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CryptoFlux Dynamo [JOAT]CryptoFlux Dynamo: Velocity Scalping Strategy WHAT THIS STRATEGY IS CryptoFlux Dynamo is an open-source Pine Script v6 strategy designed for momentum-based scalping on cryptocurrency perpetual futures. It combines multiple technical analysis methods into a unified system that adapts its behavior based on current market volatility conditions. This script is published open-source so you can read, understand, and modify the complete logic. The description below explains everything the strategy does so that traders who cannot read Pine Script can fully understand how it works before using it. HOW THIS STRATEGY IS ORIGINAL AND WHY THE INDICATORS ARE COMBINED This strategy uses well-known indicators (MACD, EMA, RSI, MFI, Bollinger Bands, Keltner Channels, ATR). The originality is not in the individual indicators themselves, but in the specific way they are integrated into a regime-adaptive system. Here is the detailed justification for why these components are combined and how they work together: The Problem Being Solved: Standard indicator-based strategies use fixed thresholds. For example, a typical MACD strategy might enter when the histogram crosses above zero. However, in cryptocurrency markets, volatility changes dramatically throughout the day and week. A MACD crossover during a low-volatility consolidation period has very different implications than the same crossover during a high-volatility trending period. Using the same entry thresholds and stop distances in both conditions leads to either: Too many false signals during consolidation (if thresholds are loose) Missing valid opportunities during expansion (if thresholds are tight) Stops that are too tight during volatility spikes (causing premature exits) Stops that are too wide during compression (giving back profits) The Solution Approach: This strategy first classifies the current volatility regime using normalized ATR (ATR as a percentage of price), then dynamically adjusts ALL other parameters based on that classification. This creates a context-aware system rather than a static threshold comparison. How Each Component Contributes to the System: ATR-Based Regime Classification (The Foundation) The strategy calculates ATR over 21 periods, smooths it with a 13-period EMA to reduce noise from wicks, then divides by price to get a normalized percentage. This ATR% is classified into three regimes: - Compression (ATR% < 0.8%): Market is consolidating, breakouts are more likely but false signals are common - Expansion (ATR% 0.8% - 1.6%): Normal trending conditions - Velocity (ATR% > 1.6%): High volatility, larger moves but also larger adverse excursions This regime classification then controls stop distances, profit targets, trailing stop offsets, and signal strength requirements. The regime acts as a "meta-parameter" that tunes the entire system. EMA Ribbon (8/21/34) - Trend Structure Detection The three EMAs establish trend direction and structure. When EMA 8 > EMA 21 > EMA 34, the trend structure is bullish. The slope of the middle EMA (21) is calculated over 8 bars and converted to degrees using arctangent. This slope measurement quantifies trend strength, not just direction. Why these specific periods? The 8/21/34 sequence follows Fibonacci-like spacing and provides good separation on 5-minute cryptocurrency charts. The fast EMA (8) responds to immediate price action, the mid EMA (21) represents the short-term trend, and the slow EMA (34) acts as a trend filter. The EMA ribbon works with the regime classification: during compression regimes, the strategy requires stronger ribbon alignment before entry because false breakouts are more common. MACD (8/21/5) - Momentum Measurement The MACD uses faster parameters (8/21/5) than the standard (12/26/9) because cryptocurrency markets move faster than traditional markets. The histogram is smoothed with a 5-period EMA to reduce noise. The key innovation is the adaptive histogram baseline. Instead of using a fixed threshold, the strategy calculates a rolling baseline from the smoothed absolute histogram value, then multiplies by a sensitivity factor (1.15). This means the threshold for "significant momentum" automatically adjusts based on recent momentum levels. The MACD works with the regime classification: during velocity regimes, the histogram baseline is effectively higher because recent momentum has been stronger, preventing entries on relatively weak momentum. RSI (21 period) and MFI (21 period) - Independent Momentum Confirmation RSI measures momentum using price changes only. MFI (Money Flow Index) measures momentum using price AND volume. By requiring both to confirm, the strategy filters out price moves that lack volume support. The 21-period length is longer than typical (14) to reduce noise on 5-minute charts. The trigger threshold (55 for longs, 45 for shorts) is slightly offset from 50 to require momentum in the trade direction, not just neutral readings. These indicators work together: a signal requires RSI > 55 AND MFI > 55 for longs. This dual confirmation reduces false signals from price manipulation or low-volume moves. Bollinger Bands (1.5 mult) and Keltner Channels (1.8 mult) - Squeeze Detection When Bollinger Bands contract inside Keltner Channels, volatility is compressing and a breakout is likely. This is the "squeeze" condition. When the bands expand back outside the channels, the squeeze "releases." The strategy uses a 1.5 multiplier for Bollinger Bands (tighter than standard 2.0) and 1.8 for Keltner Channels. These values were chosen to identify meaningful squeezes on 5-minute cryptocurrency charts without triggering too frequently. The squeeze detection works with the regime classification: squeeze releases during compression regimes receive additional signal strength points because breakouts from consolidation are more significant. Volume Impulse Detection - Institutional Participation Filter The strategy calculates a volume baseline (34-period SMA) and standard deviation. A "volume impulse" is detected when current volume exceeds the baseline by 1.15x OR when the volume z-score exceeds 0.5. This filter ensures entries occur when there is meaningful market participation, not during low-volume periods where price moves are less reliable. Volume impulse is required for all entries and adds points to the composite signal strength score. Cycle Oscillator - Trend Alignment Filter The strategy calculates a 55-period EMA as a cycle basis, then measures price deviation from this basis as a percentage. When price is more than 0.15% above the cycle basis, the cycle is bullish. When more than 0.15% below, the cycle is bearish. This filter prevents counter-trend entries. Long signals require bullish cycle alignment; short signals require bearish cycle alignment. BTC Dominance Filter (Optional) - Market Regime Filter The strategy can optionally use BTC.D (Bitcoin Dominance) as a market regime filter. When BTC dominance is rising (slope > 0.12), the market is in "risk-off" mode and long entries on altcoins are filtered. When dominance is falling (slope < -0.12), short entries are filtered. This filter is optional because the BTC.D data feed may lag during low-liquidity periods. How The Components Work Together (The Mashup Justification): The strategy uses a composite scoring system where each signal pathway contributes points: Trend Break pathway (30 points): Requires EMA ribbon alignment + positive slope + price breaks above recent structure high Momentum Surge pathway (30 points): Requires MACD histogram > adaptive baseline + MACD line > signal + RSI > 55 + MFI > 55 + volume impulse Squeeze Release pathway (25 points): Requires BB inside KC (squeeze) then release + momentum bias + histogram confirmation Micro Pullback pathway (15 points): Requires shallow retracement to fast EMA within established trend + histogram confirmation + volume impulse Additional modifiers: +5 points if volume impulse is present, -5 if absent +5 points in velocity regime, -2 in compression regime +5 points if cycle is aligned, -5 if counter-trend A trade only executes when the composite score reaches the minimum threshold (default 55) AND all filters agree (session, cycle bias, BTC dominance if enabled). This scoring system is the core innovation: instead of requiring ALL conditions to be true (which would generate very few signals) or ANY condition to be true (which would generate too many false signals), the strategy requires ENOUGH conditions to be true, with different conditions contributing different weights based on their reliability. HOW THE STRATEGY CALCULATES ENTRIES AND EXITS Entry Logic: 1. Calculate current volatility regime from ATR% 2. Calculate all indicator values (MACD, EMA, RSI, MFI, squeeze, volume) 3. Evaluate each signal pathway and sum points 4. Check all filters (session, cycle, dominance, kill switch) 5. If composite score >= 55 AND all filters pass, generate entry signal 6. Calculate position size based on risk per trade and regime-adjusted stop distance 7. Execute entry with regime name as comment Position Sizing Formula: RiskCapital = Equity * (0.65 / 100) StopDistance = ATR * StopMultiplier(regime) RawQuantity = RiskCapital / StopDistance MaxQuantity = Equity * (12 / 100) / Price Quantity = min(RawQuantity, MaxQuantity) Quantity = round(Quantity / 0.001) * 0.001 This ensures each trade risks approximately 0.65% of equity regardless of volatility, while capping total exposure at 12% of equity. Stop Loss Calculation: Stop distance is ATR multiplied by a regime-specific multiplier: Compression regime: 1.05x ATR (tighter stops because moves are smaller) Expansion regime: 1.55x ATR (standard stops) Velocity regime: 2.1x ATR (wider stops to avoid premature exits during volatility) Take Profit Calculation: Target distance is ATR multiplied by regime-specific multiplier and base risk/reward: Compression regime: 1.6x ATR * 1.8 base R:R * 0.9 regime bonus = approximately 2.6x ATR Expansion regime: 2.05x ATR * 1.8 base R:R * 1.0 regime bonus = approximately 3.7x ATR Velocity regime: 2.8x ATR * 1.8 base R:R * 1.15 regime bonus = approximately 5.8x ATR Trailing Stop Logic: When adaptive trailing is enabled, the strategy calculates a trailing offset based on ATR and regime: Compression regime: 1.1x base offset (looser trailing to avoid noise) Expansion regime: 1.0x base offset (standard) Velocity regime: 0.8x base offset (tighter trailing to lock in profits during fast moves) The trailing stop only activates when it would be tighter than the initial stop. Momentum Fail-Safe Exits: The strategy closes positions early if momentum reverses: Long positions close if MACD histogram turns negative OR EMA ribbon structure breaks (fast EMA crosses below mid EMA) Short positions close if MACD histogram turns positive OR EMA ribbon structure breaks This prevents holding through momentum reversals even if stop loss hasn't been hit. Kill Switch: If maximum drawdown exceeds 6.5%, the strategy disables new entries until manually reset. This prevents continued trading during adverse conditions. HOW TO USE THIS STRATEGY Step 1: Apply to Chart Use a 5-minute chart of a high-liquidity cryptocurrency perpetual (BTC/USDT, ETH/USDT recommended) Ensure at least 200 bars of history are loaded for indicator stabilization Use standard candlestick charts only (not Heikin Ashi, Renko, or other non-standard types) Step 2: Understand the Visual Elements EMA Ribbon: Three lines (8/21/34 periods) showing trend structure. Bullish when stacked upward, bearish when stacked downward. Background Color: Shows current volatility regime - Indigo/dark blue = Compression (low volatility) - Purple = Expansion (normal volatility) - Magenta/pink = Velocity (high volatility) Bar Colors: Reflect signal strength divergence. Brighter colors indicate stronger directional bias. Triangle Markers: Entry signals. Up triangles below bars = long entry. Down triangles above bars = short entry. Dashboard (top-right): Real-time display of regime, ATR%, signal strengths, position status, stops, targets, and risk metrics. Step 3: Interpret the Dashboard Regime: Current volatility classification (Compression/Expansion/Velocity) ATR%: Normalized volatility as percentage of price Long/Short Strength: Current composite signal scores (0-100) Cycle Osc: Price deviation from 55-period EMA as percentage Dominance: BTC.D slope and filter status Position: Current position direction or "Flat" Stop/Target: Current stop loss and take profit levels Kill Switch: Status of drawdown protection Volume Z: Current volume z-score Impulse: Whether volume impulse condition is met Step 4: Adjust Parameters for Your Needs For more conservative trading: Increase "Minimum Composite Signal Strength" to 65 or higher For more aggressive trading: Decrease to 50 (but expect more false signals) For higher timeframes (15m+): Increase "Structure Break Window" to 12-15, increase "RSI Momentum Trigger" to 58 For lower liquidity pairs: Increase "Volume Impulse Multiplier" to 1.3, increase slippage in strategy properties To disable short selling: Uncheck "Enable Short Structure" To disable BTC dominance filter: Uncheck "BTC Dominance Confirmation" STRATEGY PROPERTIES (BACKTEST SETTINGS) These are the exact settings used in the strategy's Properties dialog box. You must use these same settings when evaluating the backtest results shown in the publication: Initial Capital: $100,000 Justification: This amount is higher than typical retail accounts. I chose this value to demonstrate percentage-based returns that scale proportionally. The strategy uses percentage-based position sizing (0.65% risk per trade), so a $10,000 account would see the same percentage returns with 10x smaller position sizes. The absolute dollar amounts in the backtest should be interpreted as percentages of capital. Commission: 0.04% (commission_value = 0.04) Justification: This reflects typical perpetual futures exchange fees. Major exchanges charge between 0.02% (maker) and 0.075% (taker). The 0.04% value is a reasonable middle estimate. If your exchange charges different fees, adjust this value accordingly. Higher fees will reduce net profitability. Slippage: 1 tick Justification: This is conservative for liquid pairs like BTC/USDT on major exchanges during normal conditions. For less liquid altcoins or during high volatility, actual slippage may be higher. If you trade less liquid pairs, increase this value to 2-3 ticks for more realistic results. Pyramiding: 1 Justification: No position stacking. The strategy holds only one position at a time. This simplifies risk management and prevents overexposure. calc_on_every_tick: true Justification: The strategy evaluates on every price update, not just bar close. This is necessary for scalping timeframes where waiting for bar close would miss opportunities. Note that this setting means backtest results may differ slightly from bar-close-only evaluation. calc_on_order_fills: true Justification: The strategy recalculates immediately after order fills for faster response to position changes. RISK PER TRADE JUSTIFICATION The default risk per trade is 0.65% of equity. This is well within the TradingView guideline that "risking more than 5-10% on a trade is not typically considered viable." With the 12% maximum exposure cap, even if the strategy takes multiple consecutive losses, the total risk remains manageable. The kill switch at 6.5% drawdown provides additional protection by halting new entries during adverse conditions. The position sizing formula ensures that stop distance (which varies by regime) is accounted for, so actual risk per trade remains approximately 0.65% regardless of volatility conditions. SAMPLE SIZE CONSIDERATIONS For statistically meaningful backtest results, you should select a dataset that generates at least 100 trades. On 5-minute BTC/USDT charts, this typically requires: 2-3 months of data during normal market conditions 1-2 months during high-volatility periods 3-4 months during low-volatility consolidation periods The strategy's selectivity (requiring 55+ composite score plus all filters) means it generates fewer signals than less filtered approaches. If your backtest shows fewer than 100 trades, extend the date range or reduce the minimum signal strength threshold. Fewer than 100 trades produces statistically unreliable results. Win rate, profit factor, and other metrics can vary significantly with small sample sizes. STRATEGY DESIGN COMPROMISES AND LIMITATIONS Every strategy involves trade-offs. Here are the compromises made in this design and the limitations you should understand: Selectivity vs. Opportunity Trade-off The 55-point minimum threshold filters many potential trades. This reduces false signals but also misses valid setups that don't meet all criteria. Lowering the threshold increases trade frequency but decreases win rate. There is no "correct" threshold; it depends on your preference for fewer higher-quality signals vs. more signals with lower individual quality. Regime Classification Lag The ATR-based regime detection uses historical data (21 periods + 13-period smoothing). It cannot predict sudden volatility spikes. During flash crashes or black swan events, the strategy may be classified in the wrong regime for several bars before the classification updates. This is an inherent limitation of any lagging indicator. Indicator Parameter Sensitivity The default parameters (MACD 8/21/5, EMA 8/21/34, RSI 21, etc.) are tuned for BTC/ETH perpetuals on 5-minute charts during 2024 market conditions. Different assets, timeframes, or market regimes may require different parameters. There is no guarantee that parameters optimized on historical data will perform similarly in the future. BTC Dominance Filter Limitations The CRYPTOCAP:BTC.D data feed may lag during low-liquidity periods or weekends. The dominance slope calculation uses a 5-bar SMA, adding additional delay. If you notice the filter behaving unexpectedly, consider disabling it. Backtest vs. Live Execution Differences TradingView backtesting does not replicate actual broker execution. Key differences: Backtests assume perfect fills at calculated prices; real execution involves order book depth, latency, and partial fills The calc_on_every_tick setting improves backtest realism but still cannot capture sub-bar price action or order book dynamics Commission and slippage settings are estimates; actual costs vary by exchange, time of day, and market conditions Funding rates on perpetual futures are not modeled in backtests and can significantly impact profitability over time Exchange-specific limitations (position limits, liquidation mechanics, order types) are not modeled Market Condition Dependencies This strategy is designed for trending and breakout conditions. During extended sideways consolidation with no clear direction, the strategy may generate few signals or experience whipsaws. No strategy performs well in all market conditions. Cryptocurrency-Specific Risks Cryptocurrency markets operate 24/7 without session boundaries. This means: No natural "overnight" risk reduction Volatility can spike at any time Liquidity varies significantly by time of day Exchange outages or issues can occur at any time WHAT THIS STRATEGY DOES NOT DO To be straightforward about limitations: This strategy does not guarantee profits. Past backtest performance does not indicate future results. This strategy does not predict the future. It reacts to current conditions based on historical patterns. This strategy does not account for funding rates, which can significantly impact perpetual futures profitability. This strategy does not model exchange-specific execution issues (partial fills, requotes, outages). This strategy does not adapt to fundamental news events or black swan scenarios. This strategy is not optimized for all market conditions. It may underperform during extended consolidation. IMPORTANT RISK WARNINGS Past performance does not guarantee future results. The backtest results shown reflect specific historical market conditions and parameter settings. Markets change constantly, and strategies that performed well historically may underperform or lose money in the future. A single backtest run does not constitute proof of future profitability. Trading involves substantial risk of loss. Cryptocurrency derivatives are highly volatile instruments. You can lose your entire investment. Only trade with capital you can afford to lose completely. This is not financial advice. This strategy is provided for educational and informational purposes only. It does not constitute investment advice, trading recommendations, or any form of financial guidance. The author is not a licensed financial advisor. You are responsible for your own decisions. Before using this strategy with real capital: Thoroughly understand the code and logic by reading the open-source implementation Forward test with paper trading or very small positions for an extended period Verify that commission, slippage, and execution assumptions match your actual trading environment Understand that live results will differ from backtest results Consider consulting with a qualified financial advisor No guarantees or warranties. This strategy is provided "as is" without any guarantees of profitability, accuracy, or suitability for any purpose. The author is not responsible for any losses incurred from using this strategy. OPEN-SOURCE CODE STRUCTURE The strategy code is organized into these sections for readability: Configuration Architecture: Input parameters organized into logical groups (Core Controls, Optimization Constants, Regime Intelligence, Signal Pathways, Risk Architecture, Visualization) Helper Functions: calcQty() for position sizing, clamp01() and normalize() for value normalization, calcMFI() for Money Flow Index calculation Core Indicator Engine: EMA ribbon, ATR and regime classification, MACD with adaptive baseline, RSI, MFI, volume analytics, cycle oscillator, BTC dominance filter, squeeze detection Signal Pathway Logic: Trend break, momentum surge, squeeze release, micro pullback pathways with composite scoring Entry/Exit Orchestration: Signal filtering, position sizing, entry execution, stop/target calculation, trailing stop logic, momentum fail-safe exits Visualization Layer: EMA plots, regime background, bar coloring, signal labels, dashboard table You can read and modify any part of the code. Understanding the logic before deployment is strongly recommended. - Made with passion by officialjackofalltrades
Strategia Pine Script®
di officialjackofalltrades
Sector Flow AnalysisSector Flow Analysis - Track market leadership and rotation across 11 major sector ETFs with real-time performance rankings. Key Features: Monitors all 11 S&P sector ETFs (XLK, XLF, XLI, XLU, XLV, XLP, XLY, XLB, XLRE, XLC, XLE) Configurable lookback period (5-60 days) for performance calculation Color-coded risk indicators: 🟢 Risk-On sectors leading (bullish), 🟠 Risk-Off sectors leading (defensive), 🟡 Neutral sectors Display top N sectors (1-11) to declutter your chart Fully customizable positioning, text size, and color Clean, minimal table overlay that won't obstruct your chart Use Cases: Identify sector rotation patterns and market leadership changes Gauge market sentiment (risk-on vs risk-off behavior) Find opportunities by tracking which sectors are gaining/losing momentum Confirm trend strength when your stock's sector is leading Perfect companion to comprehensive market analysis tools for a complete picture of sector dynamics.
Indicatore Pine Script®
di danny_dahan
Account GuardianAccount Guardian: Dynamic Risk/Reward Overlay Introduction Account Guardian is an open-source indicator for TradingView designed to help traders evaluate trade setups before entering positions. It automatically calculates Risk-to-Reward ratios based on market structure, displays visual Stop Loss and Take Profit zones, and provides real-time position sizing recommendations. The indicator addresses a fundamental question every trader should ask before entering a trade: "Does this setup make mathematical sense?" Account Guardian answers this question visually and numerically, helping traders avoid impulsive entries with poor risk profiles. Core Functionality Account Guardian performs four primary functions: Detects swing highs and swing lows to identify logical stop loss placement levels Calculates Risk-to-Reward ratios for both long and short setups in real-time Displays visual SL/TP zones on the chart for immediate trade planning Computes position sizing based on your account size and risk tolerance The goal is to provide traders with instant feedback on whether a potential trade meets their minimum risk/reward criteria before committing capital. How It Works Swing Detection The indicator uses pivot point detection to identify recent swing highs and swing lows on the chart. These swing points serve as logical areas for stop loss placement: For Long Trades: The most recent swing low becomes the stop loss level. Price breaking below this level would invalidate the bullish thesis. For Short Trades: The most recent swing high becomes the stop loss level. Price breaking above this level would invalidate the bearish thesis. The swing detection lookback period is configurable, allowing you to adjust sensitivity based on your trading timeframe and style. It automatically adjusts the tp and sl when it is applied to your chart so it is always moving up and down! Risk/Reward Calculation Once swing levels are identified, the indicator calculates: Entry Price: Current close price (where you would enter) Stop Loss: Recent swing low (for longs) or swing high (for shorts) Risk: Distance from entry to stop loss Take Profit: Entry plus (Risk × Target Multiplier) R:R Ratio: Reward divided by Risk The R:R ratio is then evaluated against your configured thresholds to determine if the setup is valid, marginal, or poor. Visual Elements SL/TP Zones When enabled, the indicator draws colored boxes on the chart showing: Red Zone: Stop Loss area - the region between your entry and stop loss Green/Gold/Red Zone: Take Profit area - colored based on R:R quality The color coding provides instant visual feedback: Green: R:R meets or exceeds your "Good R:R" threshold (default 3:1) Gold: R:R meets minimum threshold but below "Good" (between 2:1 and 3:1) Red: R:R below minimum threshold - setup should be avoided Swing Point Markers Small circles mark detected swing points on the chart: Green circles: Swing lows (potential support / long SL levels) Red circles: Swing highs (potential resistance / short SL levels) Dashboard Panel The dashboard in the top-right corner displays comprehensive trade planning information: R:R Row: Current Risk-to-Reward ratio for long and short setups Status Row: VALID, OK, BAD, or N/A based on R:R thresholds Stop Loss Row: Exact price level for stop loss placement Take Profit Row: Exact price level for take profit placement Pos Size Row: Recommended position size based on your risk parameters Risk $ Row: Dollar amount at risk per trade Position Sizing Logic The indicator calculates position size using the formula: Position Size = Risk Amount / Risk per Unit Where: Risk Amount = Account Size × (Risk Percentage / 100) Risk per Unit = Entry Price - Stop Loss Price For example, with a $10,000 account risking 1% per trade ($100), if your entry is at 100 and stop loss at 98 (risk of 2 per unit), your position size would be 50 units. Input Parameters Swing Detection: Swing Lookback: Number of bars to look back for pivot detection (default: 10). Higher values find more significant swing points but may be slower to update. Target Multiplier: Multiplier applied to risk to calculate take profit distance (default: 2). A value of 2 means TP is 2× the distance of SL from entry. Risk/Reward Thresholds: Minimum R:R: Minimum acceptable Risk-to-Reward ratio (default: 2.0). Setups below this show as "BAD" in red. Good R:R: Threshold for excellent setups (default: 3.0). Setups at or above this show as "VALID" in green. Account Settings: Account Size ($): Your trading account size in dollars (default: 10,000). Used for position sizing calculations. Risk Per Trade (%): Percentage of account to risk per trade (default: 1.0%). Professional traders typically risk 0.5-2% per trade. Display: Show SL/TP Zones: Toggle visibility of the colored zone boxes on chart (default: enabled) Show Dashboard: Toggle visibility of the information panel (default: enabled) Analyze Direction: Choose to analyze Long only, Short only, or Both directions (default: Both) How to Use This Indicator Basic Workflow: Add the indicator to your chart Configure your account size and risk percentage in the settings Set your minimum and good R:R thresholds based on your trading rules Look at the dashboard to see current R:R for potential long and short entries Only consider trades where the status shows "VALID" or at minimum "OK" Use the displayed SL and TP levels for your order placement Use the position size recommendation to determine lot/contract size Interpreting the Dashboard: VALID (Green): Excellent setup - R:R meets your "Good" threshold. This is the ideal scenario for taking a trade. OK (Gold): Acceptable setup - R:R meets minimum but isn't optimal. Consider taking if other confluence factors align. BAD (Red): Poor setup - R:R below minimum threshold. Avoid this trade or wait for better entry. N/A (Gray): Cannot calculate - usually means no valid swing point detected yet. Best Practices: Use this indicator as a filter, not a signal generator. It tells you IF a trade makes sense, not WHEN to enter. Combine with your existing entry strategy - use Account Guardian to validate setups from other analysis. Adjust the swing lookback based on your timeframe. Lower timeframes may need smaller lookback values. Be honest with your account size input - accurate position sizing requires accurate inputs. Consider the target multiplier carefully. Higher multipliers mean larger potential reward but lower probability of hitting TP. Alerts The indicator includes four alert conditions: Good Long Setup: Triggers when long R:R reaches or exceeds your "Good R:R" threshold Good Short Setup: Triggers when short R:R reaches or exceeds your "Good R:R" threshold Bad Long Setup: Triggers when long R:R falls below your minimum threshold Bad Short Setup: Triggers when short R:R falls below your minimum threshold These alerts can help you monitor multiple charts and get notified when favorable setups appear. Technical Implementation The indicator is built using Pine Script v6 and includes: Pivot-based swing detection using ta.pivothigh() and ta.pivotlow() Dynamic box drawing for visual SL/TP zones Table-based dashboard for clean information display Color-coded visual feedback system Persistent variable tracking for swing levels Code Structure: // Swing Detection float swingHi = ta.pivothigh(high, swingLen, swingLen) float swingLo = ta.pivotlow(low, swingLen, swingLen) // R:R Calculation for Long float longSL = recentSwingLo float longRisk = entry - longSL float longTP = entry + (longRisk * targetMult) float longRR = (longTP - entry) / longRisk // Position Sizing float riskAmount = accountSize * (riskPct / 100) float posSize = riskAmount / longRisk Limitations The indicator uses historical swing points which may not always represent optimal SL placement for your specific strategy Position sizing assumes you can trade fractional units - adjust accordingly for instruments with minimum lot sizes R:R calculations assume linear price movement and don't account for gaps or slippage The indicator doesn't predict price direction - it only evaluates the mathematical viability of a setup Swing detection has inherent lag due to the lookback period required for pivot confirmation Recommended Settings by Trading Style Scalping (1-5 minute charts): Swing Lookback: 5-8 Target Multiplier: 1-2 Minimum R:R: 1.5 Good R:R: 2.0 Day Trading (15-60 minute charts): Swing Lookback: 8-12 Target Multiplier: 2 Minimum R:R: 2.0 Good R:R: 3.0 Swing Trading (4H-Daily charts): Swing Lookback: 10-20 Target Multiplier: 2-3 Minimum R:R: 2.5 Good R:R: 4.0 Why Risk/Reward Matters Many traders focus solely on win rate, but profitability depends on the combination of win rate AND risk/reward ratio. Consider these scenarios: 50% win rate with 1:1 R:R = Breakeven (before costs) 50% win rate with 2:1 R:R = Profitable 40% win rate with 3:1 R:R = Profitable 60% win rate with 1:2 R:R = Losing money Account Guardian helps ensure you only take trades where the math works in your favor, even if you're wrong more often than you're right. Disclaimer This indicator is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation. Trading involves substantial risk of loss and is not suitable for all investors. The calculations provided by this indicator are based on historical price data and mathematical formulas that may not accurately predict future price movements. Position sizing recommendations are estimates based on user inputs and should be verified before placing actual trades. Always consider factors such as leverage, margin requirements, and broker-specific rules when determining actual position sizes. The Risk-to-Reward ratios displayed are theoretical calculations based on swing point detection. Actual trade outcomes will vary based on market conditions, execution quality, and other factors not captured by this indicator. Past performance does not guarantee future results. Users should thoroughly test any trading approach in a demo environment before risking real capital. The authors and publishers of this indicator are not responsible for any losses or damages arising from its use. Always consult with a qualified financial advisor before making investment decisions.
Indicatore Pine Script®
di officialjackofalltrades
Cantillon Risk Calculator [Free]Overview Stop guessing your position size. The Cantillon Risk Calculator is a lightweight utility that instantly tells you exactly how many units (Contracts/Coins) to buy based on your account size and risk percentage. How to Use Open Settings. Enter your Account Size (e.g., $10,000) and Risk % (e.g., 1%). Type in your Stop Loss price. The table instantly shows your Position Size. Looking for High Probability Setups? Risk management is only half the battle. To find where to enter using Institutional Order Blocks and Sigma Bands, you need the Cantillon Terminal .
Indicatore Pine Script®
di Thecantillonreport
My Price Curtain by @magasineMy Price Curtain by @magasine Functional Description My Price Curtain is a high-performance visual analysis tool designed to provide traders with immediate context regarding price positioning relative to institutional benchmarks. Unlike standard moving averages, this indicator creates a "curtain" of data that dynamically colors the chart background and provides real-time performance metrics to identify trend dominance at a glance. Key Features & Differential Value Multi-Method Dynamic Benchmarking: Choose between five different calculation methods: SMA, EMA, WMA, RMA, or a manual Fixed Price. This allows you to switch from a standard technical trend (MA) to a "break-even" or "entry point" analysis (Fixed Price) instantly. Intelligent Visual Feedback: The "Curtain" logic automatically colors the chart background—Green for Bullish dominance and Red for Bearish dominance—reducing cognitive load during fast-paced sessions. Advanced Statistical Tracking: The indicator includes a built-in Performance Table that tracks the percentage of bars closing above or below the selected benchmark. This helps traders quantify the strength of a trend over the entire visible dataset. Precision Labeling & Distance Analysis: A dynamic, color-coded label tracks the price on the Y-axis. It calculates and displays the exact percentage distance from the price to the benchmark in real-time, helping to identify overextended moves. Optional Deviation Zones: Enable visual "Safety Zones" (boxes) that project a user-defined percentage deviation from the average, assisting in identifying potential volatility expansion or exhaustion areas. Trading Utilities Trend Confirmation: Use the background color and "Bars Above" percentage to confirm if you are trading with the path of least resistance. Scalping & Intraday Support: The "Distance" metric is essential for scalpers to avoid entering trades too far from the average (mean reversion risk). Custom Strategy Benchmark: Use the "Fixed Price" mode to set your specific entry price and see your real-time performance and "curtain" status relative to your position.
Indicatore Pine Script®
di magasine
Position size calculatorA clean position size calculator designed specifically for leverage traders. It calculates your position size, potential profit, and risk-to-reward ratio (R/R) based on fixed dollar risk. Simply enter your entry price, stop-loss, take-profit, and risk in USD to receive precise results. The position size is currently calculated using the following risk-based formula: Position Size = Risk ($) / Stop-Loss distance. This approach keeps risk constant regardless of leverage. All colors are fully customizable to seamlessly fit your chart theme. If you have ideas for additional calculation models or if you find any issues, leave a comment and help improve the tool.
Indicatore Pine Script®
di tezulie
Trade Time & Position Duration Monitor (Multi-Entry)Overview Active Position Hold Timer is a specialized risk management tool designed to track the "psychological time cost" of your trades. Beyond just monitoring price, it focuses on the precise duration your position stays in profit or loss. Key Features: Real-time Duration Tracking: Automatically calculates total time in profit vs. loss. Max Loss Streak: Records the longest continuous time in a losing state. Multi-Entry Cost Averaging: Supports initial entry plus 2 scale-ins. Dual Language Interface: Switch between English and Traditional Chinese. Time-Based Alerts: Set custom alerts for total or consecutive duration. Calculation Logic: Baseline Calculation: Starts accumulating once entry time is reached. Profit/Loss Detection: Compares Close Price to Average Cost on every bar. Live Update: Uses timenow for second-by-second dashboard updates. 中文說明 本指標專為管理「心理時間成本」而設計。它不僅監控價格，更專注於記錄倉位處於盈虧狀態的精確時間，幫助交易者克服持倉時的心理壓力。 主要功能： 即時時長統計：自動計算總浮盈/浮虧時間，以及當前連續狀態的持續時間。 歷史最大浮虧時長：記錄該筆交易中，最長一次連續虧損的時間壓力。 多筆進場計算：支援初始進場加上兩次加倉，自動繪製動態成本線。 雙語切換：表格界面支援中/英文切換。 多維度警報：可針對總時間或連續時間設定警報，提醒您交易是否持有過久。 計算邏輯： 基準計時：當時間超過進場點後，根據圖表週期累加時長。 盈虧判定：每根 K 棒收盤時，自動比較收盤價與平均成本。 即時秒級更新：加入 **Live Timer** 邏輯，計算當前 K 棒已跳動的時間，確保儀表板數據每秒更新。 壓力追蹤：持續追蹤並記錄最長的連續虧損時長。
Indicatore Pine Script®
di awsl
X-Trend Macro Command CenterX-Trend Macro Command Center (MCC) | Institutional Grade Dashboard 📝 Description Body The Invisible Engine of the Market Revealed. Traders often focus solely on Price Action, ignoring the massive underwater currents that actually drive trends: Global Liquidity, Inflation, and Central Bank Policy. We created X-Trend Macro Command Center (MCC) to solve this problem. This is not just an indicator. It is a fundamental heads-up display that bridges the gap between technical charts and macroeconomic reality. 💡 The Idea & Philosophy Markets don't move in a vacuum. Bull runs are fueled by M2 Money Supply expansion and negative real yields. Crashes are triggered by liquidity crunches and aggressive rate hikes. X-Trend MCC was built to give retail traders the same "Macro Awareness" that institutional desks possess. It aggregates fragmented economic data from Federal Reserve databases (FRED) directly onto your chart in real-time. 🚀 Application & Logic This tool is designed for Trend Traders, Crypto Investors, and Macro Analysts. Identify the Regime: Instantly see if the environment is "RISK ON" (High Liquidity, Low Real Rates) or "RISK OFF" (Monetary Tightening). Validate the Trend: Don't buy the dip if Liquidity (M2) is crashing. Don't short the rally if Real Yields are negative. Multi-Region Analysis: Switch instantly between economic powerhouses (US, China, Japan) to see where the capital is flowing. 📊 Dashboard Metrics Explained Every row in the Command Center tells a specific story about the economy: Interest Rate: The "Gravity" of finance. Higher rates weigh down risk assets (Stocks/Crypto). Inflation (YoY): The erosion of purchasing power. We calculate this dynamically based on CPI data. Real Yield (The "Golden" Metric): Calculated as Interest Rate - Inflation. Green: Real Yield is low/negative. Cash is trash, assets fly. Red: Real Yield is high. Cash is King, assets struggle. US Debt & GDP: Fiscal health indicators formatted in Trillions ($T). Watch the Debt-to-GDP ratio—if it spikes >120%, expect currency debasement. M2 Money Supply: The fuel tank of the market. Tracks the total amount of money in circulation. ↗ Trend: Liquidity is entering the system (Bullish). ↘ Trend: Liquidity is drying up (Bearish). 🧩 The X-Trend Ecosystem X-Trend MCC is just the tip of the iceberg. This module is part of the larger X-Trend Project — a comprehensive suite of algorithmic tools being developed to quantify market chaos. While our Price Action algorithms (Lite/Pro/Ultra) handle the Micro, the MCC handles the Macro. Technical Note: Data Sources: Direct connection to FRED (Federal Reserve Economic Data). Zero Repainting: Historical data is requested strictly using closed bars to ensure accuracy. Open Source: We believe in transparency. The code is open for study under MPL 2.0. Build by Dev0880 | X-Trend © 2025
Indicatore Pine Script®
di Dev0880
Index Construction Tool🙏🏻 The most natural mathematical way to construct an index || portfolio, based on contraharmonic mean || contraharmonic weighting. If you currently traded assets do not satisfy you, why not make your own ones? Contraharmonic mean is literally a weighted mean where each value is weighted by itself. ... Now let me explain to you why contraharmonic weighting is really so fundamental in two ways: observation how the industry (prolly unknowably) converged to this method, and the real mathematical explanation why things are this way. How it works in the industry. In indexes like TVC:SPX or TVC:DJI the individual components (stocks) are weighted by market capitalization. This market cap is made of two components: number of shares outstanding and the actual price of the stock. While the number of shares holds the same over really long periods of time and changes rarely by corporate actions , the prices change all the time, so market cap is in fact almost purely based on prices itself. So when they weight index legs by market cap, it really means they weight it by stock prices. That’s the observation: even tho I never dem saying they do contraharmonic weighting, that’s what happens in reality. Natural explanation Now the main part: how the universe works. If you build a logical sequence of how information ‘gradually’ combines, you have this: Suppose you have the one last datapoint of each of 4 different assets; The next logical step is to combine these datapoints somehow in pairs. Pairs are created only as ratios , this reveals relationships between components, this is the only step where these fundamental operations are meaningful, they lose meaning with 3+ components. This way we will have 16 pairs: 4 of them would be 1s, 6 real ratios, and 6 more inverted ratios of these; Then the next logical step is to combine all the pairs (not the initial single assets) all together. Naturally this is done via matrices, by constructing a 4x4 design matrix where each cell will be one of these 16 pairs. That matrix will have ones in the main diagonal (because these would be smth like ES/ES, NQ/NQ etc). Other cells will be actual ratios, like ES/NQ, RTY/YM etc; Then the native way to compress and summarize all this structure is to do eigendecomposition . The only eigenvector that would be meaningful in this case is the principal eigenvector, and its loadings would be what we were hunting for. We can multiply each asset datapoint by corresponding loading, sum them up and have one single index value, what we were aiming for; Now the main catch: turns out using these principal eigenvector loadings mathematically is Exactly the same as simply calculating contraharmonic weights of those 4 initial assets. We’re done here. For the sceptics, no other way of constructing the design matrix other than with ratios would result in another type of a defined mean. Filling that design matrix with ratios Is the only way to obtain a meaningful defined mean, that would also work with negative numbers. I’m skipping a couple of details there tbh, but they don’t really matter (we don’t need log-space, and anyways the idea holds even then). But the core idea is this: only contraharmonic mean emerges there, no other mean ever does. Finally, how to use the thing: Good news we don't use contraharmonic mean itself because we need an internals of it: actual weights of components that make this contraharmonic mean, (so we can follow it with our position sizes). This actually allows us to also use these weights but not for addition, but for subtraction. So, the script has 2 modes (examples would follow): Addition: the main one, allows you to make indexes, portfolios, baskets, groups, whatever you call it. The script will simply sum the weighted legs; Subtraction: allows you to make spreads, residual spreads etc. Important: the script will subtract all the symbols From the first one. So if the first we have 3 symbols: YM, ES, RTY, the script will do YM - ES - RTY, weights would be applied to each. At the top tight corner of the script you will see a lil table with symbols and corresponding weights you wanna trade: these are ‘already’ adjusted for point value of each leg, you don’t need to do anything, only scale them all together to meet your risk profile. Symbols have to be added the way the default ones are added, one line : one symbol. Pls explore the script’s Style setting: You can pick a visualization method you like ! including overlays on the main chart pane ! Script also outputs inferred volume delta, inferred volume and inferred tick count calculated with the same method. You can use them in further calculations. ... Examples of how you can use it ^^ Purple dotted line: overlay from ICT script, turned on in Style settings, the contraharmonic mean itself calculated from the same assets that are on the chart: CME_MINI:RTY1! , CME_MINI:ES1! , CME_MINI:NQ1! , CBOT_MINI:YM1! ^^ precious metals residual spread ( COMEX:GC1! COMEX:SI1! NYMEX:PL1! ) ^^ CBOT:ZC1! vs CBOT:ZW1! grain spread ^^ BDI (Bid Dope Index), constructed from: NYSE:MO , NYSE:TPB , NYSE:DGX , NASDAQ:JAZZ , NYSE:IIPR , NASDAQ:CRON , OTC:CURLF , OTC:TCNNF ^^ NYMEX:CL1! & ICEEUR:BRN1! basket ^^ resulting index price, inferred volume delta, inferred volume and inferred tick count of CME_MINI:NQ1! vs CME_MINI:ES1! spread ... Synthetic assets is the whole new Universe you can jump into and never look back, if this is your way ... ∞
Indicatore Pine Script®
di gorx1
Ichimoku Trading Checklist - 5 Rules🧠 Description This indicator implements a rule-based checklist built on Ichimoku Kinko Hyo, complemented with RSI and price structure, designed to help traders objectively evaluate whether a bullish setup is valid or not. ⚠️ This indicator does NOT generate buy or sell signals. ⚠️ It is NOT a trading system or financial advice. The core philosophy is discipline and consistency: If there is no setup, there is no trade. ________________________________________ ✅ The 5 Rules Evaluated 1. Chikou Span above price (26 bars back) Confirms that current price is above historical price, validating a bullish context. 2. Bullish TK Cross (Tenkan-sen > Kijun-sen) Measures bullish momentum within the Ichimoku framework. 3. Bullish divergence or convergence between RSI and price Evaluates relative strength using recent RSI pivots and price structure. 4. Kumo breakout followed by a valid pullback Requires a bullish cloud breakout and a pullback that respects the structure. 5. Bullish Kumo (green cloud / twist) Confirms that the Ichimoku cloud supports a bullish bias. ________________________________________ 🚦 Decision Traffic Light (Final Row) The last row of the table provides a traffic-light style summary: • 🟢 5/5 rules met → Valid setup • 🟡 1–4 rules met → Incomplete setup • 🔴 0 rules met → No trade Core message displayed: “No setup, No trade!” 🚫 ________________________________________ 🎨 Customization Through the Inputs panel, users can customize: • Header, body, and footer background colors • Traffic-light colors and icons (🟢 🟡 🔴) • Text alignment (left / center / right) • Optional rule counter (x/5) ⚠️ Tables do not use TradingView’s Style tab; all customization is handled via Inputs. ________________________________________ ⏱️ Timeframe The indicator is timeframe-agnostic, but it was designed and tested primarily on the 1H timeframe, where Ichimoku and RSI structure tend to be more consistent. ________________________________________ ⚠️ Disclaimer This script is provided for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. Trading involves risk, and all decisions remain the sole responsibility of the user. Remember that every strategy is based on probabilities and scenarios that you have already tested in hundreds of trades. ________________________________________ 👤 Author © Yesid Correa Cano Pine Script v6 License: Mozilla Public License 2.0 (MPL-2.0)
Indicatore Pine Script®
di Yesid_Correa_Cano
Volatility Targeting: Single Asset [BackQuant]Volatility Targeting: Single Asset An educational example that demonstrates how volatility targeting can scale exposure up or down on one symbol, then applies a simple EMA cross for long or short direction and a higher timeframe style regime filter to gate risk. It builds a synthetic equity curve and compares it to buy and hold and a benchmark. Important disclaimer This script is a concept and education example only . It is not a complete trading system and it is not meant for live execution. It does not model many real world constraints, and its equity curve is only a simplified simulation. If you want to trade any idea like this, you need a proper strategy() implementation, realistic execution assumptions, and robust backtesting with out of sample validation. Single asset vs the full portfolio concept This indicator is the single asset, long short version of the broader volatility targeted momentum portfolio concept. The original multi asset concept and full portfolio implementation is here: That portfolio script is about allocating across multiple assets with a portfolio view. This script is intentionally simpler and focuses on one symbol so you can clearly see how volatility targeting behaves, how the scaling interacts with trend direction, and what an equity curve comparison looks like. What this indicator is trying to demonstrate Volatility targeting is a risk scaling framework. The core idea is simple: If realized volatility is low relative to a target, you can scale position size up so the strategy behaves like it has a stable risk budget. If realized volatility is high relative to a target, you scale down to avoid getting blown around by the market. Instead of always being 1x long or 1x short, exposure becomes dynamic. This is often used in risk parity style systems, trend following overlays, and volatility controlled products. This script combines that risk scaling with a simple trend direction model: Fast and slow EMA cross determines whether the strategy is long or short. A second, longer EMA cross acts as a regime filter that decides whether the system is ACTIVE or effectively in CASH. An equity curve is built from the scaled returns so you can visualize how the framework behaves across regimes. How the logic works step by step 1) Returns and simple momentum The script uses log returns for the base return stream: ret = log(price / price ) It also computes a simple momentum value: mom = price / price - 1 In this version, momentum is mainly informational since the directional signal is the EMA cross. The lookback input is shared with volatility estimation to keep the concept compact. 2) Realized volatility estimation Realized volatility is estimated as the standard deviation of returns over the lookback window, then annualized: vol = stdev(ret, lookback) * sqrt(tradingdays) The Trading Days/Year input controls annualization: 252 is typical for traditional markets. 365 is typical for crypto since it trades daily. 3) Volatility targeting multiplier Once realized vol is estimated, the script computes a scaling factor that tries to push realized volatility toward the target: volMult = targetVol / vol This is then clamped into a reasonable range: Minimum 0.1 so exposure never goes to zero just because vol spikes. Maximum 5.0 so exposure is not allowed to lever infinitely during ultra low volatility periods. This clamp is one of the most important “sanity rails” in any volatility targeted system. Without it, very low volatility regimes can create unrealistic leverage. 4) Scaled return stream The per bar return used for the equity curve is the raw return multiplied by the volatility multiplier: sr = ret * volMult Think of this as the return you would have earned if you scaled exposure to match the volatility budget. 5) Long short direction via EMA cross Direction is determined by a fast and slow EMA cross on price: If fast EMA is above slow EMA, direction is long. If fast EMA is below slow EMA, direction is short. This produces dir as either +1 or -1. The scaled return stream is then signed by direction: avgRet = dir * sr So the strategy return is volatility targeted and directionally flipped depending on trend. 6) Regime filter: ACTIVE vs CASH A second EMA pair acts as a top level regime filter: If fast regime EMA is above slow regime EMA, the system is ACTIVE. If fast regime EMA is below slow regime EMA, the system is considered CASH, meaning it does not compound equity. This is designed to reduce participation in long bear phases or low quality environments, depending on how you set the regime lengths. By default it is a classic 50 and 200 EMA cross structure. Important detail, the script applies regime_filter when compounding equity, meaning it uses the prior bar regime state to avoid ambiguous same bar updates. 7) Equity curve construction The script builds a synthetic equity curve starting from Initial Capital after Start Date . Each bar: If regime was ACTIVE on the previous bar, equity compounds by (1 + netRet). If regime was CASH, equity stays flat. Fees are modeled very simply as a per bar penalty on returns: netRet = avgRet - (fee_rate * avgRet) This is not realistic execution modeling, it is just a simple turnover penalty knob to show how friction can reduce compounded performance. Real backtesting should model trade based costs, spreads, funding, and slippage. Benchmark and buy and hold comparison The script pulls a benchmark symbol via request.security and builds a buy and hold equity curve starting from the same date and initial capital. The buy and hold curve is based on benchmark price appreciation, not the strategy’s asset price, so you can compare: Strategy equity on the chart symbol. Buy and hold equity for the selected benchmark instrument. By default the benchmark is TVC:SPX, but you can set it to anything, for crypto you might set it to BTC, or a sector index, or a dominance proxy depending on your study. What it plots If enabled, the indicator plots: Strategy Equity as a line, colored by recent direction of equity change, using Positive Equity Color and Negative Equity Color . Buy and Hold Equity for the chosen benchmark as a line. Optional labels that tag each curve on the right side of the chart. This makes it easy to visually see when volatility targeting and regime gating change the shape of the equity curve relative to a simple passive hold. Metrics table explained If Show Metrics Table is enabled, a table is built and populated with common performance statistics based on the simulated daily returns of the strategy equity curve after the start date. These include: Net Profit (%) total return relative to initial capital. Max DD (%) maximum drawdown computed from equity peaks, stored over time. Win Rate percent of positive return bars. Annual Mean Returns (% p/y) mean daily return annualized. Annual Stdev Returns (% p/y) volatility of daily returns annualized. Variance of annualized returns. Sortino Ratio annualized return divided by downside deviation, using negative return stdev. Sharpe Ratio risk adjusted return using the risk free rate input. Omega Ratio positive return sum divided by negative return sum. Gain to Pain total return sum divided by absolute loss sum. CAGR (% p/y) compounded annual growth rate based on time since start date. Portfolio Alpha (% p/y) alpha versus benchmark using beta and the benchmark mean. Portfolio Beta covariance of strategy returns with benchmark returns divided by benchmark variance. Skewness of Returns actually the script computes a conditional value based on the lower 5 percent tail of returns, so it behaves more like a simple CVaR style tail loss estimate than classic skewness. Important note, these are calculated from the synthetic equity stream in an indicator context. They are useful for concept exploration, but they are not a substitute for professional backtesting where trade timing, fills, funding, and leverage constraints are accurately represented. How to interpret the system conceptually Vol targeting effect When volatility rises, volMult falls, so the strategy de risks and the equity curve typically becomes smoother. When volatility compresses, volMult rises, so the system takes more exposure and tries to maintain a stable risk budget. This is why volatility targeting is often used as a “risk equalizer”, it can reduce the “biggest drawdowns happen only because vol expanded” problem, at the cost of potentially under participating in explosive upside if volatility rises during a trend. Long short directional effect Because direction is an EMA cross: In strong trends, the direction stays stable and the scaled return stream compounds in that trend direction. In choppy ranges, the EMA cross can flip and create whipsaws, which is where fees and regime filtering matter most. Regime filter effect The 50 and 200 style filter tries to: Keep the system active in sustained up regimes. Reduce exposure during long down regimes or extended weakness. It will always be late at turning points, by design. It is a slow filter meant to reduce deep participation, not to catch bottoms. Common applications This script is mainly for understanding and research, but conceptually, volatility targeting overlays are used for: Risk budgeting normalize risk so your exposure is not accidentally huge in high vol regimes. System comparison see how a simple trend model behaves with and without vol scaling. Parameter exploration test how target volatility, lookback length, and regime lengths change the shape of equity and drawdowns. Framework building as a reference blueprint before implementing a proper strategy() version with trade based execution logic. Tuning guidance Lookback lower values react faster to vol shifts but can create unstable scaling, higher values smooth scaling but react slower to regime changes. Target volatility higher targets increase exposure and drawdown potential, lower targets reduce exposure and usually lower drawdowns, but can under perform in strong trends. Signal EMAs tighter EMAs increase trade frequency, wider EMAs reduce churn but react slower. Regime EMAs slower regime filters reduce false toggles but will miss early trend transitions. Fees if you crank this up you will see how sensitive higher turnover parameter sets are to friction. Final note This is a compact educational demonstration of a volatility targeted, long short single asset framework with a regime gate and a synthetic equity curve. If you want a production ready implementation, the correct next step is to convert this concept into a strategy() script, add realistic execution and cost modeling, test across multiple timeframes and market regimes, and validate out of sample before making any decision based on the results.
Indicatore Pine Script®
di BackQuant
2
Pair Creation🙏🏻 The one and only pair construction tech you need, unlike others: Applies one consistent operation to all the data features (not only prices). Then, the script outputs these, so you can apply other calculations on these outputs. calculates a very fast and native volatility based hedge ratio, that also takes into account point value (think SPY vs ES) so you can easily use it in position sizing Has built-in forward pricing aka cost of carry model , so you can de-drift pairs from cost of carry, discover spot price of oil based on futures, and ofc find arbitrage opportunities Also allows to make a pair as a product of 2 series, useful for triangular arbitrage This script can make a pair in 2 ways: Ratio, by dividing leg 1 by leg 2 Product, by multiplying leg 1 by leg 2 The real mathematically right way to construct a pair is a ratio/product (Spreads are in fact = 2 legged portfolio, but I ain't told ya that ok). Why? Because a pair of 2 entities has a mathematically unique beauty, it allows direct comparisons and relationship analysis, smth you can't do directly with 3 and more components. Multiplication (think inversions like (EURUSD -> USDEUR), and use cases for triangular arbitrage) is useful sometimes too. ... Quickguide: First, "Legs" are pair components: make a pair of related assets. Don’t be guided exclusively by clustering, cointegrations, mutual information etc. Common sense and exogenous info can easily made them all Forward pricing model: is useful when u work with spot vs futures pairs. Otherwise: put financing, storage and yield all on zeros, this way u will turn it off and have a pure ratio/product of 2 legs. Look at the 2 numbers on the script’s status line: the first one would always be 1), and the second one is a variable. First number (always 1) is multiplier for your position size on leg 1 The second number is the multiplier for your position size on leg 2 in the opposite direction. If both legs are related, trading your sizes with these multipliers makes you do statistical arbitrage -> trading ~ volatility in risk free mode, while the relationship between the assets is still in place. Also guys srsly, nobody ‘ever’ made a universal law that somewhy somehow for whatever secret conspiracy reason one shall only trade pairs in mean reverting style xd. You can do whatever you want: Tilt hedge ratio significantly based on relative strength of legs Trade the pair in momentum style Ignore hedge ratio all together And more and more, the limit is your imagination, e.g.: Anticipate hedge ratio changes based on exogenous info and act accordingly Scalp a pair just like any other asset Make a pair out of 2 pairs Like I mean it, whatever you desire About forward pricing model: It’s applied only to leg 2; Direct: takes spot price and finds out implied futures price Inverse: takes futures price and finds out implied spot price (try on oil) Pls read online how to choose parameters, it’s open access reliable info About the hedge ratio I use: You prolly noticed the way I prefer to use inferred volumes vs the “real” ones. In pairs it’s especially meaningful, because real volumes lose sense in pair creation. And while volumes are closely tied to volatility, the inferred volumes ‘Are’ volatility irl (and later can be converted to currency space by using point value, allowing direct comparisons symbol vs symbol). This hedge ratio is a good example of how discovering the real nature of entities beats making 100s of inventions, why domain knowledge and proper feature engineering beats difficult bulky models, neural networks etc. How simple data understanding & operations on it is all you need. This script simply does this: Takes inferred volume delta of both assets, makes a ratio, normalizes it by tick sizes and points values of both legs, calculates a typical value of this series. That’s it, no step 2, we’re done. No Kalman filters, no TLS regression, no vine copulas, or whatever new fancy keywords you can come up with etc. ... ^^ comparing real ES prices vs theoretical ones by forward-pricing model. Financing: 0.04, yield 0.0175 ^^ EURUSD, 6E futures with theoretical futures price calculated with interest rate differential 0.02 (4% USD - 2% EUR interest rates) ^^4 different pairs (RTY/ES, YM/ES, NQ/ES, ES/ZN) each with different plot style (pick one you like in script's Style settings) ^^ YM/RTY pair, each plot represents ratio of different features: ratio of prices, ratio of inferred volume deltas, ratio of inferred volumes, ratio of inferred tick counts (also can be turned on/off in Style settings) ... How can u upgrade it and make a step forward yourself: On tradingview missing values are automatically fixed by backfilling, and this never becomes a thing until you hit high frequency data. You can do better and use Kalman filter for filling missing values. Script contains the functions I use everywhere to calculate inferred volume delta, inferred volume, and inferred tick count. ... ∞
Indicatore Pine Script®
di gorx1
Realtime Position CalculatorRisk management is the single most important factor in trading success. This indicator automates the process of position sizing in real-time based on your account risk and a dynamic technical Stop Loss. It eliminates the need for manual calculations and helps you execute trades faster while adhering to strict risk management rules. How it Works The indicator visually places a Stop Loss line based on recent market structure (Highs/Lows) and instantly calculates the required position size (Contracts/Lots) to match your defined monetary risk. 1. Dynamic Stop Loss : It identifies the highest high (for Shorts) or lowest low (for Longs) over a user-defined lookback period. 2. Position Calculation : It calculates the distance between the current price and the Stop Loss level. 3. Formula : Contract Size = Risk Amount / (Distance * Point Value) 4. Actual vs. Target Risk : Because of the rounding, the script calculates and displays the Actual Risk (e.g., $95) alongside your Target Risk (e.g., $100), so you know exactly what is at stake. Key Features Real-time Calculation : Updates instantly as price moves. Copy Trading Support : Includes an "Account Multiplier" setting. If you trade 10 accounts via a copy trader, set the multiplier to 10. The indicator will show the total contract size needed across all accounts. Point Value Support : Works for Stocks/Crypto (Point Value = 1) and Futures (e.g., ES = 50, NQ = 20). Customizable UI : Toggle specific data on/off in the label (e.g., hide price, show only contracts). Adjustable label offset to keep the chart clean. Settings Guide Trade Direction : Toggle between Long and Short setups. Add the indicator two times and set another for Longs and another for Shorts so you can see both direction at the same time. Risk Amount : Your max risk in currency (e.g., $100). Lookback : How many bars back to look for the SL pivot (e.g., 10 bars). Point Value : Crucial for Futures. Use 1.0 for Crypto/Stocks. Use tick value/point value for futures (e.g., 50 for ES). Account Multiplier : Multiply the position size for multiple accounts. Label Offset : Move the information label to the right to avoid overlapping with price action. Disclaimer This tool is for informational and educational purposes only. Always verify calculations manually before executing trades. Past performance is not indicative of future results.
Indicatore Pine Script®
di ViltsuFIN
Adaptive Risk Management [sgbpulse]1. Introduction: Adaptive Risk Management is an advanced indicator designed to provide traders with a comprehensive risk management tool directly on the chart. Instead of relying on complex manual calculations, the indicator automates all critical steps of trade planning. It dynamically calculates the estimated Entry Price , the Stop Loss location, the required Position Size (Quantity) based on your capital and risk limits, and the three Take Profit targets based on your defined Reward/Risk ratios. The indicator displays all these essential data points clearly and visually on the chart, ensuring you always know the potential risk-reward profile of every trade. ARM : The A daptive R isk M anagement every trader needs to ARM themselves with. 2. The Critical Importance of Risk Management Proper risk management is the cornerstone of successful trading. Consistent profitability in the market is impossible without rigorously defining risk limits. Risk Control: This starts by setting the maximum risk amount you are willing to lose in a single trade (Risk per Trade), and limiting the total capital allocated to the position (Max Capital per Trade). Defining Boundaries (Stop Loss & Take Profit): It is mandatory to define a technical Stop Loss and a Take Profit target. A fundamental rule of risk management is that the Reward/Risk Ratio (R/R) must be a minimum of 1:1. 3. Core Features, Adaptivity, and Customization The Adaptive Risk Management indicator is engineered for use across all major trading styles, including Swing Trading, Intraday Trading, and Scalping, providing consistent risk control regardless of the chosen timeframe. Real-Time Dynamic Adaptivity: The indicator calculates all risk management parameters (Entry, Stop Loss, Quantity) dynamically with every new bar, thus adapting instantly to changing market conditions. Trend Direction Adjustment: Define the analysis direction (Long/Uptrend or Short/Downtrend). Intraday Session Data Control: Full control over whether lookback calculations will include data from Extended Trading Hours (ETH), or if the daily calculations will start actively only from the first bar of Regular Trading Hours (RTH). Status Validation: The indicator performs critical status checks and displays clear Warning Messages if risk conditions are not met. 4. Intuitive Visualization and Real-Time Data Dynamic Tracking Lines: The Entry Price and Stop Loss lines are updated with every new bar. Crucially, the length of these lines dynamically reflects the calculation's lookback range (e.g., the extent of Lookback Bars or the location of the confirmed Pivot Point), providing a visual anchor for the calculated price. Risk and Reward Zones: The indicator creates a graphical background fill between Entry and Stop Loss (marked with the risk color) and between Entry and the Reward Targets (marked with the reward color). Essential Information Labels: Labels are placed at the end of each line, providing critical data: Estimated Entry Price, Stock/Contract Quantity (Quantity), Total Entry Amount, Estimated Stop Loss, Risk per Share, Total Financial Risk (Risk Amount), Exit Amount, Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3. 4.1. Data Window Metrics (16 Full Series) The indicator displays 16 full data series in the TradingView Data Window, allowing precise tracking of every calculation parameter: Entry Data: Estimated Entry, Quantity, Entry Amount. Risk Data (Stop Loss): Estimated Stop Loss, Risk per Share, Risk Amount, Exit Amount. Reward Data (Take Profit): Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3. 4.2. Instant Tracking in the Status Line The indicator displays 6 critical parameters continuously in the indicator's Status Line: Estimated Entry, Quantity, Estimated Stop Loss, Estimated Take Profit 1/2/3. 5. Detailed Indicator Inputs 5.1 General Focused Trend: Defines the analysis direction (Uptrend / Downtrend). Max Capital per Trade: The maximum amount allocated to purchasing stocks/contracts (in account currency). Risk per Trade: The maximum amount the user is willing to risk in this single trade (in account currency). ATR Length: The lookback period for the Average True Range (ATR) calculation. 5.2 Intraday Session Data Control Regular Hours Limitation : If enabled, all daily lookback calculations (for Entry/Stop Loss anchor points) will begin strictly from the first Regular Trading Hours (RTH) bar. This limits the lookback range to the current RTH session, excluding preceding Extended Trading Hours (ETH) data. Only relevant for Intraday charts. Default: False (Off) 5.3 Entry Inputs Entry Method: Selects the entry price calculation method: Current Price: Uses the closing price of the current bar as the estimated entry point (Market Entry). ATR Real Bodies Margin : - Uptrend: Calculates the Maximum Real Body over the lookback period + the calculated safety margin. - Downtrend: Calculates the Minimum Real Body over the lookback period - the calculated safety margin. ATR Bars Margin : - Uptrend: Calculates the Maximum High price over the lookback period + the calculated safety margin. - Downtrend: Calculates the Minimum Low price over the lookback period - the calculated safety margin. Lookback Bars: The number of bars used to calculate the extremes in the ATR-based entry methods (Relevant only for ATR Real Bodies Margin and ATR Bars Margin methods). ATR Multiplier (Entry): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to determine the estimated Entry Price. 5.4 Risk Inputs (Stop Loss) Risk Method: Selects the Stop Loss price calculation method. ATR Current Price Margin : - Uptrend: Entry Price - the calculated safety margin. - Downtrend: Entry Price + the calculated safety margin. ATR Current Bar Margin : - Uptrend: Current Bar's Low price - the calculated safety margin. - Downtrend: Current Bar's High price + the calculated safety margin. ATR Bars Margin : - Uptrend: Lowest Low over lookback period - the calculated safety margin. - Downtrend: Highest High over lookback period + the calculated safety margin. ATR Pivot Margin : - Uptrend: The first confirmed Pivot Low point - the calculated safety margin. - Downtrend: The first confirmed Pivot High point + the calculated safety margin. Lookback Bars: The lookback period for finding the extreme price used in the 'ATR Bars Margin' calculation. ATR Multiplier (Risk): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to place the estimated Stop Loss. Note: If set to 0, the Stop Loss will be placed exactly at the technical anchor point, provided the Minimum Margin Value is also 0. Minimum Margin Value: The minimum price value (e.g., $0.01) the Stop Loss margin buffer must be. Pivot (Left / Right): The number of bars required on either side of the pivot bar for confirmation (relevant only for the ATR Pivot Margin method). 5.5 Reward Inputs (Take Profit) Show Take Profit 1/2/3: ON/OFF switch to control the visibility of each Take Profit target. Reward/Risk Ratio 1/ 2/ 3: Defines the R/R ratio for the profit target. Must be ≥1.0. 6. Indicator Status/Warning Messages In situations where the Stop Loss location cannot be calculated logically and validly, often caused by a mismatch between the configured Focused Trend (Uptrend/Downtrend) and the actual price action, the indicator will display a warning message, explaining the reason and suggesting corrective action. Status Message 1: Pivot reference unavailable Condition: The Stop Loss is set to the "ATR Pivot Margin" method, but the anchor point (Pivot) is missing or inaccessible. Message Displayed: "Pivot reference unavailable. Wait for valid price action, or adjust the Regular Hours Limitation setting or Pivot Left/Right inputs." Status Message 2: Calculated Stop Loss is unsafe Condition: The calculated Stop Loss is placed illogically or unsafely relative to the trend direction and the Entry price. Message Displayed: "Calculated Stop Loss is unsafe for current trend. Wait for valid price action or adjust SL Lookback/Multiplier." 7. Summary The Adaptive Risk Management (ARM) indicator provides a seamless and systematic approach to trade execution and risk control. By dynamically automating all critical trade parameters—from Entry Price and Stop Loss placement to Position Sizing and Take Profit targets—ARM removes emotional bias and ensures every trade adheres strictly to your predefined risk profile. Key Benefits: Systematic Risk Control: Strict enforcement of maximum capital allocation and risk per trade limits. Adaptivity: Dynamic calculation of prices and quantities based on real-time market data (ATR and Lookback). Clarity and Trust: Clear on-chart visualization, precise data metrics (16 series), and unambiguous Status/Warning Messages ensure transparency and reliability. ARM allows traders to focus on strategy and analysis, confident that their execution complies with the core principles of professional risk management. Important Note: Trading Risk This indicator is intended for educational and informational purposes only and does not constitute investment advice or a recommendation for trading in any form whatsoever. Trading in financial markets involves significant risk of capital loss. It is important to remember that past performance is not indicative of future results. All trading decisions are your sole responsibility. Never trade with money you cannot afford to lose.
Indicatore Pine Script®
di sgbpulse
FAIR VALUE CEDEARSFair Value CEDEARS y ETFs Important: load together with the CEDEARdata library. Returns the “Fair Value” of CEDEAR and CEDEAR-based ETF prices traded on ByMA, using as a reference the price of the underlying ordinary share or ETF traded on the NYSE or NASDAQ. It multiplies the NYSE/NASDAQ price by the CEDEAR or ETF conversion ratio and converts the currency to ARS or Dólar MEP using the exchange rate implied by the AL30/AL30C ratio for tickers quoted in ARS (e.g., AAPL) and AL30D/AL30C for tickers quoted in Dólar MEP (e.g., AAPLD). If the CEDEAR or ETF quote is higher than Fair Value, it highlights the difference in red; if it is lower, it highlights it in green. If any of the markets is closed or in an auction period, it notifies the user and changes the background color. By default, the CEDEAR or ETF quote used is the last price, but the user may choose to use the BID or OFFER instead. This allows CEDEAR and ETF buyers to compare Fair Value against the OFFER, while sellers may prefer to measure Fair Value against the BID of the local instrument. BCBA:AAPL BCBA:AAPLD NASDAQ:AAPL BCBA:SPY BCBA:TSLA BCBA:TSLAD CEDEARS ETFs ByMA
Indicatore Pine Script®
di Eco Valores
7
Absorption RatioThe Hidden Connections Between Markets Financial markets are not isolated islands. When panic spreads, seemingly unrelated assets suddenly begin moving in lockstep. Stocks, bonds, commodities, and currencies that normally provide diversification benefits start falling together. This phenomenon, where correlations spike during crises, has devastated portfolios throughout history. The Absorption Ratio provides a quantitative measure of this hidden fragility. The concept emerged from research at State Street Associates, where Mark Kritzman, Yuanzhen Li, Sebastien Page, and Roberto Rigobon developed a novel application of principal component analysis to measure systemic risk. Their 2011 paper in the Journal of Portfolio Management demonstrated that when markets become tightly coupled, the variance explained by the first few principal components increases dramatically. This concentration of variance signals elevated systemic risk. What the Absorption Ratio Measures Principal component analysis, or PCA, is a statistical technique that identifies the underlying factors driving a set of variables. When applied to asset returns, the first principal component typically captures broad market movements. The second might capture sector rotations or risk-on/risk-off dynamics. Additional components capture increasingly idiosyncratic patterns. The Absorption Ratio measures the fraction of total variance absorbed or explained by a fixed number of principal components. In the original research, Kritzman and colleagues used the first fifth of the eigenvectors. When this fraction is high, it means a small number of factors are driving most of the market movements. Assets are moving together, and diversification provides less protection than usual. Consider an analogy: imagine a room full of people having independent conversations. Each person speaks at different times about different topics. The total "variance" of sound in the room comes from many independent sources. Now imagine a fire alarm goes off. Suddenly everyone is talking about the same thing, moving in the same direction. The variance is now dominated by a single factor. The Absorption Ratio captures this transition from diverse, independent behavior to unified, correlated movement. The Implementation Approach TradingView does not support matrix algebra required for true principal component analysis. This implementation uses a closely related proxy: the average absolute correlation across a universe of major asset classes. This approach captures the same underlying phenomenon because when assets are highly correlated, the first principal component explains more variance by mathematical necessity. The asset universe includes eight ETFs representing major investable categories: SPY and QQQ for large cap US equities, IWM for small caps, EFA for developed international markets, EEM for emerging markets, TLT for long-term treasuries, GLD for gold, and USO for oil. This selection provides exposure to equities across geographies and market caps, plus traditional diversifying assets. From eight assets, there are twenty-eight unique pairwise correlations. The indicator calculates each using a rolling window, takes the absolute value to measure coupling strength regardless of direction, and averages across all pairs. This average correlation is then transformed to match the typical range of published Absorption Ratio values. The transformation maps zero average correlation to an AR of 0.50 and perfect correlation to an AR of 1.00. This scaling aligns with empirical observations that the AR typically fluctuates between 0.60 and 0.95 in practice. Interpreting the Regimes The indicator classifies systemic risk into four regimes based on AR levels. The Extreme regime occurs when the AR exceeds 0.90. At this level, nearly all asset classes are moving together. Diversification has largely failed. Historically, this regime has coincided with major market dislocations: the 2008 financial crisis, the 2020 COVID crash, and significant correction periods. Portfolios constructed under normal correlation assumptions will experience larger drawdowns than expected. The High regime, between 0.80 and 0.90, indicates elevated systemic risk. Correlations across asset classes are above normal. This often occurs during the build-up to stress events or during volatile periods where fear is spreading but has not reached panic levels. Risk management should be more conservative. The Normal regime covers AR values between 0.60 and 0.80. This represents typical market conditions where some correlation exists between assets but diversification still provides meaningful benefits. Standard portfolio construction assumptions are reasonable. The Low regime, below 0.60, indicates that assets are behaving relatively independently. Diversification is working well. Idiosyncratic factors dominate returns rather than systematic risk. This environment is favorable for active management and security selection strategies. The Relationship to Portfolio Construction The implications for portfolio management are significant. Modern portfolio theory assumes correlations are stable and uses historical estimates to construct efficient portfolios. The Absorption Ratio reveals that this assumption is violated precisely when it matters most. When AR is elevated, the effective number of independent bets in a diversified portfolio shrinks. A portfolio holding stocks, bonds, commodities, and real estate might behave as if it holds only one or two positions during high AR periods. Position sizing based on normal correlation estimates will underestimate portfolio risk. Conversely, when AR is low, true diversification opportunities expand. The same nominal portfolio provides more independent return streams. Risk can be deployed more aggressively while maintaining the same effective exposure. Component Analysis The indicator separately tracks equity correlations and cross-asset correlations. These components tell different stories about market structure. Equity correlations measure coupling within the stock market. High equity correlation indicates broad risk-on or risk-off behavior where all stocks move together. This is common during both rallies and selloffs driven by macroeconomic factors. Stock pickers face headwinds when equity correlations are elevated because individual company fundamentals matter less than market beta. Cross-asset correlations measure coupling between different asset classes. When stocks, bonds, and commodities start moving together, traditional hedges fail. The classic 60/40 stock/bond portfolio, for example, assumes negative or low correlation between equities and treasuries. When cross-asset correlation spikes, this assumption breaks down. During the 2022 market environment, for instance, both stocks and bonds fell significantly as inflation and rate hikes affected all assets simultaneously. High cross-asset correlation warned that the usual defensive allocations would not provide their expected protection. Mean Reversion Characteristics Like most risk metrics, the Absorption Ratio tends to mean-revert over time. Extremely high AR readings eventually normalize as panic subsides and assets return to more independent behavior. Extremely low readings tend to rise as some level of systematic risk always reasserts itself. The indicator tracks AR in statistical terms by calculating its Z-score relative to the trailing distribution. When AR reaches extreme Z-scores, the probability of normalization increases. This creates potential opportunities for strategies that bet on mean reversion in systemic risk. A buy signal triggers when AR recovers from extremely elevated levels, suggesting the worst of the correlation spike may be over. A sell signal triggers when AR rises from unusually low levels, warning that complacency about diversification benefits may be excessive. Momentum and Trend The rate of change in AR carries information beyond the absolute level. Rapidly rising AR suggests correlations are increasing and systemic risk is building. Even if AR has not yet reached the high regime, acceleration in coupling should prompt increased vigilance. Falling AR momentum indicates normalizing conditions. Correlations are decreasing and assets are returning to more independent behavior. This often occurs in the recovery phase following stress events. Practical Application For asset allocators, the AR provides guidance on how much diversification benefit to expect from a given allocation. During high AR periods, reducing overall portfolio risk makes sense because the usual diversifiers provide less protection. During low AR periods, standard or even aggressive allocations are more appropriate. For risk managers, the AR serves as an early warning indicator. Rising AR often precedes large market moves and volatility spikes. Tightening risk limits before correlations reach extreme levels can protect capital. For systematic traders, the AR provides a regime filter. Mean reversion strategies may work better during high AR periods when panics create overshooting. Momentum strategies may work better during low AR periods when trends can develop independently across assets. Limitations and Considerations The proxy methodology introduces some approximation error relative to true PCA-based AR calculations. The asset universe, while representative, does not include all possible diversifiers. Correlation estimates are inherently backward-looking and can change rapidly. The transformation from average correlation to AR scale is calibrated to match typical published ranges but is not mathematically equivalent to the eigenvalue ratio. Users should interpret levels directionally rather than as precise measurements. Correlation regimes can persist longer than expected. Mean reversion signals indicate elevated probability of normalization but do not guarantee timing. High AR can remain elevated throughout extended crisis periods. References Kritzman, M., Li, Y., Page, S., and Rigobon, R. (2011). Principal Components as a Measure of Systemic Risk. Journal of Portfolio Management, 37(4), 112-126. Kritzman, M., and Li, Y. (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal, 66(5), 30-41. Billio, M., Getmansky, M., Lo, A., and Pelizzon, L. (2012). Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors. Journal of Financial Economics, 104(3), 535-559.
Indicatore Pine Script®
di EdgeTools
RiskCraft - Advanced Risk Management SystemRiskCraft – Risk Intelligence Dashboard Trade like you actually respect risk "I know the setup looks good… but how much am I actually risking right now?" RiskCraft is an open-source Pine Script v6 indicator that keeps risk transparent directly on the chart. It is not a signal generator; it is a risk desk that calculates size, frames volatility, and reminds you when your behaviour drifts away from the plan. Core utilities Calculates professional-style position sizing in real time. Reads volatility and market regime before position size is confirmed. Adjusts risk based on the trader’s emotional state and confidence inputs. Maps session risk across Asian, London, and New York hours. Draws exactly one stop line and one target line in the preferred direction. Provides rotating education tips plus contextual warnings when risk escalates. It is intentionally conservative and keeps you in the game long enough for any separate entry logic to matter. --- Chart layout checklist Use a clean chart on a liquid symbol (e.g., AMEX:SPY or major FX pairs). Main RiskCraft dashboard placed on the right edge. Session Risk box on the left with UTC time visible. Floating risk badge above price. Stop/target guide lines enabled. Education panel visible in the bottom-right corner. --- 1. On-chart components Right-side dashboard : account risk %, position size/value, stop, target, risk/reward, regime, trend strength, emotional state, behavioural score, correlation, and preferred trade direction. Session Risk box : highlights active session (Asian, London, NY), current UTC time, and risk label (High/Med/Low) per session. Floating risk badge : keeps actual account risk percent visible with colour-coded wording from Ultra Cautious to Very Aggressive. Stop/target lines : exactly one dashed stop and one dashed target aligned with the preferred bias. Education panel : rotates core principles and AI-style warnings tied to volatility, risk %, and behaviour flags. --- 2. Volatility engine – ATR with context 📈 atr = ta.atr(atrLength) atrPercent = (atr / close) * 100 atrSMA = ta.sma(atr, atrLength) volatilityRatio = atr / atrSMA isHighVol = volatilityRatio > volThreshold ATR vs ATR SMA shows how wild price is relative to recent history. Volatility ratio above the threshold flips isHighVol , which immediately trims risk. An ATR percentile rank over the last 100 bars indicates calm versus chaotic regimes. Daily ATR sampling via request.security() gives higher time-frame context for intraday sessions. When volatility spikes the script dials position size down automatically instead of cheering for maximum exposure. --- 3. Market regime radar – Danger or Drift 🌊 ema20 = ta.ema(close, 20) ema50 = ta.ema(close, 50) ema200 = ta.ema(close, 200) trendScore = (close > ema20 ? 1 : -1) + (ema20 > ema50 ? 1 : -1) + (ema50 > ema200 ? 1 : -1) = ta.dmi(14, 14) Regimes covered: Danger : high volatility with weak trend. Volatile : volatility elevated but structure still directional. Choppy : low ADX and noisy action. Trending : directional flows without extreme volatility. Mixed : anything between. Each regime maps to a 1–10 risk score and a multiplier that feeds the final position size. Danger and Choppy clamp size; Trending restores normal risk. --- 4. Behaviour engine – trader inputs matter 🧠 You provide: Emotional state : Confident, Neutral, FOMO, Revenge, Fearful. Confidence : slider from 1 to 10. Toggle for behavioural adjustment on/off. Behind the scenes: Each state triggers an emotional multiplier . Confidence produces a confidence multiplier . Combined they form behavioralFactor and a 0–100 Behavioural Score . High-risk emotions or low conviction clamp the final risk. Calm inputs allow normal size. The dashboard prints both fields to keep accountability on-screen. --- 5. Correlation guardrail – avoid stacking identical risk 📊 Optional correlation mode compares the active symbol to a reference (default AMEX:SPY ): corrClose = request.security(correlationSymbol, timeframe.period, close) priceReturn = ta.change(close) / close corrReturn = ta.change(corrClose) / corrClose correlation = calcCorrelation() Absolute correlation above the threshold applies a correlation multiplier (< 1) to reduce size. Dashboard row shows the live correlation and reference ticker. When disabled, the row simply echoes the current symbol, keeping the table readable. --- 6. Position sizing engine – heart of the script 💰 baseRiskAmount = accountSize * (baseRiskPercent / 100) adjustedRisk = baseRiskAmount * behavioralFactor * regimeAdjustment * volAdjustment * correlationAdjustment finalRiskAmount = math.min(adjustedRisk, accountSize * (maxRiskCap / 100)) stopDistance = atr * atrStopMultiplier takeProfit = atr * atrTargetMultiplier positionSize = stopDistance > 0 ? finalRiskAmount / stopDistance : 0 positionValue = positionSize * close Outputs shown on the dashboard: Position size in units and value in currency. Actual risk % back on account after adjustments. Risk/Reward derived from ATR-based stop and target. --- 7. Intelligent trade direction – bias without signals 🎯 Direction score ingredients: EMA stack alignment. Price versus EMA20. RSI momentum relative to 50. MACD line vs signal. Directional Movement (DI+/DI–). The resulting Trade Direction row prints LONG, SHORT, or NEUTRAL. No orders are generated—this is guidance so you only risk capital when the structure supports it. --- 8. Stop/target guide lines – two lines only ✂️ if showStopLines if preferLong // long stop below, target above else if preferShort // short stop above, target below Lines refresh each bar to keep clutter low. When the direction score is neutral, no lines appear. Use them as visual anchors, not auto-orders. --- 9. Session Risk map – global volatility clock 🌍 Tracks Asian, London, and New York windows via UTC. Computes average ATR per session versus global ATR SMA. Labels each session High/Med/Low and colours the cells accordingly. Top row shows the active session plus current UTC time so you always know the regime you are trading. One glance tells you whether you are trading quiet drift or the part of the day that hunts stops. --- 10. Floating risk badge – honesty above price 🪪 Text ranges from Ultra Cautious through Very Aggressive. Colour matches the risk palette inputs (High/Med/Low). Updates on the last bar only, keeping historical clutter off the chart. Account risk becomes impossible to ignore while you stare at price. --- 11. Education engine & warnings 📚 Rotates evergreen principles (risk 1–2%, journal trades, respect plan). Triggers contextual warnings when volatility and risk % conflict. Flags when emotional state = FOMO or Revenge. Highlights sub-standard risk/reward setups. When multiple danger flags stack, an AI-style warning overrides the tip text so you can course-correct before capital is exposed. --- 12. Alerts – hard guard rails 🚨 Excessive Risk Alert : actual risk % crosses custom threshold. High Volatility Alert : ATR behaviour signals danger regime. Emotional State Warning : FOMO or Revenge selected. Poor Risk/Reward Alert : risk/reward drops below your standard. All alerts reinforce discipline; none suggest entries or exits. --- 13. Multi-market behaviour 🕒 Intraday (1m–1h): session box and badge react quickly; ideal for scalpers needing constant risk context. Higher time frames (1D–1W): dashboard shifts slowly, supporting swing planning. Asset classes confirmed in validation: crypto majors, large-cap equities, indices, major FX pairs, and liquid commodities. Risk logic is price-based, so it adapts across markets without bespoke tuning. 15. Key inputs & recommended defaults Account Size : 10,000 (modify to match actual account; min 100). Base Risk % : 1.0 with a Maximum Risk Cap of 2.5%. ATR Period : 14, Stop Multiplier 2.0, Target Multiplier 3.0. High Vol Threshold : 1.5 for ATR ratio. Behavioural Adjustment : enabled by default; disable for fixed risk. Correlation Check : optional; default symbol AMEX:SPY , threshold 0.7. Display toggles : main dashboard, risk badge, session map, education panel, and stop lines can be individually disabled to reduce clutter. 16. Usage notes & limits Indicator mode only; no automated entries or exits. Trade history panel intentionally disabled (requires strategy context). Correlation analysis depends on additional data requests and may lag slightly on illiquid symbols. Session timing uses UTC; adjust expectations if you trade localized instruments. HTF ATR sampling uses daily data, so bar replay on lower charts may show brief data gaps while HTF loads. What does everyone think RISK really means?
Indicatore Pine Script®
di officialjackofalltrades
2