Courses / Master Trader Program

Quantitative Methods

Statistical edge-finding techniques

Quantitative trading uses statistical methods to find and exploit market inefficiencies.

Edge-finding methods: 1. Mean reversion testing: Z-score analysis on price deviations 2. Momentum factors: Rate of change and relative strength 3. Pair trading: Statistical arbitrage between correlated instruments 4. Regime switching models: Hidden Markov Models for market states 5. Machine learning: Classification of market conditions

Statistical significance: - Minimum 200 trades for system validation - P-value < 0.05 for statistical significance - Out-of-sample testing is mandatory - Bootstrap methods for confidence intervals

The most important quantitative concept: positive expected value over a large sample size is all you need.

Pro tip: If your system has positive expected value and you can execute it consistently, the math guarantees long-term profitability. Trust the math..

Common mistake: Believing a system works based on 20 trades. Better: 20 trades is meaningless statistically. Need 200+ for any confidence..

Key takeaways

• Statistical methods find market inefficiencies
• 200+ trades for statistical significance
• Out-of-sample testing is mandatory
• Positive expected value is the only edge you need

Quiz

• For statistical significance, you need: Answer: 200+ trades. 200+ trades provides sufficient sample size for statistical significance.

Educational content, not financial advice.