ALMA & UT Bot Confluence StrategyALMA & UT Bot Confluence Strategy
This is a comprehensive trend-following and momentum strategy designed to identify high-probability trade setups by combining multiple layers of confirmation. It is built around an ALMA (Arnaud Legoux Moving Average) and a long-term EMA, and then enhances signal quality with the popular UT Bot indicator, a Volume Filter, and an adaptive hold mechanism.
The primary goal of this strategy is to filter out market noise, avoid low liquidity traps, and provide more robust and selective trading logic by adapting its timing to changing market volatility.
Key Features and How It Works
This strategy is not a simple crossover system. An entry signal is generated by the confluence of only a few conditions:
Underlying Trend and Signal Engine:
ALMA (Arnaud Legoux Moving Average): Provides a responsive, low-latency signal line for entries. EMA (Exponential Moving Average): A longer-term EMA acts as a primary trend filter, ensuring trades are executed only in line with the overall market trend.
Confirmation Layer:
UT Bot Confirmation: A trade is considered valid only when the UT Bot indicator provides a relevant buy or sell signal. This acts as a strong secondary confirmation, reducing false entries.
Advanced Filters for Signal Quality:
Volume Filter: This is an important safety mechanism that prevents trades from being executed in low-volume, illiquid markets where price action can be erratic and unreliable.
Momentum Filter (ADX and RSI): The strategy uses the ADX to check for sufficient market momentum and the RSI to ensure it doesn't enter overbought/oversold zones.
Volatility Filter (Bollinger Bands): This helps prevent entries when the price deviates too far from its average, preventing "buying at the top" or "selling at the bottom." Adaptive Timing (Dynamic Cool-Down):
Instead of a fixed waiting period between trades, this strategy uses a dynamic cooling-down period based on the ATR. It automatically waits longer during periods of high volatility (to prevent volatility) and becomes more responsive in calmer markets. How to Use This Strategy:
Long Entry (BUY): When all bullish conditions align, a green "BUY" triangle appears below the price.
Short Entry (SELL): When all bearish conditions align, a red "SELL" triangle appears above the price.
Trend Visualization: The chart background is color-coded according to UT Bot's trend direction (Green for an uptrend, Red for a downtrend), allowing for at-a-glance market analysis.
Double Exit Strategy Options
You have full control over how you exit trades:
Classic SL/TP: Use a standard Stop-Loss and Take-Profit order based on ATR (Average True Range) multipliers. UT Bot Trailing Stop (Recommended): A dynamic exit mechanism that follows the price allows your winning trades to catch up to larger trends while protecting your profits.
Disclaimer
This script is for educational purposes only and should not be construed as financial advice. Past performance is not indicative of future results. All trades involve risk. Before risking any capital, we strongly recommend extensively backtesting this strategy across your preferred assets and timeframes to understand its behavior and find settings that suit your personal trading style.
The author recommends using this strategy with Heikin-Ashi candlesticks. Using this method will significantly increase the strategy's trading success rate and profitability in backtests.
You should change the settings according to your preferred chart time range. You can find the best value for you by observing the value changes you make on the chart.
Pesquisar nos scripts por "backtesting"
VRD-5: Volume Reversal Detector (5 Bars)Overview
This Pine Script indicator detects potential trend reversals based on volume patterns over a 5-bar period. It identifies accumulation (bullish) and distribution (bearish) patterns using volume analysis combined with price action.
Key Features
Volume Analysis:
Compares current volume to a 34-period SMA
Identifies strong/weak volume using configurable thresholds
Calculates volume "energy" as a 5-bar average ratio
Pattern Detection:
Bearish Signal: Looks for decreasing volume after a strong volume bar
Bullish Signal: Looks for increasing volume after weak volume bars
Visualization:
Colored volume histogram (bullish/bearish/neutral)
SMA volume line
Labels for detected signals
Customization Options:
Adjustable lookback period (3-10 bars)
Configurable thresholds for volume strength
Strict mode requiring confirming price action
Suggested Improvements
Performance Optimization:
Reduce the max_labels_count (currently 500) to improve performance
Consider using barstate.isconfirmed for more efficient calculations
Enhanced Visualization:
Add arrows on price chart for better visibility
Include a background color highlight for signal periods
Add option to display the energy level as a separate line
Additional Features:
Incorporate RSI or MACD for confirmation
Add multi-timeframe analysis capability
Include a strategy version for backtesting
Code Structure:
Separate the logic into distinct functions for better readability
Add more detailed comments for complex calculations
Consider using varip for real-time updates if needed
User Experience:
Add input options for label text size/position
Include sound options for alerts
Add a toggle for the information table
This indicator provides a solid foundation for volume-based reversal detection that could be further enhanced with these improvements while maintaining its core functionality.
Multi - Timeframe 3 EMA Bull/Bear Table此指标是一个图标指标，适用于短线交易以及中线交易，它明确的显示出来了用EMA来表示方向指示，1分钟不可使用，此图表更新了多次以及修改了多次，在实际回测中有明显的提醒作用，不过多用于参考，不可作为主要指标使用，代码稍复杂如有加以改进的地方请提出，其中核心使用了EMA的20，50，200周期来作为参考，目的是能识别多周期和时间的方向指示，注意：此指标建议仅用于方向参考，不用于主要指标交易。
This indicator is a graphical indicator suitable for short-term and medium-term trading. It clearly shows the direction indicated by the EMA. It cannot be used for 1-minute intervals. This chart has been updated and modified multiple times, and it has a significant alerting effect in actual backtesting; however, it is mainly for reference and should not be used as the primary indicator. The code is somewhat complex, so please suggest improvements if there are any. The core uses the 20, 50, and 200 EMA periods as references, with the aim of identifying the direction indicators across multiple periods and timeframes. Note: This indicator is recommended only for directional reference and not for main indicator trading.
Bar TimeBar Time is a simple utility for traders who rely on backtesting, Bar Replay, and detailed price action analysis. It solves a common but frustrating problem: knowing the exact time of the bar you are looking at.
While most time indicators show your computer's live clock time, this tool displays the bar's own timestamp, perfectly synchronized with your chart's data and timezone.
Why Is This Important?
When you are deep in a Bar Replay session or analyzing a historical setup, the live clock is irrelevant. You need to know when that critical breakout or reversal candle actually happened. Was it during the pre-market? At the London open? In the last five minutes of the US session? This indicator provides that vital context instantly, without you needing to squint at the small print on the x-axis.
Key Use Cases
1. Mastering Bar Replay
As you click through bars in Replay mode, the displayed time updates with each new bar. This allows you to simulate a live trading session with full awareness of the time of day, helping you train your decision-making under more realistic conditions.
2. Analyzing Screener Signals
This is one of the most powerful uses. Imagine your screener finds a "BUY" signal on a stock from two bars ago. You switch to that stock's chart to investigate. Instead of hunting for the exact bar, this tool instantly shows you the date and time of the bar you are currently hovering over. It dramatically speeds up the workflow of moving from a screener alert to actionable analysis.
3. Detailed Price Action Study
Quickly identify key session timings, see how price reacts to news events at a specific time, or analyze intraday volume patterns with complete temporal clarity.
Features & Customization
The tool is designed to be lightweight, efficient, and fully customizable to match your charting environment.
Timezone-Aware Accuracy: Automatically detects your chart's timezone for a perfect match between the label and the x-axis.
Fully Customizable Position: Place the time display in any of nine screen positions (e.g., Top Left, Bottom Center) using a simple dropdown menu.
Custom Colors: Easily set the background and text colors to blend seamlessly with your chart's theme.
Vertical Line Timeline 10 Inputs by LK**Vertical Line Timeline (10 Inputs)**
This TradingView indicator plots vertical lines on your chart at up to **10 specific times of day**. You can define each time in **HH.MM format** (e.g., `9.30` for 9:30 AM). When the current bar’s time matches any of the defined times (based on the chart’s timezone), the indicator automatically draws a **full-height vertical line** at that bar.
**Features:**
* **Up to 10 custom time inputs** (HH.MM format)
* **Custom color** for each time marker
* **Adjustable line width** (1–6 px)
* **Solid or dotted style** toggle
* **Full-height vertical lines** (extend through the entire chart height)
* Works on any intraday timeframe where bar start times can match the defined times
* No labels or extra elements — clean and minimal display
**Use cases:**
* Marking important market sessions (e.g., London Open, New York Open, Asian Close)
* Highlighting personal trade execution windows
* Visual cues for strategy backtesting or time-based setups
Breakout Volume Momentum [5m]Breakout Volume Momentum Indicator (Pine Script v5)
This TradingView Pine Script v5 indicator plots a green dot below a 5-minute price bar whenever all the breakout and volume conditions are met. It is optimized for live intraday trading (not backtesting) and includes customizable inputs for thresholds and trading session times. Key features and conditions of this indicator:
Gap Up Threshold: Current price is up at least X% (default 20%) from the previous day’s close (uses higher-timeframe daily data) before any signal can trigger.
Relative Volume (RVOL): Current bar’s volume is at least Y× (default 2×) the average volume of the last 20 bars. This ensures unusually high volume is present, indicating strong interest.
Trend Alignment: Price is trading above the VWAP (Volume-Weighted Average Price) and above a fast EMA. In addition, the fast EMA (default 9) is above the slower EMA (default 20) to confirm bullish momentum
tradingview.com
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. These filters ensure the stock is in an intraday uptrend (above the average price and rising EMAs).
Intraday Breakout (optional): Optionally require the price to break above the recent intraday high (default last 30 bars). If enabled, a signal only occurs when the stock exceeds its prior range high, confirming a breakout. This can be toggled on/off in the settings.
Avoid Parabolic Spikes: The script skips any bar with an excessively large range (default >12% from low to high), to avoid triggering on spiky or unsustainable parabolic candles.
Time Window Filter: Signals are restricted to a specific session window (by default 09:30 – 11:00 exchange time, typically the morning session) and will not trigger outside these hours. The session window is adjustable via inputs
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.
Alerts: An alert condition is provided so you can set a Trading View alert to send a push notification when a green dot signal fires. The alert message includes the ticker and price at the time of signal.
Structure From Start – MTF (body-close BOS)Displays higher-timeframe market structure from a chosen start date using body-close BOS logic, with trend state, guard levels, and BOS markers plotted on your current chart.
Multi-Timeframe Market Structure with Body-Close BOS Logic
This indicator tracks market structure from a chosen start date on a higher-timeframe (HTF) of your choice, then displays it on your current chart for intraday context.
It detects swing highs/lows using pivot logic, confirms Break of Structure (BOS) only when a candle closes beyond the swing level (body-close rule), and maintains the “valid swing” level that invalidates the current bias.
Key Features:
• Works on any HTF you select (e.g., H1, H4) while you operate on lower TFs like M5 or M1.
• Start reading structure from any date/time you choose for focused backtesting or scenario analysis.
• Highlights trend state (long/short/neutral) with background colors.
• Plots the active “guard” level (valid swing high/low) that would flip bias if broken.
• Marks BOS events directly on your trading TF, updating only when the HTF candle closes.
Ideal for combining a clear higher-timeframe bias with lower-timeframe execution, without manually tracking HTF structure changes during live markets.
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.
Keltner Channel Based Grid Strategy # KC Grid Strategy - Keltner Channel Based Grid Trading System
## Strategy Overview
KC Grid Strategy is an innovative grid trading system that combines the power of Keltner Channels with dynamic position sizing to create a mean-reversion trading approach. This strategy automatically adjusts position sizes based on price deviation from the Keltner Channel center line, implementing a systematic grid-based approach that capitalizes on market volatility and price oscillations.
## Core Principles
### Keltner Channel Foundation
The strategy builds upon the Keltner Channel indicator, which consists of:
- **Center Line**: Moving average (EMA or SMA) of the price
- **Upper Band**: Center line + (ATR/TR/Range × Multiplier)
- **Lower Band**: Center line - (ATR/TR/Range × Multiplier)
### Grid Trading Logic
The strategy implements a sophisticated grid system where:
1. **Position Direction**: Inversely correlated to price position within the channel
- When price is above center line → Short positions
- When price is below center line → Long positions
2. **Position Size**: Proportional to distance from center line
- Greater deviation = Larger position size
3. **Grid Activation**: Positions are adjusted only when the difference exceeds a predefined grid threshold
### Mathematical Foundation
The core calculation uses the KC Rate formula:
```
kcRate = (close - ma) / bandWidth
targetPosition = kcRate × maxAmount × (-1)
```
This creates a mean-reversion system where positions increase as price moves further from the mean, expecting eventual return to equilibrium.
## Parameter Guide
### Time Range Settings
- **Start Date**: Beginning of strategy execution period
- **End Date**: End of strategy execution period
### Core Parameters
1. **Number of Grids (NumGrid)**: Default 12
- Controls grid sensitivity and position adjustment frequency
- Higher values = More frequent but smaller adjustments
- Lower values = Less frequent but larger adjustments
2. **Length**: Default 10
- Period for moving average and volatility calculations
- Shorter periods = More responsive to recent price action
- Longer periods = Smoother, less noisy signals
3. **Grid Coefficient (kcRateMult)**: Default 1.33
- Multiplier for channel width calculation
- Higher values = Wider channels, less frequent trades
- Lower values = Narrower channels, more frequent trades
4. **Source**: Default Close
- Price source for calculations (Close, Open, High, Low, etc.)
- Close price typically provides most reliable signals
5. **Use Exponential MA**: Default True
- True = Uses EMA (more responsive to recent prices)
- False = Uses SMA (equal weight to all periods)
6. **Bands Style**: Default "Average True Range"
- **Average True Range**: Smoothed volatility measure (recommended)
- **True Range**: Current bar's volatility only
- **Range**: Simple high-low difference
## How to Use
### Setup Instructions
1. **Apply to Chart**: Add the strategy to your desired timeframe and instrument
2. **Configure Parameters**: Adjust settings based on market characteristics:
- Volatile markets: Increase Grid Coefficient, reduce Number of Grids
- Stable markets: Decrease Grid Coefficient, increase Number of Grids
3. **Set Time Range**: Define your backtesting or live trading period
4. **Monitor Performance**: Watch strategy performance metrics and adjust as needed
### Optimal Market Conditions
- **Range-bound markets**: Strategy performs best in sideways trending markets
- **High volatility**: Benefits from frequent price oscillations around the mean
- **Liquid instruments**: Ensures efficient order execution and minimal slippage
### Position Management
The strategy automatically:
- Calculates optimal position sizes based on account equity
- Adjusts positions incrementally as price moves through grid levels
- Maintains risk control through maximum position limits
- Executes trades only during specified time periods
## Risk Warnings
### ⚠️ Important Risk Considerations
1. **Trending Market Risk**:
- Strategy may underperform or generate losses in strong trending markets
- Mean-reversion assumption may fail during sustained directional moves
- Consider market regime analysis before deployment
2. **Leverage and Position Size Risk**:
- Strategy uses pyramiding (up to 20 positions)
- Large positions may accumulate during extended moves
- Monitor account equity and margin requirements closely
3. **Volatility Risk**:
- Sudden volatility spikes may trigger multiple rapid position adjustments
- Consider volatility filters during high-impact news events
- Backtest across different volatility regimes
4. **Execution Risk**:
- Strategy calculates on every tick (calc_on_every_tick = true)
- May generate frequent orders in volatile conditions
- Ensure adequate execution infrastructure and consider transaction costs
5. **Parameter Sensitivity**:
- Performance highly dependent on parameter optimization
- Over-optimization may lead to curve-fitting
- Regular parameter review and adjustment may be necessary
## Suitable Scenarios
### Ideal Market Conditions
- **Sideways/Range-bound markets**: Primary use case
- **Mean-reverting instruments**: Forex pairs, some commodities
- **Stable volatility environments**: Consistent ATR patterns
- **Liquid markets**: Major currency pairs, popular stocks/indices
## Important Notes
### Strategy Limitations
1. **No Stop Loss**: Strategy relies on mean reversion without traditional stop losses
2. **Capital Requirements**: Requires sufficient capital for grid-based position sizing
3. **Market Regime Dependency**: Performance varies significantly across different market conditions
## Disclaimer
This strategy is provided for educational and research purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Users should thoroughly test the strategy and understand its mechanics before risking real capital. The author assumes no responsibility for trading losses incurred through the use of this strategy.
---
# KC网格策略 - 基于肯特纳通道的网格交易系统
## 策略概述
KC网格策略是一个创新的网格交易系统，它将肯特纳通道的力量与动态仓位调整相结合，创建了一个均值回归交易方法。该策略根据价格偏离肯特纳通道中心线的程度自动调整仓位大小，实施系统化的网格方法，利用市场波动和价格振荡获利。
## 核心原理
### 肯特纳通道基础
该策略建立在肯特纳通道指标之上，包含：
- **中心线**: 价格的移动平均线（EMA或SMA）
- **上轨**: 中心线 + (ATR/TR/Range × 乘数)
- **下轨**: 中心线 - (ATR/TR/Range × 乘数)
### 网格交易逻辑
该策略实施复杂的网格系统：
1. **仓位方向**: 与价格在通道中的位置呈反向关系
- 当价格高于中心线时 → 空头仓位
- 当价格低于中心线时 → 多头仓位
2. **仓位大小**: 与距离中心线的距离成正比
- 偏离越大 = 仓位越大
3. **网格激活**: 只有当差异超过预定义的网格阈值时才调整仓位
### 数学基础
核心计算使用KC比率公式：
```
kcRate = (close - ma) / bandWidth
targetPosition = kcRate × maxAmount × (-1)
```
这创建了一个均值回归系统，当价格偏离均值越远时仓位越大，期望最终回归均衡。
## 参数说明
### 时间范围设置
- **开始日期**: 策略执行期间的开始时间
- **结束日期**: 策略执行期间的结束时间
### 核心参数
1. **网格数量 (NumGrid)**: 默认12
- 控制网格敏感度和仓位调整频率
- 较高值 = 更频繁但较小的调整
- 较低值 = 较少频繁但较大的调整
2. **长度**: 默认10
- 移动平均线和波动率计算的周期
- 较短周期 = 对近期价格行为更敏感
- 较长周期 = 更平滑，噪音更少的信号
3. **网格系数 (kcRateMult)**: 默认1.33
- 通道宽度计算的乘数
- 较高值 = 更宽的通道，较少频繁的交易
- 较低值 = 更窄的通道，更频繁的交易
4. **数据源**: 默认收盘价
- 计算的价格来源（收盘价、开盘价、最高价、最低价等）
- 收盘价通常提供最可靠的信号
5. **使用指数移动平均**: 默认True
- True = 使用EMA（对近期价格更敏感）
- False = 使用SMA（对所有周期等权重）
6. **通道样式**: 默认"平均真实范围"
- **平均真实范围**: 平滑的波动率测量（推荐）
- **真实范围**: 仅当前K线的波动率
- **范围**: 简单的高低价差
## 使用方法
### 设置说明
1. **应用到图表**: 将策略添加到您所需的时间框架和交易品种
2. **配置参数**: 根据市场特征调整设置：
- 波动市场：增加网格系数，减少网格数量
- 稳定市场：减少网格系数，增加网格数量
3. **设置时间范围**: 定义您的回测或实盘交易期间
4. **监控表现**: 观察策略表现指标并根据需要调整
### 最佳市场条件
- **区间震荡市场**: 策略在横盘趋势市场中表现最佳
- **高波动性**: 受益于围绕均值的频繁价格振荡
- **流动性强的品种**: 确保高效的订单执行和最小滑点
### 仓位管理
策略自动：
- 根据账户权益计算最优仓位大小
- 随着价格在网格水平移动逐步调整仓位
- 通过最大仓位限制维持风险控制
- 仅在指定时间段内执行交易
## 风险警示
### ⚠️ 重要风险考虑
1. **趋势市场风险**:
- 策略在强趋势市场中可能表现不佳或产生损失
- 在持续方向性移动期间均值回归假设可能失效
- 部署前考虑市场制度分析
2. **杠杆和仓位大小风险**:
- 策略使用金字塔加仓（最多20个仓位）
- 在延长移动期间可能积累大仓位
- 密切监控账户权益和保证金要求
3. **波动性风险**:
- 突然的波动性激增可能触发多次快速仓位调整
- 在高影响新闻事件期间考虑波动性过滤器
- 在不同波动性制度下进行回测
4. **执行风险**:
- 策略在每个tick上计算（calc_on_every_tick = true）
- 在波动条件下可能产生频繁订单
- 确保充足的执行基础设施并考虑交易成本
5. **参数敏感性**:
- 表现高度依赖于参数优化
- 过度优化可能导致曲线拟合
- 可能需要定期参数审查和调整
## 适用场景
### 理想市场条件
- **横盘/区间震荡市场**: 主要用例
- **均值回归品种**: 外汇对，某些商品
- **稳定波动性环境**: 一致的ATR模式
- **流动性市场**: 主要货币对，热门股票/指数
## 注意事项
### 策略限制
1. **无止损**: 策略依赖均值回归而无传统止损
2. **资金要求**: 需要充足资金进行基于网格的仓位调整
3. **市场制度依赖性**: 在不同市场条件下表现差异显著
## 免责声明
该策略仅供教育和研究目的。过往表现不保证未来结果。交易涉及重大损失风险，并非适合所有投资者。用户应在投入真实资金前彻底测试策略并理解其机制。作者对使用此策略产生的交易损失不承担任何责任。
---
**Strategy Version**: Pine Script v6
**Author**: Signal2Trade
**Last Updated**: 2025-8-9
**License**: Open Source (Mozilla Public License 2.0)
Mutanabby_AI | Ultimate Algo | Remastered+Overview
The Mutanabby_AI Ultimate Algo Remastered+ represents a sophisticated trend-following system that combines Supertrend analysis with multiple moving average confirmations. This comprehensive indicator is designed specifically for identifying high-probability trend continuation and reversal opportunities across various market conditions.
Core Algorithm Components
**Supertrend Foundation**: The primary signal generation relies on a customizable Supertrend indicator with adjustable sensitivity (1-20 range). This adaptive trend-following tool uses Average True Range calculations to establish dynamic support and resistance levels that respond to market volatility.
**SMA Confirmation Matrix**: Multiple Simple Moving Averages (SMA 4, 5, 9, 13) provide layered confirmation for signal strength. The algorithm distinguishes between regular signals and "Strong" signals based on SMA 4 vs SMA 5 relationship, offering traders different conviction levels for position sizing.
**Trend Ribbon Visualization**: SMA 21 and SMA 34 create a visual trend ribbon that changes color based on their relationship. Green ribbon indicates bullish momentum while red signals bearish conditions, providing immediate visual trend context.
**RSI-Based Candle Coloring**: Advanced 61-tier RSI system colors candles with gradient precision from deep red (RSI ≤20) through purple transitions to bright green (RSI ≥79). This visual enhancement helps traders instantly assess momentum strength and overbought/oversold conditions.
Signal Generation Logic
**Buy Signal Criteria**:
- Price crosses above Supertrend line
- Close price must be above SMA 9 (trend confirmation)
- Signal strength determined by SMA 4 vs SMA 5 relationship
- "Strong Buy" when SMA 4 ≥ SMA 5
- Regular "Buy" when SMA 4 < SMA 5
**Sell Signal Criteria**:
- Price crosses below Supertrend line
- Close price must be below SMA 9 (trend confirmation)
- Signal strength based on SMA relationship
- "Strong Sell" when SMA 4 ≤ SMA 5
- Regular "Sell" when SMA 4 > SMA 5
Advanced Risk Management System
**Automated TP/SL Calculation**: The indicator automatically calculates stop loss and take profit levels using ATR-based measurements. Risk percentage and ATR length are fully customizable, allowing traders to adapt to different market conditions and personal risk tolerance.
**Multiple Take Profit Targets**:
- 1:1 Risk-Reward ratio for conservative profit taking
- 2:1 Risk-Reward for balanced trade management
- 3:1 Risk-Reward for maximum profit potential
**Visual Risk Display**: All risk management levels appear as both labels and optional trend lines on the chart. Customizable line styles (solid, dashed, dotted) and positioning ensure clear visualization without chart clutter.
**Dynamic Level Updates**: Risk levels automatically recalculate with each new signal, maintaining current market relevance throughout position lifecycles.
Visual Enhancement Features
**Customizable Display Options**: Toggle trend ribbon, TP/SL levels, and risk lines independently. Decimal precision adjustments (1-8 decimal places) accommodate different instrument price formats and personal preferences.
**Professional Label System**: Clean, informative labels show entry points, stop losses, and take profit targets with precise price levels. Labels automatically position themselves for optimal chart readability.
**Color-Coded Momentum**: The gradient RSI candle coloring system provides instant visual feedback on momentum strength, helping traders assess market energy and potential reversal zones.
Implementation Strategy
**Timeframe Optimization**: The algorithm performs effectively across multiple timeframes, with higher timeframes (4H, Daily) providing more reliable signals for swing trading. Lower timeframes work well for day trading with appropriate risk adjustments.
**Sensitivity Adjustment**: Lower sensitivity values (1-5) generate fewer but higher-quality signals, ideal for conservative approaches. Higher sensitivity (15-20) increases signal frequency for active trading styles.
**Risk Management Integration**: Use the automated risk calculations as baseline parameters, adjusting risk percentage based on account size and market conditions. The 1:1, 2:1, 3:1 targets enable systematic profit-taking strategies.
Market Application
**Trend Following Excellence**: Primary strength lies in capturing significant trend movements through the Supertrend foundation with SMA confirmation. The dual-layer approach reduces false signals common in single-indicator systems.
**Momentum Assessment**: RSI-based candle coloring provides immediate momentum context, helping traders assess signal strength and potential continuation probability.
**Range Detection**: The trend ribbon helps identify ranging conditions when SMA 21 and SMA 34 converge, alerting traders to potential breakout opportunities.
Performance Optimization
**Signal Quality**: The requirement for both Supertrend crossover AND SMA 9 confirmation significantly improves signal reliability compared to basic trend-following approaches.
**Visual Clarity**: The comprehensive visual system enables rapid market assessment without complex calculations, ideal for traders managing multiple instruments.
**Adaptability**: Extensive customization options allow fine-tuning for specific markets, trading styles, and risk preferences while maintaining the core algorithm integrity.
## Non-Repainting Design
**Educational Note**: This indicator uses standard TradingView functions (Supertrend, SMA, RSI) with normal behavior patterns. Real-time updates on current candles are expected and standard across all technical indicators. Historical signals on closed candles remain fixed and unchanged, ensuring reliable backtesting and analysis.
**Signal Confirmation**: Final signals are confirmed only when candles close, following standard technical analysis principles. The algorithm provides clear distinction between developing signals and confirmed entries.
Technical Specifications
**Supertrend Parameters**: Default sensitivity of 4 with ATR length of 11 provides balanced signal generation. Sensitivity range from 1-20 allows adaptation to different market volatilities and trading preferences.
**Moving Average Configuration**: SMA periods of 8, 9, and 13 create multi-layered trend confirmation, while SMA 21 and 34 form the visual trend ribbon for broader market context.
**Risk Management**: ATR-based calculations with customizable risk percentage ensure dynamic adaptation to market volatility while maintaining consistent risk exposure principles.
Recommended Settings
**Conservative Approach**: Sensitivity 4-5, RSI length 14, higher timeframes (4H, Daily) for swing trading with maximum signal reliability.
**Active Trading**: Sensitivity 6-8, RSI length 8-10, intermediate timeframes (1H) for balanced signal frequency and quality.
**Scalping Setup**: Sensitivity 10-15, RSI length 5-8, lower timeframes (15-30min) with enhanced risk management protocols.
## Conclusion
The Mutanabby_AI Ultimate Algo Remastered+ combines proven trend-following principles with modern visual enhancements and comprehensive risk management. The algorithm's strength lies in its multi-layered confirmation approach and automated risk calculations, providing both novice and experienced traders with clear signals and systematic trade management.
Success with this system requires understanding the relationship between signal strength indicators and adapting sensitivity settings to match current market conditions. The comprehensive visual feedback system enables rapid decision-making while the automated risk management ensures consistent trade parameters.
Practice with different sensitivity settings and timeframes to optimize performance for your specific trading style and risk tolerance. The algorithm's systematic approach provides an excellent framework for disciplined trend-following strategies across various market environments.
TCP | Market Session | Session Analyzer📌 TCP | Market Session Indicator | Crypto Version
A powerful, real-time market session visualization tool tailored for crypto traders. Track the heartbeat of Asia, Europe, and US trading hours directly on your chart with live session boxes, behavioral analysis, liquidity grab detection, and countdown timers. Know when the action starts, how the market behaves, and where the traps lie.
🔰 Introduction:
Trade the Right Hours with the Right Tools
Time matters in trading. Most significant moves happen during key sessions—and knowing when and how each session unfolds can give you a sharp edge. The TCP Market Session Indicator, developed by Trade City Pro (TCP), puts professional session tracking and behavioral insights at your fingertips.
Whether you're a scalper or swing trader, this indicator gives you the timing context to enter and exit trades with greater confidence and clarity.
🕒 Core Features
• Live Session Boxes :
Highlight active ranges during Asia, Europe, and US sessions with dynamic high/low updates.
• Session Start/End Labels :
Know exactly when each session begins and ends plotted clearly on your chart with context.
• Session Behavior Analysis :
At the end of each session, the indicator classifies the price action as:
- Trend Up
- Trend Down
- Consolidation
- Manipulation
• Liquidity Grab Detection: Automatically detects possible stop hunts (fake breakouts) and marks them on the chart with precision filters (volume, ATR, reversal).
• Session Countdown Table: A live dashboard showing:
- Current active session
- Time left in session
- Upcoming session and how many minutes until it starts
- Utility time converter (e.g. 90 min = 01:30)
• Vertical Session Lines: Visualize past and upcoming session boundaries with customizable history and future range.
• Multi-Day Support: Draw session ranges for previous, current, and future days for better backtesting and forecasting.
⚙️ Settings Panel
Customize everything to fit your trading style and schedule:
• Session Time Settings:
Set the opening and closing time for each session manually using UTC-based minute inputs.
→ For example, enter Asia Start: 0, Asia End: 480 for 00:00–08:00 UTC.
This gives full flexibility to adjust session hours to match your preferred market behavior.
• Enable or Disable Elements:
Toggle the visibility of each session (Asia, Europe, US), as well as:
- Session Boxes
- Countdown Table
- Session Lines
- Liquidity Grab Labels
• Timezone Selection:
Choose between using UTC or your chart’s local timezone for session calculations.
• Customization Options:
Select number of past and future days to draw session data
Adjust vertical line transparency
Fine-tune label offset and spacing for clean layout
📊 Smart Session Boxes
Each session box tracks high, low, open, and close in real time, providing visual clarity on market structure. Once a session ends, the box closes, and the behavior type is saved and labeled ideal for spotting patterns across sessions.
• Asia: Green Box
• Europe: Orange Box
• US: Blue Box
💡 Why Use This Tool?
• Perfect Timing: Don’t get chopped in low-liquidity hours. Focus on sessions where volume and volatility align.
• Pattern Recognition: Study how price behaves session-to-session to build better strategies.
• Trap Detection: Spot manipulation moves (liquidity grabs) early and avoid common retail pitfalls.
• Macro Session Mapping: Use as a foundational layer to align trades with market structure and news cycles.
🔍 Example Use Case
You're watching BTC at 12:45 UTC. The indicator tells you:
The Asia session just ended (label shows “Asia Session End: Trend Up”)
Europe session starts in 15 minutes
A liquidity grab just triggered at the previous high—label confirmed
Now you know who’s active, what the market just did, and what’s about to start—all in one glance.
✅ Why Traders Trust It
• Visual & Intuitive: Fully chart-based, no clutter, no guessing
• Crypto-Focused: Designed specifically for 24/7 crypto markets (not outdated forex models)
• Non-Repainting: All labels and boxes stay as printed—no tricks
• Reliable: Tested across multiple exchanges, pairs, and timeframes
🧩 Built by Trade City Pro (TCP)
The TCP Market Session Indicator is part of a suite of professional tools used by over 150,000 traders. It’s coded in Pine Script v6 for full compatibility with TradingView’s latest capabilities.
🔗 Resources
• Tutorial: Learn how to analyze sessions like a pro in our TradingView guide:
"TradeCityPro Academy: Session Mapping & Liquidity Traps"
• More Tools: Explore our full library of indicators on
Options Strategy V2.0📈 Options Strategy V2.0 – Intraday Reversal-Resilient Momentum System
Overview:
This strategy is designed specifically for intraday SPY, TSLA, MSFT, etc. options trading (0DTE or 1DTE), using high-probability signals derived from a confluence of technical indicators: EMA crossovers, RSI thresholds, ATR-based risk control, and volume spikes. The strategy aims to capture strong directional moves while avoiding overtrading, thanks to a built-in cooldown logic and optional time/session filters.
⚙️ Core Concept
The strategy executes trades only in the direction of the prevailing trend, determined by short- and long-term Exponential Moving Averages (EMA). Entry signals are generated when the Relative Strength Index (RSI) confirms momentum in the direction of the trend, and volume spikes suggest institutional activity.
To increase adaptability and user control, it includes a highly customizable parameter set for both long and short entries independently.
📌 Key Features
✅ Trend-Following Logic
Long entries are only allowed when EMA(short) > EMA(long)
Short entries are only allowed when EMA(short) < EMA(long)
✅ RSI Confirmation
Long: Requires RSI crossover above a configurable threshold
Short: Requires RSI crossunder below a configurable threshold
Optional rejection filters: Entry blocked above/below specific RSI extremes
✅ Volume Spike Filter
Confirms institutional participation by comparing current volume to an average multiplied by a user-defined factor.
✅ ATR-Based Risk Management
Both Stop Loss (SL) and Take Profit (TP) are dynamically calculated using ATR × a multiplier.
TP/SL ratio is fully configurable.
✅ Cooldown Control
After every trade, the system waits for a set number of bars before allowing new entries.
This prevents overtrading and increases signal quality.
Optionally, cooldown is ignored for reversal trades, ensuring the system can react immediately to a confirmed trend change.
✅ Candle Body Filter (Noise Control)
Avoids trades on candles with too small bodies relative to wicks (often noise or indecision candles).
✅ VWAP Confirmation (Optional)
Ensures price is trading above VWAP for long entries, or below for short entries.
✅ Time & Session Filters
Trades only during regular market hours (09:30–16:00 EST).
No-trade zone (e.g., 14:15–15:45 EST) to avoid low-liquidity traps or late-day whipsaws.
✅ End-of-Day Auto Close
All open positions are force-closed at 15:55 EST, protecting against overnight risk (especially relevant for 0DTE options).
📊 Visual Aids
EMA plots show trend direction
VWAP line provides real-time mean-reversion context
Stop Loss and Take Profit lines appear dynamically with each trade
Alerts notify of entry signals and exit triggers
🔧 Customization Panel
Nearly every element of the strategy can be tailored:
EMA lengths (short and long, for both sides)
RSI thresholds and length
ATR length, SL multiplier, and TP/SL ratio
Volume spike sensitivity
Minimum EMA distance filter
Candle body ratio filter
Session restrictions
Cooldown logic (duration + reversal exception)
This makes the strategy extremely versatile, allowing both conservative and aggressive configurations depending on the trader’s profile and the market context.
📌 Example Use Case: SPY Options (0DTE or 1DTE)
This system was designed and tested specifically for SPY and other intraday options trading, where:
Delta is around 0.50 or higher
Trades are short-lived (often 1–5 candles)
You aim to trade 1–3 signals per day, filtering out weak entries
🚫 Important Notes
It is not a scalping strategy; it relies on confirmed breakouts with trend support
No pyramiding or re-entries without cooldown to preserve risk integrity
Should be used with real-time alerts and manual broker execution
📈 Alerts Included
📈 Long Entry Signal
📉 Short Entry Signal
⚠️ Auto-closed all positions at 15:55 EST
✅ Proven Settings – Real Trades + Backtest Results
The current version of the strategy includes the optimal settings I’ve arrived at through extensive backtesting, as well as 3 months of real trading with consistent profitability. These results reflect real-world execution under live market conditions using 0DTE SPY options, with disciplined trade management and risk control.
🧠 Final Thoughts
Options Strategy V2.0 is a robust, highly tunable intraday strategy that blends momentum, trend-following, and volume confirmation. It is ideal for disciplined traders focused on SPY or other 0DTE/1DTE options, and it includes guardrails to reduce false signals and improve execution timing.
Perfect for those who seek precision, flexibility, and risk-defined setups—not blind automation.
%ATR + ΔClose HighlightScript Overview
This indicator displays on your chart:
Table of the last N bars that passed the ATR-based range filter:
Columns: Bar #, High, Range (High–Low), Low
Summary row: ATR(N), suggested Stop-Loss (SL = X % of ATR), and the current bar’s range as a percentage of ATR
Red badge on the most recent bar showing ΔClose% (the absolute difference between today’s and yesterday’s close, expressed as % of ATR)
Background highlights:
Blue fill under the most recent bar that met the filter
Yellow fill under bars that failed the filter
Hidden plots of ATR, %ATR, and ΔClose% (for use in strategies or alerts)
All table elements, fills, and plots can be toggled off with a single switch so that only the red ΔClose% badge remains visible.
Inputs
Setting Description Default
Length (bars) Lookback period for ATR and range filter (bars) 5
Upper deviation (%) Upper filter threshold (% of average ATR) 150%
Lower deviation (%) Lower filter threshold (% of average ATR) 50%
SL as % of ATR Stop-loss distance (% of ATR) 10%
Label position Table position relative to bar (“above” or “below”) above
Vertical offset (×ATR) Vertical spacing from the bar in ATR units 2.0
Show table & ATR plots Show or hide table, background highlights, and plots true
How It Works
ATR Calculation & Filtering
Computes average True Range over the last N bars.
Marks bars whose daily range falls within the specified upper/lower deviation band.
Table Construction
Gathers up to N most recent bars that passed the filter (or backfills from the most recent pass).
Formats each bar’s High, Low, and Range into fixed-width columns for neat alignment.
Stop-Loss & Percent Metrics
Calculates a recommended SL distance as a percentage of ATR.
Computes today’s bar range and ΔClose (absolute change in close) as % of ATR.
Chart Display
Table: Shows detailed per-bar data and summary metrics.
Background fills: Blue for the latest valid bar, yellow for invalid bars.
Hidden plots: ATR, %ATR, and ΔClose% (useful for backtesting).
Red badge: Always visible on the right side of the last bar, displaying ΔClose%.
Tips
Disable the table & ATR plots to reduce chart clutter—leave only the red ΔClose% badge for a minimalist volatility alert.
Use the hidden ATR fields (plot outputs) in TradingView Strategies or Alerts to automate volatility-based entries/exits.
Adjust the deviation band to capture “normal” intraday moves vs. outsized volatility spikes.
Load this script on any US market chart (stocks, futures, crypto, etc.) to instantly visualize recent volatility structure, set dynamic SL levels, and highlight today’s price change relative to average true range.
Multi-Timeframe SFP + SMTImportant: Please Read First
This indicator is not a "one size fits all" solution. It is a professional and complex tool that requires you to learn how to use it, in addition to backtesting different settings to discover what works best for your specific trading style and the assets you trade. The default settings provided are my personal preferences for trading higher-timeframe setups, but you are encouraged to experiment and find your own optimal configuration.
Please note that while this initial version is solid, it may still contain small errors or bugs. I will be actively working on improving the indicator over time. Also, be aware that the script is not written for maximum efficiency and may be resource-intensive, but this should not pose a problem for most users.
The source code for this indicator is open. If you truly want to understand precisely how all the logic works, you can copy and paste the code into an AI assistant like Gemini or ChatGPT and ask it to explain any part of the script to you.
Author's Preferred Settings (Guideline)
As a starting point, here are the settings I personally use for my trading:
SFP Timeframe: 4-Hour (Strength: 5-5)
Max Lookback: 35 Bars
Raid Expiration: 1 Bar
SFP Lines Limit: 1
SMT Timeframe 1: 30-Minute (Strength: 2-2) with 3-Minute LTF Detection.
SMT Timeframe 2: 15-Minute (Strength: 3-3) with 3-Minute LTF Detection.
SMT Timeframe 3: 1-Hour (Strength: 1-1) with 3-Minute LTF Detection.
SMT Timeframe 4: 15-Minute (Strength: 1-1) with 3-Minute LTF Detection.
Multi-Timeframe SMT: An Overview
This indicator is a powerful tool designed to identify high-probability trading setups by combining two key institutional concepts: Swing Failure Patterns (SFP) on a higher timeframe and Smart Money Technique (SMT) divergences on a lower timeframe. A key feature is the ability to configure and run up to four independent SMT analyses simultaneously, allowing you to monitor for divergences across multiple timeframes (e.g., 15m, 1H, 4H) from a single indicator.
Its primary purpose is to generate automated signals through TradingView's alert system. By setting up alerts, the script runs server-side, monitoring the market for you. When a setup presents itself, it will send a push notification to your device, allowing you to personally evaluate the trade without being tied to your screen.
The Strategy: HTF Liquidity Sweeps into LTF SMT
The core strategy is built on a classic institutional trading model:
Wait for a liquidity sweep on a significant high timeframe (e.g., 4-hour, Daily).
Once liquidity is taken, look for a confirmation of a shift in market structure on a lower timeframe.
This indicator uses an SMT divergence as that confirmation signal, indicating that smart money may be stepping in to reverse the price.
How It Works: The Two-Step Process
The indicator's logic follows a precise two-step process to generate a signal:
Step 1: The Swing Failure Pattern (SFP)
First, the indicator identifies a high-timeframe liquidity sweep. This is configured in the "Swing Failure Pattern (SFP) Timeframe" settings.
It looks for a candle that wicks above a previous high (or below a previous low) but then closes back within the range of that pivot. This action is known as a "raid" or a "swing failure," suggesting the move failed to find genuine momentum.
Step 2: The SMT Divergence
The moment a valid SFP is confirmed, the indicator's multiple SMT engines activate.
Each engine begins monitoring the specific SMT timeframe you have configured (e.g., "SMT Timeframe 1," "SMT Timeframe 2," etc.) for a Smart Money Technique (SMT) divergence.
An SMT divergence occurs when two closely correlated assets fail to move in sync. For example, after a raid on a high, Asset A makes a new high, but Asset B fails to do so. This disagreement suggests weakness and a potential reversal.
When the script finds this divergence, it plots the SMT line and triggers an alert.
The Power of Alerts
The true strength of this indicator lies in its alert capabilities. You can create alerts for both unconfirmed and confirmed SMTs.
Enable Alerts LTF Detection: These alerts trigger when an unconfirmed, potential SMT is spotted on the lower "LTF Detection" timeframe. While not yet confirmed, these early alerts can notify you of a potential move before it fully happens, allowing you to be ahead of the curve and find the best possible trade entries.
Enable Alerts Confirmed SMT: These alerts trigger only when a permanent, confirmed SMT line is plotted on your chosen SMT timeframe. These signals are more reliable but occur later than the early detection alerts.
Key Concepts Explained
What is Pivot Strength?
Pivot Strength determines how significant a high or low needs to be to qualify as a valid structural point. A setting of 5-5, for example, means that for a candle's high to be considered a valid pivot high, its high must be higher than the highs of the 5 candles to its left and the 5 candles to its right.
Higher Strength (e.g., 5-5, 8-8): Creates fewer, but more significant, pivots. This is ideal for identifying major structural highs and lows on higher timeframes.
Lower Strength (e.g., 2-2, 3-3): Creates more pivots, making it suitable for identifying the smaller shifts in momentum on lower timeframes.
Raid Expiration & Validity
An SFP signal is not valid forever. The "Raid Expiration" setting determines how many SFP timeframe bars can pass after a raid before that signal is considered "stale" and can no longer be used to validate an SMT. This ensures your SMT divergences are always in response to recent liquidity sweeps.
Why You Must Be on the Right Chart Timeframe to See SMT Lines
Pine Script™ has a fundamental rule: an indicator running on a chart can only "see" the bars of that chart's timeframe or higher.
When the SMT logic is set to the 15-minute timeframe, it calculates its pivots based on 15-minute data. To accurately plot lines connecting these pivots, you must be on a 15-minute chart or lower (e.g., 5-minute, 1-minute).
If you are on a higher timeframe chart, like the 1-hour, the 15-minute bars do not exist on that chart, so the indicator has no bars to draw the lines on.
This is precisely why the alert system is so powerful. You can set your alert to run on the 15-minute timeframe, and TradingView's servers will monitor that timeframe for you, sending a notification regardless of what chart you are currently viewing.
Trading Hours Filterfor those of us with certain trading hours and schedules and wait for certain setups, this is great for backtesting or live testing trade setups in your timeframe. Adjust times in pine editor as needed.
DeltaTrace ForecastDeltaTrace Forecast is a forward-looking projection tool that visualizes the probable directional path of price using a multi-timeframe momentum model rooted in volatility-adjusted nonlinear dynamics. Rather than relying on traditional indicators that react to price after the fact, DeltaTrace estimates future price motion by tracing the progression of momentum changes across expanding timeframes—then scaling those deltas using adaptive volatility to forecast a plausible path forward.
At its core, DeltaTrace constructs a momentum vector from a series of smoothed z-scores derived from increasing multiples of the current chart's timeframe. These z-scores are normalized using a hyperbolic tangent function (tanh), which compresses extreme values and emphasizes meaningful deviations without being overly sensitive to outliers. This nonlinear normalization ensures that explosive moves are weighted with less distortion, while still preserving the shape and direction of the underlying trend.
Once the z-scores are calculated for a range of 12 timeframes (from 1× the current timeframe up to 12×), the indicator computes the first difference between each adjacent pair. These differences—or deltas—represent the change in momentum from one timeframe to the next. In this structure, a strong positive delta implies momentum is strengthening as we look into higher timeframes, while a negative delta reflects waning or reversing strength.
However, not all deltas are treated equally. To make the projection adaptive to market volatility and temporally meaningful, each delta is scaled by the square root of its corresponding timeframe multiple, weighted by the ATR (Average True Range) of the base timeframe. This square-root volatility scaling mirrors the behavior of Brownian motion and reflects the natural geometric diffusion of price over time. By applying this scaling, the model tempers its forecast according to recent volatility while maintaining proportional distance over longer time horizons.
The result is a chain of projected price steps—11 in total—starting from the current closing price. These steps are cumulative, meaning each one builds upon the previous, forming a continuously adjusted polyline that represents the most recent forecast path of price. Each point in the forecast line is directional: if the next projected point is above the last, the segment is colored green (upward momentum); if below, it is colored red (downward momentum). This color coding gives immediate visual feedback on the nature of the projected path and allows for intuitive at-a-glance interpretation.
What makes DeltaTrace unique is its combination of ideas from signal processing, time-series momentum analysis, and volatility theory. Instead of relying on static support/resistance levels or lagging moving averages, it dynamically adapts to both momentum curvature and volatility structure. This allows it to be used not just for trend confirmation, but also for top-down bias fading, reversal anticipation, and path-following strategies.
Traders can use DeltaTrace in a variety of ways depending on their style:
For trend traders, a consistent upward or downward curve in the forecast suggests directional continuation and can be used for position sizing or confirmation of bias.
For mean-reversion traders, exaggerated divergence between the current price and the first few forecast points may indicate temporary exhaustion or overextension.
For scalpers or intraday traders, the short-term bend or flattening of the initial segments can reveal early signs of weakening momentum or build-up before breakout.
For swing traders, the full shape of the polyline gives an evolving map of market rhythm across time compression, allowing for context-aware decision-making.
It’s important to understand that this is a path projection tool, not a precise price target predictor. The forecast does not attempt to predict exact price levels at exact bars, but rather illustrates how the market might evolve if the current multi-timeframe momentum structure persists. Like all models, it should be interpreted probabilistically and used in conjunction with other confirmation signals, risk management tools, or strategy frameworks.
Inputs allow customization of the z-score calculation length and ATR window to tune the sensitivity of the model. The color scheme for up/down forecast segments can also be adjusted for personal preference. Additionally, users can toggle the polyline forecast on or off, which may be useful for pairing this indicator with others in a crowded chart layout.
Because the forecast path is calculated only on the last bar, it does not repaint or shift once the candle closes—preserving historical accuracy for visual inspection and backtesting reference. However, it is also sensitive to changes in volatility and momentum structure, meaning it updates each bar as conditions evolve, making it most effective in real-time decision support.
DeltaTrace Forecast is particularly well-suited for traders who want a deeper understanding of hidden momentum shifts across timeframes without relying on traditional trend-following tools. It reveals the shape of future possibility based on present dynamics, offering a compact yet powerful visualization of directional bias, transition risk, and path strength.
To maximize its utility, consider pairing DeltaTrace with volume profiles, order flow tools, higher timeframe zones, or market structure indicators. Used in context, it becomes a powerful companion to both systematic and discretionary trading styles—especially for those who appreciate a blend of mathematics and intuition in their market analysis.
This indicator is not based on magic or black-box logic; every component—from the z-score standardization to the volatility-adjusted deltas—is fully transparent and grounded in simple, interpretable mechanics. If you're looking for a reliable way to visualize multi-timeframe bias and momentum diffusion, DeltaTrace provides a unique lens through which to interpret future potential in an ever-shifting market landscape.
MSTY-WNTR Rebalancing SignalMSTY-WNTR Rebalancing Signal
## Overview
The **MSTY-WNTR Rebalancing Signal** is a custom TradingView indicator designed to help investors dynamically allocate between two YieldMax ETFs: **MSTY** (YieldMax MSTR Option Income Strategy ETF) and **WNTR** (YieldMax Short MSTR Option Income Strategy ETF). These ETFs are tied to MicroStrategy (MSTR) stock, which is heavily influenced by Bitcoin's price due to MSTR's significant Bitcoin holdings.
MSTY benefits from upward movements in MSTR (and thus Bitcoin) through a covered call strategy that generates income but caps upside potential. WNTR, on the other hand, provides inverse exposure, profiting from MSTR declines but losing in rallies. This indicator uses Bitcoin's momentum and MSTR's relative strength to signal when to hold MSTY (bullish phases), WNTR (bearish phases), or stay neutral, aiming to optimize returns by switching allocations at key turning points.
Inspired by strategies discussed in crypto communities (e.g., X posts analyzing MSTR-linked ETFs), this indicator promotes an active rebalancing approach over a "set and forget" buy-and-hold strategy. In simulated backtests over the past 12 months (as of August 4, 2025), the optimized version has shown potential to outperform holding 100% MSTY or 100% WNTR alone, with an illustrative APY of ~125% vs. ~6% for MSTY and ~-15% for WNTR in one scenario.
**Important Disclaimer**: This is not financial advice. Past performance does not guarantee future results. Always consult a financial advisor. Trading involves risk, and you could lose money. The indicator is for educational and informational purposes only.
## Key Features
- **Momentum-Based Signals**: Uses a Simple Moving Average (SMA) on Bitcoin's price to detect bullish (price > SMA) or bearish (price < SMA) trends.
- **RSI Confirmation**: Incorporates MSTR's Relative Strength Index (RSI) to filter signals, avoiding overbought conditions for MSTY and oversold for WNTR.
- **Visual Cues**:
- Green upward triangle for "Hold MSTY".
- Red downward triangle for "Hold WNTR".
- Yellow cross for "Switch" signals.
- Background color: Green for MSTY, red for WNTR.
- **Information Panel**: A table in the top-right corner displays real-time data: BTC Price, SMA value, MSTR RSI, and current Allocation (MSTY, WNTR, or Neutral).
- **Alerts**: Configurable alerts for holding MSTY, holding WNTR, or switching.
- **Optimized Parameters**: Defaults are tuned (SMA: 10 days, RSI: 15 periods, Overbought: 80, Oversold: 20) based on simulations to reduce whipsaws and capture trends effectively.
## How It Works
The indicator's logic is straightforward yet effective for volatile assets like Bitcoin and MSTR:
1. **Primary Trigger (Bitcoin Momentum)**:
- Calculate the SMA of Bitcoin's closing price (default: 10-day).
- Bullish: Current BTC price > SMA → Potential MSTY hold.
- Bearish: Current BTC price < SMA → Potential WNTR hold.
2. **Secondary Filter (MSTR RSI Confirmation)**:
- Compute RSI on MSTR stock (default: 15-period).
- For bullish signals: If RSI > Overbought (80), signal Neutral (avoid overextended rallies).
- For bearish signals: If RSI < Oversold (20), signal Neutral (avoid capitulation bottoms).
3. **Allocation Rules**:
- Hold 100% MSTY if bullish and not overbought.
- Hold 100% WNTR if bearish and not oversold.
- Neutral otherwise (e.g., during choppy or extreme markets) – consider holding cash or avoiding trades.
4. **Rebalancing**:
- Switch signals trigger when the hold changes (e.g., from MSTY to WNTR).
- Recommended frequency: Weekly reviews or on 5% BTC moves to minimize trading costs (aim for 4-6 trades/year).
This approach leverages Bitcoin's influence on MSTR while mitigating the risks of MSTY's covered call drag during downtrends and WNTR's losses in uptrends.
## Setup and Usage
1. **Chart Requirements**:
- Apply this indicator to a Bitcoin chart (e.g., BTCUSD on Binance or Coinbase, daily timeframe recommended).
- Ensure MSTR stock data is accessible (TradingView supports it natively).
2. **Adding to TradingView**:
- Open the Pine Editor.
- Paste the script code.
- Save and add to your chart.
- Customize inputs if needed (e.g., adjust SMA/RSI lengths for different timeframes).
3. **Interpretation**:
- **Green Background/Triangle**: Allocate 100% to MSTY – Bitcoin is in an uptrend, MSTR not overbought.
- **Red Background/Triangle**: Allocate 100% to WNTR – Bitcoin in downtrend, MSTR not oversold.
- **Yellow Switch Cross**: Rebalance your portfolio immediately.
- **Neutral (No Signal)**: Panel shows "Neutral" – Hold cash or previous position; reassess weekly.
- Monitor the panel for key metrics to validate signals manually.
4. **Backtesting and Strategy Integration**:
- Convert to a strategy script by changing `indicator()` to `strategy()` and adding entry/exit logic for automated testing.
- In simulations (e.g., using Python or TradingView's backtester), it has outperformed buy-and-hold in volatile markets by ~100-200% relative APY, but results vary.
- Factor in fees: ETF expense ratios (~0.99%), trading commissions (~$0.40/trade), and slippage.
5. **Risk Management**:
- Use with a diversified portfolio; never allocate more than you can afford to lose.
- Add stop-losses (e.g., 10% trailing) to protect against extreme moves.
- Rebalance sparingly to avoid over-trading in sideways markets.
- Dividends: Reinvest MSTY/WNTR payouts into the current hold for compounding.
## Performance Insights (Simulated as of August 4, 2025)
Based on synthetic backtests modeling the last 12 months:
- **Optimized Strategy APY**: ~125% (by timing switches effectively).
- **Hold 100% MSTY APY**: ~6% (gains from BTC rallies offset by downtrends).
- **Hold 100% WNTR APY**: ~-15% (losses in bull phases outweigh bear gains).
In one scenario with stronger volatility, the strategy achieved ~4533% APY vs. 10% for MSTY and -34% for WNTR, highlighting its potential in dynamic markets. However, these are illustrative; real results depend on actual BTC/MSTR movements. Test thoroughly on historical data.
## Limitations and Considerations
- **Data Dependency**: Relies on accurate BTC and MSTR data; delays or gaps can affect signals.
- **Market Risks**: Bitcoin's volatility can lead to false signals (whipsaws); the RSI filter helps but isn't perfect.
- **No Guarantees**: This indicator doesn't predict the future. MSTR's correlation to BTC may change (e.g., due to regulatory events).
- **Not for All Users**: Best for intermediate/advanced traders familiar with ETFs and crypto. Beginners should paper trade first.
- **Updates**: As of August 4, 2025, this is version 1.0. Future updates may include volume filters or EMA options.
If you find this indicator useful, consider leaving a like or comment on TradingView. Feedback welcome for improvements!
Auto-Pivot Levels with Alerts and 4 methods [ChartWhizzperer]🚀 Auto-Pivot Levels – Dynamic Edition
Now with
Live Mode,
4 Pivot Methods
PineConnector-Ready Alerts!
Free, Open Source, Pine Script v6-compliant.
🟢 NEW: Live Mode (Ultra-Dynamic, Repainting) – Switchable in UI!
Instantly switch between Classic (session-based, repaint-free) and Live (rolling window, real-time, repainting) using the simple checkbox in the settings!
Live Mode recalculates all pivots on every tick/bar, using the current high/low/close for the chosen session (daily, weekly, monthly).
Perfect for:
Scalping and high-frequency trading
Real-time bot/automation setups (PineConnector-ready)
Fast-moving or breakout markets
Classic Mode: For traditional, stable levels based on confirmed session data – ideal for backtesting and trading history.
📊 Four Calculation Methods (Choose What Fits YOU):
1️⃣ Classic
Standard pivot calculation.
Based on previous session’s High, Low, Close.
Simple, proven, and suitable for any asset.
2️⃣ Fibonacci
Projects levels using Fibonacci ratios of the prior session’s range.
Great for traders who want to align pivots with fib retracements and extensions.
3️⃣ Camarilla
Uses unique multipliers for support/resistance, focusing on mean reversion and volatility.
Popular among futures and forex day traders.
4️⃣ Woodie
Puts extra weight on previous Close for more responsive pivots.
Often used in trending or choppy conditions.
Switch methods anytime in the UI – the script recalculates instantly and keeps your chart clean!
🔔 Level-Specific Alerts – PineConnector Ready!
Dedicated alert for EVERY level and direction (Up/Down):
Pivot (P), R1, R2, R3, S1, S2, S3
No configuration hassle:
All alerts are pre-defined in the TradingView Alert Panel.
Machine-readable message format:
PIVOT=R1 DIR=UP SYMBOL={{ticker}} PRICE={{close}}
Direct plug-and-play with PineConnector, webhooks, Discord, Telegram, bots, and other automation tools.
Never miss a breakout, reversal, or key support/resistance touch.
🛠 Powerful Customization & Performance
Session selection: Daily, Weekly, Monthly (choose what suits your trading style).
Show/hide any level (Pivot, R1–R3, S1–S3) for minimal chart clutter.
Color selection for each level to match your theme or highlight key pivots.
Auto-cleanup: Old lines and labels are cleared on every recalculation or session change for maximum performance and visual clarity.
Zero runtime errors: Strict Pine Script v6 practices for stability.
💡 How To Use – Quick Start
Add the indicator to your TradingView chart.
Pick your calculation method (Classic, Fibonacci, Camarilla, Woodie).
Set session type (Daily, Weekly, Monthly).
Switch between Classic and Live Mode with a single click in settings.
Customize your levels (on/off, colors).
Open the Alert Panel, select any pre-configured alert (e.g. "R2 Cross Down"), and go live!
Connect with PineConnector or any webhook system instantly using the pre-formatted alert messages.
🤖 Who Is It For?
Active scalpers & bot traders: Live Mode + PineConnector-ready alerts = instant, automated reactions.
Swing and position traders: Use Classic Mode for stable, repaint-free levels.
Strategy developers: Seamless integration into automated and manual trading workflows.
🏷 License & Community
Open Source, Non-Commercial:
Free for personal & educational use under CC BY-NC-SA 4.0.
Feedback, bug reports & ideas:
Drop a comment, or contact me for feature requests.
Trade smart. Trade dynamic. Unlock the true power of pivots – with ChartWhizzperer !
HMA Strategy HMA Strat (Hull Moving Average Strategy) Indicator Description
The HMA Strat is a trend-following strategy that uses a dual Hull Moving Average system. It helps identify continuation and high-probability reversal signals in both bullish and bearish market conditions. The strategy aims to reduce noise while maintaining sensitivity to changes in price momentum by comparing the standard Hull Moving Average (HMA) to a smoothed version.
This strategy is ideal for traders who focus on systematic backtesting, momentum entry, and simple charts. It features integrated plotting, color-zoning, and strategic actions based on TradingView's strategy engine. The system provides dynamic long and short signals based on crossover logic.
Key Features
Dual HMA Framework: To improve signal quality and reduce choppy trend identification, it compares a regular HMA with a smoothed version (HMA3).
Entries Based on Crossover
ALPHA 999This indicator is designed to provide traders with clear and reliable signals by combining essential technical tools into one simplified framework. Whether you’re trading crypto, stocks, or forex, this tool helps identify potential entry and exit points using . It is well-suited for both intraday and swing traders, offering customizable inputs, multi-timeframe compatibility, and real-time alerts. The indicator displays intuitive visual cues directly on the chart, making it easy to interpret market conditions and respond quickly. Ideal for traders who prefer clean charts with actionable signals, this tool enhances decision-making without overwhelming complexity. While powerful on its own, it can be paired with other indicators for greater confirmation and accuracy. Please note: this indicator is for educational purposes only and should be used with proper risk management and backtesting before applying to live trades.
MA Crossover Detector
The Moving Average Crossover Detector is a custom indicator that visually shows buy and sell signals clearly on the chart. based on the crossing of two moving averages — a popular and beginner-friendly tool in technical analysis.
It plots two moving averages — One fast (short period) and one slow (long period) — and highlights crossover points:
✅ Buy Signal (Golden Cross) – When the fast MA crosses above the slow MA.
❌ Sell Signal (Death Cross) – When the fast MA crosses below the slow MA.
✅ Features
Visual: Clearly shows crossovers on the chart.
Customizable: Choose periods, types, styles, etc.
Alert-ready: You can set alerts for crossovers.
The Moving Average (MA) Crossover Strategy is one of the simplest and most widely used strategies in technical analysis for trading stocks, forex, crypto, and other markets. It relies on the interaction between two moving averages to generate buy and sell signals.
Core Components
Short-Term Moving Average (Fast MA) : Reacts quickly to price changes (e.g., 9-period or 20-period).
Long-Term Moving Average (Slow MA) : Reacts more slowly to price changes (e.g., 21-period or 200-period).
How the Strategy Works
Bullish Crossover (Golden Cross):
Occurs when the fast MA crosses above the slow MA. Interpreted as a buy signal, indicating a potential uptrend.
Bearish Crossover (Death Cross):
Occurs when the fast MA crosses below the slow MA. Interpreted as a sell signal, indicating a potential downtrend.
Common Variants
Short-term trading
9 EMA
21 EMA
Swing trading
20 SMA
50 SMA
Long-term investing
50 SMA
200 SMA
Pros
Easy to understand and implement
Works well in trending markets
Can be automated for backtesting and execution
Cons
Lagging indicator: MAs are based on past prices, so signals come after the move has started.
Choppy markets = whipsaws: Generates false signals in sideways/range-bound conditions.
May underperform in volatile or mean-reverting environments
Tips for Improvement
Use confirmation tools : e.g., RSI, MACD, volume analysis, price action
Add filters : Trend filter (ADX), volatility filter (ATR), or time filter (session-based)
Combine with price structure : Support/resistance, breakouts, pullbacks
Canonical Momenta Indicator [T1][T69]📌 Overview
The Canonical Momenta Indicator models trend pressure using a Lagrangian-based momentum engine combined with reflexivity theory to detect bursts in price movement influenced by herd behavior and volume acceleration.
🧠 Features
Lagrangian-based kinetic model combining velocity and acceleration
Reflexivity burst detection with directional scoring
Adaptive momentum-weighted output (adaptiveCMI)
Buy 🐋 / Sell 🐻 labels when reflexivity confirms direction
Fully parameterized for customization
⚙️ How to Use
This indicator helps traders:
Detect reflexive bursts in market activity driven by sharp price movement + volume spikes
Capture herd-driven directional moves early.
Gauge market pressure using a kinetic-potential energy model.
Suggested signals:
🐋 Reflexive Up: Strong bullish momentum spike confirmed by volume and positive lagrangian pressure
🐻 Reflexive Down: Strong bearish dump confirmed by volume and negative lagrangian burst
🔧 Configuration
MA Lookback Length - Smoothing for baseline price & energy calculation
Reflexivity Momentum Threshold - Price momentum trigger for burst detection
Reflexivity Lookback - Period over which bursts are counted
Reflexivity Window - Minimum burst sum to trigger signal label
Volume Spike Threshold - % above average volume to qualify as burst
📊 Behavior Description
The indicator computes a Lagrangian energy:
Kinetic Energy = (velocity² + 0.5 * acceleration²)
Potential Energy = deviation from moving average (distance²)
Lagrangian = Potential − Kinetic (higher = overextension)
Then, reflexive bursts are triggered when:
Price is rising or falling over short window (burstMvmnt)
Volume is above average by a user-defined multiple
Each bar gets a burst score:
+1 for up-burst
−1 for down-burst
0 otherwise
⚠️ Risk Profile Based on Lookback Settings
Risk Level | Description | Recommended Lookback
🟥 High | Extremely sensitive to bursts, prone to false signals | 7–10
🟨 Moderate | Balanced reflexivity with trend confirmation | 11–20
🟩 Low | Filters out most noise, slower to react | 21+
🧪 Advanced Tips
Combine with moving average slope for trend filtering
Use divergence between adaptiveCMI and price to detect exhaustion
Works well in crypto, commodities, and volatile assets
⚠️ Limitations
Sensitive to high volatility noise if volMult is too low
Designed for higher timeframes (1H, 4H, Daily) for reliability
Doesn’t confirm direction in sideways markets — pair with other filters
📝 Disclaimer
This tool is provided for educational and informational purposes. Always do your own backtesting and use proper risk management.
BarUtils: Get Bar Index from DateLibrary "BarUtils"
getBarIndexFromDate(targetTimestamp)
Parameters:
targetTimestamp (int)
**Description**:
This utility provides a reliable way to calculate the `bar_index` of a specific calendar date, regardless of chart resolution. It's especially useful for anchoring scripts to historical events, labeling macroeconomic moments, or marking custom time-based signals that must remain consistent across timeframes.
Unlike hardcoded `bar_index - N` approaches, this function dynamically estimates the number of bars between a given `timestamp()` and the current bar using the actual time-per-bar (`time - time `). It works correctly on intraday, daily, weekly, and monthly charts.
### 💡 **Function Provided**:
import TradeTitan120/BarUtils/1
* `getBarIndexFromDate(int targetTimestamp)`
→ Returns the estimated `bar_index` that aligns with a given timestamp
### ✅ **Use Cases**:
* Marking past events like FOMC meetings, market crashes, or personal signals
* Backtesting entry/exit conditions from specific calendar dates
* Anchoring visual elements (shapes, lines, labels) across resolutions
This tool is simple, fast, and built for accuracy. Use it to enhance multi-timeframe compatibility in any script.
