How does volatility affect the model's behavior?

Higher local volatility triggers a higher predator growth rate, reflecting the tendency for momentum-based strategies to thrive during volatile market conditions.

What is the primary benefit of the Adaptive Predator-Prey skill?

It allows trading models to dynamically adjust their sensitivity to market changes, preventing the performance degradation often seen with fixed-coefficient models in shifting environments.

What mathematical model does this skill utilize?

It is based on the Lotka-Volterra equations, modeling the interaction between 'Predators' (trend followers) and 'Prey' (mean-reversion traders) to detect market regimes.

How does the exponential decay cooldown improve trading?

Instead of a binary gate that blocks all trades for a set period, it gradually increases the allowed edge, enabling cautious, partial re-entry as confidence grows.

Adaptive Predator-Prey Dynamics

Name: Adaptive Predator-Prey Dynamics
Author: smith6jt-cop

bysmith6jt-cop

数据科学与机器学习

Optimizes trading regime detection by dynamically adjusting Lotka-Volterra coefficients based on real-time market volatility and momentum.

关于

This skill enhances Claude's ability to manage and tune complex trading signals using adaptive predator-prey dynamics. It replaces static parameters with market-responsive variables, allowing for faster predator growth during high volatility and stronger regime interactions during momentum shifts. By implementing an exponential decay cooldown and strengthening macro-regime influence, the skill ensures that fine-scale trading chains remain synchronized with broader market trends while providing a sophisticated, risk-managed approach to trade re-entry.

主要功能

Enhanced macro-regime influence weighting (increased from 12% to 30%)
Volatility-adaptive predator growth rates using dynamic 'a' coefficients
0 GitHub stars
Exponential decay cooldown for gradual, non-binary trade re-entry
Momentum-responsive interaction scaling for decisive regime shifts
Dynamic volatility gating based on historical percentile scaling

使用场景

Automated regime detection in algorithmic trading systems
Fine-tuning signal sensitivity in high-volatility market environments
Implementing graduated position scaling and risk-managed re-entry logic

关于

主要功能

Enhanced macro-regime influence weighting (increased from 12% to 30%)
Volatility-adaptive predator growth rates using dynamic 'a' coefficients
0 GitHub stars
Exponential decay cooldown for gradual, non-binary trade re-entry
Momentum-responsive interaction scaling for decisive regime shifts
Dynamic volatility gating based on historical percentile scaling

使用场景

Automated regime detection in algorithmic trading systems
Fine-tuning signal sensitivity in high-volatility market environments
Implementing graduated position scaling and risk-managed re-entry logic