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Who published Quant Ml Trading?

mahmoud20138 (@mahmoud20138) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Quant Ml Trading by mahmoud20138 — Agent Skill

Name: Quant Ml Trading
Author: mahmoud20138

Quant Ml Trading

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", "se

mahmoud20138 4055b45 4.8 KB Updated Sep 17, 2026

File contents

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

Related Skills

---name: quant-ml-tradingdescription: 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", "se---> **Skill:** Quant Ml Trading | **Domain:** trading | **Category:** quantitative | **Level:** expert> **Tags:** `trading`, `quant`, `machine-learning`, `genetic`, `reinforcement-learning`, `signals`# Quant & ML Trading — Complete Toolkit## OverviewFive powerful tools for quantitative strategy work:1. **Strategy Validation** — backtest tearsheets, walk-forward OOS, Monte Carlo, sensitivity, A/B testing2. **Genetic Optimizer** — evolve strategy parameters using evolutionary algorithms3. **Decay Monitor** — detect when a live strategy is losing its edge4. **RL Trade Agent** — reinforcement learning agent (DQN) that learns to trade from market replay5. **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.mdParameter optimization / genetic algorithm / evolve strategy? → Load references/genetic-optimizer.mdStrategy decay / live vs backtest divergence / RL agent? → Load references/tensortrade-rl.mdCombine signals / meta-model / AI signal aggregation? → Load references/ai-signal-aggregator.mdQuant workflow / performance metrics / key formulas? → Load references/quantitative-trading.mdStats / time series / ARIMA / GARCH / regression / Fama-French? → Load references/statistics-timeseries.mdML models / feature engineering / regime detection / NLP? → Load references/ml-trading.mdBacktesting rules / execution algos / factor strategies (detail)? → Load references/backtesting-execution.mdData cleaning / feature pipeline / anomaly detection / persistent storage / ETL? → Load references/data-science-pipeline.mdMultiple topics? → Load all relevant reference files```## Anti-Overfitting Rules (ALL skills)1. Always use walk-forward OOS — never optimize on all available data2. Monte Carlo bootstrap required before any live deployment3. GA/RL results MUST be validated on unseen temporal data4. Meta-model must be re-trained monthly with rolling walk-forward5. Live vs backtest Sharpe gap > 0.5 = pause the strategy immediately------## Related Skills- [Ai Pattern Recognition](../ml-trading.md)- [Backtesting Sim](../backtesting-sim.md)- [Statistical Analysis](../statistics-timeseries.md)- [Algorithmic Strategies](../backtesting-sim.md)

Frequently asked questions