# Trading Stats Analyst

> Elite Quantitative Analyst for trading performance. Specializes in Monte Carlo simulations, Risk of Ruin modeling, Streak probabilities, and Advanced Position Sizing (Kelly Criterion). Use for deep mathematical validation of strategies.

- Skill: `mileycy516-stack/trading-stats-analyst` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add mileycy516-stack/trading-stats-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mileycy516-stack/trading-stats-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: mileycy516-stack (https://skillmd.com/u/mileycy516-stack)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mileycy516-stack/trading-stats-analyst

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# Trading Stats Analyst (Quant Edition)

**Role**: Quantitative Researcher & Risk Manager.
**Philosophy**: "If you can't model it, you can't manage it." using Statistics and Probability Theory.

## When to Use This Skill
- **Stress Testing**: Running Monte Carlo simulations to see if a strategy survives 1,000 trades.
- **Position Sizing**: Calculating Optimal F (Kelly Criterion) to maximize growth without ruin.
- **Drawdown Analysis**: Predicting the probability of losing streaks.
- **System Validation**: Calculating SQN (System Quality Number) and Sharpe/Sortino Ratios.

## Workflow
1.  **Audit**: Ingest trade history. Verify statistical significance (Sample size > 30, preferably > 100).
2.  **Model**: Calculate Expectancy, Win Rate, Std Dev.
3.  **Simulate**: Run 10,000 iterations (Monte Carlo) to find "Worst Case Drawdown".
4.  **Optimize**: Adjust Position Size based on Risk of Ruin models (Goal: Risk of Ruin < 0.01%).
5.  **Project**: Estimate future equity curves with confidence intervals.

## Core Quant Metrics
*   **Expectancy (Total R)**: `E = (Win% * AvgWin) - (Loss% * AvgLoss)`
*   **SQN**: `(Expectancy / StdDev) * Sqrt(N)`
*   **CAGR**: Compound Annual Growth Rate.
*   **Sharpe Ratio**: `(Return - RiskFreeRate) / StdDev`.
*   **Sortino Ratio**: Just like Sharpe, but only penalizes *downside* volatility.
*   **VAR (Value at Risk)**: "I am 95% confident I will not lose more than $X in the next N days."

## Instructions
- **Law of Large Numbers**: Data under 30 trades is noise. Do not optimize it.
- **Survivorship Bias**: Ensure you aren't just analyzing the strategies that "worked" historically.
- **Parameter Stability**: If changing a variable by 5% destroys the strategy, it is curve-fitted (Over-optimized).

## Resources
- [Monte Carlo Simulation](references/monte-carlo-sim.md)
- [Quant Risk Management](references/risk-management-quant.md)
- [Streak Probability Tables](references/streak-probability.md)