance series float The calculated between-bar variance (σ²btw), either biased or unbiased. sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wBtwStdDev(source, weight, length, biased)   Weighted Between Standard Deviation.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float σbtw uniform if Σw = 0. wBtwStdErr(source, weight, length, biased)   Weighted Between Standard Error.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float √(σ²btw / N_eff) uniform if Σw = 0. wTotVar(mu, sigma, weight, length, biased)   Weighted Total Variance (= between-group + within-group). Useful when each bar represents an aggregate with its own mean* and pre-estimated σ (e.g., second-level ranges inside a 1-minute bar). Assumes the *weight* series applies to both the group means and their σ estimates.   Parameters:      mu (float) : series float Group means (e.g., HL2 of 1-second bars).      sigma (float) : series float Pre-estimated σ of each group (same basis).      weight (float) : series float Weight series (volume, ticks, …).      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: varBtw series float The between-bar variance component (σ²btw). varWtn series float The within-bar variance component (σ²wtn). sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wTotStdDev(mu, sigma, weight, length, biased)   Weighted Total Standard Deviation.   Parameters:      mu (float) : series float Group means (e.g., HL2 of 1-second bars).      sigma (float) : series float Pre-estimated σ of each group (same basis).      weight (float) : series float Weight series (volume, ticks, …).      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float σtot. wTotStdErr(mu, sigma, weight, length, biased)   Weighted Total Standard Error. SE = √( total variance / N_eff ) with the same effective sample size logic as `wster()`.   Parameters:      mu (float) : series float Group means (e.g., HL2 of 1-second bars).      sigma (float) : series float Pre-estimated σ of each group (same basis).      weight (float) : series float Weight series (volume, ticks, …).      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float √(σ²tot / N_eff). wLinReg(source, weight, length)   Weighted Linear Regression.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.   Returns: mid series float The estimated value of the regression line at the most recent bar. slope series float The slope of the regression line. intercept series float The intercept of the regression line. wResVar(source, weight, midLine, slope, length, biased)   Weighted Residual Variance. linear regression – optionally biased (population) or unbiased (sample).   Parameters:      source (float) : series float Data series.      weight (float) : series float Weighting series (volume, etc.).      midLine (float) : series float Regression value at the last bar.      slope (float) : series float Slope per bar.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population variance (σ²_P), denominator ≈ N_eff. false → sample variance (σ²_S), denominator ≈ N_eff - 2. (Adjusts for 2 degrees of freedom lost to the regression).   Returns: variance series float The calculated residual variance (σ²res), either biased or unbiased. sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wResStdDev(source, weight, midLine, slope, length, biased)   Weighted Residual Standard Deviation.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      midLine (float) : series float Regression value at the last bar.      slope (float) : series float Slope per bar.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float σres; uniform if Σw = 0. wResStdErr(source, weight, midLine, slope, length, biased)   Weighted Residual Standard Error.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      midLine (float) : series float Regression value at the last bar.      slope (float) : series float Slope per bar.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float √(σ²res / N_eff); uniform if Σw = 0. wLRTotVar(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Total Variance **around the window’s weighted mean μ**. σ²_tot = E_w ⟶ *within-group variance* + Var_w ⟶ *residual variance* + Var_w ⟶ *trend variance* where each bar i in the look-back window contributes m_i = *mean* (e.g. 1-sec HL2) σ_i = *sigma* (pre-estimated intrabar σ) w_i = *weight* (volume, ticks, …) ŷ_i = b₀ + b₁·x (value of the weighted LR line) r_i = m_i − ŷ_i (orthogonal residual)   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: varRes series float The residual variance component (σ²res). varWtn series float The within-bar variance component (σ²wtn). varTrd series float The trend variance component (σ²trd), explained by the linear regression. sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wLRTotStdDev(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Total Standard Deviation.   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √(σ²tot). wLRTotStdErr(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Total Standard Error. SE = √( σ²_tot / N_eff ) with N_eff = Σw² / Σw² (like in wster()).   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √((σ²res, σ²wtn, σ²trd) / N_eff). wLRLocTotStdDev(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Local Total Standard Deviation. Measures the total "noise" (within-bar + residual) around the trend.   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √(σ²wtn + σ²res). wLRLocTotStdErr(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Local Total Standard Error.   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √((σ²wtn + σ²res) / N_eff). wLSma(source, weight, length)   Weighted Least Square Moving Average.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.   Returns: series float Least square weighted mean. Falls back to unweighted regression if Σw = 0.
Bibliothèque Pine Script®
par AustrianTradingMachine
Z-Score Momentum | MisinkoMasterThe Z-Score Momentum is a new trend analysis indicator designed to catch reversals, and shifts in trends by comparing the "positive" and "negative" momentum by using the Z-Score. This approach helps traders and investors get unique insight into the market of not just Crypto, but any market. A deeper dive into the indicator First, I want to cover the "Why?", as I believe it will ease of the part of the calculation to make it easier to understand, as by then you will understand how it fits the puzzle. I had an attempt to create a momentum oscillator that would catch reversals and provide high tier accuracy while maintaining the main part => the speed. I thought back to many concepts, divergences between averages? - Did not work Maybe a MACD rework? - Did not work with what I tried :( So I thought about statistics, Standard Deviation, Z-Score, Sharpe/Sortino/Omega ratio... Wait, was that the Z-Score? I only tried the For Loop version of it :O So on my way back from school I formulated a concept (originaly not like this but to that later) that would attempt to use the Z-Score as an accurate momentum oscillator. Many ideas were falling out of the blue, but not many worked. After almost giving up on this, and going to go back to developing my strategies, I tried one last thing: What if we use divergences in the average, formulated like a Z-score? Surprise-surprise, it worked! Now to explain what I have been so passionately yapping about, and to connect the pieces of the puzzle once and for all: The indicator compares the "strength" of the bullish/bearish factors (could be said differently, but this is my "speach bubble", and I think this describes it the best) What could we use for the "bullish/bearish" factors? How about high & low? I mean, these are by definitions the highest and lowest points in price, which I decided to interpret as: The highest the bull & bear "factors" achieved that bar. The problem here is comparison, I mean high will ALWAYS > low, unless the asset decided to unplug itself and stop moving, but otherwise that would be unfair. Now if I use my Z-score, it will get higher while low is going up, which is the opposite of what I want, the bearish "factor" is weaker while we go up! So I sat on my ret*rded a*s for 25 minutes, completly ignoring the fact the number "-1" exists. Surprise surprise, multiplying the Z-Score of the low by -1 did what I wanted! Now it reversed itself (magically). Now while the low keeps going down, the bear factor increases, and while it goes up the bear factor lowers. This was btw still too noisy, so instead of the classic formula: a = current value b = average value c = standard deviation of a Z = (a-b)/c I used: a = average value over n/2 period b = average value over n period c = standard deviation of a Z = (a-b)/c And then compared the Z-Score of High to the Z-Score of Low by basic subtraction, which gives us final result and shows us the strength of trend, the direction of the trend, and possibly more, which I may have not found. As always, this script is open source, so make sure to play around with it, you may uncover the treasure that I did not :) Enjoy Gs!
Indicateur Pine Script®
par MisinkoMaster
First Passage Time - Distribution AnalysisThe First Passage Time (FPT) Distribution Analysis indicator is a sophisticated probabilistic tool that answers one of the most critical questions in trading: "How long will it take for price to reach my target, and what are the odds of getting there first?" Unlike traditional technical indicators that focus on what might happen, this indicator tells you when it's likely to happen. Mathematical Foundation: First Passage Time Theory What is First Passage Time? First Passage Time (FPT) is a concept in stochastic processes that measures the time it takes for a random process to reach a specific threshold for the first time. Originally developed in physics and mathematics, FPT has applications in: Quantitative Finance: Option pricing, risk management, and algorithmic trading Neuroscience: Modeling neural firing patterns Biology: Population dynamics and disease spread Engineering: Reliability analysis and failure prediction The Mathematics Behind It This indicator uses Geometric Brownian Motion (GBM), the same stochastic model used in the Black-Scholes option pricing formula: dS = μS dt + σS dW Where: S = Asset price μ = Drift (trend component) σ = Volatility (uncertainty component) dW = Wiener process (random walk) Through Monte Carlo simulation, the indicator runs 1,000+ price path simulations to statistically determine: When each threshold (+X% or -X%) is likely to be hit Which threshold is hit first (directional bias) How often each scenario occurs (probability distribution) 🎯 How This Indicator Works Core Algorithm Workflow: Calculate Historical Statistics Measures recent price volatility (standard deviation of log returns) Calculates drift (average directional movement) Annualizes these metrics for meaningful comparison Run Monte Carlo Simulations Generates 1,000+ random price paths based on historical behavior Tracks when each path hits the upside (+X%) or downside (-X%) threshold Records which threshold was hit first in each simulation Aggregate Statistical Results Calculates percentile distributions (10th, 25th, 50th, 75th, 90th) Computes "first hit" probabilities (upside vs downside) Determines average and median time-to-target Visual Representation Displays thresholds as horizontal lines Shows gradient risk zones (purple-to-blue) Provides comprehensive statistics table 📈 Use Cases 1. Options Trading Selling Options: Determine if your strike price is likely to be hit before expiration Buying Options: Estimate probability of reaching profit targets within your time window Time Decay Management: Compare expected time-to-target vs theta decay Example: You're considering selling a 30-day call option 5% out of the money. The indicator shows there's a 72% chance price hits +5% within 12 days. This tells you the trade has high assignment risk. 2. Swing Trading Entry Timing: Wait for higher probability setups when directional bias is strong Target Setting: Use median time-to-target to set realistic profit expectations Stop Loss Placement: Understand probability of hitting your stop before target Example: The indicator shows 85% upside probability with median time of 3.2 days. You can confidently enter long positions with appropriate position sizing. 3. Risk Management Position Sizing: Larger positions when probability heavily favors one direction Portfolio Allocation: Reduce exposure when probabilities are near 50/50 (high uncertainty) Hedge Timing: Know when to add protective positions based on downside probability Example: Indicator shows 55% upside vs 45% downside—nearly neutral. This signals high uncertainty, suggesting reduced position size or wait for better setup. 4. Market Regime Detection Trending Markets: High directional bias (70%+ one direction) Range-bound Markets: Balanced probabilities (45-55% both directions) Volatility Regimes: Compare actual vs theoretical minimum time Example: Consistent 90%+ bullish bias across multiple timeframes confirms strong uptrend—stay long and avoid counter-trend trades. First Hit Rate (Most Important!) Shows which threshold is likely to be hit FIRST: Upside %: Probability of hitting upside target before downside Downside %: Probability of hitting downside target before upside These always sum to 100% ⚠️ Warning: If you see "Low Hit Rate" warning, increase this parameter! Advanced Parameters Drift Mode Allows you to explore different scenarios: Historical: Uses actual recent trend (default—most realistic) Zero (Neutral): Assumes no trend, only volatility (symmetric probabilities) 50% Reduced: Dampens trend effect (conservative scenario) Use Case: Switch to "Zero (Neutral)" to see what happens in a pure volatility environment, useful for range-bound markets. Distribution Type Percentile: Shows 10%, 25%, 50%, 75%, 90% levels (recommended for most users) Sigma: Shows standard deviation levels (1σ, 2σ)—useful for statistical analysis ⚠️ Important Limitations & Best Practices Limitations Assumes GBM: Real markets have fat tails, jumps, and regime changes not captured by GBM Historical Parameters: Uses recent volatility/drift—may not predict regime shifts No Fundamental Events: Cannot predict earnings, news, or macro shocks Computational: Runs only on last bar—doesn't give historical signals Remember: Probabilities are not certainties. Use this indicator as part of a comprehensive trading plan with proper risk management. Created by: Henrique Centieiro. feedback is more than welcome!
Indicateur Pine Script®
par HenriqueCentieiro
15
Anchored Probability Cone by TenozenFirst of all, credit to @nasu_is_gaji for the open source code of Log-Normal Price Forecast! He teaches me alot on how to use polylines and inverse normal distribution from his indicator, so check it out! What is this indicator all about? This indicator draws a probability cone that visualizes possible future price ranges with varying levels of statistical confidence using Inverse Normal Distribution , anchored to the start of a selected timeframe (4h, W, M, etc.) Feutures: Anchored Cone: Forecasts begin at the first bar of each chosen higher timeframe, offering a consistent point for analysis. Drift & Volatility-Based Forecast: Uses log returns to estimate market volatility (smoothed using VWMA) and incorporates a trend angle that users can set manually. Probabilistic Price Bands: Displays price ranges with 5 customizable confidence levels (e.g., 30%, 68%, 87%, 99%, 99,9%). Dynamic Updating: Recalculates and redraws the cone at the start of each new anchor period. How to use: Choose the Anchored Timeframe (PineScript only be able to forecast 500 bars in the future, so if it doesn't plot, try adjusting to a lower anchored period). You can set the Model Length, 100 sample is the default. The higher the sample size, the higher the bias towards the overall volatility. So better set the sample size in a balanced manner. If the market is inside the 30% conifidence zone (gray color), most likely the market is sideways. If it's outside the 30% confidence zone, that means it would tend to trend and reach the other probability levels. Always follow the trend, don't ever try to trade mean reversions if you don't know what you're doing, as mean reversion trades are riskier. That's all guys! I hope this indicator helps! If there's any suggestions, I'm open for it! Thanks and goodluck on your trading journey!
Indicateur Pine Script®
par Tenozen
1
StatPivot- Dynamic Range Analyzer - indicator [PresentTrading]Hello everyone! In the following few open scripts, I would like to share various statistical tools that benefit trading. For this time, it is a powerful indicator called StatPivot- Dynamic Range Analyzer that brings a whole new dimension to your technical analysis toolkit. This tool goes beyond traditional pivot point analysis by providing comprehensive statistical insights about price movements, helping you identify high-probability trading opportunities based on historical data patterns rather than subjective interpretations. Whether you're a day trader, swing trader, or position trader, StatPivot's real-time percentile rankings give you a statistical edge in understanding exactly where current price action stands within historical contexts. Welcome to share your opinions! Looking forward to sharing the next tool soon! █ Introduction and How it is Different StatPivot is an advanced technical analysis tool that revolutionizes retracement analysis. Unlike traditional pivot indicators that only show static support/resistance levels, StatPivot delivers dynamic statistical insights based on historical pivot patterns. Its key innovation is real-time percentile calculation - while conventional tools require new pivot formations before updating (often too late for trading decisions), StatPivot continuously analyzes where current price stands within historical retracement distributions. Furthermore, StatPivot provides comprehensive statistical metrics including mean, median, standard deviation, and percentile distributions of price movements, giving traders a probabilistic edge by revealing which price levels represent statistically significant zones for potential reversals or continuations. By transforming raw price data into statistical insights, StatPivot helps traders move beyond subjective price analysis to evidence-based decision making. █ Strategy, How it Works: Detailed Explanation 🔶 Pivot Point Detection and Analysis The core of StatPivot's functionality begins with identifying significant pivot points in the price structure. Using the parameters left and right, the indicator locates pivot highs and lows by examining a specified number of bars to the left and right of each potential pivot point: Copyp_low = ta.pivotlow(low, left, right) p_high = ta.pivothigh(high, left, right) For a point to qualify as a pivot low, it must have left higher lows to its left and right higher lows to its right. Similarly, a pivot high must have left lower highs to its left and right lower highs to its right. This approach ensures that only significant turning points are recognized. 🔶 Percentage Change Calculation Once pivot points are identified, StatPivot calculates the percentage changes between consecutive pivot points: For drops (when a pivot low is lower than the previous pivot low): CopydropPercent = (previous_pivot_low - current_pivot_low) / previous_pivot_low * 100 For rises (when a pivot high is higher than the previous pivot high): CopyrisePercent = (current_pivot_high - previous_pivot_high) / previous_pivot_high * 100 These calculations quantify the magnitude of each market swing, allowing for statistical analysis of historical price movements. 🔶 Statistical Distribution Analysis StatPivot computes comprehensive statistics on the historical distribution of drops and rises: Average (Mean): The arithmetic mean of all recorded percentage changes CopyavgDrop = array.avg(dropValues) Median: The middle value when all percentage changes are arranged in order CopymedianDrop = array.median(dropValues) Standard Deviation: Measures the dispersion of percentage changes from the average CopystdDevDrop = array.stdev(dropValues) Percentiles (25th, 75th): Values below which 25% and 75% of observations fall Copyq1 = array.get(sorted, math.floor(cnt * 0.25)) q3 = array.get(sorted, math.floor(cnt * 0.75)) VaR95: The maximum expected percentage drop with 95% confidence Copyvar95D = array.get(sortedD, math.floor(nD * 0.95)) Coefficient of Variation (CV): Measures relative variability CopycvD = stdDevDrop / avgDrop These statistics provide a comprehensive view of market behavior, enabling traders to understand the typical ranges and extreme moves. 🔶 Real-time Percentile Ranking StatPivot's most innovative feature is its real-time percentile calculation. For each current price, it calculates: The percentage drop from the latest pivot high: CopycurrentDropPct = (latestPivotHigh - close) / latestPivotHigh * 100 The percentage rise from the latest pivot low: CopycurrentRisePct = (close - latestPivotLow) / latestPivotLow * 100 The percentile ranks of these values within the historical distribution: CopyrealtimeDropRank = (count of historical drops <= currentDropPct) / total drops * 100 This calculation reveals exactly where the current price movement stands in relation to all historical movements, providing crucial context for decision-making. 🔶 Cluster Analysis To identify the most common retracement zones, StatPivot performs a cluster analysis by dividing the range of historical drops into five equal intervals: CopyrangeSize = maxVal - minVal For each interval boundary: Copyboundaries = minVal + rangeSize * i / 5 By counting the number of observations in each interval, the indicator identifies the most frequently occurring retracement zones, which often serve as significant support or resistance areas. 🔶 Expected Price Targets Using the statistical data, StatPivot calculates expected price targets: CopytargetBuyPrice = close * (1 - avgDrop / 100) targetSellPrice = close * (1 + avgRise / 100) These targets represent statistically probable price levels for potential entries and exits based on the average historical behavior of the market. █ Trade Direction StatPivot functions as an analytical tool rather than a direct trading signal generator, providing statistical insights that can be applied to various trading strategies. However, the data it generates can be interpreted for different trade directions: For Long Trades: Entry considerations: Look for price drops that reach the 70-80th percentile range in the historical distribution, suggesting a statistically significant retracement Target setting: Use the Expected Sell price or consider the average rise percentage as a reasonable target Risk management: Set stop losses below recent pivot lows or at a distance related to the statistical volatility (standard deviation) For Short Trades: Entry considerations: Look for price rises that reach the 70-80th percentile range, indicating an unusual extension Target setting: Use the Expected Buy price or average drop percentage as a target Risk management: Set stop losses above recent pivot highs or based on statistical measures of volatility For Range Trading: Use the most common drop and rise clusters to identify probable reversal zones Trade bounces between these statistically significant levels For Trend Following: Confirm trend strength by analyzing consecutive higher pivot lows (uptrend) or lower pivot highs (downtrend) Use lower percentile retracements (20-30th percentile) as entry opportunities in established trends █ Usage StatPivot offers multiple ways to integrate its statistical insights into your trading workflow: Statistical Table Analysis: Review the comprehensive statistics displayed in the data table to understand the market's behavior. Pay particular attention to: Average drop and rise percentages to set reasonable expectations Standard deviation to gauge volatility VaR95 for risk assessment Real-time Percentile Monitoring: Watch the real-time percentile display to see where the current price movement stands within the historical distribution. This can help identify: Extreme movements (90th+ percentile) that might indicate reversal opportunities Typical retracements (40-60th percentile) that might continue further Shallow pullbacks (10-30th percentile) that might represent continuation opportunities in trends Support and Resistance Identification: Utilize the plotted pivot points as key support and resistance levels, especially when they align with statistically significant percentile ranges. Target Price Setting: Use the expected buy and sell prices calculated from historical averages as initial targets for your trades. Risk Management: Apply the statistical measurements like standard deviation and VaR95 to set appropriate stop loss levels that account for the market's historical volatility. Pattern Recognition: Over time, learn to recognize when certain percentile levels consistently lead to reversals or continuations in your specific market, and develop personalized strategies based on these observations. █ Default Settings The default settings of StatPivot have been carefully calibrated to provide reliable statistical analysis across a variety of markets and timeframes, but understanding their effects allows for optimal customization: Left Bars (30) and Right Bars (30): These parameters determine how pivot points are identified. With both set to 30 by default: A pivot low must be the lowest point among 30 bars to its left and 30 bars to its right A pivot high must be the highest point among 30 bars to its left and 30 bars to its right Effect on performance: Larger values create fewer but more significant pivot points, reducing noise but potentially missing important market structures. Smaller values generate more pivot points, capturing more nuanced movements but potentially including noise. Table Position (Top Right): Determines where the statistical data table appears on the chart. Effect on performance: No impact on analytical performance, purely a visual preference. Show Distribution Histogram (False): Controls whether the distribution histogram of drop percentages is displayed. Effect on performance: Enabling this provides visual insight into the distribution of retracements but can clutter the chart. Show Real-time Percentile (True): Toggles the display of real-time percentile rankings. Effect on performance: A critical setting that enables the dynamic analysis of current price movements. Disabling this removes one of the key advantages of the indicator. Real-time Percentile Display Mode (Label): Chooses between label display or indicator line for percentile rankings. Effect on performance: Labels provide precise information at the current price point, while indicator lines show the evolution of percentile rankings over time. Advanced Considerations for Settings Optimization: Timeframe Adjustment: Higher timeframes generally benefit from larger Left/Right values to identify truly significant pivots, while lower timeframes may require smaller values to capture shorter-term swings. Volatility-Based Tuning: In highly volatile markets, consider increasing the Left/Right values to filter out noise. In less volatile conditions, lower values can help identify more potential entry and exit points. Market-Specific Optimization: Different markets (forex, stocks, commodities) display different retracement patterns. Monitor the statistics table to see if your market typically shows larger or smaller retracements than the current settings are optimized for. Trading Style Alignment: Adjust the settings to match your trading timeframe. Day traders might prefer settings that identify shorter-term pivots (smaller Left/Right values), while swing traders benefit from more significant pivots (larger Left/Right values). By understanding how these settings affect the analysis and customizing them to your specific market and trading style, you can maximize the effectiveness of StatPivot as a powerful statistical tool for identifying high-probability trading opportunities.
Indicateur Pine Script®
par PresentTrading
1
DCA Simulation for CryptoCommunity v1.1Overview This script provides a detailed simulation of a Dollar-Cost Averaging (DCA) strategy tailored for crypto traders. It allows users to visualize how their DCA strategy would perform historically under specific parameters. The script is designed to help traders understand the mechanics of DCA and how it influences average price movement, budget utilization, and trade outcomes. Key Features: Combines Interval and Safety Order DCA: Interval DCA: Regular purchases based on predefined time intervals. Safety Order DCA: Additional buys triggered by percentage price drops. Interactive Visualization: Displays buy levels, average price, and profit-taking points on the chart. Allows traders to assess how their strategy adapts to price movements. Comprehensive Dashboard: Tracks money spent, contracts acquired, and budget utilization. Shows maximum amounts used if profit-taking is active. Dynamic Safety Orders: Resets safety orders when a new higher high is established. Customizable Parameters: Adjustable buy frequency, safety order settings, and profit-taking levels. Suitable for traders with varying budgets and risk tolerances. Default Strategy Settings: Account Size: Default account size is set to $10,000 to represent a realistic budget for the average trader. Commission & Slippage: Includes realistic trading fees and slippage assumptions to ensure accurate backtesting results. Risk Management: Defaults to risking no more than 5% of the account balance per trade. Sample Size: Optimized to generate a minimum of 100 trades for meaningful statistical analysis. Users can adjust parameters to fit longer timeframes or different datasets. Usage Instructions: Configure Your Strategy: Set the base order, safety order size, and buy frequency based on your preferred DCA approach. Analyze Historical Performance: Use the chart and dashboard to understand how the strategy performs under different market conditions. Optimize Parameters: Adjust settings to align with your risk tolerance and trading objectives. Important Notes: This script is for educational and simulation purposes. It is not intended to provide financial advice or guarantee profitability. If the strategy's default settings do not meet your needs, feel free to adjust them while keeping risk management in mind. TradingView limits the number of open trades to 999, so reduce the buy frequency if necessary to fit longer timeframes.
Stratégie Pine Script®
par jthegit
MathHelpersLibrary "MathHelpers" Overview A collection of helper functions for designing indicators and strategies. calculateATR(length, log)   Calculates the Average True Range (ATR) or Log ATR based on the 'log' parameter. Sans Wilder's Smoothing   Parameters:      length (simple int)      log (simple bool)   Returns: float The calculated ATR value. Returns Log ATR if `log` is true, otherwise returns standard ATR. CDF(z)   Computes the Cumulative Distribution Function (CDF) for a given value 'z', mimicking the CDF function in "Statistically Sound Indicators" by Timothy Masters.   Parameters:      z (simple float)   Returns: float The CDF value corresponding to the input `z`, ranging between 0 and 1. logReturns(lookback)   Calculates the logarithmic returns over a specified lookback period.   Parameters:      lookback (simple int)   Returns: float The calculated logarithmic return. Returns `na` if insufficient data is available.
Bibliothèque Pine Script®
par HuntGatherTrade
Volume StatsDescription: Volume Stats displays volume data and statistics for every day of the year, and is designed to work on "1D" timeframe. The data is displayed in a table with columns being months of the year, and rows being days of each month. By default, latest data is displayed, but you have an option to switch to data of the previous year as well. The statistics displayed for each day is: - volume - % of total yearly volume - % of total monthly volume The statistics displayed for each column (month) is: - monthly volume - % of total yearly volume - sentiment (was there more bullish or bearish volume?) - min volume (on which day of the month was the min volume) - max volume (on which day of the month was the max volume) The cells change their colors depending on whether the volume is bullish or bearish, and what % of total volume the current cell has (either yearly or monthly). The header cells also change their color (based either on sentiment or what % of yearly volume the current month has). This is the first (and free) version of the indicator, and I'm planning to create a "PRO" version of this indicator in future. Parameters: - Timezone - Cell data -> which data to display in the cells (no data, volume or percentage) - Highlight min and max volume -> if checked, cells with min and max volume (either monthly or yearly) will be highlighted with a dot or letter (depending on the "Cell data" input) - Cell stats mode -> which data to use for color and % calculation (All data = yearly, Column = monthly) - Display data from previous year -> if checked, the data from previous year will be used - Header color is calculated from -> either sentiment or % of the yearly volume - Reverse theme -> the table colors are automatically changed based on the "Dark mode" of Tradingview, this checkbox reverses the logic (so that darker colors will be used when "Dark mode" is off, and lighter colors when it's on) - Hide logo -> hides the cat logo (PLEASE DO NOT HIDE THE CAT) Conclusion: Let me know what you think of the indicator. As I said, I'm planning to make a PRO version with more features, for which I already have some ideas, but if you have any suggestions, please let me know.
Indicateur Pine Script®
par sabricat
analytics_tablesLibrary "analytics_tables" 📝 Description This library provides the implementation of several performance-related statistics and metrics, presented in the form of tables. The metrics shown in the afforementioned tables where developed during the past years of my in-depth analalysis of various strategies in an atempt to reason about the performance of each strategy. The visualization and some statistics where inspired by the existing implementations of the "Seasonality" script, and the performance matrix implementations of @QuantNomad and @ZenAndTheArtOfTrading scripts. While this library is meant to be used by my strategy framework "Template Trailing Strategy (Backtester)" script, I wrapped it in a library hoping this can be usefull for other community strategy scripts that will be released in the future. 🤔 How to Guide To use the functionality this library provides in your script you have to import it first! Copy the import statement of the latest release by pressing the copy button below and then paste it into your script. Give a short name to this library so you can refer to it later on. The import statement should look like this: import jason5480/analytics_tables/1 as ant There are three types of tables provided by this library in the initial release. The stats table the metrics table and the seasonality table. Each one shows different kinds of performance statistics. The table UDT shall be initialized once using the `init()` method. They can be updated using the `update()` method where the updated data UDT object shall be passed. The data UDT can also initialized and get updated on demend depending on the use case A code example for the StatsTable is the following: var ant.StatsData statsData = ant.StatsData.new() statsData.update(SideStats.new(), SideStats.new(), 0) if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed)) var statsTable = ant.StatsTable.new().init(ant.getTablePos('TOP', 'RIGHT')) statsTable.update(statsData) A code example for the MetricsTable is the following: var ant.StatsData statsData = ant.StatsData.new() statsData.update(ant.SideStats.new(), ant.SideStats.new(), 0) if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed)) var metricsTable = ant.MetricsTable.new().init(ant.getTablePos('BOTTOM', 'RIGHT')) metricsTable.update(statsData, 10) A code example for the SeasonalityTable is the following: var ant.SeasonalData seasonalData = ant.SeasonalData.new().init(Seasonality.monthOfYear) seasonalData.update() if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed)) var seasonalTable = ant.SeasonalTable.new().init(seasonalData, ant.getTablePos('BOTTOM', 'LEFT')) seasonalTable.update(seasonalData) 🏋️‍♂️ Please refer to the "EXAMPLE" regions of the script for more advanced and up to date code examples! Special thanks to @Mrcrbw for the proposal to develop this library and @DCNeu for the constructive feedback 🏆. getTablePos(ypos, xpos)   Get table position compatible string   Parameters:      ypos (simple string) : The position on y axise      xpos (simple string) : The position on x axise   Returns: The position to be passed to the table method init(this, pos, height, width, positiveTxtColor, negativeTxtColor, neutralTxtColor, positiveBgColor, negativeBgColor, neutralBgColor)   Initialize the stats table object with the given colors in the given position   Namespace types: StatsTable   Parameters:      this (StatsTable) : The stats table object      pos (simple string) : The table position string      height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells      width (simple float) : The width of the table as a percentage of the charts height. By default, 0 auto-adjusts the width based on the text inside the cells      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      neutralTxtColor (simple color) : The text color when neutral      positiveBgColor (simple color) : The background color with transparency when positive      negativeBgColor (simple color) : The background color with transparency when negative      neutralBgColor (simple color) : The background color with transparency when neutral method init(this, pos, height, width, neutralBgColor)   Initialize the metrics table object with the given colors in the given position   Namespace types: MetricsTable   Parameters:      this (MetricsTable) : The metrics table object      pos (simple string) : The table position string      height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells      width (simple float) : The width of the table as a percentage of the charts width. By default, 0 auto-adjusts the width based on the text inside the cells      neutralBgColor (simple color) : The background color with transparency when neutral method init(this, seas)   Initialize the seasonal data   Namespace types: SeasonalData   Parameters:      this (SeasonalData) : The seasonal data object      seas (simple Seasonality) : The seasonality of the matrix data method init(this, data, pos, maxNumOfYears, height, width, extended, neutralTxtColor, neutralBgColor)   Initialize the seasonal table object with the given colors in the given position   Namespace types: SeasonalTable   Parameters:      this (SeasonalTable) : The seasonal table object      data (SeasonalData) : The seasonality data of the table      pos (simple string) : The table position string      maxNumOfYears (simple int) : The maximum number of years that fit into the table      height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells      width (simple float) : The width of the table as a percentage of the charts width. By default, 0 auto-adjusts the width based on the text inside the cells      extended (simple bool) : The seasonal table with extended columns for performance      neutralTxtColor (simple color) : The text color when neutral      neutralBgColor (simple color) : The background color with transparency when neutral method update(this, wins, losses, numOfInconclusiveExits)   Update the strategy info data of the strategy   Namespace types: StatsData   Parameters:      this (StatsData) : The strategy statistics object      wins (SideStats)      losses (SideStats)      numOfInconclusiveExits (int) : The number of inconclusive trades method update(this, stats, positiveTxtColor, negativeTxtColor, negativeBgColor, neutralBgColor)   Update the stats table object with the given data   Namespace types: StatsTable   Parameters:      this (StatsTable) : The stats table object      stats (StatsData) : The stats data to update the table      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      negativeBgColor (simple color) : The background color with transparency when negative      neutralBgColor (simple color) : The background color with transparency when neutral method update(this, stats, buyAndHoldPerc, positiveTxtColor, negativeTxtColor, positiveBgColor, negativeBgColor)   Update the metrics table object with the given data   Namespace types: MetricsTable   Parameters:      this (MetricsTable) : The metrics table object      stats (StatsData) : The stats data to update the table      buyAndHoldPerc (float) : The buy and hold percetage      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      positiveBgColor (simple color) : The background color with transparency when positive      negativeBgColor (simple color) : The background color with transparency when negative method update(this)   Update the seasonal data based on the season and eon timeframe   Namespace types: SeasonalData   Parameters:      this (SeasonalData) : The seasonal data object method update(this, data, positiveTxtColor, negativeTxtColor, neutralTxtColor, positiveBgColor, negativeBgColor, neutralBgColor, timeBgColor)   Update the seasonal table object with the given data   Namespace types: SeasonalTable   Parameters:      this (SeasonalTable) : The seasonal table object      data (SeasonalData) : The seasonal cell data to update the table      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      neutralTxtColor (simple color) : The text color when neutral      positiveBgColor (simple color) : The background color with transparency when positive      negativeBgColor (simple color) : The background color with transparency when negative      neutralBgColor (simple color) : The background color with transparency when neutral      timeBgColor (simple color) : The background color of the time gradient SideStats   Object that represents the strategy statistics data of one side win or lose   Fields:      numOf (series int)      sumFreeProfit (series float)      freeProfitStDev (series float)      sumProfit (series float)      profitStDev (series float)      sumGain (series float)      gainStDev (series float)      avgQuantityPerc (series float)      avgCapitalRiskPerc (series float)      avgTPExecutedCount (series float)      avgRiskRewardRatio (series float)      maxStreak (series int) StatsTable   Object that represents the stats table   Fields:      table (series table) : The actual table      rows (series int) : The number of rows of the table      columns (series int) : The number of columns of the table StatsData   Object that represents the statistics data of the strategy   Fields:      wins (SideStats)      losses (SideStats)      numOfInconclusiveExits (series int)      avgFreeProfitStr (series string)      freeProfitStDevStr (series string)      lossFreeProfitStDevStr (series string)      avgProfitStr (series string)      profitStDevStr (series string)      lossProfitStDevStr (series string)      avgQuantityStr (series string) MetricsTable   Object that represents the metrics table   Fields:      table (series table) : The actual table      rows (series int) : The number of rows of the table      columns (series int) : The number of columns of the table SeasonalData   Object that represents the seasonal table dynamic data   Fields:      seasonality (series Seasonality)      eonToMatrixRow (map)      numOfEons (series int)      mostRecentMatrixRow (series int)      balances (matrix)      returnPercs (matrix)      maxDDs (matrix)      eonReturnPercs (array)      eonCAGRs (array)      eonMaxDDs (array) SeasonalTable   Object that represents the seasonal table   Fields:      table (series table) : The actual table      headRows (series int) : The number of head rows of the table      headColumns (series int) : The number of head columns of the table      eonRows (series int) : The number of eon rows of the table      seasonColumns (series int) : The number of season columns of the table      statsRows (series int)      statsColumns (series int) : The number of stats columns of the table      rows (series int) : The number of rows of the table      columns (series int) : The number of columns of the table      extended (series bool) : Whether the table has additional performance statistics
Bibliothèque Pine Script®
par jason5480
3