Initial commit with translated description

references/methodology.md

@@ -0,0 +1,227 @@
+# Backtesting Methodology Reference
+
+## Table of Contents
+
+1. Core Testing Techniques
+2. Stress Testing Methods
+3. Parameter Sensitivity Analysis
+4. Slippage and Friction Modeling
+5. Sample Size Guidelines
+6. Market Regime Analysis
+7. Common Pitfalls and Biases
+
+## 1. Core Testing Techniques
+
+### "Beat Ideas to Death" Approach
+
+**Core principle**: Add friction and punishment to find strategies that break the least, not those that profit the most on paper.
+
+**Key techniques**:
+- Multiple stop loss variations
+- Different profit targets
+- Realistic + exaggerated commissions
+- Worst-case fills
+- Extended time periods
+- Multiple market regimes
+
+### The 80/20 Rule for R&D Time
+
+- 20% generating and codifying ideas
+- 80% stress testing and trying to break them
+
+## 2. Stress Testing Methods
+
+### Execution Friction Tests
+
+**Required friction additions**:
+- Realistic commissions (actual broker rates)
+- Pessimistic slippage (1.5-2x typical)
+- Worst-case entry fills (ask + 1-2 ticks)
+- Worst-case exit fills (bid - 1-2 ticks)
+- Order rejection scenarios
+- Partial fills
+
+### Parameter Robustness Tests
+
+Test across multiple configurations:
+- Entry timing variations (±15-30 minutes)
+- Stop loss distances (50%, 75%, 100%, 125%, 150% of baseline)
+- Profit targets (80%, 90%, 100%, 110%, 120% of baseline)
+- Position sizing rules
+- Filter thresholds
+
+**Goal**: Find "plateau" performance where small parameter changes don't drastically alter results.
+
+### Time-Based Robustness
+
+**Minimum requirements**:
+- Test across at least 5-10 years
+- Include multiple market regimes:
+  - Bull markets
+  - Bear markets
+  - High volatility periods
+  - Low volatility periods
+  - Trending markets
+  - Range-bound markets
+
+**Year-by-year analysis**: Strategy should show positive expectancy in majority of years, not rely on 1-2 exceptional years.
+
+## 3. Parameter Sensitivity Analysis
+
+### Heat Map Analysis
+
+Create 2D heat maps varying two parameters simultaneously:
+- Profit target (rows) × Stop loss (columns)
+- Entry time (rows) × Exit time (columns)
+- Volatility filter (rows) × Volume filter (columns)
+
+**Interpretation**:
+- Robust strategies show "plateaus" of consistent performance
+- Fragile strategies show "spikes" or narrow optimal ranges
+- Avoid strategies with performance cliffs at parameter boundaries
+
+### Walk-Forward Analysis
+
+1. Optimize parameters on training period (e.g., Year 1-2)
+2. Test with those parameters on validation period (Year 3)
+3. Roll forward and repeat
+4. Compare in-sample vs out-of-sample performance
+
+**Warning signs**:
+- Out-of-sample performance <50% of in-sample
+- Frequent need to re-optimize parameters
+- Parameters that change dramatically between periods
+
+## 4. Slippage and Friction Modeling
+
+### Realistic Slippage Assumptions
+
+**By market capitalization**:
+- Mega cap (>$200B): 0.01-0.02%
+- Large cap ($10B-$200B): 0.02-0.05%
+- Mid cap ($2B-$10B): 0.05-0.10%
+- Small cap ($300M-$2B): 0.10-0.20%
+- Micro cap (<$300M): 0.20-0.50%+
+
+**By order type**:
+- Market orders: Higher slippage
+- Limit orders: Lower slippage but potential non-fills
+- Stop orders: Significant slippage in volatile conditions
+
+### Conservative Testing Approach
+
+Use 1.5-2x typical slippage estimates for stress testing:
+- If typical slippage is 0.05%, test with 0.075-0.10%
+- If typical is 0.10%, test with 0.15-0.20%
+
+**Rationale**: Strategies that survive pessimistic assumptions often perform better in practice than in backtests.
+
+## 5. Sample Size Guidelines
+
+### Minimum Trade Requirements
+
+**Statistical significance thresholds**:
+- Absolute minimum: 30 trades
+- Preferred minimum: 100 trades
+- High confidence: 200+ trades
+
+**Why large samples matter**:
+- Reduces impact of outliers
+- Provides statistical confidence
+- Reveals true edge vs luck
+
+### Time Period Considerations
+
+**Minimum testing period**: 5 years
+**Preferred testing period**: 10+ years
+
+**Must include**:
+- At least one full market cycle
+- Multiple volatility regimes
+- Different Federal Reserve policy environments
+
+## 6. Market Regime Analysis
+
+### Regime Classification
+
+**Volatility-based regimes**:
+- Low volatility: VIX <15
+- Normal volatility: VIX 15-25
+- High volatility: VIX 25-35
+- Extreme volatility: VIX >35
+
+**Trend-based regimes**:
+- Strong uptrend: Market +10%+ over 6 months
+- Moderate uptrend: Market +5% to +10% over 6 months
+- Sideways: Market -5% to +5% over 6 months
+- Downtrend: Market <-5% over 6 months
+
+### Performance Requirements by Regime
+
+**Robust strategy characteristics**:
+- Positive expectancy in majority of regimes
+- Acceptable (not necessarily best) in all regimes
+- No catastrophic failures in any single regime
+- Understanding of which regime causes weakness
+
+## 7. Common Pitfalls and Biases
+
+### Survivorship Bias
+
+**Issue**: Testing only on currently-trading stocks ignores delisted/bankrupt companies.
+
+**Solution**: Use survivorship-bias-free datasets that include historical delistings.
+
+### Look-Ahead Bias
+
+**Issue**: Using information not available at the time of trade.
+
+**Examples**:
+- Using EOD data for intraday decisions
+- Using next-day's open for today's close decisions
+- Calculating indicators with future data points
+
+**Prevention**: Strict timestamp control and data alignment checks.
+
+### Curve-Fitting (Over-Optimization)
+
+**Warning signs**:
+- Too many parameters (>5-7)
+- Highly specific parameter values (e.g., RSI = 37.3)
+- Perfect backtest results
+- Large performance drop in validation period
+
+**Prevention techniques**:
+- Limit parameters to essential ones only
+- Use round numbers when possible
+- Require out-of-sample testing
+- Analyze parameter sensitivity
+
+### Sample Selection Bias
+
+**Issue**: Testing only on hand-picked examples (e.g., known market leaders).
+
+**Problem**: Ignoring all stocks that met criteria but failed creates false impression of strategy quality.
+
+**Solution**: Test on ALL historical examples meeting the criteria, not just successful outcomes.
+
+### Hindsight Bias
+
+**Issue**: Using outcome knowledge to influence decisions.
+
+**Prevention for systematic trading**:
+- Define all rules in advance
+- No manual intervention based on hindsight
+- Test rules across all cases, not cherry-picked examples
+
+### Data Mining Bias
+
+**Issue**: Testing hundreds of strategies until finding one that "works" by random chance.
+
+**Risk**: With enough attempts, random data will produce seemingly profitable patterns.
+
+**Mitigation**:
+- Have hypothesis before testing
+- Require economic logic for the edge
+- Use Bonferroni correction for multiple comparisons
+- Demand higher significance thresholds (p < 0.01 instead of p < 0.05)