GOLD_30MIN_ALLINONEA comprehensive 30 minute trading tool for XAUUSD trading.
Use in combination of the indicator: 1 minute Easy Scalping Sys v3.0 (by BulltradingAM).
Rules:
1. A solid break out (measure breakout strength from the other indicator mentioned above) from the London session high or low (Orange Boxes), during the first 3 30Min candles of NYC session (Blue Boxes).
2. open position in the direction of the break out, set SL on London session high/low and TP on 1:1 RR or Bollinger Band outer line (for trending trades) and Bollinger Band Base line (for pullbacks and trend reversal trades).
3. No long trades in Bollinger red section and no short trades in Bollinger green section.
More Information:
You need the indicator only for the breakout candle momentum strength with the following indicator settings:
Timeframe 1: 1 Day
Timeframe 2: 30 Minutes
Timeframe 3: 30 Minutes
Timeframe 4: 30 Minutes
and set the week candles fill to blank for easy identification.
You will not need ATRs or Hulls lines or anything else from the other indicator.
Wyszukaj w skryptach "欧元汇率走势30天"
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.
VWAP CALENDARThe VWAP CALENDAR indicator plots up to 20 anchored Volume-Weighted Average Price (VWAP) lines on your chart, each starting from a user-defined date and time (e.g., April 20, 2024). Designed for simplicity, it helps traders visualize VWAPs for key events or dates, with customizable labels and colors. The indicator is optimized for crypto markets (e.g., BTC/USD) but works with any symbol providing volume data.
Features: Multiple VWAPs: Configure up to 20 independent VWAPs, each with a custom anchor date and time.
Dynamic Labels: Labels update in real-time, aligning precisely with each VWAP line’s price level, positioned to the right of the chart for clarity.
Customizable Settings: Adjust label text (e.g., “Event A”), line colors, line widths (1–5 pixels), text colors, and text sizes (8–40 points, default 22).
Bubble or No-Background Labels: Choose between bubble-style labels (with colored backgrounds) or plain text labels without backgrounds.
Timeframe Support: Accurate on daily, 4-hour, 1-hour, and 30-minute charts for anchors within ~1.5 years (e.g., April 20, 2024, from August 2025).
Limitations: VWAP accuracy for anchors like April 20, 2024 (~477 days back) is reliable on 1-hour and larger timeframes. Below 30-minute (e.g., 15-minute, 24-minute), VWAPs may start later or be unavailable due to TradingView’s 5,000-bar historical data limit. For distant anchors, use 4-hour or daily charts to ensure accuracy.
Requires sufficient chart history (e.g., premium account or deep exchange data) for older anchors on 1-hour or 30-minute charts.
Usage Notes: Set anchor dates via the indicator settings (e.g., “2024-04-20 00:00”).
Enable/disable individual VWAPs as needed.
Zoom out to load maximum chart history for best results, especially on 1-hour or 30-minute timeframes.
Ideal for crypto symbols with continuous trading data, but verify data availability for other markets.
Disclaimer:
This is a free indicator provided as-is.
Daily High/Low Close Breakout - GOLD### **Daily High/Low Close Breakout Indicator**
This indicator is a powerful tool for identifying potential breakout opportunities based on the previous day's price action. It's built on a unique time-based logic that defines key support and resistance levels for the trading day.
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### **How the Indicator Works**
The indicator operates in two main phases:
1. **Calculation Period (00:00 to 16:30 Tehran Time):** The indicator first observes the price action from the start of the day until 16:30. During this time, it records the highest and lowest **closing prices** of all candles. The chart background is shaded gray to visually mark this period.
2. **Trading Period (16:30 to 16:30 the next day):** At 16:30, the highest and lowest close levels are finalized and drawn as horizontal lines. These levels then become the primary breakout zones for the next 24 hours. The indicator will generate signals whenever the price crosses these lines.
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### **Trading Signals**
The indicator uses a simple and effective crossover logic for its signals:
* **BUY Signal:** A signal is generated when a candle's closing price **crosses above** the high close line.
* **SELL Signal:** A signal is generated when a candle's closing price **crosses below** the low close line.
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### **Important Usage Guidelines**
For optimal performance, please follow these specific recommendations:
* **Timeframe:** This indicator is designed and optimized to be used exclusively on the **15-minute timeframe**. Using it on other timeframes may produce inconsistent or unreliable results.
* **Primary Asset:** The logic for this indicator was developed and backtested primarily for **Gold (XAUUSD)**. Its performance and win rate have been observed to be the most consistent on this asset.
* **Asset Restriction:** It is strongly recommended to **avoid using this indicator on other currency pairs or assets**, as it has not been optimized for their specific market behavior.
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### **Disclaimer**
*This indicator is provided for informational and educational purposes only. It is not financial advice. Past performance is not a guarantee of future results. All trading decisions should be based on your own research and risk analysis. Always use proper risk management.*
Contrarian Investor📌 Indicator Overview
Name:Contrarian investor
Purpose: Identify oversold or overbought conditions for simple reversal trades.
Key Features:
Uses the 200-period moving average (200MA) to determine the market trend.
Uses RSI to detect oversold and overbought levels.
Includes a signal interval filter to prevent excessive signals.
📌 Signal Conditions
BUY (Reversal Buy)
Price is below the 200MA
RSI is below the oversold threshold (default: 30)
When both conditions are met, a "BUY" label is plotted below the bar.
SELL (Reversal Sell)
Price is above the 200MA
RSI is above the overbought threshold (default: 70)
When both conditions are met, a "SELL" label is plotted above the bar.
📌 Parameters
MA Length: Default 200 (used for trend detection)
RSI Length: Default 14
RSI Oversold: Default 30 (trigger for BUY signals)
RSI Overbought: Default 70 (trigger for SELL signals)
Signal Interval (bars): Default 10 (prevents duplicate signals)
📌 How to Use
Use the 200MA to confirm the trend direction.
Wait for RSI to reach extreme levels (oversold or overbought).
When a "BUY" or "SELL" label appears, consider a potential entry.
For better accuracy, combine with support/resistance or price action confirmation.
📌 Notes
This indicator is designed as a supplementary tool, not a standalone entry system.
Adjust the signal interval based on your trading style (e.g., shorter for scalping, longer for swing trading).
In strong trending markets, reversal signals may fail frequently, so additional confluence is recommended.
You need to adjust the settings depending on the market conditions.
This indicator is not intended for use during strong trending markets, such as after major economic news releases.
It is best suited for range-bound markets and scalping within a few-dollar price range.
📌 インジケーターの概要
名前：Contrarian investor
目的：過剰に売られた/買われたタイミングでの逆張りシグナルを簡単に確認
特徴：
200MAを基準にトレンド方向を判定
RSIで売られすぎ・買われすぎを検出
過剰なシグナルを防ぐための「シグナル間隔制限」付き
📌 シグナルの条件
BUY（逆張り買い）
現在の価格が 200MAより下
RSIが 設定値（初期値30）以下
この条件で「BUY」ラベルがチャート下に表示されます。
SELL（逆張り売り）
現在の価格が 200MAより上
RSIが 設定値（初期値70）以上
この条件で「SELL」ラベルがチャート上に表示されます。
📌 パラメータ設定
MA期間：デフォルト200（200MAで長期トレンドを判定）
RSI期間：デフォルト14
RSI売られすぎ：デフォルト30（BUYの発生条件）
RSI買われすぎ：デフォルト70（SELLの発生条件）
シグナル間隔（バー）：デフォルト10（重複シグナル防止）
📌 使い方
200MAでトレンド方向を確認
RSIが極端な水準に達したら逆張りシグナル発生
「BUY」または「SELL」のラベルが出たら検討
他のテクニカル（サポレジ・プライスアクション）と組み合わせると精度向上
📌 注意点
単独でのエントリー判断には使わず、補助的に活用するのが推奨
シグナル間隔は調整可能（例：スキャルピングなら短め、スイングなら長め）
トレンドが強い相場では逆張りシグナルが連続して外れる可能性あり
相場環境によって設定を変える必要がある
指標発表後など強いトレンドが出る時ではなくレンジ相場で数ドル幅のスキャルピングをするのに向いている。
BUY in HASH RibbonsHash Ribbons Indicator (BUY Signal)
A TradingView Pine Script v6 implementation for identifying Bitcoin miner capitulation (“Springs”) and recovery phases based on hash rate data. It marks potential low-risk buying opportunities by tracking short- and long-term moving averages of the network hash rate.
⸻
Key Features
• Hash Rate SMAs
• Short-term SMA (default: 30 days)
• Long-term SMA (default: 60 days)
• Phase Markers
• Gray circle: Short SMA crosses below long SMA (start of capitulation)
• White circles: Ongoing capitulation, with brighter white when the short SMA turns upward
• Yellow circle: Short SMA crosses back above long SMA (end of capitulation)
• Orange circle: Buy signal once hash rate recovery aligns with bullish price momentum (10-day price SMA crosses above 20-day price SMA)
• Display Modes
• Ribbons: Plots the two SMAs as colored bands—red for capitulation, green for recovery
• Oscillator: Shows the percentage difference between SMAs as a histogram (red for negative, blue for positive)
• Optional Overlays
• Bitcoin halving dates (2012, 2016, 2020, 2024) with dashed lines and labels
• Raw hash rate data in EH/s
• Alerts
• Configurable alerts for capitulation start, recovery, and buy signals
⸻
How It Works
1. Data Source: Fetches daily hash rate values from a selected provider (e.g., IntoTheBlock, Quandl).
2. Capitulation Detection: When the 30-day SMA falls below the 60-day SMA, miners are likely capitulating.
3. Recovery Identification: A rising 30-day SMA during capitulation signals miner recovery.
4. Buy Signal: Confirmed when the hash rate recovery coincides with a bullish shift in price momentum (10-day price SMA > 20-day price SMA).
⸻
Inputs
Hash Rate Short SMA: 30 days
Hash Rate Long SMA: 60 days
Plot Signals: On
Plot Halvings: Off
Plot Raw Hash Rate: Off
⸻
Considerations
• Timeframe: Best applied on daily charts to capture meaningful miner behavior.
• Data Reliability: Ensure the chosen hash rate source provides consistent, gap-free data.
• Risk Management: Use alongside other technical indicators (e.g., RSI, MACD) and fundamental analysis.
• Backtesting: Evaluate performance over different market cycles before live deployment.
Overheat Oscillator with DivergenceIndicator Description
The Overheat Oscillator with Divergence is an advanced technical indicator designed for the TradingView platform, assisting traders in identifying potential market reversal points by analyzing price momentum and volume, as well as detecting divergences. The indicator combines trend strength assessment with signal smoothing to provide clear indications of market overheat or oversold conditions. An optional divergence detection feature allows for the identification of discrepancies between price movement and the oscillator's value, which may signal upcoming trend changes.
The indicator is displayed in a separate panel below the price chart and offers visual cues through a color gradient, horizontal reference lines, and a dynamic market sentiment table. Users can customize numerous parameters, such as calculation periods, sentiment thresholds, line colors, and visualization styles, making the indicator a versatile tool for various trading strategies.
How the Indicator Works
The indicator is based on the following key components:
Oscillator Calculations
The indicator analyzes price candles, assigning a score based on their nature. A bullish candle (when the closing price is higher than the opening price) receives a score of +1.0, while a bearish candle (when the closing price is lower than the opening price) receives a score of -1.0. This scoring reflects the strength of price movement over a given period.
The score is modified by a volume multiplier (default: 2.0) if the candle's volume exceeds the volume's simple moving average (SMA, default: calculated over 20 candles). This ensures that candles with higher volume have a greater impact on the oscillator's value, better capturing significant market movements driven by increased trading activity. For example, a bullish candle with high volume may receive a score of +2.0 instead of +1.0, amplifying the bullish signal.
The scores are summed over a specified number of candles (default: 20), normalized to a 0–100 range, and then smoothed using a simple moving average (SMA, default: 5 periods) to reduce noise and improve signal clarity.
Color Gradient
The oscillator's values are visualized using a color gradient that changes based on the oscillator's level:
Green: Market cooldown (values below the Gradient Min threshold).
Yellow: Neutral sentiment (values between Gradient Min and Gradient Yellow).
Orange: Elevated activity (values between Gradient Yellow and Gradient Orange).
Red: Market overheat (values above Gradient Orange).
The color gradient is applied as the background in the oscillator panel, facilitating quick assessment of market sentiment.
Reference Levels
The indicator displays customizable horizontal lines for key thresholds (e.g., Overheat Threshold, Oversold Threshold, Gradient Min, Yellow, Orange, Max). These lines are visible only at the height of the last few oscillator candles, preventing chart clutter and helping users focus on current values.
Users can also define three custom horizontal lines with selectable styles (solid, dotted, dashed) and colors. These lines serve as auxiliary tools, e.g., for marking personal support/resistance levels, but do not affect the oscillator's signals or background colors.
Market Sentiment
The indicator displays sentiment labels in a table located in the top-right corner of the panel, dynamically updating based on the oscillator's value:
Cooled: Values below Gradient Yellow (default: 35).
Neutral: Values between Gradient Yellow and Gradient Orange (default: 60).
Excited: Values between Gradient Orange and Overheat Threshold (default: 70).
Overheated: Values above Overheat Threshold (default: 70).
The Overheat Threshold and Oversold Threshold are critical for displaying the "Overheated" and "Cooled" labels in the sentiment table, enabling users to quickly identify extreme market conditions. The labels update when key thresholds are crossed, and their colors match the oscillator's gradient.
Divergence Detection
The indicator offers optional detection of regular bullish and bearish divergences:
Bullish Divergence: Occurs when the price forms a lower low, but the oscillator forms a higher low, suggesting a weakening downtrend.
Bearish Divergence: Occurs when the price forms a higher high, but the oscillator forms a lower high, suggesting a weakening uptrend.
Divergences are marked on the chart with labels ("Bull" for bullish, "Bear" for bearish) and lines indicating pivot points. They are calculated with a delay equal to the Lookback Right setting (default: 5 candles), meaning signals appear after pivot confirmation in the specified lookback period. The indicator also generates alerts for users when a divergence is detected.
Indicator Settings
Main Settings (SETTINGS)
Period Length: Specifies the number of candles used for oscillator calculations (default: 20).
Volume SMA Period: The period for the volume's simple moving average (default: 20).
Volume Multiplier: Multiplier applied to candle scores when volume exceeds the average (default: 2.0).
SMA Length: The period for smoothing the oscillator with a simple moving average (default: 5).
Thresholds (THRESHOLDS)
Overheat Threshold: Level indicating market overheat (default: 70). This value determines when the sentiment table displays the "Overheated" label, signaling a potential peak in an uptrend.
Oversold Threshold: Level indicating market cooldown (default: 30). This value determines when the sentiment table displays the "Cooled" label, signaling a potential bottom in a downtrend.
Gradient Min (Green): Lower threshold for the green gradient (default: 20).
Gradient Yellow Threshold: Threshold for the yellow gradient (default: 35).
Gradient Orange Threshold: Threshold for the orange gradient (default: 60).
Gradient Max (Red): Upper threshold for the red gradient (default: 70).
Visualization (VISUALIZATION)
Signal Line Color: Color of the oscillator line (default: dark red, RGB(5, 0, 0)).
Show Reference Lines: Enables/disables the display of threshold lines (default: enabled).
Divergence Settings (DIVERGENCE SETTINGS)
Calculate Divergence: Enables/disables divergence detection (default: disabled).
Lookback Right: Number of candles back for pivot analysis (default: 5).
Lookback Left: Number of candles to the left for pivot analysis (default: 5).
Line Style (STYLE)
Custom Line 1, 2, 3 Value: Levels for custom horizontal lines (default: 70, 50, 30).
Custom Line 1, 2, 3 Color: Colors for custom lines (default: black, RGB(0, 0, 0)).
Custom Line 1, 2, 3 Style: Line styles (solid, dotted, dashed; default: dashed, dotted, dashed).
How to Use the Indicator
Adding to the Chart
Add the indicator to your TradingView chart by searching for "Overheat Oscillator with Divergence."
Configure the settings according to your trading strategy.
Signal Interpretation
Overheated: Values above the Overheat Threshold (default: 70) in the sentiment table may indicate a potential uptrend peak.
Cooled: Values below the Oversold Threshold (default: 30) in the sentiment table may suggest a potential downtrend bottom.
Divergences:
Bullish: Look for "Bull" labels on the chart, indicating potential upward reversals (calculated with a Lookback Right delay).
Bearish: Look for "Bear" labels, indicating potential downward reversals (calculated with a Lookback Right delay).
Customization
Experiment with settings such as period length, volume multiplier, or gradient thresholds to tailor the indicator to your trading style (e.g., scalping, medium-term trading).
Usage Examples
Scalping: Set a shorter period (e.g., Period Length = 10, SMA Length = 3) and monitor rapid sentiment changes and divergences on lower timeframes (e.g., 5-minute charts).
Medium-Term Trading: Use default settings or increase Period Length (e.g., 30) and SMA Length (e.g., 7) for more stable signals on hourly or daily charts.
Reversal Detection: Enable divergence detection and observe "Bull" or "Bear" labels in conjunction with overheat/cooled levels in the sentiment table.
Notes
The indicator performs best when used in conjunction with other technical analysis tools, such as support/resistance lines, moving averages, or Fibonacci levels.
Divergences may serve as early signals but do not always guarantee immediate trend reversals—confirmation with other indicators is recommended.
Test different settings on historical data to find the optimal configuration for your chosen market and timeframe.
First FVG📘 Indicator Description (English)
First FVG – NY Open is a TradingView indicator designed to automatically identify the first Fair Value Gap (FVG) that appears during the New York session, following the ICT (Inner Circle Trader) methodology.
It highlights institutional inefficiencies in price caused by imbalanced price action and helps traders spot high-probability entry zones, especially after the 9:30 AM EST (New York Open).
⚙️ How It Works
Session time: The indicator scans for FVGs starting at 9:32 AM (allowing 3 candles after the NY Open to form).
FVG Conditions:
Bullish FVG: When the high of 2 candles ago is lower than the low of the current candle and the middle candle is bullish.
Bearish FVG: When the low of 2 candles ago is higher than the high of the current candle and the middle candle is bearish.
Only the first FVG per session is drawn, as taught by ICT for setups like Judas Swing or NY Reversal models.
A colored box is drawn to represent the FVG zone.
A dotted horizontal line (CE) is drawn at the midpoint of the FVG box (Consequent Encroachment), a key level watched by smart money traders.
A dashed vertical line is drawn at 9:30 NY time to mark the open.
🧠 How to Use It
Wait for the NY Open (9:30 AM EST) – the indicator becomes active at 9:32 AM.
Watch for the first FVG box of the day. This is often a high-probability reaction zone.
Use the CE line (center of the FVG) as a reference for entries, rejections, or liquidity grabs.
Combine with market structure, PD Arrays, and liquidity concepts as taught by ICT for confluence.
The FVG box and CE line will extend forward for several candles for visual clarity.
🎛️ Customizable Settings
Session time (default: 09:32–16:00 NY)
FVG box color (up/down)
Text color
Max number of days to keep boxes on chart
Option to show or hide the 9:30 NY Open vertical line
Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features.
Core Functionality
1. Signal Generation Logic
Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes).
Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273).
RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active.
Buy Signal: A buy is triggered if, after setup:
Price rises back above the level,
The RSI rate of change (ROC) indicates exhaustion of the drop,
The current bar shows positive momentum.
2. Trade Management
Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically.
Exit Signals: The script signals exit based on price action relative to these risk management levels.
3. Filters & Enhancements
Parabolic Move Filter: Prevents entries during extreme price moves.
Dead Cat Bounce Filter: Avoids false signals after sharp reversals.
Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts).
Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement.
User Inputs & Customization
Trade Direction: Toggle between LONG and SHORT signal generation.
Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements.
Trade Settings: Set take profit, stop loss, and trailing stop behavior.
Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis.
Backtesting: Integrated backtester with summary and detailed statistics tables.
Technical Features
Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics.
Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations.
Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart.
Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry).
Intended Use
Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD).
Timeframe: 30-second chart.
Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support.
Notable Customization & Extension Points
Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum".
Displacement Logic: Future update to use "FVG concept" for displacement.
High-Contrast RSI: Optional visual enhancements for RSI extremes.
Time-based Stop: Consideration for adding a time-based stop mechanism.
This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
MTF Candles [Fadi x MMT]MTF Candles
Overview
The MTF Candles indicator is a powerful tool designed for traders who want to visualize higher timeframe (HTF) candles directly on their current chart. Built with flexibility and precision in mind, this Pine Script indicator displays up to six higher timeframe candles, complete with customizable styling, sweeps, midpoints, fair value gaps (FVGs), volume imbalances, and trace lines. It’s perfect for multi-timeframe analysis, helping traders identify key levels, market structure, and potential trading opportunities with ease.
Key Features
- Multi-Timeframe Candles : Display up to six higher timeframe candles (e.g., 5m, 15m, 30m, 4H, 1D, 1W) on your chart, with configurable timeframes and visibility.
- Sweeps Detection : Identify liquidity sweeps (highs/lows) with customizable line styles, widths, and colors, plus optional alerts for confirmed bullish or bearish sweeps.
- Midpoint Lines : Plot the midpoint (average of high and low) of the previous HTF candle, with customizable color, width, and style for enhanced market analysis.
- Fair Value Gaps (FVGs) : Highlight gaps between non-adjacent candles, indicating potential areas of interest for price action.
- Volume Imbalances : Detect and display volume imbalances between adjacent candles, aiding in spotting significant price levels.
- Trace Lines : Connect HTF candle open, close, high, and low prices to their respective chart bars, with customizable styles and optional price labels.
- Custom Daily Open Times : Support for custom daily candle open times (Midnight, 8:30, or 9:30) to align with specific market sessions.
- Dynamic Labels : Show timeframe names, remaining time until the next HTF candle, and interval labels (e.g., day of the week for daily candles) with adjustable positions and sizes.
- Highly Customizable : Fine-tune candle appearance, spacing, padding, and visual elements to suit your trading style.
How It Works
The indicator renders HTF candles as boxes (bodies) and lines (wicks) on the right side of the chart, with each timeframe offset for clarity. It dynamically updates candles in real-time, tracks their highs and lows, and displays sweeps and midpoints when conditions are met. FVGs and volume imbalances are calculated based on candle relationships, and trace lines link HTF candle levels to their originating bars on the chart.
Sweep Logic
- A bearish sweep occurs when the current candle’s high exceeds the previous candle’s high, but the close is below it.
- A bullish sweep occurs when the current candle’s low falls below the previous candle’s low, but the close is above it.
- Sweeps are visualized as horizontal lines and can trigger alerts when confirmed on the next candle.
Midpoint Logic
- A midpoint line is drawn at the average of the previous HTF candle’s high and low, extending until the next HTF candle forms.
- Useful for identifying potential support/resistance or mean reversion levels.
Imbalance Detection
- FVGs : Identified when a candle’s low is above the next-but-one candle’s high (or vice versa), indicating a price gap.
- Volume Imbalances : Detected between adjacent candles where the body of one candle doesn’t overlap with the next, signaling potential liquidity zones.
Settings
Timeframe Settings
- HTF 1–6 : Enable/disable up to six higher timeframes (default: 5m, 15m, 30m, 4H, 1D, 1W) and set the maximum number of candles to display per timeframe (default: 4).
- Limit to Next HTFs : Restrict the number of active timeframes (1–6).
Styling
- Body, Border, Wick Colors : Customize bull and bear candle colors (default: light gray for bulls, dark gray for bears).
- Candle Width : Adjust the width of HTF candles (1–4).
- Padding and Spacing : Set the offset from the current price action and spacing between candles and timeframes.
Label Settings
- HTF Label : Show/hide timeframe labels (e.g., "15m", "4H") at the top/bottom of candle sets.
- Remaining Time : Display the countdown to the next HTF candle.
Interval Value: Show day of the week for daily candles or time for intraday candles.
- Label Position/Alignment : Choose to display labels at the top, bottom, or both, and align them with the highest/lowest candles or follow individual candle sets.
Imbalance Settings
- Fair Value Gap : Enable/disable FVGs with customizable color (default: semi-transparent gray).
- Volume Imbalance : Enable/disable volume imbalances with customizable color (default: semi-transparent red).
Trace Settings
- Trace Lines : Enable/disable lines connecting HTF candle levels to their chart bars, with customizable colors, styles (solid, dashed, dotted), and sizes.
- Price Labels : Show price levels for open, close, high, and low trace lines.
- Anchor : Choose whether trace lines anchor to the first or last enabled timeframe.
Sweep Settings
- Show Sweeps : Enable/disable sweep detection and visualization.
- Sweep Line : Customize color, width, and style (solid, dashed, dotted).
- Sweep Alert : Enable alerts for confirmed sweeps.
Midpoint Settings
- Show Midpoint : Enable/disable midpoint lines.
- Midpoint Line : Customize color (default: orange), width, and style (solid, dashed, dotted).
Custom Daily Open
Custom Daily Candle Open : Choose between Midnight, 8:30, or 9:30 (America/New_York) for daily candle opens.
Usage
- Add the indicator to your TradingView chart.
- Configure the desired higher timeframes (HTF 1–6) and enable/disable features via the settings panel.
- Adjust styling, labels, and spacing to match your chart preferences.
Use sweeps, midpoints, FVGs, and volume imbalances to identify key levels for trading decisions.
- Enable sweep alerts to receive notifications for confirmed liquidity sweeps.
Notes
Performance: The indicator is optimized for up to 500 boxes, lines, and labels, with a maximum of 5000 bars back. Can be slow at a time
Time Zone: Custom daily opens use the America/New_York time zone for consistency with major financial markets.
Compatibility: Ensure selected HTFs are valid (higher than the chart’s timeframe and divisible by it for intraday periods).
Price Reaction Analysis by Day of WeekOverview
The "Price Reaction Analysis by Day of Week" indicator is a tool that enables traders to analyze historical price reaction patterns to technical indicator signals on a selected day of the week. It examines price behavior on a chosen candle (from 1 to 30) in the next day or subsequent days after a signal, depending on the timeframe, and provides success rate statistics to support data-driven trading decisions. The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week comparisons. Lower timeframes generate more signals due to the higher number of candles per day.
Key Features
1. Flexible Technical Indicator Selection
Users can choose one of four technical indicators: RSI, SMI, MA, or Bollinger Bands. Each indicator has configurable parameters, such as:
RSI length, oversold/overbought levels.
SMI length, %K and %D smoothing, signal levels.
MA length.
Bollinger Bands length and multiplier.
2. Day-of-Week Analysis
The indicator allows users to select a day of the week (Monday, Tuesday, Wednesday, Thursday, Friday) for generating signals. It analyzes price reactions on a selected candle (from 1 to 30) in the next day or subsequent days after the signal. Examples:
On a daily timeframe, a signal on Monday can be analyzed for the first, fourth, or later candle (up to 30) in subsequent days (e.g., Tuesday, Wednesday).
On timeframes lower than 1 day (e.g., 12H, 8H, 6H, 4H, 1H, 15M), the analysis targets the selected candle in the next day or subsequent days. For example, on a 4H timeframe, you can analyze the second Tuesday candle following a Monday signal. The maximum timeframe is 1 day to ensure consistent day-of-week analysis.
3. Visual Signals
Signals for the analysis period are marked with background highlights in real-time when the indicator’s conditions are met. The last highlighted candle of the selected day is always analyzed. Arrows are displayed on the chart at the candle specified by the “Candles to Compare” setting (e.g., the first candle if set to 1):
Green upward triangles (below the candle) for successful buy signals (the closing price of the selected candle is higher than the signal candle’s close).
Red downward triangles (above the candle) for successful sell signals (the closing price of the selected candle is lower than the signal candle’s close).
Gray “x” marks for unsuccessful signals (no price reversal in the expected direction). Arrow positions are intuitive: buy signals below the candle, sell signals above. Highlights and arrows do not require waiting for future signals but are essential for calculating statistics.
Note: The first candle of the next day may appear shifted on the chart due to timezone differences, which can affect the timing of signal appearance.
4. Signal Conditions (Highlights) for Each Indicator
RSI: The oscillator is in oversold (buy) or overbought (sell) zones.
SMI: SMI returns from oversold (buy) or overbought (sell) zones.
MA: Price crosses the MA (upward for buy, downward for sell).
Bollinger Bands: Price returns inside the bands (from below for buy, from above for sell).
5. Success Rate Statistics
A table in the top-right corner of the chart displays:
The number of buy and sell signals for the selected day of the week.
The percentage of cases where the price of the selected candle in the next day or subsequent days reversed as expected (e.g., rising after a buy signal). Statistics are based on comparing the closing price of the signal candle with the closing price of the selected candle (e.g., first, fourth) in the next day or subsequent days.
Important: Statistics do not account for price movements within the candle or after its close. The price on the selected candle (e.g., fourth) may be lower than earlier candles but still higher than the signal candle, counting as a positive buy signal, though it does not guarantee profit.
6. Date Range
Users can specify the analysis date range, enabling strategy testing on historical data from a chosen period. Ensure the start and end dates are set correctly.
Applications
The indicator is designed for traders who want to leverage historical patterns for position planning. Examples:
On a 4-hour timeframe: If a sell signal highlight appears on Monday and statistics show an 80% chance that the fourth Tuesday candle is bearish, traders may consider playing a correction at the open of that candle.
On a daily timeframe: If a highlight indicates market overheating, traders may consider entering a position at the open of the first candle after the signal (e.g., Tuesday), provided statistics suggest an edge. Users can analyze the signal on the first candle and check later candles to validate results, increasing confidence in consistent patterns.
Key Settings
Indicator Type: Choose between RSI, SMI, MA, or Bollinger Bands.
Selected Day: Monday, Tuesday, Wednesday, Thursday, or Friday.
Candles to Compare: The number of the candle in the next day or subsequent days (from 1 to 30).
Indicator Parameters: Lengths, levels (e.g., oversold/overbought for RSI).
Background Colors: Configurable highlights for buy and sell signals.
Notes
Timeframes: The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week patterns. Timeframes lower than 1 day generate more signals due to the higher number of candles per day.
Candle Shift: The first candle of the next day may appear shifted on the chart due to timezone differences, affecting the timing of signals across markets or platforms.
Statistical Limitations: Results are based on the closing prices of the selected candle, ignoring fluctuations in earlier candles, within the candle, or subsequent price movements. Traders must assess whether entering at the open or after the close of the selected candle is profitable.
Testing: Effectiveness depends on historical data and parameter settings. Testing different configurations across markets and timeframes is recommended.
Who Is It For?
Swing and position traders who base decisions on technical analysis and historical patterns.
Market analysts seeking patterns in price behavior by day of the week.
TradingView users of all experience levels, thanks to an intuitive interface and flexible settings.
Tuga SupertrendDescription
This strategy uses the Supertrend indicator enhanced with commission and slippage filters to capture trends on the daily chart. It’s designed to work on any asset but is especially effective in markets with consistent movements.
Use the date inputs to set the backtest period (default: from January 1, 2018, through today, June 30, 2025).
The default input values are optimized for the daily chart. For other timeframes, adjust the parameters to suit the asset you’re testing.
Release Notes
June 30, 2025
• Updated default backtest period to end on June 30, 2025.
• Default commission adjusted to 0.1 %.
• Slippage set to 3 ticks.
• Default slippage set to 3 ticks.
• Simplified the strategy name to “Tuga Supertrend”.
Default Parameters
Parameter Default Value
Supertrend Period 10
Multiplier (Factor) 3
Commission 0.1 %
Slippage 3 ticks
Start Date January 1, 2018
End Date June 30, 2025
Boomerang Trading Indicator# Boomerang News Trading Indicator
## Overview
The Boomerang Trading Indicator is designed to identify potential reversal opportunities following major economic news releases. This indicator analyzes the initial market reaction to news events and provides visual cues for potential counter-trend trading opportunities based on Fibonacci retracement levels.
## How It Works
### News Event Detection
- Automatically detects major news release times (NFP, CPI, FOMC, etc.)
- Analyzes the first significant price movement following news releases
- Requires minimum candle size threshold to filter out weak reactions
### First Move Analysis
The indicator employs multiple analytical methods to determine the initial market direction:
**Simple Analysis (High Confidence):**
- When the news candle has ≥70% body-to-total ratio, uses straightforward bullish/bearish classification
**Advanced Analysis (Complex Cases):**
- Volume-weighted direction analysis
- Momentum and wick pattern analysis
- Market structure and gap analysis
- Weighted voting system combining all methods
### Entry Signal Generation
Based on the "boomerang" concept where markets often reverse after initial news reactions:
**For Bullish First Moves (Price Up Initially):**
- Generates SHORT entry signals when price retraces to 1.25-1.5 Fibonacci levels
- Visual: Red triangles above price bars
**For Bearish First Moves (Price Down Initially):**
- Generates LONG entry signals when price retraces to -0.25 to -0.5 Fibonacci levels
- Visual: Green triangles below price bars
## Key Features
### Visual Elements
- **Fibonacci Levels**: Displays key retracement levels based on the initial reaction range
- **Entry Zones**: Clear visual marking of optimal entry areas
- **Direction Arrows**: Shows the initial market reaction direction
- **Target Levels**: Displays profit target zones at 50% and 100% retracement levels
### Information Panel
Real-time display showing:
- Current setup status
- First move direction and body percentage
- Recommended trade direction
- Key price levels (reaction high/low)
- Profit targets with historical success rates
### Alert System
- Pre-news warnings (customizable timing)
- News event notifications
- Setup activation alerts
- Entry signal notifications
### Success Tracking
- Visual "BOOM!" animations when targets are hit
- Target 1 (50% level): ~95% historical success rate
- Target 2 (Main target): ~80% historical success rate
## Configuration Options
### Time Settings
- News release hour and minute (customizable for different events)
- Pre-news alert timing
- Setup duration (default 60 bars after news)
### Fibonacci Levels
- Adjustable retracement percentages
- Customizable target levels
- Mid-level importance weighting
### Risk Management
- Minimum reaction candle size filter
- Maximum risk point setting
- Visual risk/reward display
### Display Options
- Toggle Fibonacci level visibility
- Toggle target level display
- Toggle animation effects
- Customizable alert preferences
## Applicable News Events
This indicator is designed for high-impact economic releases:
- Non-Farm Payrolls (NFP) - First Friday, 8:30 AM ET
- Consumer Price Index (CPI) - Monthly, 8:30 AM ET
- Producer Price Index (PPI) - Monthly, 8:30 AM ET
- Gross Domestic Product (GDP) - Quarterly, 8:30 AM ET
- FOMC Interest Rate Decisions - 8 times yearly, 2:00 PM ET
## Trading Strategy Framework
### Core Principle
Markets often overreact to news initially, then reverse toward more rational price levels. This "boomerang effect" creates short-term trading opportunities.
### Entry Strategy
1. Wait for significant initial reaction (>10 points minimum)
2. Identify the initial direction using multi-factor analysis
3. Trade opposite to the initial reaction when price reaches sweet spot zones
4. Use Fibonacci retracement levels as entry triggers
### Risk Management
- Always use appropriate position sizing
- Set stop losses beyond recent swing levels
- Consider market volatility and news importance
- Monitor for setup invalidation signals
## Important Notes
### Educational Purpose
This indicator is for educational and analytical purposes. Users should:
- Thoroughly test strategies in demo environments
- Understand the risks involved in news trading
- Consider market conditions and volatility
- Use proper risk management techniques
### Market Considerations
- High volatility during news events increases both opportunity and risk
- Spreads may widen significantly during news releases
- Different brokers may have varying execution conditions
- Economic calendar timing may vary between sources
### Limitations
- Past performance does not guarantee future results
- Market conditions can change, affecting strategy effectiveness
- News events may have unexpected outcomes affecting normal patterns
- Technical analysis should be combined with fundamental analysis
## Version Information
- Compatible with TradingView Pine Script v5
- Designed for 1-minute timeframe optimal performance
- Works on major forex pairs, indices, and commodities
- Regular updates based on market condition changes
---
**Disclaimer:** This indicator is provided for educational purposes only. Trading involves substantial risk and is not suitable for all investors. Past performance is not indicative of future results. Users should conduct their own research and consider their financial situation before making trading decisions.
Uptrick: Fusion Trend Reversion SystemOverview
The Uptrick: Fusion Trend Reversion System is a multi-layered indicator designed to identify potential price reversals during intraday movement while keeping traders informed of the dominant short-term trend. It blends a composite fair value model with deviation logic and a refined momentum filter using the Relative Strength Index (RSI). This tool was created with scalpers and short-term traders in mind and is especially effective on lower timeframes such as 1-minute, 5-minute, and 15-minute charts where price dislocations and quick momentum shifts are frequent.
Introduction
This indicator is built around the fusion of two classic concepts in technical trading: identifying trend direction and spotting potential reversion points. These are often handled separately, but this system merges them into one process. It starts by computing a fair value price using five moving averages, each with its own mathematical structure and strengths. These include the exponential moving average (EMA), which gives more weight to recent data; the simple moving average (SMA), which gives equal weight to all periods; the weighted moving average (WMA), which progressively increases weight with recency; the Arnaud Legoux moving average (ALMA), known for smoothing without lag; and the volume-weighted average price (VWAP), which factors in volume at each price level.
All five are averaged into a single value — the raw fusion line. This fusion acts as a dynamically balanced centerline that adapts to price conditions with both smoothing and responsiveness. Two additional exponential moving averages are applied to the raw fusion line. One is slower, giving a stable trend reference, and the other is faster, used to define momentum and cloud behavior. These two lines — the fusion slow and fusion fast — form the backbone of trend and signal logic.
Purpose
This system is meant for traders who want to trade reversals without losing sight of the underlying directional bias. Many reversal indicators fail because they act too early or signal too frequently in choppy markets. This script filters out noise through two conditions: price deviation and RSI confirmation. Reversion trades are considered only when the price moves a significant distance from fair value and RSI suggests a legitimate shift in momentum. That filtering process gives the trader a cleaner, higher-quality signal and reduces false entries.
The indicator also visually supports the trader through colored bars, up/down labels, and a filled cloud between the fast and slow fusion lines. These features make the market context immediately visible: whether the trend is up or down, whether a reversal just occurred, and whether price is currently in a high-risk reversion zone.
Originality and Uniqueness
What makes this script different from most reversal systems is the way it combines layers of logic — not just to detect signals, but to qualify and structure them. Rather than relying on a single MA or a raw RSI level, it uses a five-MA fusion to create a baseline fair value that incorporates speed, stability, and volume-awareness.
On top of that, the system introduces a dual-smoothing mechanism. It doesn’t just smooth price once — it creates two layers: one to follow the general trend and another to track faster deviations. This structure lets the script distinguish between continuation moves and possible turning points more effectively than a single-line or single-metric system.
It also uses RSI in a more refined way. Instead of just checking if RSI is overbought or oversold, the script smooths RSI and requires directional confirmation. Beyond that, it includes signal memory. Once a signal is generated, a new one will not appear unless the RSI becomes even more extreme and curls back again. This memory-based gating reduces signal clutter and prevents repetition, a rare feature in similar scripts.
Why these indicators were merged
Each moving average in the fusion serves a specific role. EMA reacts quickly to recent price changes and is often favored in fast-trading strategies. SMA acts as a long-term filter and smooths erratic behavior. WMA blends responsiveness with smoothing in a more balanced way. ALMA focuses on minimizing lag without losing detail, which is helpful in fast markets. VWAP anchors price to real trade volume, giving a sense of where actual positioning is happening.
By combining all five, the script creates a fair value model that doesn’t lean too heavily on one logic type. This fusion is then smoothed into two separate EMAs: one slower (trend layer), one faster (signal layer). The difference between these forms the basis of the trend cloud, which can be toggled on or off visually.
RSI is then used to confirm whether price is reversing with enough force to warrant a trade. The RSI is calculated over a 14-period window and smoothed with a 7-period EMA. The reason for smoothing RSI is to cut down on noise and avoid reacting to short, insignificant spikes. A signal is only considered if price is stretched away from the trend line and the smoothed RSI is in a reversal state — below 30 and rising for bullish setups, above 70 and falling for bearish ones.
Calculations
The script follows this structure:
Calculate EMA, SMA, WMA, ALMA, and VWAP using the same base length
Average the five values to form the raw fusion line
Smooth the raw fusion line with an EMA using sens1 to create the fusion slow line
Smooth the raw fusion line with another EMA using sens2 to create the fusion fast line
If fusion slow is rising and price is above it, trend is bullish
If fusion slow is falling and price is below it, trend is bearish
Calculate RSI over 14 periods
Smooth RSI using a 7-period EMA
Determine deviation as the absolute difference between current price and fusion slow
A raw signal is flagged if deviation exceeds the threshold
A raw signal is flagged if RSI EMA is under 30 and rising (bullish setup)
A raw signal is flagged if RSI EMA is over 70 and falling (bearish setup)
A final signal is confirmed for a bullish setup if RSI EMA is lower than the last bullish signal’s RSI
A final signal is confirmed for a bearish setup if RSI EMA is higher than the last bearish signal’s RSI
Reset the bullish RSI memory if RSI EMA rises above 30
Reset the bearish RSI memory if RSI EMA falls below 70
Store last signal direction and use it for optional bar coloring
Draw the trend cloud between fusion fast and fusion slow using fill()
Show signal labels only if showSignals is enabled
Bar and candle colors reflect either trend slope or last signal direction depending on mode selected
How it works
Once the script is loaded, it builds a fusion line by averaging five different types of moving averages. That line is smoothed twice into a fast and slow version. These two fusion lines form the structure for identifying trend direction and signal areas.
Trend bias is defined by the slope of the slow line. If the slow line is rising and price is above it, the market is considered bullish. If the slow line is falling and price is below it, it’s considered bearish.
Meanwhile, the script monitors how far price has moved from that slow line. If price is stretched beyond a certain distance (set by the threshold), and RSI confirms that momentum is reversing, a raw reversion signal is created. But the script only allows that signal to show if RSI has moved further into oversold or overbought territory than it did at the last signal. This blocks repetitive, weak entries. The memory is cleared only if RSI exits the zone — above 30 for bullish, below 70 for bearish.
Once a signal is accepted, a label is drawn. If the signal toggle is off, no label will be shown regardless of conditions. Bar colors are controlled separately — you can color them based on trend slope or last signal, depending on your selected mode.
Inputs
You can adjust the following settings:
MA Length: Sets the period for all moving averages used in the fusion.
Show Reversion Signals: Turns on the plotting of “Up” and “Down” labels when a reversal is confirmed.
Bar Coloring: Enables or disables colored bars based on trend or signal direction.
Show Trend Cloud: Fills the space between the fusion fast and slow lines to reflect trend bias.
Bar Color Mode: Lets you choose whether bars follow trend logic or last signal direction.
Sens 1: Smoothing speed for the slow fusion line — higher values = slower trend.
Sens 2: Smoothing speed for the fast line — lower values = faster signal response.
Deviation Threshold: Minimum distance price must move from fair value to trigger a signal check.
Features
This indicator offers:
A composite fair value model using five moving average types.
Dual smoothing system with user-defined sensitivity.
Slope-based trend definition tied to price position.
Deviation-triggered signal logic filtered by RSI reversal.
RSI memory system that blocks repetitive signals and resets only when RSI exits overbought or oversold zones.
Real-time tracking of the last signal’s direction for optional bar coloring.
Up/Down labels at signal points, visible only when enabled.
Optional trend cloud between fusion layers, visualizing current market bias.
Full user control over smoothing, threshold, color modes, and visibility.
Conclusion
The Fusion Trend-Reversion System is a tool for short-term traders looking to fade price extremes without ignoring trend bias. It calculates fair value using five diverse moving averages, smooths this into two dynamic layers, and applies strict reversal logic based on RSI deviation and momentum strength. Signals are triggered only when price is stretched and momentum confirms it with increasingly strong behavior. This combination makes the tool suitable for scalping, intraday entries, and fast market environments where precision matters.
Disclaimer
This indicator is for informational and educational purposes only. It does not constitute financial advice. All trading involves risk, and no tool can predict market behavior with certainty. Use proper risk management and do your own research before making trading decisions.
LilSpecCodes1. Killzone Background Highlighting:
It highlights 4 key market sessions:
Killzone Time (EST) Color
Silver Bullet 9:30 AM – 12:00 PM Light Blue
London Killzone 2:00 AM – 5:00 AM Light Green
NY PM Killzone 1:30 PM – 4:00 PM Light Purple
Asia Open 7:00 PM – 11:00 PM Light Red
These are meant to help you focus during high-probability trading times.
__________________________________________________
2. Previous Day High/Low (PDH/PDL):
Plots green line = PDH
Plots red line = PDL
Tracks the current day’s session high/low and sets it as PDH/PDL on a new trading day
CHANGES WITH ETH/RTH
3. Inside Bar Marker:
Plots a small black triangle under bars where the high is lower than the previous bar’s high and the low is higher than the previous bar’s low (inside bars)
Useful for spotting potential breakout or continuation setups
4. Vertical Time Markers (White Dashed Lines)
Time (EST) Label
4:00 AM End of London Silver Bullet
9:30 AM NYSE Open
10:00 AM Start of NY Silver Bullet
11:00 AM End of NY Silver Bullet
11:30 AM (Customizable Input)
3:00 PM PM Killzone Ends
3:15 PM Futures Market Close
7:15 PM Asia Session Watch
Volume Weighted Average Price Dynamic Slope [sgbpulse]VWAP Dynamic Slope: A Comprehensive Indicator for Trend Identification and Smart Trading
Introducing VWAP Dynamic Slope, an innovative TradingView indicator that harnesses the power of Volume Weighted Average Price (VWAP) and enhances it with immediate visual feedback. The indicator colors the VWAP line based on its slope, allowing you to quickly and easily identify the direction and strength of the current trend for the asset, providing advanced tools for in-depth analysis.
What is VWAP and Why is it so Important?
VWAP (Volume Weighted Average Price) is an indicator that represents the average price at which an asset has traded, weighted by the volume traded at each price level. Unlike a simple moving average, VWAP gives greater weight to trades executed with high volume, making it a reliable measure of the asset's "true" or "fair" price within a given period. Many institutional traders use VWAP as a central reference point for evaluating the effectiveness of entries and exits. An asset trading above its VWAP is considered to have bullish momentum, and below it – bearish momentum.
How it Works: Dynamic VWAP Slope Analysis
VWAP Dynamic Slope analyzes the inclination of the VWAP line and displays it using an intuitive color scheme:
Positive Slope (Uptrend): When the VWAP points upwards, signaling positive momentum, the default color will be green.
Negative Slope (Downtrend): When the VWAP points downwards, signaling negative momentum, the default color will be orange.
Trend Change (CHG): When a change in the VWAP's trend direction occurs, a "CHG" label will be displayed. The label's color will be green if the change is to an uptrend, and orange if the change is to a downtrend.
Identifying Steep Slopes for Increased Momentum:
The indicator's uniqueness lies in its ability to identify "steep" slopes – rapid and particularly strong changes in the VWAP's direction. This indicates exceptionally strong momentum:
Steep Positive Slope: The VWAP color will change to dark green, indicating significant buying pressure.
Steep Negative Slope: The VWAP color will change to dark red, indicating significant selling pressure.
Dynamic Momentum Strength Label: In situations of steep slope (positive or negative), a dynamic label will be displayed with the change value of the VWAP at that point. This label allows you to monitor momentum strength, intensification, or weakening in real-time.
Advanced Analytical Tools for Complete Control
VWAP Dynamic Slope provides you with unprecedented flexibility through a variety of customizable tools:
Multiple VWAP Anchors and Visual Marking:
Common Time Anchors: Choose whether the VWAP resets at the beginning of each Session (daily), Week, Month, Quarter, Year, Decade, or Century.
Advanced Intraday Anchors: Within the Session, you can choose to calculate VWAP specifically for Pre-Market, Regular Hours, and Post-Market hours. This option is particularly crucial for intraday traders.
Important Event Anchors: The indicator allows for VWAP resets at significant milestones such as Earnings, Dividends, and Splits, for analyzing the market's immediate reaction.
Visual Anchor Marking: To enhance clarity and orientation, a Label ⚓ can be displayed at each selected anchor point, helping to immediately identify the start point of the VWAP calculation in the chosen context.
Customizable Bands (Up to Three on Each Side):
Add up to three Bands above and below the VWAP to identify areas of deviation and excursion from the average price. You have two calculation options:
Standard Deviation: Based on volatility and statistical distance from the VWAP.
Percentage: Defines fixed percentage-based bands from the VWAP.
Key Pre-Market Levels (Pre-Market High/Low):
Display the Pre-Market High and Low levels as separate lines on the chart. These lines often serve as important psychological support and resistance zones, allowing you to see how the VWAP behaves near them.
Full Customization and Precise Control:
VWAP Source Selection: Determine which price data type will be used for the VWAP calculation. The default is HLC3 (average of High, Low, and Close), but any other relevant data source available in TradingView can be selected.
Offset: Set an offset for the VWAP line, allowing you to shift it left or right on the time axis by a chosen number of bars.
Customizable Colors: Choose your preferred colors for each slope state, Pre-Market High/Low lines, and Bands.
Setting the "Steepness" Threshold (Per-mille Price Change Per Minute ‱/min with Auto-Adjustment): Determine the sensitivity for identifying a steep slope by setting the required change threshold in VWAP in terms of per-mille price change per minute (‱/min). The indicator performs smart adjustment for any timeframe you select on the chart (e.g., 30 seconds, 1 minute, 5 minutes, 10 minutes, etc.), ensuring that the "steepness" setting maintains consistency and relevance.
Examples for Setting the Steepness Threshold:
Suppose you set the steepness threshold to 0.3‱/min (per-mille price change per minute).
On a 30-second chart: The indicator will check if the VWAP changed by 0.15 ‱/min (half of the per-minute threshold) within a single bar. If so, the slope will be considered steep. Explanation: Since 30 seconds is half a minute, the indicator looks for a change that is half of the threshold set for a full minute.
On a 1-minute chart: The indicator will check if the VWAP changed by 0.3 ‱/min (the full per-minute threshold) within a single bar. If so, the slope will be considered steep. Explanation: Here, the bar represents a full minute, so we check the full threshold.
On a 5-minute chart: The indicator will check if the VWAP changed by 1.5 ‱/min (5 times the per-minute threshold) within a single bar. If so, the slope will be considered steep. Explanation: A 5-minute bar contains 5 minutes, so the cumulative change in VWAP needs to be 5 times greater to be considered "steep" on the same scale.
In summary, this setting allows you to precisely and uniformly control the sensitivity of steep slope detection across all timeframes, providing immense flexibility in analyzing the asset's momentum.
Advantages of Using Per-mille Price Change Per Minute (‱/min)
Using per-mille price change per minute (‱/min) offers several key advantages for your indicator:
Normalized and Objective Measurement: It provides a uniform scale for the VWAP's rate of change, regardless of the asset's price or nominal value. A 0.1 per-mille change per minute always carries the same relative significance.
Comparison Across Different Asset Prices: Using per-mille allows for direct comparison of VWAP movement strength between assets trading at very different prices (e.g., a $100 asset versus a $1 asset), enabling an understanding of true momentum without bias from the nominal price.
Smart Timeframe Agnostic Adjustment: This is a critical capability. The indicator automatically adjusts the per-mille per minute threshold you set to any chart timeframe (30 seconds, 1 minute, 5 minutes, etc.), maintaining consistency in "steepness" detection without manual recalibration.
Precise Momentum Identification: This measurement precisely identifies when the VWAP's rate of change becomes significant, and when momentum strengthens or weakens, contributing to more informed trading decisions.
In short, per-mille change per minute (‱/min) provides accuracy, consistency, and flexibility in identifying VWAP momentum changes, with smart adaptation across all timeframes.
Who is this Indicator For?
VWAP Dynamic Slope is a powerful tool for:
Intraday Traders: For quick identification of intraday trend directions and momentum across any timeframe, with specific consideration for Pre-Market, Regular Hours, or Post-Market VWAP, and incorporating key pre-market levels.
Swing Traders and Long-Term Investors: For analyzing longer-term trends based on periodic and event-driven VWAP anchors.
Beginner Traders: As an excellent visual aid for understanding the relationship between price, volume, and trend direction, and how different anchor points, pre-market levels, and data sources influence price behavior.
Experienced Traders: For integration with existing strategies, gaining additional confirmation for trend strength identification, and highly precise and flexible parameter calibration.
VWAP Dynamic Slope provides a rich, multi-dimensional layer of information about the VWAP, helping you make more informed trading decisions in real-time, within the context of your chosen asset.
Advanced Petroleum Market Model (APMM)Advanced Petroleum Market Model (APMM): A Multi-Factor Fundamental Analysis Framework for Oil Market Assessment
## 1. Introduction
The petroleum market represents one of the most complex and globally significant commodity markets, characterized by intricate supply-demand dynamics, geopolitical influences, and substantial price volatility (Hamilton, 2009). Traditional fundamental analysis approaches often struggle to synthesize the multitude of relevant indicators into actionable insights due to data heterogeneity, temporal misalignment, and subjective weighting schemes (Baumeister & Kilian, 2016).
The Advanced Petroleum Market Model addresses these limitations through a systematic, quantitative approach that integrates 16 verified fundamental indicators across five critical market dimensions. The model builds upon established financial engineering principles while incorporating petroleum-specific market dynamics and adaptive learning mechanisms.
## 2. Theoretical Framework
### 2.1 Market Efficiency and Information Integration
The model operates under the assumption of semi-strong market efficiency, where fundamental information is gradually incorporated into prices with varying degrees of lag (Fama, 1970). The petroleum market's unique characteristics, including storage costs, transportation constraints, and geopolitical risk premiums, create opportunities for fundamental analysis to provide predictive value (Kilian, 2009).
### 2.2 Multi-Factor Asset Pricing Theory
Drawing from Ross's (1976) Arbitrage Pricing Theory, the model treats petroleum prices as driven by multiple systematic risk factors. The five-factor decomposition (Supply, Inventory, Demand, Trade, Sentiment) represents economically meaningful sources of systematic risk in petroleum markets (Chen et al., 1986).
## 3. Methodology
### 3.1 Data Sources and Quality Framework
The model integrates 16 fundamental indicators sourced from verified TradingView economic data feeds:
Supply Indicators:
- US Oil Production (ECONOMICS:USCOP)
- US Oil Rigs Count (ECONOMICS:USCOR)
- API Crude Runs (ECONOMICS:USACR)
Inventory Indicators:
- US Crude Stock Changes (ECONOMICS:USCOSC)
- Cushing Stocks (ECONOMICS:USCCOS)
- API Crude Stocks (ECONOMICS:USCSC)
- API Gasoline Stocks (ECONOMICS:USGS)
- API Distillate Stocks (ECONOMICS:USDS)
Demand Indicators:
- Refinery Crude Runs (ECONOMICS:USRCR)
- Gasoline Production (ECONOMICS:USGPRO)
- Distillate Production (ECONOMICS:USDFP)
- Industrial Production Index (FRED:INDPRO)
Trade Indicators:
- US Crude Imports (ECONOMICS:USCOI)
- US Oil Exports (ECONOMICS:USOE)
- API Crude Imports (ECONOMICS:USCI)
- Dollar Index (TVC:DXY)
Sentiment Indicators:
- Oil Volatility Index (CBOE:OVX)
### 3.2 Data Quality Monitoring System
Following best practices in quantitative finance (Lopez de Prado, 2018), the model implements comprehensive data quality monitoring:
Data Quality Score = Σ(Individual Indicator Validity) / Total Indicators
Where validity is determined by:
- Non-null data availability
- Positive value validation
- Temporal consistency checks
### 3.3 Statistical Normalization Framework
#### 3.3.1 Z-Score Normalization
The model employs robust Z-score normalization as established by Sharpe (1994) for cross-indicator comparability:
Z_i,t = (X_i,t - μ_i) / σ_i
Where:
- X_i,t = Raw value of indicator i at time t
- μ_i = Sample mean of indicator i
- σ_i = Sample standard deviation of indicator i
Z-scores are capped at ±3 to mitigate outlier influence (Tukey, 1977).
#### 3.3.2 Percentile Rank Transformation
For intuitive interpretation, Z-scores are converted to percentile ranks following the methodology of Conover (1999):
Percentile_Rank = (Number of values < current_value) / Total_observations × 100
### 3.4 Exponential Smoothing Framework
Signal smoothing employs exponential weighted moving averages (Brown, 1963) with adaptive alpha parameter:
S_t = α × X_t + (1-α) × S_{t-1}
Where α = 2/(N+1) and N represents the smoothing period.
### 3.5 Dynamic Threshold Optimization
The model implements adaptive thresholds using Bollinger Band methodology (Bollinger, 1992):
Dynamic_Threshold = μ ± (k × σ)
Where k is the threshold multiplier adjusted for market volatility regime.
### 3.6 Composite Score Calculation
The fundamental score integrates component scores through weighted averaging:
Fundamental_Score = Σ(w_i × Score_i × Quality_i)
Where:
- w_i = Normalized component weight
- Score_i = Component fundamental score
- Quality_i = Data quality adjustment factor
## 4. Implementation Architecture
### 4.1 Adaptive Parameter Framework
The model incorporates regime-specific adjustments based on market volatility:
Volatility_Regime = σ_price / μ_price × 100
High volatility regimes (>25%) trigger enhanced weighting for inventory and sentiment components, reflecting increased market sensitivity to supply disruptions and psychological factors.
### 4.2 Data Synchronization Protocol
Given varying publication frequencies (daily, weekly, monthly), the model employs forward-fill synchronization to maintain temporal alignment across all indicators.
### 4.3 Quality-Adjusted Scoring
Component scores are adjusted for data quality to prevent degraded inputs from contaminating the composite signal:
Adjusted_Score = Raw_Score × Quality_Factor + 50 × (1 - Quality_Factor)
This formulation ensures that poor-quality data reverts toward neutral (50) rather than contributing noise.
## 5. Usage Guidelines and Best Practices
### 5.1 Configuration Recommendations
For Short-term Analysis (1-4 weeks):
- Lookback Period: 26 weeks
- Smoothing Length: 3-5 periods
- Confidence Period: 13 weeks
- Increase inventory and sentiment weights
For Medium-term Analysis (1-3 months):
- Lookback Period: 52 weeks
- Smoothing Length: 5-8 periods
- Confidence Period: 26 weeks
- Balanced component weights
For Long-term Analysis (3+ months):
- Lookback Period: 104 weeks
- Smoothing Length: 8-12 periods
- Confidence Period: 52 weeks
- Increase supply and demand weights
### 5.2 Signal Interpretation Framework
Bullish Signals (Score > 70):
- Fundamental conditions favor price appreciation
- Consider long positions or reduced short exposure
- Monitor for trend confirmation across multiple timeframes
Bearish Signals (Score < 30):
- Fundamental conditions suggest price weakness
- Consider short positions or reduced long exposure
- Evaluate downside protection strategies
Neutral Range (30-70):
- Mixed fundamental environment
- Favor range-bound or volatility strategies
- Wait for clearer directional signals
### 5.3 Risk Management Considerations
1. Data Quality Monitoring: Continuously monitor the data quality dashboard. Scores below 75% warrant increased caution.
2. Regime Awareness: Adjust position sizing based on volatility regime indicators. High volatility periods require reduced exposure.
3. Correlation Analysis: Monitor correlation with crude oil prices to validate model effectiveness.
4. Fundamental-Technical Divergence: Pay attention when fundamental signals diverge from technical indicators, as this may signal regime changes.
### 5.4 Alert System Optimization
Configure alerts conservatively to avoid false signals:
- Set alert threshold at 75+ for high-confidence signals
- Enable data quality warnings to maintain system integrity
- Use trend reversal alerts for early regime change detection
## 6. Model Validation and Performance Metrics
### 6.1 Statistical Validation
The model's statistical robustness is ensured through:
- Out-of-sample testing protocols
- Rolling window validation
- Bootstrap confidence intervals
- Regime-specific performance analysis
### 6.2 Economic Validation
Fundamental accuracy is validated against:
- Energy Information Administration (EIA) official reports
- International Energy Agency (IEA) market assessments
- Commercial inventory data verification
## 7. Limitations and Considerations
### 7.1 Model Limitations
1. Data Dependency: Model performance is contingent on data availability and quality from external sources.
2. US Market Focus: Primary data sources are US-centric, potentially limiting global applicability.
3. Lag Effects: Some fundamental indicators exhibit publication lags that may delay signal generation.
4. Regime Shifts: Structural market changes may require model recalibration.
### 7.2 Market Environment Considerations
The model is optimized for normal market conditions. During extreme events (e.g., geopolitical crises, pandemics), additional qualitative factors should be considered alongside quantitative signals.
## References
Baumeister, C., & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. *Journal of Economic Perspectives*, 30(1), 139-160.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. McGraw-Hill.
Brown, R. G. (1963). *Smoothing, Forecasting and Prediction of Discrete Time Series*. Prentice-Hall.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. *Journal of Business*, 59(3), 383-403.
Conover, W. J. (1999). *Practical Nonparametric Statistics* (3rd ed.). John Wiley & Sons.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. *Journal of Finance*, 25(2), 383-417.
Hamilton, J. D. (2009). Understanding crude oil prices. *Energy Journal*, 30(2), 179-206.
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. *American Economic Review*, 99(3), 1053-1069.
Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons.
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. *Journal of Economic Theory*, 13(3), 341-360.
Sharpe, W. F. (1994). The Sharpe ratio. *Journal of Portfolio Management*, 21(1), 49-58.
Tukey, J. W. (1977). *Exploratory Data Analysis*. Addison-Wesley.
RSI Multi-TF TabRSI Multi-Timeframe Table 📊
A tool for multi-timeframe RSI analysis with visual overbought/oversold level highlighting.
Description
This indicator calculates the Relative Strength Index (RSI) for the current chart and displays RSI values across five additional timeframes (15m, 1h, 4h, 1d, 1w) in a dynamic table. The color-coded system simplifies identifying overbought (>70), oversold (<30), and neutral zones. Visual signals on the chart enhance analysis for the current timeframe.
Key Features
✅ Multi-Timeframe Analysis :
Track RSI across 15m, 1h, 4h, 1d, and 1w in a compact table.
Color-coded alerts:
🔴 Red — Overbought (potential pullback),
🔵 Blue — Oversold (potential rebound),
🟡 Yellow — Neutral zone.
✅ Visual Signals :
Background shading for oversold/overbought zones on the main chart.
Horizontal lines at 30 and 70 levels for reference.
✅ Customizable Settings :
Adjust RSI length (default: 14), source (close, open, high, etc.), and threshold levels.
How to Use
Table Analysis :
Compare RSI values across timeframes to spot divergences (e.g., overbought on 15m vs. oversold on D).
Use colors for quick decisions.
Chart Signals :
Blue background suggests bullish potential (oversold), red hints at bearish pressure (overbought).
Always confirm with other tools (volume, trends, or candlestick patterns).
Examples :
RSI(1h) > 70 while RSI(4h) < 30 → Possible reversal upward.
Sustained RSI(1d) above 50 may indicate a bullish trend.
Settings
RSI Length : Period for RSI calculation (default: 14).
RSI Source : Data source (close, open, high, low, hl2, hlc3, ohlc4).
Overbought/Oversold Levels : Thresholds for alerts (default: 70/30).
Important Notes
No direct trading signals : Use this as an analytical tool, not a standalone strategy.
Test strategies historically and consider market context before trading.
Canuck Trading Projection IndicatorCanuck Trading Projection Indicator
Overview
The Canuck Trading Projection Indicator is a powerful PineScript v6 tool designed for TradingView to project potential bullish and bearish price trajectories based on historical price and volume movements. It provides traders with actionable insights by estimating future price targets and assigning confidence levels to each outlook, helping to identify probable market directions across any timeframe. Ideal for both short-term and long-term traders, this indicator combines momentum analysis, RSI filtering, support/resistance detection, and time-weighted trend analysis to deliver robust projections.
Features
Bullish and Bearish Projections: Forecasts price targets for upward (bullish) and downward (bearish) movements over a user-defined projection period (default 20 bars).
Confidence Levels: Assigns percentage confidence scores to each outlook, reflecting the likelihood of the projected price based on historical trends, volatility, and volume.
RSI Filter: Incorporates a 14-period Relative Strength Index (RSI) to validate trends, requiring RSI > 50 for bullish and RSI < 50 for bearish signals.
Support/Resistance Detection: Adjusts confidence levels when projections are near key swing highs/lows (within 2% of average price), boosting confidence by 5% for alignments.
Time-Based Weighting: Prioritizes recent price movements in trend analysis, giving more weight to newer bars for improved relevance.
Customizable Inputs: Allows users to tailor lookback period, projection bars, RSI period, confidence threshold, colors, and label positioning.
Forced Label Spacing: Prevents overlap of bullish and bearish text labels, even for tight projections, using fixed vertical slots when price differences are small (<2% of average price).
Timeframe Flexibility: Works seamlessly across all TradingView timeframes (e.g., 30-minute, hourly, daily, weekly, monthly), adapting projections to the chart’s resolution.
Clean Visualization: Displays projections as green (bullish) and red (bearish) dashed lines, with non-overlapping text labels at the projection endpoints showing price targets and confidence levels.
How It Works
The indicator analyzes historical price and volume data over a user-defined lookback period (default 50 bars) to calculate:
Momentum: Combines price changes and volume to assess trend strength, using a weighted moving average (WMA) for directional bias.
Trend Analysis: Counts bullish (price up, volume above average, RSI > 50) and bearish (price down, volume above average, RSI < 50) trends, weighting recent bars more heavily.
Projections:
Bullish Slope: Positive or flat when momentum is upward, scaled by price change and momentum intensity.
Bearish Slope: Negative or flat when momentum is downward, amplified by bearish confidence for stronger projections.
Projects prices forward by 20 bars (default) using current close plus slope times projection bars.
Confidence Levels:
Base confidence derived from the proportion of bullish/bearish trends, with a 5% minimum to avoid zero confidence.
Adjusted by volatility (lower volatility increases confidence), volume trends, and proximity to support/resistance levels.
Visualization:
Draws projection lines from the current close to the 20-bar future target.
Places text labels at line endpoints, showing price targets and confidence percentages, with forced spacing for readability.
Input Parameters
Lookback Period (default: 50): Number of bars for historical analysis (minimum 10).
Projection Bars (default: 20): Number of bars to project forward (minimum 5).
Confidence Threshold (default: 0.6): Minimum confidence for strong trend indication (0.1 to 1.0).
Bullish Projection Line Color (default: Green): Color for bullish projection line and label.
Bearish Projection Line Color (default: Red): Color for bearish projection line and label.
RSI Period (default: 14): Period for RSI momentum filter (minimum 5).
Label Vertical Offset (%) (default: 1.0): Base offset for labels as a percentage of price range (0.1% to 5.0%).
Minimum Label Spacing (%) (default: 2.0): Minimum vertical spacing between labels for tight projections (0.5% to 10.0%).
Usage Instructions
Add to Chart: Copy the script into TradingView’s Pine Editor, save, and add the indicator to your chart.
Select Timeframe: Apply to any timeframe (e.g., 30-minute, hourly, daily, weekly, monthly) to match your trading strategy.
Interpret Outputs:
Green Line/Label: Bullish price target and confidence (e.g., "Bullish: 414.37, Confidence: 35%").
Red Line/Label: Bearish price target and confidence (e.g., "Bearish: 279.08, Confidence: 41.3%").
Higher confidence indicates a stronger likelihood of the projected outcome.
Adjust Inputs:
Modify Lookback Period to focus on shorter/longer historical trends (e.g., 20 for short-term, 100 for long-term).
Change Projection Bars to adjust forecast horizon (e.g., 10 for shorter, 50 for longer).
Tweak RSI Period or Confidence Threshold for sensitivity to momentum or trend strength.
Customize Colors for visual preference.
Increase Minimum Label Spacing if labels overlap in volatile markets.
Combine with Analysis: Use alongside other indicators (e.g., moving averages, Bollinger Bands) or fundamental analysis to confirm signals, as projections are probabilistic.
Example: TSLA Across Timeframes
Using live TSLA data (close ~346.46 USD, May 31, 2025), the indicator produces:
30-Minute: Bullish 341.93 (13.3%), Bearish 327.96 (86.7%) – Strong bearish sentiment due to intraday volatility.
1-Hour: Bullish 342.00 (33.9%), Bearish 327.50 (62.3%) – Bearish but less intense, reflecting hourly swings.
4-Hour: Bullish 345.52 (73.4%), Bearish 344.44 (19.0%) – Flat outlook, indicating consolidation.
Daily: Bullish 391.26 (68.8%), Bearish 302.22 (31.2%) – Bullish bias from recent uptrend, bearish tempered by longer lookback.
Weekly: Bullish 414.37 (35.0%), Bearish 279.08 (41.3%) – Wide range, reflecting annual volatility.
Monthly: Bullish 396.70 (54.9%), Bearish 296.93 (10.2%) – Long-term bullish optimism.
These results align with market dynamics: short-term intervals capture volatility, while longer intervals smooth trends, providing balanced outlooks.
Notes
Accuracy: Projections are estimates based on historical data and should be used with other analysis tools. Confidence levels indicate likelihood, not certainty.
Timeframe Sensitivity: Short-term intervals (e.g., 30-minute) show larger price swings and higher confidence due to volatility, while longer intervals (e.g., monthly) are more stable.
Customization: Adjust inputs to match your trading style (e.g., shorter lookback for day trading, longer for swing trading).
Performance: Tested on volatile stocks like TSLA, NVIDIA, and others, ensuring robust performance across markets.
Limitations: May produce conservative bearish projections in strong uptrends due to momentum weighting. Adjust lookback or projection_bars for sensitivity.
Feedback
If you encounter issues (e.g., label overlap, projection mismatches), please share your timeframe, settings, or a screenshot. Suggestions for enhancements (e.g., additional filters, visual tweaks) are welcome!
Disclaimer
The Canuck Trading Projection Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
MACD + RSI + EMA + BB + ATR Day Trading StrategyEntry Conditions and Signals
The strategy implements a multi-layered filtering approach to entry conditions, requiring alignment across technical indicators, timeframes, and market conditions .
Long Entry Requirements
Trend Filter: Fast EMA (9) must be above Slow EMA (21), price must be above Fast EMA, and higher timeframe must confirm uptrend
MACD Signal: MACD line crosses above signal line, indicating increasing bullish momentum
RSI Condition: RSI below 70 (not overbought) but above 40 (showing momentum)
Volume & Volatility: Current volume exceeds 1.2x 20-period average and ATR shows sufficient market movement
Time Filter: Trading occurs during optimal hours (9:30-11:30 AM ET) when market volatility is typically highest
Exit Strategies
The strategy employs multiple exit mechanisms to adapt to changing market conditions and protect profits :
Stop Loss Management
Initial Stop: Placed at 2.0x ATR from entry price, adapting to current market volatility
Trailing Stop: 1.5x ATR trailing stop that moves up (for longs) or down (for shorts) as price moves favorably
Time-Based Exits: All positions closed by end of trading day (4:00 PM ET) to avoid overnight risk
Best Practices for Implementation
Settings
Chart Setup: 5-minute timeframe for execution with 15-minute chart for trend confirmation
Session Times: Focus on 9:30-11:30 AM ET trading for highest volatility and opportunity
Reversal Trap Sniper – Verified VersionReversal Trap Sniper
Overview
Reversal Trap Sniper is a counterintuitive momentum-following strategy that identifies "reversal traps"—situations where traders expect a market reversal based on RSI, but the price continues trending. By detecting these failed reversal signals, the strategy enters trades in the trend direction, often catching strong follow-through moves.
How It Works
The system monitors the Relative Strength Index (RSI). When RSI moves above the overbought level (e.g., 70) and then drops back below it, many traders interpret this as a sell signal.
However, this strategy treats such moves with caution. If the RSI pulls back below the overbought threshold but the price continues to rise, the system considers it a "reversal trap"—a fakeout.
In such cases, instead of going short, the strategy enters a long position, assuming that the trend is still valid and those betting on a reversal may fuel a breakout.
Similarly, if RSI rises above the oversold level from below, but price continues falling, a short trade is triggered.
Entries are followed by ATR-based stop-loss and dynamic take-profit (2× risk), with a fallback time-based exit after 30 bars.
Key Features
- Detects failed RSI-based reversals ("traps")
- Follows momentum after the trap is triggered
- Uses ATR for dynamic stop-loss and take-profit
- Auto-exit after a fixed bar count (30 bars)
- Visual markers on chart for transparency
- Realistic trading assumptions: 0.05% commission, slippage, and capped pyramiding
Parameter Explanation
RSI Length (14): Standard RSI calculation period
Overbought/Oversold Levels (70/30): Common thresholds used by many traders
ATR Length (14): Used to define stop-loss and target dynamically
Risk-Reward Ratio (2.0): Take-profit is set at 2× the stop-loss distance
Max Holding Bars (30): Ensures trades don’t remain open indefinitely
Pyramiding (10): Allows scaling into trades, simulating real-world strategy stacking
Originality Note
This strategy inverts traditional RSI logic. Instead of treating overbought/oversold conditions as signals for reversal, it waits for those signals to fail. Only after such failures, confirmed by continued price action in the same direction, does the system enter trades. This logic is based on the behavioral observation that failed reversal signals often trigger stronger trend continuation—making this strategy uniquely positioned to exploit trap scenarios.
Disclaimer
This script is for educational and research purposes only. Trading involves risk, and past performance does not guarantee future results. Always test thoroughly before applying with live capital.
RSI mura visionOverview
The Enhanced RSI with Custom 40/60 Zones is a Pine Script™ v6 open-source indicator that builds on the classic Relative Strength Index by adding two additional horizontal levels at 40 and 60, alongside the standard 30/70. These extra zones help you identify early momentum shifts and distinguish trending markets from ranging ones with greater precision.
Key Features & Originality
* Custom Mid-Zones (40/60): Standard RSI signals can be noisy around the 50 midpoint. By marking 40 as a “weak momentum” threshold and 60 as a “strong momentum” confirmation, you get clearer entry and exit cues.
* Color-Coded Zones: The RSI line changes color when crossing 40, 50, 60, 70, and 30, letting you visually spot momentum acceleration or deceleration.
* Configurable Alerts: Built-in alert conditions fire when RSI crosses 40 or 60 in either direction, so you never miss a potential trend onset or exhaustion.
* Lightweight & Clean: No external dependencies, no look-ahead bias, and minimal repainting—ideal for both novice and professional traders.
How It Works
1. Momentum Decomposition: The standard 14-period RSI measures overbought/oversold extremes. Adding 40/60 lets you see when momentum shifts from neutral to bullish (crossing above 60) or bearish (dropping below 40) earlier than the classic 70/30 thresholds.
2. Trend Confirmation vs. Pullbacks: Readings between 40–60 often correspond to healthy pullbacks within a trend. A bounce off 40 suggests continuation; a rejection at 60 warns of a deeper pullback or reversal.
Usage & Inputs
* RSI Length (default 14): Period for calculating RSI.
* Level Inputs: Customize levels for overbought (70), support (60), neutral (50), weak (40), and oversold (30).
* Alert Toggles: Enable/disable alerts on each cross.
Why This Adds Value
* Early Signals: Capture trend beginnings before the market reaches extreme overbought/oversold levels.
* Noise Reduction: Filter sideways chop by watching the 40–60 corridor.
* Flexibility: Works on any timeframe or ticker.
Pine Script™ Version: v6
Open-Source License: MPL-2.0
Feel free to fork, modify, and share.
Extended-hours Volume vs AVOL// ──────────────────────────────────────────────────────────────────────────────
// Extended-Hours Volume vs AVOL • HOW IT WORKS & HOW TO TRADE IT
// ──────────────────────────────────────────────────────────────────────────────
//
// ░ What this indicator is
// ------------------------
// • It accumulates PRE-MARKET (04:00-09:30 ET) and AFTER-HOURS (16:00-20:00 ET)
// volume on intraday charts and compares that running total with the stock’s
// 21-day average daily volume (“AVOL” by default).
// • Three live read-outs are shown in the data-window/table:
//
// AH – volume traded since the 16:00 ET close
// PM – volume traded before the 09:30 ET open
// Ext – AH + PM (updates in pre-market only)
// %AVOL – Ext ÷ AVOL × 100 (updates in pre-market)
//
// • It is intended for U.S. equities but the session strings can be edited for
// other markets.
//
// ░ Why it matters
// ----------------
// Big extended-hours volume almost always precedes outsized intraday range.
// By quantifying that volume as a % of “normal” trade (AVOL), you can filter
// which gappers and news names deserve focus *before* the bell rings.
//
// ░ Quick-start trade plan (educational template – tune to taste)
// ----------------------------------------------------------------
// 1. **Scan** the watch-list between 08:30-09:25 ET.
// ► Keep charts on 1- or 5-minute candles with “Extended Hours” ✔ checked.
// 2. **Filter** by `Ext` or `%AVOL`:
// – Skip if < 10 % → very low interest
// – Flag if 20-50 % → strong interest, Tier-1 candidate
// – Laser-focus if > 50 % → crowd favourite; expect liquidity & range
// 3. **Opening Range Breakout (long example)**
// • Preconditions: Ext ≥ 20 % & price above yesterday’s close.
// • Let the first 1- or 5-min bar complete after 09:30.
// • Stop-buy 1 tick above that bar (or pre-market high – whichever higher).
// • Initial stop below that bar low (or pre-market low).
// • First target = 1R or next HTF resistance.
// 4. **Red-to-Green reversal (gap-down long)**
// • Ext ≥ 30 % but pre-market gap is negative.
// • Enter as price reclaims yesterday’s close on live volume.
// • Stop under reclaim bar; scale out into VWAP / first liquidity pocket.
// 5. **Risk** – size so the full stop is ≤ 1 R of account. Volume fade or
// loss of %AVOL slope is a reason to tighten or exit early.
//
// ░ Tips
// ------
// • AVOL look-back can be changed in the input panel (21 days ⇒ ~1 month).
// • To monitor several symbols, open a multi-chart layout and sort your
// watch-list by %AVOL descending – leaders float to the top automatically.
// • Replace colour constants with hex if the namespace ever gets shadowed.
//
// ░ Disclaimer
// ------------
// For educational purposes only. Not financial advice. Trade your own plan.
//
// ──────────────────────────────────────────────────────────────────────────────
