মিন রিভার্সন মডেল
কোয়ান্টিটেটিভ ফান্ডের মিন রিভার্সন স্ট্র্যাটেজি মাস্টার করুন। Z-score অ্যানালাইসিস, বোলিঞ্জার ব্যান্ড রিভার্সন এবং স্ট্যাটিস্টিক্যাল আর্বিট্রাজ শিখুন।
Mean Reversion Models
Master mean reversion strategies used by quantitative funds. Learn z-score analysis, Bollinger Band reversion, pairs trading, and statistical arbitrage approaches.
The difference between gambling and trading is QUANTIFICATION. If you cannot express your edge as a number, you don't have an edge — you have a hope. Quantitative methods turn intuition into measurable, repeatable processes.
Core Principles
Quantitative trading is built on these foundations:
- Everything is probability — no certainty, only statistical edges
- Large sample sizes — individual trades are meaningless, systems matter
- Mathematical expectancy — positive expected value over time
- Risk of ruin — survival first, profits second
- Process over outcome — judge decisions, not results
Practical Framework
Step 1: Define Your Edge
- What is your win rate?
- What is your average win vs average loss?
- What is your expectancy per trade?
Step 2: Size Appropriately
- Use Kelly Criterion or fixed fractional sizing
- Never risk more than your edge justifies
- Account for correlation between positions
Step 3: Validate Statistically
- Minimum 100+ trade sample for significance
- Out-of-sample testing required
- Walk-forward validation for robustness
Step 4: Monitor & Adapt
- Track equity curve vs expected behavior
- Reduce size during drawdowns beyond 1 SD
- Pause if strategy degrades beyond 2 SD
The #1 killer of quantitative strategies is overfitting — finding patterns in historical noise that don't persist forward. Always use out-of-sample data and walk-forward testing. If a strategy looks "too good" in backtest, it probably IS too good to be true.