Şimdi başlat

Educational

Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns. The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets. The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks. Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991). MATHEMATICAL METHODOLOGY The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t. Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns. Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively. ADVANCED RISK METRICS The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification. The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions. The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced. Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture. To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds. The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns. COLOR SCHEMES AND VISUALIZATION The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools. The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments. Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations. PRACTICAL APPLICATIONS Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles. The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions. Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable. Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements. Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals. STATISTICAL SIGNIFICANCE AND VALIDATION The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns. Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns. Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference. IMPLEMENTATION BEST PRACTICES Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment. Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives. Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification. ALERT SYSTEM AND AUTOMATION The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations. New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken. TRADING STRATEGIES Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods. Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles. Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions. LIMITATIONS AND CONSIDERATIONS Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates. Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities. Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical. Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging. TECHNICAL IMPLEMENTATION Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible. Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis. The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations. Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences. CONCLUSION The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions. The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases. Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes. BIBLIOGRAPHY Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228. Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58. Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152. Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill. Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91. Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138. Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64. Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
Pine Script® göstergesi
EdgeTools tarafından
8
TPC Strategy XAUUSD - M5 with Fixed SL/TPThis script implements a trend-following strategy for XAUUSD on the 5-minute chart, using 200 EMA and 21 EMA to filter direction. Entries are triggered based on RSI, MACD crossovers, and price action alignment. It includes fixed Stop Loss (15 pips) and Take Profit (22.5 pips) with visual SL/TP lines, BUY/SELL labels, and alert conditions for automated notifications. Designed for intraday scalping and low-risk entries during trending conditions.
Pine Script® stratejisi
sagarnale97 tarafından
Multiple Ema's This indicator plots five customizable Exponential Moving Averages (EMAs) directly on your chart, helping you analyze price trends and identify potential support/resistance zones more effectively. Features: Five EMAs with adjustable lengths: Quickly set the periods for each EMA (default: 10, 20, 50, 100, 200). Clear, color-coded lines: Each EMA is plotted with a distinct color for easy visualization: EMA 1 (Green) EMA 2 (Orange) EMA 3 (Blue) EMA 4 (Purple) EMA 5 (Brown) Overlay on price chart: All curves are shown directly on your main chart for seamless trend analysis. How to Use: Use this indicator to: Identify short-, medium-, and long-term trends by observing the relationships and crossovers between the EMAs. Spot momentum shifts and potential entry/exit opportunities when price crosses above or below multiple EMAs. Fine-tune EMA periods to your own trading strategy using the input settings. Ideal for: Traders and investors seeking a flexible, multi-timeframe EMA solution for stocks, forex, crypto, or any market. Tip: Experiment with EMA lengths to match your trading style or combine with other indicators for even stronger signals!
Pine Script® göstergesi
SimplifiedTrading001 tarafından
Combo 2/20 EMA & Bandpass Filter by TamarokDescription: This strategy combines a 2/20 exponential moving average (EMA) crossover with a custom bandpass filter to generate buy and sell signals. Use the Fast EMA and Slow EMA inputs to adjust trend sensitivity, and the Bandpass Filter Length, Delta, and Zones to fine-tune momentum turns. Signals occur when both EMA and BPF agree in direction, with optional reversal and time filters. How to use: 1. Add the script to your chart in TradingView. 2. Adjust the EMA and BP Filter parameters to match your asset’s volatility. 3. Enable ‘Reverse Signals’ to trade counter-trend, or use the time filter to limit sessions. 4. Set alerts on Long Alert and Short Alert for automated notifications. Inspiration: Based on HPotter’s original combo strategy (Stocks & Commodities Mar 2010). Updated to Pine Script v6 with streamlined code and alerts. WARNING: For purpose educate only
Pine Script® stratejisi
tamarok tarafından
STOCK SCHOOL | SWING TRACKER Swing Tracker is a powerful tool that automatically identifies Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), and Lower Lows (LL) directly on the chart, helping traders clearly understand market structure and trend direction. Designed for price action traders, it works seamlessly across all timeframes and instruments, offering clean visual labels for swing points to spot trend continuations or potential reversals. Whether you're following the trend or looking for structure shifts, Swing Tracker keeps you aligned with price action for smarter, more confident trading decisions.
Pine Script® göstergesi
stockschoolindia tarafından
1
Opening-Range BreakoutNote: Default trading date range looks mediocre. Set date range to "Entire History" to see full effect of the strategy. 50.91% profitable trades, 1.178 profit factor, steady profits and limited drawdown. Total P&L: $154,141.18, Max Drawdown: $18,624.36. High R^2 █ Overview The Opening-Range Breakout strategy is a mechanical, session‑based day‑trading system designed to capture the initial burst of directional momentum immediately following the market open. It defines a user‑configurable “opening range” window, measures its high and low boundaries, then places breakout stop orders at those levels once the range closes. Built‑in filters on minimum range width, reward‑to‑risk ratios, and optional reversal logic help refine entries and manage risk dynamically. █ How It Works Opening‑Range Formation Between 9:30–10:15 AM ET (configurable), the script tracks the highest high and lowest low to form the day’s opening range box. On the first bar after the range window closes, the range high (OR_high) and low (OR_low) are “locked in.” Range‑Width Filter To avoid false breakouts in low‑volatility mornings, the range must be at least X% of the current price (default 0.35%). If the measured opening-range width < minimum threshold, no orders are placed that day. Entry & Order Placement Long: a stop‑buy order at the opening‑range high. Short: a stop‑sell order at the opening‑range low. Only one side can trigger (or both if reverse logic is enabled after a losing trade). Risk Management Once triggered, each trade uses an ATR‑style stop-loss defined as a percentage retracement of the range (default 50% of range width). Profit target is set at a configurable Reward/Risk Ratio (default 1.1×). Optional: Reverse on Stop‑Loss – if the initial breakout loses, immediately reverse into the opposite side on the same day. Session Exit Any open positions are closed at the end of the regular trading day (default 3:45 PM ET window end, with hard flat at session close). Visual cues are provided via green (range high) and red (range low) step‑line plots directly on the chart, allowing you to see the range box and breakout triggers in real time. █ Why It Works Early Momentum Capture: The first 15 – 60 minutes of trading encapsulate overnight news digestion and institutional order flow, creating a well‑defined volatility “range.” Mechanical Discipline: Clear, rule‑based entries and exits remove emotional guesswork, ensuring consistency. Volatility Filtering: By requiring a minimum range width, the system avoids choppy, low‑range days where false breakouts are common. Dynamic Sizing: Stops and targets scale with the opening range, adapting automatically to each day’s volatility environment. █ How to Use Set Your Instruments & Timeframe -Apply to any futures contract on a 1‑ to 5‑minute chart. -Ensure chart timezone is set to America/New_York. Configure Inputs -Opening‑Range Window: e.g. “0930-1015” for a 45‑minute range. -Min. OR Width (%): e.g. 0.35 for 0.35% of current price. -Reward/Risk Ratio: e.g. 1.1 for a modest profit target above your stop. -Max OR Retracement %: e.g. 50 to set stop at 50% of range width. -One Trade Per Day: toggle to limit to a single breakout. -Reverse on Stop Loss: toggle to flip direction after a losing breakout. Monitor the Chart -Watch the green and red range boundaries form during the session open. -Orders will automatically submit on the first bar after the range window closes, conditioned on your filters. Review & Adjust -Backtest across multiple months to validate performance on your preferred contract. -Tweak range duration, minimum width, and R/R multiple to fit your risk tolerance and desired win‑rate vs. expectancy balance. █ Settings Reference Input Defaults Opening‑Range Window - Time window to form OR (HHMM-HHMM) - 0930–1015 Regular Trading Day - Full session for EOD flat (HHMM-HHMM) - 0930–1545 Min. OR Width (%) - Minimum OR size as % of close to trigger orders - 0.35 Reward/Risk Ratio - Profit target multiple of stop‑loss distance - 1.1 Max OR Retracement (%) - % of OR width to use as stop‑loss distance - 50 One Trade Per Day - Limit to a single breakout order per day - false Reverse on Stop Loss - Reverse direction immediately after a losing trade - true Disclaimer This strategy description and any accompanying code are provided for educational purposes only and do not constitute financial advice or a solicitation to trade. Futures trading involves substantial risk, including possible loss of capital. Past performance is not indicative of future results. Traders should assess their own risk tolerance and conduct thorough backtesting and forward-testing before committing real capital.
Pine Script® stratejisi
fabledforman tarafından
1
Valuation Tool + Williams %R by QDEEDValuation + Williams %R Indicator This indicator combines relative valuation and momentum to help identify overvalued and undervalued conditions in key macro assets: DXY (US Dollar Index) GC1! (Gold Futures) ZB1! (30-Year US Treasury Bond Futures) Inspired by Larry Williams' techniques, this tool uses a rescaled comparison of asset prices and overlays the Williams %R momentum oscillator. What it shows: When the value line is above 0, the asset may be overvalued relative to the others. When it's below 0, the asset may be undervalued. The Williams %R adds a timing layer, indicating overbought/oversold momentum zones.
Pine Script® göstergesi
QDEED tarafından
2 Asset Optimal PortfolioThis script calculates and plots either the Sharpe Ratio or Sortino Ratio for a two-asset portfolio using historical price data, allowing users to analyse how different allocations affect portfolio performance over a specified lookback period. Features: Determine the weights of 2 assets and how they affect the the Sharpe or Sortino ratio. Adjust timeframe to suit your personal investment timeframe. User Inputs: 1. Asset 1 and Asset 2: Choose any two symbols to evaluate (default is BTCUSD for both). 2. Look Back Length: Number of past bars (days) to use for calculations (default is 365). 3. Source: Price source for returns (default is close). 4. Ratio: Select which ratio to plot — Sharpe or Sortino. 5. % of Asset 1: Portfolio weight (from 0 to 1) for Asset 1.
Pine Script® göstergesi
QuantChook tarafından
Futures Trade Entry Indicator (MTF)Work in Progress with the help of Claude. Not ready for active trading. Public for help improving. This will be a multi-timeframe futures trading indicator with supply/demand zones, Fair Value Gaps (FVGs), and sophisticated entry/exit logic.I've created a comprehensive Pine Script v6 indicator that matches all your specifications! Here's what the script includes: ## **Key Features Implemented:** ### **🔄 Multi-Timeframe Logic** - **1H Chart Data**: Detects Supply Zones, Demand Zones, and Fair Value Gaps - **Current Timeframe**: Monitors for re-entry and confirmation signals ### **📊 Zone Detection** - **Supply Zones**: Identified using pivot highs with configurable strength - **Demand Zones**: Identified using pivot lows with touch validation - **Fair Value Gaps**: Both bullish and bearish FVGs detected automatically - **Auto-Expiry**: Zones expire after 24 hours (configurable) ### **⚡ Entry Logic** - **Dual Confirmation Required**: - ✅ Engulfing candle pattern (bullish/bearish) - ✅ Market structure shift (HH→LL or LL→HH) - **Zone Re-entry**: Price must be within identified zones/FVGs ### **🎯 Probability System** - **Smart Scoring**: Based on zone age, strength, and risk/reward ratio - **Color-Coded**: Green (High), Yellow (Medium), Red (Low) - **Real-time Calculation**: Updates with each potential entry ### **🎨 Visual Elements** - **Colored Zones**: Supply (red), Demand (green), FVGs (blue/orange) - **Entry Labels**: 🟩 LONG / 🟥 SHORT markers - **Probability Labels**: Display confidence levels - **Confirmation Shapes**: Triangle indicators for pattern completion ### **⚙️ Manual Controls** All the requested toggles are available in the settings panel: - Show/Hide Supply Zones - Show/Hide Demand Zones - Show/Hide FVGs - Show/Hide Labels - Show/Hide Probability - Zone strength and expiry settings - Custom colors for all elements ### **🔔 Alert System** - Entry opportunity alerts - Includes probability assessment - Ticker symbol identification ## **Usage Instructions:** 1. **Apply to 15m chart** for active trading signals 2. **Configure settings** based on your preferences 3. **Set up alerts** for automated notifications 4. **Monitor probability levels** for trade quality assessment The script automatically handles the complex multi-timeframe analysis while keeping the interface clean and user-friendly. All zones update dynamically and expire appropriately to avoid clutter. Would you like me to adjust any specific parameters or add additional features?
Pine Script® göstergesi
A_warrior tarafından
1
Multi timeframe trendDESCRIPTION This indicator, Multi Timeframe Trend, is a powerful tool designed to give traders a comprehensive overview of market trends across multiple timeframes using a single, customizable Exponential Moving Average (EMA). It visually displays whether the price is trading above or below the EMA on each timeframe, helping traders quickly determine the dominant trend at a glance. The real-time dashboard is plotted directly on your chart and color-coded to show bullish (green) or bearish (red) conditions per timeframe, from 15 minutes to 1 week. It is especially helpful for identifying trend alignment across multiple timeframes—an essential component of many professional trading strategies. USER INPUTS * Enter the EMA length – Adjust the EMA period used in the trend calculation (default: 200) * Table Size – Choose how large the on-chart table appears: "tiny", "small", "normal", or "large" INDICATOR LOGIC * The indicator calculates the EMA for each of the following timeframes: 1W, 1D, 4H, 1H, 30M, and 15M * It checks whether the current close is above or below each EMA and labels it as: * Bullish if close > EMA * Bearish if close < EMA * Each timeframe’s trend is displayed in a dynamic table in the top-right corner of the chart * The background color of each cell changes according to trend condition for quick visual interpretation * Real-time responsiveness: handles both historical and live bars to maintain accurate, flicker-free updates WHY IT IS UNIQUE * Combines multiple timeframe trend analysis into a single glance * Clean and color-coded dashboard overlay for real-time trading decisions * Avoids repainting using barstate logic for accurate trend updates * Fully customizable table size and EMA length * Works on any chart, including stocks, crypto, forex, indices HOW USERS CAN BENEFIT FROM IT * Multi-timeframe confirmation: Easily confirm alignment across timeframes before entering a trade * Avoid false signals by ensuring higher timeframe trends agree with lower timeframe setups * Enhance strategy filters: Use as a trend filter in combination with your existing entry indicators * Quick market analysis: No need to switch between charts or manually calculate EMAs * Visual clarity: Trend conditions are easy to read and interpret in real-time
Pine Script® göstergesi
Shivam_Mandrai tarafından
2