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Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment ## 1. Introduction Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985). The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014). ## 2. Theoretical Foundation ### 2.1 Momentum Theory and Market Anomalies The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015). ### 2.2 Drawdown Analysis and Risk Management Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators. ### 2.3 Adaptive Smoothing and Market Regimes The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets. ## 3. Methodology ### 3.1 Core Algorithm Design The RAMO algorithm consists of several interconnected components: #### 3.1.1 Risk-Adjusted Momentum Calculation The fundamental innovation of RAMO lies in its risk adjustment mechanism: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor) Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05) This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952). #### 3.1.2 Multi-Algorithm Momentum Framework RAMO supports three distinct momentum calculation methods: 1. Rate of Change: Traditional percentage-based momentum (Pring, 2002) 2. Price Momentum: Absolute price differences 3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997) This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013). ### 3.2 Leading Indicator Components #### 3.2.1 Momentum Acceleration Analysis The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes: Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n) This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969). #### 3.2.2 Linear Regression Prediction RAMO incorporates linear regression-based prediction to project momentum values forward: Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset) This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes. #### 3.2.3 Volume-Based Exhaustion Detection The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns: Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20) This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989). ### 3.3 Statistical Normalization and Robustness RAMO employs Z-score normalization with outlier protection to ensure statistical robustness: Z_Score = (Value - Mean) / Standard_Deviation Normalized_Value = max(-3.5, min(3.5, Z_Score)) This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions. ### 3.4 Adaptive Threshold Calculation Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992): Upper_Threshold = Mean + (Multiplier × Standard_Deviation) Lower_Threshold = Mean - (Multiplier × Standard_Deviation) This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992). ## 4. Implementation Details ### 4.1 Adaptive Smoothing Algorithm The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility: Volatility_Percentile = Percentrank(Volatility, 100) Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100) This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995). ### 4.2 Risk Environment Classification RAMO classifies market conditions into three risk environments: - Low Risk: Current_DD < 30% × Max_DD - Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD - High Risk: Current_DD ≥ 70% × Max_DD This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014). ## 5. Signal Generation and Interpretation ### 5.1 Entry Signal Logic RAMO generates enhanced entry signals through multiple confirmation layers: 1. Primary Signal: Crossover between indicator and signal line 2. Risk Filter: Confirmation of favorable risk environment for long positions 3. Leading Component: Early warning signals via acceleration analysis 4. Exhaustion Filter: Volume-based reversal detection This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992). ### 5.2 Divergence Analysis RAMO incorporates both traditional and leading divergence detection: - Traditional Divergence: Price and indicator divergence over 3-5 periods - Slope Divergence: Momentum slope versus price direction - Acceleration Divergence: Changes in momentum acceleration This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999). ## 6. Empirical Advantages and Applications ### 6.1 Risk-Adjusted Performance The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980). ### 6.2 Regime Awareness RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments. ### 6.3 Institutional Applications The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013). ## 7. Limitations and Future Research ### 7.1 Parameter Sensitivity Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions. ### 7.2 Market Microstructure Considerations RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance. ### 7.3 Transaction Cost Integration Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004). ## References Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons. Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press. Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985. Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764. Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84. Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall. Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291. Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill. Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082. Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91. Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance. Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library. Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill. Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790. Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314. Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239. Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Pine Script®指標
由EdgeTools提供
Performance Metrics With Bracketed Rebalacing [BackQuant]Performance Metrics With Bracketed Rebalancing The Performance Metrics With Bracketed Rebalancing script offers a robust method for assessing portfolio performance, integrating advanced portfolio metrics with different rebalancing strategies. With a focus on adaptability, the script allows traders to monitor and adjust portfolio weights, equity, and other key financial metrics dynamically. This script provides a versatile approach for evaluating different trading strategies, considering factors like risk-adjusted returns, volatility, and the impact of portfolio rebalancing. Please take the time to read the following: Key Features and Benefits of Portfolio Methods Bracketed Rebalancing: Bracketed Rebalancing is an advanced strategy designed to trigger portfolio adjustments when an asset's weight surpasses a predefined threshold. This approach minimizes overexposure to any single asset while maintaining flexibility in response to market changes. The strategy is particularly beneficial for mitigating risks that arise from significant asset weight fluctuations. The following image illustrates how this method reacts when asset weights cross the threshold: Daily Rebalancing: Unlike the bracketed method, Daily Rebalancing adjusts portfolio weights every trading day, ensuring consistent asset allocation. This method aims for a more even distribution of portfolio weights, making it a suitable option for traders who prefer less sensitivity to individual asset volatility. Here's an example of Daily Rebalancing in action: No Rebalancing: For traders who prefer a passive approach, the "No Rebalancing" option allows the portfolio to remain static, without any adjustments to asset weights. This method may appeal to long-term investors or those who believe in the inherent stability of their selected assets. Here’s how the portfolio looks when no rebalancing is applied: Portfolio Weights Visualization: One of the standout features of this script is the visual representation of portfolio weights. With adjustable settings, users can track the current allocation of assets in real-time, making it easier to analyze shifts and trends. The following image shows the real-time weight distribution across three assets: Rolling Drawdown Plot: Managing drawdown risk is a critical aspect of portfolio management. The Rolling Drawdown Plot visually tracks the drawdown over time, helping traders monitor the risk exposure and performance relative to the peak equity levels. This feature is essential for assessing the portfolio's resilience during market downturns: Daily Portfolio Returns: Tracking daily returns is crucial for evaluating the short-term performance of the portfolio. The script allows users to plot daily portfolio returns to gain insights into daily profit or loss, helping traders stay updated on their portfolio’s progress: Performance Metrics Net Profit (%): This metric represents the total return on investment as a percentage of the initial capital. A positive net profit indicates that the portfolio has gained value over the evaluation period, while a negative value suggests a loss. It's a fundamental indicator of overall portfolio performance. Maximum Drawdown (Max DD): Maximum Drawdown measures the largest peak-to-trough decline in portfolio value during a specified period. It quantifies the most significant loss an investor would have experienced if they had invested at the highest point and sold at the lowest point within the timeframe. A smaller Max DD indicates better risk management and less exposure to significant losses. Annual Mean Returns (% p/y): This metric calculates the average annual return of the portfolio over the evaluation period. It provides insight into the portfolio's ability to generate returns on an annual basis, aiding in performance comparison with other investment opportunities. Annual Standard Deviation of Returns (% p/y): This measure indicates the volatility of the portfolio's returns on an annual basis. A higher standard deviation signifies greater variability in returns, implying higher risk, while a lower value suggests more stable returns. Variance: Variance is the square of the standard deviation and provides a measure of the dispersion of returns. It helps in understanding the degree of risk associated with the portfolio's returns. Sortino Ratio: The Sortino Ratio is a variation of the Sharpe Ratio that only considers downside risk, focusing on negative volatility. It is calculated as the difference between the portfolio's return and the minimum acceptable return (MAR), divided by the downside deviation. A higher Sortino Ratio indicates better risk-adjusted performance, emphasizing the importance of avoiding negative returns. Sharpe Ratio: The Sharpe Ratio measures the portfolio's excess return per unit of total risk, as represented by standard deviation. It is calculated by subtracting the risk-free rate from the portfolio's return and dividing by the standard deviation of the portfolio's excess return. A higher Sharpe Ratio indicates more favorable risk-adjusted returns. Omega Ratio: The Omega Ratio evaluates the probability of achieving returns above a certain threshold relative to the probability of experiencing returns below that threshold. It is calculated by dividing the cumulative probability of positive returns by the cumulative probability of negative returns. An Omega Ratio greater than 1 indicates a higher likelihood of achieving favorable returns. Gain-to-Pain Ratio: The Gain-to-Pain Ratio measures the return per unit of risk, focusing on the magnitude of gains relative to the severity of losses. It is calculated by dividing the total gains by the total losses experienced during the evaluation period. A higher ratio suggests a more favorable balance between reward and risk. www.linkedin.com Compound Annual Growth Rate (CAGR) (% p/y): CAGR represents the mean annual growth rate of the portfolio over a specified period, assuming the investment has been compounding over that time. It provides a smoothed annual rate of growth, eliminating the effects of volatility and offering a clearer picture of long-term performance. Portfolio Alpha (% p/y): Portfolio Alpha measures the portfolio's performance relative to a benchmark index, adjusting for risk. It is calculated using the Capital Asset Pricing Model (CAPM) and represents the excess return of the portfolio over the expected return based on its beta and the benchmark's performance. A positive alpha indicates outperformance, while a negative alpha suggests underperformance. Portfolio Beta: Portfolio Beta assesses the portfolio's sensitivity to market movements, indicating its exposure to systematic risk. A beta greater than 1 suggests the portfolio is more volatile than the market, while a beta less than 1 indicates lower volatility. Beta is used to understand the portfolio's potential for gains or losses in relation to market fluctuations. Skewness of Returns: Skewness measures the asymmetry of the return distribution. A positive skew indicates a distribution with a long right tail, suggesting more frequent small losses and fewer large gains. A negative skew indicates a long left tail, implying more frequent small gains and fewer large losses. Understanding skewness helps in assessing the likelihood of extreme outcomes. Value at Risk (VaR) 95th Percentile: VaR at the 95th percentile estimates the maximum potential loss over a specified period, given a 95% confidence level. It provides a threshold value such that there is a 95% probability that the portfolio will not experience a loss greater than this amount. Conditional Value at Risk (CVaR): CVaR, also known as Expected Shortfall, measures the average loss exceeding the VaR threshold. It provides insight into the tail risk of the portfolio, indicating the expected loss in the worst-case scenarios beyond the VaR level. These metrics collectively offer a comprehensive view of the portfolio's performance, risk exposure, and efficiency. By analyzing these indicators, investors can make informed decisions, balancing potential returns with acceptable levels of risk. Conclusion The Performance Metrics With Bracketed Rebalancing script provides a comprehensive framework for evaluating and optimizing portfolio performance. By integrating advanced metrics, adaptive rebalancing strategies, and visual analytics, it empowers traders to make informed decisions in managing their investment portfolios. However, it's crucial to consider the implications of rebalancing strategies, as academic research indicates that predictable rebalancing can lead to market impact costs. Therefore, adopting flexible and less predictable rebalancing approaches may enhance portfolio performance and reduce associated costs.
Pine Script®指標
由BackQuant提供
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance. By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements. Key Features 1. Cross-Sectional Z-Score Calculation: The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance. 2. Asset Ranking and Allocation: The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets. 3. Rolling Window for Mean and Standard Deviation Calculations: The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data. 4. Mean and Standard Deviation Visualization: The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions. 5. Top & Bottom Performer Tables: The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio. 6. Customizable Parameters: The strategy allows traders to customize several key parameters, including: Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations. Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to. Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference. 7. Forward Test & Out-of-Sample Testing: The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions. 8. Visual Feedback and Alerts: The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments. 9. Risk Management via Z-Scores and Std Dev: The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction. 10. Note on Mean Reversion Strategy: If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy. References www.uts.edu.au onlinelibrary.wiley.com www.cmegroup.com Final Thoughts The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
Pine Script®指標
由BackQuant提供
Money Risk Management with Trade Tracking Overview The Money Risk Management with Trade Tracking indicator is a powerful tool designed for traders on TradingView to simplify trade simulation and risk management. Unlike the TradingView Strategy Tester, which can be complex for beginners, this indicator provides an intuitive, beginner-friendly interface to evaluate trading strategies in a realistic manner, mirroring real-world trading conditions. Built on the foundation of open-source contributions from LuxAlgo and TCP, this indicator integrates external indicator signals, overlays take-profit (TP) and stop-loss (SL) levels, and provides detailed money management analytics. It empowers traders to visualize potential profits, losses, and risk-reward ratios, making it easier to understand the financial outcomes of their strategies. Key Features Signal Integration: Seamlessly integrates with external long and short signals from other indicators, allowing traders to overlay TP/SL levels based on their preferred strategies. Realistic Trade Simulation: Simulates trades as they would occur in real-world scenarios, accounting for initial capital, risk percentage, leverage, and compounding effects. Money Management Dashboard: Displays critical metrics such as current capital, unrealized P&L, risk amount, potential profit, risk-reward ratio, and trade status in a customizable, beginner-friendly table. TP/SL Visualization: Plots TP and SL levels on the chart with customizable styles (solid, dashed, dotted) and colors, along with optional labels for clarity. Performance Tracking: Tracks total trades, win/loss counts, win rate, and profit factor, providing a clear overview of strategy performance. Liquidation Risk Alerts: Warns traders if stop-loss levels risk liquidation based on leverage settings, enhancing risk awareness. Benefits for Traders Beginner-Friendly: Simplifies the complexities of the TradingView Strategy Tester, offering an intuitive interface for new traders to simulate and evaluate trades without confusion. Real-World Insights: Helps traders understand the actual profit or loss potential of their strategies by factoring in capital, risk, and leverage, bridging the gap between theoretical backtesting and real-world execution. Enhanced Decision-Making: Provides clear, real-time analytics on risk-reward ratios, unrealized P&L, and trade performance, enabling informed trading decisions. Customizable and Flexible: Allows customization of TP/SL settings, table positions, colors, and sizes, catering to individual trader preferences. Risk Management Focus: Encourages disciplined trading by highlighting risk amounts, potential profits, and liquidation risks, fostering better financial planning. Why This Indicator Stands Out Many traders struggle to translate backtested strategy results into real-world outcomes due to the abstract nature of percentage-based profitability metrics. This indicator addresses that challenge by providing a practical, user-friendly tool that simulates trades with real-world parameters like capital, leverage, and compounding. Its open-source nature ensures accessibility, while its integration with other indicators makes it versatile for various trading styles. How to Use Add to TradingView: Copy the Pine Script code into TradingView’s Pine Editor and add it to your chart. Configure Inputs: Set your initial capital, risk percentage, leverage, and TP/SL values in the indicator settings. Select external long/short signal sources if integrating with other indicators. Monitor Dashboards: Use the Money Management and Target Dashboard tables to track trade performance and risk metrics in real time. Analyze Results: Review win rates, profit factors, and P&L to refine your trading strategy. Credits This indicator builds upon the open-source contributions of LuxAlgo and TCP , whose efforts in sharing their code have made this tool possible. Their dedication to the trading community is deeply appreciated.
Pine Script®指標
由mishy_Champ提供
Session HighlightsCrypto relevant global equity market open/close indicator, high opacity background highlights follow the following color scheme & daily time ranges (times in EST): Orange: 8:00 PM to 9:30 PM (Sunday - Thursday): Japan/South Korea Yellow: 9:30 PM to +1D 4:00 AM (Sunday - Thursday): Hong Kong Aqua: 8:00 AM to 9:30 AM (Monday - Friday): US Premarket / Macro Data Release Blue: 9:30 AM to 4:00 PM (Monday - Friday): US White: 4:00 PM to +2D 6:00 PM (Friday - Sunday): Weekend *Market Holidays not accounted for
Pine Script®指標
由bobbyjacowleff提供
The LEAP Contest - Symbol & Max Position Table TrackerDescription: This indicator tracks the maximum contracts allowed to be traded for TradingView’s *"The Leap"* Contest. It displays a horizontal table at the bottom right of your chart showing up to 20 symbols along with their maximum allowable open contract positions. Use case: Designed specifically for traders participating in *The Leap* Contest on TradingView. Users need to enter the symbol and the maximum contracts allowed for that symbol in the settings menu for each new contest. It provides a quick reference to ensure compliance with contest rules on maximum position sizes. How it works: The table shows two rows: the top row displays the symbol name, and the bottom row shows the max contract limit. If the currently loaded chart symbol matches any symbol in the list, its text color changes to yellow . Customization: Symbols and limits must be updated in the indicator’s settings before each contest to reflect the current rules.
Pine Script®指標
由Midgar-提供
Futures Margin Lookup TableThis script applies a table to the upper right corner of the screen, which provides the intraday and overnight margin requirements of the currently selected symbol. In this indicator the user must provide the broker data in the form of specifically formatted text blocks. The data for which should be found on the broker website. The purpose for it's creation is due to the non-standard way each individual broker may price their margins and lack of information within TradingView when connected to some (maybe all) brokers, including when paper trading, as the flat percentage rule is not accurate. An example of information for NinjaTrader could look like this MES;Micro S&P;$50;$2406 ES;E-Mini S&P;$500;$24,053 GC;Gold;$500;$16500 NQ;E-Mini Nasdaq;$1,000;$34,810 FDAX;Dax Index;€2,000;€44,311 Each symbol begins a new line, and the values on that line are separated by semicolons (;) Each line consists of the following... SYMBOL : Search string used to match to the beginning of the current chart symbol. NAME: Human readable name INTRA: Intraday trading margin requirement per contract OVERNIGHT: Overnight trading margin requirement per contract The script simply finds a matching line within your provided information using the current chart symbol. So for example the continuous chart for NQ1! would match to the user specified line starting with NQ... as would the individual contract dates such as NQM2025, NQK2025, etc. NOTES: There is a possibility that symbols with similar starting characters could match. If this is the case put the longer symbol higher in the list. There is also a line / character limit to the text input fields within pinescript Ensure the text you paste into them is not truncated. If so there are 3 input fields for just this purpose. Find the last complete line and continue the remaining symbol lines on the subsequent inputs.
Pine Script®指標
由sannyasi007提供
PCA Regime-Adjusted MomentumSummary The PCA Regime-Adjusted Momentum (PCA-RAM) is an advanced market analysis tool designed to provide nuanced insights into market momentum and structural stability. It moves beyond traditional indicators by using Principal Component Analysis (PCA) to deconstruct market data into its most essential patterns. The indicator provides two key pieces of information: A smoothed momentum signal based on the market's dominant underlying trend. A dynamic regime filter that gauges the stability and clarity of the market's structure, advising you when to trust or fade the momentum signals. This allows traders to not only identify potential shifts in momentum but also to understand the context and confidence behind those signals. Core Concepts & Methodology The strength of this indicator lies in its sound, data-driven methodology. 1. Principal Component Analysis (PCA) At its core, the indicator analyzes a rolling window (default 50 periods) of standardized market data (Open, High, Low, Close, and Volume). PCA is a powerful statistical technique that distills this complex, 5-dimensional data into its fundamental, uncorrelated components of variance. We focus on the First Principal Component (PC1), which represents the single most dominant pattern or "theme" driving the market's behavior in the lookback window. 2. The Momentum Signal Instead of just looking at price, we project the current market data onto this dominant underlying pattern (PC1). This gives us a raw "projection score" that measures how strongly the current bar aligns with the historically dominant market structure. This raw score is then smoothed using two an exponential moving averages (a fast and a slow line) to create a clear, actionable momentum signal, similar in concept to a MACD. 3. The Dynamic Regime Filter This is arguably the indicator's most powerful feature. It answers the question: "How clear is the current market picture?" It calculates the Market Concentration Ratio, which is the percentage of total market variance explained by PC1 alone. A high ratio indicates that the market is moving in a simple, one-dimensional way (e.g., a clear, strong trend). A low ratio indicates the market is complex, multi-dimensional, and choppy, with no single dominant theme. Crucially, this filter is dynamic. It compares the current concentration ratio to its own recent average, allowing it to adapt to any asset or timeframe. It automatically learns what "normal" and "choppy" look like for the specific chart you are viewing. How to Interpret the Indicator The indicator is displayed in a separate pane with two key visual elements: The Momentum Lines (White & Gold) White Line: The "Fast Line," representing the current momentum. Gold Line: The "Slow Line," acting as the trend confirmation. Bullish Signal: A crossover of the White Line above the Gold Line suggests a shift to positive momentum. Bearish Signal: A crossover of the White Line below the Gold Line suggests a shift to negative momentum. The Regime Filter (Purple & Dark Red Background) This is your confidence gauge. Navy Blue Background (High Concentration): The market structure is stable, simple, and trending. Momentum signals are more reliable and should be given higher priority. Dark Red Background (Low Concentration): The market structure is complex, choppy, or directionless. Momentum signals are unreliable and prone to failure or "whipsaws." This is a signal to be cautious, tighten stops, or potentially stay out of the market. Potential Trading Strategies This tool is versatile and can be used in several ways: 1. Primary Signal Strategy Condition: Wait for the background to turn Purple, confirming a stable, high-confidence regime. Entry: Take the next crossover signal from the momentum lines (White over Gold for long, White under Gold for short). Exit/Filter: Consider exiting positions or ignoring new signals when the background turns Navy. 2. As a Confirmation or Filter for Your Existing Strategy Do you have a trend-following system? Only enable its long and short signals when the PCA-RAM background is Purple. Do you have a range-trading or mean-reversion system? It might be most effective when the PCA-RAM background is Navy, indicating a lack of a clear trend. 3. Advanced Divergence Analysis Look for classic divergences between price and the momentum lines. For example, if the price is making a new high, but the Gold Line is making a lower high, it may indicate underlying weakness in the trend, even on a Purple background. This divergence signal is more powerful because it shows that the new price high is not being confirmed by the market's dominant underlying pattern.
Pine Script®指標
由marcosag90提供
Correlation MA – 15 Assets + Average (Optional)This indicator calculates the moving average of the correlation coefficient between your charted asset and up to 15 user-selected symbols. It helps identify uncorrelated or inversely correlated assets for diversification, pair trading, or hedging. Features: ✅ Compare your current chart against up to 15 assets ✅ Toggle assets on/off individually ✅ Custom correlation and MA lengths ✅ Real-time average correlation line across enabled assets ✅ Horizontal lines at +1, 0, and -1 for easy visual reference Ideal for: Portfolio diversification analysis Finding low-correlation stocks Mean-reversion & pair trading setups Crypto, equities, ETFs To use: set the benchmark chart (e.g. TSLA), choose up to 15 assets, and adjust settings as needed. Look for assets with correlation near 0 or negative values for uncorrelated performance.
Pine Script®指標
由GhostPivot提供
80-20_DCA-Alert The idea for this indicator comes from the book “$1,000 To $1,000,000 Proven Strategies for Triple Leveraged ETF Success” by B.D. Collins. In the book, he describes a charming 80/20 DCA strategy with a stronger price weighting when prices fall in order to trade leveraged ETFs. This indicator is applied to the chart of the unleveraged (!) underlying or index of the ETF. You can then use the alarm function to receive a (daily) update on how much of the cash should currently be invested into the corresponding leveraged ETF. Depending on whether the price is above or below the freely definable levels, a different weighting is recommended. The default settings are based on B.D. Collins' original strategy and are as follows: At the beginning of each quarter, if the price of the unleveraged underlying (index) of the ETF - is between 0 and 15% below the ATH, 20% of the saved cash balance is invested - between 16 and 25% below the ATH, 40% of the saved cash balance is invested - between 26 and 35% below the ATH, 60% of the saved cash balance is invested - greater than 35% below the ATH, 80% of the saved cash balance is invested More details in his book. This is not financial advice. Trading with leveraged ETFs is very risky and can lead to extreme losses Good Luck and may the force be with us
Pine Script®指標
由Hastom提供
Dr Avinash Talele momentum indicaterTrend and Volatility Metrics EMA10, EMA20, EMA50: Show the percentage distance of the current price from the 10, 20, and 50-period Exponential Moving Averages. Positive values indicate the price is above the moving average (bullish momentum). Negative values indicate the price is below the moving average (bearish or corrective phase). Use: Helps traders spot if a stock is extended or pulling back to support. RVol (Relative Volume): Compares current volume to the 20-day average. Positive values mean higher-than-average trading activity (potential institutional interest). Negative values mean lower activity (less conviction). Use: High RVol often precedes strong moves. ADR (Average Daily Range): Shows the average daily price movement as a percentage. Use: Higher ADR = more volatility = more trading opportunities. 50D Avg. Vol & 50D Avg. Vol ₹: The 50-day average volume (in millions) and value traded (in crores). Use: Confirms liquidity and suitability for larger trades. ROC (Rate of Change) Section 1W, 1M, 3M, 6M, 12M: Show the percentage price change over the last 1 week, 1 month, 3 months, 6 months, and 12 months. Positive values (green) = uptrend, Negative values (red) = downtrend. Use: Quickly see if the stock is gaining or losing momentum over different timeframes. Momentum Section 1M, 3M, 6M: Show the percentage gain from the lowest price in the last 1, 3, and 6 months. Use: Measures how much the stock has bounced from recent lows, helping find strong rebounds or new leaders. 52-Week High/Low Section From 52WH / From 52WL: Show how far the current price is from its 52-week high and low, as a percentage. Closer to 52WH = strong uptrend; Closer to 52WL = possible value or turnaround setup. Use: Helps traders identify stocks breaking out to new highs or rebounding off lows. U/D Ratio U/D Ratio: The ratio of up-volume to down-volume over the last 50 days. Above 1 = more buying volume (bullish), Below 1 = more selling volume (bearish). Use: Confirms accumulation or distribution. How This Table Helps Analysts and Traders Instant Trend Assessment: With EMA distances and ROC, analysts can instantly see if the stock is trending, consolidating, or reversing. Momentum Confirmation: ROC and Momentum sections highlight stocks with strong recent moves, ideal for momentum and breakout traders. Liquidity and Volatility Check: Volume and ADR ensure the stock is tradable and has enough price movement to justify a trade. Relative Positioning: 52-week high/low stats show whether the stock is near breakout levels or potential reversal zones. Volume Confirmation: RVol and U/D ratio help confirm if moves are backed by real buying/selling interest. Actionable Insights: By combining these metrics, traders can filter for stocks with strong trends, robust momentum, and institutional backing—ideal for swing, position, or even intraday trading.
Pine Script®指標
由dravinashmedicaloncology提供
LTA - Futures Contract Size CalculatorLTA - Futures Contract Size Calculator This indicator helps futures traders calculate the potential stop-loss (SL) value for their trades with ease. Simply input your entry price, stop-loss price, and number of contracts, and the indicator will compute the ticks moved, price movement, and total SL value in USD. Key Features: Supports a wide range of futures contracts, including: Index Futures: E-mini S&P 500 (ES), Micro E-mini S&P 500 (MES), E-mini Nasdaq-100 (NQ), Micro E-mini Nasdaq-100 (MNQ) Commodity Futures: Crude Oil (CL), Gold (GC), Micro Gold (MGC), Silver (SI), Micro Silver (SIL), Platinum (PL), Micro Platinum (MPL), Natural Gas (NG), Micro Natural Gas (MNG) Bond Futures: 30-Year T-Bond (ZB) Currency Futures: Euro FX (6E), Japanese Yen (6J), Australian Dollar (6A), British Pound (6B), Canadian Dollar (6C), Swiss Franc (6S), New Zealand Dollar (6N) Displays key metrics in a clean table (bottom-right corner): Instrument, Entry Price, Stop-Loss Price, Number of Contracts, Tick Size, Ticks Moved, Price Movement, and Total SL Value. Automatically calculates based on the selected instrument’s tick size and tick value. User-friendly interface with a dark theme for better visibility. How to Use: Add the indicator to your chart. Select your instrument from the dropdown (ensure it matches your chart’s symbol, e.g., "NG1!" for NATURAL GAS (NG)). Input your Entry Price, Stop-Loss Price, and Number of Contracts. View the results in the table, including the Total SL Value in USD. Ideal For: Futures traders looking to quickly assess stop-loss risk. Beginners and pros trading indices, commodities, bonds, or currencies. Note: Ensure your chart symbol matches the selected instrument for accurate calculations. For best results, test with a few contracts and price levels to confirm the output. This description is tailored for TradingView’s audience, providing a clear overview of the indicator’s functionality, supported instruments, and usage instructions. It also includes a note to help users avoid common pitfalls (e.g., mismatched symbols). If you’d like to adjust the tone, add more details, or include specific TradingView tags (e.g., , ), let me know!
Pine Script®指標
由lorenzocorrado_提供
Zen Lab Checklist - FNSThe Zen Lab Checklist - FNS is a simple yet powerful visual trading assistant designed to help traders maintain discipline and consistency in their trading routines. This provides a customizable on-screen checklist. This indicator allows traders to verify key conditions before entering a trade which will help identify trade quality and promote structured trading habits. This indicator is ideal for discretionary traders who follow a consistent set of entry rules. ✅ Key Features Customizable Checklist Items: Define up to 6 checklist labels with on/off toggle switches to track your trade criteria. Visual Feedback: Each checklist item displays a ✅ checkmark when conditions are met or a ❌ cross when not. Colors are visually distinct — green for confirmed, red for not confirmed. Progress Tracker: A "Trade Score" footer calculates a "trade score" percentage, helping you quickly assess the trade idea quality and readiness. Table Position Control: Easily adjust the table’s position on your chart (e.g., top-right, middle-center, bottom-left) using a dropdown menu. Custom Styling Options: - Change background and font color of checklist rows. - Set font size (tiny to huge). - Set the header and footer colors separately for visual contrast. (default is green background with white font) 📌 How to Use - Open the indicator settings. - Label your checklist items to match your personal or strategy-specific rules. - Toggle the corresponding switches based on your trade setup conditions. - Review the on-chart checklist and "Trade Score" to confirm your trade decision. 🎯 Why Use This? - Discipline: Keeps you aligned with your trading plan. - Clarity: Clear visual indicator of trade readiness. - Efficiency: Saves time by centralizing your checklist visually on your chart. - Custom Fit: Adapt the labels and styling to match your strategy or preferences. ⚠️ Notes This is a manual checklist, meaning you control the toggle switches based on your judgment. Ideal for discretionary traders who follow a consistent set of entry rules.
Pine Script®指標
由makinbills提供
AsturRiskPanelIndicator Summary ATR Engine Length & Smoothing: Choose how many bars to use (default 14) and the smoothing method (RMA/SMA/EMA/WMA). Median ATR: Computes a rolling median of ATR over a user-defined look-back (default 14) to derive a “scalp” target. Scalp Target Automatically set at ½ × median ATR, snapped to the nearest tick. Optional rounding to whole points for simplicity. Stop Calculation ATR Multiplier: Scales current ATR by a user input (default 1.5) to produce your stop distance in points (and ticks when appropriate). Distortion Handling: Switches between point-only and point + tick displays based on contract specifications. Risk & Sizing Risk % of account per trade (default 2 %). Calculates dollar risk per contract and optimal contract count. Displays all metrics (scalp, stop, risk/contract, max contracts, max risk, account size) in a customizable on-chart table. ATR-Based Stop Placement Guidelines Trade Context ATR Multiplier Notes Tight Range Entry 1.0 × ATR High-conviction, precise entries. Expect more shake-outs. Standard Trend Entry 1.5 × ATR Balanced for H2/L2, MTR, DT/DB entries. Breakouts/Microchannels 2.0 × ATR Wide stops through chop—Brooks-style breathing room. How to Use Select ATR Settings Pick an ATR length (e.g. 14) and smoothing (RMA for stability). Adjust the median length if you want a faster/slower scalp line. Align Multiplier with Your Setup For tight-range entries, set ATR Multiplier ≈ 1.0. For standard trend trades, leave at 1.5. For breakout/pullback setups, increase to 2.0 or more. Customize Risk Parameters Enter your account size and desired risk % per trade (e.g. 2 %). The table auto-calculates how many contracts you can take. Read the On-Chart Table Scalp shows your intraday target. Stop gives Brooks-style stop distance in points (and ticks). Risk/Contract is the dollar risk per contract. Max Contracts tells you maximum position size. Max Risk confirms total dollar exposure. Visual Confirmation Place your entry, then eyeball the scalp and stop levels against chart structure (e.g. swing highs/lows). Adjust the ATR multiplier if market context shifts (e.g. volatility spikes). By blending this sizing panel with contextual ATR multipliers, you’ll consistently give your trades the right amount of “breathing room” while keeping risk in check.
Pine Script®指標
由Govy42提供
Profit Guard ProProfitGuard Pro ProfitGuard Pro is a risk management and profit calculation tool that helps traders optimize their trades by handling position sizing, risk management, leverage, and take profit calculations. With support for both cumulative and non-cumulative take profit strategies, this versatile indicator provides the insights you need to maximize your trading strategy. How to Use ProfitGuard Pro: Load the Indicator: Add ProfitGuard Pro to your chart in TradingView. Set Your Entry Position: Input your desired entry price. Define Your Stop Loss: Enter the price at which your trade will exit to minimize losses. Add Take Profit Levels: Input your TP1, TP2, TP3, and TP4 levels, as needed. If you want fewer take profit levels, adjust the number of TPs in the settings menu. You can choose between 1 to 4 take profit levels based on your strategy. Adjust Risk Settings: Specify your account size and risk percentage to calculate position size and leverage. Choose Cumulative or Non-Cumulative Mode: Toggle cumulative profit mode to either recalculate position sizes as each take profit is hit or keep position sizes static for each TP. Once set up, ProfitGuard Pro will automatically calculate your position size, leverage, and potential profits for each take profit level, providing a clear visual on your chart to guide your trading decisions. Key Features: Risk Management: Calculate your risk percentage based on account size and stop loss. Visualize risk in dollar terms and percentage of your account. Position Size & Leverage: Automatically calculate the ideal position size and leverage for your trade based on your entry, stop loss, and risk settings. Ensure you are trading with the appropriate leverage for your account size. Cumulative vs Non-Cumulative Profit Mode: Cumulative Mode: Adjusts position size after each take profit is reached, recalculating for remaining contracts. Non-Cumulative Mode: Treats each take profit as a separate calculation using the full position size. Take Profit Levels: Set up to 4 customizable take profit levels. Adjust percentage values for each TP target, and visualize them on your chart with easy-to-read lines. Profit Calculation: Displays potential profits for each take profit level based on whether cumulative or non-cumulative mode is selected. Calculate your risk-reward ratio dynamically at each TP. Customizable Visuals: Easily customize the table's size, position, and color scheme to fit your chart. Visualize key trade details like leverage, contracts, margin, and profits directly on your chart. Short and Long Position Support: Automatically adjusts calculations based on whether you're trading long or short.
Pine Script®指標
由svstaats提供
Value at Risk (VaR/CVaR) - Stop Loss ToolThis script calculates Value at Risk (VaR) and Conditional Value at Risk (CVaR) over a configurable T-bar forward horizon, based on historical T-bar log returns. It plots projected price thresholds that reflect the worst X% of historical return outcomes, helping set statistically grounded stop-loss levels. A 95% 5-day VaR of −3% means: “In the worst 5% of all historical 5-day periods, losses were 3% or more.” If you're bullish, and your thesis is correct, price should not behave like one of those worst-case scenarios. So if the market starts trading below that 5-day VaR level, it may indicate that your long bias is invalidated, and a stop-loss near that level can help protect against further downside consistent with tail-risk behavior. How it's different: Unlike ATR or standard deviation-based methods, which measure recent volatility magnitude, VaR/CVaR incorporate both the magnitude and **likelihood** (5% chance for example) of adverse moves. This makes it better suited for risk-aware position sizing and exits grounded in actual historical return distributions. How to use for stop placement: - Set your holding horizon (T) and confidence level (e.g., 95%) in the inputs. - The script plots a price level below which only the worst 5% (or chosen %) of T-bar returns have historically occurred (VaR). - If price approaches or breaches the VaR line, your bullish/bearish thesis may be invalidated. - CVaR gives a deeper threshold: the average loss **if** things go worse than VaR — useful for a secondary or emergency stop. FURTHER NOTES FROM SOURCE CODE: //======================================================================// // If you're bullish (expecting the price to go up), then under normal circumstances, prices should not behave like they do on the worst-case days. // If they are — you're probably wrong, or something unexpected is happening. Basically, returns shouldn't be exhibiting downside tail-like behavior if you're bullish. // VaR(95%, T) gives the threshold below which the price falls only 5% of the time historically, over T days/bars and considering N historical samples. // CVaR tells you the expected/average price level if that adverse move continues // Caveats: // For a variety of reasons, VaR underestimates volatility, despite using historical returns directly rather than making normality assumptions // as is the case with the standard historicalvol/bollinger band/stdev/ATR approaches) // Volatility begets volatility (volatility clustering), and VaR is not a conditional probability on recent volatility so it likely underestimates the true volatility of an adverse event // Regieme shifts occur (bullish phase after prolonged bearish behavior), so upside/short VaR would underestimate the best-case days in the beginning of that move, depending on lookahead horizon/sampling period // News/events happen, and maybe your sampling period doesn't contain enough event-driven returns to form reliable stats // In general of course, this tool assumes past return distributions are reflective of forward risk (not the case in non-stationary time series) // Thus, this tool is not predictive — it shows historical tail risk, not guaranteed outcomes. // Also, when forming log-returns, overlapping windows of returns are used (to get more samples), but this introduces autocorrelation (if it wasn't there already). This means again, the true VaR is underestimated. // Description: // This script calculates and plots both Value at Risk (VaR) and // Conditional Value at Risk (CVaR) for a given confidence level, using // historical log returns. It computes both long-side (left tail) and // short-side (right tail) risk, and converts them into price thresholds (red and green lines respectively). // // Key Concepts: // - VaR: "There is a 95% chance the loss will be less than this value over T days. Represents the 95th-percentile worst empirical returns observed in the sampling period, over T bars. // - CVaR: "Given that the loss exceeds the VaR, the average of those worst 5% losses is this value. (blue line)" Expected tail loss. If the worst case breached, how bad can it get on average // - For shorts, the script computes the mirror (right-tail) equivalents. // - Use T-day log returns if estimating risk over multiple days forward. // - You can see instances where the VaR for time T, was surpassed historically with the "backtest" boolean // // Usage for Stop-Loss: // - LONG POSITIONS: // • 95th percentile means, 5% of the time (1 in 20 times) you'd expect to get a VaR level loss (touch the red line), over the next T bars. // • VaR threshold = minimum price expected with (1 – confidence)% chance. // • CVaR threshold = expected price if that worst-case zone is breached. // → Use as potential stop-loss (VaR) or disaster stop (CVaR). If you're bullish (and you're right), price should not be exhibiting returns consistent with the worst 5% of days/T_bars historically. //======================================================================//
Pine Script®指標
由scakolli提供
1A Monthly P&L Table - Using Library1A Monthly P&L Table: Track Your Performance Month-by-Month Overview: The 1A Monthly P&L Table is a straightforward yet powerful indicator designed to give you an immediate overview of your asset's (or strategy's) percentage performance on a monthly basis. Displayed conveniently in the bottom-right corner of your chart, this tool helps you quickly assess historical gains and losses, making it easier to analyze trends in performance over time. Key Features: Monthly Performance at a Glance: Clearly see the percentage change for each past month. Cumulative P&L: A running total of the displayed monthly P&L is provided, giving you a quick sum of performance over the selected period. Customizable Display: Months to Display: Choose how many past months you want to see in the table (from 1 to 60 months). Text Size: Adjust the text size (Tiny, Small, Normal, Large, Huge) to fit your viewing preferences. Text Color: Customize the color of the text for better visibility against your chart background. Intraday & Daily Compatibility: The table is optimized to display on daily and intraday timeframes, ensuring it's relevant for various trading styles. (Note: For very long-term analysis on weekly/monthly charts, you might consider other tools, as this focuses on granular monthly P&L.) How It Works: The indicator calculates the percentage change from the close of the previous month to the close of the current month. For the very first month displayed, it calculates the P&L from the opening price of the chart's first bar to the close of that month. This data is then neatly organized into a table, updated on the last bar of the day or session. Ideal For: Traders and investors who want a quick, visual summary of monthly performance. Analyzing seasonal trends or consistent periods of profitability/drawdown. Supplementing backtesting results with a clear month-by-month breakdown. Settings: Text Color: Changes the color of all text within the table. Text Size: Controls the font size of the table content. Months to Display: Determines the number of recent months included in the table.
Pine Script®指標
由forexfederation提供
ZenAlgo - DominatorThis indicator provides a structured multi-ticker overview of market momentum and relative strength by analyzing short-term price behavior across selected assets in comparison with broader crypto dominance and Bitcoin/ETH performance. Ticker and Market Data Handling The script accepts up to 9 user-defined symbols (tickers) along with BTCUSD and ETHUSD. For each symbol: It retrieves the current price. It also requests the daily opening price from the "D" timeframe to compute intraday percentage change. For BTC, ETH, and dominance (sum of BTC, USDT, and USDC dominance), daily change is calculated using this same method. This comparison enables tracking relative performance from the daily open, which provides meaningful insight into intraday strength or weakness among different assets. Dominance Logic The indicator aggregates dominance data from BTC , USDT , and USDC using TradingView’s CRYPTOCAP indices. This combined dominance is used as a reference in directional and status calculations. ETH dominance is also analyzed independently. Changes in dominance are used to infer whether market attention is shifting toward Bitcoin/stablecoins (typically indicating risk-off sentiment) or away from them (typically risk-on behavior, benefiting altcoins). Price Direction Estimation The script estimates directional bias using an EMA-based deviation technique: A short EMA (user-defined lookback , default 4 bars) is calculated. The current close is compared to the EMA to assess directional bias. Recent candle changes are also inspected to confirm a consistent short-term trend (e.g., 3 consecutive higher closes for "up"). A small threshold is used to avoid classifying flat movements as trends. This directionality logic is applied separately to: The selected ticker's price BTC price Combined dominance This allows the script to contextualize the movement of each asset within broader market conditions. Market Status Evaluation A custom function analyzes ETH and BTC dominance trends along with their relative strength to define the overall market regime: Altseason is identified when BTC dominance is declining, ETH dominance rising, and ETH outperforms BTC. BTC Season occurs when BTC dominance is rising, ETH dominance falling, and BTC outperforms ETH. If neither condition is met, the state is Neutral . This classification is shown alongside each ticker's row in the table and helps traders assess whether market conditions favor Bitcoin, Ethereum, or altcoins in general. Ticker Status Classification Each ticker is analyzed independently using the earlier directional logic. Its status is then determined as follows: Full Bull : Ticker is trending up while dominance is declining or BTC is also rising. Bullish : Ticker is trending up but not supported by broader bullish context. Bearish : Ticker is trending down but without broader confirmation. Full Bear : Ticker is trending down while dominance rises or BTC falls. Neutral : No strong directional bias or conflicting context. This classification reflects short-term momentum and macro alignment and is color-coded in the results table. Table Display and Plotting A configurable table is shown on the chart, which: Displays the name and status of each selected ticker. Optionally includes BTC, ETH, and market state. Uses color-coding for intuitive interpretation. Additionally, price changes from the daily open are plotted for each selected ticker, BTC, ETH, and combined dominance. These values are also labeled directly on the chart. Labeling and UX Enhancements Labels next to the current candle display price and percent change for each active ticker and for BTC, ETH, and combined dominance. Labels update each bar, and old labels are deleted to avoid clutter. Ticker names are dynamically shortened by stripping exchange prefixes. How to Use This Indicator This tool helps traders: Spot early rotations between Bitcoin and altcoins. Identify intraday momentum leaders or laggards. Monitor which tickers align with or diverge from broader market trends. Detect possible sentiment shifts based on dominance trends. It is best used on lower to mid timeframes (15m–4h) to capture intraday to short-term shifts. Users should cross-reference with longer-term trend tools or structural indicators when making directional decisions. Interpretation of Values % Change : Measures intraday move from daily open. Strong positive/negative values may indicate breakouts or reversals. Status : Describes directional strength relative to market conditions. Market State : Gives a general bias toward BTC dominance, ETH strength, or altcoin momentum. Limitations & Considerations The indicator does not analyze liquidity or volume directly. All logic is based on short-term movements and may produce false signals in ranging or low-volume environments. Dominance calculations rely on external CRYPTOCAP indices, which may differ from exchange-specific flows. Added Value Over Other Free Tools Unlike basic % change tables or price overlays, this indicator: Integrates dominance-based macro context into ticker evaluation. Dynamically classifies market regimes (BTC season / Altseason). Uses multi-factor logic to determine ticker bias, avoiding single-metric interpretation. Displays consolidated information in a table and chart overlays for rapid assessment.
Pine Script®指標
由ZenAlgo_Official提供