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Simple Leveraged PnLThis script shows your live trade PnL, ROE, R:R ratio, margin, leverage, entry, TP, and SL directly on the chart. It draws: Green/red zones for your Take Profit and Stop Loss ranges. A pinned info card (movable to any corner of the chart) showing all key trade details in one place. You can fully customize: Card position (top/middle/bottom × left/middle/right) Text size, colors, and background Zone transparency It works for both Long and Short positions and updates in real time.
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WinnersBeatTheMarket tarafından
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research. The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007). Theoretical Foundation The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin. The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing. The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006). Scientific Implementation This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research: Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly). Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies. Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration. ASSET SPECIFIC IMPLEMENTATION Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities. Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010). Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013). Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations. Risk Management Integration The calculator integrates sophisticated risk assessment through two primary modes: Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class. Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics. Fractional Kelly Approaches Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate. The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles. Empirical Validation and Performance Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period. Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies. Practical Implementation Guidelines Successful Kelly implementation requires systematic application with attention to several critical factors: Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures. Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations. Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly. Professional Features and Customization The calculator provides comprehensive customization options for professional applications: Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments. Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity. Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class. Limitations and Considerations Like any analytical tool, the Kelly Criterion has important limitations that users must understand: Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999). Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications. Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high. Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003). Advanced Applications and Extensions Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments. Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations. Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources. Comprehensive Usage Instructions and Practical Examples Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters. Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth. Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility. Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences. Detailed Practical Examples Example 1: SPY Swing Trading Strategy Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses. Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application. The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile. Example 2: EURUSD Day Trading with Stop Losses A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters. Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example. Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves. Example 3: ES1! Futures Systematic Strategy Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms. Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading. Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading. Example 4: MES1! Micro-Futures for Smaller Accounts Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity. Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments. Example 5: Bitcoin Swing Trading Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators. Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics. Advanced Usage Scenarios Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins. Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur. Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions. Troubleshooting and Optimization Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist. Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves. Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk. Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies. Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management. Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements. Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure. Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current. Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives. Professional Applications This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies. Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics. Conclusion The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications. Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods. The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results. References Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons. Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92. Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press. Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press. Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176. Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147. Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons. Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926. Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press. MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954. MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42. Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423. Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill. Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific. Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education. Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons. Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31. Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR. Further Reading For comprehensive understanding of Kelly Criterion applications and advanced implementations: MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons. Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House. Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons. Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
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EdgeTools tarafından
Signal Stack MeterWhat it is A lightweight “go or no‑go” meter that combines your manual read of Structure, Location, and Momentum with automatic context from volatility and macro timing. It surfaces a single, tradeable answer on the chart: OK to engage or Standby. Why traders like it You keep your discretion and nuance, and the meter adds guardrails. It prevents good trade ideas from being executed in the wrong conditions. What it measures Manual buckets you set each day: Structure, Location, Momentum from 0 to 2 Volatility from VIX, term structure, ATR 5 over 60, and session gaps Time windows for CPI, NFP, and FOMC with ET inputs and an exchange‑offset Total score and a simple gate: threshold plus a “strong bucket” rule you choose How to use in 30 seconds Pick a preset for your market. Set Structure, Location, Momentum to 0, 1, or 2. Leave defaults for the auto metrics while you get a feel. Read the header. When it says OK to engage, you have both your read and the context. Defaults we recommend OK threshold: 5 Strong bucket rule: Either Structure or Location equals 2 VIX triggers: 22 and 1.25× the 20‑SMA Term mode: Diff at 0.00 tolerance. Ratio mode at 1.00+ is available ATR 5/60 defense: 1.25. Offense cue: 0.85 or lower ATR smoothing: 1 Gap mode: RTH with 0.60× ATR5 wild gap. ON wild range at 0.80× ATR5 CPI window 08:25 to 08:40 ET. FOMC window 13:50 to 14:30 ET ET to exchange offset: −60 for CME index futures. Set to 0 for NYSE symbols like SPY Alert cadence: Once per RTH session. Snooze first 30 minutes optional New since the last description Parity with Defense Mode for presets, sessions, ratio vs diff term mode, ATR smoothing, RTH‑key cadence, and snooze options Event windows in ET with a simple offset to your exchange time Alternate row backgrounds and full color control for readability Exposed series for automation: EngageOK(1=yes) plus TotalScore Debug toggle to see ATR ratio, term, and gap measurements directly Notes Dynamic alerts require “Any alert() function call”. The meter is designed to sit opposite Defense Mode on the chart. Use the position input to avoid overlap.
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Uplift32 tarafından
Market Regime Matrix [Alpha Extract]A sophisticated market regime classification system that combines multiple technical analysis components into an intelligent scoring framework to identify and track dominant market conditions. Utilizing advanced ADX-based trend detection, EMA directional analysis, volatility assessment, and crash protection protocols, the Market Regime Matrix delivers institutional-grade regime classification with BULL, BEAR, and CHOP states. The system features intelligent scoring with smoothing algorithms, duration filters for stability, and structure-based conviction adjustments to provide traders with clear, actionable market context. 🔶 Multi-Component Regime Engine Integrates five core analytical components: ADX trend strength detection, EMA-200 directional bias, ROC momentum analysis, Bollinger Band volatility measurement, and zig-zag structure verification. Each component contributes to a sophisticated scoring system that evaluates market conditions across multiple dimensions, ensuring comprehensive regime assessment with institutional precision. // Gate Keeper: ADX determines market type is_trending = adx_value > adx_trend_threshold is_ranging = adx_value <= adx_trend_threshold is_maximum_chop = adx_value <= adx_chop_threshold // BULL CONDITIONS with Structure Veto if price_above_ema and di_bullish if use_structure_filter and isBullStructure raw_bullScore := 5.0 // MAXIMUM CONVICTION: Strong signals + Bull structure else if use_structure_filter and not isBullStructure raw_bullScore := 3.0 // REDUCED: Strong signals but broken structure 🔶 Intelligent Scoring System Employs a dynamic 0-5 scale scoring mechanism for each regime type (BULL/BEAR/CHOP) with adaptive conviction levels. The system automatically adjusts scores based on signal alignment, market structure confirmation, and volatility conditions. Features decision margin requirements to prevent false regime changes and includes maximum conviction thresholds for high-probability setups. 🔶 Advanced Structure Filter Implements zig-zag based market structure analysis using configurable deviation thresholds to identify significant pivot points. The system tracks Higher Highs/Higher Lows (HH/HL) for bullish structure and Lower Lows/Lower Highs (LL/LH) for bearish structure, applying structure veto logic that reduces conviction when price action contradicts the underlying trend framework. // Define Market Structure (Bull = HH/HL, Bear = LL/LH) isBullStructure = not na(last_significant_high) and not na(prev_significant_high) and not na(last_significant_low) and not na(prev_significant_low) and last_significant_high > prev_significant_high and last_significant_low > prev_significant_low isBearStructure = not na(last_significant_high) and not na(prev_significant_high) and not na(last_significant_low) and not na(prev_significant_low) and last_significant_low < prev_significant_low and last_significant_high < prev_significant_high 🔶 Superior Engine Components Features dual-layer regime stabilization through score smoothing and duration filtering. The score smoothing component reduces noise by averaging raw scores over configurable periods, while the duration filter requires minimum regime persistence before confirming changes. This eliminates whipsaws and ensures regime transitions represent genuine market shifts rather than temporary fluctuations. 🔶 Crash Detection & Active Penalties Incorporates sophisticated crash detection using Rate of Change (ROC) analysis with severity classification. When crash conditions are detected, the system applies active penalties (-5.0) to BULL and CHOP scores while boosting BEAR conviction based on crash severity. This ensures immediate regime response to major market dislocations and drawdown events. // === CRASH OVERRIDE (Active Penalties) === is_crash = roc_value < crash_threshold if is_crash // Calculate crash severity crash_severity = math.abs(roc_value / crash_threshold) crash_bonus = 4.0 + (crash_severity - 1.0) * 2.0 // ACTIVE PENALTIES: Force bear dominance raw_bearScore := math.max(raw_bearScore, crash_bonus) raw_bullScore := -5.0 // ACTIVE PENALTY raw_chopScore := -5.0 // ACTIVE PENALTY ❓How It Works 🔶 ADX-Based Market Classification The Market Regime Matrix uses ADX (Average Directional Index) as the primary gatekeeper to distinguish between trending and ranging market conditions. When ADX exceeds the trend threshold, the system activates BULL/BEAR regime logic using DI+/DI- crossovers and EMA positioning. When ADX falls below the ranging threshold, CHOP regime logic takes precedence, with maximum conviction assigned during ultra-low ADX periods. 🔶 Dynamic Conviction Scaling Each regime receives conviction ratings from UNCERTAIN to MAXIMUM based on signal alignment and score magnitude. MAXIMUM conviction (5.0 score) requires perfect signal alignment plus favorable market structure. The system progressively reduces conviction when signals conflict or structure breaks, ensuring traders understand the reliability of each regime classification. 🔶 Regime Transition Management Implements decision margin requirements where new regimes must exceed existing regimes by configurable thresholds before transitions occur. Combined with duration filtering, this prevents premature regime changes and maintains stability during consolidation periods. The system tracks both raw regime signals and final regime output for complete transparency. 🔶 Visual Regime Mapping Provides comprehensive visual feedback through colored candle overlays, background regime highlighting, and real-time information tables. The system displays regime history, conviction levels, structure status, and key metrics in an organized dashboard format. Regime changes trigger immediate visual alerts with detailed transition information. 🔶 Performance Optimization Features efficient array management for zig-zag calculations, smart variable updating to prevent recomputation, and configurable debug modes for strategy development. The system maintains optimal performance across all timeframes while providing institutional-grade analytical depth. Why Choose Market Regime Matrix ? The Market Regime Matrix represents the evolution of market regime analysis, combining traditional technical indicators with modern algorithmic decision-making frameworks. By integrating multiple analytical dimensions with intelligent scoring, structure verification, and crash protection, it provides traders with institutional-quality market context that adapts to changing conditions. The sophisticated filtering system eliminates noise while preserving responsiveness, making it an essential tool for traders seeking to align their strategies with dominant market regimes and avoid adverse market environments.
Pine Script® göstergesi
AlphaExtract tarafından
Sat Stacking Strategies Simulation (SSSS)Sat Stacking Strategies Simulation (SSSS) This indicator simulates and compares different Bitcoin stacking strategies over time, allowing you to visualize performance, cost basis, and stacking behavior directly on your chart. Core Features: Three Stacking Strategies • Trend-Based – Stack only when price is above/below a long-term SMA. • Stack the Dip – Buy during sharp pullbacks or oversold conditions. • Price Zone – Stack only in “cheap”, “fair”, or “expensive” zones based on a simulated Short-Term Holder (STH) cost basis. Always Stack Benchmark Compare your chosen strategy against a simple “Always Stack” approach for a real-world DCA reference. Performance Metrics Table Track: • Total Fiat Added • Total BTC Accumulated • Current Value • Average Cost per BTC • PnL % • CAGR • Sharpe Ratio & Stdev • Stack Events & Time Underwater Advanced Options • Simulate cash-secured puts on unused fiat. • Simulate covered calls on BTC holdings. • Roll over unused stacking amounts for future buys. This tool is designed for Bitcoiners, stackers, and DCA enthusiasts who want to backtest and visualize their stacking plan—whether you keep it simple or go full quant. Sometimes the best alpha is just showing up every week with your wallet open… and occasionally wearing a helmet. 🪖💰
Pine Script® göstergesi
piriya33 tarafından
1
RISK MANAGEMENT CALCULATOR V3📊 RISK MANAGEMENT CALCULATOR – Lot Size, Profit & R:R Tool This script is designed to help traders instantly calculate lot size, expected profit, and risk/reward ratio based on their trade plan. ✅ Features: Input your Risk Amount ($), Entry, Stop Loss, and up to 3 Take Profits Calculates: ✅ Lot Size based on risk ✅ Split profits per TP level (equally weighted) ✅ Total Profit & Risk/Reward (R:R) Displays everything in a clean bottom-right table Optimized for both: 🖥️ Desktop mode (larger layout) 📱 Mobile mode (toggle compact view) 💡 How to Use: Enter your planned Entry, Stop Loss, and Risk Amount Set any TP1, TP2, or TP3 prices (set TP to 0 if not used) The system will auto-compute your ideal lot size and show estimated profits 🔧 Parameters: Risk Amount ($) – how much you’re willing to lose Entry Price – your trade entry Stop Loss Price – your SL level Take Profit 1/2/3 – optional TP targets Pip Value – profit/loss per point for 1 standard lot 📱 Mobile Mode – compact the table for small screens 🔐 Notes: No trades are executed – this is a risk planning tool only Designed for all markets (forex, gold, indices, crypto) TP profits are equally split (e.g. 2 TP = 50% / 50%)
Pine Script® göstergesi
danwang94 tarafından
RISK MANAGEMENT CALCULATOR📊 RISK MANAGEMENT CALCULATOR – Lot Size, Profit & R:R Tool This script is designed to help traders instantly calculate lot size, expected profit, and risk/reward ratio based on their trade plan. ✅ Features: Input your Risk Amount ($), Entry, Stop Loss, and up to 3 Take Profits Calculates: ✅ Lot Size based on risk ✅ Split profits per TP level (equally weighted) ✅ Total Profit & Risk/Reward (R:R) Displays everything in a clean bottom-right table Optimized for both: 🖥️ Desktop mode (larger layout) 📱 Mobile mode (toggle compact view) 💡 How to Use: Enter your planned Entry, Stop Loss, and Risk Amount Set any TP1, TP2, or TP3 prices (set TP to 0 if not used) The system will auto-compute your ideal lot size and show estimated profits 🔧 Parameters: Risk Amount ($) – how much you’re willing to lose Entry Price – your trade entry Stop Loss Price – your SL level Take Profit 1/2/3 – optional TP targets Pip Value – profit/loss per point for 1 standard lot 📱 Mobile Mode – compact the table for small screens 🔐 Notes: No trades are executed – this is a risk planning tool only Designed for all markets (forex, gold, indices, crypto) TP profits are equally split (e.g. 2 TP = 50% / 50%)
Pine Script® göstergesi
danwang94 tarafından
ATR % Line from LoD/HoDATR % Line Trading Indicator - Entry Filter Tool This Pine Script creates a sophisticated ATR (Average True Range) percentage-based entry filter indicator for TradingView that helps traders avoid buying overextended stocks and identify optimal entry zones based on volatility. Core Functionality - Entry Discipline The script calculates a maximum entry threshold by taking a percentage of the Average True Range (ATR) and projecting it from the current day's low. This creates a dynamic "no-buy zone" that adapts to market volatility, helping traders avoid purchasing stocks that have already moved too far from their daily base. Key Calculation: Measures the ATR over a specified period (default: 14 bars) Takes a user-defined percentage of that ATR (default: 25%) Projects this distance from the day's low to establish a maximum entry threshold Entry Rule: Avoid buying when price exceeds this ATR% level from the daily low or high. Visual Features Entry Threshold Line: Draws a horizontal line at the calculated maximum entry level Line extends forward for clear visualization of the "no-buy zone" Red zones above this line indicate overextended conditions Fully customizable appearance with color, width, and style options Smart Entry Alerts: Optional labels show the ATR percentage threshold and exact price level Visual confirmation when stocks are trading in acceptable entry zones vs. extended areas Real-Time Monitoring Table: Displays current distance from daily low as ATR percentage Shows whether current price is in "safe entry zone" or "extended territory" Customizable display options for clean chart analysis Practical Applications for Entry Management Avoiding Extended Entries: Primary Use: Don't initiate long positions when price is more than X% ATR from the daily low Prevents buying stocks that have already made their daily move Reduces risk of buying at temporary tops within the trading session Entry Zone Identification: Price trading below the ATR% line = potential entry opportunity Price trading above the ATR% line = wait for pullback or skip the trade Combines volatility analysis with momentum discipline Risk Management Benefits: Improved Entry Timing: Enter closer to daily support levels Better Risk/Reward: Shorter distance to stop loss (daily low) Reduced Chasing: Systematic approach prevents FOMO-driven entries Volatility Awareness: Higher volatility stocks get wider acceptable entry ranges Configuration for Entry Filtering Key Settings for Entry Management: ATR Percentage: Set your maximum acceptable extension (15-30% common for day trading) Reference Point: Use "Low" to measure extension from daily base Line Style: Make highly visible to clearly see entry threshold Alert Integration: Visual confirmation of entry-friendly zones Typical Usage Scenarios: Conservative Entries: 15-20% ATR from daily low Moderate Extensions: 25-35% ATR for stronger momentum plays Aggressive Setups: 40%+ ATR for breakout situations (use with caution) Entry Strategy Integration Pre-Market Planning: Set ATR% threshold based on stock's typical volatility Identify key levels where entries become unfavorable Plan alternative entry strategies for extended stocks Intraday Execution: Monitor real-time ATR% extension from daily low Avoid new long positions when threshold is exceeded Wait for pullbacks to re-enter acceptable entry zones This tool transforms volatility analysis into practical entry discipline, helping traders maintain consistent entry standards and avoid the costly mistake of chasing overextended stocks. By respecting ATR-based extension limits, traders can improve their entry timing and overall trade profitability.
Pine Script® göstergesi
BigWaveChartist tarafından
2 Asset Optimal PortfolioThis script calculates and plots either the Sharpe Ratio or Sortino Ratio for a two-asset portfolio using historical price data, allowing users to analyse how different allocations affect portfolio performance over a specified lookback period. Features: Determine the weights of 2 assets and how they affect the the Sharpe or Sortino ratio. Adjust timeframe to suit your personal investment timeframe. User Inputs: 1. Asset 1 and Asset 2: Choose any two symbols to evaluate (default is BTCUSD for both). 2. Look Back Length: Number of past bars (days) to use for calculations (default is 365). 3. Source: Price source for returns (default is close). 4. Ratio: Select which ratio to plot — Sharpe or Sortino. 5. % of Asset 1: Portfolio weight (from 0 to 1) for Asset 1.
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QuantChook tarafından
🌊 Reinhart-Rogoff Financial Instability Index (RR-FII)Overview The Reinhart-Rogoff Financial Instability Index (RR-FII) is a multi-factor indicator that consolidates historical crisis patterns into a single risk score ranging from 0 to 100. Drawing from the extensive research in "This Time is Different: Eight Centuries of Financial Crises" by Carmen M. Reinhart and Kenneth S. Rogoff, the RR-FII translates nearly a millennium of crisis data into practical insights for financial markets. What It Does The RR-FII acts like a real-time financial weather forecast by tracking four key stress indicators that historically signal the build-up to major financial crises. Unlike traditional indicators based only on price, it takes a broader view, examining the global market's interconnected conditions to provide a holistic assessment of systemic risk. The Four Crisis Components - Capital Flow Stress (Default weight: 25%) - Data analyzed: Volatility (ATR) and price movements of the selected asset. - Detects abrupt volatility surges or sharp price falls, which often precede debt defaults due to sudden stops in capital inflow. - Commodity Cycle (Default weight: 20%) - Data analyzed: US crude oil prices (customizable). - Watches for significant declines from recent highs, since commodity price troughs often signal looming crises in emerging markets. - Currency Crisis (Default weight: 30%) - Data analyzed: US Dollar Index (DXY, customizable). - Flags if the currency depreciates by more than 15% in a year, aligning with historical criteria for currency crashes linked to defaults. - Banking Sector Health (Default weight: 25%) - Data analyzed: Performance of financial sector ETFs (e.g., XLF) relative to broad market benchmarks (SPY). - Monitors for underperformance in the financial sector, a strong indicator of broader financial instability. Risk Scale Interpretation - 0-20: Safe – Low systemic risk, normal conditions. - 20-40: Moderate – Some signs of stress, increased caution advised. - 40-60: Elevated – Multiple risk factors, consider adjusting positions. - 60-80: High – Significant probability of crisis, implement strong risk controls. - 80-100: Critical – Several crisis indicators active, exercise maximum caution. Visual Features - The main risk line changes color with increasing risk. - Background colors show different risk zones for quick reference. - Option to view individual component scores. - A real-time status table summarizes all component readings. - Crisis event markers appear when thresholds are breached. - Customizable alerts notify users of changing risk levels. How to Use - Apply as an overlay for broad risk management at the portfolio level. - Adjust position sizes inversely to the crisis index score. - Use high index readings as a warning to increase vigilance or reduce exposure. - Set up alerts for changes in risk levels. - Analyze using various timeframes; daily and weekly charts yield the best macro insights. Customizable Settings - Change the weighting of each crisis factor. - Switch commodity, currency, banking sector, and benchmark symbols for customized views or regional focus. - Adjust thresholds and visual settings to match individual risk preferences. Academic Foundation Rooted in rigorous analysis of 66 countries and 800 years of data, the RR-FII uses empirically validated relationships and thresholds to assess systemic risk. The indicator embodies key findings: financial crises often follow established patterns, different types of crises frequently coincide, and clear quantitative signals often precede major events. Best Practices - Use RR-FII as part of a comprehensive risk management strategy, not as a standalone trading signal. - Combine with fundamental analysis for complete market insight. - Monitor for differences between component readings and the overall index. - Favor higher timeframes for a broader macro view. - Adjust component importance to suit specific market interests. Important Disclaimers - RR-FII assesses risk using patterns from past crises but does not predict future events. - Historical performance is not a guarantee of future results. - Always employ proper risk management. - Consider this tool as one element in a broader analytical toolkit. - Even with high risk readings, markets may not react immediately. Technical Requirements - Compatible with Pine Script v6, suitable for all timeframes and symbols. - Pulls data automatically for USOIL, DXY, XLF, and SPY. - Operates without repainting, using only confirmed data. The RR-FII condenses centuries of financial crisis knowledge into a modern risk management tool, equipping investors and traders with a deeper understanding of when systemic risks are most pronounced.
Pine Script® göstergesi
ungroundedpilot tarafından
🧪 Yuri Garcia Smart Money Strategy FULL (Slope Divergence))📣 Yuri Garcia – Smart Money Strategy FULL This is my private Smart Money Concept strategy, designed for my family and community to learn, trade, and grow sustainably. 🔑 How it works: ✅ Volume Cluster Zones: Automatically detects areas where strong buyers or sellers concentrate, acting as dynamic S/R levels. ✅ HTF Institutional Zones (4H): Higher timeframe trend filter ensures you’re always trading in the direction of major flows. ✅ Wick Pullback Filter: Confirms price rejects the zone, catching smart money traps and reversals. ✅ Cumulative Delta (CVD): Confirms whether buyers or sellers are truly in control. ✅ Slope-Based Divergence: Optional hidden divergence between price & CVD to spot reversals others miss. ✅ ATR Dynamic SL/TP: Adapts stop loss and take profit to live volatility with adjustable risk/reward. 🧩 Visual Markers Explained: 🟦 Blue X: Price inside HTF zone 🟨 Yellow X: Price inside Volume Cluster zone 🟧 Orange Circle: Wick pullback detected 🟥 Red Square: CVD confirms order flow strength 🔼 Aqua Triangle Up: Bullish slope divergence 🔽 Purple Triangle Down: Bearish slope divergence 🟢 Green Triangle Up: Final Long Entry confirmed 🔴 Red Triangle Down: Final Short Entry confirmed ⚡ Who is this for? This strategy is best suited for traders who understand smart money concepts, order flow, and want an adaptive framework to trade major assets like BTC, Gold, SP500, NASDAQ, or FX pairs. 🔒 Important Use responsibly, backtest extensively, and combine with solid risk management. This is for educational purposes only. ✨ Credits Built with ❤️ by Yuri Garcia – dedicated to my family & community. ✅ How to use it 1️⃣ Add to chart 2️⃣ Adjust inputs for your asset & timeframe 3️⃣ Enable/disable slope divergence filter to match your style 4️⃣ Set your alerts with built-in conditions
Pine Script® stratejisi
YGD_CONSULTING_LLC tarafından
PnL_EMA_TRACK12_PRO_3.3_full_adjusted# Multi-Ticker Support Manage up to 12 tickers simultaneously. - For each symbol, input share quantities, entry prices, and two optional additional entry points (E2, E3) with their own shares and offset percentages. - Dynamic handling of inputs using arrays for easier maintenance and scalability. # Average Cost and PnL Calculation - Computes weighted average entry costs across all position parts (E1 and optionally E2 and E3). - Calculates real-time Profit & Loss (PnL) both in USD and percentage relative to the current price. - Color-coded values: green for profit, red for loss — for quick visual feedback. # Moving Averages as Benchmarks - Uses daily EMAs (10, 21, 65) and 15-minute SMA 200 as reference levels. - Calculates percentage deviations of these moving averages from the average entry price. - Calculates dollar differences based on the total shares held. # Chart Visualization - Draws a dashed yellow line for the average cost of each position. - Optionally draws two additional lines and labels for E2 (blue) and E3 (purple) if activated. - Lines extend to the right to emphasize current relevance. - Labels can be positioned left or right, with customizable horizontal offset. # Interactive Table in Chart - Positions the info table in any chosen corner or center of the chart (top/right/left/middle, etc.). - Displays symbol, PnL (dollar and percentage), and deviations to key EMAs and SMA. - Colors PnL values according to profit or loss for instant clarity. # User-Friendly Settings - Flexible font size options for both the table and labels. - Customizable colors for positive and negative values (default green/red). - Choice of label position and X-axis offset to fit your chart style.
Pine Script® göstergesi
RhyTar tarafından
Ticker Industry and Competitor LookupThe Ticker Industry and Competitor Lookup is a comprehensive indicator that provides instant access to industry classification data and competitive intelligence for any ticker symbol. Built using the advanced SIC_TICKER_DATA library, this tool delivers professional-grade sector analysis with enterprise-level performance. It's a simple yet great tool for competitor research, sector studies, portfolio diversification, and investment decision-making. This indicator is a simple tool built on based on our SIC_TICKER_DATA library to demonstrate the use cases of the library. In this case, you enter a ticker and it displays the sector, SIC or Standard Industrial Classification which is a SEC identifier, and more importantly, the competitors that are listed to be in the exact same SIC by SEC. There isn't much to say about the indicator itself but we strongly recommend checking out the SIC_TICKER_DATA library we just published to learn more about the types of indicators you can build using it.
Pine Script® göstergesi
EdgeTerminal tarafından
Checklist Dashboard Table# Checklist Dashboard Table – ICT/SMC Trading Helper Overview The “Checklist Dashboard Table” is a TradingView indicator designed to help traders structure, organize, and validate their market analyses following the ICT/SMC (Inner Circle Trader / Smart Money Concepts) methodology. It provides a visual and interactive checklist directly on your chart, ensuring you never miss a crucial step in your decision-making process. Key Features - Visual Checklist : All your trading criteria are displayed as color-coded checkboxes (green for validated, red for not validated), making your analysis process both clear and efficient. - Clear Separation Between Analysis and Confirmations : - Analysis : Reminders for your routine, such as timeframe selection (M3 to H4), trend analysis via RSI, and identification of key zones (Midnight Open, SSL/BSL, Asian High/Low). - Confirmations : Six customizable criteria to check off as you validate your setup (clear trend, OB + FVG, OTE zone, Premium/Discount, R/R > 1:2, CBDR/Midnight). - Personal Notes Section : Keep your trade entries, observations, or comments in a dedicated field in the indicator’s settings. Your notes are displayed right in the checklist for quick reference and journaling. - Elegant and Compact Display : The table is styled for readability and can be positioned anywhere on your chart. - Quick Customization : Instantly update any criterion or your personal notes via the script settings. How to Use 1. Add the indicator to your chart. 2. Review the “Analysis” section as your pre-trade routine reminder. 3. Check off the “Confirmations” criteria as you validate your entry strategy. 4. Write your trade notes or comments in the provided notes section. 5. Use the checklist to reinforce discipline and repeatability in your trading. Why Use This Checklist? - Prevents you from skipping important steps in your analysis. - Reinforces trading discipline and consistency. - Allows you to document and review your trade decisions for ongoing improvement. Who Is It For? Perfect for ICT/SMC traders, but also valuable for anyone looking to organize and systematize their trading process. Happy trading!
Pine Script® göstergesi
Eamda tarafından
Dynamic SL/TP Levels (ATR or Fixed %)This indicator, "Dynamic SL/TP Levels (ATR or Fixed %)", is designed to help traders visualize potential stop loss (SL) and take profit (TP) levels for both long and short positions, refreshing dynamically on each new bar. It assumes entry at the current bar's close price and uses a fixed 1:2 risk-reward ratio (TP is twice the distance of SL in the profit direction). Levels are displayed in a compact table in the chart pane for easy reference, without cluttering the main chart with lines. Key Features: Calculation Modes: ATR-Based (Dynamic): SL distance is derived from the Average True Range (ATR) multiplied by a user-defined factor (default 1.5x). This adapts to the asset's volatility, providing breathing room based on recent price movements. Fixed Percentage: SL is set as a direct percentage of the current close price (default 0.5%), offering consistent gaps regardless of volatility. Long and Short Support: Calculates and shows SL/TP for longs (SL below close, TP above) and shorts (SL above close, TP below), with toggles to hide/show each. Real-Time Updates: Levels recalculate every bar, making them readily available for entry decisions in your trading system. Display: Outputs to a table in the top-right pane, showing precise values formatted to the asset's tick size (e.g., full decimal places for crypto). How to Use: Add the indicator to your chart via TradingView's Pine Editor or library. Adjust settings: Toggle "Use ATR?" on/off to switch modes. Set "ATR Length" (default 14) and "ATR Multiplier for SL" for dynamic mode. Set "Fixed SL %" for percentage mode. Enable/disable "Show Long Levels" or "Show Short Levels" as needed. Interpret the table: Use the displayed SL/TP values when your strategy signals an entry. For risk management, combine with position sizing (e.g., risk 1% of account per trade based on SL distance). Example: On a volatile asset like BTC, ATR mode might set a wider SL for realism; on stable pairs, fixed % ensures predictability. This tool promotes disciplined trading by tying levels to price action or fixed rules, but it's not financial advice—always backtest and use with your full strategy. Feedback welcome!
Pine Script® göstergesi
MonkeyPhone tarafından
TrendZonesTrendZones This is an indicator which I use, have tested, tweaked and added features to for use in my trend following investing system. I got the idea for it when for some reason I was looking for a dynamic reference to measure the height of a channel or something. In search of this I made MA’s of the high and low borders of a Donchian channel which turned out to be two near parallel and stunningly smooth curves. This visual was so appealing that I immediately tried to turn it into a replacement for the KeltCOG which I previously used in my system. First I created a curve in the middle of the upper and lower curves, which I called COG (Center Of Gravity). Then I decided to enter only one lookback and let the script create a Donchian channel with half the lookback and use this to create the curves with an MA of whole lookback. For this reason the minimum lookback is set to 14, enough room for the Donchian Channel of 7 periods. This Donchian ChanneI has a special way of calculating the borders, involving a 5 period Median value. Thanks to this these borders are really a resistance and support level, which won’t change at a whim, e.g. when a ‘dead cat bounce’ occurs. I prevented the Donchian channel to show itself between the curves and only pop out from behind these. These pop outs now function as “strong trend zones”. I gave it colors (blue:-strong up, green: moderate up, orange: moderate down, red: strong down, near COG: gray, curves horizontal: gray) and it looked very appealing. I tested it in different time frames. In some weekend, when I was bored, I observed for a few hours the minute chart of bitcoin. It turned out that you can reliably tell that an uptrend ends when the candles go under the COG beginning a downtrend. Uptrend starts again once the candles go above COG. As Trends on minute charts only last around half an hour, this entertainment made the potential of this indicator very clear to me in just one afternoon. Risk Management, Safe Level and Logical Stops. In the inputs are settings for “Risk Tolerance”, and to activate “Show Logical Stop Level” (activated in example chart) and “Show Safe Level”. As a rule of thump a trade should not expose the invested capital to a risk of losing more than 2 percent. I divided my investment capital in ten equal parts which are allocated to ten different stocks or other instruments or kept liquid. This means that when a position is closed by triggering a Stop with a loss of 20 percent, the invested capital suffers only 2 percent (20% x 10% = 2%). This is why the value for “Risk Tolerance” has a default of 20. Because I put my Stops on the lower curve, a “Safe Level” can be calculated such that when you buy for a price below or at this level, the stop will protect the position sufficiently. Because I only buy when the instrument is in uptrend, the buying price should be between COG and Safe Level. Although I never do that, putting the stop at other curves is feasible and when you want to widen the stop (I never lower my stops btw) in a downtrend situation, even 1 ATR below the “Low Border”. I call these “Logical Stop Levels”, marked with dark green circles on the lower curve when safe buying by placing the Stoploss on this curve is possible, gray circles on the other curves, on the Upper Curve navy when price enters very profitable level. In a downtrend situation maroon circles appear. Target lines When I open a position I always set a Stoploss and a Target, for this purpose two types of Target values can be set and corresponding Target lines activated. These lines are drawn above the “High Border” at the set distance. If one expects some price to be used, differences will occur. Other Features Support Zone, this is 1 ATR below the “Low Border”, the maroon circles of the “Logal Stops” are placed on this “Support level”. Stop distance and Channel Width. (activated in example chart) These are reported in a two cell table in the right lower corner of the main panel. I created this because I want to be able to check the volatility, whether the channel shows a situation in which safe buying in most levels of the channel is possible or what risk you take when you buy now and set the Stop at the nearest logical level (which is not always the “Lower curve”). This feature comes in handy for creating a setup I propose in the “Day Trading Fantasy” below. Some General and User Settings. I never activate this, perhaps you will. Use Of TrendZones In My System. Create a list of stocks in uptrend. I define ‘stock in uptrend’ as in uptrend zone in all three monthly, weekly and daily charts, all three should at the same time be in uptrend. The advantage of TrendZones is that you can immediately see in which zone the candle moves. Opening a position in a stock from the above list. I do this only when in both the daily and weekly the green dot on the lower curve indicates a buying opportunity. This is usually not the case in most of the items of the list, this feature thus provides a good timing for opening a position. Sometimes you need to wait a few weeks for this to happen. Setting a target over a position. For this I use the Target percent line of the weekly chart with the default value of 10. Updating the Stoploss and Target values. Every week or two weeks I set these to the new values of the “Lower Curve” and the Target line of the weekly. Attention: never shift down Stops, only up or let them stay the same when the curve moves down. I never use Stop levels on other curves. I Check the charts whenever I like to do this. Close the position when the uptrend obviously shifts down. Otherwise I let the profits run until the Target triggers which closes the position with some profit. For selecting stocks an checking charts for volume events, I also use a subpanel indicator called “TZanalyser”, which borrows the visual of my “Fibonacci Zone Oscillator”, is based on TrendZones and includes code from my REVE indicators. I intend to publish that as well. Day Trading Fantasy. Day trading is an attempt to earn a dime by opening a position in the morning and close it during the day again with a profit (or a loss). Before the market closes, you close all day trading positions. In my fantasy the “Logical Stop Level” is repurposed for use as entry point and the ATR-based Target line is used to provide a target setting in an intraday chart, like e.g. 15 minute. To do this the “Safe Level” should be limited to between Channel width and COG. This can be done by showing “Safe Level” and “Channel Width” and then set “Risk Tolerance” to around the shown Channel Width. In this setting you can then wait for the green circle to show up for entering your trade and protect it with the stop. I don’t know if this works fine or if it’s better than other day trade systems, because I don’t do day trading. Take care and have fun.
Pine Script® göstergesi
eykpunter tarafından
21