Match on Selectable Percentage Change + RangeIndicator Overview:
Match on Selectable Percentage Change + Range is a powerful analytical tool designed for traders and analysts who want to identify historical price bars that match a specific percentage variation, and then evaluate how price evolved in the following days. It combines precision filtering with visual tabular feedback, making it ideal for pattern recognition, backtesting, and scenario analysis.
What It Does
This indicator scans historical bars to find instances where the percentage change between two consecutive closes matches a user-defined target (± a customizable tolerance). Once matches are found, it displays:
The date of each match (most recent first)
The actual variation searched
The percentage change after 2, 10, 20, and 30 bars
The min-max range (in %) over those same periods
All results are shown in a dynamic table directly on the chart.
Inputs & Controls
Input Description
Which variation do you want to analyze? (%)
Set the target percentage change to look for (e.g. 2.5%)
% deviation from the variation to be considered (%) Define the tolerance range around the target (e.g. ±0.5%)
Bars to analyze (max 9999) Set how many past bars to scan
Show match table Toggle to enable/disable the entire table
Show percentage variations (2d, 10d, 20d, 30d) Toggle to show/hide post-match percentage changes
Show min-max ranges (2d, 10d, 20d, 30d) Toggle to show/hide post-match high/low ranges
Table Structure
Each row in the table represents a historical match. Columns include:
Date: When the match occurred
Variation in: The actual % change that triggered the match
2d / 10d / 20d / 30d: % change after those days
Min-Max 2d / 10d / 20d / 30d: Range of price movement after those days
Color coding helps quickly identify bullish (green) vs bearish (red) outcomes.
Use Cases
Backtesting: See how similar past moves evolved over time
Scenario modeling: Estimate potential outcomes after a known variation
Pattern recognition: Spot recurring setups or volatility clusters
Risk analysis: Understand post-variation drawdowns and upside potential
Tips for Use
Use tighter deviation (e.g. 0.3%) for precision, or wider (e.g. 1%) for broader pattern capture.
Combine with other indicators to validate setups (e.g. volume, RSI, trend filters).
Toggle off variation or range columns to focus only on the metrics you need.
Cari dalam skrip untuk "backtest"
ICT 1st Presented FVG After RTH OpenICT 1st Presented FVG After RTH Open
Overview
This indicator identifies and tracks the first Fair Value Gap (FVG) that forms after the Regular Trading Hours (RTH) open, based on Inner Circle Trader (ICT) concepts. It monitors price behavior and reaction to this initial FVG throughout the trading session.
Key Features
📊 Smart FVG Detection
• Automatically identifies the first valid FVG after RTH open (default: 9:30-10:00 AM ET)
• Filters noise using ATR-based minimum gap size validation
• Option to display all FVGs or just the first one
• Visual distinction between the first FVG and subsequent ones
⏰ Customizable Time Settings
• Adjustable RTH window (default: 9:30-10:00 AM)
• Multiple timezone support (New York, Chicago, London, Tokyo)
• Flexible tracking duration and sampling intervals
📈 Price Reaction Tracking
• Monitors price behavior relative to the first FVG over time
• Tracks whether price remains above, below, or inside the FVG zone
• Records price distance from FVG boundaries
• Displays real-time data in an easy-to-read table
• Volume tracking at each sample interval
🎨 Visual Elements
• Color-coded FVG boxes (green for bullish, red for bearish)
• Timestamp labels showing when each FVG formed
• Extendable boxes to track ongoing validity
• Optional background highlighting during RTH window
• Customizable table positions and display options
🔔 Alert System
• Visual markers on chart for easy backtesting
• Real-time programmatic alerts with detailed FVG information
• TradingView alert conditions for custom notifications
• Alerts include price range, gap size, and timestamp
Settings
Time Configuration:
• Timezone selection
• RTH start/end times
• Tracking duration (default: 120 minutes)
• Sample interval (default: 5 minutes)
FVG Validation:
• ATR length for gap size calculation
• Minimum gap size as ATR percentage
• Option to show all valid FVGs
Display Options:
• Custom colors for bullish/bearish FVGs
• Label visibility toggle
• Box extension options
• Maximum historical FVGs to display
• Info and reaction table positions
Use Cases
1. Entry Timing: Use the first FVG as a potential entry zone when price returns to fill the gap
2. Trend Confirmation: Monitor whether price respects or violates the first FVG
3. Session Analysis: Track how the first inefficiency of the session plays out over time
4. Backtesting: Visual markers allow easy historical analysis of FVG behavior
How It Works
The indicator waits for RTH to begin, then identifies the first three-candle pattern that creates a valid Fair Value Gap. Once detected, it:
1. Marks the FVG zone with a colored box
2. Begins tracking price position at regular intervals
3. Records data in a reaction table showing price behavior over time
4. Continues monitoring until the tracking duration expires or a new trading day begins
Notes
• Resets daily to track each session independently
• Works on any timeframe, though lower timeframes (1-5 min) are recommended for intraday FVG detection
• The "first presented" FVG concept emphasizes the importance of the initial inefficiency created after market open
• Historical FVGs are preserved up to the display limit for reference
This indicator is designed for traders familiar with ICT concepts and Fair Value Gap trading strategies. It combines automated detection with comprehensive tracking to help identify high-probability trading opportunities.
MACD Enhanced [DCAUT]█ MACD Enhanced
📊 ORIGINALITY & INNOVATION
The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently.
This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies.
The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms.
📐 MATHEMATICAL FOUNDATION
The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines.
Available Algorithms:
The implementation supports a comprehensive spectrum of technical analysis algorithms:
Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting)
Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive)
Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential)
Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility)
Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation)
Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing)
Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Histogram Interpretation:
Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities
Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities
Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift
Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration
Advanced Signal Recognition:
Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals
Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup
Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones
🎯 STRATEGIC APPLICATIONS
Comprehensive Trend Confirmation Strategies:
Primary Trend Validation Protocol:
Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line
Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening
Use secondary confirmation from MACD line angle: steep angles (>45°) indicate strong trends, shallow angles suggest consolidation
Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend)
Entry Timing Techniques:
Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero
Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns
Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals
Advanced Divergence Trading Systems:
Regular Divergence Recognition:
Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals
Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management
Hidden Divergence Strategies:
Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks
Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies
Multi-Timeframe Coordination Framework:
Three-Timeframe Analysis Structure:
Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction
Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions
Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias
Timeframe Synchronization Rules:
Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions
Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions
Conflicting signals between timeframes = Wait for alignment or use smaller position sizes
1H MACD signals only valid when aligned with both higher timeframes
Algorithm Considerations by Market Type:
Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions
Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market
Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required
Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise
Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches.
📋 DETAILED PARAMETER CONFIGURATION
Comprehensive Source Selection Strategy:
Price Source Analysis and Optimization:
Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows
HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions
HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth
OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis
Parameter Configuration Principles:
Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness.
Length Parameter Considerations:
Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment
Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings
Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics
Comprehensive Algorithm Selection Framework:
MACD Line Algorithm Decision Matrix:
EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior
SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes
HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise
KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio
Signal Line Algorithm Optimization Strategies:
Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics
Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals
Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA)
Parameter Sensitivity Considerations:
Impact of Parameter Changes:
Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics
Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms
Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Response Characteristics by Algorithm:
Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise
Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability
Highest Stability: SMA, RMA, TMA - reduced noise but increased lag
Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions
Noise Filtering Capabilities:
Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings.
Market Condition Adaptability:
Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility.
Comparative Performance vs Traditional MACD:
Algorithm Flexibility: 21 algorithms vs 1 fixed EMA
Signal Quality: Reduced false signals through noise filtering algorithms
Market Adaptability: Optimizable for any market condition vs fixed behavior
Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching
Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
CD - Promedio de Posición ManualIndicador que muestra el precio promedio a medida que vas construyendo la posicion, util para hacer backtest.
Indicator that shows the average price as you build your position, useful for backtesting.
[blackcat] L2 Trend LinearityOVERVIEW
The L2 Trend Linearity indicator is a sophisticated market analysis tool designed to help traders identify and visualize market trend linearity by analyzing price action relative to dynamic support and resistance zones. This powerful Pine Script indicator utilizes the Arnaud Legoux Moving Average (ALMA) algorithm to calculate weighted price calculations and generate dynamic support/resistance zones that adapt to changing market conditions. By visualizing market zones through colored candles and histograms, the indicator provides clear visual cues about market momentum and potential trading opportunities. The script generates buy/sell signals based on zone crossovers, making it an invaluable tool for both technical analysis and automated trading strategies. Whether you're a day trader, swing trader, or algorithmic trader, this indicator can help you identify market regimes, support/resistance levels, and potential entry/exit points with greater precision.
FEATURES
Dynamic Support/Resistance Zones: Calculates dynamic support (bear market zone) and resistance (bull market zone) using weighted price calculations and ALMA smoothing
Visual Market Representation: Color-coded candles and histograms provide immediate visual feedback about market conditions
Smart Signal Generation: Automatic buy/sell signals generated from zone crossovers with clear visual indicators
Customizable Parameters: Four different ALMA smoothing parameters for various timeframes and trading styles
Multi-Timeframe Compatibility: Works across different timeframes from 1-minute to weekly charts
Real-time Analysis: Provides instant feedback on market momentum and trend direction
Clear Visual Cues: Green candles indicate bullish momentum, red candles indicate bearish momentum, and white candles indicate neutral conditions
Histogram Visualization: Blue histogram shows bear market zone (below support), aqua histogram shows bull market zone (above resistance)
Signal Labels: "B" labels mark buy signals (price crosses above resistance), "S" labels mark sell signals (price crosses below support)
Overlay Functionality: Works as an overlay indicator without cluttering the chart with unnecessary elements
Highly Customizable: All parameters can be adjusted to suit different trading strategies and market conditions
HOW TO USE
Add the Indicator to Your Chart
Open TradingView and navigate to your desired trading instrument
Click on "Indicators" in the top menu and select "New"
Search for "L2 Trend Linearity" or paste the Pine Script code
Click "Add to Chart" to apply the indicator
Configure the Parameters
ALMA Length Short: Set the short-term smoothing parameter (default: 3). Lower values provide more responsive signals but may generate more false signals
ALMA Length Medium: Set the medium-term smoothing parameter (default: 5). This provides a balance between responsiveness and stability
ALMA Length Long: Set the long-term smoothing parameter (default: 13). Higher values provide more stable signals but with less responsiveness
ALMA Length Very Long: Set the very long-term smoothing parameter (default: 21). This provides the most stable support/resistance levels
Understand the Visual Elements
Green Candles: Indicate bullish momentum when price is above the bear market zone (support)
Red Candles: Indicate bearish momentum when price is below the bull market zone (resistance)
White Candles: Indicate neutral market conditions when price is between support and resistance zones
Blue Histogram: Shows bear market zone when price is below support level
Aqua Histogram: Shows bull market zone when price is above resistance level
"B" Labels: Mark buy signals when price crosses above resistance
"S" Labels: Mark sell signals when price crosses below support
Identify Market Regimes
Bullish Regime: Price consistently above resistance zone with green candles and aqua histogram
Bearish Regime: Price consistently below support zone with red candles and blue histogram
Neutral Regime: Price oscillating between support and resistance zones with white candles
Generate Trading Signals
Buy Signals: Look for price crossing above the bull market zone (resistance) with confirmation from green candles
Sell Signals: Look for price crossing below the bear market zone (support) with confirmation from red candles
Confirmation: Always wait for confirmation from candle color changes before entering trades
Optimize for Different Timeframes
Scalping: Use shorter ALMA lengths (3-5) for 1-5 minute charts
Day Trading: Use medium ALMA lengths (5-13) for 15-60 minute charts
Swing Trading: Use longer ALMA lengths (13-21) for 1-4 hour charts
Position Trading: Use very long ALMA lengths (21+) for daily and weekly charts
LIMITATIONS
Whipsaw Markets: The indicator may generate false signals in choppy, sideways markets where price oscillates rapidly between support and resistance
Lagging Nature: Like all moving average-based indicators, there is inherent lag in the calculations, which may result in delayed signals
Not a Standalone Tool: This indicator should be used in conjunction with other technical analysis tools and risk management strategies
Market Structure Dependency: Performance may vary depending on market structure and volatility conditions
Parameter Sensitivity: Different markets may require different parameter settings for optimal performance
No Volume Integration: The indicator does not incorporate volume data, which could provide additional confirmation signals
Limited Backtesting: Pine Script limitations may restrict comprehensive backtesting capabilities
Not Suitable for All Instruments: May perform differently on stocks, forex, crypto, and futures markets
Requires Confirmation: Signals should always be confirmed with other indicators or price action analysis
Not Predictive: The indicator identifies current market conditions but does not predict future price movements
NOTES
ALMA Algorithm: The indicator uses the Arnaud Legoux Moving Average (ALMA) algorithm, which is known for its excellent smoothing capabilities and reduced lag compared to traditional moving averages
Weighted Price Calculations: The bear market zone uses (2low + close) / 3, while the bull market zone uses (high + 2close) / 3, providing more weight to recent price action
Dynamic Zones: The support and resistance zones are dynamic and adapt to changing market conditions, making them more responsive than static levels
Color Psychology: The color scheme follows traditional trading psychology - green for bullish, red for bearish, and white for neutral
Signal Timing: The signals are generated on the close of each bar, ensuring they are based on complete price action
Label Positioning: Buy signals appear below the bar (red "B" label), while sell signals appear above the bar (green "S" label)
Multiple Timeframes: The indicator can be applied to multiple timeframes simultaneously for comprehensive analysis
Risk Management: Always use proper risk management techniques when trading based on indicator signals
Market Context: Consider the overall market context and trend direction when interpreting signals
Confirmation: Look for confirmation from other indicators or price action patterns before entering trades
Practice: Test the indicator on historical data before using it in live trading
Customization: Feel free to experiment with different parameter combinations to find what works best for your trading style
THANKS
Special thanks to the TradingView community and the Pine Script developers for creating such a powerful and flexible platform for technical analysis. This indicator builds upon the foundation of the ALMA algorithm and various moving average techniques developed by technical analysis pioneers. The concept of dynamic support and resistance zones has been refined over decades of market analysis, and this script represents a modern implementation of these timeless principles. We acknowledge the contributions of all traders and developers who have contributed to the evolution of technical analysis and continue to push the boundaries of what's possible with algorithmic trading tools.
Market Outlook Score (MOS)Overview
The "Market Outlook Score (MOS)" is a custom technical indicator designed for TradingView, written in Pine Script version 6. It provides a quantitative assessment of market conditions by aggregating multiple factors, including trend strength across different timeframes, directional movement (via ADX), momentum (via RSI changes), volume dynamics, and volatility stability (via ATR). The MOS is calculated as a weighted score that ranges typically between -1 and +1 (though it can exceed these bounds in extreme conditions), where positive values suggest bullish (long) opportunities, negative values indicate bearish (short) setups, and values near zero imply neutral or indecisive markets.
This indicator is particularly useful for traders seeking a holistic "outlook" score to gauge potential entry points or market bias. It overlays on a separate pane (non-overlay mode) and visualizes the score through horizontal threshold lines and dynamic labels showing the numeric MOS value along with a simple trading decision ("Long", "Short", or "Neutral"). The script avoids using the plot function for compatibility reasons (e.g., potential TradingView bugs) and instead relies on hline for static lines and label.new for per-bar annotations.
Key features:
Multi-Timeframe Analysis: Incorporates slope data from 5-minute, 15-minute, and 30-minute charts to capture short-term trends.
Trend and Strength Integration: Uses ADX to weight trend bias, ensuring stronger signals in trending markets.
Momentum and Volume: Includes RSI momentum impulses and volume deviations for added confirmation.
Volatility Adjustment: Factors in ATR changes to assess market stability.
Customizable Inputs: Allows users to tweak periods for lookback, ADX, and ATR.
Decision Labels: Automatically classifies the MOS into actionable categories with visual labels.
This indicator is best suited for intraday or swing trading on volatile assets like stocks, forex, or cryptocurrencies. It does not generate buy/sell signals directly but can be combined with other tools (e.g., moving averages or oscillators) for comprehensive strategies.
Inputs
The script provides three user-configurable inputs via TradingView's input panel:
Lookback Period (lookback):
Type: Integer
Default: 20
Range: Minimum 10, Maximum 50
Purpose: Defines the number of bars used in slope calculations for trend analysis. A shorter lookback makes the indicator more sensitive to recent price action, while a longer one smooths out noise for longer-term trends.
ADX Period (adxPeriod):
Type: Integer
Default: 14
Range: Minimum 5, Maximum 30
Purpose: Sets the smoothing period for the Average Directional Index (ADX) and its components (DI+ and DI-). Standard value is 14, but shorter periods increase responsiveness, and longer ones reduce false signals.
ATR Period (atrPeriod):
Type: Integer
Default: 14
Range: Minimum 5, Maximum 30
Purpose: Determines the period for the Average True Range (ATR) calculation, which measures volatility. Adjust this to match your trading timeframe—shorter for scalping, longer for positional trading.
These inputs allow customization without editing the code, making the indicator adaptable to different market conditions or user preferences.
Core Calculations
The MOS is computed through a series of steps, blending trend, momentum, volume, and volatility metrics. Here's a breakdown:
Multi-Timeframe Slopes:
The script fetches data from higher timeframes (5m, 15m, 30m) using request.security.
Slope calculation: For each timeframe, it computes the linear regression slope of price over the lookback period using the formula:
textslope = correlation(close, bar_index, lookback) * stdev(close, lookback) / stdev(bar_index, lookback)
This measures the rate of price change, where positive slopes indicate uptrends and negative slopes indicate downtrends.
Variables: slope5m, slope15m, slope30m.
ATR (Average True Range):
Calculated using ta.atr(atrPeriod).
Represents average volatility over the specified period. Used later to derive volatility stability.
ADX (Average Directional Index):
A detailed, manual implementation (not using built-in ta.adx for customization):
Computes upward movement (upMove = high - high ) and downward movement (downMove = low - low).
Derives +DM (Plus Directional Movement) and -DM (Minus Directional Movement) by filtering non-relevant moves.
Smooths true range (trur = ta.rma(ta.tr(true), adxPeriod)).
Calculates +DI and -DI: plusDI = 100 * ta.rma(plusDM, adxPeriod) / trur, similarly for minusDI.
DX: dx = 100 * abs(plusDI - minusDI) / max(plusDI + minusDI, 0.0001).
ADX: adx = ta.rma(dx, adxPeriod).
ADX values above 25 typically indicate strong trends; here, it's normalized (divided by 50) to influence the trend bias.
Volume Delta (5m Timeframe):
Fetches 5m volume: volume_5m = request.security(syminfo.tickerid, "5", volume, lookahead=barmerge.lookahead_on).
Computes a 12-period SMA of volume: avgVolume = ta.sma(volume_5m, 12).
Delta: (volume_5m - avgVolume) / avgVolume (or 0 if avgVolume is zero).
This measures relative volume spikes, where positive deltas suggest increased interest (bullish) and negative suggest waning activity (bearish).
MOS Components and Final Calculation:
Trend Bias: Average of the three slopes, normalized by close price and scaled by 100, then weighted by ADX influence: (slope5m + slope15m + slope30m) / 3 / close * 100 * (adx / 50).
Emphasizes trends in strong ADX conditions.
Momentum Impulse: Change in 5m RSI(14) over 1 bar, divided by 50: ta.change(request.security(syminfo.tickerid, "5", ta.rsi(close, 14), lookahead=barmerge.lookahead_on), 1) / 50.
Captures short-term momentum shifts.
Volatility Clarity: 1 - ta.change(atr, 1) / max(atr, 0.0001).
Measures ATR stability; values near 1 indicate low volatility changes (clearer trends), while lower values suggest erratic markets.
MOS Formula: Weighted average:
textmos = (0.35 * trendBias + 0.25 * momentumImpulse + 0.2 * volumeDelta + 0.2 * volatilityClarity)
Weights prioritize trend (35%) and momentum (25%), with volume and volatility at 20% each. These can be adjusted in code for experimentation.
Trading Decision:
A variable mosDecision starts as "Neutral".
If mos > 0.15, set to "Long".
If mos < -0.15, set to "Short".
Thresholds (0.15 and -0.15) are hardcoded but can be modified.
Visualization and Outputs
Threshold Lines (using hline):
Long Threshold: Horizontal dashed green line at +0.15.
Short Threshold: Horizontal dashed red line at -0.15.
Neutral Line: Horizontal dashed gray line at 0.
These provide visual reference points for MOS interpretation.
Dynamic Labels (using label.new):
Placed at each bar's index and MOS value.
Text: Formatted MOS value (e.g., "0.2345") followed by a newline and the decision (e.g., "Long").
Style: Downward-pointing label with gray background and white text for readability.
This replaces a traditional plot line, showing exact values and decisions per bar without cluttering the chart.
The indicator appears in a separate pane below the main price chart, making it easy to monitor alongside price action.
Usage Instructions
Adding to TradingView:
Copy the script into TradingView's Pine Script editor.
Save and add to your chart via the "Indicators" menu.
Select a symbol and timeframe (e.g., 1-minute for intraday).
Interpretation:
Long Signal: MOS > 0.15 – Consider bullish positions if supported by other indicators.
Short Signal: MOS < -0.15 – Potential bearish setups.
Neutral: Between -0.15 and 0.15 – Avoid trades or wait for confirmation.
Watch for MOS crossings of thresholds for momentum shifts.
Combine with price patterns, support/resistance, or volume for better accuracy.
Limitations and Considerations:
Lookahead Bias: Uses barmerge.lookahead_on for multi-timeframe data, which may introduce minor forward-looking bias in backtesting (use with caution).
No Alerts Built-In: Add custom alerts via TradingView's alert system based on MOS conditions.
Performance: Tested for compatibility; may require adjustments for illiquid assets or extreme volatility.
Backtesting: Use TradingView's strategy tester to evaluate historical performance, but remember past results don't guarantee future outcomes.
Customization: Edit weights in the MOS formula or thresholds to fit your strategy.
This indicator distills complex market data into a single score, aiding decision-making while encouraging users to verify signals with additional analysis. If you need modifications, such as restoring plot functionality or adding features, provide details for further refinement.
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.
Hidden Markov Model [Extension] | FractalystWhat's the indicator's purpose and functionality?
The Hidden Markov Model is specifically designed to integrate with the Quantify Trading Model framework, serving as a probabilistic market regime identification system for institutional trading analysis.
Hidden Markov Models are particularly well-suited for market regime detection because they can model the unobservable (hidden) state of the market, capture probabilistic transitions between different states, and account for observable market data that each state generates.
The indicator uses Hidden Markov Model mathematics to automatically detect distinct market regimes such as low-volatility bull markets, high-volatility bear markets, or range-bound consolidation periods.
This approach provides real-time regime probabilities without requiring optimization periods that can lead to overfitting, enabling systematic trading based on genuine probabilistic market structure.
How does this extension work with the Quantify Trading Model?
The Hidden Markov Model | Fractalyst serves as a probabilistic state estimation engine for systematic market analysis.
Instead of relying on traditional technical indicators, this system automatically identifies market regimes using forward algorithm implementation with three-state probability calculation (bullish/neutral/bearish), Viterbi decoding process for determining most likely regime sequence without repainting, online parameter learning with adaptive emission probabilities based on market observations, and multi-feature analysis combining normalized returns, volatility comprehensive regime assessment.
The indicator outputs regime probabilities and confidence levels that can be used for systematic trading decisions, portfolio allocation, or risk management protocols.
Why doesn't this use optimization periods like other indicators?
The Hidden Markov Model | Fractalyst deliberately avoids optimization periods to prevent overfitting bias that destroys out-of-sample performance.
The system uses a fixed mathematical framework based on Hidden Markov Model theory rather than optimized parameters, probabilistic state estimation using forward algorithm calculations that work across all market conditions, online learning methodology with adaptive parameter updates based on real-time market observations, and regime persistence modeling using fixed transition probabilities with 70% diagonal bias for realistic regime behavior.
This approach ensures the regime detection signals remain robust across different market cycles without the performance degradation typical of over-optimized traditional indicators.
Can this extension be used independently for discretionary trading?
No, the Hidden Markov Model | Fractalyst is specifically engineered for systematic implementation within institutional trading frameworks.
The indicator is designed to provide regime filtering for systematic trading algorithms and risk management systems, enable automated backtesting through mathematical regime identification without subjective interpretation, and support institutional-level analysis when combined with systematic entry/exit models.
Using this indicator independently would miss the primary value proposition of systematic regime-based strategy optimization that institutional frameworks provide.
How do I integrate this with the Quantify Trading Model?
Integration enables institutional-grade systematic trading through advanced machine learning and statistical validation:
- Add both HMM Extension and Quantify Trading Model to your chart
- Select HMM Extension as the bias source using input.source()
- Quantify automatically uses the extension's bias signals for entry/exit analysis
- The built-in machine learning algorithms score optimal entry and exit levels based on trend intensity, and market structure patterns identified by the extension
The extension handles all bias detection complexity while Quantify focuses on optimal trade timing, position sizing, and risk management along with PineConnector automation
What markets and assets does the indicator Extension work best on?
The Hidden Markov Model | Fractalyst performs optimally on markets with sufficient price movement since the system relies on statistical analysis of returns, volatility, and momentum patterns for regime identification.
Recommended asset classes include major forex pairs (EURUSD, GBPUSD, USDJPY) with high liquidity and clear regime transitions, stock index futures (ES, NQ, YM) providing consistent regime behavior patterns, individual equities (large-cap stocks with sufficient volatility for regime detection), cryptocurrency markets (BTC, ETH with pronounced regime characteristics), and commodity futures (GC, CL showing distinct market cycles and regime transitions).
These markets provide sufficient statistical variation in returns and volatility patterns, ensuring the HMM system's mathematical framework can effectively distinguish between bullish, neutral, and bearish regime states.
Any timeframe from 15-minute to daily charts provides sufficient data points for regime calculation, with higher timeframes (4H, Daily) typically showing more stable regime identification with fewer false transitions, while lower timeframes (30m, 1H) provide more responsive regime detection but may show increased noise.
Acceptable Timeframes and Portfolio Integration:
- Any timeframe that can be evaluated within Quantify Trading Model's backtesting engine is acceptable for live trading implementation.
Legal Disclaimers and Risk Acknowledgments
Trading Risk Disclosure
The HMM Extension is provided for informational, educational, and systematic bias detection purposes only and should not be construed as financial, investment, or trading advice. The extension provides institutional analysis but does not guarantee profitable outcomes, accurate bias predictions, or positive investment returns.
Trading systems utilizing bias detection algorithms carry substantial risks including but not limited to total capital loss, incorrect bias identification, market regime changes, and adverse conditions that may invalidate analysis. The extension's performance depends on accurate data, TradingView infrastructure stability, and proper integration with Quantify Trading Model, any of which may experience data errors, technical failures, or service interruptions that could affect bias detection accuracy.
System Dependency Acknowledgment
The extension requires continuous operation of multiple interconnected systems: TradingView charts and real-time data feeds, accurate reporting from exchanges, Quantify Trading Model integration, and stable platform connectivity. Any interruption or malfunction in these systems may result in incorrect bias signals, missed transitions, or unexpected analytical behavior.
Users acknowledge that neither Fractalyst nor the creator has control over third-party data providers, exchange reporting accuracy, or TradingView platform stability, and cannot guarantee data accuracy, service availability, or analytical performance. Market microstructure changes, reporting delays, exchange outages, and technical factors may significantly affect bias detection accuracy compared to theoretical or backtested performance.
Intellectual Property Protection
The HMM Extension, including all proprietary algorithms, classification methodologies, three-state bias detection systems, and integration protocols, constitutes the exclusive intellectual property of Fractalyst. Unauthorized reproduction, reverse engineering, modification, or commercial exploitation of these proprietary technologies is strictly prohibited and may result in legal action.
Liability Limitation
By utilizing this extension, users acknowledge and agree that they assume full responsibility and liability for all trading decisions, financial outcomes, and potential losses resulting from reliance on the extension's bias detection signals. Fractalyst shall not be liable for any unfavorable outcomes, financial losses, missed opportunities, or damages resulting from the development, use, malfunction, or performance of this extension.
Past performance of bias detection accuracy, classification effectiveness, or integration with Quantify Trading Model does not guarantee future results. Trading outcomes depend on numerous factors including market regime changes, pattern evolution, institutional behavior shifts, and proper system configuration, all of which are beyond the control of Fractalyst.
User Responsibility Statement
Users are solely responsible for understanding the risks associated with algorithmic bias detection, properly configuring system parameters, maintaining appropriate risk management protocols, and regularly monitoring extension performance. Users should thoroughly validate the extension's bias signals through comprehensive backtesting before live implementation and should never base trading decisions solely on automated bias detection.
This extension is designed to provide systematic institutional flow analysis but does not replace the need for proper market understanding, risk management discipline, and comprehensive trading methodology. Users should maintain active oversight of bias detection accuracy and be prepared to implement manual overrides when market conditions invalidate analysis assumptions.
Terms of Service Acceptance
Continued use of the HMM Extension constitutes acceptance of these terms, acknowledgment of associated risks, and agreement to respect all intellectual property protections. Users assume full responsibility for compliance with applicable laws and regulations governing automated trading system usage in their jurisdiction.
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities.
Theoretical Foundation
Momentum Factor Research
The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)².
The momentum calculation methodology follows the academic standard:
Momentum(t) = / P(t-n) × 100
Where P(t) is the current price and n is the lookback period.
The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator.
Indicator Architecture
Core Components
1. Data Collection and Processing
The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods).
2. Composite Oscillator Calculation
Following Fama-French factor construction methodology, the indicator offers two weighting schemes:
- Equal Weight: Each active stock receives identical weighting (1/n)
- Market Cap Weight: Reserved for future enhancement
3. Oscillator Transformation Functions
The indicator provides five distinct oscillator types, each with established technical analysis foundations:
a) Momentum Oscillator (Default)
- Pure rate-of-change calculation
- Centered around zero
- Direct implementation of Jegadeesh & Titman methodology
b) RSI (Relative Strength Index)
- Wilder's (1978) relative strength methodology
- Transformed to center around zero for consistency
- Scale: -50 to +50
c) Stochastic Oscillator
- George Lane's %K methodology
- Measures current position within recent range
- Transformed to center around zero
d) Williams %R
- Larry Williams' range-based oscillator
- Inverse stochastic calculation
- Adjusted for zero-centered display
e) CCI (Commodity Channel Index)
- Donald Lambert's mean reversion indicator
- Measures deviation from moving average
- Scaled for optimal visualization
Operational Modes
Mode 1: Magnificent 7 Analysis
Tracks the collective momentum of the seven constituent stocks. This mode is optimal for:
- Technology sector analysis
- Growth stock momentum assessment
- Large-cap performance tracking
Mode 2: S&P 500 Equal Weight Comparison
Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides:
- Broader market momentum context
- Size-neutral market analysis
- Comparison baseline for relative performance
Mode 3: Relative Performance Analysis
Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables:
- Sector rotation analysis
- Style factor assessment (Growth vs. Value)
- Relative strength identification
Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum
Signal Generation and Thresholds
Signal Classification
The indicator generates three signal states:
- Bullish: Oscillator > Upper Threshold (default: +2.0%)
- Bearish: Oscillator < Lower Threshold (default: -2.0%)
- Neutral: Oscillator between thresholds
Relative Performance Signals
In relative performance mode, specialized thresholds apply:
- Outperformance: Relative momentum > +1.0%
- Underperformance: Relative momentum < -1.0%
Alert System
Comprehensive alert conditions include:
- Threshold crossovers (bullish/bearish signals)
- Zero-line crosses (momentum direction changes)
- Relative performance shifts
- Breadth Analysis Component
The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into:
- Strong Breadth (>60%): Broad-based momentum
- Weak Breadth (<40%): Narrow momentum leadership
- Mixed Breadth (40-60%): Neutral momentum distribution
Visual Design and User Interface
Theme-Adaptive Display
The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences.
Professional Data Table
A comprehensive data table displays:
- Current oscillator value and percentage
- Active mode and oscillator type
- Signal status and strength
- Component breakdowns (in relative performance mode)
- Breadth percentage
- Active threshold levels
Custom Color Options
Users can override default colors with custom selections for:
- Neutral conditions (default: Material Blue)
- Bullish signals (default: Material Green)
- Bearish signals (default: Material Red)
Practical Applications
Portfolio Management
- Sector Allocation: Use relative performance mode to time technology sector exposure
- Risk Management: Monitor breadth deterioration as early warning signal
- Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions
Market Analysis
- Trend Identification: Zero-line crosses indicate momentum regime changes
- Divergence Analysis: Compare MAG7 performance against broader market
- Volatility Assessment: Oscillator range and frequency provide volatility insights
Strategy Development
- Factor Timing: Implement growth factor timing strategies
- Momentum Strategies: Develop systematic momentum-based approaches
- Risk Parity: Use breadth metrics for risk-adjusted portfolio construction
Configuration Guidelines
Parameter Selection
- Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment
- Smoothing Period (1-50): Higher values reduce noise but increase lag
- Thresholds: Adjust based on historical volatility and strategy requirements
Timeframe Considerations
- Daily Charts: Optimal for swing trading and medium-term analysis
- Weekly Charts: Suitable for long-term trend analysis
- Intraday Charts: Useful for short-term tactical decisions
Limitations and Considerations
Market Concentration Risk
The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions.
Momentum Persistence
While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years).
Correlation Dynamics
During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity.
Performance Metrics and Backtesting
The indicator includes hidden plots for comprehensive backtesting:
- Individual stock momentum values
- Composite breadth percentage
- S&P 500 Equal Weight momentum
- Relative performance calculations
These metrics enable quantitative strategy development and historical performance analysis.
References
¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
OptionHawk1. What makes the script original?
• Unique concept: It integrates a Keltner based custom supertrend with a multi-EMA energy visualization, ATR based multi target management, and on chart options (CALL/PUT) trade signals—creating a toolkit not found in typical public scripts.
• Innovative use: Instead of off the shelf indicators, it reinvents them:
• Keltner bands used as dynamic Supertrend triggers.
• Fifteen EMAs layered for “energy” zones (bullish/bearish heatmaps).
• ATR dynamically scales multi-TP levels and stop loss.
These are creatively fused into a unified signal and automation engine.
________________________________________
2. What value does it provide to traders?
• Clear entries & exits: Labels for entry price/time, five TP levels, and SL structure eliminate guesswork.
• Visualization & automation: Real-time bar coloring and energy overlays allow quick momentum reads.
• Targeted to common pain points: Many traders struggle with manual TP/SL and entry timing—this automates that process.
• Ready for real use: Just plug into intraday (e.g., 5 min) or swing setups; no manual calculations. Signals are actionable out of the box.
________________________________________
3. Why invite only (worth paying)?
• Proprietary fusion: Public indicators like Supertrend or EMA are common—but your layered use, ATR based scaling, and label logic are exclusive.
• Auto-generated options format: Unique labeling for CALL/PUT, with graphical on chart signals, isn’t offered freely elsewhere.
• Time-saver & edge-provider: Saves traders hours of configuration and enhances consistency—worth the subscription cost over piecing together mash ups.
________________________________________
4. How does it work?
• Signal backbone: Custom supertrend uses Keltner bands crossing with close for direction, filtered by trend direction EMAs.
• Multi time logic: Trend defined by crossover of price over dynamic SMA thresholds built from ATR.
• Energy bar-colors/EMAs: 15 fast EMAs color-coded green/red to instantly show momentum.
• Entry logic: “Bull” when close crosses above supertrend; “Bear” when crosses below.
• Risk management: SL set at previous bar; up to 5 ATR scaled targets (or percentage based).
• Options formatted alerts: CALL/PUT labels with ₹¬currency values, embedded timestamp, SL/TP all printed on the chart.
________________________________________
5. How should traders use it?
• Best markets & timeframes: Ideal for intraday / low timeframe (1 15m) setups and 1 hour swing trades in equities, indices, options.
• Conditions: Works best in trending or volatility driven sessions—visible via Keltner bands and EMA energy alignment.
• Recommended combo: Use alongside volume filters or broader cycles; when supertrend & energy EMAs align, validation is stronger.
________________________________________
6. Proof of effectiveness?
• On chart visuals: Entry/exit labels, confirmed labels, TP and SL markers make past hits obvious.
• Real trade examples: Highlighted both bull & bear setups with full profit realization or SL hits.
• Performance is paint tested: Easy to showcase historic signals across multiple tickers.
• Data-backed: Users can export chart data to calculate win rate and avg return per trade.
________________________________________
Summary Pitch:
OptionHawk offers a holistic, execution-ready trading tool:
1. Proprietary blend of Keltner-supertrend and layered EMAs—beyond standard scripts.
2. Automates entries, multi-tier targets, SL, and options-format labels.
3. Visual energy overlays for quick momentum readings.
4. Use-tested in intraday and swing markets.
5. Installs on chart and works immediately—no setup complexity.
It's not a public indicator package; it's a self-contained, plug and play trade catalyst—worth subscribing for active traders seeking clarity, speed, and structure in their decision-making.
6. While OptionHawk is designed for clarity and structure, no script can predict the market. Always use with discretion and proper risk management.
---------------------------------------------------------------------------------------------------------------------
OptionHawk: A Comprehensive Trend-Following & Volatility-Adaptive Trading System
The "OptionHawk" script is a sophisticated trading tool designed to provide clear, actionable signals for options trading by combining multiple technical indicators and custom logic. It aims to offer a holistic view of market conditions, identifying trend direction, momentum, and potential entry/exit points with dynamic stop-loss and take-profit levels.
________________________________________
1. Why These Specific Indicators and Code Elements?
The "OptionHawk" script is a strategic fusion of the Supertrend indicator (modified with Keltner Channels), a multi-EMA "Energy" ribbon, dynamic trend lines (based on SMA and ATR), a 100-period Trend Filter EMA, and comprehensive trade management logic (SL/TP). My reason and motivation for this mashup stem from a desire to create a robust system that accounts for various market aspects often overlooked by individual indicators:
• Supertrend with Keltner Channels: The standard Supertrend is effective for trend identification but can sometimes generate whipsaws in volatile or ranging markets. By integrating Keltner Channels into the Supertrend calculation, the volatility measure becomes more adaptive, using the (high - low) range within the Keltner Channel for its ATR-like component. This aims to create a more responsive yet less prone-to-false-signals Supertrend.
• Multi-EMA "Energy" Ribbon: This visually striking element, composed of 15 EMAs, provides a quick glance at short-to-medium term momentum and potential support/resistance zones. When these EMAs are stacked and moving in one direction, it indicates strong "energy" behind the trend, reinforcing the signals from other indicators.
• Dynamic Trend Lines (SMA + ATR): These lines offer a visual representation of support and resistance that adapts to market volatility. Unlike static trend lines, their ATR-based offset ensures they remain relevant across different market conditions and asset classes, providing context for price action relative to the underlying trend.
• 100-Period Trend Filter EMA: A longer-period EMA acts as a higher-timeframe trend filter. This is crucial for confirming the direction identified by the faster-acting Supertrend, helping to avoid trades against the prevailing broader trend.
• Comprehensive Trade Management Logic: The script integrates automated calculation and display of stop-loss (SL) and multiple take-profit (TP) levels, along with trade confirmation and "TP Hit" labels. This is critical for practical trading, providing immediate, calculated risk-reward parameters that individual indicators typically don't offer.
This combination is driven by the need for a multi-faceted approach to trading that goes beyond simple signal generation to include trend confirmation, volatility adaptation, and essential risk management.
________________________________________
2. What Problem or Need Does This Mashup Solve?
This mashup addresses several critical gaps that existing individual indicators often fail to fill:
• Reliable Trend Identification in Volatile Markets: While Supertrend is good, it can be late or whipsaw. Integrating Keltner Channels helps it adapt to changing volatility, providing more reliable trend signals.
• Confirmation of Signals: A common pitfall of relying on a single indicator is false signals. "OptionHawk" uses the multi-EMA "Energy" ribbon and the 100-period EMA to confirm the trend identified by the Keltner-Supertrend, reducing false entries.
• Dynamic Support/Resistance & Trend Context: Static support and resistance levels can quickly become irrelevant. The dynamic SMA + ATR trend lines provide continually adjusting zones that reflect the current market's true support and resistance, giving traders a better understanding of price action within the trend.
• Integrated Risk and Reward Management: Most indicators just give entry signals. This script goes a significant step further by automatically calculating and displaying clear stop-loss and up to five take-profit levels (either ATR-based or percentage-based). This is a vital component for structured trading, allowing traders to pre-define their risk and reward for each trade.
• Visual Clarity and Actionable Information: Instead of requiring traders to layer multiple indicators manually, "OptionHawk" integrates them into a single, cohesive display with intuitive bar coloring, shape plots, and informative labels. This reduces cognitive load and presents actionable information directly on the chart.
In essence, "OptionHawk" provides a more comprehensive, adaptive, and actionable trading framework than relying on isolated indicators.
________________________________________
3. How Do the Components Work Together?
The various components of "OptionHawk" interact in a synergistic and often sequential manner to generate signals and manage trades:
• Keltner-Supertrend as the Primary Signal Generator: The supertrend function, enhanced by keltner_channel, is the core of the system. It identifies potential trend reversals and continuation signals (bullish/bearish crosses of the supertrendLine). The sensitivity and factor inputs directly influence how closely the Supertrend follows price and its responsiveness to volatility.
• Multi-EMA "Energy" Ribbon for Momentum and Confirmation: The 15 EMAs (from ema1 to ema15) are plotted to provide a visual representation of short-term momentum. When the price is above these EMAs and they are spread out and pointing upwards, it suggests strong bullish "energy." Conversely, when price is below them and they are pointing downwards, it indicates bearish "energy." This ribbon serves as a simultaneous visual confirmation for the Supertrend signals; a buy signal from Supertrend is stronger if the EMA ribbon is also indicating upward momentum.
• Dynamic Trend Lines for Context and Confirmation: The sma_high and sma_low lines, incorporating ATR, act as dynamic support and resistance. The trend variable, determined by price crossing these lines, provides an overarching directional bias. This component works conditionally with the Supertrend; a bullish Supertrend signal is more potent if the price is also above the sma_high (indicating an uptrend).
• 100-Period Trend Filter EMA for Macro Trend Confirmation: The ema100 acts as a macro trend filter. Supertrend signals are typically considered valid if they align with the direction of the ema100. For example, a "BUY" signal from the Keltner-Supertrend is ideally taken only if the price is also above the ema100, signifying that the smaller trend aligns with the larger trend. This is a conditional filter.
• Trade Confirmation and SL/TP Logic (Sequential and Conditional):
• Once a bull or bear signal is generated by the Keltner-Supertrend, the tradeSignalCall or tradeSignalPut is set to true.
• A confirmation step then occurs for a "BUY" signal, the script checks if the close of the next bar is higher than the entry bar's close. For a "SELL" signal, it checks if the close of the next bar is lower. This is a sequential confirmation step aimed at filtering out weak signals.
• Upon a confirmed signal, the stop-loss (SL) is immediately set based on the previous bar's low (for calls) or high (for puts).
• Multiple take-profit (TP) levels are calculated and stored in arrays. These can be based on a fixed percentage or dynamic ATR multiples, based on user input.
• The TP HIT logic continuously monitors price action simultaneously against these pre-defined target levels, displaying labels when a target is reached. The SL HIT logic similarly monitors for a stop-loss breach.
In summary, the Supertrend generates the initial signal, which is then confirmed by the dynamic trend lines and the 100-period EMA, and visually reinforced by the EMA "Energy" ribbon. The trade management logic then takes over, calculating and displaying vital risk-reward parameters.
________________________________________
4. What is the Purpose of the Mashup Beyond Simply Merging Code?
The purpose of "OptionHawk" extends far beyond merely combining different indicator codes; it's about creating a structured and informed decision-making process for options trading. The key strategic insights and functionalities added by combining these elements are:
• Enhanced Signal Reliability and Reduced Noise: By requiring multiple indicators to align (e.g., Keltner-Supertrend signal confirmed by EMA trend filter and dynamic trend lines), the script aims to filter out false signals and whipsaws that commonly plague individual indicators. This leads to higher-probability trade setups.
• Adaptive Risk Management: The integration of ATR into both the Supertrend calculation and the dynamic stop-loss/take-profit levels makes the entire system adaptive to current market volatility. This means stop-losses and targets are not static but expand or contract with the market's price swings, promoting more realistic risk management.
• Clear Trade Entry and Exit Framework: The script provides a complete trading plan with each signal: a clear entry point, a precise stop-loss, and multiple cascading take-profit levels. This holistic approach empowers traders to manage their trades effectively from initiation to conclusion, rather than just identifying a potential entry.
• Visual Confirmation of Market Strength: The "Energy" ribbon and dynamic trend lines provide an immediate visual understanding of the market's momentum and underlying trend strength, helping traders gauge conviction behind a signal.
• Improved Backtesting and Analysis: By combining these elements into one script, traders can more easily backtest a comprehensive strategy rather than trying to manually combine signals from multiple overlaying indicators, leading to more accurate strategy analysis.
• Suitability for Options Trading: Options contracts are highly sensitive to price movement and volatility. This script's focus on confirmed trend identification, dynamic volatility adaptation, and precise risk management makes it particularly well-suited for the nuanced demands of options trading, where timing and defined risk are paramount.
________________________________________
5. What New Functionality or Insight Does Your Script Offer?
"OptionHawk" offers several new functionalities and insights that significantly enhance decision-making, improve accuracy, and provide clearer signals and better timing for traders:
• "Smart" Supertrend: By basing the Supertrend's volatility component on the Keltner Channel's range instead of a simple ATR, the Supertrend becomes more sensitive to price action within its typical bounds while still adapting to broader market volatility. This can lead to earlier and more relevant trend change signals.
• Multi-Confirmation System: The script doesn't just provide a signal; it layers multiple confirmations (Keltner-Supertrend, multi-EMA "Energy" coloration, dynamic trend lines, and the 100-period EMA). This multi-layered validation significantly improves the accuracy of signals by reducing the likelihood of false positives.
• Automated and Dynamic Risk-Reward Display: This is a major functionality enhancement. The automatic calculation and clear display of stop-loss and five distinct take-profit levels (based on either ATR or percentage) directly on the chart, along with "TP HIT" and "SL HIT" labels, streamline the trading process. Traders no longer need to manually calculate these crucial levels, leading to enhanced decision-making and better risk management.
• Visual Trend "Energy" and Momentum: The vibrant coloring of the multi-EMA ribbon based on price relative to the EMA provides an intuitive and immediate visual cue for market momentum and "energy." This offers an insight into the strength of the current move, which isn't available from single EMA plots.
• Post-Signal Confirmation: The "Confirmation" label appearing on the bar after a signal, if the price continues in the signaled direction, adds an extra layer of real-time validation. This helps to improve signal timing by waiting for initial follow-through.
• Streamlined Options Trading Planning: For options traders, having clear entry prices, stop-losses, and multiple target levels directly annotated on the chart is invaluable. It helps in quickly assessing potential premium movements and managing positions effectively.
In essence, "OptionHawk" transitions from a collection of indicators to a semi-automated trading assistant, providing a comprehensive, visually rich, and dynamically adaptive framework for making more informed and disciplined trading decisions.
----------------------------------------------------------------------------------------------------------------
Performance & Claims
1. What is the claimed performance of the script or strategy?
Answer: The script does not claim any specific performance metrics (e.g., win rate, profit factor, percentage gains). It's an indicator designed to identify potential buy/sell signals and target/stop-loss levels. The labels it generates ("BUY CALL," "BUY PUT," "TP HIT," "SL HIT") are informational based on its internal logic, not a representation of actual trading outcomes.
2. Is there any proof or backtesting to support this claim?
Answer: No, the provided code does not include any backtesting functionality or historical performance proof. As an indicator, it simply overlays visual signals on the chart. To obtain backtesting results, the logic would need to be implemented as a Pine Script strategy with entry/exit rules and commission/slippage considerations.
3. Are there any unrealistic or exaggerated performance expectations being made?
Answer: The script itself does not make any performance expectations. It avoids quantitative claims. However, if this script were presented to users with implied promises of profit based solely on the visual signals, that would be unrealistic.
4. Have you clearly stated the limitations of the performance data (e.g., “based on backtesting only”)?
Answer: There is no statement of performance data or its limitations because the script doesn't generate performance data.
5. Do you include a disclaimer that past results do not guarantee future performance?
Answer: No, the script does not include any disclaimers about past or future performance. This is typically found in accompanying documentation or marketing materials for a trading system, not within the indicator's code itself.
________________________________________
Evidence & Transparency
6. How are your performance results measured (e.g., profit factor, win rate, Sharpe ratio)?
Answer: Performance results are not measured by this script. It's an indicator.
7. Are these results reproducible by others using the same script and settings?
Answer: The visual signals and calculated levels (Supertrend line, EMAs, target/SL levels) generated by the script are reproducible on TradingView when applied to the same instrument, timeframe, and with the same input settings. However, the actual trading results (profit/loss) are not generated or reproducible by this indicator.
8. Do you include enough data (charts, equity curves, trade logs) to support your claims?
Answer: No, the script does not include or generate equity curves or trade logs. It provides visual labels on the chart, which can be seen as a form of "data" to support the signal generation, but not the performance claims (as none are made by the code).
________________________________________
Future Expectations
9. Are you making any predictions about future market performance?
Answer: No, the script does not make any explicit predictions about future market performance. Its signals are based on historical price action and indicator calculations.
10. Have you stated clearly that the future is fundamentally uncertain?
Answer: No, the script does not contain any statements about the uncertainty of the future.
11. Are forward-looking statements presented with caution and appropriate language?
Answer: The script does not contain any forward-looking statements beyond the visual signals it generates based on real-time data.
________________________________________
Risk & Disclosure
12. Have you disclosed the risks associated with using your script or strategy?
Answer: No, the script does not include any risk disclosures. This is typically found in external documentation.
13. Do you explain that trading involves potential loss as well as gain?
Answer: No, the script does not contain any explanation about the potential for loss in trading.
________________________________________
Honesty & Integrity
14. Have you avoided hype words like “guaranteed,” “foolproof,” or “no losses”?
Answer: Yes, the script itself avoids these hype words. The language used within the code is technical and describes the indicator's logic.
15. Is your language grounded and realistic rather than promotional?
Answer: Yes, the language within the provided Pine Script code is grounded and realistic as it pertains to the technical implementation of an indicator.
16. Are you leaving out any important details that might mislead users (e.g., selective performance snapshots)?
Answer: From the perspective of the code itself, no, it's not "leaving out" performance details because it's not designed to generate them. However, if this indicator were to be presented as a "strategy" that implies profitability without accompanying disclaimers, backtesting results, and risk disclosures, then that external presentation could be misleading. The script focuses on signal generation and visual representation.
⚠️ Disclaimer:
This indicator is for informational and educational purposes only. It does not guarantee any future results or performance. All trading involves risk. Please assess your own risk tolerance and consult a licensed financial advisor if needed. Past performance does not indicate future returns.
RSI Shifting Band Oscillator | QuantMAC📊 RSI Shifting Band Oscillator | QuantMAC
🎯 Overview
The RSI Shifting Band Oscillator represents a breakthrough in adaptive technical analysis, combining the innovative dual-stage RSI processing with dynamic volatility bands to create an oscillator that automatically adjusts to changing market momentum conditions. This cutting-edge indicator goes beyond traditional static approaches by using smoothed RSI to dynamically shift band width based on momentum transitions, providing superior signal accuracy across different market regimes.
🔧 Key Features
Revolutionary Dual RSI Technology: Proprietary two-stage RSI calculation with exponential smoothing that measures momentum transitions in real-time
Dynamic Adaptive Bands: Self-adjusting volatility bands that expand and contract based on RSI distance from equilibrium
Dual Trading Modes: Flexible Long/Short or Long/Cash strategies for different trading preferences
Advanced Performance Analytics: Comprehensive metrics including Sharpe, Sortino, and Omega ratios
Smart Visual System: Dynamic color coding with 9 professional color schemes
Precision Backtesting: Date range filtering with detailed historical performance analysis
Real-time Signal Generation: Clear entry/exit signals with customizable threshold sensitivity
Position Sizing Intelligence: Half Kelly criterion for optimal risk management
📈 How The Dual RSI Technology Works
The Dual RSI system is the heart of this indicator's innovation. Unlike traditional RSI implementations, this approach analyzes the smoothed momentum transitions between different RSI states, providing early warning signals for momentum regime changes.
RSI Calculation Process:
Calculate traditional RSI using specified length and price source
Apply exponential moving average smoothing to reduce noise
Measure RSI distance from neutral 50 level to determine momentum strength
Use RSI deviation to dynamically adjust standard deviation multipliers
Create adaptive bands that respond to momentum conditions
Generate normalized oscillator values for clear signal interpretation
The genius of this dual RSI approach lies in its ability to detect when markets are transitioning between momentum and consolidation periods before traditional indicators catch up. This provides traders with a significant edge in timing entries and exits.
⚙️ Comprehensive Parameter Control
RSI Settings:
RSI Length: Controls the lookback period for momentum analysis (default: 14)
RSI Smoothing: Reduces noise in RSI calculations using EMA (default: 20)
Source: Price input selection (close, open, high, low, etc.)
Oscillator Settings:
Base Length: Foundation moving average for band calculations (default: 40)
Standard Deviation Length: Period for volatility measurement (default: 26)
SD Multiplier: Base band width adjustment (default: 2.7)
Oscillator Multiplier: Scaling factor for oscillator values (default: 100)
Signal Thresholds:
Long Threshold: Bullish signal trigger level (default: 90)
Short Threshold: Bearish signal trigger level (default: 56)
🎨 Advanced Visual System
Main Chart Elements:
Dynamic Shifting Bands: Upper and lower bands that automatically adjust width based on RSI momentum
Adaptive Fill Zone: Color-coded area between bands showing current market state
Basis Line: Moving average foundation displayed as subtle reference points
Smart Bar Coloring: Candles change color based on oscillator state for instant visual feedback
Oscillator Pane:
Normalized RSI Oscillator: Main signal line centered around zero with dynamic coloring
Threshold Lines: Horizontal reference lines for entry/exit levels
Zero Line: Central reference for oscillator neutrality
Color State Indication: Line colors change based on bullish/bearish conditions
📊 Professional Performance Metrics
The built-in analytics suite provides institutional-grade performance measurement:
Net Profit %: Total strategy return percentage
Maximum Drawdown %: Worst peak-to-trough decline
Win Rate %: Percentage of profitable trades
Profit Factor: Ratio of gross profits to gross losses
Sharpe Ratio: Risk-adjusted return measurement
Sortino Ratio: Downside-focused risk adjustment
Omega Ratio: Probability-weighted performance ratio
Half Kelly %: Optimal position sizing recommendation
Total Trades: Complete transaction count
🎯 Strategic Trading Applications
Long/Short Mode: ⚡
Maximizes profit potential by capturing both upward and downward price movements. The dual RSI technology helps identify when momentum is strengthening or weakening, allowing for optimal position switches between long and short.
Long/Cash Mode: 🛡️
Conservative approach ideal for retirement accounts or risk-averse traders. The indicator's adaptive nature helps identify the best times to be invested versus sitting in cash, protecting capital during adverse market conditions.
🚀 Unique Advantages
Traditional Indicators vs RSI Shifting Bands:
Static vs Dynamic: While most indicators use fixed parameters, RSI bands adapt in real-time
Lagging vs Leading: Dual RSI detects momentum transitions before they fully manifest
One-Size vs Adaptive: The same settings work across different market conditions
Simple vs Intelligent: Advanced momentum analysis provides superior market insight
💡 Professional Setup Guide
For Day Trading (Short-term):
RSI Length: 10-12
RSI Smoothing: 15-18
Base Length: 25-30
Thresholds: Long 85, Short 60
For Swing Trading (Medium-term):
RSI Length: 14-16 (default range)
RSI Smoothing: 20-25
Base Length: 40-50
Thresholds: Long 90, Short 56 (defaults)
For Position Trading (Long-term):
RSI Length: 18-21
RSI Smoothing: 25-30
Base Length: 60-80
Thresholds: Long 92, Short 50
🧠 Advanced Trading Techniques
RSI Divergence Analysis:
Watch for divergences between price action and smoothed RSI readings. When price makes new highs/lows but RSI doesn't confirm, it often signals upcoming reversals.
Band Width Interpretation:
Expanding Bands: Increasing momentum, expect larger price moves
Contracting Bands: Decreasing momentum, prepare for potential breakouts
Band Touches: Price touching outer bands often signals reversal opportunities
Multi-Timeframe Analysis:
Use RSI oscillator on higher timeframes for trend direction and lower timeframes for precise entry timing.
⚠️ Important Risk Disclaimers
Past performance is not indicative of future results. This indicator represents advanced technical analysis but should never be used as the sole basis for trading decisions.
Critical Risk Factors:
Market Conditions: No indicator performs equally well in all market environments
Backtesting Limitations: Historical performance may not reflect future market behavior
Momentum Risk: Adaptive indicators can be sensitive to extreme momentum conditions
Parameter Sensitivity: Different settings may produce significantly different results
Capital Risk: Always use appropriate position sizing and stop-loss protection
📚 Educational Benefits
This indicator provides exceptional learning opportunities for understanding:
Advanced RSI analysis and momentum measurement techniques
Adaptive indicator design and implementation
The relationship between momentum transitions and price movements
Professional risk management using Kelly Criterion principles
Modern oscillator interpretation and signal generation
🔍 Market Applications
The RSI Shifting Band Oscillator works across various markets:
Forex: Excellent for currency pair momentum analysis
Stocks: Individual equity and index trading
Commodities: Adaptive to commodity market momentum cycles
Cryptocurrencies: Handles extreme momentum variations effectively
Futures: Professional derivatives trading applications
🔧 Technical Innovation
The RSI Shifting Band Oscillator represents years of research into adaptive technical analysis. The proprietary dual RSI calculation method has been optimized for:
Computational Efficiency: Fast calculation even on high-frequency data
Noise Reduction: Advanced smoothing without excessive lag
Market Adaptability: Automatic adjustment to changing conditions
Signal Clarity: Clear, actionable trading signals
🔔 Updates and Evolution
The RSI Shifting Band Oscillator | QuantMAC continues to evolve with regular updates incorporating the latest research in adaptive technical analysis. The code is thoroughly documented for transparency and educational purposes.
Trading Notice: Financial markets involve substantial risk of loss. The RSI Shifting Band Oscillator is a sophisticated technical analysis tool designed to assist in trading decisions but cannot guarantee profitable outcomes.
---
Master The Markets With Adaptive Intelligence! 🎯📈
Malama's Heikin CountMalama's Heikin Count is a Pine Script indicator designed to enhance price action analysis by combining Heikin Ashi candlestick calculations with a normalized measurement of upper and lower shadow sizes. The indicator overlays Heikin Ashi candles on the chart and displays the relative sizes of upper and lower shadows as numerical labels (scaled from 1 to 10) for candles within the last two days, starting from 9:00 AM each day. This tool aims to help traders identify the strength of price movements and potential reversals by quantifying the significance of candlestick shadows in the context of Heikin Ashi’s smoothed price data. It is particularly useful for day traders and swing traders who rely on candlestick patterns to gauge market sentiment and momentum.
The indicator solves the problem of interpreting raw candlestick data by providing a smoothed visualization through Heikin Ashi candles and a simplified, numerical representation of shadow sizes. This allows traders to quickly assess whether a candle’s upper or lower shadow indicates strong buying or selling pressure, aiding in decision-making for entries, exits, or reversals.
Originality and Usefulness
Originality: While Heikin Ashi candles are a well-known technique for smoothing price data and reducing noise, Malama's Heikin Count introduces a novel feature by calculating and normalizing the sizes of upper and lower shadows relative to the total candle height. Unlike standard Heikin Ashi implementations, which focus solely on candle body trends, this indicator quantifies shadow proportions and presents them on a standardized 1–10 scale. This normalization makes it easier for traders to compare shadow significance across different timeframes and assets without needing to manually interpret raw measurements. The restriction of shadow size labels to the last two days from 9:00 AM ensures relevance for active trading sessions, avoiding clutter from older data.
Usefulness: The indicator is particularly valuable for traders who combine candlestick pattern analysis with trend-following strategies. By integrating Heikin Ashi’s trend-smoothing capabilities with shadow size metrics, it provides a unique perspective on market dynamics. For example, large upper shadows (high normalized values) may indicate rejection at resistance levels, while large lower shadows may suggest support or buying pressure. Unlike other open-source Heikin Ashi indicators, which typically focus only on candle plotting, this script’s shadow size normalization and time-based filtering offer a distinctive tool for intraday and short-term trading strategies.
Detailed Methodology ("How It Works")
The core logic of Malama's Heikin Count revolves around three main components: Heikin Ashi candle calculations, shadow size analysis, and time-based filtering for label display. Below is a breakdown of how these components work together:
Heikin Ashi Candle Calculations:
The script calculates Heikin Ashi candles to smooth price data and reduce market noise, making trends easier to identify.
Formulas:
haClose = (open + high + low + close) / 4: The Heikin Ashi close is the average of the current bar’s open, high, low, and close prices.
haOpen = na(haOpen ) ? (open + close) / 2 : (haOpen + haClose ) / 2: The Heikin Ashi open is either the average of the current bar’s open and close (for the first bar) or the average of the previous Heikin Ashi open and close.
haHigh = max(high, max(haOpen, haClose)): The Heikin Ashi high is the maximum of the current bar’s high, Heikin Ashi open, and Heikin Ashi close.
haLow = min(low, min(haOpen, haClose)): The Heikin Ashi low is the minimum of the current bar’s low, Heikin Ashi open, and Heikin Ashi close.
These calculations produce smoothed candles that emphasize trend direction and reduce the impact of short-term price fluctuations.
Shadow Size Analysis:
The script calculates the upper and lower shadows of each Heikin Ashi candle to assess market sentiment.
Formulas:
upperShadow = haHigh - max(haClose, haOpen): Measures the length of the upper shadow (distance from the top of the candle body to the high).
lowerShadow = min(haClose, haOpen) - haLow: Measures the length of the lower shadow (distance from the bottom of the candle body to the low).
totalHeight = haHigh - haLow: Calculates the total height of the candle (from high to low).
upperShadowPercentage = (upperShadow / totalHeight) * 100: Converts the upper shadow length to a percentage of the total candle height.
lowerShadowPercentage = (lowerShadow / totalHeight) * 100: Converts the lower shadow length to a percentage of the total candle height.
Normalization: The normalizeShadowSize function scales the shadow percentages to a 1–10 range using math.round(value / 10). This ensures that shadow sizes are presented in an easily interpretable format, where 1 represents a very small shadow (less than 10% of the candle height) and 10 represents a very large shadow (90–100% of the candle height). The normalization caps values between 1 and 10 for consistency.
Time-Based Filtering:
The script only displays shadow size labels for candles within the last two days, starting from 9:00 AM each day. This is achieved by calculating a start timestamp using timestamp(year(timenow), month(timenow), dayofmonth(timenow) - daysBack, startHour, startMinute), where daysBack = 2, startHour = 9, and startMinute = 0.
The condition time >= startTime ensures that labels are only plotted for candles within this time window, keeping the chart relevant for recent trading activity and avoiding clutter from older data.
Signal Generation:
The script does not generate explicit buy or sell signals but provides visual cues through shadow size labels. Large upper shadow sizes (e.g., 8–10) may indicate selling pressure or resistance, while large lower shadow sizes may suggest buying pressure or support. Traders can use these metrics in conjunction with the Heikin Ashi candle colors (green for bullish, red for bearish) to make trading decisions.
Strategy Results and Risk Management
Backtesting: The script is an indicator and does not include built-in backtesting or strategy logic for generating buy/sell signals. As such, it does not assume specific commission, slippage, or account sizing parameters. Traders using this indicator should incorporate it into their existing strategies, applying their own risk management rules.
Risk Management Guidance:
Traders can use the shadow size labels to inform risk management decisions. For example, a large upper shadow (e.g., 8–10) at a resistance level may prompt a trader to set a tighter stop-loss above the candle’s high, anticipating a potential reversal. Conversely, a large lower shadow at a support level may suggest a wider stop-loss below the low to account for volatility.
Default settings (e.g., 2-day lookback, 9:00 AM start) are designed to focus on recent price action, which is suitable for intraday and short-term swing trading. Traders should combine the indicator with other tools (e.g., support/resistance levels, trendlines) to define risk limits, such as risking 5–10% of equity per trade.
The indicator does not enforce specific risk management settings, allowing traders to customize their approach based on their risk tolerance and trading style.
User Settings and Customization
The script includes the following user-customizable inputs:
Days Back (daysBack = 2):
Description: Controls the lookback period for displaying shadow size labels. The default value of 2 means labels are shown for candles within the last two days.
Impact: Increasing daysBack extends the time window for label display, which may be useful for longer-term analysis but could clutter the chart. Decreasing it focuses on more recent data, ideal for intraday trading.
Start Hour (startHour = 9) and Start Minute (startMinute = 0):
Description: Defines the start time of the trading day (default is 9:00 AM). Labels are only shown for candles after this time each day within the lookback period.
Impact: Traders can adjust these settings to align with their preferred trading session (e.g., 9:30 AM for U.S. market open). Changing the start time shifts the time window for label display, affecting which candles are analyzed.
These settings allow traders to tailor the indicator to their trading timeframe and session preferences, ensuring that the shadow size labels remain relevant to their analysis.
Visualizations and Chart Setup
The indicator plots the following elements on the chart:
Heikin Ashi Candles:
Plotted using plotcandle(haOpen, haClose, haHigh, haLow), these candles overlay the standard price chart.
Color Coding: Green candles indicate bullish momentum (Heikin Ashi close ≥ open), while red candles indicate bearish momentum (Heikin Ashi close < open).
These candles provide a smoothed view of price trends, making it easier to identify trend direction and continuations.
Shadow Size Labels:
Upper Shadow Labels: Displayed above each candle at the Heikin Ashi high, showing the normalized upper shadow size (1–10). These labels are green with white text and use the label.style_label_down style for clear visibility.
Lower Shadow Labels: Displayed below each candle at the Heikin Ashi low, showing the normalized lower shadow size (1–10). These labels are red with white text and use the label.style_label_up style.
Labels are only shown for candles within the last two days from 9:00 AM, ensuring that only recent and relevant data is visualized.
Debugging Labels (Optional):
A blue label at the bottom of the chart displays the text "Upper: Lower: " for each candle, showing both shadow sizes for debugging purposes. This can be removed or commented out if not needed, as it is primarily for development use.
The visualizations are designed to be minimal and focused, ensuring that traders can quickly interpret the Heikin Ashi trend and shadow size metrics without unnecessary clutter. The use of color-coded candles and labels enhances readability, while the time-based filtering keeps the chart clean and relevant.
Malama's Candle Sniper Malama's Candle Sniper
This Pine Script is an overlay indicator crafted for TradingView to detect and highlight a variety of bullish and bearish candlestick patterns directly on the price chart. Its primary goal is to assist traders in identifying potential reversal or continuation signals by marking these patterns with labeled visual cues. The indicator is versatile, applicable across different markets (e.g., stocks, forex, cryptocurrencies) and timeframes, making it a valuable tool for enhancing technical analysis and informing trading decisions.
Originality and Usefulness
While the candlestick patterns detected by this script are well-established in technical analysis, "Malama's Candle Sniper" stands out due to its comprehensive nature. It consolidates the detection of numerous patterns—ranging from engulfing patterns to doji variations and multi-candle formations—into a single, unified indicator. This eliminates the need for traders to apply multiple individual indicators, streamlining their charting process and saving time.
The indicator’s usefulness lies in its ability to:
Provide Visual Clarity: Labels are plotted on the chart when patterns are detected, offering immediate recognition of potential trading opportunities.
Broad Pattern Coverage: It identifies both bullish and bearish patterns, accommodating various market conditions and trading strategies.
This makes it an ideal tool for traders who incorporate candlestick analysis into their decision-making, whether for spotting trend reversals or confirming ongoing momentum.
How It Works
"Malama's Candle Sniper" operates by defining helper functions in Pine Script that evaluate whether specific candlestick pattern conditions are met for the current bar. Each function returns a boolean value (true/false) based on predefined criteria involving the open, high, low, and close prices of the candles. The script then checks for transitions from false to true (i.e., a pattern newly appearing) and plots a corresponding label on the chart.
Bullish Patterns Detected
The script identifies the following bullish patterns, which typically signal potential upward price movements:
Bullish Engulfing: A small bearish candle followed by a larger bullish candle that engulfs it.
Three White Soldiers: Three consecutive bullish candles with higher closes.
Bullish Three Line Strike: Three bullish candles followed by a bearish candle that doesn’t negate the prior uptrend.
Three Inside Up: A bearish candle, a smaller bullish candle within its range, and a strong bullish confirmation candle.
Dragonfly Doji: A doji with a long lower wick and little to no upper wick, opening and closing near the high.
Piercing Line: A bearish candle followed by a bullish candle that opens below the prior low and closes above the midpoint of the prior candle.
Bullish Marubozu: A strong bullish candle with no upper or lower wicks.
Bullish Abandoned Baby: A bearish candle, a doji gapped below it, and a bullish candle gapped above the doji.
Rising Window: A gap up between two candles, with the current low above the prior high.
Hammer: A candle with a small body and a long lower wick, indicating rejection of lower prices.
Morning Star: A three-candle pattern with a bearish candle, a small-bodied middle candle, and a strong bullish candle.
Bearish Patterns Detected
The script also detects these bearish patterns, which often indicate potential downward price movements:
Bearish Engulfing: A small bullish candle followed by a larger bearish candle that engulfs it.
Three Black Crows: Three consecutive bearish candles with lower closes.
Bearish Three Line Strike: Three bearish candles followed by a bullish candle that doesn’t reverse the downtrend.
Three Inside Down: A bullish candle, a smaller bearish candle within its range, and a strong bearish confirmation candle.
Gravestone Doji: A doji with a long upper wick and little to no lower wick, opening and closing near the low.
Dark Cloud Cover: A bullish candle followed by a bearish candle that opens above the prior high and closes below the midpoint of the prior candle.
Bearish Marubozu: A strong bearish candle with no upper or lower wicks.
Bearish Abandoned Baby: A bullish candle, a doji gapped above it, and a bearish candle gapped below the doji.
Falling Window: A gap down between two candles, with the current high below the prior low.
Hanging Man: A candle with a small body and a long lower wick after an uptrend, signaling potential reversal.
Label Plotting
When a pattern is detected (i.e., its condition transitions from false to true):
Bullish Patterns: A label is plotted at the high of the bar, using a green background with white text and a downward-pointing style (e.g., "Bull Engulf" for Bullish Engulfing).
Bearish Patterns: A label is plotted at the low of the bar, using a red background with white text and an upward-pointing style (e.g., "Bear Engulf" for Bearish Engulfing).
This visual distinction helps traders quickly differentiate between bullish and bearish signals and their precise locations on the chart.
Strategy and Risk Management
Backtesting: "Malama's Candle Sniper" is strictly an indicator and does not include backtesting capabilities or automated trading signals. It does not simulate trades or provide performance statistics such as win rates or profit/loss metrics.
Risk Management: As an informational tool, it lacks built-in risk management features. Traders must independently implement strategies like stop-loss orders, take-profit levels, or position sizing to manage risk when acting on the detected patterns. For example, a trader might place a stop-loss below a Hammer pattern’s low or above a Hanging Man’s high to limit potential losses.
User Settings and Customization
Inputs: The script does not offer user-configurable inputs. All pattern detection logic is hardcoded, meaning traders cannot adjust parameters such as lookback periods or pattern sensitivity through the interface.
Customization: Advanced users with Pine Script knowledge can modify the code directly to:
Add or remove patterns.
Adjust the conditions (e.g., tweak the wick-to-body ratio for a Hammer).
Change label styles or colors.
However, the default version is fixed and ready-to-use as is.
Visualizations and Chart Setup
Plotted Elements:
Bullish Labels: Appear at the candle’s high with a green background, white text, and a downward-pointing arrow (e.g., "Hammer").
Bearish Labels: Appear at the candle’s low with a red background, white text, and an upward-pointing arrow (e.g., "Hanging Man").
Chart Setup: The indicator is configured as an overlay (overlay=true), meaning it integrates seamlessly with the price chart. Labels are displayed directly on the candlesticks, eliminating the need for a separate pane and keeping the focus on price action.
Usage Example
To use "Malama's Candle Sniper":
Add the indicator to your TradingView chart via the Indicators menu.
Observe the price chart for green (bullish) or red (bearish) labels as they appear.
Analyze the context of each pattern (e.g., trend direction, support/resistance levels) to decide on potential trades.
Apply your own entry, exit, and risk management rules based on the signals.
For instance, spotting a "Morning Star" label during a downtrend near a support level might prompt a trader to consider a long position, while a "Dark Cloud Cover" at resistance could signal a short opportunity.
[blackcat] L3 Projected Magic-9 SequenceOVERVIEW
The L3 Projected Magic-9 Sequence indicator is a sophisticated tool designed to help traders identify potential trend reversals through a unique sequence of price movements. By calculating projected highs and lows based on previous bar conditions, this script provides valuable insights into possible future market directions. It plots these key levels on the chart and highlights specific sequential patterns that often precede significant reversals, offering traders a visual advantage in their decision-making process 📈💡.
FEATURES
Projections: Calculates and plots projected highs and lows based on intricate conditions derived from previous bars' open, close, high, and low prices. These projections serve as dynamic support and resistance levels, helping traders anticipate potential turning points in the market 📊.
Sequential Patterns:
Identifies various sequential patterns known as "Magic" sequences, such as Magic-9 and Magic-13.
Labels these sequences directly on the chart for easy identification: 5, 6, 7, 8, 9, 12, 13 for both bullish and bearish trends.
Provides additional labels when these sequences align with projected highs or lows, enhancing the reliability of the signal 🏷️.
Differentiates between trend and sideways phases using the Magic-9 Project Range. Traditional sequences generating buy and sell signals of 9 and 13 during sideways swings are displayed indistinguishably from other numbers. However, the 9 and 13 generated by breakouts are highlighted with red and green labels for better visibility 🚦.
Project Range Adjustment:
The Project Range is automatically adjusted by Multiple Time Frame (MTF).
A higher cycle is selected as the baseline of the Project Range based on the current operating cycle, ensuring adaptability to varying market conditions ⏳.
Customization:
Offers customizable colors for plotted lines and labels, allowing users to tailor the appearance to their preferences 🎨.
Adjustable settings for lookback periods and other parameters to fine-tune the indicator according to individual trading styles.
Automatic Timeframe Selection:
Automatically selects the most suitable timeframe for data fetching, ensuring optimal performance across different chart intervals ⏳.
Ensures compatibility with various trading strategies, whether short-term intraday or long-term positional trading.
HOW TO USE
Adding the Indicator:
Open your TradingView platform and navigate to the chart where you want to apply the indicator.
Click on the "Indicators" button at the top of the screen and search for L3 Projected Magic-9 Sequence.
Select the indicator from the list and add it to your chart.
Understanding Projections:
Once added, observe the plotted projected highs and lows on your chart.
These lines represent anticipated support and resistance levels based on complex calculations involving previous bar data.
Identifying Sequential Patterns:
Look for labels such as 5, 6, 7, 8, 9, 12, and 13 appearing on the chart.
These labels signify specific sequential patterns that often precede market reversals.
Pay special attention to labels that include arrows (e.g., 9▼, 13▲), indicating alignment with projected highs or lows.
Note the differentiation between trend and sideways phases:
During sideways swings, traditional sequences generating buy and sell signals of 9 and 13 are displayed indistinguishably from other numbers.
Breakout-generated 9 and 13 are highlighted with red and green labels for clear identification.
Combining with Other Tools:
While the L3 Projected Magic-9 Sequence offers powerful insights, it is essential to combine its signals with other technical analysis tools.
Use moving averages, volume indicators, or candlestick patterns to confirm the validity of the identified sequences before executing trades.
LIMITATIONS
Market Conditions: The indicator performs best in trending markets but may generate false signals during periods of consolidation or range-bound movement 🌐.
Complexity: Due to its reliance on specific sequential patterns, some traders might find the concept challenging to grasp initially. Thorough testing and understanding are crucial before deploying it in live trading environments.
Data Dependency: Accurate projections depend on having sufficient historical data. Insufficient data may lead to less reliable results.
NOTES
Backtesting: Before implementing the indicator in real-time trading, conduct extensive backtesting to evaluate its effectiveness under various market conditions.
Risk Management: Always adhere to proper risk management principles, even when relying on robust indicators like this one. Set stop-loss orders and position sizes accordingly to protect your capital 🛡️.
Continuous Learning: Stay updated with the latest developments and adjustments made to the indicator by following community discussions and official updates from the author.
BIN Based Support and Resistance [SS]This indicator presents a version of an alternative way to determine support and resistance, using a method called "Bins".
Bins provide for a flexible and interesting way to determine support and resistance levels.
First off, let's discuss BINS:
Bins are ranges or containers into which your data points can be sorted. For example, if you're grouping ages, you might have bins like 0–18, 19–35, 36–50, and 51+. Any data point within these intervals gets placed in the corresponding bin.
Binning simplifies complex data sets by grouping values into categories. This is useful for such things as
Visualizing data in histograms or bar charts.
Reducing noise and highlighting trends.
This indicator groups the price action into 10 separate bins. It determines the Support / Resistance level by averaging the values in the Bins to find an iteration of the "central tendency" or average reoccurring value.
Pros and Cons
Since this is a different approach to support and resistance, I think its important to highlight some of the pros and advantages, but also be open about the cons.
First off the PROS
Bin Based Support and Resistance Levels dynamically adjust to ranges as opposed to hard / fast peaks and valleys. This makes them better at analyzing price action vs simply drawing lines at random peaks and valleys.
Because Bins are analyzing ALL PA within a period's max and min range, Bin Support and Resistance can actually be used similar to Volume profile, where you are able to identify a pseudo-POC, or areas where price tends to consolidate. Take a look at this example on SPY:
You can see these 2 SR lines are close together. This represents that this general price range is an area where price likes to accumulate/consolidate. You can see the SPY ended up coming back to this range and consolidating there for a bit.
This is a strength of using a BIN based approach to calculating support and resistance, because as indicated before, it looks at price action vs peaks and valleys.
As a tip, these areas are areas you want to wait for a break in one direction or the other.
The indicator provides for backtest results of the support and resistance lines, to see how many times certain areas acted as resistance or support. Because this is analyzing and distributing PA evenly throughout the period's max and min, the indicator can tell you which areas tend to have higher rejection zones and which have higher support zones.
Now the CONS
Because bin based SR take an average approach, the SR lines can sometimes be slightly broken before the ticker finds rejection:
To combat this, make sure there is confirmed support. How the indicator actually backtests these lines is by waiting to see if the ticker has 3 consecutive closes above the support line or below the resistance line. So these are things to be mindful of.
It doesn't consider pivots. Most support and resistance indicators either identify max and min peaks and valleys or use pivot points. Pivot points are a great way to identify peaks and valleys and thus by extension support and resistance. However, this is also somewhat of a strength, as using BINS forces the indicator to consider ALL price action and not just the extremes (highs and lows).
Can be slightly skewed in highly volatile environments. Any time there is a massive drop or rally, it can skew the indicator to give extreme ranges to both ends. For example, the Tariff news collapse on ES1!:
Owning to limitations in lookback length, sometimes the min and max range can be exceeded and other traditional areas of support / resistance is where a ticker will find support.
Using the indicator
Here are some basic use/functionalities of the indicator:
Selecting display of backtest results: You can select to have the backtest results shown in a table:
Or directly on the lines:
Inversely, you can toggle them off completely:
You can modify the lookback length. The suggested lookback length is between 250 to 500 candles on smaller timeframes. I also suggest 252 on daily timeframes (which represents 1 trading year).
And that's the indicator!
It is very easy to use, so you should pick it up in no time!
Enjoy and as always, 🚀🚀 safe trades! 🚀🚀
Auto Fib Retracement with Buy/SellKey Features of the Advanced Script:
Multi-Timeframe (MTF) Analysis:
We added an input for the higher timeframe (higher_tf), where the trend is checked on a higher timeframe to confirm the primary trend direction.
Complex Trend Detection:
The trend is determined not only by the current timeframe but also by the trend on the higher timeframe, giving a more comprehensive and reliable signal.
Dynamic Fibonacci Levels:
Fibonacci lines are plotted dynamically, extending them based on price movement, with the Fibonacci retracement drawn only when a trend is identified.
Background Color & Labels:
A background color is added to give a clear indication of the trend direction. Green for uptrend, red for downtrend. It makes it visually easier to understand the current market structure.
"Buy" or "Sell" labels are shown directly on the chart to mark possible entry points.
Strategy and Backtesting:
The script includes strategy commands (strategy.entry and strategy.exit), which allow for backtesting the strategy in TradingView.
Stop loss and take profit conditions are added (loss=100, profit=200), which can be adjusted according to your preferences.
Next Steps:
Test with different timeframes: Try changing the higher_tf to different timeframes (like "60" or "240") and see how it affects the trend detection.
Adjust Fibonacci settings: Modify how the Fibonacci levels are calculated or add more Fibonacci levels like 38.2%, 61.8%, etc.
Optimize Strategy Parameters: Fine-tune the entry/exit logic by adjusting stop loss, take profit, and other strategy parameters.
This should give you a robust foundation for creating advanced trend detection strategies
Custom Buy and Sell Signal with Body Ratio and RSI
Indicator Overview:
Name: Custom Buy and Sell Signal with Body Ratio and RSI
Description: This indicator is designed to detect buy and sell opportunities by analyzing the body size and wicks of candles in combination with the RSI indicator and volume. It helps identify trend reversals under high-volume market conditions, which enhances the reliability of the signals.
Indicator Features:
RSI (Relative Strength Index): The RSI indicator is used to assess oversold (RSI < 40) or overbought (RSI > 60) conditions. These zones signal potential reversals when combined with other technical signals.
Candle Body Analysis:
The indicator compares the size of the current and previous candles to validate signals.
For a buy signal, the current candle must be bullish and have a body size proportional to that of the previous bearish candle.
Similarly, for a sell signal, the current candle must be bearish with a body size comparable to the previous bullish candle.
Wick Validation:
The indicator analyzes the wick length to reinforce or exclude signals.
For a buy signal, the lower wick of the bullish candle must be shorter than that of the previous bearish candle.
For a sell signal, the upper wick of the bearish candle must be shorter than that of the previous bullish candle and smaller than 30% of the candle's body.
High Volume:
Signals are only generated when the volume exceeds a certain threshold, ensuring that signals are issued in active market conditions.
The minimum volume should be adjusted based on the asset. For example, for gold, a minimum volume of 9000 is recommended.
Trading Strategy:
Buy Signals:
A bearish (red) candle is followed by a bullish (green) candle with a body size that is comparable to the previous candle (0.9 to 3 times the body size).
The lower wick of the bullish candle is shorter than that of the previous bearish candle, confirming the validity of the signal.
The RSI must be below 40, indicating an oversold condition.
The volume must exceed the defined threshold (e.g., > 9000 for gold) to confirm an active market.
Sell Signals:
A bullish (green) candle is followed by a bearish (red) candle with a comparable body size.
The upper wick of the bearish candle must be shorter than that of the previous bullish candle and must not exceed 30% of the body size.
The RSI must be above 60, indicating an overbought condition.
The volume must also exceed the minimum threshold for a valid signal.
Usage Guidelines:
Volume Adjustment: It is crucial to adjust the volume threshold depending on the asset you're trading. For example, for assets like gold, a minimum volume of 9000 is recommended to filter out weak signals. Each asset has a different volume dynamic, so test different thresholds on historical data to find the optimal setting.
Time Frame:
It is recommended to use this indicator on a 1-hour (1H) chart for the best signal relevance. This time frame provides a good balance between reactivity and filtering false signals.
Confluence:
Combine the signals from this indicator with other tools like support and resistance levels, moving averages, or chart patterns to increase your chances of success. Confluence of indicators improves the reliability of signals.
Risk Management:
Implement strict risk management. Use stop-losses based on volatility, such as ATR (Average True Range), or the wick size to determine exit points.
Backtesting:
Before using it live, conduct backtesting on various assets to fine-tune the parameters, especially the volume threshold, and to verify performance across different market conditions.
This indicator is an excellent tool for traders looking to identify trend reversals based on solid technical criteria such as RSI, candle structure, and volume. It is particularly effective on volatile assets with precise volume adjustment.
Multi-Moving Average Buy/Sell IndicatorThis Multi-Moving Average Buy/Sell Indicator is a powerful and customizable tool designed to help traders identify potential buy and sell signals based on the interaction between price and multiple moving averages. Whether you're a day trader, swing trader, or long-term investor, this indicator provides clear visual cues and alerts to help you make informed trading decisions.
Key Features
1. Multiple Moving Averages
The indicator calculates four key moving averages:
9-period MA
20-period MA
50-period MA
180-period MA
You can choose the type of moving average:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
2. Custom Timeframe
Select a custom timeframe from a user-friendly dropdown menu:
1 Minute
5 Minutes
15 Minutes
30 Minutes
1 Hour
4 Hours
Daily
Weekly
The indicator dynamically adjusts to the selected timeframe, making it suitable for all trading styles.
3. Buy/Sell Signals
Buy Signal: Triggered when the price crosses above any of the moving averages.
Sell Signal: Triggered when the price crosses below any of the moving averages.
Signals are displayed as labels on the chart:
Green "BUY" Label: Below the bar when a buy signal is triggered.
Red "SELL" Label: Above the bar when a sell signal is triggered.
4. Visualization
Toggle the visibility of all moving averages using the showAllMAs input.
Moving averages are plotted with distinct colors for easy identification:
9 MA: Blue
20 MA: Orange
50 MA: Purple
180 MA: Teal
5. Alerts
The indicator generates alerts for buy and sell signals, which can be used for notifications or automated trading.
How to Use
Add the Indicator:
Open TradingView and go to the Pine Script Editor.
Copy and paste the script into the editor.
Click Add to Chart.
Configure Inputs:
maType: Choose the type of moving average (SMA, EMA, WMA).
timeframe: Select a custom timeframe (e.g., "1 Minute", "Daily").
showSignals: Toggle to show or hide buy/sell signals.
showAllMAs: Toggle to show or hide all moving averages.
Interpret the Signals:
Look for green "BUY" labels below the bars for potential buy opportunities.
Look for red "SELL" labels above the bars for potential sell opportunities.
Set Alerts:
Use the built-in alert system to get notified when buy or sell signals are triggered.
Example Use Cases
Day Trading
Use a 1-minute or 5-minute timeframe with an EMA for quick signals.
Example Inputs:
maType = "EMA"
timeframe = "5 Minutes"
showAllMAs = true
Swing Trading
Use a daily timeframe with an SMA for longer-term signals.
Example Inputs:
maType = "SMA"
timeframe = "Daily"
showAllMAs = false
Why Use This Indicator?
Versatility: Suitable for all trading styles and timeframes.
Customization: Choose your preferred moving average type and timeframe.
Clear Signals: Easy-to-read buy/sell labels and moving averages.
Alerts: Never miss a trading opportunity with built-in alerts.
Limitations
False Signals:
The indicator may generate false signals in choppy or sideways markets. Always combine it with other tools (e.g., RSI, volume analysis) for better accuracy.
Timeframe Dependency:
The effectiveness of the signals depends on the selected timeframe. Shorter timeframes may produce more signals but with higher noise.
No Backtesting:
The script does not include backtesting functionality. Test the strategy manually on historical data.
Customization Options
Add More Moving Averages: Modify the script to include additional moving averages (e.g., 200 MA).
Change Signal Logic: Adjust the conditions for buy/sell signals (e.g., require confirmation from multiple moving averages).
Add Alerts for Specific MAs: Create separate alerts for signals based on specific moving averages (e.g., only 9 MA or 50 MA).
High-Probability IndicatorExplanation of the Code
Trend Filter (EMA):
A 50-period Exponential Moving Average (EMA) is used to determine the overall trend.
trendUp is true when the price is above the EMA.
trendDown is true when the price is below the EMA.
Momentum Filter (RSI):
A 14-period RSI is used to identify overbought and oversold conditions.
oversold is true when RSI ≤ 30.
overbought is true when RSI ≥ 70.
Volatility Filter (ATR):
A 14-period Average True Range (ATR) is used to measure volatility.
ATR is multiplied by a user-defined multiplier (default: 2.0) to set a volatility threshold.
Ensures trades are only taken during periods of sufficient volatility.
Entry Conditions:
Long Entry: Price is above the EMA (uptrend), RSI is oversold, and the candle range exceeds the ATR threshold.
Short Entry: Price is below the EMA (downtrend), RSI is overbought, and the candle range exceeds the ATR threshold.
Exit Conditions:
Take Profit: A fixed percentage above/below the entry price.
Stop Loss: A fixed percentage below/above the entry price.
Visualization:
The EMA is plotted on the chart.
Background colors highlight uptrends and downtrends.
Buy and sell signals are displayed as labels on the chart.
Alerts:
Alerts are triggered for buy and sell signals.
How to Use the Indicator
Trend Filter:
Only take trades in the direction of the trend (e.g., long in an uptrend, short in a downtrend).
Momentum Filter:
Look for oversold conditions in an uptrend for long entries.
Look for overbought conditions in a downtrend for short entries.
Volatility Filter:
Ensure the candle range exceeds the ATR threshold to avoid low-volatility trades.
Risk Management:
Use the built-in take profit and stop loss levels to manage risk.
Optimization Tips
Backtesting:
Test the indicator on multiple timeframes and assets to evaluate its performance.
Adjust the input parameters (e.g., EMA length, RSI length, ATR multiplier) to optimize for specific markets.
Combination with Other Strategies:
Add additional filters, such as volume analysis or support/resistance levels, to improve accuracy.
Risk Management:
Use proper position sizing and risk-reward ratios to maximize profitability.
Disclaimer
No indicator can guarantee an 85% win ratio due to the inherent unpredictability of financial markets. This script is provided for educational purposes only. Always conduct thorough backtesting and paper trading before using any strategy in live trading.
Let me know if you need further assistance or enhancements!
PDF MA For Loop [BackQuant]PDF MA For Loop
Introducing the PDF MA For Loop, an innovative trading indicator that combines Probability Density Function (PDF) smoothing with a dynamic for-loop scoring mechanism. This advanced tool provides traders with precise trend-following signals, helping to identify long and short opportunities with improved clarity and adaptability to market conditions.
If you would like to check out the stand alone PDF Moving Average:
Core Concept: Probability Density Function (PDF) Smoothing
The PDF smoothing method is a unique approach that applies adaptive weights to price data based on a Probability Density Function. This ensures that recent data points receive appropriate emphasis while maintaining a smooth transition across the data set. The result is a moving average that is not only smoother but also more responsive to market changes.
Key parameters in PDF smoothing:
Variance : Controls the spread of the PDF, where a higher value results in broader smoothing and a lower value makes the moving average more sensitive.
Mean : Centers the PDF around a specific value, influencing the weighting and responsiveness of the smoothing process.
By combining PDF smoothing with traditional moving averages (EMA or SMA), the indicator creates a hybrid signal that balances responsiveness and reliability.
For-Loop Scoring Mechanism
At the heart of this indicator is the for-loop scoring mechanism, which evaluates the smoothed PDF moving average over a defined range of historical data points. This process assigns a score to the current market condition based on whether the PDF moving average is greater than or less than previous values.
Long Signal: A long signal is generated when the score exceeds the Long Threshold (default set at 40), indicating upward momentum.
Short Signal: A short signal is triggered when the score crosses below the Short Threshold (default set at -10), suggesting potential downward momentum.
This dynamic scoring system ensures that the indicator remains adaptive, capturing trends and shifts in market sentiment effectively.
Customization Options
The PDF MA For Loop includes a variety of customizable settings to fit different trading styles and strategies:
Calculation Settings
Price Source : Select the input price for the calculation (default is the close price).
Smoothing Method : Choose between EMA or SMA for the additional smoothing layer, providing flexibility to adapt to market conditions.
Smoothing Period : Adjust the lookback period for the smoothing function, with shorter periods providing more sensitivity and longer periods offering greater stability.
Variance & Mean : Fine-tune the PDF function parameters to control the weighting of the smoothing process.
Signal Settings
Thresholds : Customize the upper and lower thresholds to define the sensitivity of the long and short signals.
For Loop Range : Set the range of historical data points analyzed by the for-loop, influencing the depth of the scoring mechanism.
UI Settings
Signal Line Width: Adjust the thickness of the plotted signal line for better visibility.
Candle Coloring: Enable or disable the coloring of candlesticks based on trend direction (green for long, red for short, gray for neutral).
Background Coloring: Add background shading to highlight long and short signals for an enhanced visual experience.
Alerts and Automation
The indicator includes built-in alert conditions to notify traders of important market events:
Long Signal Alert: Notifies when the score exceeds the upper threshold, indicating a bullish trend.
Short Signal Alert: Notifies when the score crosses below the lower threshold, signaling a bearish trend.
These alerts can be configured for real-time notifications, allowing traders to respond quickly to market changes without constant chart monitoring.
Trading Applications
The PDF MA For Loop is versatile and can be applied across various trading strategies and market conditions:
Trend Following: The PDF smoothing method combined with for-loop scoring makes this indicator particularly effective for identifying and following trends.
Reversal Trading: By observing the thresholds and score, traders can anticipate potential reversals when the trend shifts from long to short (or vice versa).
Risk Management: The dynamic thresholds and scoring provide clear signals, allowing traders to enter and exit trades with greater confidence and precision.
Final Thoughts
The PDF MA For Loopis merges advanced mathematical concepts with practical trading tools. By leveraging Probability Density Function smoothing and a dynamic for-loop scoring system, it provides traders with clear, actionable signals while adapting to market conditions.
Whether you’re looking for an edge in trend-following strategies or seeking precision in identifying reversals, this indicator offers the flexibility and power to enhance your trading decisions
As always, backtesting and integrating the PDF MA For Loop into a comprehensive trading strategy is recommended for optimal performance, as no single indicator should be used in isolation.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
[blackcat]L3 Strong Power Wave █ OVERVIEW
The script is an indicator named ' L3 Strong Power Wave' (SPW) designed to identify buy and sell signals based on the contraction and expansion of candlestick patterns. It calculates various indicators and plots them on a separate chart panel. The main purpose is to detect when candlestick patterns transition from contracting to expanding (buy signal) and from expanding to contracting (sell signal).
█ LOGICAL FRAMEWORK
The script is structured into several key sections:
Input Parameters and Initialization: The script uses indicator() to define the title, short title, and other properties.
Custom Functions: Several custom functions are defined for calculations, including calculate_weighted_moving_average, calculate_spw_variation, and calculate_strong_power_wave.
Calculations: The script performs complex calculations for the SPW indicators using multiple ta.alma and ta.sma functions.
Plotting: The indicators are plotted on the chart using plot().
Signal Detection: The script detects buy and sell signals based on changes in candlestick sizes.
Labeling: Buy and sell signals are indicated on the chart using label.new().
The flow of data and logic involves first calculating the SPW indicators, then plotting them, and finally detecting and labeling the buy and sell signals based on candlestick pattern changes.
█ CUSTOM FUNCTIONS
1 — calculate_weighted_moving_average(src, length, weight) :
• Purpose: Calculates a weighted moving average of the source data.
• Parameters: src (source data), length (period length), weight (weighting factor).
• Return Value: Weighted moving average value.
2 — calculate_spw_variation(base, multiplier) :
• Purpose: Computes a variation of the base value using a simple moving average and a multiplier.
• Parameters: base (base value), multiplier (multiplier factor).
• Return Value: Calculated variation value.
3 — calculate_strong_power_wave(src) :
• Purpose: Calculates multiple SPW indicators including various ta.alma and ta.sma values.
• Parameters: src (source data, typically close).
• Return Value: A tuple containing calculated SPW indicators.
█ KEY POINTS AND TECHNIQUES
• Weighted Moving Average: The script uses a custom function to calculate a weighted moving average, which can provide more emphasis on recent data points.
• Exponential Moving Averages (ALMA): The script uses ta.alma to smooth data, which is useful for identifying trends.
• Custom Indicators: The script defines and uses multiple custom indicators, demonstrating how to create and integrate complex calculations.
• Signal Detection: The script uses logical conditions to detect buy and sell signals based on candlestick pattern changes, showcasing practical application of technical analysis concepts.
• Labeling: The use of label.new() to mark buy and sell signals provides clear visual cues on the chart.
█ EXTENDED KNOWLEDGE AND APPLICATIONS
• Enhancements: The script could be enhanced by adding additional filters or parameters to refine signal accuracy.
• Backtesting: Implementing backtesting to evaluate the effectiveness of the buy and sell signals.
• Optimization: Optimizing the parameters of the moving averages and multipliers to better suit different market conditions.
• Alternative Indicators: Exploring other indicators that could complement or replace the SPW indicators.
• Related Concepts: Understanding the principles of candlestick pattern analysis and how they can be integrated into Pine Script.
FS Scorpion TailKey Features & Components:
1. Custom Date & Chart-Based Controls
The software allows users to define whether they want signals to start on a specific date (useSpecificDate) or base calculations on the visible chart’s range (useRelativeScreenSumLeft and useRelativeScreenSumRight).
Users can input the number of stocks to buy/sell per signal and decide whether to sell only for profit.
2. Technical Indicators Used
EMA (Exponential Moving Average): Users can define the length of the EMA and specify if buy/sell signals should occur when the EMA is rising or falling.
MACD (Moving Average Convergence Divergence): MACD crossovers, slopes of the MACD line, signal line, and histogram are used for generating buy/sell signals.
ATR (Average True Range): Signals are generated based on rising or falling ATR.
Aroon Indicator: Buy and sell signals are based on the behavior of the Aroon upper and lower lines.
RSI (Relative Strength Index): Tracks whether the RSI and its moving average are rising or falling to generate signals.
Bollinger Bands: Buy/sell signals depend on the basis, upper, and lower band behavior (rising or falling).
3. Signal Detection
The software creates arrays for each indicator to store conditions for buy/sell signals.
The allTrue() function checks whether all conditions for buy/sell signals are true, ensuring that only valid signals are plotted.
Signals are differentiated between buy-only, sell-only, and both buy and sell (dual signal).
4. Visual Indicators
Vertical Lines: When buy, sell, or dual signals are detected, vertical lines are drawn at the corresponding bar with configurable colors (green for buy, red for sell, silver for dual).
Buy/Sell Labels: Visual labels are plotted directly on the chart to denote buy or sell signals, allowing for clear interpretation of the strategy.
5. Cash Flow & Metrics Display
The software maintains an internal ledger of how many stocks are bought/sold, their prices, and whether a profit is being made.
A table is displayed at the bottom right of the chart, showing:
Initial investment
Current stocks owned
Last buy price
Market stake
Net profit
The table background turns green for profit and red for loss.
6. Dynamic Decision Making
Buy Condition: If a valid buy signal is generated, the software decrements the cash balance and adds stocks to the inventory.
Sell Condition: If the sell signal is valid (and meets the profit requirement), stocks are sold, and cash is incremented.
A fallback check ensures the sell logic prevents selling more stocks than are available and adjusts stock holding appropriately (e.g., sell half).
Customization and Usage
Indicator Adjustments: The user can choose which indicators to activate (e.g., EMA, MACD, RSI) via input controls. Each indicator has specific customizable parameters such as lengths, slopes, and conditions.
Signal Flexibility: The user can adjust conditions for buying and selling based on various technical indicators, which adds flexibility in implementing trading strategies. For example, users may require the RSI to be higher than its moving average or trigger sales only when MACD crosses under the signal line.
Profit Sensitivity: The software allows the option to sell only when a profit is assured by checking if the current price is higher than the last buy price.
Summary of Usage:
Indicator Selection: Enable or disable technical indicators like EMA, MACD, RSI, Aroon, ATR, and Bollinger Bands to fit your trading strategy.
Custom Date/Chart Settings: Choose whether to calculate based on specific time ranges or visible portions of the chart.
Dynamic Signal Plotting: Once buy or sell conditions are met, the software will visually plot signals on your chart, giving clear entry and exit points.
Investment Tracking: Real-time tracking of stock quantities, investments, and profit ensures a clear view of your trading performance.
Backtesting: Use this software for backtesting your strategy by analyzing how buy and sell signals would have performed historically based on the chosen indicators.
Conclusion
The FS Scorpion Tail software is a robust and flexible trading tool, allowing traders to develop custom strategies based on multiple well-known technical indicators. Its visual aid, coupled with real-time investment tracking, makes it valuable for systematic traders looking to automate or refine their trading approach.
Savitzky Golay Median Filtered RSI [BackQuant]Savitzky Golay Median Filtered RSI
Introducing BackQuant's Savitzky Golay Median Filtered RSI, a cutting-edge indicator that enhances the classic Relative Strength Index (RSI) by applying both a Savitzky-Golay filter and a median filter to provide smoother and more reliable signals. This advanced approach helps reduce noise and captures true momentum trends with greater precision. Let’s break down how the indicator works, the features it offers, and how it can improve your trading strategy.
Core Concept: Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100, with levels above 70 typically indicating overbought conditions and levels below 30 indicating oversold conditions. However, the standard RSI can sometimes generate noisy signals, especially in volatile markets, making it challenging to identify reliable entry and exit points.
To improve upon the traditional RSI, this indicator introduces two powerful filters: the Savitzky-Golay filter and a median filter.
Savitzky-Golay Filter: Smoothing with Precision
The Savitzky-Golay filter is a digital filtering technique used to smooth data while preserving important features, such as peaks and trends. Unlike simple moving averages that can distort important price data, the Savitzky-Golay filter uses polynomial regression to fit the data, providing a more accurate and less lagging result.
In this script, the Savitzky-Golay filter is applied to the RSI values to smooth out short-term fluctuations and provide a more reliable signal. By using a window size of 5 and a polynomial degree of 2, the filter effectively reduces noise without compromising the integrity of the underlying price movements.
Median Filter: Reducing Outliers
After applying the Savitzky-Golay filter, the median filter is applied to the smoothed RSI values. The median filter is particularly effective at removing short-lived outliers, further enhancing the accuracy of the RSI by reducing the impact of sudden and temporary price spikes or drops. This combination of filters creates an ultra-smooth RSI that is better suited for detecting true market trends.
Long and Short Signals
The Savitzky Golay Median Filtered RSI generates long and short signals based on user-defined threshold levels:
Long Signals: A long signal is triggered when the filtered RSI exceeds the Long Threshold (default set at 176). This indicates that momentum is shifting upward, and it may present a good buying opportunity.
Short Signals: A short signal is generated when the filtered RSI falls below the Short Threshold (default set at 162). This suggests that momentum is weakening, potentially signaling a selling opportunity or exit from a long position.
These threshold levels can be adjusted to suit different market conditions and timeframes, allowing traders to fine-tune the sensitivity of the indicator.
Customization and Visualization Options
The Savitzky Golay Median Filtered RSI comes with several customization options, enabling traders to tailor the indicator to their specific needs:
Calculation Source: Select the price source for the RSI calculation (default is OHLC4, but it can be changed to close, open, high, or low prices).
RSI Period: Adjust the lookback period for the RSI calculation (default is 14).
Median Filter Length: Control the length of the median filter applied to the smoothed RSI, affecting how much noise is removed from the signal.
Threshold Levels: Customize the long and short thresholds to define the sensitivity for generating buy and sell signals.
UI Settings: Choose whether to display the RSI and thresholds on the chart, color the bars according to trend direction, and adjust the line width and colors used for long and short signals.
Visual Feedback: Color-Coded Signals and Thresholds
To make the signals easier to interpret, the indicator offers visual feedback by coloring the price bars and the RSI plot according to the current market trend:
Green Bars indicate long signals when momentum is bullish.
Red Bars indicate short signals when momentum is bearish.
Gray Bars indicate neutral or undecided conditions when no clear signal is present.
In addition, the Long and Short Thresholds can be plotted directly on the chart to provide a clear reference for when signals are triggered, allowing traders to visually gauge the strength of the RSI relative to its thresholds.
Alerts for Automation
For traders who prefer automated notifications, the Savitzky Golay Median Filtered RSI includes built-in alert conditions for long and short signals. You can configure these alerts to notify you when a buy or sell condition is met, ensuring you never miss a trading opportunity.
Trading Applications
This indicator is versatile and can be used in a variety of trading strategies:
Trend Following: The combination of Savitzky-Golay and median filtering makes this RSI particularly useful for identifying strong trends without being misled by short-term noise. Traders can use the long and short signals to enter trades in the direction of the prevailing trend.
Reversal Trading: By adjusting the threshold levels, traders can use this indicator to spot potential reversals. When the RSI moves from overbought to oversold levels (or vice versa), it may signal a shift in market direction.
Swing Trading: The smoothed RSI provides a clear signal for short to medium-term price movements, making it an excellent tool for swing traders looking to capitalize on momentum shifts.
Risk Management: The filtered RSI can be used as part of a broader risk management strategy, helping traders avoid false signals and stay in trades only when the momentum is strong.
Final Thoughts
The Savitzky Golay Median Filtered RSI takes the classic RSI to the next level by applying advanced smoothing techniques that reduce noise and improve signal reliability. Whether you’re a trend follower, swing trader, or reversal trader, this indicator provides a more refined approach to momentum analysis, helping you make better-informed trading decisions.
As with all indicators, it is important to backtest thoroughly and incorporate sound risk management strategies when using the Savitzky Golay Median Filtered RSI in your trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
