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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Internals Elite NYSE [Beta]Overview:
This indicator is designed to provide traders with a quick overview of key market internals and metrics in a single, easy-to-read table displayed directly on the chart. It incorporates a variety of metrics that help gauge market sentiment, momentum, and overall market conditions.
The table dynamically updates in real-time and uses color-coding to highlight significant changes or thresholds, allowing traders to quickly interpret the data and make informed trading decisions.
Features:
Market Internals:
TICK: Measures the difference between the number of stocks ticking up versus those ticking down on the NYSE. Green or red background indicates if it crosses a user-defined threshold.
Advance/Decline (ADD): Shows the net number of advancing versus declining stocks on the NYSE. Color-coded to show positive, negative, or neutral conditions.
Volatility Metrics:
VIX Change (%): Displays the percentage change in the Volatility Index (VIX), a key gauge of market fear or complacency. Color-coded for direction.
VIX Price: Displays the current VIX price with thresholds to indicate low, medium, or high volatility.
Other Market Metrics:
DXY Change (%): Percentage change in the US Dollar Index (DXY), indicating dollar strength or weakness.
VWAP Deviation (%): Percentage of stocks above VWAP (Volume Weighted Average Price), helping traders assess intraday buying and selling pressure.
Asset-Specific Metrics:
BTCUSD Change (%): Percentage change in Bitcoin (BTC) price, useful for monitoring cryptocurrency sentiment.
SPY Change (%): Percentage change in the S&P 500 ETF (SPY), a proxy for the overall stock market.
Current Ticker Change (%): Percentage change in the currently selected ticker on the chart.
US10Y Change (%): Percentage change in the yield of the 10-Year US Treasury Note (TVC:US10Y), an important macroeconomic indicator.
Customizable Appearance:
Adjustable text size to suit your chart layout.
User-defined thresholds for key metrics (e.g., TICK, ADD, VWAP, VIX).
Dynamic Table Placement:
You can position the table anywhere on the chart: top-right, top-left, bottom-right, bottom-left, middle-right, or middle-left.
How to Use:
Add the Indicator to Your Chart:
Apply the indicator to your chart from the Pine Script editor in TradingView.
Customize the Inputs:
Adjust the thresholds for TICK, ADD, VWAP, and VIX according to your trading style.
Enable or disable the metrics you want to see in the table by toggling the display options for each metric (e.g., Show TICK, Show BTC, Show SPY).
Set the table placement to your preferred position on the chart.
Interpret the Table:
Look for color-coded cells to quickly identify significant changes or breaches of thresholds.
Positive values are typically shown in green, negative values in red, and neutral/insignificant changes in gray.
Use metrics like TICK and ADD to gauge market breadth and momentum.
Refer to VWAP deviation to assess intraday buying or selling pressure.
Monitor the VIX and US10Y changes to stay aware of macroeconomic and volatility shifts.
Incorporate Into Your Strategy:
Use the indicator alongside technical analysis to confirm setups or identify areas of caution.
Keep an eye on correlated metrics (e.g., VIX and SPY) for broader market context.
Use BTCUSD or DXY as additional indicators of risk-on/risk-off sentiment.
Ideal Users:
Day Traders: Quickly gauge intraday market conditions and momentum.
Swing Traders: Identify broader sentiment shifts using metrics like ADD, DXY, and US10Y.
Macro Investors: Stay updated on key macroeconomic indicators like the 10-Year Treasury yield (US10Y) and the US Dollar Index (DXY).
This indicator serves as a comprehensive tool for understanding market conditions at a glance, enabling traders to act decisively based on the latest data.
Advanced Economic Indicator by USCG_VetAdvanced Economic Indicator by USCG_Vet
tldr:
This comprehensive TradingView indicator combines multiple economic and financial metrics into a single, customizable composite index. By integrating key indicators such as the yield spread, commodity ratios, stock indices, and the Federal Reserve's QE/QT activities, it provides a holistic view of the economic landscape. Users can adjust the components and their weights to tailor the indicator to their analysis, aiding in forecasting economic conditions and market trends.
Detailed Description
Overview
The Advanced Economic Indicator is designed to provide traders and investors with a powerful tool to assess the overall economic environment. By aggregating a diverse set of economic indicators and financial market data into a single composite index, it helps identify potential turning points in the economy and financial markets.
Key Features:
Comprehensive Coverage: Includes 14 critical economic and financial indicators.
Customizable Components: Users can select which indicators to include.
Adjustable Weights: Assign weights to each component based on perceived significance.
Visual Signals: Clear plotting with threshold lines and background highlights.
Alerts: Set up alerts for when the composite index crosses user-defined thresholds.
Included Indicators
Yield Spread (10-Year Treasury Yield minus 3-Month Treasury Yield)
Copper/Gold Ratio
High Yield Spread (HYG/IEF Ratio)
Stock Market Performance (S&P 500 Index - SPX)
Bitcoin Performance (BLX)
Crude Oil Prices (CL1!)
Volatility Index (VIX)
U.S. Dollar Index (DXY)
Inflation Expectations (TIP ETF)
Consumer Confidence (XLY ETF)
Housing Market Index (XHB)
Manufacturing PMI (XLI ETF)
Unemployment Rate (Inverse SPY as Proxy)
Federal Reserve QE/QT Activities (Fed Balance Sheet - WALCL)
How to Use the Indicator
Configuring the Indicator:
Open Settings: Click on the gear icon (⚙️) next to the indicator's name.
Inputs Tab: You'll find a list of all components with checkboxes and weight inputs.
Including/Excluding Components
Checkboxes: Check or uncheck the box next to each component to include or exclude it from the composite index.
Default State: By default, all components are included.
Adjusting Component Weights:
Weight Inputs: Next to each component's checkbox is a weight input field.
Default Weights: Pre-assigned based on economic significance but fully adjustable.
Custom Weights: Enter your desired weight for each component to reflect your analysis.
Threshold Settings:
Bearish Threshold: Default is -1.0. Adjust to set the level below which the indicator signals potential economic downturns.
Bullish Threshold: Default is 1.0. Adjust to set the level above which the indicator signals potential economic upswings.
Setting the Timeframe:
Weekly Timeframe Recommended: Due to the inclusion of the Fed's balance sheet data (updated weekly), it's best to use this indicator on a weekly chart.
Changing Timeframe: Select 1W (weekly) from the timeframe options at the top of the chart.
Interpreting the Indicator:
Composite Index Line
Plot: The blue line represents the composite economic indicator.
Movement: Observe how the line moves relative to the threshold lines.
Threshold Lines
Zero Line (Gray Dotted): Indicates the neutral point.
Bearish Threshold (Red Dashed): Crossing below suggests potential economic weakness.
Bullish Threshold (Green Dashed): Crossing above suggests potential economic strength.
Background Highlights
Red Background: When the composite index is below the bearish threshold.
Green Background: When the composite index is above the bullish threshold.
No Color: When the composite index is between the thresholds.
Understanding the Components
1. Yield Spread
Description: The difference between the 10-year and 3-month U.S. Treasury yields.
Economic Significance: An inverted yield curve (negative spread) has historically preceded recessions.
2. Copper/Gold Ratio
Description: The price ratio of copper to gold.
Economic Significance: Copper is tied to industrial demand; gold is a safe-haven asset. The ratio indicates risk sentiment.
3. High Yield Spread (HYG/IEF Ratio)
Description: Ratio of high-yield corporate bonds (HYG) to intermediate-term Treasury bonds (IEF).
Economic Significance: Reflects investor appetite for risk; widening spreads can signal credit stress.
4. Stock Market Performance (SPX)
Description: S&P 500 Index levels.
Economic Significance: Broad measure of U.S. equity market performance.
5. Bitcoin Performance (BLX)
Description: Bitcoin Liquid Index price.
Economic Significance: Represents risk appetite in speculative assets.
6. Crude Oil Prices (CL1!)
Description: Front-month crude oil futures price.
Economic Significance: Influences inflation and consumer spending.
7. Volatility Index (VIX)
Description: Market's expectation of volatility (fear gauge).
Economic Significance: High VIX indicates market uncertainty; inverted in the indicator to align directionally.
8. U.S. Dollar Index (DXY)
Description: Value of the U.S. dollar relative to a basket of foreign currencies.
Economic Significance: Affects international trade and commodity prices; inverted in the indicator.
9. Inflation Expectations (TIP ETF)
Description: iShares TIPS Bond ETF prices.
Economic Significance: Reflects market expectations of inflation.
10. Consumer Confidence (XLY ETF)
Description: Consumer Discretionary Select Sector SPDR Fund prices.
Economic Significance: Proxy for consumer confidence and spending.
11. Housing Market Index (XHB)
Description: SPDR S&P Homebuilders ETF prices.
Economic Significance: Indicator of the housing market's health.
12. Manufacturing PMI (XLI ETF)
Description: Industrial Select Sector SPDR Fund prices.
Economic Significance: Proxy for manufacturing activity.
13. Unemployment Rate (Inverse SPY as Proxy)
Description: Inverse of the SPY ETF price.
Economic Significance: Represents unemployment trends; higher inverse SPY suggests higher unemployment.
14. Federal Reserve QE/QT Activities (Fed Balance Sheet - WALCL)
Description: Total assets held by the Federal Reserve.
Economic Significance: Indicates liquidity injections (QE) or withdrawals (QT); impacts interest rates and asset prices.
Customization and Advanced Usage
Adjusting Weights:
Purpose: Emphasize components you believe are more predictive or relevant.
Method: Increase or decrease the weight value next to each component.
Example: If you think the yield spread is particularly important, you might assign it a higher weight.
Disclaimer
This indicator is for educational and informational purposes only. It is not financial advice. Trading and investing involve risks, including possible loss of principal. Always conduct your own analysis and consult with a professional financial advisor before making investment decisions.
BetaBeta , also known as the Beta coefficient, is a measure that compares the volatility of an individual underlying or portfolio to the volatility of the entire market, typically represented by a market index like the S&P 500 or an investible product such as the SPY ETF (SPDR S&P 500 ETF Trust). A Beta value provides insight into how an asset's returns are expected to respond to market swings.
Interpretation of Beta Values
Beta = 1: The asset's volatility is in line with the market. If the market rises or falls, the asset is expected to move correspondingly.
Beta > 1: The asset is more volatile than the market. If the market rises or falls, the asset's price is expected to rise or fall more significantly.
Beta < 1 but > 0: The asset is less volatile than the market. It still moves in the same direction as the market but with less magnitude.
Beta = 0: The asset's returns are not correlated with the market's returns.
Beta < 0: The asset moves in the opposite direction to the market.
Example
A beta of 1.20 relative to the S&P 500 Index or SPY implies that if the S&P's return increases by 1%, the portfolio is expected to increase by 12.0%.
A beta of -0.10 relative to the S&P 500 Index or SPY implies that if the S&P's return increases by 1%, the portfolio is expected to decrease by 0.1%. In practical terms, this implies that the portfolio is expected to be predominantly 'market neutral' .
Calculation & Default Values
The Beta of an asset is calculated by dividing the covariance of the asset's returns with the market's returns by the variance of the market's returns over a certain period (standard period: 1 years, 250 trading days). Hint: It's noteworthy to mention that Beta can also be derived through linear regression analysis, although this technique is not employed in this Beta Indicator.
Formula: Beta = Covariance(Asset Returns, Market Returns) / Variance(Market Returns)
Reference Market: Essentially any reference market index or product can be used. The default reference is the SPY (SPDR S&P 500 ETF Trust), primarily due to its investable nature and broad representation of the market. However, it's crucial to note that Beta can also be calculated by comparing specific underlyings, such as two different stocks or commodities, instead of comparing an asset to the broader market. This flexibility allows for a more tailored analysis of volatility and correlation, depending on the user's specific trading or investment focus.
Look-back Period: The standard look-back period is typically 1-5 years (250-1250 trading days), but this can be adjusted based on the user's preference and the specifics of the trading strategy. For robust estimations, use at least 250 trading days.
Option Delta: An optional feature in the Beta Indicator is the ability to select a specific Delta value if options are written on the underlying asset with Deltas less than 1, providing an estimation of the beta-weighted delta of the position. It involves multiplying the beta of the underlying asset by the delta of the option. This addition allows for a more precise assessment of the underlying asset's correspondence with the overall market in case you are an options trader. The default Delta value is set to 1, representing scenarios where no options on the underlying asset are being analyzed. This default setting aligns with analyzing the direct relationship between the asset itself and the market, without the layer of complexity introduced by options.
Calculation: Simple or Log Returns: In the calculation of Beta, users have the option to choose between using simple returns or log returns for both the asset and the market. The default setting is 'Simple Returns'.
Advantages of Using Beta
Risk Management: Beta provides a clear metric for understanding and managing the risk of a portfolio in relation to market movements.
Portfolio Diversification: By knowing the beta of various assets, investors can create a balanced portfolio that aligns with their risk tolerance and investment goals.
Performance Benchmarking: Beta allows investors to compare an asset's risk-adjusted performance against the market or other benchmarks.
Beta-Weighted Deltas for Options Traders
For options traders, understanding the beta-weighted delta is crucial. It involves multiplying the beta of the underlying asset by the delta of the option. This provides a more nuanced view of the option's risk relative to the overall market. However, it's important to note that the delta of an option is dynamic, changing with the asset's price, time to expiration, and other factors.
RedK Relative Strength Ribbon: RS Ribbon and RS ChartsRedK Relative Strength Ribbon (RedK RS_Ribbon) is TA tool that plots the Relative Strength of the current chart symbol against another symbol, or an index of choice. It enables us to see when a stock is gaining strength (or weakness) relative to (an index that represents) the market, and when it hits new highs or lows of that relative strength, which may lead to better trading decisions.
I searched TV for existing RS indicators but didn't find what I really wanted, so I put this together and added some additional features for my own use. It started as a simple RS line with new x-weeks Hi/Lo markers, then evolved into what you see here in v1.0 with the ability to plot a full RS chart in regular or HA candle types. Hope this will be useful to some other growth traders here on TV.
What is Relative Strength (RS)
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(RS is a comprehensive concept in TA, below is a quick summary - please research further if it's not already a familiar topic)
Relative Strength (RS) is a technical concept / indicator used mainly by growth / swing / momentum traders to compare the performance of one security or asset against another. RS measures the price performance of a specific security relative to a benchmark, such as an index or another asset. It's not to be confused with the famous Relative Strength Index (RSI) technical indicator
For example, In the context of comparing a stock's relative strength to the SPY (S&P 500) index, the relative strength calculation involves dividing the stock's price or price-related value (e.g., close price) by the corresponding value of the SPY index. The resulting ratio (and its trend over time) indicates the relative performance of the stock compared to the index.
Traders and investors use relative strength analysis to identify securities that have been showing relative strength or weakness compared to a benchmark, which can help in making investment decisions or identifying the "market leaders" and potential trading opportunities.
There are so many books and documentation about the RS concept and its importance to identify market leaders, especially when recovering from a bear market - if you're interested in the concept, please search more about it and review some of that literature. There's also a more detailed definition of Relative Strength in this article on Invstopedia
RedK RS_Ribbon features and options
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The indicator settings provide many options and features - see the settings box below
- Change / choose base symbol
The default is to use SPY as the base symbol - so we're comparing the chart's symbol to a proxy of the S&P 500 - Some traders may prefer to use the QQQ - or other index or ETF that acts as a proxy for the industry / sector / market they are trading
- RS Calculation / RS line
we use the simple form of the RS calculation,
RS = closing price of current chart symbol / closing price of the base symbol (default is SPY) * 100
some RS documentation will use the Rate of Change (RoC) - but that's not what we're using here.
- The RS_Ribbon
* Once the RS line is plotted, it made sense to add couple of moving averages to it, to make it easier to observe the trend of the RS and the changes in that trend as you can see in the sample chart on top.
* The RS_Ribbon is made up of a fast and slow moving averages and will change color (green / red) based on detected trend RS direction - the 2 MA types and lengths can be changed until you get the setup that provides the best view for you of the RS trend over time. My preferred settings are used as defaults here.
- Identifying New (x)Week Hi/Lo RS Values
* Most traders would be interested when the calculated RS hits a new 52-week high or low value.
* There are cases where we may want to see when a new RS Hi/Lo has been hit for a different period - for example, a quarter (13 weeks)
* the number of weeks can be changed as well as adjusting the numbers of trading days per week (if needed for certain symbols/exchanges)
- Working with Different Timeframes
* Now these "markers" will only be available in the daily and weekly timeframes and there is a good reason for that, it's not the fact that i'm lazy :) and that enabling this in timeframes lower than 1D would have been some heavy lifting, but the reality is that with RS, we're really interested if a "day's close" hits a new RS high or low value against the moving window of x weeks (and the weeks close also) - if you think of this more, at lower TF, RS can hit a lower value that never end up registering on the daily closing and that causes a lot of visual confusion. So i took the "cleaner way out" of that issue.
* note that you can choose a different timeframe for the RS_Ribbon than the chart - if you do, please make sure the chart is at a lower timeframe than the indicator's - (and in that case remember to hide the candles because they won't make much sense)
i wanted to leverage TV's built-in multi-Timeframe (MTF) support with the caveat that using the indicator at lower TF with a chart at a higher TF (example chart at 1Wk and indicator at 1D) will show inaccurate results. If this sounds confusing, keep the indicator TF same as the chart.
the example here shows a 2-Hr chart against 1D RS_Ribbon
- Using RS Charts and RS Candles
* Beside the ability to plot the RS "closing" value with the RS line, the indicator provides the ability to show a "full" RS Chart with candles that represent the relative values of open, high, low. and close against the base symbol.
* the RS Charts can be used for regular chart analysis, for example, we can identify common chart patterns like Cup & Handle, VCP, Head & Shoulder..etc using these charts .. which can provide some edge over the price charts
* for the Heikin Ashi fans, I added the ability to choose classic or HA candles for the chart. note you have to enable the option to show the RS candles first before you choose the option to switch to HA.
The chart below shows a side-by-side comparison on the 2 RS chart types
Closing remarks
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* RS is a good way to identify market/sector leaders (who will usually recover from a bear market before others) - and enable us to see the strength that comes from the broader makrket versus the one that comes from the stock's own performance and identify good trading opportunities
* I'll continue to update this work and alerts will come in next version - but wanted to check initial reaction and value
* as usual, if you decide to use this in your chart analysis, it's necessary to combine with other momentum, trend, ...etc indicators and do not make trading decision only based on the signales from a single indicator
Modern Portfolio Management IndicatorAfter weeks of grueling over this indicator, I am excited to be releasing it!
Intro:
This is not a sexy, technical or math based indicator that will give you buy and sell signals or anything fancy, but it is an indicator that I created in hopes to bridge a gap I have noticed. That gap is the lack of indicators and technical resources for those who also like to plan their investments. This indicator is tailored to those who are either established investors and to those who are looking to get into investing but don't really know where to start.
The premise of this indicator is based on Modern Portfolio Theory (MPT). Before we get into the indicator itself, I think its important to provide a quick synopsis of MPT.
About MPT:
Modern Portfolio Theory (MPT) is an investment framework that was developed by Harry Markowitz in the 1950s. It is based on the idea that an investor can optimize their investment portfolio by considering the trade-off between risk and return. MPT emphasizes diversification and holds that the risk of an individual asset should be assessed in the context of its contribution to the overall portfolio's risk. The theory suggests that by diversifying investments across different asset classes with varying levels of risk, an investor can achieve a more efficient portfolio that maximizes returns for a given level of risk or minimizes risk for a desired level of return. MPT also introduced the concept of the efficient frontier, which represents the set of portfolios that offer the highest expected return for a given level of risk. MPT has been widely adopted and used by investors, financial advisors, and portfolio managers to construct and manage portfolios.
So how does this indicator help with MPT?
The thinking and theory that went behind this indicator was this: I wanted an indicator, or really just a "way" to test and back-test ticker performance over time and under various circumstances and help manage risk.
Over the last 3 years we have seen a massive bull market, followed by a pretty huge bear market, followed by a very unexpected bull market. We have been and continue to be plagued with economic and political uncertainty that seems to constantly be looming over everyone with each waking day. Some people have liquidated their retirement investments, while others are fomoing in to catch this current bull run. But which tickers are sound and how tickers and funds have compared amongst each other remains somewhat difficult to ascertain, absent manually reviewing and calculating each ticker individually.
That is where this indicator comes in. This indicator permits the user to define up to 5 equities that they are potentially interested in investing in, or are already invested in. The user can then select a specific period in time, say from the beginning of 2022 till now. The user can then define how much they want to invest in each company by number of shares, so if they want to buy 1 share a week, or 2 shares a month, they can input these variables into the indicator to draw conclusions. As many brokers are also now permitting fractional share trading, this ability is also integrated into the indicator. So for shares, you can put in, say, 0.25 shares of SPY and the indicator will accept this and account for this fractional share.
The indicator will then show you a portfolio summary of what your earnings and returns would be for the defined period. It will provide a percent return as well as the projected P&L based on your desired investment amount and frequency.
But it goes beyond just that, you can also have the indicator display a simple forecasting projection of the portfolio. It will show the projected P&L and % Return over various periods in time on each of the ticker (see image below):
The indicator will also break down your portfolio allocation, it will show where the majority of your holdings are and where the majority of your P&L in coming from (best performers will show a green fill and worst will show a red fill, see image below):
This colour coding also extends to the portfolio breakdown itself.
Dollar cost averaging (DCA) is incorporated into the indicator itself, by assuming ongoing contributions. If you want to stop contributions at a certain point, you just select your end time for contributions at the point in which you would stop contributing.
The indicator also provides some basic fundamental information about the company tickers (if applicable). Simply select the "Fundamental" chart and it will display a breakdown of the fundamentals, including dividends paid, market cap and earnings yield:
The indicator also provides a correlation assessment of each holding against each other holding. This emphasizes the profound role of diversification on portfolios. The less correlation you have in your portfolio among your holdings, the better diversified you are. As well, if you have holdings that are perfectly inverse other holdings, you have a pseudo hedge against the downturn of one of your holdings. This is even more helpful if the inverse is a company with solid fundamentals.
In the below example you will see NASDAQ:IRDM in the portfolio. You will be able to see that NASDAQ:IRDM has a slight inverse relationship to SPY:
Yet IRDM has solid fundamentals and is performing well fundamentally. Thus, this makes IRDIM a solid addition to your portfolio as it can potentially hedge against a downturn for SPY and is less risky than simply holding an inverse leveraged share on SPY which is most likely just going to cost you money than make you money.
Concluding remarks:
There are many fun and interesting things you can do with this indicator and I encourage you to try it out and have fun with it! The overall objective with the indicator is to help you plan for your portfolio and not necessarily to manage your portfolio. If you have a few stocks you are looking at and contemplating investing in, this will help you run some theoretical scenarios with this stock based on historical performance and also help give you a feel of how it will perform in the future based on past behaviour.
It is important to remember that past behaviour does not indicate future behaviour, but the indicator provides you with tools to get a feel for how a stock has performed under various circumstances and get a general feel of the fundamentals of the company you could potentially be investing in.
Please note, this indicator is not meant to replace full, fundamental analyses of individual companies. It is simply meant to give you a "gist" of how companies are fundamentally and how they have performed historically.
I hope you enjoy it!
Safe trades everyone!
Trend Correlation HeatmapHello everyone!
I am excited to release my trend correlation heatmap, or trend heatmap for short.
Per usual, I think its important to explain the theory before we get into the use of the indicator, so let's get into the theory!
The theory:
So what is a correlation?
Correlation is the relationship one variable has to another. Correlations are the basis of everything I do as a quantitative trader. From the correlation between the same variables (i.e. autocorrelation), the correlation between other variables (i.e. VIX and SPY, SPY High and SPY Low, DXY and ES1! close, etc.) and, as well, the correlation between price and time (time series correlation).
This may sound very familiar to you, especially if you are a user, observer or follower of my ideas and/or indicators. Ninety-five percent of my indicators are a function of one of those three things. Whether it be a time series based indicator (i.e.my time series indicator), whether it be autocorrelation (my autoregressive cloud indicator or my autocorrelation oscillator) or whether it be regressive in nature (i.e. my SPY Volume weighted close, or even my expected move which uses averages in lieu of regressive approaches but is foundational in regression principles. Or even my VIX oscillator which relies on the premise of correlations between tickers.) So correlation is extremely important to me and while its true I am more of a regression trader than anything, I would argue that I am more of a correlation trader, because correlations are the backbone of how I develop math models of stocks.
What I am trying to stress here is the importance of correlations. They really truly are foundational to any type of quantitative analysis for stocks. And as such, understanding the current relationship a stock has to time is pivotal for any meaningful analysis to be conducted.
So what is correlation to time and what does it tell us?
Correlation to time, otherwise known and commonly referred to as "Time Series", is the relationship a ticker's price has to the passing of time. It is displayed in the traditional Pearson Correlation Coefficient or R value and can be any value from -1 (strong negative relationship, i.e. a strong downtrend) to + 1 (i.e. a strong positive relationship, i.e. a strong uptrend). The higher or lower the value the stronger the up or downtrend is.
As such, correlation to time tells us two very important things. These are:
a) The direction of the stock; and
b) The strength of the trend.
Let's take a look at an example:
Above we have a chart of QQQ. We can see a trendline that seems to fit well. The questions we ask as traders are:
1. What is the likelihood QQQ breaks down from this trendline?
2. What is the likelihood QQQ continues up?
3. What is the likelihood QQQ does a false breakdown?
There are numerous mathematical approaches we can take to answer these questions. For example, 1 and 2 can be answered by use of a Cumulative Distribution Density analysis (CDDA) or even a linear or loglinear regression analysis and 3 can be answered, more or less, with a linear regression analysis and standard error ascertainment, or even just a general comparison using a data science approach (such as cosine similarity or Manhattan distance).
But, the reality is, all 3 of these questions can be visualized, at least in some way, by simply looking at the correlation to time. Let's look at this chart again, this time with the correlation heatmap applied:
If we look at the indicator we can see some pivotal things. These are:
1. We have 4, very strong uptrends that span both higher AND lower timeframes. We have a strong uptrend of 0.96 on the 5 minute, 50 candle period. We have a strong uptrend at the 300 candle lookback period on the 1 minute, we have a strong uptrend on the 100 day lookback on the daily timeframe period and we have a strong uptrend on the 5 minute on the 500 candle lookback period.
2. By comparison, we have 3 downtrends, all of which have correlations less than the 4 uptrends. All of the downtrends have a correlation above -0.8 (which we would want lower than -0.8 to be very strong), and all of the uptrends are greater than + 0.80.
3. We can also see that the uptrends are not confined to the smaller timeframes. We have multiple uptrends on multiple timeframes and both short term (50 to 100 candles) and long term (up to 500 candles).
4. The overall trend is strengthening to the upside manifested by a positive Max Change and a Positive Min change (to be discussed later more in-depth).
With this, we can see that QQQ is actually very strong and likely will continue at least some upside. If we let this play out:
We continued up, had one test and then bounced.
Now, I want to specify, this indicator is not a panacea for all trading. And in relation to the 3 questions posed, they are best answered, at least quantitatively, not only by correlation but also by the aforementioned methods (CDDA, etc.) but correlation will help you get a feel for the strength or weakness present with a stock.
What are some tangible applications of the indicator?
For me, this indicator is used in many ways. Let me outline some ways I generally apply this indicator in my day and swing trading:
1. Gauging the strength of the stock: The indictor tells you the most prevalent behavior of the stock. Are there more downtrends than uptrends present? Are the downtrends present on the larger timeframes vs uptrends on the shorter indicating a possible bullish reversal? or vice versa? Are the trends strengthening or weakening? All of these things can be visualized with the indicator.
2. Setting parameters for other indicators: If you trade EMAs or SMAs, you may have a "one size fits all" approach. However, its actually better to adjust your EMA or SMA length to the actual trend itself. Take a look at this:
This is QQQ on the 1 hour with the 200 EMA with 200 standard deviation bands added. If we look at the heatmap, we can see, yes indeed 200 has a fairly strong uptrend correlation of 0.70. But the strongest hourly uptrend is actually at 400 candles, with a correlation of 0.91. So what happens if we change the EMA length and standard deviation to 400? This:
The exact areas are circled and colour coded. You can see, the 400 offers more of a better reference point of supports and resistances as well as a better overall trend fit. And this is why I never advocate for getting married to a specific EMA. If you are an EMA 200 lover or 21 or 51, know that these are not always the best depending on the trend and situation.
Components of the indicator:
Ah okay, now for the boring stuff. Let's go over the functionality of the indicator. I tried to keep it simple, so it is pretty straight forward. If we open the menu here are our options:
We have the ability to toggle whichever timeframes we want. We also have the ability to toggle on or off the legend that displays the colour codes and the Max and Min highest change.
Max and Min highest change: The max and min highest change simply display the change in correlation over the previous 14 candles. An increasing Max change means that the Max trend is strengthening. If we see an increasing Max change and an increasing Min change (the Min correlation is moving up), this means the stock is bullish. Why? Because the min (i.e. ideally a big negative number) is going up closer to the positives. Therefore, the downtrend is weakening.
If we see both the Max and Min declining (red), that means the uptrend is weakening and downtrend is strengthening. Here are some examples:
Final Thoughts:
And that is the indicator and the theory behind the indicator.
In a nutshell, to summarize, the indicator simply tracks the correlation of a ticker to time on multiple timeframes. This will allow you to make judgements about strength, sentiment and also help you adjust which tools and timeframes you are using to perform your analyses.
As well, to make the indicator more user friendly, I tried to make the colours distinctively different. I was going to do different shades but it was a little difficult to visualize. As such, I have included a toggle-able legend with a breakdown of the colour codes!
That's it my friends, I hope you find it useful!
Safe trades and leave your questions, comments and feedback below!
Accelerating Dual Momentum ScoreThis is a score metric used by the Accelerating Dual Momentum strategy.
According to the website you referenced when you created, the strategy is as follows:
Strategy Rules
This strategy allocates 100% of of the portfolio to one asset each month.
1. On the last trading day of each month, calculate the “momentum score” for the S&P 500 ( SPY ) and the international small cap equities (SCZ). The momentum score is the average of the 1, 3, and 6-month total return for each asset.
2. If the momentum score of SCZ > SPY and is greater than 0, invest in SCZ.
3. If the momentum score of SPY > SCZ and is greater than 0, invest in SPY .
4. If neither momentum score is greater than 0, calculate the 1-month total return for long-term US Treasuries ( TLT ) and US TIPS (TIP). Invest in whichever has the higher return.
Source: portfoliodb.co
Volatility/Volume ImpactWe often hear statements such as follow the big volume to project possible price movements. Or low volatility is good for trend. How much of it is statistically right for different markets. I wrote this small script to study the impact of Volatility and Volume on price movements.
Concept is as below:
Compare volume with a reference median value. You can also use moving average or other types for this comparison.
If volume is higher than median, increment positive value impact with change in close price. If volume is less than median, then increment negative value impact with change in close price.
With this we derive pvd and nvd which are measure of price change when volume is higher and lower respectively. pvd measures the price change when volume is higher than median whereas nvd measures price change when volume is lower than median.
Calculate correlation of pvd and nvd with close price to see what is impacting the price by higher extent.
Colors are applied to plots which have higher correlation to price movement. For example, if pvd has higher correlation to price movement, then pvd is coloured green whereas nvd is coloured silver. Similarly if nvd has higher correlation to price then nvd is coloured in red whereas pvd is coloured in silver.
Similar calculation also applied for volatility.
With this, you can observe how price change is correlated to high/low volume and volatility.
Let us see some examples on different markets.
Example 1: AMEX:SPY
From the chart snapshot below, it looks evident that SPY always thrive when there is low volatility and LOW VOLUME!!
Example 2: NASDAQ:TSLA
The picture will be different if you look at individual stocks. For Tesla, the price movement is more correlated to high volume (unlike SPY where low volume days define the trend)
Example 3: KUCOIN:BTCUSDT
Unlike stocks and indices, high volatility defined the trend for BTC for long time. It thrived when volatility is more. We can see that high volume is still major influencer in BTC price movements.
Settings are very simple and self explanatory.
Hint: You can also move the indicator to chart overlay for better visualisation of comparison with close price.
Linear Regression Relative Strength[image/x/iZvwDWEY/
Relative Strength indicator comparing the current symbol to SPY (or any other benchmark). It may help to pick the right assets to complement the portfolio build around core ETFs such as SPY.
The general idea is to show if the current symbol outperforms or underperforms the benchmark (SPY by default) when bought some certain time ago. Relative performance is displayed as percent and is calculated for three different time ranges - short (1 mo by default), mid (1 quarter), and long (half a year). To smooth the volatility, the script uses linear regression to estimate the trend and takes the start and the end points of the linear regression line to compute the relative strength.
It is important to remember that the script shows the gain relative to SPY (or other selected benchmark), not the asset's gain. Therefore, it may indicate that the asset is profitable, but it still may lose value if SPY is in downtrend.
Therefore, it is crucial to check other indicators before making a decision. In the example above, standard linear regression for one quarter is used to indicate the direction of the trend.
Drawdown RangeHello death eaters, presenting a unique script which can be used for fundamental analysis or mean reversion based trades.
Process of deriving this table is as below:
Find out ATH for given day
Calculate the drawdown from ATH for the day and drawdown percentage
Based on the drawdown percentage, increment the count of basket which is based on input iNumber of ranges . For example, if number of ranges is 5, then there will be 5 baskets. First basket will fit drawdown percentage 0-20% and each subsequent ones will accommodate next 20% range.
Repeat the process from start to last bar. Once done, table will plot how much percentage of days belong to which basket.
For example, from the below chart of NASDAQ:AAPL
We can deduce following,
Historically stock has traded within 1% drawdown from ATH for 6.59% of time. This is the max amount of time stock has stayed in specific range of drawdown from ATH.
Stock has traded at the drawdown range of 82-83% from ATH for 0.17% of time. This is the least amount of time the stock has stayed in specific range of drawdown from ATH.
At present, stock is trading 2-3% below ATH and this has happened for about 2.46% of total days in trade
Maximum drawdown the stock has suffered is 83%
Lets take another example of NASDAQ:TSLA
Stock is trading at 21-22% below ATH. But, historically the max drawdown range where stock has traded is within 0-1%. Now, if we make this range to show 20 divisions instead of 100, it will look something like this:
Table suggests that stock is trading about 20-25% below ATH - which is right. But, table also suggests that stock has spent most number of days within this drawdown range when we divide it by 20 baskets instad of 100. I would probably wait for price to break out of this range before going long or short. At present, it seems a stage ranging stage. I might think about selling PUTs or covered CALLs outside this range.
Similarly, if you look at AMEX:SPY , 36% of the time, price has stayed within 5% from ATH - makes it a compelling bull case!!
NYSE:BABA is trading at 50-55% below ATH - which is the most it has retraced so far. In general, it is used to be within 15-20% from ATH
NOW, Bit of explanation on input options.
Number of Ranges : Says how many baskets the drawdown map needs to be divided into.
Reference : You can take ATH as reference or chose a time window between which the highest need to be considered for drawdown. This can be useful for megacaps which has gone beyond initial phase of uncertainity. There is no point looking at 80% drawdown AAPL had during 1990s. More approriate to look at it post 2000s where it started making higher impact and growth.
Cumulative Percentage : When this is unchecked, percentage division shows 0-nth percentage instad of percentage ranges. For example this is how it looks on SPY:
We can see that SPY has remained within 6% from ATH for more than 50% of the time.
Hope this is helpful. Happy trading :)
PS: this can be used in conjunction with Drawdown-Price-vs-Fundamentals to pick value stocks at discounted price while also keeping an eye on range tendencies of it.
Thanks to @mattX5 for the ideas and discussion today :)
Altered True Strength Indicator (TSI) Reupload-
Altered TSI provides a slightly more volatile signal that demonstrates extremities in price action with greater success than standard TSI. In addition, I added bull/bear cross indicators (green/red) to make it easier to notice the crosses to save time when the market is moving fast (I couldn't find a regular TSI script with this addition). Finally, the signal also has overextension parameters (red and green lines)
I think this is best used on Intraday time frames as the signals respond to volatility very well and using Heikin Ashi candles, trend is more visual. In this particular example, I am showing SPY on the 3m time chart (my favorite short time frame) and the signal alone provided many opportunities for trades when using simple divergences and countering overextension direction when short term (blue) signal crosses either
In the first example (purple lines), SPY ramps but it was a dull signal given the signal strength flatlining- we would be looking for a short entry. When the signal fires, it provides a clean $1.50 move down in spy.
In the second example (orange), the blue signal provides a nice V shape (rebound signal) in which we are looking for a long entry. 390.50 is a strong SPY support in confluence with 2nd std dev VWAP extension, but disregarding that bull signal fires resulting in a 2 dollar move upwards. Exit is provided when blue line crosses green overextension.
In the third example (white), we are searching for a short entry at 392.5 resistance in confluence with divergently higher highs. Bear cross signal when fired and a significant cross is visible provides a $2.50 move to the downside with a potential exit provided when blue line crosses red overextension line in confluence with previous LOD area.
In the fourth example (green), we watch as the blue line provides a V pattern, we are searching for a long entry. If you didn't take a riskier long at 2nd std dev VWAP overextension with V recovery on blue line at red overextension for a ride to vwap, then you are looking for a secondary entry long as you wouldn't take the trade at resistance (vwap). Bullishly divergent lows provide this entry and the signal does not bear cross at all (but looking for significant crosses is more important even if the signal were to make a minor bear cross). Bullishly divergent double bottom provides a long entry to end of day with a nice clean signal for a $5.00 move until eod or when signal crosses overextension range.
Ideally, close to the money options or SPY/SPXS/SPXL are best used in the intraday time frame.
Again, this is not a standalone indicator but it's best used in conjunction with other indicators/trading strategies
Any questions feel free to comment
Candlestick RSThis is a candlestick charted Relative Strength indicator. It compares the chosen stock's progress compared to that of the SPY ETF ... ( SPY is used so it should hopefully update intraday). I use this indicator to see which stocks are outperforming the market.
Input Variable Descriptions:
Ratio: this variable is a float (0 to 1) that is basically how close the Candlestick RS is to the actual price action of the chart. (1.0 being right on top of it, 0.0 being as far away as possible from it)
Ballpark SPY price: this variable has to be constant, and due to the way pinescript works, you have to manually put in a ballpark of what SPY is at.
Neither of these variables influences the actual data of the indicator, but rather how it is shown on screen. It's difficult to describe, so I recommend you messing around with the variables and see what changes.
Hope this helps, I find this useful, so I figured I'd publish this... This is my first pine script so forgive me for any errors, just want to help :)
Alpha Options System# Apex Options Sniper - Advanced Multi-Signal Day Trading System
## 🎯 Overview
**Apex Options Sniper** is a professional-grade, multi-signal trading indicator specifically engineered for high-probability day trading of weekly options. This comprehensive system combines 10+ technical indicators into a sophisticated scoring algorithm that identifies optimal entry points with institutional-level precision.
Perfect for traders of SPY, QQQ, and high-volume stocks, this indicator eliminates guesswork by providing clear BUY CALLS and BUY PUTS signals based on multiple technical confluences.
---
## 🚀 Key Features
### **Multi-Signal Confluence Engine**
- **10+ Technical Indicators** working in harmony
- **Weighted Scoring System** (0-30+ points) for signal strength
- **Real-time Signal Classification**: Strong vs Moderate signals
- **False Signal Reduction** through multi-confirmation requirements
### **Advanced Momentum Analysis**
- ✅ RSI with Divergence Detection (bullish & bearish)
- ✅ Stochastic Oscillator (oversold/overbought + crossovers)
- ✅ MACD with crossover and momentum confirmation
- ✅ Automatic divergence spotting for reversal trades
### **Sophisticated Trend Detection**
- ✅ Triple EMA System (9/21/50) with alignment scoring
- ✅ SuperTrend Indicator with trend flip alerts
- ✅ VWAP for institutional price levels
- ✅ Multi-timeframe trend confirmation
### **Professional Volume Analysis**
- ✅ Volume Spike Detection (vs 20-period average)
- ✅ OBV (On-Balance Volume) with divergence detection
- ✅ Order Flow Analysis (buy vs sell pressure)
- ✅ Relative volume ratio display
### **Advanced Pattern Recognition**
- ✅ Bollinger Band Squeeze detection (volatility expansion)
- ✅ BB breakout signals (major move initiation)
- ✅ Automatic Support & Resistance levels (pivot-based)
- ✅ Price reaction scoring at key levels
### **Built-in Risk Management**
- ✅ ATR-based Stop Loss calculations
- ✅ Customizable Risk:Reward ratios
- ✅ Position sizing recommendations
- ✅ Real-time profit target calculations
### **Comprehensive Visual Dashboard**
- ✅ Live scoring breakdown for all indicators
- ✅ Individual signal strength display
- ✅ Bull vs Bear score comparison
- ✅ Color-coded signal status
- ✅ Risk management metrics
---
## 📊 How It Works
### **Scoring System**
The indicator assigns points based on technical conditions:
| **Category** | **Max Points** | **Conditions** |
|-------------|---------------|----------------|
| Momentum (RSI/Stoch) | 8 | Oversold/overbought + divergences |
| MACD | 4 | Crossovers + momentum direction |
| Trend (EMAs) | 6 | EMA alignment + SuperTrend |
| Volume | 4 | Spikes + OBV divergences |
| Order Flow | 2 | Buy/sell pressure imbalance |
| Bollinger Bands | 2 | Squeeze + breakouts |
| Support/Resistance | 2 | Price at key levels |
| VWAP | 1 | Above/below institutional level |
### **Signal Thresholds**
- **🚀 STRONG CALLS**: Bull score ≥6, Net score ≥4
- **📈 CALLS**: Bull score ≥4, Net score ≥2
- **🔥 STRONG PUTS**: Bear score ≥6, Net score ≤-4
- **📉 PUTS**: Bear score ≥4, Net score ≤-2
### **Multi-Timeframe Filter**
Optional higher timeframe confirmation reduces false signals by ensuring the broader trend supports your trade direction.
---
## 🎮 How to Use
### **Installation**
1. Open TradingView Pine Editor
2. Paste the complete indicator code
3. Click "Add to Chart"
4. Customize settings to your preference
### **Recommended Settings**
**For SPY/QQQ Day Trading:**
- Timeframe: 1-minute or 5-minute
- Strong Signal Threshold: 6
- Moderate Signal Threshold: 4
- Multi-timeframe Confluence: ON
**For Individual Stocks:**
- Timeframe: 5-minute or 15-minute
- Increase SuperTrend multiplier to 3.5-4.0
- Enable all advanced features
**For Scalping:**
- Timeframe: 1-minute
- Use STRONG signals only (6+)
- Tight stop loss (1.0-1.5 ATR multiplier)
### **Best Trading Times**
- **9:30-11:00 AM EST** - Highest volume, strongest signals
- **2:00-4:00 PM EST** - Afternoon momentum plays
- Avoid 11:30 AM-1:30 PM EST (lunch chop)
---
## 📈 Signal Interpretation
### **What You'll See on Chart:**
**Visual Signals:**
- 🟢 **Green Triangle (CALLS)**: Bullish entry point
- 🟢 **Large Green Triangle (STRONG CALLS)**: High-confidence bullish entry
- 🔴 **Red Triangle (PUTS)**: Bearish entry point
- 🔴 **Large Red Triangle (STRONG PUTS)**: High-confidence bearish entry
- 💎 **Small Diamonds**: RSI/OBV divergences (reversal warning)
**Dashboard Information:**
- Individual indicator values and signals
- Real-time score breakdown
- Bull/Bear score totals
- ATR stop loss levels
### **Entry Rules:**
✅ **High Probability Trades (Take These):**
- Strong signal (6+ score)
- 3+ indicators confirming
- Volume spike present
- SuperTrend aligned
- Higher timeframe confirms
⚠️ **Moderate Trades (Smaller Position):**
- Moderate signal (4-5 score)
- 2+ indicators confirming
- Normal volume
- Mixed trend signals
❌ **Avoid These:**
- Conflicting signals (Bull score ≈ Bear score)
- Low volume
- During major news events
- Bollinger squeeze without breakout direction
---
## 🛡️ Risk Management Guide
### **Position Sizing:**
- **Strong Signals (6+)**: 3-5% of portfolio
- **Moderate Signals (4-5)**: 2-3% of portfolio
- **Low Conviction**: 1-2% or skip
### **Stop Loss Strategy:**
- Use ATR-based stops (displayed in dashboard)
- Default: 1.5x ATR from entry
- Weekly options: 30-50% premium loss maximum
- Never hold through stop loss hoping for recovery
### **Profit Targets:**
- **Quick Scalps**: 25-50% gain (15-30 min)
- **Day Trades**: 50-100% gain (same day exit)
- **Swing**: 100-200% gain (1-2 days max for weeklies)
- **Take partial profits** at first target, let rest run
### **Time Decay Management (Weekly Options):**
- Monday-Wednesday: Hold overnight acceptable on strong signals
- Thursday: Close by EOD unless very strong conviction
- Friday: Avoid holding overnight, theta decay accelerates
---
## 🔔 Alert Configuration
### **Recommended Alerts:**
**Essential Alerts:**
1. 🚀 Strong Buy Calls
2. 🔥 Strong Buy Puts
**Advanced Alerts:**
3. 💎 RSI Bullish Divergence
4. ⚠️ RSI Bearish Divergence
5. 🔶 Bollinger Band Squeeze
6. ✅ SuperTrend Bull Flip
7. ❌ SuperTrend Bear Flip
**Alert Setup:**
- Set frequency: "Once Per Bar Close"
- Enable for all devices
- Use webhook for automation (optional)
---
## 💡 Pro Trading Tips
### **Maximize Win Rate:**
1. **Wait for confluence** - Best trades have 3+ indicators aligned
2. **Respect the dashboard** - Check WHY it's signaling (which indicators)
3. **Volume is king** - Signals with volume spikes are significantly more reliable
4. **Use BB Squeeze** - When squeeze + signal = explosive directional move
5. **SuperTrend flips** - Major trend change confirmations, very powerful
6. **Watch for divergences** - Diamond markers = hidden reversal opportunities
### **Common Mistakes to Avoid:**
❌ Trading every signal (be selective)
❌ Ignoring volume (volume confirms everything)
❌ Fighting the higher timeframe trend
❌ Oversizing positions on moderate signals
❌ Holding weekly options too long (theta decay)
❌ Trading during lunch hour (11:30-1:30 EST)
### **Advanced Techniques:**
- **Divergence + Support/Resistance** = Highest probability reversals
- **BB Squeeze + EMA alignment** = Explosive trend continuations
- **SuperTrend flip + Volume spike** = Major trend change entries
- **Multiple timeframe analysis** - Check 5m signal on 1m chart for precision entries
---
## 📊 Indicator Components Explained
### **RSI (Relative Strength Index)**
- Measures momentum and overbought/oversold conditions
- Divergences signal potential reversals before they happen
- Score: 2-3 points for extremes and divergences
### **Stochastic Oscillator**
- Confirms momentum extremes
- Crossovers provide entry timing
- Score: 1-2 points
### **MACD (Moving Average Convergence Divergence)**
- Trend following momentum indicator
- Crossovers signal momentum shifts
- Score: 1-3 points based on signal strength
### **EMA System (9/21/50)**
- Dynamic support and resistance
- Alignment shows trend strength
- Price position relative to EMAs scores 1-2 points
### **SuperTrend**
- Volatility-based trend indicator
- Reduces whipsaws in choppy conditions
- Trend flips are major signals (2 points)
### **Bollinger Bands**
- Volatility measurement
- Squeeze = calm before the storm
- Breakouts = directional move initiation (2 points)
### **Volume Analysis**
- Confirms price movement legitimacy
- Spikes validate signals (2 points)
- OBV divergences predict reversals (2 points)
### **Order Flow**
- Buy vs sell pressure measurement
- Institutional footprint detection
- Score: 2 points for strong imbalances
---
## 🎓 Learning Path
### **Beginner (Week 1-2):**
- Use STRONG signals only
- Focus on high-volume stocks (SPY/QQQ)
- Trade only first hour of market
- Use paper trading first
### **Intermediate (Week 3-4):**
- Add moderate signals to your arsenal
- Learn to read the dashboard
- Understand why each signal triggers
- Start combining with support/resistance
### **Advanced (Month 2+):**
- Use divergence signals
- Trade BB squeeze breakouts
- Optimize settings for your style
- Develop your own confluence rules
---
## ⚙️ Customization Guide
### **Adjustable Parameters:**
**Momentum Settings:**
- RSI Length (default: 14)
- RSI Oversold/Overbought levels (30/70)
- Stochastic Length (14)
**Trend Settings:**
- EMA periods (9/21/50)
- SuperTrend ATR Length (10)
- SuperTrend Multiplier (3.0)
**Volume Settings:**
- Volume MA Length (20)
- Volume Spike Threshold (1.5x)
**Advanced Settings:**
- Bollinger Band Length (20)
- BB Standard Deviation (2.0)
- Pivot Lookback (10)
**Signal Thresholds:**
- Strong Signal Score (default: 6)
- Moderate Signal Score (default: 4)
**Risk Management:**
- ATR Length (14)
- Stop Loss Multiplier (1.5)
- Risk:Reward Ratio (2.0)
---
## 📈 Performance Optimization
### **For Volatile Markets (VIX > 25):**
- Increase SuperTrend multiplier to 4.0
- Raise signal thresholds (+1 point)
- Tighten stop losses (1.0-1.2 ATR)
### **For Ranging Markets:**
- Focus on RSI extremes and divergences
- Use BB squeeze signals
- Ignore moderate signals
- Wait for support/resistance confirmation
### **For Trending Markets:**
- Follow SuperTrend direction religiously
- Use EMA alignment signals
- Allow wider stops (2.0 ATR)
- Take partial profits, let winners run
---
## 🔍 Troubleshooting
**Too Many Signals:**
- Increase signal thresholds to 7/5
- Enable multi-timeframe filter
- Trade only STRONG signals
**Missing Signals:**
- Decrease thresholds to 5/3
- Disable multi-timeframe filter
- Check that all features are enabled
**Whipsaw in Choppy Markets:**
- Increase SuperTrend multiplier
- Require volume spike confirmation
- Avoid trading 11:30 AM-1:30 PM EST
---
## 🏆 Best Practices
✅ **Always check:**
1. Dashboard shows why signal triggered
2. Volume confirms the move
3. Not during news events
4. Adequate time until expiration
✅ **Risk Management:**
1. Never risk more than 2% per trade
2. Use stops religiously
3. Take profits at targets
4. Don't revenge trade
✅ **Journal Your Trades:**
1. Entry price and signal strength
2. Which indicators triggered
3. Exit price and profit/loss
4. What worked and what didn't
---
## 📞 Support & Updates
This indicator is designed to evolve with market conditions. Recommended to:
- Review settings monthly
- Backtest on your favorite instruments
- Adjust thresholds based on your risk tolerance
- Keep a trading journal to track performance
---
## ⚠️ Disclaimer
This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Options trading involves substantial risk and is not suitable for all investors. Past performance does not guarantee future results. Always:
- Do your own research and due diligence
- Never invest more than you can afford to lose
- Consider consulting with a financial advisor
- Practice with paper trading before using real money
- Understand options Greeks (Delta, Theta, Gamma, Vega)
- Be aware of earnings dates and major news events
**No indicator is 100% accurate. Use proper risk management and trade responsibly.**
---
## 📊 Version History
**v1.0 - Initial Release**
- Multi-signal confluence system
- 10+ technical indicators
- Advanced dashboard
- ATR-based risk management
- Comprehensive alert system
---
## 🎯 Final Thoughts
**Apex Options Sniper** transforms complex technical analysis into clear, actionable signals. By combining multiple proven indicators with sophisticated scoring logic, it helps traders identify high-probability setups while managing risk effectively.
**Success Keys:**
- Quality over quantity (be selective)
- Risk management is everything
- Volume confirms the signal
- Confluence increases probability
- Discipline beats emotion
**Trade smart. Trade with confidence. Trade with Apex Options Sniper.**
---
*For questions, suggestions, or to share your success stories, please comment below or send a message.*
**Happy Trading! 🚀📈**
Real Relative Strength Indicator### What is RRS (Real Relative Strength)?
RRS is a volatility-normalized relative strength indicator that shows you – in real time – whether your stock, crypto, or any asset is genuinely beating or lagging the broader market after adjusting for risk and volatility. Unlike the classic “price ÷ SPY” line that gets completely fooled by volatility regimes, RRS answers the only question that actually matters to professional traders:
“Is this ticker moving better (or worse) than the market on a risk-adjusted basis right now?”
It does this by measuring the excess momentum of your ticker versus a benchmark (SPY, QQQ, BTC, etc.) and then dividing that excess by the average volatility (ATR) of both instruments. The result is a clean, centered-around-zero oscillator that works the same way in calm markets, crash markets, or parabolic bull runs.
### How to Use the RRS Indicator (Aqua/Purple Area Version) in Practice
The indicator is deliberately simple to read once you know the rules:
Positive area (aqua) means genuine outperformance.
Negative area (purple) means genuine underperformance.
The farther from zero, the stronger the leadership or weakness.
#### Core Signals and How to Trade Them
- RRS crossing above zero → one of the highest-probability long signals in existence. The asset has just started outperforming the market on a risk-adjusted basis. Enter or add aggressively if price structure agrees.
- RRS crossing below zero → leadership is ending. Tighten stops, take partial or full profits, or flip short if you trade both sides.
- RRS above +2 (bright aqua area) → clear leadership. This is where the real money is made in bull markets. Trail stops, add on pullbacks, let winners run.
- RRS below –2 (bright purple area) → clear distribution or capitulation. Avoid new longs, consider short entries or protective puts.
- Extreme readings above +4 or below –4 (background tint appears) → rare, very high-conviction moves. Treat these like once-a-month opportunities.
- Divergence (not plotted here, but easy to spot visually): price making new highs while the aqua area is shrinking → distribution. Price making new lows while the purple area is shrinking → hidden buying and coming reversal.
#### Best Settings by Style and Asset Class
For stocks and ETFs: keep benchmark as SPY (or QQQ for tech-heavy names) and length 14–20 on daily/4H charts.
For crypto: change the benchmark to BTCUSD (or ETHUSD) immediately — otherwise the reading is meaningless. Length 10–14 works best on 1H–4H crypto charts because volatility is higher.
For day trading: drop length to 10–12 and use 15-minute or 5-minute charts. Signals are faster and still extremely clean.
#### Highest-Edge Setups (What Actually Prints Money)
- RRS crosses above zero while price is still below a major moving average (50 EMA, 200 SMA, etc.) → early leadership, often catches the exact bottom of a new leg up.
- RRS already deep aqua (+3 or higher) and price pulls back to support without RRS dropping below +1 → textbook add-on or re-entry zone.
- RRS deep purple and suddenly turns flat or starts curling up while price is still falling → hidden accumulation, usually the exact low tick.
That’s it. Master these few rules and the RRS becomes one of the most powerful edge tools you will ever use for rotation trading...
O'Neil Market TimingBill O'Neil Market Timing Indicator - User Guide
Overview
This Pine Script indicator implements William O'Neil's market timing methodology, which assigns one of four distinct states to a market index (such as SPY or QQQ) to help traders identify optimal market conditions for investing. The indicator is designed to work exclusively on Daily timeframe charts.
The Four Market States
The indicator tracks the market through four distinct states, with specific transition rules between them:
1. Confirmed Uptrend (Green)
- Meaning: The market is in a healthy uptrend with institutional support
- Action: Favorable conditions for building positions in leading stocks
- Can transition to: State 2 (Uptrend Under Pressure)
2. Uptrend Under Pressure (Yellow)
- Meaning: The uptrend is showing signs of weakness with increasing distribution
- Action: Be cautious, tighten stops, reduce position sizes
- Can transition to: State 1 (Confirmed Uptrend) or State 3 (Downtrend)
3. Downtrend (Red)
- Meaning: The market is in a confirmed downtrend
- Action: Stay mostly in cash, avoid new purchases
- Can transition to: State 4 (Rally Attempt)
4. Rally Attempt (Pink/Fuchsia)
- Meaning: The market is attempting to bottom and reverse
- Action: Watch for Follow-Through Day to confirm new uptrend
- Can transition to: State 1 (Confirmed Uptrend) or State 3 (Downtrend)
Key Concepts
Distribution Day
A distribution day occurs when:
1. The index closes down by more than the critical percentage (default 0.2%)
2. Volume is higher than the previous day's volume
Distribution days indicate institutional selling and are marked with red triangles on the indicator.
Follow-Through Day
A follow-through day occurs during a Rally Attempt when:
1. The index closes up by more than the critical percentage (default 1.6%)
2. Volume is higher than the previous day's volume
A Follow-Through Day confirms a new uptrend and triggers the transition from Rally Attempt to Confirmed Uptrend.
State Transition Logic
Valid Transitions
The system only allows specific transitions:
- 1 → 2: When distribution days reach the "pressure number" (default 5) within the lookback period (default 25 bars)
- 2 → 1: When distribution days drop below the pressure number
- 2 → 3: When distribution days reach "downtrend number" (default 7) AND price drops by "downtrend criterion" (default 6%) from the lookback high
- 3 → 4: When the market doesn't make a new low for 3 consecutive days
- 4 → 3: When a new low is made, undercutting the downtrend low
- 4 → 1: When a Follow-Through Day occurs during the Rally Attempt
Input Parameters
Distribution Day Parameters
- Distribution Day % Threshold (default 0.2%, range 0.1-2.0%)
- Minimum percentage decline required to qualify as a distribution day. While 0.2% seems to be the canonical number I see in literature about this, I use a much higher threshold (at least 0.5%)
Follow-Through Day Parameters
- Follow-Through Day % Threshold (default 1.6%, range 1.0-2.0%)
- Minimum percentage gain required to qualify as a follow-through day
### State Transition Parameters
- Pressure Number (default 5, range 3-6)
- Number of distribution days needed to transition from Confirmed Uptrend to Uptrend Under Pressure
- Lookback Period (default 25 bars, range 20-30)
- Number of days to count distribution days
- Downtrend Number (default 7, range 4-10)
- Number of distribution days needed (with price drop) to transition to Downtrend
- Downtrend % Drop from High (default 6%, range 5-10%)
- Percentage drop from lookback high required for downtrend confirmation
Visual Settings
- Color customization for each state
- Table position selection (Top Left, Top Right, Bottom Left, Bottom Right)
## How to Use This Indicator
### Installation
1. Open TradingView and navigate to SPY or QQQ (or another major index)
2. **Important**: Switch to the Daily (1D) timeframe
3. Click on "Indicators" at the top of the chart
4. Click "Pine Editor" at the bottom of the screen
5. Copy and paste the Pine Script code
6. Click "Add to Chart"
### Interpretation
**When the indicator shows:**
- **Green (State 1)**: Market is healthy - consider adding quality positions
- **Yellow (State 2)**: Exercise caution - tighten stops, be selective
- **Red (State 3)**: Defensive mode - preserve capital, avoid new buys
- **Pink (State 4)**: Watch closely - prepare for potential Follow-Through Day
### The Information Table
The table displays:
- **Current State**: The current market condition
- **Distribution Days**: Number of distribution days in the lookback period
- **Lookback Period**: Number of bars being analyzed
- **Rally Attempt Day**: (Only in State 4) Days into the current rally attempt
### Visual Elements
1. **State Line**: A stepped line showing the current state (1-4)
2. **Red Triangles**: Mark each distribution day
3. **Horizontal Reference Lines**: Dotted lines marking each state level
4. **Color-Coded Display**: The state line changes color based on the current market condition
## Trading Strategy Guidelines
### In Confirmed Uptrend (State 1)
- Build positions in stocks breaking out of proper bases
- Use normal position sizing
- Focus on stocks showing institutional accumulation
- Hold winners as long as they act properly
### In Uptrend Under Pressure (State 2)
- Take partial profits in extended positions
- Tighten stop losses
- Be more selective with new entries
- Reduce overall exposure
### In Downtrend (State 3)
- Move to cash or maintain very light exposure
- Avoid new purchases
- Focus on preservation of capital
- Use the time for research and watchlist building
### In Rally Attempt (State 4)
- Stay mostly in cash but prepare
- Build a watchlist of strong stocks
- On Day 4+ of the rally attempt, watch for Follow-Through Day
- If FTD occurs, begin cautiously adding positions
## Best Practices
1. **Use with Major Indices**: This indicator works best with SPY, QQQ, or other broad market indices
2. **Daily Timeframe Only**: The indicator is designed for daily bars - do not use on intraday timeframes
3. **Combine with Stock Analysis**: Use the market state as a filter for individual stock decisions
4. **Respect the Signals**: When the market enters Downtrend, reduce exposure regardless of individual stock setups
5. **Monitor Distribution Days**: Pay attention when distribution days accumulate - it's a warning sign
6. **Wait for Follow-Through**: Don't jump back in too early during Rally Attempt - wait for confirmation
## Alert Conditions
The indicator includes built-in alert conditions for:
- State changes (entering any of the four states)
- Distribution Day detection
- Follow-Through Day detection during Rally Attempt
To set up alerts:
1. Click the "Alert" button while the indicator is on your chart
2. Select "O'Neil Market Timing"
3. Choose your desired alert condition
4. Configure notification preferences
## Customization Tips
### For More Sensitive Detection
- Lower the "Pressure Number" to 3-4
- Lower the "Distribution Day % Threshold" to 0.15%
- Reduce the "Downtrend Number" to 5-6
### For More Conservative Detection
- Raise the "Pressure Number" to 6
- Raise the "Distribution Day % Threshold" to 0.3-0.5%
- Increase the "Downtrend Number" to 8-9
### For Different Market Conditions
- **Bull Market**: Consider slightly higher thresholds
- **Bear Market**: Consider slightly lower thresholds
- **Volatile Market**: May need to increase percentage thresholds
## Limitations and Considerations
1. **Not a Crystal Ball**: The indicator identifies conditions but doesn't predict the future
2. **False Signals**: Follow-Through Days can fail - use proper risk management
3. **Whipsaws Possible**: In choppy markets, the indicator may switch states frequently
4. **Confirmation Lag**: By design, there's a lag as the system waits for confirmation
5. **Works Best with Price Action**: Combine with your analysis of individual stocks
## Historical Context
This methodology is based on William J. O'Neil's decades of market research, documented in books like "How to Make Money in Stocks" and through Investor's Business Daily. O'Neil's research showed that:
- Most major market tops are preceded by accumulation of distribution days
- Most successful rallies begin with a Follow-Through Day on Day 4-7 of a rally attempt
- Identifying market state helps prevent buying during unfavorable conditions
## Troubleshooting
**Problem**: Indicator shows "Initializing"
- **Solution**: Let the chart load at least 5 bars to establish the initial state
**Problem**: No distribution day markers appear
- **Solution**: Verify you're on daily timeframe and check if volume data is available
**Problem**: Table not visible
- **Solution**: Check the table position setting and ensure it's not off-screen
**Problem**: State seems to change too frequently
- **Solution**: Increase the lookback period or adjust threshold parameters
## Support and Further Learning
For deeper understanding of this methodology:
- Read "How to Make Money in Stocks" by William J. O'Neil
- Study Investor's Business Daily's "Market Pulse"
- Review historical market tops and bottoms to see the pattern
- Practice identifying distribution days and follow-through days manually
## Version History
**Version 1.0** (November 2025)
- Initial implementation
- Four-state system with proper transitions
- Distribution day detection and marking
- Follow-through day detection
- Customizable parameters
- Information table display
- Alert conditions
---
## Quick Reference Card
| State | Number | Color | Action |
|-------|--------|-------|--------|
| Confirmed Uptrend | 1 | Green | Buy quality setups |
| Uptrend Under Pressure | 2 | Yellow | Tighten stops, be selective |
| Downtrend | 3 | Red | Cash position, no new buys |
| Rally Attempt | 4 | Pink | Watch for Follow-Through Day |
**Distribution Day**: Down > 0.2% on higher volume (red triangle)
**Follow-Through Day**: Up > 1.6% on higher volume during Rally Attempt (triggers State 4→1)
---
*Remember: This indicator is a tool to help identify market conditions. It should be used as part of a comprehensive trading strategy that includes proper risk management, position sizing, and individual stock analysis.*
Also, I created this with the help of an AI coding framework, and I didn't exhaustively test it. I don't actually use this for my own trading, so it's quite possible that it's materially wrong, and that following this will lead to poor investment decisions.. This is "copy left" software, so feel free to alter this to your own tastes, and claim authorship.
26 EMA Reversal LogicThis indicator identifies two distinct price behaviours on the daily charts of SPY, SPX, QQQ, or IXIC, using the 26-period EMA as a reference. It plots one signal per downtrend — either a yellow circle (bearish continuation) or a green circle (bullish reversal) — and locks further signals until price closes above the 26 EMA.
The yellow circles are when we close below the 26-day EMA and the next day we make a lower low.
The green circles are when we close below the 26-day EMA and the next day we actually open higher and that low is never revisited.
Symbol Restriction
Only works on: SPY, SPX, QQQ, IXIC
On any other symbol, the script will display an error and stop.
Timeframe Restriction
DAILY chart only — will show an error on any other timeframe.
Core Logic: Two-Candle Pattern Detection
Both signals start with the same Day 1 condition:
Day 1: The candle closes below the 26 EMA
From there, Day 2 determines the signal:
Yellow Circle (Bearish Continuation)
Plotted BELOW the Day 2 candle
Conditions:
Day 1 closed below the 26 EMA
Day 2 makes a lower low than Day 1’s low → low < low Interpretation:
Price is weakening — pushing to new lows below the EMA.
Confirms downward momentum.
Green Circle (Bullish Reversal / Failed Breakdown)
Plotted ABOVE the Day 2 candle
Conditions:
Day 1 closed below the 26 EMA
Day 2 opens higher than Day 1’s close → open > close
Day 2’s low never revisits Day 1’s low → low >= low Interpretation:
Buyers defend the prior low with a higher open — classic false breakdown.
Suggests a potential reversal higher.
One Signal Per Downtrend (Lock & Reset)
After either a yellow or green circle is plotted, no more circles appear
Prevents clutter — focuses on first meaningful reaction
Reset Rule:
Lock is released only when price closes above the 26 EMA
Best Used On
Daily timeframe
SPY, SPX, QQQ, IXIC only
With trend, volume, or broader market context
Low Range Predictor [NR4/NR7 after WR4/WR7/WR20, within 1-3Days]Indicator Overview
The Low Range Predictor is a TradingView indicator displayed in a single panel below the chart. It spots volatility contraction setups (NR4/NR7 within 1–3 days of WR4/WR7/WR20) to predict low-range moves (e.g., <0.5% daily on SPY) over 2–5 days, perfect for your weekly 15/22 DTE put calendar spread strategy.
What You See
• Red Histograms (WR, Volatility Climax):
• WR4: Half-length red bars, widest range in 4 bars.
• WR7: Three-quarter-length red bars, widest in 7 bars.
• WR20: Full-length red bars, widest in 20 bars.
• Green Histograms (NR, Entry Signals):
• NR4: Half-length green bars, only on NR4 days (tightest range in 4 bars) within 1–3 days of a WR4.
• NR7: Full-length green bars, only on NR7 days within 1–3 days of a WR7.
• Panel: All signals (red WR4/WR7/WR20, green NR4/NR7) show in one panel below the chart, with green bars marking put calendar entry days.
Probabilities
• Volatility Contraction:
• NR4 after WR4: 65–70% chance of daily ranges <0.5% on SPY for 2–5 days (ATR drops 20–30%). Occurs ~2–3 times/month.
• NR7 after WR7: 60–65% chance of similar low ranges, less frequent (~1–2 times/month).
• Backtest (SPY, 2000–2025): 65% of NR4/NR7 signals lead to reduced volatility (<0.7% daily range) vs. 50% for random days.
• Signal Frequency: NR4 signals are more common than NR7, ideal for weekly entries. WR20 provides context but isn’t tied to NR signals.
Regular Trading Hours Opening Range Gap (RTH ORG)### Regular Trading Hours (RTH) Gap Indicator with Quartile Levels
**Overview**
Discover overnight gaps in index futures like ES, YM, and NQ, or stocks like SPY, with this enhanced Pine Script v6 indicator. It visualizes the critical gap between the previous RTH close (4:15 PM ET for futures, 4:00 PM for SPY) and the next RTH open (9:30 AM ET), helping traders spot potential price sensitivity formed during after-hours trading.
**Key Features**
- **Standard Gap Boxes**: Semi-transparent boxes highlight the gap range, with optional text labels showing day-of-week and "RTH" identifier.
- **Midpoint Line**: A customizable dashed line at the 50% level, with price labels for quick reference.
- **New: Quartile Lines (25% & 75%)**: Dotted lines (default width 1) mark the quarter and three-quarter points within the gap, ideal for finer intraday analysis. Toggle on/off, adjust style/color/width, and add labels.
- **High-Low Gap Variant**: Optional boxes and midlines for gaps between the prior close's high/low and the open's high/low—perfect for wick-based overlaps on lower timeframes (5-min or below recommended).
- **RTH Close Lines**: Extend previous close levels with dotted lines and price tags.
- **Customization Galore**: Extend elements right, limit historical displays (default: 3 gaps), no-plot sessions (e.g., avoid weekends), and time offsets for non-US indices.
**How to Use**
Apply to 15-min or lower charts for best results. Toggle "extend right" for ongoing levels. SPY auto-adjusts for its 4 PM close.
Tested on major indices—enhance your gap trading strategy today! Questions? Drop a comment.
Thanks to twingall for supplying the original code.
Thanks to The Inner Circle Trader (ICT) for the logical and systematic application.
Normalized Portfolio TrackerThis script lets you create, visualize, and track a custom portfolio of up to 15 assets directly on TradingView.
It calculates a synthetic "portfolio index" by combining multiple tickers with user-defined weights, automatically normalizing them so the total allocation always equals 100%.
All assets are scaled to a common starting point, allowing you to compare your portfolio’s performance versus any benchmark like SPY, QQQ, or BTC.
🚀 Goal
This script helps traders and investors:
• Understand the combined performance of their portfolio.
• Normalize diverse assets into a single synthetic chart .
• Make portfolio-level insights without relying on external spreadsheets.
🎯 Use Cases
• Backtest your portfolio allocations directly on the chart.
• Compare your portfolio vs. benchmarks like SPY, QQQ, BTC.
• Track thematic baskets (commodities, EV supply chain, regional ETFs).
• Visualize how each component contributes to overall performance.
📊 Features
• Weighted Portfolio Performance : Combines selected assets into a synthetic value series.
• Base Price Alignment : Each asset is normalized to its starting price at the chosen date.
• Dynamic Portfolio Table : Displays symbols, normalized weights (%), equivalent shares (based on each asset’s start price, sums to 100 shares), and a total row that always sums to 100%.
• Multi-Asset Support : Works with stocks, ETFs, indices, crypto, or any TradingView-compatible symbol.
⚙️ Configuration
Flexible Portfolio Setup
• Add up to 15 assets with custom weight inputs.
• You can enter any arbitrary numbers (e.g. 30, 15, 55).
• The script automatically normalizes all weights so the total allocation always equals 100%.
Start Date Selection
• Choose any custom start date to normalize all assets.
• The portfolio value is then scaled relative to the main chart symbol, so you can directly compare portfolio performance against benchmarks like SPY or QQQ.
Chart Styles
• Candlestick chart
• Heikin Ashi chart
• Line chart
Custom Display
• Adjustable colors and line widths
• Optionally display asset list, normalized weights, and equivalent shares
⚙️ How It Works
• Fetch OHLC data for each asset.
• Normalizes weights internally so totals = 100%.
• Stores each asset’s base price at the selected start date.
• Calculates equivalent “shares” for each allocation.
• Builds a synthetic portfolio value series by summing weighted contributions.
• Renders as Candlestick, Heikin Ashi, or Line chart.
• Adds a portfolio info table for clarity.
⚠️ Notes
• This script is for visualization only . It does not place trades or auto-rebalance.
• Weight inputs are automatically normalized, so you don’t need to enter exact percentages.
Relative Performance Indicator - TrendSpider StyleRelative Performance Indicator - TrendSpider Style
📈 Overview
This Relative Performance (RP) indicator measures how your stock is performing compared to a benchmark index, displayed as a percentile ranking from 0-100. Based on TrendSpider's methodology, it answers the critical question: "Is this stock a leader or a laggard?"
Unlike simple ratio charts, this indicator uses percentile ranking to normalize relative performance, making it easy to identify when a stock is showing exceptional strength (>80) or concerning weakness (<20) compared to its historical relationship with the benchmark.
✨ Key Features
Three Calculation Modes:
Quarterly: 3-month relative performance for swing trading
Yearly: Weighted 4-quarter performance for position trading
TechRank: Composite of 6 technical indicators for multi-factor analysis
Clean Visual Design:
Green fills above 80 (strong outperformance)
Red fills below 20 (significant underperformance)
Dotted median line at 50 for quick reference
Current value label for instant reading
Flexible Benchmarks:
Compare against major indices (SPY, QQQ, IWM)
Sector ETFs for within-sector analysis
Custom symbols for specialized comparisons
Built-in Alerts:
Strong performance zone entry (>80)
Weak performance zone entry (<20)
Median crossovers (50 level)
📊 How To Use
Buy Signals:
RP crosses above 80: Stock entering leadership status
RP holding above 60: Maintaining relative strength
RP rising while price consolidating: Accumulation phase
Sell/Avoid Signals:
RP drops below 50: Losing relative strength
RP below 20: Significant underperformance
RP falling while price rising: Bearish divergence
Sector Rotation:
Compare multiple assets to find strongest sectors
Rotate into high RP assets (>70)
Exit low RP positions (<30)
🎯 Reading The Values
80-100: Exceptional outperformance - Strong buy/hold
60-80: Moderate outperformance - Hold positions
40-60: Market perform - No edge
20-40: Underperformance - Caution/reduce
0-20: Severe underperformance - Avoid/exit
⚙️ Calculation Method
Calculates percentage performance of both your stock and the benchmark
Finds the performance differential
Ranks this differential against historical values using percentile analysis
Normalizes to 0-100 scale for easy interpretation
This percentile approach adapts to different market conditions and volatility regimes, providing consistent signals whether in trending or choppy markets.
💡 Pro Tips
For Growth Stocks: Use quarterly mode with QQQ as benchmark
For Value Stocks: Use yearly mode with SPY as benchmark
For Small Caps: Compare against IWM, not SPY
For Sector Analysis: Use sector ETFs (XLK, XLF, XLE, etc.)
Combine with Price Action: High RP + price breakout = powerful signal
⚠️ Important Notes
RP is relative, not absolute - stocks can fall with high RP if the market falls harder
Choose appropriate benchmarks for meaningful comparisons
Best used in conjunction with price action and volume analysis
Historical lookback period affects sensitivity (adjustable in settings)
🔧 Customization
Fully customizable visual settings, thresholds, calculation periods, and smoothing options. Adjust the normalization lookback period (default 252 days) to fine-tune sensitivity to your trading timeframe.
📌 Credit
Inspired by TrendSpider's Relative Performance implementation, adapted for TradingView with enhanced customization options and Pine Script v6 optimization.
Tags to include: relativeperformance, relativestrength, percentile, ranking, sectorrotation, benchmark, outperformance, trendspider, marketbreadth, strengthindicator
Category: Momentum Indicators / Trend Analysis
Feel free to modify this description to match your style or add any specific points you want to emphasize!
Opening Range IndicatorComplete Trading Guide: Opening Range Breakout Strategy
What Are Opening Ranges?
Opening ranges capture the high and low prices during the first few minutes of market open. These levels often act as key support and resistance throughout the trading day because:
Heavy volume occurs at market open as overnight orders execute
Institutional activity is concentrated during opening minutes
Price discovery happens as market participants react to overnight news
Psychological levels are established that traders watch all day
Understanding the Three Timeframes
OR5 (5-Minute Range: 9:30-9:35 AM)
Most sensitive - captures immediate market reaction
Quick signals but higher false breakout rate
Best for scalping and momentum trading
Use for early entry when conviction is high
OR15 (15-Minute Range: 9:30-9:45 AM)
Balanced approach - most popular among day traders
Moderate sensitivity with better reliability
Good for swing trades lasting several hours
Primary timeframe for most strategies
OR30 (30-Minute Range: 9:30-10:00 AM)
Most reliable but slower signals
Lower false breakout rate
Best for position trades and trend following
Use when looking for major moves
Core Trading Strategies
Strategy 1: Basic Breakout
Setup:
Wait for price to break above OR15 high or below OR15 low
Enter on the breakout candle close
Stop loss: Opposite side of the range
Target: 2-3x the range size
Example:
OR15 range: $100.00 - $102.00 (Range = $2.00)
Long entry: Break above $102.00
Stop loss: $99.50 (below OR15 low)
Target: $104.00+ (2x range size)
Strategy 2: Multiple Confirmation
Setup:
Wait for OR5 break first (early signal)
Confirm with OR15 break in same direction
Enter on OR15 confirmation
Stop: Below OR30 if available, or OR15 opposite level
Why it works:
Multiple timeframe confirmation reduces false signals and increases probability of sustained moves.
Strategy 3: Failed Breakout Reversal
Setup:
Price breaks OR15 level but fails to hold
Wait for re-entry into the range
Enter reversal trade toward opposite OR level
Stop: Recent breakout high/low
Target: Opposite side of range + extension
Key insight: Failed breakouts often lead to strong moves in the opposite direction.
Advanced Techniques
Range Quality Assessment
High-Quality Ranges (Trade these):
Range size: 0.5% - 2% of stock price
Clean boundaries (not choppy)
Volume spike during range formation
Clear rejection at range levels
Low-Quality Ranges (Avoid these):
Very narrow ranges (<0.3% of stock price)
Extremely wide ranges (>3% of stock price)
Choppy, overlapping candles
Low volume during formation
Volume Confirmation
For Breakouts:
Look for volume spike (2x+ average) on breakout
Declining volume often signals false breakout
Rising volume during range formation shows interest
Market Context Filters
Best Conditions:
Trending market days (SPY/QQQ with clear direction)
Earnings reactions or news-driven moves
High-volume stocks with good liquidity
Volatility above average (VIX considerations)
Avoid Trading When:
Extremely low volume days
Major economic announcements pending
Holidays or half-days
Choppy, sideways market conditions
Risk Management Rules
Position Sizing
Conservative: Risk 0.5% of account per trade
Moderate: Risk 1% of account per trade
Aggressive: Risk 2% maximum per trade
Stop Loss Placement
Inside the range: Quick exit but higher stop-out rate
Outside opposite level: More room but larger risk
ATR-based: 1.5-2x Average True Range below entry
Profit Taking
Target 1: 1x range size (take 50% off)
Target 2: 2x range size (take 25% off)
Runner: Trail remaining 25% with moving stops
Specific Entry Techniques
Breakout Entry Methods
Method 1: Immediate Entry
Enter as soon as price closes above/below range
Fastest entry but highest false signal rate
Best for strong momentum situations
Method 2: Pullback Entry
Wait for breakout, then pullback to range level
Enter when price bounces off former resistance/support
Better risk/reward but may miss some moves
Method 3: Volume Confirmation
Wait for breakout + volume spike
Enter after volume confirmation candle
Reduces false signals significantly
Multiple Timeframe Entries
Aggressive: OR5 break → immediate entry
Conservative: OR5 + OR15 + OR30 all align → enter
Balanced: OR15 break with OR30 support → enter
Common Mistakes to Avoid
1. Trading Poor-Quality Ranges
❌ Don't trade ranges that are too narrow or too wide
✅ Focus on clean, well-defined ranges with good volume
2. Ignoring Volume
❌ Don't chase breakouts without volume confirmation
✅ Always check for volume spike on breakouts
3. Over-Trading
❌ Don't force trades when ranges are unclear
✅ Wait for high-probability setups only
4. Poor Risk Management
❌ Don't risk more than planned or use tight stops in volatile conditions
✅ Stick to predetermined risk levels
5. Fighting the Trend
❌ Don't fade breakouts in strongly trending markets
✅ Align trades with overall market direction
Daily Trading Routine
Pre-Market (8:00-9:30 AM)
Check overnight news and earnings
Review major indices (SPY, QQQ, IWM)
Identify potential opening range candidates
Set alerts for range breakouts
Market Open (9:30-10:00 AM)
Watch opening range formation
Note volume and price action quality
Mark key levels on charts
Prepare for breakout signals
Trading Session (10:00 AM - 4:00 PM)
Execute breakout strategies
Manage existing positions
Trail stops as profits develop
Look for additional setups
Post-Market Review
Analyze winning and losing trades
Review range quality vs. outcomes
Identify improvement areas
Prepare for next session
Best Stocks/ETFs for Opening Range Trading
Large Cap Stocks (Best for beginners):
AAPL, MSFT, GOOGL, AMZN, TSLA
High liquidity, predictable behavior
Good range formation most days
ETFs (Consistent patterns):
SPY, QQQ, IWM, XLF, XLE
Excellent liquidity
Clear range boundaries
Mid-Cap Growth (Advanced traders):
Stocks with good volume (1M+ shares daily)
Recent news catalysts
Clean technical patterns
Performance Optimization
Track These Metrics:
Win rate by range type (OR5 vs OR15 vs OR30)
Average R/R (risk vs reward ratio)
Best performing market conditions
Time of day performance
Continuous Improvement:
Keep detailed trade journal
Review failed breakouts for patterns
Adjust position sizing based on win rate
Refine entry timing based on backtesting
Final Tips for Success
Start small - Paper trade or use tiny positions initially
Focus on quality - Better to miss trades than take bad ones
Stay disciplined - Stick to your rules even during losing streaks
Adapt to conditions - What works in trending markets may fail in choppy conditions
Keep learning - Markets evolve, so should your approach
The opening range strategy is powerful because it captures natural market behavior, but like all strategies, it requires practice, discipline, and proper risk management to be profitable long-term.
EvoTrend-X Indicator — Evolutionary Trend Learner ExperimentalEvoTrend-X Indicator — Evolutionary Trend Learner
NOTE: This is an experimental Pine Script v6 port of a Python prototype. Pine wasn’t the original research language, so there may be small quirks—your feedback and bug reports are very welcome. The model is non-repainting, MTF-safe (lookahead_off + gaps_on), and features an adaptive (fitness-based) candidate selector, confidence gating, and a volatility filter.
⸻
What it is
EvoTrend-X is adaptive trend indicator that learns which moving-average length best fits the current market. It maintains a small “population” of fast EMA candidates, rewards those that align with price momentum, and continuously selects the best performer. Signals are gated by a multi-factor Confidence score (fitness, strength vs. ATR, MTF agreement) and a volatility filter (ATR%). You get a clean Fast/Slow pair (for the currently best candidate), optional HTF filter, a fitness ribbon for transparency, and a themed info panel with a one-glance STATUS readout.
Core outputs
• Selected Fast/Slow EMAs (auto-chosen from candidates via fitness learning)
• Spread cross (Fast – Slow) → visual BUY/SELL markers + alert hooks
• Confidence % (0–100): Fitness ⊕ Distance vs. ATR ⊕ MTF agreement
• Gates: Trend regime (Kaufman ER), Volatility (ATR%), MTF filter (optional)
• Candidate Fitness Ribbon: shows which lengths the learner currently prefers
• Export plot: hidden series “EvoTrend-X Export (spread)” for downstream use
⸻
Why it’s different
• Evolutionary learning (on-chart): Each candidate EMA length gets rewarded if its slope matches price change and penalized otherwise, with a gentle decay so the model forgets stale regimes. The best fitness wins the right to define the displayed Fast/Slow pair.
• Confidence gate: Signals don’t light up unless multiple conditions concur: learned fitness, spread strength vs. volatility, and (optionally) higher-timeframe trend.
• Volatility awareness: ATR% filter blocks low-energy environments that cause death-by-a-thousand-whipsaws. Your “why no signal?” answer is always visible in the STATUS.
• Preset discipline, Custom freedom: Presets set reasonable baselines for FX, equities, and crypto; Custom exposes all knobs and honors your inputs one-to-one.
• Non-repainting rigor: All MTF calls use lookahead_off + gaps_on. Decisions use confirmed bars. No forward refs. No conditional ta.* pitfalls.
⸻
Presets (and what they do)
• FX 1H (Conservative): Medium candidates, slightly higher MinConf, modest ATR% floor. Good for macro sessions and cleaner swings.
• FX 15m (Active): Shorter candidates, looser MinConf, higher ATR% floor. Designed for intraday velocity and decisive sessions.
• Equities 1D: Longer candidates, gentler volatility floor. Suits index/large-cap trend waves.
• Crypto 1H: Mid-short candidates, higher ATR% floor for 24/7 chop, stronger MinConf to avoid noise.
• Custom: Your inputs are used directly (no override). Ideal for systematic tuning or bespoke assets.
⸻
How the learning works (at a glance)
1. Candidates: A small set of fast EMA lengths (e.g., 8/12/16/20/26/34). Slow = Fast × multiplier (default ×2.0).
2. Reward/decay: If price change and the candidate’s Fast slope agree (both up or both down), its fitness increases; otherwise decreases. A decay constant slowly forgets the distant past.
3. Selection: The candidate with highest fitness defines the displayed Fast/Slow pair.
4. Signal engine: Crosses of the spread (Fast − Slow) across zero mark potential regime shifts. A Confidence score and gates decide whether to surface them.
⸻
Controls & what they mean
Learning / Regime
• Slow length = Fast ×: scales the Slow EMA relative to each Fast candidate. Larger multiplier = smoother regime detection, fewer whipsaws.
• ER length / threshold: Kaufman Efficiency Ratio; above threshold = “Trending” background.
• Learning step, Decay: Larger step reacts faster to new behavior; decay sets how quickly the past is forgotten.
Confidence / Volatility gate
• Min Confidence (%): Minimum score to show signals (and fire alerts). Raising it filters noise; lowering it increases frequency.
• ATR length: The ATR window for both the ATR% filter and strength normalization. Shorter = faster, but choppier.
• Min ATR% (percent): ATR as a percentage of price. If ATR% < Min ATR% → status shows BLOCK: low vola.
MTF Trend Filter
• Use HTF filter / Timeframe / Fast & Slow: HTF Fast>Slow for longs, Fast threshold; exit when spread flips or Confidence decays below your comfort zone.
2) FX index/majors, 15m (active intraday)
• Preset: FX 15m (Active).
• Gate: MinConf 60–70; Min ATR% 0.15–0.30.
• Flow: Focus on session opens (LDN/NY). The ribbon should heat up on shorter candidates before valid crosses appear—good early warning.
3) SPY / Index futures, 1D (positioning)
• Preset: Equities 1D.
• Gate: MinConf 55–65; Min ATR% 0.05–0.12.
• Flow: Use spread crosses as regime flags; add timing from price structure. For adds, wait for ER to remain trending across several bars.
4) BTCUSD, 1H (24/7)
• Preset: Crypto 1H.
• Gate: MinConf 70–80; Min ATR% 0.20–0.35.
• Flow: Crypto chops—volatility filter is your friend. When ribbon and HTF OK agree, favor continuation entries; otherwise stand down.
⸻
Reading the Info Panel (and fixing “no signals”)
The panel is your self-diagnostic:
• HTF OK? False means the higher-timeframe EMAs disagree with your intended side.
• Regime: If “Chop”, ER < threshold. Consider raising the threshold or waiting.
• Confidence: Heat-colored; if below MinConf, the gate blocks signals.
• ATR% vs. Min ATR%: If ATR% < Min ATR%, status shows BLOCK: low vola.
• STATUS (composite):
• BLOCK: low vola → increase Min ATR% down (i.e., allow lower vol) or wait for expansion.
• BLOCK: HTF filter → disable HTF or align with the HTF tide.
• BLOCK: confidence → lower MinConf slightly or wait for stronger alignment.
• OK → you’ll see markers on valid crosses.
⸻
Alerts
Two static alert hooks:
• BUY cross — spread crosses up and all gates (ER, Vol, MTF, Confidence) are open.
• SELL cross — mirror of the above.
Create them once from “Add Alert” → choose the condition by name.
⸻
Exporting to other scripts
In your other Pine indicators/strategies, add an input.source and select EvoTrend-X → “EvoTrend-X Export (spread)”. Common uses:
• Build a rule: only trade when exported spread > 0 (trend filter).
• Combine with your oscillator: oscillator oversold and spread > 0 → buy bias.
⸻
Best practices
• Let it learn: Keep Learning step moderate (0.4–0.6) and Decay close to 1.0 (e.g., 0.99–0.997) for smooth regime memory.
• Respect volatility: Tune Min ATR% by asset and timeframe. FX 1H ≈ 0.10–0.20; crypto 1H ≈ 0.20–0.35; equities 1D ≈ 0.05–0.12.
• MTF discipline: HTF filter removes lots of “almost” trades. If you prefer aggressive entries, turn it off and rely more on Confidence.
• Confidence as throttle:
• 40–60%: exploratory; expect more signals.
• 60–75%: balanced; good daily driver.
• 75–90%: selective; catch the clean stuff.
• 90–100%: only A-setups; patient mode.
• Watch the ribbon: When shorter candidates heat up before a cross, momentum is forming. If long candidates dominate, you’re in a slower trend cycle.
⸻
Non-repainting & safety notes
• All request.security() calls use lookahead=barmerge.lookahead_off, gaps=barmerge.gaps_on.
• No forward references; decisions rely on confirmed bar data.
• EMA lengths are simple ints (no series-length errors).
• Confidence components are computed every bar (no conditional ta.* traps).
⸻
Limitations & tips
• Chop happens: ER helps, but sideways microstructure can still flicker—use Confidence + Vol filter as brakes.
• Presets ≠ oracle: They’re sensible baselines; always tune MinConf and Min ATR% to your venue and session.
• Theme “Auto”: Pine cannot read chart theme; “Auto” defaults to a Dark-friendly palette.
⸻
Publisher’s Screenshots Checklist
1) FX swing — EURUSD 1H
• Preset: FX 1H (Conservative)
• Params: MinConf=70, ATR Len=14, Min ATR%=0.12, MTF ON (TF=4H, 20/50)
• Show: Clear BUY cross, STATUS=OK, green regime background; Fitness Ribbon visible.
2) FX intraday — GBPUSD 15m
• Preset: FX 15m (Active)
• Params: MinConf=60, ATR Len=14, Min ATR%=0.20, MTF ON (TF=60m)
• Show: SELL cross near London session open. HTF lines enabled (translucent).
• Caption: “GBPUSD 15m • Active session sell with MTF alignment.”
3) Indices — SPY 1D
• Preset: Equities 1D
• Params: MinConf=60, ATR Len=14, Min ATR%=0.08, MTF ON (TF=1W, 20/50)
• Show: Longer trend run after BUY cross; regime shading shows persistence.
• Caption: “SPY 1D • Trend run after BUY cross; weekly filter aligned.”
4) Crypto — BINANCE:BTCUSDT 1H
• Preset: Crypto 1H
• Params: MinConf=75, ATR Len=14, Min ATR%=0.25, MTF ON (TF=4H)
• Show: BUY cross + quick follow-through; Ribbon warming (reds/yellows → greens).
• Caption: “BTCUSDT 1H • Momentum break with high confidence and ribbon turning.”
