Is quant-ml-trading safe to install?

SaferSkills independently audited quant-ml-trading (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

plugins/tradecraft/skills/quant-ml-trading/SKILL.md · 1 file

plugins/tradecraft/skills/quant-ml-trading/SKILL.md7.7 KB · Markdown

name: quant-ml-trading

description: >

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.

related_skills:

- ml-trading

- backtesting-sim

- statistics-timeseries

- backtesting-sim

tags:

- trading

- quant

- machine-learning

- genetic

- reinforcement-learning

- signals

skill_level: expert

kind: reference

category: trading/quant

status: active

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

quant-ml-trading— agent skill

Is quant-ml-trading safe to install?

Quant & ML Trading — Complete Toolkit

Overview

Reference Files

Quick Decision Guide

Anti-Overfitting Rules (ALL skills)

Audit the pieces. Scan the whole. Decide.

Is quant-ml-trading safe to install?

Quant & ML Trading — Complete Toolkit

Overview

Reference Files

Quick Decision Guide

Anti-Overfitting Rules (ALL skills)

Related Skills

Audit the pieces. Scan the whole. Decide.