Rachev Regime AnalyzerRachev Regime Analyzer ~ GForge
What It Does
Measures the ratio of extreme gains to extreme losses to identify whether markets favor bulls or bears. When your best moves are bigger than your worst moves, conditions are bullish. When the opposite is true, conditions are bearish.
Simple Interpretation:
Ratio > 1.2 → Bullish regime (tail gains exceed tail losses)
Ratio < 0.8 → Bearish regime (tail losses exceed tail gains)
Between → Neutral/transitional
Key Features
Two Modes:
Single Asset: Analyze current chart
Multi-Asset: Aggregate regime across 5 assets with custom weights (great for gauging overall crypto/market conditions)
Customizable:
Lookback period (20-200 bars)
Tail percentile (what counts as "extreme")
Bullish/bearish thresholds
6 color schemes
Optional MA smoothing
Visual Signals:
Buy/sell markers at threshold crosses
Background regime coloring
Info table with current values and confidence score
Configurable alerts
How to Use
Choose lookback period based on your timeframe (40-60 bars is a good start)
Watch for threshold crosses - these mark regime changes
Check confidence score - higher = more reliable
Use multi-asset mode to see if entire market is shifting (not just one coin)
Best combined with: Trend indicators, support/resistance, volume analysis
Parameters
Lookback: More bars = smoother, less responsive
Alpha (0.10): Defines extreme events - lower = more extreme
Thresholds: Adjust based on asset volatility
Return Type: Log returns recommended for most assets
What Makes It Useful
Unlike simple volatility measures, this shows asymmetry - whether extreme moves favor upside or downside. A ratio of 1.5 means your extreme gains are 50% larger than extreme losses - that's actionable information about risk-reward dynamics.
Multi-asset aggregation is particularly powerful for crypto traders wanting to gauge if BTC, ETH, SOL, etc. are all showing similar regime characteristics.
Disclaimer
Educational tool only. Not financial advice. Use proper risk management. No indicator works in isolation - always consider broader market context.
Developed by GForge
Comments and feedback welcome! 👍
Tailrisk
EVaR Indicator and Position SizingThe Problem:
Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress.
Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing.
The Solution: Entropic Value at Risk (EVAR)
EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets.
Entropy is more adaptive than standard deviations and volatility measures.
I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui.
Key advantages of EVAR over traditional risk measures:
Superior tail risk capture: More accurately quantifies the probability of extreme market moves
Adaptability to market regimes: Self-calibrates to changing volatility environments
Non-parametric flexibility: Makes less assumptions about the underlying return distribution
Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :)
Mathematically, EVAR is defined as:
EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))}
Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior.
Technical Implementation
This indicator implements EVAR through a q-exponential approach from Tsallis statistics:
Returns Calculation: Price returns are calculated over the lookback period
Moment Generating Function: Approximated using q-exponentials to account for fat tails
EVAR Computation: Derived from the MGF and confidence parameter
Normalization: Scaled to for intuitive visualization
Position Sizing: Inversely modulated based on normalized EVAR
The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions.
Indicator Components
1. EVAR Risk Visualization
Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1)
Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones
2. Position Sizing Matrix
Risk Assessment: Current risk level and raw EVAR value
Position Recommendations: Percentage allocation, dollar value, and quantity
Stop Parameters: Mathematically derived stop price with percentage distance
Drawdown Projection: Maximum theoretical loss if stop is triggered
Interpretation and Application
The normalized EVAR reading provides a probabilistic risk assessment:
< 0.3: Low risk environment with minimal tail concerns
0.3-0.5: Moderate risk with standard tail behavior
0.5-0.7: Elevated risk with increased probability of significant moves
> 0.7: High risk environment with substantial tail risk present
Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated.
Parameter Optimization
For optimal risk assessment across market conditions:
Lookback Period: Determines the historical window for risk calculation
Q Parameter: Controls tail sensitivity (higher values increase conservatism)
Confidence Level: Sets the statistical threshold for risk assessment
For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches.
Practical Applications
Adaptive Risk Management: Quantify and respond to changing tail risk conditions
Volatility-Normalized Positioning: Maintain consistent exposure across market regimes
Black Swan Detection: Early identification of potential extreme market conditions
Portfolio Construction: Apply consistent risk-based sizing across diverse instruments
This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets.
Enjoy!
