Quant ml trading

Skill mahmoud20138/Tradecraft/plugins/tradecraft/skills/quant-ml-trading

102 Claude Code skills across 7 categories -- trading strategies, Azure, VSCode extensions, AI prompts, and custom automation skills

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npx -y skills add mahmoud20138/Tradecraft --skill quant-ml-trading

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Complete quantitative and ML-powered trading toolkit — strategy validation, genetic optimization, decay monitoring, reinforcement learning, signal aggregation, data science pipelines, and statistical/quant foundations. Use this skill for: "generate a backtest report", "create tearsheet", "equity curve", "Monte Carlo simulation", "strategy report", "performance tearsheet", "backtest results", "drawdown analysis", "strategy statistics", "risk report", "publish backtest", "PDF report", "HTML report", "walk-forward", "out of sample validation", "rolling optimization", "WFO", "anchored walk forward", "parameter stability", "robustness test", "rolling backtest", "adaptive optimization", "parameter reoptimization", "stress test", "bootstrap simulation", "worst case scenario", "parameter sensitivity", "confidence interval", "ruin probability", "survival analysis", "tail risk", "heatmap", "parameter sweep", "optimization surface", "3D surface", "parameter landscape", "which parameters matter", "robust parameters", "sensitivity analysis", "parameter grid search", "A/B test strategies", "compare strategies", "which strategy is better", "statistical comparison", "strategy comparison", "head to head", "variant testing", "significance test", "optimize strategy", "evolve parameters", "genetic algorithm", "breed strategies", "parameter optimization", "auto-optimize", "find best parameters", "evolutionary search", "mutation", "crossover", "fitness function", "population-based optimization", "is my strategy still working", "strategy decay", "alpha decay", "edge erosion", "performance degradation", "parameter drift", "when to retire a strategy", "strategy health check", "is the edge gone", "strategy monitoring", "live vs backtest divergence", "reinforcement learning trading", "RL agent", "deep Q-network trading", "PPO trading", "train an agent", "learn from replay", "reward function trading", "AI agent that learns to trade", "gym trading environment", "state space market", "action space trading", "combine all signals", "aggregate strategies", "meta strategy", "AI signal", "ensemble signal", "which signal to follow", "best signal now", "combine everything", "master signal", "AI recommendation", "backtesting framework", "walk-forward analysis", "overfitting prevention", "survivorship bias", "look-ahead bias", "performance metrics", "Sharpe", "Sortino", "Calmar", "Information Ratio", "win rate", "profit factor", "expectancy", "R-multiple", "SQN", "ARIMA models", "GARCH volatility", "regression analysis", "Fama-French factors", "machine learning trading", "random forest", "XGBoost", "LSTM", "feature engineering for trading", "market regime detection", "NLP sentiment analysis", "algorithmic execution", "TWAP", "VWAP", "implementation shortfall", "factor investing", "value", "momentum", "size", "quality", "low volatility", "statistical arbitrage", "cointegration", "mean reversion statistics", "Z-score trading", "analyze trading data", "build ML models on price data", "feature engineering on market data", "store retrieve analysis results", "run data science on my pairs", "find patterns in the data", "statistical analysis of EURUSD", "cluster market regimes", "anomaly detection on price", "store my analysis", "retrieve past analysis", "data pipeline", "machine learning on forex data", "train a model", "feature importance", "large-scale data processing", "ETL", "persistent result storage", "data science pipeline", "data cleaning", "data validation", "OHLCV cleaning", or any strategy validation, genetic optimization, decay detection, RL trading, ML signal aggregation, quantitative analysis, or data science task applied to trading.

SKILL.md

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Skill: Quant Ml Trading | Domain: trading | Category: quantitative | Level: expert Tags: trading, quant, machine-learning, genetic, reinforcement-learning, signals

Quant & ML Trading — Complete Toolkit

Overview

Five powerful tools for quantitative strategy work:

Strategy Validation — backtest tearsheets, walk-forward OOS, Monte Carlo, sensitivity, A/B testing
Genetic Optimizer — evolve strategy parameters using evolutionary algorithms
Decay Monitor — detect when a live strategy is losing its edge
RL Trade Agent — reinforcement learning agent (DQN) that learns to trade from market replay
AI Signal Aggregator — combine signals from all strategies using weighted voting + ML meta-model

Reference Files

File	Contents
`references/strategy-validation.md`	Tearsheet, walk-forward WFO, Monte Carlo, sensitivity heatmaps, A/B testing
`references/genetic-optimizer.md`	Gene encoding, fitness functions, GA engine, anti-overfitting safeguards
`references/tensortrade-rl.md`	Strategy decay monitor (CUSUM, rolling Sharpe, live vs backtest) + RL trading environment + DQN agent
`references/ai-signal-aggregator.md`	Weighted voting, ML meta-learner, confidence calibration, trade decision engine
`references/quantitative-trading.md`	Quant workflow diagram, performance metrics table, pitfalls table, key formulas (Sharpe/Kelly/Z-score/IR/Expectancy)
`references/statistics-timeseries.md`	Descriptive stats, return distributions, stationarity (ADF/KPSS), ARIMA models, GARCH volatility, regression, Fama-French factors, cointegration
`references/ml-trading.md`	Supervised learning (XGBoost/LSTM/RF configs), feature engineering, regime detection (K-Means/HMM/GMM), RL, NLP sentiment, model validation
`references/backtesting-execution.md`	10 backtesting rules, walk-forward, Monte Carlo, detailed performance metrics, 8 pitfalls, TWAP/VWAP/IS execution, factor strategies
`references/data-science-pipeline.md`	DataPipeline (OHLCV clean/validate/resample), FeatureEngine, StatisticalAnalysis, ModelFactory, TradingDataStore (persistent storage)

Quick Decision Guide

Strategy tearsheet / backtest report / Monte Carlo?
  → Load references/strategy-validation.md

Parameter optimization / genetic algorithm / evolve strategy?
  → Load references/genetic-optimizer.md

Strategy decay / live vs backtest divergence / RL agent?
  → Load references/tensortrade-rl.md

Combine signals / meta-model / AI signal aggregation?
  → Load references/ai-signal-aggregator.md

Quant workflow / performance metrics / key formulas?
  → Load references/quantitative-trading.md

Stats / time series / ARIMA / GARCH / regression / Fama-French?
  → Load references/statistics-timeseries.md

ML models / feature engineering / regime detection / NLP?
  → Load references/ml-trading.md

Backtesting rules / execution algos / factor strategies (detail)?
  → Load references/backtesting-execution.md

Data cleaning / feature pipeline / anomaly detection / persistent storage / ETL?
  → Load references/data-science-pipeline.md

Multiple topics?
  → Load all relevant reference files

Anti-Overfitting Rules (ALL skills)

Always use walk-forward OOS — never optimize on all available data
Monte Carlo bootstrap required before any live deployment
GA/RL results MUST be validated on unseen temporal data
Meta-model must be re-trained monthly with rolling walk-forward
Live vs backtest Sharpe gap > 0.5 = pause the strategy immediately