📊 Market Index Backtest & ETF Probability Analyzer

Stop trading on emotions and pure gut feeling. Get 100% deterministic, data-backed historical win rates and expected returns for 2X Leveraged and -2X Inverse ETFs before placing your trades.

💡 Executive Summary & Core Value Proposition

When the market crashes or surges abruptly, emotional retail traders often panic sell at the bottom or FOMO buy at the top. The Market Index Backtest Analyzer provides an empirical, quantitative reality check.

By analyzing 5 to 10 years of historical daily OHLCV data across major Korean (KOSPI, KOSDAQ) and US indices (S&P 500, NASDAQ), this skill computes the exact mathematical probability of a rebound or further continuation.

• Eliminate Emotion: Base your position sizing on hard mathematical win rates rather than fear.
• Bi-Directional Statistical Edge: Evaluate both long (Bull 2X) and short (Bear -2X) probabilities simultaneously.
• Deterministic Python Engine: Zero LLM guesswork. Powered by pure statistical computation via financial data APIs.

⚡ Key Features & Capabilities

1. Multi-Index & Multi-Asset Support

• South Korean Markets: KOSPI (^KS11), KOSDAQ (^KQ11), KODEX 200, KODEX Leveraged, KODEX Inverse 2X.
• US Global Markets: S&P 500 (^GSPC), NASDAQ 100 (^NDX), TQQQ, SQQQ, SOXL, SOXS.

2. Multi-Timeframe Forward Horizons

Computes historical performance over three critical post-trigger horizons:

• T+1 Horizon: Next-day scalp & day-trading probability.
• T+3 Horizon: Short-term swing trading edge.
• T+5 Horizon: Weekly position holding probability.

3. Comprehensive Risk Metrics

• Win Rate (%): Percentage of historical instances resulting in positive returns.
• Average Return (%): Mean expected gain/loss across all historical sample instances.
• Maximum Drawdown (MDD): Worst-case historical loss suffered during the holding period.

🚀 How It Works (4-Step Engine Pipeline)

1. User Input Specification: You specify the index and condition (e.g., "KOSPI daily drop >= -2.0%" or "NASDAQ 3 consecutive down days").
2. Historical Data Scanning: The embedded Python engine fetches raw price action data across thousands of historical candles.
3. Statistical Aggregation: Computes forward returns for 2X Leveraged and -2X Inverse proxies.
4. Actionable Report Output: Generates an immediate visual matrix sheet with clear probability breakdowns.

📋 Sample Detailed Output Report

================================================================================
[MARKET BACKTEST REPORT: KOSPI Daily Drop >= -2.0%]
- Data Range: Last 5 Years Historical Sample
- Total Historical Trigger Instances Matched: 42 Days
================================================================================

[STRATEGY 1: 2X LEVERAGED ETF (BULL REBOUND)]
- T+1 Horizon : Win Rate 64.3% | Avg Return +1.12%
- T+3 Horizon : Win Rate 57.1% | Avg Return +0.85%
- T+5 Horizon : Win Rate 61.9% | Avg Return +1.40%

[STRATEGY 2: -2X INVERSE ETF (BEAR CONTINUATION)]
- T+1 Horizon : Win Rate 35.7% | Avg Return -1.12%
- T+3 Horizon : Win Rate 42.9% | Avg Return -0.85%
- T+5 Horizon : Win Rate 38.1% | Avg Return -1.40%

[STATISTICAL SUMMARY]
✔ Primary Edge: T+1 Bull Rebound shows a strong 64.3% win rate with positive expectancy (+1.12%).
✔ Risk Warning: Inverse strategy exhibits negative expected value across all forward horizons under this specific condition.
================================================================================

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## 🎯 Target Audience & Use Cases

- **Day Traders & Scalpers**: Instantly assess if a market crash creates a high-probability bounce setup for tomorrow morning.
- **Leverage ETF Traders (TQQQ / SOXL / KODEX 200)**: Avoid holding high-decay leverage products against statistical odds.
- **Inverse ETF Traders (SQQQ / SOXS)**: Verify if market breakdown conditions actually have historical continuation momentum.

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## ❓ Frequently Asked Questions (FAQ)

**Q1. Does this skill predict the future price?**
No. No tool can predict the future with 100% certainty. This skill provides **empirical historical probabilities** based on past data so you can trade with a statistical edge rather than guessing.

**Q2. Is the data updated in real-time?**
Yes. The underlying Python engine fetches up-to-date market data via financial APIs upon every execution.

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### ⚠️ Legal Disclaimer
*This Agent Skill provides historical quantitative data and backtested statistical analysis for informational and educational purposes only. It does not constitute financial, investment, or trading advice. Past market performance is no guarantee of future results.*