মন্টে কার্লো সিমুলেশন ও ড্রডাউন এক্সপেক্টেন্সি
ট্রেডিংয়ের জন্য মন্টে কার্লো অ্যানালাইসিস মাস্টার করুন। হাজার হাজার সম্ভাব্য ইকুইটি কার্ভ সিমুলেট করে ড্রডাউন সম্ভাব্যতা ও রুইন রিস্ক বুঝুন।
Monte Carlo Simulations for Drawdown Expectancy
Master Monte Carlo analysis for trading. Simulate thousands of possible equity curves to understand drawdown probability, ruin risk, and confidence intervals.
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.