Variable Moving Average [LazyBear]Variable Moving Average, often abbreviated as VMA, is an Exponential Moving Average developed by Tushar S. Chande. VMA automatically adjusts its smoothing constant on the basis of Market Volatility.
Use this like other Moving Averages. I have added the following options that can be enabled via options page:
- Trend Direction Indication: Green = Up trend, Blue = Potential congestion, Red = down trend.
- Color bars based on Trend
More info:
www.thewizardtrader.com
List of my other indicators:
- GDoc: docs.google.com
- Chart:
Buscar en scripts para "technical"
Price Trendlines + Break Signals█ OVERVIEW
The "Price Trendlines + Break Signals" indicator is a technical analysis tool that automatically draws trendlines based on price pivot points and detects breakout signals. Designed for traders seeking precise market signals, the indicator identifies key pivot points, draws trendlines (resistance and support), and generates breakout signals with background highlighting. It offers flexible settings and alerts for breakout signals.
█ CONCEPTS
The indicator was created to provide traders with an alternative source of signals based on trendlines. Breakouts and bounces from trendlines can signal a trend change or the end of a correction. Combining these signals with other technical analysis tools can form the basis for building diverse trading strategies.
█ FEATURES
-Pivot Point Calculation: The indicator identifies pivot points (pivot high and pivot low) based on the closing price, with configurable left and right bars for pivot detection. Setting a higher number of bars results in fewer but more significant trendlines, with a delay corresponding to the specified length. Lower values generate more trendlines, but they are less significant. Crossovers are signaled only after the trendline is drawn, so sometimes no signals appear on crossed trendlines—this indicates the price passed through the line before it was detected.
- Trendlines: Draws trendlines connecting price pivot points—upper lines for downtrends (resistance) and lower lines for uptrends (support). Lines can be extended by a specified number of bars (default: 50).
- Tolerance Margin: Trendlines are widened by a tolerance margin, calculated using the average candle body size over a specified period and its multiplier. Reducing the multiplier to zero leaves only the trendline without a margin. Breaking this zone is a condition for generating signals.
- Breakout Signals: Generates signals when the price breaks through a trendline (bullish for upper lines, bearish for lower lines), with background highlighting for signal confirmation.
Alerts: Built-in alerts for:
- Upper trendline breakout (bullish signal).
- Lower trendline breakout (bearish signal).
Customization: Allows adjustment of pivot parameters, trendline extension length, tolerance margin, line colors, fills, and signal background transparency.
█ HOW TO USE
Adding the Indicator: Add the indicator to your TradingView chart via the Pine Editor or Indicators menu.
Configuring Settings:
- Left Bars for Pivot: Number of bars back for detecting pivots (default: 10).
- Right Bars for Pivot: Number of bars forward to confirm pivots (default: 10).
- Extend past 2nd pivot: Number of bars to extend the trendline after the second pivot (default: 50, 0 = no extension).
- Average Body Periods: Period for calculating the average candle body size used for the tolerance margin (default: 100).
- Tolerance Multiplier: Multiplier for the tolerance margin based on the average candle body size (default: 1.0).
Colors and Style:
- Upper trendline (resistance): default red.
- Lower trendline (support): default green.
- Line fills: colors with transparency (default 70).
- Signal background: green for bullish signals, red for bearish signals (default transparency 85).
Interpreting Signals:
- Trendlines: Upper lines (red) indicate a downtrend, lower lines (green) indicate an uptrend. Signals appear after a trendline breakout with the tolerance margin. Each trendline generates only one breakout signal, though it may still act as resistance or support for the price.
- Breakout Signals: Green background indicates an upper trendline breakout (bullish), red background indicates a lower trendline breakout (bearish).
- Alerts: Set up alerts in TradingView for trendline breakout signals.
Combining with Other Tools: Use with support/resistance levels, Fibonacci levels, RSI, pivot points, or FVG (Fair Value Gap) for signal confirmation.
█ APPLICATIONS
The "Price Trendlines + Break Signals" indicator is designed to identify trends and potential reversal points, supporting both trend-following and contrarian strategies:
- Trend Confirmation: Trendlines indicate the direction of the price trend, and bounces from them may signal the end of a correction.
- Reversal Strategies: Breakout signals can be used as cues to enter positions in anticipation of a trend change or correction.
- Noise Filtering: The tolerance margin reduces false signals, enhancing reliability.
█ NOTES
- Trendline crossovers are signaled only after the trendline is drawn, so sometimes no signals appear on crossed trendlines—this indicates the price passed through the line before it was detected.
- Each trendline generates only one breakout signal, though it may still act as a level of support or resistance for the price.
- Setting a higher number of bars for pivots results in fewer but more significant trendlines, with a delay corresponding to the specified length. Lower values generate more trendlines, but they are less significant.
- Adjust settings (e.g., number of bars for pivots, tolerance multiplier) to suit your trading style and timeframe.
- Combine with other technical analysis tools, such as RSI, pivot points, or FVG, to enhance signal accuracy.
- For high-volatility markets, consider increasing the tolerance margin to reduce false signals.
MNQ TopStep 50K | Ultra Quality v3.0MNQ TopStep 50K | Ultra Quality v3.0 - Publish Summary
📊 Overview
A professional-grade trading indicator designed specifically for MNQ futures traders using TopStep funded accounts. Combines 7 technical confirmations with 5 advanced safety filters to deliver high-quality trade signals while managing drawdown risk.
🎯 Key Features
Core Signal System
7-Point Confirmation: VWAP, EMA crossovers, 15-min HTF trend, MACD, RSI, ADX, and Volume
Signal Grading: Each signal is rated A+ through D based on 7 quality factors
Quality Threshold: Adjustable minimum grade requirement (A+, A, B, C, D)
Advanced Safety Filters (Customizable)
Mean Reversion Filter - Prevents chasing extended moves beyond VWAP bands
ATR Spike Filter - Avoids trading during extreme volatility events
EMA Spacing Filter - Ensures proper trend separation (optional)
Momentum Filter - Requires consecutive directional bars (optional)
Multi-Timeframe Confirmation - Aligns with 15-min trend (optional)
TopStep Risk Management
Real-time drawdown tracking
Position sizing calculator based on remaining cushion
Daily loss limit monitoring
Consecutive loss protection
Max trades per day limiter
Visual Components
VWAP with 1σ, 2σ, 3σ bands
EMA 9/21 with cloud fill
15-min EMA 50 for HTF trend
Comprehensive metrics dashboard
Risk management panel
Filter status panel
Detailed trade labels with entry, stops, and targets
⚙️ Default Settings (Balanced for Regular Signals)
Technical Indicators
Fast EMA: 9 | Slow EMA: 21 | HTF EMA: 50 (15-min)
MACD: 10/22/9
RSI: 14 period | Thresholds: 52 (buy) / 48 (sell)
ADX: 14 period | Minimum: 20
ATR: 14 period | Stop: 2x | TP1: 2x | TP2: 3x
Volume: 1.2x average required
Session Settings
Default: 9:30 AM - 11:30 AM ET (adjustable)
Avoids first 15 minutes after market open
Customizable trading hours
Safety Filters (Default Configuration)
✅ Mean Reversion: Enabled (2.5σ max from VWAP)
✅ ATR Spike: Enabled (2.0x threshold)
❌ EMA Spacing: Disabled (can enable for quality)
❌ Momentum: Disabled (can enable for quality)
❌ MTF Confirmation: Disabled (can enable for quality)
Risk Controls
Minimum Signal Quality: C (adjustable to A+ for fewer/better signals)
Min Bars Between Signals: 10
Max Trades Per Day: 5
Stop After Consecutive Losses: 2
📈 Expected Performance
With Default Settings:
Signals per week: 10-15 trades
Estimated win rate: 55-60%
Risk-Reward: 1:2 (TP1) and 1:3 (TP2)
With Aggressive Settings (Min Quality = D, All Filters Off):
Signals per week: 20-25 trades
Estimated win rate: 50-55%
With Conservative Settings (Min Quality = A, All Filters On):
Signals per week: 3-5 trades
Estimated win rate: 65-70%
🚀 How to Use
Basic Setup:
Add indicator to MNQ 5-minute chart
Adjust TopStep account settings in inputs
Set your risk per trade percentage (default: 0.5%)
Configure trading session hours
Set minimum signal quality (Start with C for balanced results)
Signal Interpretation:
Green Triangle (BUY): Long signal - all confirmations aligned
Red Triangle (SELL): Short signal - all confirmations aligned
Label Details: Shows entry, stop loss, take profit levels, position size, and signal grade
Signal Grade: A+ = Elite (6-7 points) | A = Strong (5) | B = Good (4) | C = Fair (3)
Dashboard Monitoring:
Top Right: Technical metrics and market conditions
Top Left: Filter status (which filters are passing/blocking)
Bottom Right: TopStep risk metrics and position sizing
⚡ Customization Tips
For More Signals:
Lower "Minimum Signal Quality" to D
Decrease ADX threshold to 18-20
Lower RSI thresholds to 50/50
Reduce Volume multiplier to 1.1x
Disable additional filters
For Higher Quality (Fewer Signals):
Raise "Minimum Signal Quality" to A or A+
Increase ADX threshold to 25-30
Enable all 5 advanced filters
Tighten VWAP distance to 2.0σ
Increase momentum requirement to 3-4 bars
For TopStep Compliance:
Adjust "Max Total Drawdown" and "Daily Loss Limit" to match your account
Update "Already Used Drawdown" daily
Monitor the Risk Panel for cushion remaining
Use recommended contract sizing
🛡️ Risk Disclaimer
IMPORTANT: This indicator is for educational and informational purposes only.
Past performance does not guarantee future results
All trading involves substantial risk of loss
Use proper risk management and position sizing
Test thoroughly in paper trading before live use
The indicator does not guarantee profitable trades
Adjust settings based on your risk tolerance and trading style
Always comply with your broker's and TopStep's rules
MNQ TopStep 50K | Ultra Quality v3.0MNQ TopStep 50K | Ultra Quality v3.0 - Publish Summary📊 OverviewA professional-grade trading indicator designed specifically for MNQ futures traders using TopStep funded accounts. Combines 7 technical confirmations with 5 advanced safety filters to deliver high-quality trade signals while managing drawdown risk.🎯 Key FeaturesCore Signal System
7-Point Confirmation: VWAP, EMA crossovers, 15-min HTF trend, MACD, RSI, ADX, and Volume
Signal Grading: Each signal is rated A+ through D based on 7 quality factors
Quality Threshold: Adjustable minimum grade requirement (A+, A, B, C, D)
Advanced Safety Filters (Customizable)
Mean Reversion Filter - Prevents chasing extended moves beyond VWAP bands
ATR Spike Filter - Avoids trading during extreme volatility events
EMA Spacing Filter - Ensures proper trend separation (optional)
Momentum Filter - Requires consecutive directional bars (optional)
Multi-Timeframe Confirmation - Aligns with 15-min trend (optional)
TopStep Risk Management
Real-time drawdown tracking
Position sizing calculator based on remaining cushion
Daily loss limit monitoring
Consecutive loss protection
Max trades per day limiter
Visual Components
VWAP with 1σ, 2σ, 3σ bands
EMA 9/21 with cloud fill
15-min EMA 50 for HTF trend
Comprehensive metrics dashboard
Risk management panel
Filter status panel
Detailed trade labels with entry, stops, and targets
⚙️ Default Settings (Balanced for Regular Signals)Technical Indicators
Fast EMA: 9 | Slow EMA: 21 | HTF EMA: 50 (15-min)
MACD: 10/22/9
RSI: 14 period | Thresholds: 52 (buy) / 48 (sell)
ADX: 14 period | Minimum: 20
ATR: 14 period | Stop: 2x | TP1: 2x | TP2: 3x
Volume: 1.2x average required
Session Settings
Default: 9:30 AM - 11:30 AM ET (adjustable)
Avoids first 15 minutes after market open
Customizable trading hours
Safety Filters (Default Configuration)
✅ Mean Reversion: Enabled (2.5σ max from VWAP)
✅ ATR Spike: Enabled (2.0x threshold)
❌ EMA Spacing: Disabled (can enable for quality)
❌ Momentum: Disabled (can enable for quality)
❌ MTF Confirmation: Disabled (can enable for quality)
Risk Controls
Minimum Signal Quality: C (adjustable to A+ for fewer/better signals)
Min Bars Between Signals: 10
Max Trades Per Day: 5
Stop After Consecutive Losses: 2
📈 Expected PerformanceWith Default Settings:
Signals per week: 10-15 trades
Estimated win rate: 55-60%
Risk-Reward: 1:2 (TP1) and 1:3 (TP2)
With Aggressive Settings (Min Quality = D, All Filters Off):
Signals per week: 20-25 trades
Estimated win rate: 50-55%
With Conservative Settings (Min Quality = A, All Filters On):
Signals per week: 3-5 trades
Estimated win rate: 65-70%
🚀 How to UseBasic Setup:
Add indicator to MNQ 5-minute chart
Adjust TopStep account settings in inputs
Set your risk per trade percentage (default: 0.5%)
Configure trading session hours
Set minimum signal quality (Start with C for balanced results)
Signal Interpretation:
Green Triangle (BUY): Long signal - all confirmations aligned
Red Triangle (SELL): Short signal - all confirmations aligned
Label Details: Shows entry, stop loss, take profit levels, position size, and signal grade
Signal Grade: A+ = Elite (6-7 points) | A = Strong (5) | B = Good (4) | C = Fair (3)
Dashboard Monitoring:
Top Right: Technical metrics and market conditions
Top Left: Filter status (which filters are passing/blocking)
Bottom Right: TopStep risk metrics and position sizing
⚡ Customization TipsFor More Signals:
Lower "Minimum Signal Quality" to D
Decrease ADX threshold to 18-20
Lower RSI thresholds to 50/50
Reduce Volume multiplier to 1.1x
Disable additional filters
For Higher Quality (Fewer Signals):
Raise "Minimum Signal Quality" to A or A+
Increase ADX threshold to 25-30
Enable all 5 advanced filters
Tighten VWAP distance to 2.0σ
Increase momentum requirement to 3-4 bars
For TopStep Compliance:
Adjust "Max Total Drawdown" and "Daily Loss Limit" to match your account
Update "Already Used Drawdown" daily
Monitor the Risk Panel for cushion remaining
Use recommended contract sizing
🛡️ Risk DisclaimerIMPORTANT: This indicator is for educational and informational purposes only.
Past performance does not guarantee future results
All trading involves substantial risk of loss
Use proper risk management and position sizing
Test thoroughly in paper trading before live use
The indicator does not guarantee profitable trades
Adjust settings based on your risk tolerance and trading style
Always comply with your broker's and TopStep's rules
T3 [DCAUT]█ T3
📊 INDICATOR OVERVIEW
The T3 Moving Average is a smoothing indicator developed by Tim Tillson and published in Technical Analysis of Stocks & Commodities magazine (January 1998). The algorithm applies Generalized DEMA (Double Exponential Moving Average) recursively three times, creating a six-pole filtering effect that aims to balance noise reduction with responsiveness while minimizing lag relative to price changes.
📐 MATHEMATICAL FOUNDATION
Generalized DEMA (GD) Function:
The core building block is the Generalized DEMA function, which combines two exponential moving averages with weights controlled by the volume factor:
GD(input, v) = EMA(input) × (1 + v) - EMA(EMA(input)) × v
Where v is the volume factor parameter (default 0.7). This weighted combination reduces lag while maintaining smoothness by extrapolating beyond the first EMA using the double-smoothed EMA as a reference.
T3 Calculation Process:
T3 applies the GD function three times recursively:
T3 = GD(GD(GD(Price, v), v), v)
This triple nesting creates a six-pole smoothing effect (each GD applies two EMA operations, resulting in 2 × 3 = 6 total EMA calculations). The cascading refinement progressively filters noise while preserving trend information.
Step-by-Step Breakdown:
First GD application: GD1 = EMA(Price) × (1 + v) - EMA(EMA(Price)) × v - Creates initial smoothed series with lag reduction
Second GD application: GD2 = EMA(GD1) × (1 + v) - EMA(EMA(GD1)) × v - Further refines the smoothing while maintaining responsiveness
Third GD application: T3 = EMA(GD2) × (1 + v) - EMA(EMA(GD2)) × v - Final refinement produces the T3 output
Volume Factor Impact:
The volume factor (v) is the key parameter controlling the balance between smoothness and responsiveness. Tim Tillson recommended v = 0.7 as the optimal default value.
Lower volume factors (v closer to 0.0): Increase the extrapolation effect, making T3 more responsive to price changes but potentially more sensitive to noise.
Higher volume factors (v closer to 1.0): Reduce the extrapolation effect, producing smoother output with less sensitivity to short-term fluctuations but slightly more lag.
The recursive application of the volume factor through three GD stages creates a nonlinear filtering effect that achieves superior lag reduction compared to traditional moving averages of equivalent smoothness.
📊 SIGNAL INTERPRETATION
Trend Direction Signals:
Green Line (T3 Rising): Smoothed trend line is rising, may indicate uptrend, consider bullish opportunities when confirmed by other factors
Red Line (T3 Falling): Smoothed trend line is falling, may indicate downtrend, consider bearish opportunities when confirmed by other factors
Gray Line (T3 Flat): Smoothed trend line is flat, indicates unclear trend or consolidation phase
Price Crossover Signals:
Price Crosses Above T3: Price breaks above smoothed trend line, may be bullish signal, requires confirmation from other indicators
Price Crosses Below T3: Price breaks below smoothed trend line, may be bearish signal, requires confirmation from other indicators
Price Position Relative to T3: Price sustained above T3 may indicate uptrend, sustained below may indicate downtrend
Supporting Analysis Signals:
T3 Slope Angle: Steeper slopes indicate stronger trend momentum, flatter slopes suggest weakening trends
Price Deviation: Significant price separation from T3 may indicate overextension, watch for pullback or reversal
Dynamic Support/Resistance: T3 line can serve as dynamic support (in uptrends) or resistance (in downtrends) reference
🎯 STRATEGIC APPLICATIONS
Common Usage Patterns:
The T3 Moving Average can be incorporated into trading analysis in various ways. These represent common approaches used by market participants, though effectiveness varies by market conditions and requires individual testing:
Trend Filtering:
T3 can be used as a trend filter by observing the relationship between price and the T3 line. The color-coded slope (green for rising, red for falling, gray for sideways) provides visual feedback about the current trend direction of the smoothed series.
Price Crossover Analysis:
Some traders monitor crossovers between price and the T3 line as potential indication points. When price crosses the T3 line, it may suggest a change in the relationship between current price action and the smoothed trend.
Multi-Timeframe Observation:
T3 can be applied to multiple timeframes simultaneously. Observing alignment or divergence between different timeframe T3 indicators may provide context about trend consistency across time scales.
Dynamic Reference Level:
The T3 line can serve as a dynamic reference level for price action analysis. Price distance from T3, price reactions when approaching T3, and the behavior of price relative to the T3 line can all be incorporated into market analysis frameworks.
Application Considerations:
Any trading application should be thoroughly tested on historical data before implementation
T3 performance characteristics vary across different market conditions and asset types
The indicator provides smoothed trend information but does not predict future price movements
Combining T3 with other analytical tools and market context improves analysis quality
Risk management practices remain essential regardless of the analytical approach used
📋 DETAILED PARAMETER CONFIGURATION
Source Selection:
Close Price (Default): Standard choice for end-of-period trend analysis, reduces intrabar noise
HL2 (High+Low)/2: Provides balanced view of price action, considers full bar range
HLC3 or OHLC4: Incorporates more price information, may provide smoother results
Selection Impact: Different sources affect signal timing and smoothness characteristics
Length Configuration:
Shorter periods: More responsive, faster reaction, frequent signals, but higher false signal risk in choppy markets
Longer periods: Smoother output, fewer signals, better for long-term trends, but slower response
Default 14 periods is a common baseline, but optimal length varies by asset, timeframe, and market conditions
Parameter selection should be determined through backtesting rather than general recommendations
Volume Factor Configuration:
Lower values (closer to 0.0): Increase responsiveness but also noise sensitivity
Higher values (closer to 1.0): Increase smoothness but slightly more lag
Default 0.7 (Tim Tillson's recommendation) provides good balance for most applications
Optimal value depends on signal frequency versus reliability preference, test for specific use case
Parameter Optimization Approach:
There are no universal "best" parameter values - optimal settings depend on the specific asset, timeframe, market regime, and trading strategy
Start with default values (Length: 14, Volume Factor: 0.7) and adjust based on observed performance in your target market
Conduct systematic backtesting across different market conditions to evaluate parameter sensitivity
Consider that parameters optimized for historical data may not perform identically in future market conditions
Monitor performance and be prepared to adjust parameters as market characteristics evolve
📈 DESIGN FEATURES & MARKET ADAPTATION
Algorithm Design Features:
Simple Moving Average (SMA): Equal weighting across lookback period
Exponential Moving Average (EMA): Exponentially decreasing weights on historical prices
T3 Moving Average: Recursive Generalized DEMA with adjustable volume factor
Market Condition Adaptation:
Trending markets: Smoothed indicators generally align more closely with sustained directional movement
Ranging markets: All moving averages may generate more crossover signals during non-trending periods
Volatile conditions: Higher smoothing parameters reduce short-term sensitivity but increase lag
Indicator behavior relative to market conditions should be evaluated for specific applications
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. The T3 Moving Average has limitations and should not be used as the sole basis for trading decisions. Like all trend-following indicators, its performance varies with market conditions, and past signal characteristics do not guarantee future results.
Key Points:
T3 is a lagging indicator that responds to price changes rather than predicting future movements
Signals should be confirmed with other technical tools and market context
Parameters should be optimized for specific market and timeframe
Risk management and position sizing are essential
Market regime changes can affect indicator effectiveness
Test strategies thoroughly on historical data before live implementation
Consider broader market context and fundamental factors
Session Breakout Detector (SBD)Overview:
The Session Breakout Detector (SBD) is a TradingView indicator designed to identify and visualize breakouts from major trading sessions. It tracks a selected session (Tokyo, London, or New York) and detects price movements beyond the session's high or low, assisting traders in spotting potential breakout opportunities.
Key Features:
- Session Selection: Choose between Tokyo, London, or New York sessions.
- Breakout Detection Modes:
- Confirmed Bar: Detects breakouts when a candle closes beyond the session's range.
- Intrabar: Detects breakouts as soon as the price exceeds the session's high or low within a
candle.
- Visual Indicators:
- Displays session high, low, and range with a colored box for clear visualization.
- Marks breakouts with green (bullish) or red (bearish) triangles.
- Optional 50-Period SMA: Adds a 50-period Simple Moving Average to the chart for trend
analysis.
- Alerts: Configurable alerts for bullish and bearish breakouts.
Usage Instructions:
1. Select Session: Choose the desired trading session (Tokyo, London, or New York) from the
input settings.
2. Choose Breakout Detection Mode: Select between 'By confirmed bar' or 'By intrabars' based
on your trading preference.
3. Enable SMA (Optional): Toggle the 'Use SMA?' option to display the 50-period Simple Moving
Average.
4. Set Alerts: Configure alerts for breakout signals as per your trading strategy.
⚠️Note: This indicator is intended for informational purposes only and should not be construed as financial advice. Users are encouraged to conduct their own research and consider their individual risk tolerance before making trading decisions.
Kalman Filter [DCAUT]█ Kalman Filter
📊 ORIGINALITY & INNOVATION
The Kalman Filter represents an important adaptation of aerospace signal processing technology to financial market analysis. Originally developed by Rudolf E. Kalman in 1960 for navigation and guidance systems, this implementation brings the algorithm's noise reduction capabilities to price trend analysis.
This implementation addresses a common challenge in technical analysis: the trade-off between smoothness and responsiveness. Traditional moving averages must choose between being smooth (with increased lag) or responsive (with increased noise). The Kalman Filter improves upon this limitation through its recursive estimation approach, which continuously balances historical trend information with current price data based on configurable noise parameters.
The key advancement lies in the algorithm's adaptive weighting mechanism. Rather than applying fixed weights to historical data like conventional moving averages, the Kalman Filter dynamically adjusts its trust between the predicted trend and observed prices. This allows it to provide smoother signals during stable periods while maintaining responsiveness during genuine trend changes, helping to reduce whipsaws in ranging markets while not missing significant price movements.
📐 MATHEMATICAL FOUNDATION
The Kalman Filter operates through a two-phase recursive process:
Prediction Phase:
The algorithm first predicts the next state based on the previous estimate:
State Prediction: Estimates the next value based on current trend
Error Covariance Prediction: Calculates uncertainty in the prediction
Update Phase:
Then updates the prediction based on new price observations:
Kalman Gain Calculation: Determines the weight given to new measurements
State Update: Combines prediction with observation based on calculated gain
Error Covariance Update: Adjusts uncertainty estimate for next iteration
Core Parameters:
Process Noise (Q): Represents uncertainty in the trend model itself. Higher values indicate the trend can change more rapidly, making the filter more responsive to price changes.
Measurement Noise (R): Represents uncertainty in price observations. Higher values indicate less trust in individual price points, resulting in smoother output.
Kalman Gain Formula:
The Kalman Gain determines how much weight to give new observations versus predictions:
K = P(k|k-1) / (P(k|k-1) + R)
Where:
K is the Kalman Gain (0 to 1)
P(k|k-1) is the predicted error covariance
R is the measurement noise parameter
When K approaches 1, the filter trusts new measurements more (responsive).
When K approaches 0, the filter trusts its prediction more (smooth).
This dynamic adjustment mechanism allows the filter to adapt to changing market conditions automatically, providing an advantage over fixed-weight moving averages.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Visual Trend Indication:
The Kalman Filter line provides color-coded trend information:
Green Line: Indicates the filter value is rising, suggesting upward price momentum
Red Line: Indicates the filter value is falling, suggesting downward price momentum
Gray Line: Indicates sideways movement with no clear directional bias
Crossover Signals:
Price-filter crossovers generate trading signals:
Golden Cross: Price crosses above the Kalman Filter line, suggests potential bullish momentum development, may indicate a favorable environment for long positions, filter will naturally turn green as it adapts to price moving higher
Death Cross: Price crosses below the Kalman Filter line, suggests potential bearish momentum development, may indicate consideration for position reduction or shorts, filter will naturally turn red as it adapts to price moving lower
Trend Confirmation:
The filter serves as a dynamic trend baseline:
Price Consistently Above Filter: Confirms established uptrend
Price Consistently Below Filter: Confirms established downtrend
Frequent Crossovers: Suggests ranging or choppy market conditions
Signal Reliability Factors:
Signal quality varies based on market conditions:
Higher reliability in trending markets with sustained directional moves
Lower reliability in choppy, range-bound conditions with frequent reversals
Parameter adjustment can help adapt to different market volatility levels
🎯 STRATEGIC APPLICATIONS
Trend Following Strategy:
Use the Kalman Filter as a dynamic trend baseline:
Enter long positions when price crosses above the filter
Enter short positions when price crosses below the filter
Exit when price crosses back through the filter in the opposite direction
Monitor filter slope (color) for trend strength confirmation
Dynamic Support/Resistance:
The filter can act as a moving support or resistance level:
In uptrends: Filter often provides dynamic support for pullbacks
In downtrends: Filter often provides dynamic resistance for bounces
Price rejections from the filter can offer entry opportunities in trend direction
Filter breaches may signal potential trend reversals
Multi-Timeframe Analysis:
Combine Kalman Filters across different timeframes:
Higher timeframe filter identifies primary trend direction
Lower timeframe filter provides precise entry and exit timing
Trade only in direction of higher timeframe trend for better probability
Use lower timeframe crossovers for position entry/exit within major trend
Volatility-Adjusted Configuration:
Adapt parameters to match market conditions:
Low Volatility Markets (Forex majors, stable stocks): Use lower process noise for stability, use lower measurement noise for sensitivity
Medium Volatility Markets (Most equities): Process noise default (0.05) provides balanced performance, measurement noise default (1.0) for general-purpose filtering
High Volatility Markets (Cryptocurrencies, volatile stocks): Use higher process noise for responsiveness, use higher measurement noise for noise reduction
Risk Management Integration:
Use filter as a trailing stop-loss level in trending markets
Tighten stops when price moves significantly away from filter (overextension)
Wider stops in early trend formation when filter is just establishing direction
Consider position sizing based on distance between price and filter
📋 DETAILED PARAMETER CONFIGURATION
Source Selection:
Determines which price data feeds the algorithm:
OHLC4 (default): Uses average of open, high, low, close for balanced representation
Close: Focuses purely on closing prices for end-of-period analysis
HL2: Uses midpoint of high and low for range-based analysis
HLC3: Typical price, gives more weight to closing price
HLCC4: Weighted close price, emphasizes closing values
Process Noise (Q) - Adaptation Speed Control:
This parameter controls how quickly the filter adapts to changes:
Technical Meaning:
Represents uncertainty in the underlying trend model
Higher values allow the estimated trend to change more rapidly
Lower values assume the trend is more stable and slow-changing
Practical Impact:
Lower Values: Produces very smooth output with minimal noise, slower to respond to genuine trend changes, best for long-term trend identification, reduces false signals in choppy markets
Medium Values: Balanced responsiveness and smoothness, suitable for swing trading applications, default (0.05) works well for most markets
Higher Values: More responsive to price changes, may produce more false signals in ranging markets, better for short-term trading and day trading, captures trend changes earlier, adjust freely based on market characteristics
Measurement Noise (R) - Smoothing Control:
This parameter controls how much the filter trusts individual price observations:
Technical Meaning:
Represents uncertainty in price measurements
Higher values indicate less trust in individual price points
Lower values make each price observation more influential
Practical Impact:
Lower Values: More reactive to each price change, less smoothing with more noise in output, may produce choppy signals
Medium Values: Balanced smoothing and responsiveness, default (1.0) provides general-purpose filtering
Higher Values: Heavy smoothing for very noisy markets, reduces whipsaws significantly but increases lag in trend change detection, best for cryptocurrency and highly volatile assets, can use larger values for extreme smoothing
Parameter Interaction:
The ratio between Process Noise and Measurement Noise determines overall behavior:
High Q / Low R: Very responsive, minimal smoothing
Low Q / High R: Very smooth, maximum lag reduction
Balanced Q and R: Middle ground for most applications
Optimization Guidelines:
Start with default values (Q=0.05, R=1.0)
If too many false signals: Increase R or decrease Q
If missing trend changes: Decrease R or increase Q
Test across different market conditions before live use
Consider different settings for different timeframes
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Comparison with Traditional Moving Averages:
Versus Simple Moving Average (SMA):
The Kalman Filter typically responds faster to genuine trend changes
Produces smoother output than SMA of comparable length
Better noise reduction in ranging markets
More configurable for different market conditions
Versus Exponential Moving Average (EMA):
Similar responsiveness but with better noise filtering
Less prone to whipsaws in choppy conditions
More adaptable through dual parameter control (Q and R)
Can be tuned to match or exceed EMA responsiveness while maintaining smoothness
Versus Hull Moving Average (HMA):
Different noise reduction approach (recursive estimation vs. weighted calculation)
Kalman Filter offers more intuitive parameter adjustment
Both reduce lag effectively, but through different mechanisms
Kalman Filter may handle sudden volatility changes more gracefully
Response Characteristics:
Lag Time: Moderate and configurable through parameter adjustment
Noise Reduction: Good to excellent, particularly in volatile conditions
Trend Detection: Effective across multiple timeframes
False Signal Rate: Typically lower than simple moving averages in ranging markets
Computational Efficiency: Efficient recursive calculation suitable for real-time use
Optimal Use Cases:
Markets with mixed trending and ranging periods
Assets with moderate to high volatility requiring noise filtering
Multi-timeframe analysis requiring consistent methodology
Systematic trading strategies needing reliable trend identification
Situations requiring balance between responsiveness and smoothness
Known Limitations:
Parameters require adjustment for different market volatility levels
May still produce false signals during extreme choppy conditions
No single parameter set works optimally for all market conditions
Requires complementary indicators for comprehensive analysis
Historical performance characteristics may not persist in changing market conditions
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. The Kalman Filter's effectiveness varies with market conditions, tending to perform better in markets with clear trending phases interrupted by consolidation. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions, but rather as part of a comprehensive trading approach.
Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always test thoroughly with different parameter settings across various market conditions before using in live trading. No technical indicator can predict future price movements with certainty, and all trading involves risk of loss.
Global Risk Terminal – Multi-Asset Macro Sentiment IndicatorDescription:
The Global Risk Terminal is a sophisticated macro sentiment indicator that synthesizes signals from three key cross-asset relationships to produce a single, actionable risk appetite score. It is designed to help traders and investors identify whether global markets are in a risk-on (growth-seeking) or risk-off (defensive) regime. The indicator analyzes the behavior of commodities, equities, bonds, and currencies to generate a comprehensive view of market conditions.
Indicator Output:
The Global Risk Terminal produces a normalized risk score ranging from -1 to +1:
Positive values indicate risk-on conditions (growth assets favored)
Negative values indicate risk-off conditions (safe-haven assets favored)
Core Components:
Growth Pulse (Copper to Gold Ratio, HG/GC)
Purpose: Measures investor preference for industrial growth versus safe-haven assets.
Interpretation:
Rising ratio → Copper outperforming gold → Risk-on environment
Falling ratio → Gold outperforming copper → Risk-off environment
Flat ratio → Transitional market phase
Technical Implementation: Dual moving average slope method (fast MA default 20, slow MA default 40). Positive slope = +1, negative slope = -1, flat slope = 0
Equity Rotation (Russell 2000 to S&P 500 Ratio, RTY/ES)
Purpose: Tracks rotation between small-cap and large-cap equities, revealing market risk appetite.
Interpretation:
Rising ratio → Small-caps outperforming → Strong risk-on
Falling ratio → Large-caps outperforming → Defensive positioning
Technical Implementation: Dual moving average slope method (same as Growth Pulse)
Flow Gauge (10-Year Treasury to US Dollar Index, ZN/DXY)
Purpose: Captures liquidity conditions and cross-asset capital flows.
Interpretation:
Rising ratio → Treasury prices rising or USD weakening → Liquidity expansion, risk-on environment
Falling ratio → Treasury prices falling or USD strengthening → Liquidity contraction, risk-off environment
Technical Implementation: Dual moving average slope method
Composite Risk Score Calculation:
Analyze each component for trend using dual moving averages
Assign signal values: +1 (risk-on), -1 (risk-off), 0 (neutral)
Average the three signals:
Risk Score = (Growth Pulse + Equity Rotation + Flow Gauge) / 3
Optional smoothing with exponential moving average (default 3 periods) to reduce noise
Interpreting the Risk Score:
+0.66 to +1.0: Full risk-on – favor cyclical sectors, small-caps, growth strategies
+0.33 to +0.66: Moderate risk-on – mostly bullish environment, watch for fading momentum
-0.33 to +0.33: Neutral/transition – markets in flux, signals mixed, exercise caution
-0.66 to -0.33: Cautious risk-off – favor defensive sectors, reduce high-beta exposure
-1.0 to -0.66: Full risk-off – strong defensive positioning, prioritize safe-haven assets
How to Use the Global Risk Terminal to Frame Trades:
Aligning Trades with Market Regime
Risk-On (+0.33 and above): Look for buying opportunities in cyclical stocks, high-beta equities, commodities, and emerging markets. Use long entries for swing trades or intraday positions, following confirmed price action.
Risk-Off (-0.33 and below): Shift focus to defensive sectors, large-cap quality stocks, U.S. Treasuries, and safe-haven currencies. Prefer short entries or reduced exposure in risky assets.
Entry and Exit Framing
Use the risk score as a macro filter before executing trades:
Example: The risk score is +0.7 (strong risk-on). Prefer long positions in equities or commodities that are showing bullish confirmation on your regular chart.
Conversely, if the risk score is -0.7 (strong risk-off), avoid aggressive longs and consider short or defensive trades.
Watch for threshold crossings (+/-0.33, +/-0.66) as potential inflection points for adjusting position size, stop-loss levels, or sector rotation.
Confirming Trade Decisions
Combine the Global Risk Terminal with price action, volume, and trend indicators:
If equities rally but the risk score is declining, this may indicate a fragile rally driven by few leaders—trade cautiously.
If equities fall but the risk score is rising, consider counter-trend entries or buying dips.
Risk Management and Position Sizing
Strong alignment across components → increase position size and hold with wider stops
Mixed or neutral signals → reduce exposure, tighten stops, or avoid new trades
Defensive regimes → rotate into stable, low-volatility assets and increase cash buffer
Framing Trades Across Timeframes
Use the indicator as a strategic guide rather than a precise timing tool. Even without the MTF table:
Daily trend alignment → Guide swing trade bias
Shorter timeframe price action → Refine entry points and stop placement
Example: Daily chart shows +0.6 risk score → identify high-probability long setups using intraday technical patterns (breakouts, trend continuation).
Sector and Asset Rotation
Risk-On: Focus on cyclical sectors (financials, industrials, materials, energy), small-caps, high-beta instruments
Risk-Off: Focus on defensive sectors (utilities, consumer staples, healthcare), large-caps, safe-haven instruments
Alert Integration
Set alerts on the risk score to notify you when markets move from neutral to risk-on or risk-off regimes. Use these alerts to plan entries, exits, or portfolio adjustments in advance.
Customization Options:
Moving Average Length (5–100): Adjust sensitivity of trend detection
Score Smoothing (1–10): Reduce noise or see raw risk score
Visual Themes: Six preset themes (Cyber, Ocean, Sunset, Monochrome, Matrix, Custom)
Display Options: Show or hide component dashboards, main header, risk level lines, gradient fill, and component signals
Label Size: Tiny, Small, Normal, Large
Alert Conditions:
Risk score crosses above +0.66 → Strong risk-on
Risk score crosses below -0.66 → Strong risk-off
Risk score crosses zero → Neutral line
Risk score crosses above +0.33 → Moderate risk-on
Risk score crosses below -0.33 → Moderate risk-off
Data Sources:
HG1! – Copper Futures (COMEX)
GC1! – Gold Futures (COMEX)
RTY1! – Russell 2000 E-mini Futures (CME)
ES1! – S&P 500 E-mini Futures (CME)
ZN1! – 10-Year U.S. Treasury Note Futures (CBOT)
DXY – U.S. Dollar Index (ICE)
Notes and Limitations:
Works best during clear macro regimes and aligned trends
Use with price action, volume, and other technical tools
Not a standalone trading system; serves as a macro context filter
Equal weighting assumes all three components are equally important, but market conditions may vary
Past performance does not guarantee future results
Conclusion:
The Global Risk Terminal consolidates complex cross-asset signals into a simple, actionable score that informs market regime, portfolio positioning, sector rotation, and trading decisions. Its user-friendly layout and extensive customization options make it suitable for traders of all experience levels seeking macro-driven insights. By framing trades around risk score thresholds and combining macro context with tactical execution, traders can identify higher-probability opportunities and optimize position sizing, entries, and exits across a wide range of market conditions.
15-Min RSI Scalper [SwissAlgo]15-Min RSI Scalper
Tracks RSI Momentum Loss and Gain to Generate Signals
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WHAT THIS INDICATOR CALCULATES
This indicator attempts to identify RSI directional changes (RSI momentum) using a step-by-step "ladder" method. It reads RSI(14) from the next higher timeframe relative to your chart. On a 15-minute chart, it uses 1-hour RSI. On a 5-minute chart, it uses 15-minute RSI, and so on.
How the ladder logic works:
The indicator doesn't track RSI all the time. It only starts tracking when RSI crosses into potentially extreme territory (these are called "events" in the code):
For sell signals : when RSI crosses above a dynamic upper threshold (typically between 60-80, calculated as the 90th percentile of recent RSI)
For buy signals : when RSI crosses below a dynamic lower threshold (typically between 20-40, calculated as the 10th percentile of recent RSI)
Once tracking begins, RSI movement is divided into 2-point steps (boxes). The indicator counts how many boxes RSI climbs or falls.
A signal generates only when:
RSI reverses direction by at least 2 boxes (4 RSI points) from its extreme
RSI holds that reversal for 3 consecutive confirmed bars
Example: Dynamic threshold is at 68. RSI crosses above 68 → tracking starts. RSI climbs to 76 (4 boxes up). Then it drops back to 72 and stays below that level for 3 bars → sell signal prints. The buy signal works the same way in reverse.
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SIGNAL GENERATION METHODOLOGY
Sell Signal (Red Triangle)
RSI crosses above a dynamic start level (calculated as the 90th percentile of the last 1000 bars, constrained between 60-80)
Indicator tracks upward progression in 2-point boxes
RSI reverses and drops below a boundary 2 boxes below the highest box reached
RSI remains below that boundary for 3 confirmed bars
Red triangle plots above price
Reset condition: RSI returns below 50
Buy Signal (Green Triangle)
RSI crosses below a dynamic start level (10th percentile of last 1000 bars, constrained between 20-40)
Indicator tracks downward progression in 2-point boxes
RSI reverses and rises above a boundary 2 boxes above the lowest box reached
RSI remains above that boundary for 3 confirmed bars
Green triangle plots below price
Reset condition: RSI returns above 50
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TECHNICAL PARAMETERS
All parameters are hardcoded:
RSI Period: 14
Box Size: 2 RSI points
Reversal Threshold: 2 boxes (4 RSI points)
Confirmation Period: 3 bars
Reset Level: RSI 50
Sell Start Range: 60-80 (dynamic)
Buy Start Range: 20-40 (dynamic)
Lookback for Percentile: 1000 bars
Note: Since the code is open source, users can modify these hardcoded values directly in the script to adjust sensitivity. For example, increasing the confirmation period from 3 to 5 bars will produce fewer but more conservative signals. Decreasing the box size from 2 to 1 will make the indicator more responsive to smaller RSI movements.
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KEY FEATURES
Automatic Higher Timeframe RSI
When applied to a 15-minute chart, the indicator automatically reads 1-hour RSI data. This is the next standard timeframe above 15 minutes in the indicator's logic.
Dynamic Adaptive Start Levels
Sell signals use the 90th percentile of RSI over the last 1000 bars, constrained between 60-80. Buy signals use the 10th percentile, constrained between 20-40. These thresholds recalculate on each bar based on recent data.
Ladder Box System
RSI movements are tracked in 2-point boxes. The indicator requires a 2-box reversal followed by 3 consecutive bars maintaining that reversal before generating a signal.
Dual Signal Output
Red down-triangles plot above price when the sell signal conditions are met. Green up-triangles plot below the price when buy signal conditions are met.
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REPAINTING
This indicator does not repaint. All calculations use "barstate.isconfirmed" to ensure signals appear only on closed bars. The request.security() call uses lookahead=barmerge.lookahead_off to prevent forward-looking bias.
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INTENDED CHART TIMEFRAME
This indicator is designed for use on 15-minute charts. The visual reminder table at the top of the chart indicates this requirement.
On a 15-minute chart:
RSI data comes from the 1-hour timeframe
Signals reflect 1-hour momentum shifts
3-bar confirmation equals 45 minutes of price action
Using it on other timeframes will change the higher timeframe RSI source and may produce different behavior.
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WHAT THIS INDICATOR DOES NOT DO
Does not predict future price movements
Does not provide entry or exit advice
Does not guarantee profitable trades
Does not replace comprehensive technical analysis
Does not account for fundamental factors, news events, or market structure
Does not adapt to all market conditions equally
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EDUCATIONAL USE
This indicator demonstrates one approach to momentum reversal detection using:
Multi-timeframe analysis
Adaptive thresholds via percentile calculation
Step-wise momentum tracking
Multi-bar confirmation logic
It is designed as a technical study, not a trading system. Signals represent calculated conditions based on RSI behavior, not trade recommendations. Always do your own analysis before taking market positions.
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RISK DISCLOSURE
Trading involves substantial risk of loss. This indicator:
Is for educational and informational purposes only
Does not constitute financial, investment, or trading advice
Should not be used as the sole basis for trading decisions
Has not been tested across all market conditions
May produce false signals, late signals, or no signals in certain conditions
Past performance of any indicator does not predict future results. Users must conduct their own analysis and risk assessment before making trading decisions. Always use proper risk management, including stop losses and position sizing appropriate to your account and risk tolerance.
MIT LICENSE
This code is open source and provided as-is without warranties of any kind. You may use, modify, and distribute it freely under the MIT License.
Tunç ŞatıroğluTunç Şatıroğlu's Technical Analysis Suite
Description:
This comprehensive Pine Script indicator, inspired by the technical analysis teachings of Tunç Şatıroğlu, integrates six powerful TradingView indicators into a single, user-friendly suite for robust trend, momentum, and divergence analysis. Each component has been carefully selected and enhanced by beytun to improve functionality, performance, and visual clarity, aligning with Şatıroğlu's approach to technical analysis. The default configuration is meticulously set to match the exact settings of the individual indicators as used by Tunç Şatıroğlu in his training, ensuring authenticity and ease of use for followers of his methodology. Whether you're a beginner or an experienced trader, this suite provides a versatile toolkit for analyzing markets across multiple timeframes.
Included Indicators:
1. WaveTrend with Crosses (by LazyBear, modified): A momentum oscillator that identifies overbought/oversold conditions and trend reversals with clear buy/sell signals via crosses and bar color highlights.
2. Kaufman Adaptive Moving Average (KAMA) (by HPotter, modified): A dynamic moving average that adapts to market volatility, offering a smoother trend-following signal.
3. SuperTrend (by Alex Orekhov, modified): A trend-following indicator that plots dynamic support/resistance levels with buy/sell signals and optional wicks for enhanced accuracy.
4. Nadaraya-Watson Envelope (by LuxAlgo, modified): A non-linear envelope that highlights potential reversals with customizable repainting options for smoother outputs.
5. Divergence for Many Indicators v4 (by LonesomeTheBlue, modified): Detects regular and hidden divergences across multiple indicators (MACD, RSI, Stochastic, CCI, Momentum, OBV, VWMA, CMF, MFI, and more) for early reversal signals.
6. Ichimoku Cloud (TradingView built-in, modified): A multi-faceted indicator for trend direction, support/resistance, and momentum, with enhanced visuals for the Kumo Cloud.
Key Features:
- Authentic Default Settings : Pre-configured to mirror the exact parameters used by Tunç Şatıroğlu for each indicator, ensuring alignment with his proven technical analysis approach.
- Customizable Settings : Enable/disable individual indicators and fine-tune parameters to suit your trading style while retaining the option to revert to Şatıroğlu’s defaults.
- Enhanced User Experience : Modifications improve visual clarity, performance, and usability, with options like repainting smoothing for Nadaraya-Watson and adjustable Ichimoku projection periods.
- Multi-Timeframe Analysis : Combines trend-following, momentum, and divergence tools for a holistic view of market dynamics.
- Alert Conditions : Built-in alerts for SuperTrend direction changes, buy/sell signals, and divergence detections to keep you informed.
- Visual Clarity : Overlays (KAMA, SuperTrend, Nadaraya-Watson, Ichimoku) and pane-based indicators (WaveTrend, Divergences) are clearly distinguished, with customizable colors and styles.
Notes:
- The Nadaraya-Watson Envelope and Ichimoku Cloud may repaint in their default modes. Use the "Repainting Smoothing" option for Nadaraya-Watson or adjust Ichimoku settings to mitigate repainting if preferred.
- Published under the MIT License, with components licensed under GPL-3.0 (SuperTrend), CC BY-NC-SA 4.0 (Nadaraya-Watson), MPL 2.0 (Divergence), and TradingView's terms (Ichimoku Cloud).
Usage:
Add this indicator to your TradingView chart to leverage Tunç Şatıroğlu’s exact indicator configurations out of the box. Customize settings as needed to align with your strategy, and use the combined signals to identify trends, reversals, and divergences. Ideal for traders following Şatıroğlu’s methodologies or anyone seeking a powerful, all-in-one technical analysis tool.
Credits:
Original authors: LazyBear, HPotter, Alex Orekhov, LuxAlgo, LonesomeTheBlue, and TradingView.
Modifications and integration by beytun .
License:
Published under the MIT License, incorporating code under GPL-3.0, CC BY-NC-SA 4.0, MPL 2.0, and TradingView’s terms where applicable.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250.
Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65.
Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41.
Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press.
Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360.
Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449.
Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153.
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Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58.
Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462.
Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17.
Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105.
Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research.
Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
BTC TOPperThe BTC TOPper indicator is a sophisticated technical analysis tool designed to identify critical price levels where Bitcoin's weekly Simple Moving Average (SMA) intersects with historically significant All-Time High (ATH) levels. This indicator is particularly valuable for long-term trend analysis and identifying potential reversal zones in Bitcoin's price action.
Key Features:
🔹 Weekly SMA Analysis: Uses a 200-period Simple Moving Average on weekly timeframe to smooth out short-term volatility and focus on long-term trends
🔹 Persistent Historical ATH Tracking: Automatically detects and "freezes" ATH levels that have been held for more than one year, creating persistent reference levels
🔹 Multi-Level Cross Detection: Tracks up to 10 different frozen ATH levels simultaneously, providing comprehensive historical context
🔹 Visual Cross Alerts: Highlights entire weeks with red background when the weekly SMA crosses any frozen ATH level, making signals impossible to miss
🔹 Advanced Smoothing Options: Includes optional secondary moving averages (SMA, EMA, SMMA, WMA, VWMA) with Bollinger Bands for enhanced analysis
🔹 Customizable Parameters: Adjustable SMA length, offset, and smoothing settings to fit different trading strategies
How It Works:
ATH Detection: Continuously monitors for new all-time highs
Level Freezing: After an ATH is held for 1+ year, it becomes a "frozen" historical level
Cross Monitoring: Watches for intersections between the 200-week SMA and any frozen ATH level
Signal Generation: Highlights the entire week when a cross occurs, providing clear visual alerts
Trading Applications:
Long-term Trend Analysis: Identify when Bitcoin approaches historically significant resistance levels
Reversal Zone Detection: Spot potential areas where price might reverse based on historical context
Support/Resistance Confirmation: Use frozen ATH levels as dynamic support and resistance zones
Market Structure Analysis: Understand how current price relates to historical market cycles
Best Practices:
Use on weekly timeframe for optimal results
Combine with other technical indicators for confirmation
Pay attention to multiple frozen levels clustering in the same price range
Consider market context and fundamentals alongside technical signals
Settings:
Length: 200 (default) - SMA period
Source: Close price
Smoothing: Optional secondary MA with multiple types available
Bollinger Bands: Optional volatility bands around secondary MA
This indicator is ideal for Bitcoin traders and analysts who want to understand the relationship between current price action and historical market structure, particularly useful for identifying potential major reversal zones based on historical ATH levels.
CNagda-MomentumX - Institutional FlowMomentumX is designed to empower traders with a deeper understanding of market movements by focusing on Institutional Flow and advanced market structure analytics. The core goal is to identify and visualize where major market participants are operating, and to translate these complex footprints into clear, actionable trading signals — all in real time.
Real-time institutional activity mapping
Actionable entry and exit signals based on live market structure
Intuitive dashboard and dynamic chart visuals
Fully customizable modules for trend, liquidity, and order blocks
Core Logic Design
At the heart of MomentumX lies a robust algorithmic engine built to capture and surface institutional trading behavior. By leveraging advanced mathematical models, the indicator calculates institutional volume ratios and price momentum to pinpoint aggressive moves from large participants.
Institutional Volume & Price Momentum:
Utilizes custom volume indicators and price change analysis to detect strong buying or selling pressure, filtering out retail noise.
Liquidity Grab Detection & Activity Zones:
The script identifies liquidity grabs by monitoring abrupt price sweeps at major support/resistance levels—often where institutions trigger stop hunts or reversals. All critical activity zones are automatically color-coded on the chart for instant recognition.
Dashboard Visualization:
A fully dynamic dashboard table overlays live scores for accumulation, distribution, strength, and weakness—giving traders a real-time scan of market health.
Trendline & Order Block Architecture:
The logic auto-detects pivot highs/lows to draw smart trendlines, while the order block system highlights key reversal areas and breaker zones—making market structure clear and actionable.
MomentumX is packed with high-performance modules, each engineered to simplify complex market behavior and enhance decision-making for traders:
Institutional Flow Signals:
Instantly identifies spots where institutional players drive momentum, using unique volume and price activity analytics.
Bullish/Bearish Liquidity Grab Detection:
Marks abrupt price moves that signal stop hunts or reversals, letting traders anticipate snap-backs or trend shifts.
Trendline Auto-Detection:
Smartly draws trendlines based on significant swing highs and lows, automatically adjusting as price evolves.
Order Block System (Rejection/Breaker):
Spots and highlights key reversal zones with order block rectangles, confirming rejections or breakouts at strategic levels.
Dashboard and Bar Coloring:
A clean dashboard overlay presents live market scores, while dynamic bar coloring makes trend, strength, and high-activity periods instantly visible.
User Input Toggles for Each Module:
Every major feature is fully customizable—enable or disable modules to match individual trading setups or preferences.
Scripting/Development
MomentumX’s scripting process is modular, enabling clarity, scalability, and fast optimization throughout development:
Initialization & Inputs:
Start by defining all user input options, module toggles, color settings, and calculation parameters—ensuring maximum flexibility early on.
Core Calculation Functions:
Script advanced institutional volume and price momentum algorithms. Build out swing length logic, market state filters, and activity scoring methods.
Detection Engines:
Develop and integrate engines for liquidity grabs, automated trendline detection, and order block identification—each with dedicated functions for speed and precision.
Visual Overlays & Plotting:
Implement powerful plotting logic for colored bars, score dashboards, trendlines, reversal zones, and liquidity markers—making every data point clear and actionable on the chart.
Testing Handlers:
Add diagnostic panels and debug outputs to refine calculations and assure accuracy in every market environment.
Sample Trade Setups (Usage)
Cnagda MomentumX delivers clarity for multiple trading styles by providing timely, actionable setups grounded in institutional behavior and market structure. Here’s how traders can leverage the indicator for confident decision-making:
Liquidity Grab Reversal
Enter trades around detected liquidity grabs when price sweeps major support/resistance and the dashboard signals a momentum shift.
Example: Wait for a bullish/Bearish grab near market lows/high, with institutional flow turning positive/negative—enter long/short for potential mean reversion.
Order Block Breakout
Trade breakouts when price cleanly rejects or flips key order block zones highlighted on the chart.
Example: Short at a marked breaker block after a rejection signal, confirmed by a downward institutional activity spike.
Trendline Continuation
Ride established market moves by entering on trendline confirmations plotted by the auto-detect system.
Example: Go long after a trendline retest, confirmed by a green bar color and dashboard strength score.
Dashboard Confirmation
Combine dashboard metrics (strength, accumulation, distribution) with bar color overlays for multi-factor entries.
Example: Enter trades only when all market signals align in real time for maximum probability.
For Short Entry check -- Weakness : For Long Entry Check - Strength With Other Indications
MomentumX is not just another indicator – it’s your edge for reading the market like an insider. By transparently mapping institutional flow, uncovering hidden liquidity zones, and color-coding every major structure shift, MomentumX transforms complexity into actionable clarity. Whether you’re scalping, swing trading, or investing, you’ll gain a decisive, real-time advantage on every chart.
Embrace smarter decisions, adapt to changing market conditions instantly, and join a new generation of technically empowered traders.
Customize, observe, and let the market reveal opportunities in a way you’ve never experienced before.
Happy Trading
MACD Forecast [Titans_Invest]MACD Forecast — The Future of MACD in Trading
The MACD has always been one of the most powerful tools in technical analysis.
But what if you could see where it’s going, instead of just reacting to what has already happened?
Introducing MACD Forecast — the natural evolution of the MACD Full , now taken to the next level. It’s the world’s first MACD designed not only to analyze the present but also to predict the future behavior of momentum.
By combining the classic MACD structure with projections powered by Linear Regression, this indicator gives traders an anticipatory, predictive view, redefining what’s possible in technical analysis.
Forget lagging indicators.
This is the smartest, most advanced, and most accurate MACD ever created.
🍟 WHY MACD FORECAST IS REVOLUTIONARY
Unlike the traditional MACD, which only reflects current and past price dynamics, the MACD Forecast uses regression-based projection models to anticipate where the MACD line, signal line, and histogram are heading.
This means traders can:
• See MACD crossovers before they happen.
• Spot trend reversals earlier than most.
• Gain an unprecedented timing advantage in both discretionary and automated trading.
In other words: this indicator lets you trade ahead of time.
🔮 FORECAST ENGINE — POWERED BY LINEAR REGRESSION
At its core, the MACD Forecast integrates Linear Regression (ta.linreg) to project the MACD’s future behavior with exceptional accuracy.
Projection Modes:
• Flat Projection: Assumes trend continuity at the current level.
• LinReg Projection: Applies linear regression across N periods to mathematically forecast momentum shifts.
This dual system offers both a conservative and adaptive view of market direction.
📐 ACCURACY WITH FULL CUSTOMIZATION
Just like the MACD Full, this new version comes with 20 customizable buy-entry conditions and 20 sell-entry conditions — now enhanced with forecast-based rules that anticipate crossovers and trend reversals.
You’re not just reacting — you’re strategizing ahead of time.
⯁ HOW TO USE MACD FORECAST❓
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
🤖 BUILT FOR AUTOMATION AND BOTS 🤖
Whether for manual trading, quantitative strategies, or advanced algorithms, the MACD Forecast was designed to integrate seamlessly with automated systems.
With predictive logic at its core, your strategies can finally react to what’s coming, not just what already happened.
🥇 WHY THIS INDICATOR IS UNIQUE 🥇
• World’s first MACD with Linear Regression Forecasting
• Predictive Crossovers (before they appear on the chart)
• Maximum flexibility with Long & Short combinations — 20+ fully configurable conditions for tailor-made strategies
• Fully automatable for quantitative systems and advanced bots
This isn’t just an update.
It’s the final evolution of the MACD.
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 MACD > Signal Smoothing
🔹 MACD < Signal Smoothing
🔹 Histogram > 0
🔹 Histogram < 0
🔹 Histogram Positive
🔹 Histogram Negative
🔹 MACD > 0
🔹 MACD < 0
🔹 Signal > 0
🔹 Signal < 0
🔹 MACD > Histogram
🔹 MACD < Histogram
🔹 Signal > Histogram
🔹 Signal < Histogram
🔹 MACD (Crossover) Signal
🔹 MACD (Crossunder) Signal
🔹 MACD (Crossover) 0
🔹 MACD (Crossunder) 0
🔹 Signal (Crossover) 0
🔹 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
______________________________________________________
______________________________________________________
🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 MACD > Signal Smoothing
🔸 MACD < Signal Smoothing
🔸 Histogram > 0
🔸 Histogram < 0
🔸 Histogram Positive
🔸 Histogram Negative
🔸 MACD > 0
🔸 MACD < 0
🔸 Signal > 0
🔸 Signal < 0
🔸 MACD > Histogram
🔸 MACD < Histogram
🔸 Signal > Histogram
🔸 Signal < Histogram
🔸 MACD (Crossover) Signal
🔸 MACD (Crossunder) Signal
🔸 MACD (Crossover) 0
🔸 MACD (Crossunder) 0
🔸 Signal (Crossover) 0
🔸 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
______________________________________________________
______________________________________________________
🔮 Linear Regression Function 🔮
______________________________________________________
• Our indicator includes MACD forecasts powered by linear regression.
Forecast Types:
• Flat: Assumes prices will stay the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset : Offset.
• return: Linear regression curve.
______________________________________________________
______________________________________________________
⯁ UNIQUE FEATURES
______________________________________________________
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : MACD Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
🎗️ In memory of João Guilherme — your light will live on forever.
SCTI - D14SCTI - D14 Comprehensive Technical Analysis Suite
English Description
SCTI D14 is an advanced multi-component technical analysis indicator designed for professional traders and analysts. This comprehensive suite combines multiple analytical tools into a single, powerful indicator that provides deep market insights across various timeframes and methodologies.
Core Components:
1. EMA System (Exponential Moving Averages)
13 customizable EMA lines with periods ranging from 8 to 2584
Fibonacci-based periods (8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584)
Color-coded visualization for easy trend identification
Individual toggle controls for each EMA line
2. TFMA (Multi-Timeframe Moving Averages)
Cross-timeframe analysis with 3 independent EMA calculations
Real-time labels showing trend direction and price relationships
Customizable timeframes for each moving average
Percentage deviation display from current price
3. PMA (Precision Moving Average Cloud)
7-layer moving average system with customizable periods
Fill areas between moving averages for trend visualization
Support and resistance zone identification
Dynamic color-coded trend clouds
4. VWAP (Volume Weighted Average Price)
Multiple anchor points (Session, Week, Month, Quarter, Year, Earnings, Dividends, Splits)
Standard deviation bands for volatility analysis
Automatic session detection and anchoring
Statistical price level identification
5. Advanced Divergence Detector
12 technical indicators for divergence analysis (MACD, RSI, Stochastic, CCI, Williams %R, Bias, Momentum, OBV, VW-MACD, CMF, MFI, External)
Regular and hidden divergences detection
Bullish and bearish signals with visual confirmation
Customizable sensitivity and filtering options
Real-time alerts for divergence formations
6. Volume Profile & Node Analysis
Comprehensive volume distribution analysis
Point of Control (POC) identification
Value Area High/Low (VAH/VAL) calculations
Volume peaks and troughs detection
Support and resistance levels based on volume
7. Smart Money Concepts
Market structure analysis with Break of Structure (BOS) and Change of Character (CHoCH)
Internal and swing structure detection
Equal highs and lows identification
Fair Value Gaps (FVG) detection and visualization
Liquidity zones and institutional flow analysis
8. Trading Sessions
9 major trading sessions (Asia, Sydney, Tokyo, Shanghai, Hong Kong, Europe, London, New York, NYSE)
Real-time session status and countdown timers
Session volume and performance tracking
Customizable session boxes and labels
Statistical session analysis table
Key Features:
Modular Design: Enable/disable any component independently
Real-time Analysis: Live updates with market data
Multi-timeframe Support: Works across all chart timeframes
Customizable Alerts: Set alerts for any detected pattern or signal
Professional Visualization: Clean, organized display with customizable colors
Performance Optimized: Efficient code for smooth chart performance
Use Cases:
Trend Analysis: Identify market direction using multiple EMA systems
Entry/Exit Points: Use divergences and structure breaks for timing
Risk Management: Utilize volume profiles and session analysis for better positioning
Multi-timeframe Analysis: Confirm signals across different timeframes
Institutional Analysis: Track smart money flows and market structure
Perfect For:
Day traders seeking comprehensive market analysis
Swing traders needing multi-timeframe confirmation
Professional analysts requiring detailed market structure insights
Algorithmic traders looking for systematic signal generation
---
中文描述
SCTI - D14是一个先进的多组件技术分析指标，专为专业交易者和分析师设计。这个综合套件将多种分析工具整合到一个强大的指标中，在各种时间框架和方法论中提供深度市场洞察。
核心组件：
1. EMA系统（指数移动平均线）
13条可定制EMA线，周期从8到2584
基于斐波那契的周期（8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584）
颜色编码可视化，便于趋势识别
每条EMA线的独立切换控制
2. TFMA（多时间框架移动平均线）
跨时间框架分析，包含3个独立的EMA计算
实时标签显示趋势方向和价格关系
每个移动平均线的可定制时间框架
显示与当前价格的百分比偏差
3. PMA（精密移动平均云）
7层移动平均系统，周期可定制
移动平均线间填充区域用于趋势可视化
支撑阻力区域识别
动态颜色编码趋势云
4. VWAP（成交量加权平均价格）
多个锚点（交易时段、周、月、季、年、财报、分红、拆股）
标准差带用于波动性分析
自动时段检测和锚定
统计价格水平识别
5. 高级背离检测器
12个技术指标用于背离分析（MACD、RSI、随机指标、CCI、威廉姆斯%R、Bias、动量、OBV、VW-MACD、CMF、MFI、外部指标）
常规和隐藏背离检测
看涨看跌信号配视觉确认
可定制敏感度和过滤选项
背离形成的实时警报
6. 成交量分布与节点分析
全面的成交量分布分析
控制点（POC）识别
价值区域高/低点（VAH/VAL）计算
成交量峰值和低谷检测
基于成交量的支撑阻力水平
7. 聪明钱概念
市场结构分析，包括结构突破（BOS）和结构转变（CHoCH）
内部和摆动结构检测
等高等低识别
公允价值缺口（FVG）检测和可视化
流动性区域和机构资金流分析
8. 交易时区
9个主要交易时段（亚洲、悉尼、东京、上海、香港、欧洲、伦敦、纽约、纽交所）
实时时段状态和倒计时器
时段成交量和表现跟踪
可定制时段框和标签
统计时段分析表格
主要特性：
模块化设计：可独立启用/禁用任何组件
实时分析：随市场数据实时更新
多时间框架支持：适用于所有图表时间框架
可定制警报：为任何检测到的模式或信号设置警报
专业可视化：清洁、有序的显示界面，颜色可定制
性能优化：高效代码确保图表流畅运行
使用场景：
趋势分析：使用多重EMA系统识别市场方向
入场/出场点：利用背离和结构突破进行时机选择
风险管理：利用成交量分布和时段分析进行更好定位
多时间框架分析：在不同时间框架间确认信号
机构分析：跟踪聪明钱流向和市场结构
适用于：
寻求全面市场分析的日内交易者
需要多时间框架确认的摆动交易者
需要详细市场结构洞察的专业分析师
寻求系统化信号生成的算法交易者
Inversion Fair Value Gap Signals [AlgoAlpha]🟠 OVERVIEW
This script is a custom signal tool called Inversion Fair Value Gap Signals (IFVG) , designed to detect, track, and visualize fair value gaps (FVGs) and their inversions directly on price charts. It identifies bullish and bearish imbalances, monitors when these zones are mitigated or rejected, and extends them until resolution or expiration. What makes this script original is the inclusion of inversion logic—when a gap is filled, the area flips into an opposite "inversion fair value gap," creating potential reversal or continuation zones that give traders additional context beyond classic FVG analysis.
🟠 CONCEPTS
The script builds on the Smart Money Concepts (SMC) principle of fair value gaps, where inefficiencies form when price moves too quickly in one direction. Detection requires a three-bar sequence: a strong up or down move that leaves untraded price between bar highs and lows. To refine reliability, the script adds an ATR-based size filter and prevents overlap between zones. Once created, gaps are tracked in arrays until mitigation (price closing back into the gap), expiration, or transformation into an inversion zone. Inversions act as polarity flips, where bullish gaps become bearish resistance and bearish gaps become bullish support. Lower-timeframe volume data is also displayed inside zones to highlight whether buying or selling pressure dominated during gap creation.
🟠 FEATURES
Automatic detection of bullish and bearish FVGs with ATR-based thresholding.
Inversion logic: mitigated gaps flip into opposite-colored IFVG zones.
Volume text overlay inside each zone showing up vs down volume.
Visual markers (△/▽ for FVG, ▲/▼ for IFVG) when price exits a zone without mitigation.
🟠 USAGE
Apply the indicator to any chart and enable/disable bullish or bearish FVG detection depending on your focus. Use the colored gap zones as areas of interest: bullish gaps suggest possible continuation to the upside until mitigated, while bearish gaps suggest continuation down. When a gap flips into an inversion zone, treat it as potential support/resistance—bullish IFVGs below price may act as demand, while bearish IFVGs above price may act as supply. Watch the embedded up/down volume data to gauge the strength of participants during gap formation. Use the △/▽ and ▲/▼ markers to spot when price rejects gaps or inversions without filling them, which can indicate strong trending momentum. For practical use, combine alerts with your trade plan to track when new gaps form, when old ones are resolved, or when key zones flip into inversions, helping you align entries, targets, or reversals with institutional order flow logic.
TA█ TA Library
📊 OVERVIEW
TA is a Pine Script technical analysis library. This library provides 25+ moving averages and smoothing filters , from classic SMA/EMA to Kalman Filters and adaptive algorithms, implemented based on academic research.
🎯 Core Features
Academic Based - Algorithms follow original papers and formulas
Performance Optimized - Pre-calculated constants for faster response
Unified Interface - Consistent function design
Research Based - Integrates technical analysis research
🎯 CONCEPTS
Library Design Philosophy
This technical analysis library focuses on providing:
Academic Foundation
Algorithms based on published research papers and academic standards
Implementations that follow original mathematical formulations
Clear documentation with research references
Developer Experience
Unified interface design for consistent usage patterns
Pre-calculated constants for optimal performance
Comprehensive function collection to reduce development time
Single import statement for immediate access to all functions
Each indicator encapsulated as a simple function call - one line of code simplifies complexity
Technical Excellence
25+ carefully implemented moving averages and filters
Support for advanced algorithms like Kalman Filter and MAMA/FAMA
Optimized code structure for maintainability and reliability
Regular updates incorporating latest research developments
🚀 USING THIS LIBRARY
Import Library
//@version=6
import DCAUT/TA/1 as dta
indicator("Advanced Technical Analysis", overlay=true)
Basic Usage Example
// Classic moving average combination
ema20 = ta.ema(close, 20)
kama20 = dta.kama(close, 20)
plot(ema20, "EMA20", color.red, 2)
plot(kama20, "KAMA20", color.green, 2)
Advanced Trading System
// Adaptive moving average system
kama = dta.kama(close, 20, 2, 30)
= dta.mamaFama(close, 0.5, 0.05)
// Trend confirmation and entry signals
bullTrend = kama > kama and mamaValue > famaValue
bearTrend = kama < kama and mamaValue < famaValue
longSignal = ta.crossover(close, kama) and bullTrend
shortSignal = ta.crossunder(close, kama) and bearTrend
plot(kama, "KAMA", color.blue, 3)
plot(mamaValue, "MAMA", color.orange, 2)
plot(famaValue, "FAMA", color.purple, 2)
plotshape(longSignal, "Buy", shape.triangleup, location.belowbar, color.green)
plotshape(shortSignal, "Sell", shape.triangledown, location.abovebar, color.red)
📋 FUNCTIONS REFERENCE
ewma(source, alpha)
Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter.
Parameters:
source (series float) : Series of values to process.
alpha (series float) : The smoothing parameter of the filter.
Returns: (float) The exponentially weighted moving average value.
dema(source, length)
Calculates the Double Exponential Moving Average (DEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Double Exponential Moving Average value.
tema(source, length)
Calculates the Triple Exponential Moving Average (TEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triple Exponential Moving Average value.
zlema(source, length)
Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Zero-Lag Exponential Moving Average value.
tma(source, length)
Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triangular Moving Average value.
frama(source, length)
Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag. Developed by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Fractal Adaptive Moving Average value.
kama(source, length, fastLength, slowLength)
Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio. Developed by Perry J. Kaufman.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the efficiency calculation.
fastLength (simple int) : Fast EMA length for smoothing calculation. Optional. Default is 2.
slowLength (simple int) : Slow EMA length for smoothing calculation. Optional. Default is 30.
Returns: (float) The calculated Kaufman's Adaptive Moving Average value.
t3(source, length, volumeFactor)
Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics. Developed by Tim Tillson.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
volumeFactor (simple float) : Volume factor affecting responsiveness. Optional. Default is 0.7.
Returns: (float) The calculated Tilson Moving Average value.
ultimateSmoother(source, length)
Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness. Based on digital signal processing principles by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the smoothing calculation.
Returns: (float) The calculated Ultimate Smoother value.
kalmanFilter(source, processNoise, measurementNoise)
Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations. Based on the Kalman Filter algorithm developed by Rudolf Kalman (1960).
Parameters:
source (series float) : Series of values to process.
processNoise (simple float) : Process noise variance (Q). Controls adaptation speed. Optional. Default is 0.05.
measurementNoise (simple float) : Measurement noise variance (R). Controls smoothing. Optional. Default is 1.0.
Returns: (float) The calculated Kalman Filter value.
mcginleyDynamic(source, length)
Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes. Developed by John R. McGinley Jr.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the dynamic calculation.
Returns: (float) The calculated McGinley Dynamic value.
mama(source, fastLimit, slowLimit)
Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Mesa Adaptive Moving Average value.
fama(source, fastLimit, slowLimit)
Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Following Adaptive Moving Average value.
mamaFama(source, fastLimit, slowLimit)
Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA).
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: ( ) Tuple containing values.
laguerreFilter(source, length, gamma, order)
Calculates the standard N-order Laguerre Filter of a given data series. Standard Laguerre Filter uses uniform weighting across all polynomial terms. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Higher order increases lag. Optional. Default is 8.
Returns: (float) The calculated standard Laguerre Filter value.
laguerreBinomialFilter(source, length, gamma)
Calculates the Laguerre Binomial Filter of a given data series. Uses 6-pole feedback with binomial weighting coefficients. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.5.
Returns: (float) The calculated Laguerre Binomial Filter value.
superSmoother(source, length)
Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Period for the filter calculation.
Returns: (float) The calculated Super Smoother value.
rangeFilter(source, length, multiplier)
Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the average range calculation.
multiplier (simple float) : Multiplier for the smooth range. Higher values increase filtering. Optional. Default is 2.618.
Returns: ( ) Tuple containing filtered value, trend direction, upper band, and lower band.
qqe(source, rsiLength, rsiSmooth, qqeFactor)
Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals. Developed by Igor Livshin.
Parameters:
source (series float) : Series of values to process.
rsiLength (simple int) : Number of bars for the RSI calculation. Optional. Default is 14.
rsiSmooth (simple int) : Number of bars for smoothing the RSI. Optional. Default is 5.
qqeFactor (simple float) : QQE factor for volatility band width. Optional. Default is 4.236.
Returns: ( ) Tuple containing smoothed RSI and QQE trend line.
sslChannel(source, length)
Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: ( ) Tuple containing SSL Up and SSL Down lines.
ma(source, length, maType)
Calculates a Moving Average based on the specified type. Universal interface supporting all moving average algorithms.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
maType (simple MaType) : Type of moving average to calculate. Optional. Default is SMA.
Returns: (float) The calculated moving average value based on the specified type.
atr(length, maType)
Calculates the Average True Range (ATR) using the specified moving average type. Developed by J. Welles Wilder Jr.
Parameters:
length (simple int) : Number of bars for the ATR calculation.
maType (simple MaType) : Type of moving average to use for smoothing. Optional. Default is RMA.
Returns: (float) The calculated Average True Range value.
macd(source, fastLength, slowLength, signalLength, maType, signalMaType)
Calculates the Moving Average Convergence Divergence (MACD) with customizable MA types. Developed by Gerald Appel.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
signalLength (simple int) : Period for the signal line moving average.
maType (simple MaType) : Type of moving average for main MACD calculation. Optional. Default is EMA.
signalMaType (simple MaType) : Type of moving average for signal line calculation. Optional. Default is EMA.
Returns: ( ) Tuple containing MACD line, signal line, and histogram values.
dmao(source, fastLength, slowLength, maType)
Calculates the Dual Moving Average Oscillator (DMAO) of a given data series. Uses the same algorithm as the Percentage Price Oscillator (PPO), but can be applied to any data series.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
maType (simple MaType) : Type of moving average to use for both calculations. Optional. Default is EMA.
Returns: (float) The calculated Dual Moving Average Oscillator value as a percentage.
continuationIndex(source, length, gamma, order)
Calculates the Continuation Index of a given data series. The index represents the Inverse Fisher Transform of the normalized difference between an UltimateSmoother and an N-order Laguerre filter. Developed by John F. Ehlers, published in TASC 2025.09.
Parameters:
source (series float) : Series of values to process.
length (simple int) : The calculation length.
gamma (simple float) : Controls the phase response of the Laguerre filter. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Optional. Default is 8.
Returns: (float) The calculated Continuation Index value.
📚 RELEASE NOTES
v1.0 (2025.09.24)
✅ 25+ technical analysis functions
✅ Complete adaptive moving average series (KAMA, FRAMA, MAMA/FAMA)
✅ Advanced signal processing filters (Kalman, Laguerre, SuperSmoother, UltimateSmoother)
✅ Performance optimized with pre-calculated constants and efficient algorithms
✅ Unified function interface design following TradingView best practices
✅ Comprehensive moving average collection (DEMA, TEMA, ZLEMA, T3, etc.)
✅ Volatility and trend detection tools (QQE, SSL Channel, Range Filter)
✅ Continuation Index - Latest research from TASC 2025.09
✅ MACD and ATR calculations supporting multiple moving average types
✅ Dual Moving Average Oscillator (DMAO) for arbitrary data series analysis
SuperSmoother MA OscillatorSuperSmoother MA Oscillator - Ehlers-Inspired Lag-Minimized Signal Framework
Overview
The SuperSmoother MA Oscillator is a crossover and momentum detection framework built on the pioneering work of John F. Ehlers, who introduced digital signal processing (DSP) concepts into technical analysis. Traditional moving averages such as SMA and EMA are prone to two persistent flaws: excessive lag, which delays recognition of trend shifts, and high-frequency noise, which produces unreliable whipsaw signals. Ehlers’ SuperSmoother filter was designed to specifically address these flaws by creating a low-pass filter with minimal lag and superior noise suppression, inspired by engineering methods used in communications and radar systems.
This oscillator extends Ehlers’ foundation by combining the SuperSmoother filter with multi-length moving average oscillation, ATR-based normalization, and dynamic color coding. The result is a tool that helps traders identify market momentum, detect reliable crossovers earlier than conventional methods, and contextualize volatility and phase shifts without being distracted by transient price noise.
Unlike conventional oscillators, which either oversimplify price structure or overload the chart with reactive signals, the SuperSmoother MA Oscillator is designed to balance responsiveness and stability. By preprocessing price data with the SuperSmoother filter, traders gain a signal framework that is clean, robust, and adaptable across assets and timeframes.
Theoretical Foundation
Traditional MA oscillators such as MACD or dual-EMA systems react to raw or lightly smoothed price inputs. While effective in some conditions, these signals are often distorted by high-frequency oscillations inherent in market data, leading to false crossovers and poor timing. The SuperSmoother approach modifies this dynamic: by attenuating unwanted frequencies, it preserves structural price movements while eliminating meaningless noise.
This is particularly useful for traders who need to distinguish between genuine market cycles and random short-term price flickers. In practical terms, the oscillator helps identify:
Early trend continuations (when fast averages break cleanly above/below slower averages).
Preemptive breakout setups (when compressed oscillator ranges expand).
Exhaustion phases (when oscillator swings flatten despite continued price movement).
Its multi-purpose design allows traders to apply it flexibly across scalping, day trading, swing setups, and longer-term trend positioning, without needing separate tools for each.
The oscillator’s visual system - fast/slow lines, dynamic coloration, and zero-line crossovers - is structured to provide trend clarity without hiding nuance. Strong green/red momentum confirms directional conviction, while neutral gray phases emphasize uncertainty or low conviction. This ensures traders can quickly gauge the market state without losing access to subtle structural signals.
How It Works
The SuperSmoother MA Oscillator builds signals through a layered process:
SuperSmoother Filtering (Ehlers’ Method)
At its core lies Ehlers’ two-pole recursive filter, mathematically engineered to suppress high-frequency components while introducing minimal lag. Compared to traditional EMA smoothing, the SuperSmoother achieves better spectral separation - it allows meaningful cyclical market structures to pass through, while eliminating erratic spikes and aliasing. This makes it a superior preprocessing stage for oscillator inputs.
Fast and Slow Line Construction
Within the oscillator framework, the filtered price series is used to build two internal moving averages: a fast line (short-term momentum) and a slow line (longer-term directional bias). These are not plotted directly on the chart - instead, their relationship is transformed into the oscillator values you see.
The interaction between these two internal averages - crossovers, separation, and compression - forms the backbone of trend detection:
Uptrend Signal : Fast MA rises above the slow MA with expanding distance, generating a positive oscillator swing.
Downtrend Signal : Fast MA falls below the slow MA with widening divergence, producing a negative oscillator swing.
Neutral/Transition : Lines compress, flattening the oscillator near zero and often preceding volatility expansion.
This design ensures traders receive the information content of dual-MA crossovers while keeping the chart visually clean and focused on the oscillator’s dynamics.
ATR-Based Normalization
Markets vary in volatility. To ensure the oscillator behaves consistently across assets, ATR (Average True Range) normalization scales outputs relative to prevailing volatility conditions. This prevents the oscillator from appearing overly sensitive in calm markets or too flat during high-volatility regimes.
Dynamic Color Coding
Color transitions reflect underlying market states:
Strong Green : Bullish alignment, momentum expanding.
Strong Red : Bearish alignment, momentum expanding.
These visual cues allow traders to quickly gauge trend direction and strength at a glance, with expanding colors indicating increasing conviction in the underlying momentum.
Interpretation
The oscillator offers a multi-dimensional view of price dynamics:
Trend Analysis : Fast/slow line alignment and zero-line interactions reveal trend direction and strength. Expansions indicate momentum building; contractions flag weakening conditions or potential reversals.
Momentum & Volatility : Rapid divergence between lines reflects increasing momentum. Compression highlights periods of reduced volatility and possible upcoming expansion.
Cycle Awareness : Because of Ehlers’ DSP foundation, the oscillator captures market cycles more cleanly than conventional MA systems, allowing traders to anticipate turning points before raw price action confirms them.
Divergence Detection : When oscillator momentum fades while price continues in the same direction, it signals exhaustion - a cue to tighten stops or anticipate reversals.
By focusing on filtered, volatility-adjusted signals, traders avoid overreacting to noise while gaining early access to structural changes in momentum.
Strategy Integration
The SuperSmoother MA Oscillator adapts across multiple trading approaches:
Trend Following
Enter when fast/slow alignment is strong and expanding:
A fast line crossing above the slow line with expanding green signals confirms bullish continuation.
Use ATR-normalized expansion to filter entries in line with prevailing volatility.
Breakout Trading
Periods of compression often precede breakouts:
A breakout occurs when fast lines diverge decisively from slow lines with renewed green/red strength.
Exhaustion and Reversals
Oscillator divergence signals weakening trends:
Flattening momentum while price continues trending may indicate overextension.
Traders can exit or hedge positions in anticipation of corrective phases.
Multi-Timeframe Confluence
Apply the oscillator on higher timeframes to confirm the directional bias.
Use lower timeframes for refined entries during compression → expansion transitions.
Technical Implementation Details
SuperSmoother Algorithm (Ehlers) : Recursive two-pole filter minimizes lag while removing high-frequency noise.
Oscillator Framework : Fast/slow MAs derived from filtered prices.
ATR Normalization : Ensures consistent amplitude across market regimes.
Dynamic Color Engine : Aligns visual cues with structural states (expansion and contraction).
Multi-Factor Analysis : Combines crossover logic, volatility context, and cycle detection for robust outputs.
This layered approach ensures the oscillator is highly responsive without overloading charts with noise.
Optimal Application Parameters
Asset-Specific Guidance:
Forex : Normalize with moderate ATR scaling; focus on slow-line confirmation.
Equities : Balance responsiveness with smoothing; useful for capturing sector rotations.
Cryptocurrency : Higher ATR multipliers recommended due to volatility.
Futures/Indices : Lower frequency settings highlight structural trends.
Timeframe Optimization:
Scalping (1-5min) : Higher sensitivity, prioritize fast-line signals.
Intraday (15m-1h) : Balance between fast/slow expansions.
Swing (4h-Daily) : Focus on slow-line momentum with fast-line timing.
Position (Daily-Weekly) : Slow lines dominate; fast lines highlight cycle shifts.
Performance Characteristics
High Effectiveness:
Trending environments with moderate-to-high volatility.
Assets with steady liquidity and clear cyclical structures.
Reduced Effectiveness:
Flat/choppy conditions with little directional bias.
Ultra-short timeframes (<1m), where noise dominates.
Integration Guidelines
Confluence : Combine with liquidity zones, order blocks, and volume-based indicators for confirmation.
Risk Management : Place stops beyond slow-line thresholds or ATR-defined zones.
Dynamic Trade Management : Use expansions/contractions to scale position sizes or tighten stops.
Multi-Timeframe Confirmation : Filter lower-timeframe entries with higher-timeframe momentum states.
Disclaimer
The SuperSmoother MA Oscillator is an advanced trend and momentum analysis tool, not a guaranteed profit system. Its effectiveness depends on proper parameter settings per asset and disciplined risk management. Traders should use it as part of a broader technical framework and not in isolation.
Supertrend DashboardOverview
This dashboard is a multi-timeframe technical indicator dashboard based on Supertrend. It combines:
Trend detection via Supertrend
Momentum via RSI and OBV (volume)
Volatility via a basic candle-based metric (bs)
Trend strength via ADX
Multi-timeframe analysis to see whether the trend is bullish across different timeframes
It then displays this info in a table on the chart with colors for quick visual interpretation.
2️⃣ Inputs
Dashboard settings:
enableDashboard: Toggle the dashboard on/off
locationDashboard: Where the table appears (Top right, Bottom left, etc.)
sizeDashboard: Text size in the table
strategyName: Custom name for the strategy
Indicator settings:
factor (Supertrend factor): Controls how far the Supertrend lines are from price
atrLength: ATR period for Supertrend calculation
rsiLength: Period for RSI calculation
Visual settings:
colorBackground, colorFrame, colorBorder: Control dashboard style
3️⃣ Core Calculations
a) Supertrend
Supertrend is a trend-following indicator that generates bullish or bearish signals.
Logic:
Compute ATR (atr = ta.atr(atrLength))
Compute preliminary bands:
upperBand = src + factor * atr
lowerBand = src - factor * atr
Smooth bands to avoid false flips:
lowerBand := lowerBand > prevLower or close < prevLower ? lowerBand : prevLower
upperBand := upperBand < prevUpper or close > prevUpper ? upperBand : prevUpper
Determine direction (bullish / bearish):
dir = 1 → bullish
dir = -1 → bearish
Supertrend line = lowerBand if bullish, upperBand if bearish
Output:
st → line to plot
bull → boolean (true = bullish)
b) Buy / Sell Trigger
Logic:
bull = ta.crossover(close, supertrend) → close crosses above Supertrend → buy signal
bear = ta.crossunder(close, supertrend) → close crosses below Supertrend → sell signal
trigger → checks which signal was most recent:
trigger = ta.barssince(bull) < ta.barssince(bear) ? 1 : 0
1 → Buy
0 → Sell
c) RSI (Momentum)
rsi = ta.rsi(close, rsiLength)
Logic:
RSI > 50 → bullish
RSI < 50 → bearish
d) OBV / Volume Trend (vosc)
OBV tracks whether volume is pushing price up or down.
Manual calculation (safe for all Pine versions):
obv = ta.cum( math.sign( nz(ta.change(close), 0) ) * volume )
vosc = obv - ta.ema(obv, 20)
Logic:
vosc > 0 → bullish
vosc < 0 → bearish
e) Volatility (bs)
Measures how “volatile” the current candle is:
bs = ta.ema(math.abs((open - close) / math.max(high - low, syminfo.mintick) * 100), 3)
Higher % → stronger candle moves
Displayed on dashboard as a number
f) ADX (Trend Strength)
= ta.dmi(14, 14)
Logic:
adx > 20 → Trending
adx < 20 → Ranging
g) Multi-Timeframe Supertrend
Timeframes: 1m, 3m, 5m, 10m, 15m, 30m, 1H, 2H, 4H, 12H, 1D
Logic:
for tf in timeframes
= request.security(syminfo.tickerid, tf, f_supertrend(ohlc4, factor, atrLength))
array.push(tf_bulls, bull_tf ? 1.0 : 0.0)
bull_tf ? 1.0 : 0.0 → converts boolean to number
Then we calculate user rating:
userRating = (sum of bullish timeframes / total timeframes) * 10
0 → Strong Sell, 10 → Strong Buy
4️⃣ Dashboard Table Layout
Row Column 0 (Label) Column 1 (Value)
0 Strategy strategyName
1 Technical Rating textFromRating(userRating) (color-coded)
2 Current Signal Buy / Sell (based on last Supertrend crossover)
3 Current Trend Bullish / Bearish (based on Supertrend)
4 Trend Strength bs %
5 Volume vosc → Bullish/Bearish
6 Volatility adx → Trending/Ranging
7 Momentum RSI → Bullish/Bearish
8 Timeframe Trends 📶 Merged cell
9-19 1m → Daily Bullish/Bearish for each timeframe (green/red)
5️⃣ Color Logic
Green shades → bullish / trending / buy
Red / orange → bearish / weak / sell
Yellow → neutral / ranging
Example:
dashboard_cell_bg(1, 1, colorFromRating(userRating))
dashboard_cell_bg(1, 2, trigger ? color.green : color.red)
dashboard_cell_bg(1, 3, superBull ? color.green : color.red)
Makes the dashboard visually intuitive
6️⃣ Key Logic Flow
Calculate Supertrend on current timeframe
Detect buy/sell triggers based on crossover
Calculate RSI, OBV, Volatility, ADX
Request Supertrend on multiple timeframes → convert to 1/0
Compute user rating (percentage of bullish timeframes)
Populate dashboard table with colors and values
✅ The result: You get a compact, fast, multi-timeframe trend dashboard that shows:
Current signal (Buy/Sell)
Current trend (Bullish/Bearish)
Momentum, volatility, and volume cues
Trend across multiple timeframes
Overall technical rating
It’s essentially a full trend-strength scanner directly on your chart.
Advanced Trading System - [WOLONG X DBG]Advanced Multi-Timeframe Trading System
Overview
This technical analysis indicator combines multiple established methodologies to provide traders with market insights across various timeframes. The system integrates SuperTrend analysis, moving average clouds, MACD-based candle coloring, RSI analysis, and multi-timeframe trend detection to suggest potential entry and exit opportunities for both swing and day trading approaches.
Methodology
The indicator employs a multi-layered analytical approach based on established technical analysis principles:
Core Signal Generation
SuperTrend Engine: Utilizes adaptive SuperTrend calculations with customizable sensitivity (1-20) combined with SMA confirmation filters to identify potential trend changes and continuations
Braid Filter System: Implements moving average filtering using multiple MA types (McGinley Dynamic, EMA, DEMA, TEMA, Hull, Jurik, FRAMA) with percentage-based strength filtering to help reduce false signals
Multi-Timeframe Analysis: Analyzes trend conditions across 10 different timeframes (1-minute to Daily) using EMA-based trend detection for broader market context
Advanced Features
MACD Candle Coloring: Applies dynamic 4-level candle coloring system based on MACD histogram momentum and signal line relationships for visual trend strength assessment
RSI Analysis: Identifies potential reversal areas using RSI oversold/overbought conditions with SuperTrend confirmation
Take Profit Analysis: Features dual-mode TP detection using statistical slope analysis and Parabolic SAR integration for exit timing analysis
Key Components
Signal Types
Primary Signals: Green ▲ for potential long entries, Red ▼ for potential short entries with trend and SMA alignment
Reversal Signals: Small circular indicators for RSI-based counter-trend possibilities
Take Profit Markers: X-cross symbols indicating statistical TP analysis zones
Pullback Signals: Purple arrows for potential trend continuation entries using Parabolic SAR
Visual Elements
8-Layer MA Cloud: Customizable moving average cloud system with 3 color themes for trend visualization
Real-Time Dashboard: Multi-timeframe trend analysis table showing bullish/bearish status across all timeframes
Dynamic Candle Colors: 4-intensity MACD-based coloring system (ranging from light to strong trend colors)
Entry/SL/TP Labels: Automatic calculation and display of suggested entry points, stop losses, and multiple take profit levels
Usage Instructions
Basic Configuration
Sensitivity Setting: Start with default value 6
Increase (7-15) for more frequent signals in volatile markets
Decrease (3-5) for higher quality signals in trending markets
MA Filter Type: McGinley Dynamic recommended for smoother signals
Filter Strength: Set to 80% for balanced filtering, adjust based on market conditions
Signal Interpretation
Long Entry: Green ▲ suggests when price crosses above SuperTrend with bullish SMA alignment
Short Entry: Red ▼ suggests when price crosses below SuperTrend with bearish SMA alignment
Reversal Opportunities: Small circles indicate RSI-based counter-trend analysis
Take Profit Zones: X-crosses mark statistical TP areas based on slope analysis
Dashboard Analysis
Green Cells: Bullish trend detected on that timeframe
Red Cells: Bearish trend detected on that timeframe
Multi-Timeframe Confluence: Look for alignment across multiple timeframes for stronger signal confirmation
Risk Management Features
Automatic Calculations
ATR-Based Stop Loss: Dynamic stop loss calculation using ATR multiplier (default 1.9x)
Multiple Take Profit Levels: Three TP targets with 1:1, 1:2, and 1:3 risk-reward ratios
Position Sizing Guidance: Entry labels display suggested price levels for order placement
Confirmation Requirements
Trend Alignment: Requires SuperTrend and SMA confirmation before signal generation
Filter Validation: Braid filter must show sufficient strength before signals activate
Multi-Timeframe Context: Dashboard provides broader market context for decision making
Optimal Settings
Timeframe Recommendations
Scalping: 1M-5M charts with sensitivity 8-12
Day Trading: 15M-1H charts with sensitivity 6-8
Swing Trading: 4H-Daily charts with sensitivity 4-6
Market Conditions
Trending Markets: Reduce sensitivity, increase filter strength
Ranging Markets: Increase sensitivity, enable reversal signals
High Volatility: Adjust ATR risk factor to 2.0-2.5
Advanced Features
Customization Options
MA Cloud Periods: 8 customizable periods for cloud layers (default: 2,6,11,18,21,24,28,34)
Color Themes: Three professional color schemes plus transparent option
Dashboard Position: 9 positioning options with 4 size settings
Signal Filtering: Individual toggle controls for each signal type
Technical Specifications
Moving Average Types: 21 different MA calculations including advanced types (Jurik, FRAMA, VIDA, CMA)
Pullback Detection: Parabolic SAR with customizable start, increment, and maximum values
Statistical Analysis: Linear regression slope calculation for trend-based TP analysis
Important Limitations
Lagging Nature: Some signals may appear after potential entry points due to confirmation requirements
Ranging Markets: May produce false signals during extended sideways price action
High Volatility: Requires parameter adjustment during news events or unusual market conditions
Computational Load: Multiple timeframe analysis may impact performance on slower devices
No Guarantee: All signals are suggestions based on technical analysis and may be incorrect
Educational Disclaimers
This indicator is designed for educational and analytical purposes only. It represents a technical analysis tool based on mathematical calculations of historical price data and should not be considered as financial advice or trading recommendations.
Risk Warning: Trading involves substantial risk of loss and is not suitable for all investors. Past performance of any trading system or methodology is not necessarily indicative of future results. The high degree of leverage can work against you as well as for you.
Important Notes:
Always conduct your own analysis before making trading decisions
Use appropriate position sizing and risk management strategies
Never risk more than you can afford to lose
Consider your investment objectives, experience level, and risk tolerance
Seek advice from qualified financial professionals when needed
Performance Disclaimer: Backtesting results do not guarantee future performance. Market conditions change constantly, and what worked in the past may not work in the future. Always paper trade new strategies before risking real capital.
Dynamic EMA Stack Support & ResistanceEvery trader needs reliable support and resistance — but static zones and lagging indicators won't cut it in fast-moving markets. This script combines a Fibonacci-based 5-EMA stacking system and left/right pivots that create dynamic support & resistance logic to uncover real-time structural shifts & momentum zones that actually adapt to price action. This isn’t just a mashup — it’s a complete built-from-the-ground-up support & resistance engine designed for scalpers, intraday traders, and trend followers alike.
🧠 🧠 🧠What It Does🧠 🧠 🧠
This script uses two powerful engines working in sync:
1️⃣ EMA Stack (5-EMA Framework)
Built on Fibonacci-based lengths: 5, 8, 13, 21, 34, (configurable) this stack identifies:
🔹 Bullish Stack: EMAs aligned from fastest to slowest (uptrend confirmation)
🔹 Bearish Stack: EMAs aligned inversely (downtrend confirmation)
🟡 Narrowing Zones: When EMAs compress within ATR thresholds → possible breakout or reversal zone
🎯 Labels identify key transitions like:
✅"Begin Bear Trend?"
✅"Uptrend SPRT"
✅"RES?" (resistance test)
2️⃣ Pivot-Based Projection Engine
Using classic Left/Right Bar pivot logic, the script:
📌 Detects early-stage swing highs/lows before full confirmation
📈 Projects horizontal S/R lines that adapt to market structure
🔁 Keeps lines active until a new pivot replaces them
🧩 Syncs beautifully with EMA stack for confluence zones
🎯🎯🎯Key Features for Traders🎯🎯🎯
✅ Trend Detection
→ EMA order reveals real-time bias (bullish, bearish, compression)
✅ Dynamic S/R Zones
→ Historical support/resistance levels auto-draw and extend
✅ Smart Labeling
→ “SPRT”, “RES”, and “Trend?” labels for live context + testing logic
✅ Custom Candle Coloring
→ Choose from Bar Color or Full Candle Overlay modes
✅ Scalper & Swing Compatible
→ Use fast confirmations for scalping or stack consistency for longer trends
⚙️⚙️⚙️How to Use⚙️⚙️⚙️
✅Use Top/Bottom (trend state) Line Colors to quickly read trend conditions.
✅Use Pivot-based support/resistance projections to anticipate where price might pause or reverse.
✅Watch for yellow/blue zones to prepare for volatility shifts/reversals.
✅Combine with volume or momentum indicators for added confirmation.
📐📐📐Customization Options📐📐📐
✅EMA lengths (5, 8, 13, 21, 34) — fully configurable - try 21,34,55, 89, 144 for longer term trend states
✅Left/Right bar pivot settings (default: 21/5)
✅Label size, visibility, and color themes
✅Toggle line and label visibility for clean layouts
✅“Max Bars Back” to control how deep history is scanned safely
🛠🛠🛠Built-In Safeguards🛠🛠🛠
✅ATR-based filters to stabilize compression logic
✅Guarded lookback (max_bars_back) to avoid runtime errors
✅Works on any asset, any timeframe
🏁🏁🏁Final Word🏁🏁🏁
This script is not just a visual tool, it’s a complete trend and structure framework. Whether you're looking for clean trend alignment, dynamic support/resistance, or early warning labels, this system is tuned to help you react with confidence — not hindsight.
Rembember, no single indicator should be used in isolation. For best results, combine it with price action analysis, higher-timeframe context, and complementary tools like trendlines, moving averages etc Use it as part of a well-rounded trading approach to confirm setups — not to define them alone.
💡💡💡Turn logic into clarity. Structure into trades. And uncertainty into confidence.💡💡💡
Harmonic Super GuppyHarmonic Super Guppy – Harmonic & Golden Ratio Trend Analysis Framework
Overview
Harmonic Super Guppy is a comprehensive trend analysis and visualization tool that evolves the classic Guppy Multiple Moving Average (GMMA) methodology, pioneered by Daryl Guppy to visualize the interaction between short-term trader behavior and long-term investor trends. into a harmonic and phase-based market framework. By combining harmonic weighting, golden ratio phasing, and multiple moving averages, it provides traders with a deep understanding of market structure, momentum, and trend alignment. Fast and slow line groups visually differentiate short-term trader activity from longer-term investor positioning, while adaptive fills and dynamic coloring clearly illustrate trend coherence, expansion, and contraction in real time.
Traditional GMMA focuses primarily on moving average convergence and divergence. Harmonic Super Guppy extends this concept, integrating frequency-aware harmonic analysis and golden ratio modulation, allowing traders to detect subtle cyclical forces and early trend shifts before conventional moving averages would react. This is particularly valuable for traders seeking to identify early trend continuation setups, preemptive breakout entries, and potential trend exhaustion zones. The indicator provides a multi-dimensional view, making it suitable for scalping, intraday trading, swing setups, and even longer-term position strategies.
The visual structure of Harmonic Super Guppy is intentionally designed to convey trend clarity without oversimplification. Fast lines reflect short-term trader sentiment, slow lines capture longer-term investor alignment, and fills highlight compression or expansion. The adaptive color coding emphasizes trend alignment: strong green for bullish alignment, strong red for bearish, and subtle gray tones for indecision. This allows traders to quickly gauge market conditions while preserving the granularity necessary for sophisticated analysis.
How It Works
Harmonic Super Guppy uses a combination of harmonic averaging, golden ratio phasing, and adaptive weighting to generate its signals.
Harmonic Weighting : Each moving average integrates three layers of harmonics:
Primary harmonic captures the dominant cyclical structure of the market.
Secondary harmonic introduces a complementary frequency for oscillatory nuance.
Tertiary harmonic smooths higher-frequency noise while retaining meaningful trend signals.
Golden Ratio Phase : Phases of each harmonic contribution are adjusted using the golden ratio (default φ = 1.618), ensuring alignment with natural market rhythms. This reduces lag and allows traders to detect trend shifts earlier than conventional moving averages.
Adaptive Trend Detection : Fast SMAs are compared against slow SMAs to identify structural trends:
UpTrend : Fast SMA exceeds slow SMA.
DownTrend : Fast SMA falls below slow SMA.
Frequency Scaling : The wave frequency setting allows traders to modulate responsiveness versus smoothing. Higher frequency emphasizes short-term moves, while lower frequency highlights structural trends. This enables adaptation across asset classes with different volatility characteristics.
Through this combination, Harmonic Super Guppy captures micro and macro market cycles, helping traders distinguish between transient noise and genuine trend development. The multi-harmonic approach amplifies meaningful price action while reducing false signals inherent in standard moving averages.
Interpretation
Harmonic Super Guppy provides a multi-dimensional perspective on market dynamics:
Trend Analysis : Alignment of fast and slow lines reveals trend direction and strength. Expanding harmonics indicate momentum building, while contraction signals weakening conditions or potential reversals.
Momentum & Volatility : Rapid expansion of fast lines versus slow lines reflects short-term bullish or bearish pressure. Compression often precedes breakout scenarios or volatility expansion. Traders can quickly gauge trend vigor and potential turning points.
Market Context : The indicator overlays harmonic and structural insights without dictating entry or exit points. It complements order blocks, liquidity zones, oscillators, and other technical frameworks, providing context for informed decision-making.
Phase Divergence Detection : Subtle divergence between harmonic layers (primary, secondary, tertiary) often signals early exhaustion in trends or hidden strength, offering preemptive insight into potential reversals or sustained continuation.
By observing both structural alignment and harmonic expansion/contraction, traders gain a clear sense of when markets are trending with conviction versus when conditions are consolidating or becoming unpredictable. This allows for proactive trade management, rather than reactive responses to lagging indicators.
Strategy Integration
Harmonic Super Guppy adapts to various trading methodologies with clear, actionable guidance.
Trend Following : Enter positions when fast and slow lines are aligned and harmonics are expanding. The broader the alignment, the stronger the confirmation of trend persistence. For example:
A fast line crossover above slow lines with expanding fills confirms momentum-driven continuation.
Traders can use harmonic amplitude as a filter to reduce entries against prevailing trends.
Breakout Trading : Periods of line compression indicate potential volatility expansion. When fast lines diverge from slow lines after compression, this often precedes breakouts. Traders can combine this visual cue with structural supports/resistances or order flow analysis to improve timing and precision.
Exhaustion and Reversals : Divergences between harmonic components, or contraction of fast lines relative to slow lines, highlight weakening trends. This can indicate liquidity exhaustion, trend fatigue, or corrective phases. For example:
A flattening fast line group above a rising slow line can hint at short-term overextension.
Traders may use these signals to tighten stops, take partial profits, or prepare for contrarian setups.
Multi-Timeframe Analysis : Overlay slow lines from higher timeframes on lower timeframe charts to filter noise and trade in alignment with larger market structures. For example:
A daily bullish alignment combined with a 15-minute breakout pattern increases probability of a successful intraday trade.
Conversely, a higher timeframe divergence can warn against taking counter-trend trades in lower timeframes.
Adaptive Trade Management : Harmonic expansion/contraction can guide dynamic risk management:
Stops may be adjusted according to slow line support/resistance or harmonic contraction zones.
Position sizing can be modulated based on harmonic amplitude and compression levels, optimizing risk-reward without rigid rules.
Technical Implementation Details
Harmonic Super Guppy is powered by a multi-layered harmonic and phase calculation engine:
Harmonic Processing : Primary, secondary, and tertiary harmonics are calculated per period to capture multiple market cycles simultaneously. This reduces noise and amplifies meaningful signals.
Golden Ratio Modulation : Phase adjustments based on φ = 1.618 align harmonic contributions with natural market rhythms, smoothing lag and improving predictive value.
Adaptive Trend Scaling : Fast line expansion reflects short-term momentum; slow lines provide structural trend context. Fills adapt dynamically based on alignment intensity and harmonic amplitude.
Multi-Factor Trend Analysis : Trend strength is determined by alignment of fast and slow lines over multiple bars, expansion/contraction of harmonic amplitudes, divergences between primary, secondary, and tertiary harmonics and phase synchronization with golden ratio cycles.
These computations allow the indicator to be highly responsive yet smooth, providing traders with actionable insights in real time without overloading visual complexity.
Optimal Application Parameters
Asset-Specific Guidance:
Forex Majors : Wave frequency 1.0–2.0, φ = 1.618–1.8
Large-Cap Equities : Wave frequency 0.8–1.5, φ = 1.5–1.618
Cryptocurrency : Wave frequency 1.2–3.0, φ = 1.618–2.0
Index Futures : Wave frequency 0.5–1.5, φ = 1.618
Timeframe Optimization:
Scalping (1–5min) : Emphasize fast lines, higher frequency for micro-move capture.
Day Trading (15min–1hr) : Balance fast/slow interactions for trend confirmation.
Swing Trading (4hr–Daily) : Focus on slow lines for structural guidance, fast lines for entry timing.
Position Trading (Daily–Weekly) : Slow lines dominate; harmonics highlight long-term cycles.
Performance Characteristics
High Effectiveness Conditions:
Clear separation between short-term and long-term trends.
Moderate-to-high volatility environments.
Assets with consistent volume and price rhythm.
Reduced Effectiveness:
Flat or extremely low volatility markets.
Erratic assets with frequent gaps or algorithmic dominance.
Ultra-short timeframes (<1min), where noise dominates.
Integration Guidelines
Signal Confirmation : Confirm alignment of fast and slow lines over multiple bars. Expansion of harmonic amplitude signals trend persistence.
Risk Management : Place stops beyond slow line support/resistance. Adjust sizing based on compression/expansion zones.
Advanced Feature Settings :
Frequency tuning for different volatility environments.
Phase analysis to track divergences across harmonics.
Use fills and amplitude patterns as a guide for dynamic trade management.
Multi-timeframe confirmation to filter noise and align with structural trends.
Disclaimer
Harmonic Super Guppy is a trend analysis and visualization tool, not a guaranteed profit system. Optimal performance requires proper wave frequency, golden ratio phase, and line visibility settings per asset and timeframe. Traders should combine the indicator with other technical frameworks and maintain disciplined risk management practices.
Information Flow Analysis[b🔄 Information Flow Analysis: Systematic Multi-Component Market Analysis Framework
SYSTEM OVERVIEW AND ANALYTICAL FOUNDATION
The Information Flow Kernel - Hybrid combines established technical analysis methods into a unified analytical framework. This indicator systematically processes three distinct data streams - directional price momentum, volume-weighted pressure dynamics, and intrabar development patterns - integrating them through weighted mathematical fusion to produce statistically normalized market flow measurements.
COMPREHENSIVE MATHEMATICAL FRAMEWORK
Component 1: Directional Flow Analysis
The directional component analyzes price momentum through three mathematical vectors:
Price Vector: p = C - O (intrabar directional bias)
Momentum Vector: m = C_t - C_{t-1} (bar-to-bar velocity)
Acceleration Vector: a = m_t - m_{t-1} (momentum rate of change)
Directional Signal Integration:
S_d = \text{sgn}(p) \cdot |p| + \text{sgn}(m) \cdot |m| \cdot 0.6 + \text{sgn}(a) \cdot |a| \cdot 0.3
The signum function preserves directional information while absolute values provide magnitude weighting. Coefficients create a hierarchy emphasizing intrabar movement (100%), momentum (60%), and acceleration (30%).
Final Directional Output: K_1 = S_d \cdot w_d where w_d is the directional weight parameter.
Component 2: Volume-Weighted Pressure Analysis
Volume Normalization: r_v = \frac{V_t}{\overline{V_n}} where \overline{V_n} represents the n-period simple moving average of volume.
Base Pressure Calculation: P_{base} = \Delta C \cdot r_v \cdot w_v where \Delta C = C_t - C_{t-1} and w_v is the velocity weighting factor.
Volume Confirmation Function:
f(r_v) = \begin{cases}
1.4 & \text{if } r_v > 1.2 \
0.7 & \text{if } r_v < 0.8 \
1.0 & \text{otherwise}
\end{cases}
Final Pressure Output: K_2 = P_{base} \cdot f(r_v)
Component 3: Intrabar Development Analysis
Bar Position Calculation: B = \frac{C - L}{H - L} when H - L > 0 , else B = 0.5
Development Signal Function:
S_{dev} = \begin{cases}
2(B - 0.5) & \text{if } B > 0.6 \text{ or } B < 0.4 \
0 & \text{if } 0.4 \leq B \leq 0.6
\end{cases}
Final Development Output: K_3 = S_{dev} \cdot 0.4
Master Integration and Statistical Normalization
Weighted Component Fusion: F_{raw} = 0.5K_1 + 0.35K_2 + 0.15K_3
Sensitivity Scaling: F_{master} = F_{raw} \cdot s where s is the sensitivity parameter.
Statistical Normalization Process:
Rolling Mean: \mu_F = \frac{1}{n}\sum_{i=0}^{n-1} F_{master,t-i}
Rolling Standard Deviation: \sigma_F = \sqrt{\frac{1}{n}\sum_{i=0}^{n-1} (F_{master,t-i} - \mu_F)^2}
Z-Score Computation: z = \frac{F_{master} - \mu_F}{\sigma_F}
Boundary Enforcement: z_{bounded} = \max(-3, \min(3, z))
Final Normalization: N = \frac{z_{bounded}}{3}
Flow Metrics Calculation:
Intensity: I = |z|
Strength Percentage: S = \min(100, I \times 33.33)
Extreme Detection: \text{Extreme} = I > 2.0
DETAILED INPUT PARAMETER SPECIFICATIONS
Sensitivity (0.1 - 3.0, Default: 1.0)
Global amplification multiplier applied to the master flow calculation. Functions as: F_{master} = F_{raw} \cdot s
Low Settings (0.1 - 0.5): Enhanced precision for subtle market movements. Optimal for low-volatility environments, scalping strategies, and early detection of minor directional shifts. Increases responsiveness but may amplify noise.
Moderate Settings (0.6 - 1.2): Balanced sensitivity for standard market conditions across multiple timeframes.
High Settings (1.3 - 3.0): Reduced sensitivity to minor fluctuations while emphasizing significant flow changes. Ideal for high-volatility assets, trending markets, and longer timeframes.
Directional Weighting (0.1 - 1.0, Default: 0.7)
Controls emphasis on price direction versus volume and positioning factors. Applied as: K_{1,weighted} = K_1 \times w_d
Lower Values (0.1 - 0.4): Reduces directional bias, favoring volume-confirmed moves. Optimal for ranging markets where momentum may generate false signals.
Higher Values (0.7 - 1.0): Amplifies directional signals from price vectors and acceleration. Ideal for trending conditions where directional momentum drives price action.
Velocity Weighting (0.1 - 1.0, Default: 0.6)
Scales volume-confirmed price change impact. Applied in: P_{base} = \Delta C \times r_v \times w_v
Lower Values (0.1 - 0.4): Dampens volume spike influence, focusing on sustained pressure patterns. Suitable for illiquid assets or news-sensitive markets.
Higher Values (0.8 - 1.0): Amplifies high-volume directional moves. Optimal for liquid markets where volume provides reliable confirmation.
Volume Length (3 - 20, Default: 5)
Defines lookback period for volume averaging: \overline{V_n} = \frac{1}{n}\sum_{i=0}^{n-1} V_{t-i}
Short Periods (3 - 7): Responsive to recent volume shifts, excellent for intraday analysis.
Long Periods (13 - 20): Smoother averaging, better for swing trading and higher timeframes.
DASHBOARD SYSTEM
Primary Flow Gauge
Bilaterally symmetric visualization displaying normalized flow direction and intensity:
Segment Calculation: n_{active} = \lfloor |N| \times 15 \rfloor
Left Fill: Bearish flow when N < -0.01
Right Fill: Bullish flow when N > 0.01
Neutral Display: Empty segments when |N| \leq 0.01
Visual Style Options:
Matrix: Digital blocks (▰/▱) for quantitative precision
Wave: Progressive patterns (▁▂▃▄▅▆▇█) showing flow buildup
Dots: LED-style indicators (●/○) with intensity scaling
Blocks: Modern squares (■/□) for professional appearance
Pulse: Progressive markers (⎯ to █) emphasizing intensity buildup
Flow Intensity Visualization
30-segment horizontal bar graph with mathematical fill logic:
Segment Fill: For i \in : filled if \frac{i}{29} \leq \frac{S}{100}
Color Coding System:
Orange (S > 66%): High intensity, strong directional conviction
Cyan (33% ≤ S ≤ 66%): Moderate intensity, developing bias
White (S < 33%): Low intensity, neutral conditions
Extreme Detection Indicators
Circular markers flanking the gauge with state-dependent illumination:
Activation: I > 2.0 \land |N| > 0.3
Bright Yellow: Active extreme conditions
Dim Yellow: Normal conditions
Metrics Display
Balance Value: Raw master flow output ( F_{master} ) showing absolute directional pressure
Z-Score Value: Statistical deviation ( z_{bounded} ) indicating historical context
Dynamic Narrative System
Context-sensitive interpretation based on mathematical thresholds:
Extreme Flow: I > 2.0 \land |N| > 0.6
Moderate Flow: 0.3 < |N| \leq 0.6
High Volatility: S > 50 \land |N| \leq 0.3
Neutral State: S \leq 50 \land |N| \leq 0.3
ALERT SYSTEM SPECIFICATIONS
Mathematical Trigger Conditions:
Extreme Bullish: I > 2.0 \land N > 0.6
Extreme Bearish: I > 2.0 \land N < -0.6
High Intensity: S > 80
Bullish Shift: N_t > 0.3 \land N_{t-1} \leq 0.3
Bearish Shift: N_t < -0.3 \land N_{t-1} \geq -0.3
TECHNICAL IMPLEMENTATION AND PERFORMANCE
Computational Architecture
The system employs efficient calculation methods minimizing processing overhead:
Single-pass mathematical operations for all components
Conditional visual rendering (executed only on final bar)
Optimized array operations using direct calculations
Real-Time Processing
The indicator updates continuously during bar formation, providing immediate feedback on changing market conditions. Statistical normalization ensures consistent interpretation across varying market regimes.
Market Applicability
Optimal performance in liquid markets with consistent volume patterns. May require parameter adjustment for:
Low-volume or after-hours sessions
News-driven market conditions
Highly volatile cryptocurrency markets
Ranging versus trending market environments
PRACTICAL APPLICATION FRAMEWORK
Market State Classification
This indicator functions as a comprehensive market condition assessment tool providing:
Trend Analysis: High intensity readings ( S > 66% ) with sustained directional bias indicate strong trending conditions suitable for momentum strategies.
Reversal Detection: Extreme readings ( I > 2.0 ) at key technical levels may signal potential trend exhaustion or reversal points.
Range Identification: Low intensity with neutral flow ( S < 33%, |N| < 0.3 ) suggests ranging market conditions suitable for mean reversion strategies.
Volatility Assessment: High intensity without clear directional bias indicates elevated volatility with conflicting pressures.
Integration with Trading Systems
The normalized output range facilitates integration with automated trading systems and position sizing algorithms. The statistical basis provides consistent interpretation across different market conditions and asset classes.
LIMITATIONS AND CONSIDERATIONS
This indicator combines established technical analysis methods and processes historical data without predicting future price movements. The system performs optimally in liquid markets with consistent volume patterns and may produce false signals in thin trading conditions or during news-driven market events. This indicator is provided for educational and analytical purposes only and does not constitute financial advice. Users should combine this analysis with proper risk management, position sizing, and additional confirmation methods before making any trading decisions. Past performance does not guarantee future results.
Note: The term "kernel" in this context refers to modular calculation components rather than mathematical kernel functions in the formal computational sense.
As quantitative analyst Ralph Vince noted: "The essence of successful trading lies not in predicting market direction, but in the systematic processing of market information and the disciplined management of probability distributions."
— Dskyz, Trade with insight. Trade with anticipation.
