How does this skill improve trading win rates?

It uses a 'Diagnose-before-Fix' approach to identify if a strategy's logic, such as trend following, is mismatched with the current market regime, like ranging markets.

Is it suitable for cryptocurrency trading?

Absolutely. The methodology applies to any asset class, though it emphasizes the importance of validating traditional stock-market research against specific crypto data.

What are the requirements for a valid backtest using this skill?

It recommends a minimum backtesting period of 180 days with at least 100 trades to ensure results are statistically significant and not just outliers.

Can it help with parameter tuning for trading bots?

Yes, it provides specific guidelines for adjusting SL/TP ratios, leverage, and entry thresholds based on historical expectancy and statistical significance.

What kind of risk management does it provide?

The skill evaluates maximum drawdown and provides risk-mitigation configurations, such as leverage reduction and stop-loss optimization, to protect capital.

Strategy Optimization for Finance

Name: Strategy Optimization for Finance
Author: miles990

bymiles990

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データサイエンスとML

Implements a systematic methodology for diagnosing, refining, and validating trading strategies to improve win rates and returns.

概要

This skill provides a structured six-phase framework—Analyze, Diagnose, Research, Implement, Validate, and Iterate—designed to take trading strategies from poor performance to target goals. It helps users identify root causes of underperformance, such as mismatching trend-following strategies with ranging markets, and provides specific implementation patterns for parameter tuning, regime detection, and backtesting validation. It is ideal for quant developers and financial analysts looking to automate the iterative process of strategy refinement within the Claude environment.

主な機能

Systematic 6-phase iterative optimization workflow
1 GitHub stars
Strategy-to-market mapping for Grid, Mean Reversion, and Trend Following
Market regime diagnosis using ADX and volatility indicators
Automated backtesting validation and risk management protocols
Expectancy calculation and performance health checkups

ユースケース

Optimizing SL/TP ratios and leverage for high-frequency trading configurations
Improving a trading bot's win rate when performance drops below 40%
Transitioning a trend-following strategy to a grid-based approach during market consolidation

概要

主な機能

Systematic 6-phase iterative optimization workflow
1 GitHub stars
Strategy-to-market mapping for Grid, Mean Reversion, and Trend Following
Market regime diagnosis using ADX and volatility indicators
Automated backtesting validation and risk management protocols
Expectancy calculation and performance health checkups

ユースケース

Optimizing SL/TP ratios and leverage for high-frequency trading configurations
Improving a trading bot's win rate when performance drops below 40%
Transitioning a trend-following strategy to a grid-based approach during market consolidation