What are Trading Gates in this context?

Trading Gates are a series of sequential validation checks—such as risk limits, pattern matching, and confidence thresholds—that a signal must pass before an order is sent to the broker.

Why is normalization necessary for the predator-prey values?

Raw values in these dynamics tend to decay toward zero over time. Normalizing them into dominance ratios (0-1 range) ensures the filter remains effective regardless of the raw value magnitude.

Does this skill handle specific crypto trading limitations?

Yes, it includes logic to detect asset types and specifically block short-selling signals for cryptocurrencies on platforms like Alpaca that do not support them.

How does the Pattern Filter identify market regimes?

It uses normalized predator-prey dominance ratios (Lotka-Volterra dynamics) to determine if a market is trending (predator dominant) or mean-reverting (prey dominant).

Trading Gates & Pattern Filter

Name: Trading Gates & Pattern Filter
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Implements a multi-stage validation system for algorithmic trading that filters signals through pattern-matching and risk-management gates.

About

This skill enhances Claude's ability to manage complex algorithmic trading workflows by implementing a 'gate' architecture for order entry. It specializes in normalizing predator-prey dominance ratios to handle decaying signal values, ensuring trades only execute when high-win-rate patterns like VWAP reversion or momentum continuation are detected. It provides robust guardrails for portfolio exposure, risk management, and platform-specific constraints like crypto short-selling blocks, making it ideal for developers building or refining live trading systems with Alpaca or similar APIs.

Key Features

Multi-gate order validation including Confidence, Pattern, Risk, and Exposure checks
0 GitHub stars
High-win-rate pattern matching for VWAP Reversion (68%) and Momentum Continuation (71%)
Automated constraint handling for crypto assets and fractional share execution
Real-time trading gate status visualization for monitoring dashboards
Normalized predator-prey dominance ratio calculations for regime detection

Use Cases

Implementing safety guardrails and risk limits for live algorithmic trading bots
Reducing false-positive trade signals in high-volatility market regimes
Debugging trade rejection reasons through visual dashboard status indicators

About

Key Features

Multi-gate order validation including Confidence, Pattern, Risk, and Exposure checks
0 GitHub stars
High-win-rate pattern matching for VWAP Reversion (68%) and Momentum Continuation (71%)
Automated constraint handling for crypto assets and fractional share execution
Real-time trading gate status visualization for monitoring dashboards
Normalized predator-prey dominance ratio calculations for regime detection

Use Cases

Implementing safety guardrails and risk limits for live algorithmic trading bots
Reducing false-positive trade signals in high-volatility market regimes
Debugging trade rejection reasons through visual dashboard status indicators