Approx STH Unrealized Profit [Relative %]This indicator estimates the unrealized profit or loss of short-term holders (STH) without requiring on-chain data. Instead of using actual STH Realized Price (average purchase price), it employs a 155-day Simple Moving Average (SMA) to approximate the behavior of "recent buyers."
How It Works
The indicator calculates the percentage deviation between the current price and the 155-day SMA using the formula:
(Current Price - 155 SMA) / 155 SMA * 100%.
Positive values indicate profit, while negative values show loss. Key threshold levels are set at +50% (overbought) and -30% (oversold).
Trading Applications
Profit > 50% - STH are experiencing significant profits, suggesting potential correction. Consider taking partial profits.
0% < Profit < 50% - Moderate profits indicate the trend may continue. Maintain positions.
Profit ≈ 0% - Price is near STH's average entry point, showing market indecision.
-30% < Profit < 0% - STH are at a loss, potentially signaling accumulation opportunities.
Profit < -30% - Extreme oversold conditions may present buying opportunities.
Limitations
SMA only approximates STH behavior.
May produce false signals during sideways markets.
SMA lag can be noticeable in strong trending markets.
Recommendation
For improved accuracy, combine this indicator with trend-following tools (200 EMA, Volume analysis) and other technical indicators. It serves best as a supplementary tool for identifying overbought/oversold market conditions within your trading strategy.
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Intraday Volume Pulse GSK-VIZAG-AP-INDIAIntraday Volume Pulse Indicator
Overview
This indicator is designed to track and visualize intraday volume dynamics during a user-defined trading session. It calculates and displays key volume metrics such as buy volume, sell volume, cumulative delta (difference between buy and sell volumes), and total volume. The data is presented in a customizable table overlay on the chart, making it easy to monitor volume pulses throughout the session. This can help traders identify buying or selling pressure in real-time, particularly useful for intraday strategies.
The indicator resets its calculations at the start of each new day and only accumulates volume data from the specified session start time onward. It uses simple logic to classify volume as buy or sell based on candle direction:
Buy Volume: Assigned to green (up) candles or half of neutral (doji) candles.
Sell Volume: Assigned to red (down) candles or half of neutral (doji) candles.
All calculations are approximate and based on available volume data from the chart. This script does not incorporate external data sources, order flow, or tick-level information—it's purely derived from standard OHLCV (Open, High, Low, Close, Volume) bars.
Key Features
Session Customization: Define the start time of your trading session (e.g., market open) and select from common timezones like Asia/Kolkata, America/New_York, etc.
Volume Metrics:
Buy Volume: Total volume attributed to bullish activity.
Sell Volume: Total volume attributed to bearish activity.
Cumulative Delta: Net difference (Buy - Sell), highlighting overall market bias.
Total Volume: Sum of all volume during the session.
Formatted Display: Volumes are formatted for readability (e.g., in thousands "K", lakhs "L", or crores "Cr" for large numbers).
Color-Coded Table: Uses a patriotic color scheme inspired by general themes (Saffron, White, Green) with dynamic backgrounds based on positive/negative values for quick visual interpretation.
Table Options: Toggle visibility and position (top-right, top-left, etc.) for a clean chart layout.
How to Use
Add to Chart: Apply this indicator to any symbol's chart (works best on intraday timeframes like 1-min, 5-min, or 15-min).
Configure Inputs:
Session Start Hour/Minute: Set to your market's open time (default: 9:15 for Indian markets).
Timezone: Choose the appropriate timezone to align with your trading hours.
Show Table: Enable/disable the metrics table.
Table Position: Place the table where it doesn't obstruct your view.
Interpret the Table:
Monitor for spikes in buy/sell volume or shifts in cumulative delta.
Positive delta (green) suggests buying pressure; negative (red) suggests selling.
Use alongside price action or other indicators for confirmation—e.g., high total volume with positive delta could indicate bullish momentum.
Limitations:
Volume classification is heuristic and not based on actual order flow (e.g., it splits doji volume evenly).
Data accumulation starts from the session time and resets daily; historical backtesting may be limited by the max_bars_back=500 setting.
This is for educational and visualization purposes only—do not use as sole basis for trading decisions.
Calculation Details
Session Filter: Uses timestamp() to define the session start and filters bars with time >= sessionStart.
New Day Detection: Resets volumes on daily changes via ta.change(time("D")).
Volume Assignment:
Buy: Full volume if close > open; half if close == open.
Sell: Full volume if close < open; half if close == open.
Cumulative Metrics: Accumulated only during the session.
Formatting: Custom function f_format() scales large numbers for brevity.
Disclaimer
This script is for educational and informational purposes only. It does not provide financial advice or signals to buy/sell any security. Always perform your own analysis and consult a qualified financial professional before making trading decisions.
© 2025 GSK-VIZAG-AP-INDIA
Swing Point Volume Z-ScoreSWING POINT VOLUME Z-SCORE INDICATOR
A volume analysis tool that identifies statistical volume spikes at swing points with optional higher timeframe confirmation.
This indicator uses Leviathan's method of swing detection. All credit to him for his amazing work (and any mistakes mine). I was also inspired by Trading Riot, who's Capitulation indicator gave me the idea to create this one.
WHAT IT DOES
This indicator combines three analytical approaches:
- Volume Z-score calculation to measure volume significance statistically
- Automatic swing point detection (higher highs, lower lows, etc.)
- Optional higher timeframe volume confirmation
The Z-score measures how many standard deviations current volume is from the average, helping identify when volume activity is genuinely elevated rather than relying on visual assessment.
VISUAL SYSTEM
The indicator uses a color-coded approach for quick assessment:
GREEN - Normal Activity (Z-Score 1.0-2.0)
Above-average volume levels
ORANGE - Elevated Activity (Z-Score 2.0-3.0)
High volume activity that may indicate increased interest
RED - Potential Institutional Activity (Z-Score 3.0+)
Very high volume levels that could suggest significant market participation
HIGHER TIMEFRAME CONFIRMATION
When enabled, the indicator checks volume on a higher timeframe:
- Checkmark symbol indicates HTF volume also shows elevation
- X symbol indicates HTF volume doesn't confirm
- Auto-selects appropriate higher timeframe or allows manual selection
KEY FEATURES
Statistical Approach: Uses Z-score methodology rather than arbitrary volume thresholds
Adaptive Thresholds: Can adjust based on market volatility conditions
Swing Focus: Concentrates analysis on structurally important price levels
Volume Trends: Shows whether volume is accelerating or decelerating
Success Tracking: Monitors how often HTF confirmation proves effective
DISPLAY OPTIONS
Basic Mode: Essential features with clean interface
Advanced Mode: Additional customization and analytics
Label Sizing: Four size options to fit different screen setups
Table Position: Moveable info table with transparency control
Custom Colors: Adjustable for different chart themes
PRACTICAL APPLICATIONS
May help identify:
- Volume spikes at support/resistance levels
- Potential accumulation or distribution zones
- Breakout confirmation with volume backing
- Areas where larger market participants might be active
Works on all liquid markets and timeframes, though generally more effective on 15-minute charts and higher.
USAGE NOTES
This is an analytical tool that highlights statistically significant volume events. It should be used as part of a broader analysis approach rather than as a standalone trading system.
The indicator works best when combined with:
- Price action analysis
- Support and resistance identification
- Trend analysis
- Proper risk management
Default settings are designed to work well across most instruments, but users can adjust parameters based on their specific needs and trading style.
TECHNICAL DETAILS
Built with Pine Script v5
Compatible with all TradingView subscription levels
Open source code available for review and learning
Works on stocks, forex, crypto, futures, and other liquid instruments
The statistical approach helps remove some subjectivity from volume analysis, though like all technical indicators, it should be used thoughtfully as part of a complete trading plan.
Relative Volatility Mass [SciQua]The ⚖️ Relative Volatility Mass (RVM) is a volatility-based tool inspired by the Relative Volatility Index (RVI) .
While the RVI measures the ratio of upward to downward volatility over a period, RVM takes a different approach:
It sums the standard deviation of price changes over a rolling window, separating upward volatility from downward volatility .
The result is a measure of the total “volatility mass” over a user-defined period, rather than an average or normalized ratio.
This makes RVM particularly useful for identifying sustained high-volatility conditions without being diluted by averaging.
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How It Works
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1. Standard Deviation Calculation
• Computes the standard deviation of the chosen `Source` over a `Standard Deviation Length` (`stdDevLen`).
2. Directional Separation
• Volatility on up bars (`chg > 0`) is treated as upward volatility .
• Volatility on down bars (`chg < 0`) is treated as downward volatility .
3. Rolling Sum
• Over a `Sum Length` (`sumLen`), the upward and downward volatilities are summed separately using `math.sum()`.
4. Relative Volatility Mass
• The two sums are added together to get the total volatility mass for the rolling window.
Formula:
RVM = Σ(σ up) + Σ(σ down)
where σ is the standard deviation over `stdDevLen`.
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Key Features
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Directional Volatility Tracking – Differentiates between volatility during price advances vs. declines.
Rolling Volatility Mass – Shows the total standard deviation accumulation over a given period.
Optional Smoothing – Multiple MA types, including SMA, EMA, SMMA (RMA), WMA, VWMA.
Bollinger Band Overlay – Available when SMA is selected, with adjustable standard deviation multiplier.
Configurable Source – Apply RVM to `close`, `open`, `hl2`, or any custom source.
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Usage
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Trend Confirmation: High RVM values can confirm strong trending conditions.
Breakout Detection: Spikes in RVM often precede or accompany price breakouts.
Volatility Cycle Analysis: Compare periods of contraction and expansion.
RVM is not bounded like the RVI, so absolute values depend on market volatility and chosen parameters.
Consider normalizing or using smoothing for easier visual comparison.
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Example Settings
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Short-term volatility detection: `stdDevLen = 5`, `sumLen = 10`
Medium-term trend volatility: `stdDevLen = 14`, `sumLen = 20`
Enable `SMA + Bollinger Bands` to visualize when volatility is unusually high or low relative to recent history.
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Notes & Limitations
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Not a directional signal by itself — use alongside price structure, volume, or other indicators.
Higher `sumLen` will smooth short-term fluctuations but reduce responsiveness.
Because it sums, not averages, values will scale with both volatility and chosen window size.
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Credits
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Based on the Relative Volatility Index concept by Donald Dorsey (1993).
TradingView
SciQua - Joshua Danford
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.
Combined Futures Open Interest [Sam SDF-Solutions]The Combined Futures Open Interest indicator is designed to provide comprehensive analysis of market positioning by aggregating open interest data from the two nearest futures contracts. This dual-contract approach captures the complete picture of market participation, including rollover dynamics between front and back month contracts, offering traders crucial insights into institutional positioning and market sentiment.
Key Features:
Dual-Contract Aggregation: Automatically identifies and combines open interest from the first and second nearest futures contracts (e.g., ES1! + ES2!), providing a complete view of market positioning that single-contract analysis might miss.
Multi-Period Analysis: Tracks open interest changes across multiple timeframes:
1 Day: Immediate market sentiment shifts
1 Week: Short-term positioning trends
1 Month: Medium-term institutional flows
3 Months: Quarterly positioning aligned with contract expiration cycles
Smart Data Handling: Utilizes last known values when data is temporarily unavailable, preventing false signals from data gaps while clearly indicating when stale data is being used.
EMA Smoothing: Incorporates a customizable Exponential Moving Average (default 65 periods) to identify the underlying trend in open interest, filtering out daily noise and highlighting significant deviations.
Dynamic Visualization:
Color-coded main line showing directional changes (green for increases, red for decreases)
Optional fill areas between OI and EMA to visualize momentum
Separate contract lines for detailed rollover analysis
Customizable labels for significant percentage changes
Comprehensive Information Table: Displays real-time statistics including:
Current total open interest across both contracts
Period-over-period changes in absolute and percentage terms
EMA deviation metrics
Visual status indicators for quick assessment
Contract symbols and data quality warnings
Alert System: Configurable alerts for:
Significant daily changes (customizable threshold)
EMA crossovers indicating trend changes
Large percentage movements suggesting institutional activity
How It Works:
Contract Detection: The indicator automatically identifies the base futures symbol and constructs the appropriate contract codes for the two nearest expirations, or accepts manual symbol input for non-standard contracts.
Data Aggregation: Open interest data from both contracts is retrieved and summed, providing a complete picture that accounts for positions rolling between contracts.
Historical Comparison: The indicator calculates changes from multiple lookback periods (1/5/22/66 days) to show how positioning has evolved across different time horizons.
Trend Analysis: The EMA overlay helps identify whether current open interest is above or below its smoothed average, indicating momentum in position building or reduction.
Visual Feedback: The main line changes color based on daily changes, while the optional table provides detailed numerical analysis for traders requiring precise data.
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This indicator is essential for futures traders, particularly those focused on index futures, commodities, or currency futures where understanding the aggregate positioning across nearby contracts is crucial. It's especially valuable during rollover periods when positions shift between contracts, and for identifying institutional accumulation or distribution patterns that single-contract analysis might miss. By combining multiple timeframe analysis with intelligent data handling and clear visualization, it simplifies the complex task of monitoring open interest dynamics across the futures curve.
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.
Parabolic SAR Buy Zone📈 Parabolic SAR Buy Zone — Early Trend Reversal Indicator
This script highlights bullish reversals based on the Parabolic SAR (Stop and Reverse) indicator.
🧠 Key Features:
Uses SAR parameters: Start: 0.02, Increment: 0.005, Max: 0.2
Visually marks the Buy Zone when SAR falls below the price
Background is light blue to show accumulation or early reversal zones
Yellow SAR dots help identify trend direction and potential exits
Includes alerts when SAR flips from bearish to bullish, signaling potential entry points
✅ Best Used For:
Identifying early trend reversals
Swing trading setups on daily or weekly charts
Combining with volume, RSI, or support zones for confirmation
🛎️ Customize alert to stay notified when new buy zones appear on your favorite stocks or cryptos.
M3EDGE™ Relative Volume (RVOL)Relative Volume (RVOL) compares the current volume to its historical average.
🎯 Goal: Spot abnormal flows and anticipate impulsive moves.
🔍 M3EDGE™ Key Reading:
• RVOL > 2.0 → Likely institutional activity.
• RVOL > 1.5 → Heightened surveillance: potential move building.
• Price falling + high RVOL → Stealth accumulation / sell-side absorption.
• Price rising + high RVOL → Confirmed breakout with real flows.
💡 In the M3EDGE™ method, RVOL filters out false signals and validates setups by aligning flow + structure + momentum.
Applied to ETFs or stocks, it reveals what price action alone won’t show
Titan Wick Zone IndicatorThe Titan Wick Zone Indicator visually highlights the upper and lower wick regions of each candlestick on your chart, helping traders instantly identify areas where price was aggressively rejected (top wick) or absorbed (bottom wick). The indicator fills the area above the candle body to the wick high in red (sell zone), and the area below the candle body to the wick low in green (buy zone), both with adjustable opacity for clear visibility.
How to Use:
Spot Rejection and Absorption:
The red-filled upper wick zone marks where upward price moves were sharply rejected by sellers, often indicating supply, resistance, or “stop hunt” zones.
The green-filled lower wick zone marks where downward price moves were absorbed by buyers, pointing to potential demand, support, or accumulation zones.
Enhance Price Action Analysis:
Use these zones to avoid entering trades at price extremes, spot potential reversals, and find areas of confluence with support/resistance, Fibonacci levels, or order blocks.
Risk Management:
The indicator helps visualize where liquidity hunts or false breakouts may occur, so you can better place stop losses outside of volatile wick zones.
Ideal For:
Price action traders, scalpers, and swing traders seeking a visual edge in spotting supply/demand dynamics, liquidity zones, and wick-driven traps.
🏆 UNMITIGATED LEVELS ACCUMULATIONPDH TO ATH RISK FREE
All the PDL have a buy limit which starts at 0.1 lots which will duplicate at the same time the capital incresases. All of the buy limits have TP in ATH for max reward.
TFO + ADX with Histogram & SignalTrend Flow Oscillator (TFO + ADX) – Histogram + Signal
This version of the original TFO+ADX introduces a MACD-style histogram and signal line overlay for clearer momentum and trend visualization.
The Trend Flow Oscillator (TFO+ADX) blends two powerful volume-based tools — the Money Flow Index (MFI) and Chaikin Money Flow (CMF) — along with a normalized Average Directional Index (ADX). The result is a comprehensive momentum and trend strength tool that offers a more precise read on when markets are gaining or losing conviction.
⸻
How It Works
1.Money Flow Index (MFI)
• Measures volume-weighted buying/selling pressure using price and volume.
• Scaled between –1 and +1 for visual clarity.
2.Chaikin Money Flow (CMF)
• Evaluates volume distribution over time — institutional buying (accumulation) or selling (distribution).
• Also scaled between –1 and +1.
3.TFO Composite Line
• Combines MFI and CMF into a single flow reading.
• A signal line (EMA) tracks the trend of this flow.
• A histogram plots the difference between the TFO and its signal, giving clear signals on shifts in momentum.
4.Normalized ADX Overlay
• Shows trend strength on the same scale (–1 to +1).
• ADX > 0 indicates strong trending conditions.
• ADX < 0 signals weak or consolidating conditions.
⸻
Visual Interpretation
1. Histogram Bars
• Green: TFO is above the signal line → bullish momentum accelerating
• Red: TFO is below the signal line → bearish momentum building
• Bar height represents the strength of the momentum shift
2. Signal Line
• Tracks the smoothed trend of the TFO composite
• Histogram crossing above or below zero reflects momentum crossover and can act as entry or exit signals
3. TFO Raw Line (Optional)
• Still available for reference alongside the histogram
• Shows the unsmoothed blended money flow direction (MFI + CMF)
4. Extreme Zones
• Background shading appears when TFO exceeds ±1.0
• Helps highlight areas of stretched or unsustainable momentum, useful for spotting potential reversals or exhaustion
Order Block Finder (5-min Demand Zones)This highlights potential bullish order blocks on a 5-min chart when:
Candle is bullish
Small body (suggesting accumulation or absorption)
Lowest low in last X bars
Volume Z-Score [T2][T69]🧠 Overview
This indicator calculates the Z-Score of volume to identify unusual trading activity, particularly those associated with whale-like behavior. It helps traders detect aggressive accumulation, distribution, or breakout setups based on volume anomalies relative to historical norms.
🔍 Features
Z-Score plot of volume using a configurable lookback.
Dynamic bar coloring based on Z-Score magnitude.
🐋 Small Whale marker appears when Z-Score exceeds +3.
Supports manual adjustment of sensitivity through lookback bars input.
🧪 Risk Level & Behavior Reference
🔥 Aggressive (10–14) - Fast signal, high sensitivity to volume spikes. Suitable for scalping or altcoin breakouts.
⚖️ Moderate (20–30) - Balanced filtering of noise vs real movement. Recommended for most swing traders.
🛡️ Conservative (40–60+) - Filters out noise. Reacts only to sustained large volume anomalies. Ideal for longer timeframes or large-cap coins.
⚙️ How to Use (NON DIRECTIONAL INDICATOR)
Use the Z-Score to gauge the strength of volume relative to recent history.
When Z-Score > 1.5 → Considered above-average activity.
When Z-Score > 3 → Marks a 🐋 Small Whale Move, potential for high-volatility follow-through.
Combine with price action, support/resistance, or OBV for confirmation.
⚠️ Limitations
This is a statistical signal, not directional.
Works best when paired with context: supply zones, trend bias, or large candle patterns.
🧠 Advanced Tips
Use multiple risk settings (e.g., 14 vs 50) on stacked indicators to track retail vs whale behavior separately.
Works well with low-float tokens and high-leverage exchange pairs like BTC/USDT (Bybit).
📝 Disclaimer
This script is provided for educational and analytical purposes only. Do your own research and manage your risk responsibly.
Market Structure Dashboard @darshakssc📌 Market Structure Dashboard by @darshakssc is a comprehensive visual analysis tool designed to assist traders and analysts in understanding market conditions by presenting multiple key technical insights in one place.
This script does not provide buy or sell signals, but helps you interpret essential elements of market behavior — such as structure shifts, momentum conditions, trend direction, and volatility — for informed decision-making.
🔍 What This Dashboard Displays:
✅ Market Phase Detection
Identifies the current market condition as Bullish, Bearish, Accumulation, or Distribution, based on trend logic and RSI thresholds.
✅ Trend Direction (EMA-Based)
Uses customizable Fast and Slow Exponential Moving Averages (EMAs) to evaluate whether the market is trending upward or downward.
✅ Key Support & Resistance Levels
Highlights potential support and resistance areas based on structural highs and lows and pivot logic.
✅ RSI Momentum State
Tracks whether momentum is Overbought, Oversold, or Neutral, using the classic RSI indicator.
✅ Volatility Overview
Detects high or low volatility zones using ATR (Average True Range) compared to a moving average baseline.
✅ Structure Shift Markers
Displays triangle markers on the chart when a structural trend shift is detected.
✅ Custom Themes and Design
Choose between three clean themes — Classic, Modern, and Dark — for enhanced readability and aesthetics.
📊 Visual Elements
1). An intuitive table-style dashboard appears in the top-right of the chart.
2). Colored EMA overlays and plotted support/resistance circles on the price chart.
3). Structure shift indicators help visually mark potential change zones.
⚠️ Important Notice:
This tool is intended for educational and informational purposes only. It does not provide financial advice, trade recommendations, or guaranteed outcomes. Always use your own discretion and analysis, and consult a qualified financial professional before making investment decisions.
Trading involves risk and past performance does not guarantee future results.
Trend Flow Oscillator (CMF + MFI) + ADX## Trend Flow Oscillator (TFO + ADX) Indicator Description
The Trend Flow Oscillator (TFO+ADX) combines two volume-based indicators, Money Flow Index (MFI) and Chaikin Money Flow (CMF), along with the Average Directional Index (ADX) into one comprehensive oscillator. This indicator provides traders with insights into momentum, volume flow, and trend strength, clearly indicating bullish or bearish market conditions.
### How the Indicator Works:
1. **Money Flow Index (MFI)**:
* Measures buying and selling pressure based on price and volume.
* Scaled from -1 to +1 (where positive values indicate buying pressure, negative values indicate selling pressure).
2. **Chaikin Money Flow (CMF)**:
* Evaluates money flow volume over a set period, reflecting institutional buying or selling.
* Also scaled from -1 to +1 (positive values suggest bullish accumulation, negative values bearish distribution).
3. **Average Directional Index (ADX)**:
* Measures trend strength, indicating whether a market is trending or ranging.
* Scaled from -1 to +1, with values above 0 suggesting strong trends, and values near or below 0 indicating weak trends or sideways markets.
* Specifically, an ADX value of 0 means neutral trend strength; positive values indicate a strong trend.
### Indicator Levels and Interpretation:
* **Zero Line (0)**: Indicates neutral conditions. When the oscillator crosses above zero, it signals increasing bullish momentum; crossing below zero indicates bearish momentum.
* **Extreme Zones (+/- 0.75)**:
* Oscillator values above +0.75 are considered overbought or highly bullish.
* Oscillator values below -0.75 are considered oversold or highly bearish.
* The indicator features subtle background shading to visually highlight these extreme momentum areas for quick identification.
* Shading when values above or below the +/-1.0 level.
* **Color Coding**:
* Bright blue indicates strengthening bullish momentum.
* Dark blue signals weakening bullish momentum.
* Bright red indicates strengthening bearish momentum.
* Dark maroon signals weakening bearish momentum.
Smart Money Breakout Channels [AlgoAlpha]🟠 OVERVIEW
This script draws breakout detection zones called “Smart Money Breakout Channels” based on volatility-normalized price movement and visualizes them as dynamic boxes with volume overlays. It identifies temporary accumulation or distribution ranges using a custom normalized volatility metric and tracks when price breaks out of those zones—either upward or downward. Each channel represents a structured range where smart money may be active, helping traders anticipate key breakouts with added context from volume delta, up/down volume, and a visual gradient gauge for momentum bias.
🟠 CONCEPTS
The script calculates normalized price volatility by measuring the standard deviation of price mapped to a scale using the highest and lowest prices over a set lookback period. When normalized volatility reaches a local low and flips upward, a boxed channel is drawn between the highest and lowest prices in that zone. These boxes persist until price breaks out, either with a strong candle close (configurable) or by touching the boundary. Volume analysis enhances interpretation by rendering delta bars inside the box, showing volume distribution during the channel. Additionally, a real-time visual “gauge” shows where volume delta sits within the channel range, helping users spot pressure imbalances.
🟠 FEATURES
Automatic detection and drawing of breakout channels based on volatility-normalized price pivots.
Optional nested channels to allow multiple simultaneous zones or a clean single-zone view.
Gradient-filled volume gauge with dynamic pointer to show current delta pressure within the box.
Three volume visualization modes: raw volume, comparative up/down volume, and delta.
Alerts for new channel creation and confirmed bullish or bearish breakouts.
🟠 USAGE
Apply the indicator to any chart. Wait for a new breakout box to form—this occurs when volatility behavior shifts and a stable range emerges. Once a box appears, monitor price relative to its boundaries. A breakout above suggests bullish continuation, below suggests bearish continuation; signals are stronger when “Strong Closes Only” is enabled.
Watch the internal volume candles to understand where buy/sell pressure is concentrated during the box. Use the gauge on the right to interpret whether net pressure is building upward or downward before breakout to anticipate the direction.
Use alerts to catch breakout events without needing to monitor the chart constantly 🚨.
Advanced DMA Pattern Detection SystemAdvanced DMA Pattern Detection System with Smart Intelligence
Professional-grade moving average indicator that combines traditional DMA analysis with advanced pattern recognition and probabilistic forecasting.
Core Features:
6 Key DMAs (5, 10, 20, 50, 100, 200) with descriptive labels showing trading purpose
Advanced Pattern Recognition - Detects Institutional Accumulation, Distribution Phases, Bull/Bear Transitions, and Choppy Markets
Probability Engine - Assigns confidence scores (0-100%) with Low/Medium/High classifications
Historical Validation - Tracks success rate of last 20 pattern signals for real performance data
Smart Alert System - Only triggers on significant pattern changes (20%+ probability shifts)
Dual Display System:
Movable Information Table - Shows current pattern, probability, confidence level, success rate, and recommended action
Chart Alerts & Background Colors - Visual confirmation of high-confidence setups (80%+ patterns)
Traditional DMA Labels - Clear identification of each average's trading significance
Complete Customization:
Master on/off controls for entire system
Individual toggles for all components (DMAs, table, alerts, colors)
Adjustable alert sensitivity (Conservative/Medium/Aggressive)
6 table positions to fit any chart layout
Perfect For: Swing traders, position traders, and anyone wanting systematic trend analysis with quantified probability scores rather than subjective interpretation.
Bottom Line: Transforms basic moving averages into an intelligent trading system that tells you exactly what the market structure means and what to do about it.
Worthy Asset StrategyThis strategy is designed with a two-part philosophy: a regime filter and a value-based accumulation approach.
🟩 Regime Filter:
If the S&P 500 (SPX) is trading above its 200-period EMA, a green background is shown below the chart, signaling a favorable market regime.
If the SPX is below the 200 EMA, the background turns red, indicating a less favorable environment.
📉 Buy Signals:
Buy signals are generated by red candles that drop a certain percentage from their open — essentially treating these pullbacks as discount opportunities.
The idea is to accumulate more of a selected asset when it becomes temporarily cheaper.
💎 Philosophy & Execution:
I only apply this strategy to assets I’ve personally researched and believe to be fundamentally valuable.
If a Buy signal occurs and the SPX is trading above its 200 EMA (i.e., the background is green), I enter the position.
Once in the trade, I follow this logic:
If the position reaches +1.5% profit, I sell it.
If it doesn’t reach profit and goes into a loss, I simply hold.
I don’t sell at a loss because I believe in the long-term value of the asset.
If the price drops further, I accumulate more — aiming to lower my average cost and eventually exit at a profit once the asset recovers.
This approach is based on the mindset of treating drawdowns as discounts, not danger.
"The more it drops, the more I accumulate — because I see value, not risk."
This is still a work in progress, and I’m actively refining it over time.
⚠️ Note: The sell logic is not yet visible on the chart and will be added in a future update.
Trend Range Detector (Zeiierman)█ Overview
Trend Range Detector (Zeiierman) is a market structure tool that identifies and tracks periods of price compression by forming adaptive range boxes based on volatility and price movement. When prices remain stable within a defined band, the script dynamically draws a range box; when prices break out of that structure, the box highlights the breakout in real-time.
By combining a volatility-based envelope with a custom weighted centerline, this tool filters out noise and isolates truly stable zones — providing a clean framework for traders who focus on accumulation, distribution, breakout anticipation, and reversion opportunities.
Whether you're range trading, spotting trend consolidations, or looking for volatility contractions before major moves, the Trend Range Detector gives you a mathematically adaptive, visually intuitive structure that maps the heartbeat of the market.
█ How It Works
⚪ Range Formation Engine
The core of this indicator revolves around two conditions:
Distance Filter: The maximum distance between all recent closes and a dynamic centerline must remain within a volatility envelope.
Volatility Envelope: Based on an ATR(2000) multiplied by a user-defined factor to account for broader market volatility trends.
If both conditions are satisfied over the most recent length bars, a range box is drawn to visually anchor the zone.
⚪ Dynamic Breakout Coloring
When price breaks out of the top or bottom of the active range box, the box color shifts in real-time:
Blue Boxes represent areas where price has remained within a defined volatility envelope over a sustained number of bars. These zones reflect stable, low-volatility periods, often associated with consolidation, equilibrium, or market indecision.
Green Boxes for bullish breakouts.
Red Boxes for bearish breakdowns.
This allows traders to visually spot transitions from consolidation to expansion phases without relying on lagging signals.
█ Why Use a Weighted Close Instead of SMA?
A standard Simple Moving Average (SMA) treats all past closes equally, which works well in theory, but not in dynamic, fast-shifting markets. In this script, we replace the traditional SMA with a speed-weighted average that reflects how aggressively the market has moved bar-to-bar.
⚪ Here's why it matters:
Bars with higher momentum (larger price differences between closes) are given more weight.
Slow, sideways candles (typical in noise or low volume) contribute less to the calculated centerline.
This method creates a more accurate snapshot of market behavior, especially during volatile phases. As a result, the indicator adapts to market conditions more effectively, helping traders identify real consolidation zones, not just average lines distorted by flat bars or noise.
█ How to Use
⚪ Range Detection
Boxes form only when price remains consistently close to the speed-weighted mean.
Helps identify sideways zones, consolidations, and low-volatility structures where price is “charging up.”
⚪ Breakout Confirmation
Once price exits the top or bottom boundary, the box immediately highlights the direction of the break.
Use this signal in conjunction with your own momentum, volume, or trend filters for higher-confidence trades.
█ Settings
Minimum Range Length: Number of candles required for a valid range to form.
Range Width Multiplier: Adjusts the envelope around the weighted average using ATR(2000).
Highlight Box Breaks: Enables real-time coloring of breakouts and breakdowns for immediate visual feedback.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Volume MA Breakout T3 [Teyo69]🧭 Overview
Volume MA Breakout T3 highlights volume bars that exceed a dynamic moving average threshold. It helps traders visually identify volume breakouts—periods of significant buying or selling pressure—based on user-selected MA methods (SMA, EMA, DEMA).
🔍 Features
Volume Highlighting: Green bars indicate volume breakout above the MA; red bars otherwise.
Custom MA Options: Choose between SMA, EMA, or Double EMA for volume smoothing.
Dynamic Threshold: The moving average line adjusts based on user-defined length and method.
⚙️ Configuration
Length: Number of bars used for the moving average calculation (default: 14).
Method: Type of moving average to use:
"SMA" - Simple Moving Average
"EMA" - Exponential Moving Average
"Double EMA" - Double Exponential Moving Average
📈 How to Use
Apply to any chart to visualize volume behavior relative to its MA.
Look for green bars: These suggest volume is breaking out above its recent average—potential signal of momentum.
Red bars indicate normal/subdued volume.
⚠️ Limitations
Does not provide directional bias—use with price action or trend confirmation tools.
Works best with additional context (e.g., support/resistance, candle formations).
🧠 Advanced Tips
Use shorter MAs (e.g., 5–10) in volatile markets for more responsive signals.
Combine with OBV, MFI, or accumulation indicators for confluence.
📌 Notes
This is a volume-based filter, not a signal generator.
Useful for breakout traders and volume profile enthusiasts.
📜 Disclaimer
This script is for educational purposes only. Always test in a simulated environment before live trading. Not financial advice.
No Supply No Demand (NSND) – Volume Spread Analysis ToolThis indicator is designed for traders utilizing Volume Spread Analysis (VSA) techniques. It automatically detects potential No Demand (ND) and No Supply (NS) candles based on volume and price behavior, and confirms them using future price action within a user-defined number of lookahead bars.
Confirmed No Demand (ND): Detected when a bullish candle has volume lower than the previous two bars and is followed by weakness (next highs swept, close below).
Confirmed No Supply (NS): Detected when a bearish candle has volume lower than the previous two bars and is followed by strength (next lows swept, close above).
Adjustable lookahead bars parameter to control the confirmation window.
This tool helps identify potential distribution (ND) and accumulation (NS) areas, providing early signs of market turning points based on professional volume logic. The dot appears next to ND or NS.
Session HL + Candles + AMD (Nephew_Sam_)Session HL + Candles + AMD (Nephew_Sam_)
This indicator marks out intraday sessions summarized into single candles, with an additional option to mark out the HL of each session. Perfect for understanding AMD within a glance (accumulation-manipulation-distribution)
Features:
Session High/Low lines with customizable colors and labels
Optional session candles displayed on the right side of the chart
Timezone support for global traders
Customizable bull/bear candle colors
Works on timeframes up to 1 hour
Perfect for:
Identifying session liquidity levels
Tracking session ranges and breakouts
Multi-timeframe session analysis
ICT methodology traders
Settings:
Choose your timezone for accurate session detection
Toggle session candles and HL lines independently
Customize colors, line styles, and labels
Set maximum timeframe (up to 1 hour)
