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Realized Volatility (StdDev of Returns, %)Realized Volatility (StdDev of Returns, %) This indicator measures realized (historical) volatility by calculating the standard deviation of log returns over a user-defined lookback period. It helps traders and analysts observe how much the price has varied in the past, expressed as a percentage. How it works: Computes close-to-close logarithmic returns. Calculates the standard deviation of these returns over the selected lookback window. Provides three volatility measures: Daily Volatility (%): Standard deviation over the chosen period. Annualized Volatility (%): Scaled using the square root of the number of trading days per year (default = 250). Horizon Volatility (%): Scaled to a custom horizon (default = 5 days, useful for short-term views). Inputs: Lookback Period: Number of bars used for volatility calculation. Trading Days per Year: Used for annualizing volatility. Horizon (days): Adjusts volatility to a shorter or longer time frame. Notes: This is a statistical measure of past volatility, not a forecasting tool. If you change the scale to logarithmic, the indicator readibility improves. It should be used for analysis in combination with other tools and not as a standalone signal.
Pine Script® 인디케이터
risk_alpha의
Trojan Cycle: Dip & Profit Hunter📉 Crypto is changing. Your signals should too. This script doesn’t try to outguess price — it helps you track capital rotation and flow behavior in alignment with the evolving macro structure of the digital asset market. Trojan Cycle: Dip & Profit Hunter is a signal engine built to support and validate the capital rotation models outlined in the Trojan Cycle and Synthetic Rotation theses — available via RWCS_LTD’s published charts It is not a classic “buy low, sell high” tool. It is a structural filter that uses price/volume statistics to surface accumulation zones, synthetic traps, and macro context shifts — all aligned with the institutionalization of crypto post-2024. 🧠 Purpose & Value Crypto no longer follows the retail-led, halving-driven pattern of 2017 or 2021. Instead, institutional infrastructure, regulatory filters, and equity-market Trojan horses define the new path of capital. This tool helps you visualize that path by interpreting behavior through statistical imbalances and real-time momentum signals. Use it to: Track where capital is accumulating or exiting Identify signals consistent with true cycle rotation (vs. synthetic traps) Validate your macro view with real-time statistical context 🔍 How It Works The engine combines four signal layers: 1. Z-Score Logic - Measures how far price and volume have deviated from their mean - Detects dips, blowoffs, and exhaustion zones 2. Percentile Logic - Compares current price and volume to historical rank distribution - Flags statistically rare conditions (e.g. bottom 10% price, top 90% volume) 3. Combined Context Engine - Integrates both models to generate one of 36 unique output states - Each state provides a labeled market context (e.g., 🟢 Confluent Buy, 🔴 Confluent Sell, 🧨 Synthetic Trap ) 4. Momentum Spread & Divergence - Measures whether price is leading volume (trap risk) or volume is leading price (accumulation) - Outputs intuitive momentum context with emoji-coded alerts 📋 What You See 🧠 Contextual Table UI with key Z-Scores, percentiles, signals, and market commentary 🎯 Emoji-coded signals to quickly grasp high-probability setups or risk zones 🌊 Optional overlays: price/volume divergence, momentum spread 🎨 Visual table customization (size, position) and chart highlights for signal clarity 🔔 Alert System ✅ Single dynamic alert using alert() that only fires when signal context changes Prevents alert fatigue and allows clean webhook/automation integration 🧭 Use Cases For macro cycle traders: Track where we are in the Trojan Cycle using statistical context For thesis explorers: Use the 36-output signal map to match against your rotation thesis For capital rotation watchers: Identify structural setups consistent with ETF-driven or compliance-filtered flow For narrative skeptics: Avoid synthetic altseason traps where volume lags or flow dries up 🧪 Suggested Pairing for Thesis Validation To use this tool as part of a thesis-confirmation framework , pair it with: BTC.D — Bitcoin Dominance ETH/BTC — Ethereum strength vs. Bitcoin TOTALE100/ETH — Altcoin strength relative to ETH RWCS_LTD’s published charts and macro cycle models 🏁 Final Note Crypto has matured. So should your signals. This tool doesn’t try to game the next 2 candles. It helps you understand the current phase in a compliance-filtered, institutionalized rotation model. It’s not built for hype — it’s built for conviction. Explore the thesis → Validate the structure → Trade with clarity. 🚨 Disclaimer This script is not financial advice. It is an analytical tool designed to support market structure research and rotation thesis validation. Use this as part of a broader framework including technical structure, dominance charts, and macro data.
Pine Script® 인디케이터
RWCS_LTD의
2
ATR by Session Library [1CG]Library "ATRxSession" This library shows you how big the bars usually are during a trading session. It looks only at the times you choose (like New York or London hours), measures the “true range” of every bar in that session, then finds the average for that session. It keeps the last N sessions and gives you their overall average, so you can quickly see how much the market typically moves per bar during your chosen session. Call getSessionAtr(timezone, session, sessionCount) from your script, and it will return a single number: the average per-bar volatility during the chosen session, based on the last N completed sessions. This makes it easy to plug session-specific volatility into your own indicators or strategies. getSessionAtr(_timezone, _session, _sessionCount)   getSessionAtr - Computes a session-aware ATR over completed sessions.   Parameters:      _timezone (string) : (string) - Timezone string to evaluate session timing.      _session (string) : (string) - Session time range string (e.g., "0930-1600").      _sessionCount (int) : (int) - Number of past completed sessions to include in the rolling average.   Returns: (float) - The average ATR across the last N completed sessions, or na if not enough data.
Pine Script® 라이브러리
OneCleverGuy의
Size & LeverageSize and Leverage calculator for trading, using market orders. It will calculate maximum possible leverage by default in order to prioritize capital efficiency. If you wish to use manual leverage you need to manually enter it in the settings. The script rounds both auto leverage and size to your liking. Entry price is always last price. Size is the actual size you need to input, adjusted to your leverage, cost means the margin required to open the trade. I made this indicator as a binance futures user.
Pine Script® 인디케이터
Radu779의
AndrologQuartileAndrologQuartile This indicator is based on the assumption that if a candle closes in the upper or lower quartile of its range, the next candle often tends to take out the high or low of that candle. The script does two things: It calculates and displays live statistics on how often this condition occurs and how often it is successful. It highlights candles that meet the quartile condition so you can track them in real time. It is most meaningful to use this indicator on higher timeframes (from 1h upwards). You can also set an alert: once configured, the alert will always trigger for the timeframe that was active at the moment of setup. Usage tip: Click the statistics panel in the top right corner to adjust settings and alerts. Adjustable parameters: Quartiles: Default values are 25% and 75%. Min Distance: Defines how far the high/low must be from the candle’s close (in %) to be considered relevant. A smaller value is applied automatically on intraday timeframes under 5 minutes.
Pine Script® 인디케이터
trueandrolog의
Custom Support & Resistance Levels (Manual Input)This indicator lets you plot your own support levels (and can be extended for resistance) directly on the chart by entering them as comma-separated values. 📌 Supports manual input for multiple price levels. 📊 Lines are extended across the chart for clear visualization. 🎨 Dynamic coloring: Green if the current price is above the level. Red if the current price is below the level. 🧹 Old lines are automatically cleared to avoid clutter. This tool is ideal if you: Prefer to mark your own key zones instead of relying only on auto-detected levels. Want clean and simple visualization of critical price areas. 👉 Coming soon: Resistance levels input (commented in the code, can be enabled).
Pine Script® 인디케이터
Jothy_9645의
2
VSA Highlight & Relative Strength of Volume [odnac]This is a TradingView indicator combining VSA (Volume Spread Analysis) signals with a relative strength of volume visualization. The indicator has two main parts: 1. VSA Volume Highlight: Detects common VSA signals, including Stopping Volume, Buying Climax, No Supply, No Demand, Test, Up-thrust, Shakeout, Demand Absorption, and Supply Absorption. Supports a trend filter using a user-selectable moving average type (SMA, EMA, WMA, or VWMA) and length. Calculates spread and volume moving averages to determine wide/narrow spreads and high/low volume relative to the averages. Determines relative bar positions (close near high, close near low, or mid-close) to categorize VSA signals. Optionally colors the background based on the detected VSA signal. Supports alerts for each VSA signal type. 2. Relative Strength of Volume: Splits total volume into buying and selling components based on the candle’s high, low, and close. Buying volume is calculated as volume times the proportion of the candle’s close above the low. Selling volume is calculated as volume times the proportion of the candle’s close below the high. Plots buying and selling volume as colored columns in the pane. Plots total volume in the status line colored according to the dominant side (buying or selling). Inputs include: Toggle visibility for each VSA signal. Trend filter options (type and length). Volume and spread moving average lengths and multipliers for high/low volume and wide/narrow spread detection. Thresholds for close positions near high or low, and for identifying Buying Climax. Opacity for VSA volume highlights. The indicator is designed to help traders visually identify key volume patterns and analyze buying and selling pressure in the market.
Pine Script® 인디케이터
odnac의
2
Gott's Copernican Trend PredictorThe Gott's Copernican Trend Predictor predicts trend duration using the Copernican Principle - Based on astrophysicist Richard Gott's temporal prediction method. I had the idea to create this indicator after reading the book The Doomsday Calculation by William Poundstone. Background & Theory This indicator implements J. Richard Gott III's Copernican Principle - a statistical method that famously predicted the fall of the Berlin Wall and the duration of Broadway shows with remarkable accuracy. The Copernican Principle Explained Named after Copernicus who showed that Earth is not at the center of the universe, this principle assumes that you are not observing something at a special moment in time. When you observe a trend at any random point, you're statistically more likely to be seeing it during the "middle portion" of its lifetime rather than at its very beginning or end. The Mathematics Gott's formula provides a 95% confidence interval for how much longer a trend will continue: Minimum remaining duration = Current Age ÷ 39 Maximum remaining duration = Current Age × 39 The factor of 39 comes from statistical analysis where: There's only a 2.5% chance you're observing in the first 1/40th of the trend's life There's only a 2.5% chance you're observing in the last 1/40th of the trend's life This gives us 95% confidence that the trend will last between Age/39 and Age×39 How It Works Trend Detection The indicator uses dual moving averages (default: 50 & 200 period) to identify trend changes: Bullish Cross: Fast MA crosses above Slow MA → Uptrend begins Bearish Cross: Fast MA crosses below Slow MA → Downtrend begins Real-Time Predictions Once a trend is detected, the indicator continuously calculates: Trend Age: How long the current trend has been active Gott's 95% CI: Statistical range for remaining trend duration Projected End Dates: Calendar dates when the trend might end How to Use Setup Add the indicator to any timeframe (works on minutes, hours, days, weeks) Customize MA periods and type (SMA, EMA, WMA) Choose table position and font size for optimal viewing Interpretation Example: If a trend is 100 hours old: Minimum duration: 100 ÷ 39 = ~3 more hours Maximum duration: 100 × 39 = ~3,900 more hours 95% confidence: The trend will end between these times This indicator might be useful for swing traders, trend followers, and quantitative analysts. Coca-Cola example: Coca-Cola's chart shows an uptrend spanning 810 weeks, approximately 15.5 years. According to Gott's Copernican Principle, this trend age generates a 95% confidence interval predicting the trend will continue for a minimum of 20 weeks and a maximum of 31,590 weeks. On the other hand, a shorter trend age produces a proportionally smaller minimum duration and different risk profile in terms of statistical continuation probability. For this reason, more recent trends (and more recent companies) are likely to remain in trend for shorter.
Pine Script® 인디케이터
HenriqueCentieiro의
VSA Signals [odnac]This indicator applies Volume Spread Analysis (VSA) concepts to highlight important supply and demand events directly on the chart. It automatically detects common VSA patterns using price spread, relative volume, and candle structure, with optional trend filtering for higher accuracy. Features: Stopping Volume (SV): Signals potential end of a downtrend when heavy buying appears. Buying Climax (BC): Indicates exhaustion of an uptrend with heavy volume near the top. No Supply (NS): Weak selling pressure, often a bullish sign in an uptrend. No Demand (ND): Weak buying interest, often a bearish sign in a downtrend. Test: Low-volume test bar probing for supply. Up-thrust (UT): Failed breakout with long upper wick, often a bearish trap. Shakeout: Bear trap with high-volume wide down bar closing low. Demand Absorption (DA): Demand absorbing heavy selling pressure. Supply Absorption (SA): Supply absorbing heavy buying pressure. Additional Options: Background highlights for detected signals. Configurable moving average (SMA, EMA, WMA, VWMA) as a trend filter. Adjustable multipliers for volume and spread sensitivity. Legend table for quick reference of signals and meanings. Alerts available for all signals. This tool is designed to help traders spot professional accumulation and distribution activity and to improve trade timing by recognizing supply/demand imbalances in the market.
Pine Script® 인디케이터
odnac의
3
Realized Volatility (StdDev of Returns, %)📌 Realized Volatility (StdDev of Returns, %) This indicator measures realized volatility directly from price returns, instead of the common but misleading approach of calculating standard deviation around a moving average. 🔹 How it works: Computes close-to-close log returns (the most common way volatility is measured in finance). Calculates the standard deviation of these returns over a chosen lookback period (default = 200 bars). Converts results into percentages for easier interpretation. Provides three key volatility measures: Daily Realized Vol (%) – raw standard deviation of returns. Annualized Vol (%) – scaled by √250 trading days (market convention). Horizon Vol (%) – volatility over a custom horizon (default = 5 days, i.e. weekly). 🔹 Why use this indicator? Shows true realized volatility from historical returns. More accurate than measuring deviation around a moving average. Useful for traders analyzing risk, position sizing, and comparing realized vs implied volatility. ⚠️ Note: It is best used on the Daily Chart! By default, this uses log returns (which are additive and standard in quant finance). If you prefer, you can easily switch to simple % returns in the code. Volatility estimates depend on your chosen lookback length and may vary across timeframes.
Pine Script® 인디케이터
risk_alpha의
Daily Seasonality Strength + Prediction TableDaily Seasonality Strength + Prediction Table Return Estimates: This indicator uses historical price data to calculate average returns for each day (of the week or month) and uses these to predict the next day’s return. Seasonality Strength: It measures seasonality strength by comparing predicted returns with actual returns, using the inverse of MSE (higher values mean stronger seasonality). supports up to 10 assets This script is for informational and educational purposes only. It does not constitute financial, investment, or trading advice. I am not a financial advisor. Any decisions you make based on this indicator are your own responsibility. Always do your own research and consult with a qualified financial professional before making any investment decisions. Past performance is no guarantee of future results. The value of the instruments may fluctuate and is not guaranteed
Pine Script® 인디케이터
Hutmacher42의
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management. Strategic Design and Methodological Basis The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution. Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation: Manual Calculation: Provides a transparent and deterministic computation of HA values. ticker.heikinashi(): Utilizes TradingView's built-in function, employing confirmed historical bars to prevent repainting artifacts. Regular Candles: Allows for direct comparison with standard OHLC price action. This multi-methodological approach to trend smoothing is critical for robust signal generation.   Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (   Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses. EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.   Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation. Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation. Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment. Applicability The Mutanabby_AI | ATR+ strategy is particularly suited for: Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.   Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach. Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models. Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments. Implementation and Evaluation To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on TradingView. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns. I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
Pine Script™ 전략
Mutanabby_AI의
1
Cheat CodeWhy Monday & Friday Monday evening (NY): frequently seeds the weekly expansion. Its DR/IDR often acts as a weekly “starter envelope,” useful for breakout continuation or fade back into the box plays as liquidity builds. Friday evening (NY): often exposes end-of-week traps (run on stops into the close) and sets expectation boundaries into the following week. Carry these levels forward to catch Monday’s reaction to Friday’s closing structure. Typical use-cases Breakout & retest: Price closes outside the Monday DR/IDR → look for retests of the band edge for continuation. Liquidity sweep (“trap”) recognition: Friday session wicks briefly beyond Friday DR/IDR then closes back inside → watch for mean reversion early next week. Bias filter: Above both Monday DR midline and Friday DR midline → bias long until proven otherwise; the inverse for shorts. Session open confluence: Reactions at the open line frequently mark decision points for momentum vs. fade setups. (This is a levels framework, not a signals engine. Combine with your execution model: orderflow, S/R, session timing, or higher-TF bias.) Inputs & styling (quick reference) Display toggles (per day): Show DR / IDR / Middle DR / Middle IDR Show Opening Line Show DR/IDR Box (choose DR or IDR as box source) Show Price Labels Style controls (per day): Line width (1–4), style (Solid/Dashed/Dotted) Independent colors for DR, IDR, midlines, open line Box background opacity Timezone: Default America/New_York (changeable). Optional on-chart warning if your chart TZ differs. Practical notes Works on intraday charts; levels are anchored using weekly timestamps for accuracy on any symbol. Live updating: During the Mon/Fri calc windows, DR/IDR highs/lows and midlines keep updating until the session ends. Clean drawings: Lines, box, and labels are created once per session and then extended/updated—efficient on resources even with long display windows. Max elements: Script reserves ample line/box/label capacity for stability across weeks.
Pine Script® 인디케이터
shariff09z의
Correlation Heatmap Matrix [TradingFinder] 20 Assets Variable🔵 Introduction Correlation is one of the most important statistical and analytical metrics in financial markets, data mining, and data science. It measures the strength and direction of the relationship between two variables. The correlation coefficient always ranges between +1 and -1 : a perfect positive correlation (+1) means that two assets or currency pairs move together in the same direction and at a constant ratio, a correlation of zero (0) indicates no clear linear relationship, and a perfect negative correlation (-1) means they move in exactly opposite directions. While the Pearson Correlation Coefficient is the most common method for calculation, other statistical methods like Spearman and Kendall are also used depending on the context. In financial market analysis, correlation is a key tool for Forex, the Stock Market, and the Cryptocurrency Market because it allows traders to assess the price relationship between currency pairs, stocks, or coins. For example, in Forex, EUR/USD and GBP/USD often have a high positive correlation; in stocks, companies from the same sector such as Apple and Microsoft tend to move similarly; and in crypto, most altcoins show a strong positive correlation with Bitcoin. Using a Correlation Heatmap in these markets visually displays the strength and direction of these relationships, helping traders make more accurate decisions for risk management and strategy optimization. 🟣 Correlation in Financial Markets In finance, correlation refers to measuring how closely two assets move together over time. These assets can be stocks, currency pairs, commodities, indices, or cryptocurrencies. The main goal of correlation analysis in trading is to understand these movement patterns and use them for risk management, trend forecasting, and developing trading strategies. 🟣 Correlation Heatmap A correlation heatmap is a visual tool that presents the correlation between multiple assets in a color-coded table. Each cell shows the correlation coefficient between two assets, with colors indicating its strength and direction. Warm colors (such as red or orange) represent strong negative correlation, cool colors (such as blue or cyan) represent strong positive correlation, and mid-range tones (such as yellow or green) indicate correlations that are close to neutral. 🟣 Practical Applications in Markets Forex : Identify currency pairs that move together or in opposite directions, avoid overexposure to similar trades, and spot unusual divergences. Crypto : Examine the dependency of altcoins on Bitcoin and find independent movers for portfolio diversification. Stocks : Detect relationships between stocks in the same industry or find outliers that move differently from their sector. 🟣 Key Uses of Correlation in Trading Risk management and diversification: Select assets with low or negative correlation to reduce portfolio volatility. Avoiding overexposure: Prevent opening multiple positions on highly correlated assets. Pairs trading: Exploit temporary deviations between historically correlated assets for arbitrage opportunities. Intermarket analysis: Study the relationships between different markets like stocks, currencies, commodities, and bonds. Divergence detection: Spot when two typically correlated assets move apart as a possible trend change signal. Market forecasting: Use correlated asset movements to anticipate others’ behavior. Event reaction analysis: Evaluate how groups of assets respond to economic or political events. ❗ Important Note It’s important to note that correlation does not imply causation — it only reflects co-movement between assets. Correlation is also dynamic and can change over time, which is why analyzing it across multiple timeframes provides a more accurate picture. Combining correlation heatmaps with other analytical tools can significantly improve the precision of trading decisions. 🔵 How to Use The Correlation Heatmap Matrix indicator is designed to analyze and manage the relationships between multiple assets at once. After adding the tool to your chart, start by selecting the assets you want to compare (up to 20). Then, choose the Correlation Period that fits your trading strategy. Shorter periods (e.g., 20 bars) are more sensitive to recent price movements, making them suitable for short-term trading, while longer periods (e.g., 100 or 200 bars) provide a broader view of correlation trends over time. The indicator outputs a color-coded matrix where each cell represents the correlation between two assets. Warm colors like red and orange signal strong negative correlation, while cool colors like blue and cyan indicate strong positive correlation. Mid-range tones such as yellow or green suggest correlations that are close to neutral. This visual representation makes it easy to spot market patterns at a glance. One of the most valuable uses of this tool is in portfolio risk management. Portfolios with highly correlated assets are more vulnerable to market swings. By using the heatmap, traders can find assets with low or negative correlation to reduce overall risk. Another key benefit is preventing overexposure. For example, if EUR/USD and GBP/USD have a high positive correlation, opening trades on both is almost like doubling the position size on one asset, increasing risk unnecessarily. The heatmap makes such relationships clear, helping you avoid them. The indicator is also useful for pairs trading, where a trader identifies assets that are usually correlated but have temporarily diverged — a potential arbitrage or mean-reversion opportunity. Additionally, the tool supports intermarket analysis, allowing traders to see how movements in one market (e.g., crude oil) may impact others (e.g., the Canadian dollar). Divergence detection is another advantage: if two typically aligned assets suddenly move in opposite directions, it could signal a major trend shift or a news-driven move. Overall, the Correlation Heatmap Matrix is not just an analytical indicator but also a fast, visual alert system for monitoring multiple markets at once. This is particularly valuable for traders in fast-moving environments like Forex and crypto. 🔵 Settings 🟣 Logic Correlation Period : Number of bars used to calculate correlation between assets. 🟣 Display Table on Chart : Enable/disable displaying the heatmap directly on the chart. Table Size : Choose the table size (from very small to very large). Table Position : Set the table location on the chart (top, middle, or bottom in various alignments). 🟣 Symbol Custom Select Market : Choose the market type (Forex, Stocks, Crypto, or Custom). Symbol 1 to Symbol 20: In custom mode, you can define up to 20 assets for correlation calculation. 🔵 Conclusion The Correlation Heatmap Matrix is a powerful tool for analyzing correlations across multiple assets in Forex, crypto, and stock markets. By displaying a color-coded table, it visually conveys both the strength and direction of correlations — warm colors for strong negative correlation, cool colors for strong positive correlation, and mid-range tones such as yellow or green for near-zero or neutral correlation. This helps traders select assets with low or negative correlation for diversification, avoid overexposure to similar trades, identify arbitrage and pairs trading opportunities, and detect unusual divergences between typically aligned assets. With support for custom mode and up to 20 symbols, it offers high flexibility for different trading strategies, making it a valuable complement to technical analysis and risk management.
Pine Script® 인디케이터
TFlab의
5
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster Plain-English overview This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism. What Monte Carlo is and why quants rely on it • Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve. • Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon. • Core strengths in quant finance . – Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?” – Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic. – Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess. • Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.” How this indicator builds its probability cone 1) Seasonal pattern discovery The script builds two day-of-year maps as new data arrives: • A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information. • A volatility map that tracks the typical absolute return for the same calendar day. It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day. 2) Choice of randomness engine You can pick how the future shocks are generated: • Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock. • Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution. Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands. 3) Volatility scaling to current conditions Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa. 4) Many futures, summarized by percentiles The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles: • 5th and 95th → approximate 95% band (outer cone). • 16th and 84th → approximate 68% band (inner cone). • 50th → the median or “expected path.” These are drawn as polylines so you can immediately see central tendency and dispersion. 5) A historical overlay (optional) Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward. Inputs you control and how to think about them Monte Carlo Simulation • Price Series for Calculation . The source series, typically close. • Enable Probability Forecasts . Master switch for simulation and drawing. • Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon. • Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty. • Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice). • Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history. • Volatility Scaling . On by default so the cone respects today’s range context. Plotting & UI • Probability Cone . Plots the outer and inner percentile envelopes. • Expected Path . Plots the median line through the cone. • Historical Overlay . Dotted seasonal-only projection for context. • Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts. What appears on your chart • A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band. • A median path (default blue) running through the center of the cone. • An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent. • Optional historical seasonal path drawn as dotted segments for the next 30 bars. How to use it in trading 1) Position sizing and stop logic The cone translates “volatility plus seasonality” into distances. • Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play. • Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable. • If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality. 2) Entry timing with seasonal bias When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system. 3) Target selection Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive. 4) Scenario planning & “what-ifs” Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through. 5) Options and vol tactics • When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate. • When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics. Reading the probability cone like a pro • Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite. • Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained. • Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone. • Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst. Methodological notes (what the code actually does) • Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price. • Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives. • Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable. • Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation. • Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better. • Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones. • Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive. • Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history. Strengths and limitations • Strengths : – Probabilistic thinking replaces single-point guessing. – Seasonality adds a small but meaningful directional bias that many markets exhibit. – Volatility scaling adapts to the current regime so the cone stays realistic. • Limitations : – Seasonality can break around structural changes, policy shifts, or one-off events. – The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision. – The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts. – Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories. Tuning guide • Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets. • Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes. • Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong. • Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence. Workflow examples • Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band. • Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated. • Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range. Good habits • Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality. • Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt. • Validate seasonality for your symbol and timeframe; not every market has strong calendar effects. Summary The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
Pine Script® 인디케이터
BackQuant의
2
Weekly High/Low Weekday Stats by [M1rage]Патч: условная статистика по дню недельного экстремума Добавлена новая функция, позволяющая строить условное распределение по дням недели. Что нового. Два новых параметра в настройках: Condition: Weekly High on — зафиксировать день недели, в который сформировался недельный High. Condition: Weekly Low on — зафиксировать день недели, в который сформировался недельный Low. Таблица автоматически перестраивается: Левая колонка показывает — вероятности минимума недели при выбранном дне максимума. Правая колонка показывает — вероятности максимума недели при выбранном дне минимума. В заголовках колонок появляется подпись формата: Weekly Low | High=Tue Weekly High | Low=Thu --------------------------------------------------------------------------------------------------------------------- Patch: Conditional Statistics by Day of the Weekly Extremum A new feature has been added that builds a conditional distribution by weekdays. What’s new Two new settings: Condition: Weekly High on — fix the weekday on which the weekly High formed. Condition: Weekly Low on — fix the weekday on which the weekly Low formed. The table updates automatically: Left column — probabilities of the weekly Low given the selected day of the High. Right column — probabilities of the weekly High given the selected day of the Low. Column headers now display labels in the format: Weekly Low | High=Tue Weekly High | Low=Thu
Pine Script® 인디케이터
M1rage_fx의
TimeSeriesBenchmarkMeasuresLibrary "TimeSeriesBenchmarkMeasures" Time Series Benchmark Metrics. \ Provides a comprehensive set of functions for benchmarking time series data, allowing you to evaluate the accuracy, stability, and risk characteristics of various models or strategies. The functions cover a wide range of statistical measures, including accuracy metrics (MAE, MSE, RMSE, NRMSE, MAPE, SMAPE), autocorrelation analysis (ACF, ADF), and risk measures (Theils Inequality, Sharpness, Resolution, Coverage, and Pinball). ___ Reference: - github.com . - medium.com . - www.salesforce.com . - towardsdatascience.com . - github.com . mae(actual, forecasts)   In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.   Parameters:      actual (array) : List of actual values.      forecasts (array) : List of forecasts values.   Returns: - Mean Absolute Error (MAE). ___ Reference: - en.wikipedia.org . - The Orange Book of Machine Learning - Carl McBride Ellis . mse(actual, forecasts)   The Mean Squared Error (MSE) is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error approaches zero.   Parameters:      actual (array) : List of actual values.      forecasts (array) : List of forecasts values.   Returns: - Mean Squared Error (MSE). ___ Reference: - en.wikipedia.org . rmse(targets, forecasts, order, offset)   Calculates the Root Mean Squared Error (RMSE) between target observations and forecasts. RMSE is a standard measure of the differences between values predicted by a model and the values actually observed.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.      order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.      offset (int) : Forecast offset related to target, `default=0`.   Returns: - RMSE value. nmrse(targets, forecasts, order, offset)   Normalised Root Mean Squared Error.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.      order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.      offset (int) : Forecast offset related to target, `default=0`.   Returns: - NRMSE value. rmse_interval(targets, forecasts)   Root Mean Squared Error for a set of interval windows. Computes RMSE by converting interval forecasts (with min/max bounds) into point forecasts using the mean of the interval bounds, then compares against actual target values.   Parameters:      targets (array) : List of target observations.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - RMSE value for the combined interval list. mape(targets, forecasts)   Mean Average Percentual Error.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.   Returns: - MAPE value. smape(targets, forecasts, mode)   Symmetric Mean Average Percentual Error. Calculates the Mean Absolute Percentage Error (MAPE) between actual targets and forecasts. MAPE is a common metric for evaluating forecast accuracy, expressed as a percentage, lower values indicate a better forecast accuracy.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.      mode (int) : Type of method: default=0:`sum(abs(Fi-Ti)) / sum(Fi+Ti)` , 1:`mean(abs(Fi-Ti) / ((Fi + Ti) / 2))` , 2:`mean(abs(Fi-Ti) / (abs(Fi) + abs(Ti))) * 100`   Returns: - SMAPE value. mape_interval(targets, forecasts)   Mean Average Percentual Error for a set of interval windows.   Parameters:      targets (array) : List of target observations.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - MAPE value for the combined interval list. acf(data, k)   Autocorrelation Function (ACF) for a time series at a specified lag.   Parameters:      data (array) : Sample data of the observations.      k (int) : The lag period for which to calculate the autocorrelation. Must be a non-negative integer.   Returns: - The autocorrelation value at the specified lag, ranging from -1 to 1. ___ The autocorrelation function measures the linear dependence between observations in a time series at different time lags. It quantifies how well the series correlates with itself at different time intervals, which is useful for identifying patterns, seasonality, and the appropriate lag structure for time series models. ACF values close to 1 indicate strong positive correlation, values close to -1 indicate strong negative correlation, and values near 0 indicate no linear correlation. ___ Reference: - statisticsbyjim.com acf_multiple(data, k)   Autocorrelation function (ACF) for a time series at a set of specified lags.   Parameters:      data (array) : Sample data of the observations.      k (array) : List of lag periods for which to calculate the autocorrelation. Must be a non-negative integer.   Returns: - List of ACF values for provided lags. ___ The autocorrelation function measures the linear dependence between observations in a time series at different time lags. It quantifies how well the series correlates with itself at different time intervals, which is useful for identifying patterns, seasonality, and the appropriate lag structure for time series models. ACF values close to 1 indicate strong positive correlation, values close to -1 indicate strong negative correlation, and values near 0 indicate no linear correlation. ___ Reference: - statisticsbyjim.com adfuller(data, n_lag, conf)   : Augmented Dickey-Fuller test for stationarity.   Parameters:      data (array) : Data series.      n_lag (int) : Maximum lag.      conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).   Returns: - `adf` The test statistic. - `crit` Critical value for the test statistic at the 10 % levels. - `nobs` Number of observations used for the ADF regression and calculation of the critical values. ___ The Augmented Dickey-Fuller test is used to determine whether a time series is stationary or contains a unit root (non-stationary). The null hypothesis is that the series has a unit root (is non-stationary), while the alternative hypothesis is that the series is stationary. A stationary time series has statistical properties that do not change over time, making it suitable for many time series forecasting models. If the test statistic is less than the critical value, we reject the null hypothesis and conclude the series is stationary. ___ Reference: - www.jstor.org - en.wikipedia.org theils_inequality(targets, forecasts)   Calculates Theil's Inequality Coefficient, a measure of forecast accuracy that quantifies the relative difference between actual and predicted values.   Parameters:      targets (array) : List of target observations.      forecasts (array) : Matrix with list of forecasts, ordered column wise.   Returns: - Theil's Inequality Coefficient value, value closer to 0 is better. ___ Theil's Inequality Coefficient is calculated as: `sqrt(Sum((y_i - f_i)^2)) / (sqrt(Sum(y_i^2)) + sqrt(Sum(f_i^2)))` where `y_i` represents actual values and `f_i` represents forecast values. This metric ranges from 0 to infinity, with 0 indicating perfect forecast accuracy. ___ Reference: - en.wikipedia.org sharpness(forecasts)   The average width of the forecast intervals across all observations, representing the sharpness or precision of the predictive intervals.   Parameters:      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - Sharpness The sharpness level, which is the average width of all prediction intervals across the forecast horizon. ___ Sharpness is an important metric for evaluating forecast quality. It measures how narrow or wide the prediction intervals are. Higher sharpness (narrower intervals) indicates greater precision in the forecast intervals, while lower sharpness (wider intervals) suggests less precision. The sharpness metric is calculated as the mean of the interval widths across all observations, where each interval width is the difference between the upper and lower bounds of the prediction interval. Note: This function assumes that the forecasts matrix has at least 2 columns, with the first column representing the lower bounds and the second column representing the upper bounds of prediction intervals. ___ Reference: - Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. otexts.com resolution(forecasts)   Calculates the resolution of forecast intervals, measuring the average absolute difference between individual forecast interval widths and the overall sharpness measure.   Parameters:      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - The average absolute difference between individual forecast interval widths and the overall sharpness measure, representing the resolution of the forecasts. ___ Resolution is a key metric for evaluating forecast quality that measures the consistency of prediction interval widths. It quantifies how much the individual forecast intervals vary from the average interval width (sharpness). High resolution indicates that the forecast intervals are relatively consistent across observations, while low resolution suggests significant variation in interval widths. The resolution is calculated as the mean absolute deviation of individual interval widths from the overall sharpness value. This provides insight into the uniformity of the forecast uncertainty estimates across the forecast horizon. Note: This function requires the forecasts matrix to have at least 2 columns (min, max) representing the lower and upper bounds of prediction intervals. ___ Reference: - (sites.stat.washington.edu) - (www.jstor.org) coverage(targets, forecasts)   Calculates the coverage probability, which is the percentage of target values that fall within the corresponding forecasted prediction intervals.   Parameters:      targets (array) : List of target values.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - Percent of target values that fall within their corresponding forecast intervals, expressed as a decimal value between 0 and 1 (or 0% and 100%). ___ Coverage probability is a crucial metric for evaluating the reliability of prediction intervals. It measures how well the forecast intervals capture the actual observed values. An ideal forecast should have a coverage probability close to the nominal confidence level (e.g., 90%, 95%, or 99%). For example, if a 95% prediction interval is used, we expect approximately 95% of the actual target values to fall within those intervals. If the coverage is significantly lower than the nominal level, the intervals may be too narrow; if it's significantly higher, the intervals may be too wide. Note: This function requires the targets array and forecasts matrix to have the same number of observations, and the forecasts matrix must have at least 2 columns (min, max) representing the lower and upper bounds of prediction intervals. ___ Reference: - (www.jstor.org) pinball(tau, target, forecast)   Pinball loss function, measures the asymmetric loss for quantile forecasts.   Parameters:      tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.      target (float) : The actual observed value to compare against.      forecast (float) : The forecasted value.   Returns: - The Pinball loss value, which quantifies the distance between the forecast and target relative to the specified quantile level. ___ The Pinball loss function is specifically designed for evaluating quantile forecasts. It is asymmetric, meaning it penalizes underestimates and overestimates differently depending on the quantile level being evaluated. For a given quantile τ, the loss function is defined as: - If target >= forecast: (target - forecast) * τ - If target < forecast: (forecast - target) * (1 - τ) This loss function is commonly used in quantile regression and probabilistic forecasting to evaluate how well forecasts capture specific quantiles of the target distribution. ___ Reference: - (www.otexts.com) pinball_mean(tau, targets, forecasts)   Calculates the mean pinball loss for quantile regression.   Parameters:      tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.      targets (array) : The actual observed values to compare against.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - The mean pinball loss value across all observations. ___ The pinball_mean() function computes the average Pinball loss across multiple observations, making it suitable for evaluating overall forecast performance in quantile regression tasks. This function leverages the asymmetric Pinball loss function to evaluate how well forecasts capture specific quantiles of the target distribution. The choice of which column from the forecasts matrix to use depends on the quantile level: - For τ ≤ 0.5: Uses the first column (min) of forecasts - For τ > 0.5: Uses the second column (max) of forecasts This loss function is commonly used in quantile regression and probabilistic forecasting to evaluate how well forecasts capture specific quantiles of the target distribution. ___ Reference: - (www.otexts.com)
Pine Script® 라이브러리
RicardoSantos의
4
XAUUSD Strength Dashboard with VolumeXAUUSD Strength Dashboard with Volume Analysis 📌 Description This advanced Pine Script indicator provides a multi-timeframe dashboard for XAUUSD (Gold vs. USD), combining price action analysis with volume confirmation to generate high-probability trading signals. It detects: ✅ Break of Structure (BOS) ✅ Fair Value Gaps (FVG) ✅ Change of Character (CHOCH) ✅ Trendline Breaks (9/21 SMA Crossover) ✅ Volume Spikes (Confirmation of Strength) The dashboard displays strength scores (0-100%) and action recommendations (Strong Buy/Buy/Neutral/Sell/Strong Sell) across multiple timeframes, helping traders identify confluences for better trade decisions. 🎯 How It Works 1. Multi-Timeframe Analysis Fetches data from 1m, 5m, 15m, 30m, 1h, 4h, Daily, and Weekly timeframes. Compares trend direction, BOS, FVG, CHOCH, and volume spikes across all timeframes. 2. Volume-Confirmed Strength Score The Strength Score (0-100%) is calculated using: Trend Direction (25 points) → 9 SMA vs. 21 SMA Break of Structure (20 points) → New highs/lows with momentum Fair Value Gaps (10 points) → Imbalance zones Change of Character (10 points) → Shift in market structure Trendline Break (20 points) → SMA crossover confirmation Volume Spike (15 points) → High volume confirms moves Score Interpretation: ≥75% → Strong Buy (High confidence bullish move) 60-74% → Buy (Bullish but weaker confirmation) 40-59% → Neutral (No strong bias) 25-39% → Sell (Bearish but weaker confirmation) ≤25% → Strong Sell (High confidence bearish move) 3. Dashboard & Chart Markers Dashboard Table: Shows Trend, BOS, Volume, CHOCH, TL Break, Strength %, Key Level, and Action for each timeframe. Chart Markers: 🟢 Green Triangles → Bullish BOS 🔴 Red Triangles → Bearish BOS 🟢 Green Circles → Bullish CHOCH 🔴 Red Circles → Bearish CHOCH 📈 Green Arrows → Bullish Trendline Break 📉 Red Arrows → Bearish Trendline Break "Vol↑" (Lime) → Bullish Volume Spike "Vol↓" (Maroon) → Bearish Volume Spike 🚀 How to Use 1. Dashboard Interpretation Higher Timeframes (D/W) → Show the dominant trend. Lower Timeframes (1m-4h) → Help with entry timing. Strength Score ≥75% or ≤25% → Look for high-confidence trades. Volume Spikes → Confirm breakouts/reversals. 2. Trading Strategy 📈 Long (Buy) Setup: Higher TFs (D/W/4h) show bullish trend (↑). Current TF has BOS & Volume Spike. Strength Score ≥60%. Key Level (Low) holds as support. 📉 Short (Sell) Setup: Higher TFs (D/W/4h) show bearish trend (↓). Current TF has BOS & Volume Spike. Strength Score ≤40%. Key Level (High) holds as resistance. 3. Customization Adjust Volume Spike Multiplier (Default: 1.5x) → Controls sensitivity to volume spikes. Toggle Timeframes → Enable/disable higher/lower timeframes. 🔑 Key Benefits ✔ Multi-Timeframe Confluence → Avoids false signals. ✔ Volume Confirmation → Filters low-quality breakouts. ✔ Clear Strength Scoring → Removes emotional bias. ✔ Visual Chart Markers → Easy to spot key signals. This indicator is ideal for gold traders who follow institutional order flow, market structure, and volume analysis to improve their trading decisions. 🎯 Best Used With: Support/Resistance Levels Fibonacci Retracements Price Action Confirmation 🚀 Happy Trading! 🚀
Pine Script® 인디케이터
Mistab1009의
2