# Phase 3: Optimization Tools Implementation
## Overview
Implement 4 advanced optimization and analysis tools that enable autonomous strategy improvement.
## Status: Planning
**Start Date**: 2025-11-26
**Estimated Duration**: 2-3 weeks
## Phase 3 Tools (4 of 16)
### 1. `optimize()` - Hyperparameter Optimization
**Purpose**: Automatically find optimal strategy parameters using Optuna
**Input Parameters**:
- `strategy`: Strategy name or code
- `symbol`: Trading symbol
- `timeframe`: Candlestick timeframe
- `start_date`/`end_date`: Backtest date range
- `param_space`: Parameters to optimize (name, min, max, step)
- `metric`: Optimization metric (profit, sharpe, calmar, etc.)
- `n_trials`: Number of optimization trials (default: 100)
- `n_jobs`: Parallel jobs (default: 4)
**Output**:
- Best parameters found
- Best metric value
- Trial history
- Convergence plot data
- Time elapsed
**Implementation Approach**:
```python
# Pseudo-code
def optimize(strategy, symbol, timeframe, param_space, ...):
study = optuna.create_study(direction="maximize")
def objective(trial):
params = {
name: trial.suggest_float(name, min_val, max_val)
for name, min_val, max_val in param_space
}
result = backtest(strategy, symbol, timeframe, params)
return result[optimization_metric]
study.optimize(objective, n_trials=n_trials, n_jobs=n_jobs)
return study.best_params, study.best_value, study.trials
```
### 2. `walk_forward()` - Walk-Forward Analysis
**Purpose**: Validate strategy across different market periods to detect overfitting
**Input Parameters**:
- `strategy`: Strategy name
- `symbol`: Trading symbol
- `timeframe`: Candlestick timeframe
- `start_date`/`end_date`: Full date range
- `in_sample_period`: Days for optimization (default: 365)
- `out_sample_period`: Days for validation (default: 30)
- `step_forward`: Days to move window (default: 7)
- `hyperparameters`: Parameters to use for each walk
**Output**:
- Per-period statistics (in-sample vs out-sample)
- Degradation metrics (overfitting indicators)
- Equity curves for each period
- Statistical significance tests
**Implementation Approach**:
```python
def walk_forward(strategy, symbol, timeframe, ...):
results = []
current_start = start_date
while current_start < end_date:
in_sample_end = current_start + timedelta(days=in_sample_period)
out_sample_end = in_sample_end + timedelta(days=out_sample_period)
# Optimize on in-sample
optimized_params = optimize(strategy, ..., current_start, in_sample_end)
# Validate on out-sample
validation = backtest(strategy, ..., in_sample_end, out_sample_end, optimized_params)
results.append({
'in_sample': optimization,
'out_sample': validation,
'degradation': calculate_degradation(optimization, validation)
})
current_start += timedelta(days=step_forward)
return results
```
### 3. `backtest_batch()` - Parallel Backtesting
**Purpose**: Run multiple backtests concurrently for strategy comparison
**Input Parameters**:
- `strategy`: Strategy name
- `symbols`: List of trading symbols
- `timeframes`: List of timeframes
- `start_date`/`end_date`: Backtest period
- `hyperparameters`: Parameter sets to test
- `concurrent_limit`: Max parallel tests (default: 4)
**Output**:
- Results for each symbol/timeframe/params combination
- Comparison matrix
- Overall statistics
- Top-performing configurations
**Implementation Approach**:
```python
async def backtest_batch(strategy, symbols, timeframes, param_sets):
tasks = []
for symbol in symbols:
for timeframe in timeframes:
for params in param_sets:
tasks.append(
asyncio.create_task(
backtest_async(strategy, symbol, timeframe, params)
)
)
results = await asyncio.gather(*tasks)
return organize_results(results)
```
### 4. `analyze_results()` - Deep Results Analysis
**Purpose**: Extract insights from backtest results (trades, drawdown, correlations, etc.)
**Input Parameters**:
- `backtest_result`: Result from backtest() or backtest_batch()
- `analysis_type`: "basic", "advanced", "deep" (default: "basic")
- `include_trade_analysis`: Boolean
- `include_correlation`: Boolean
- `include_monte_carlo`: Boolean
**Output**:
- Trade-level analysis (Win%, Avg Win/Loss, Consecutive wins/losses, etc.)
- Drawdown analysis (Max DD, DD Duration, Recovery time)
- Return distribution (mean, std, skewness, kurtosis)
- Correlation with markets/benchmarks
- Risk-adjusted metrics (Sharpe, Sortino, Calmar, etc.)
- Monte Carlo projections
- ML-based pattern detection
**Implementation Approach**:
```python
def analyze_results(result, analysis_type):
analysis = {}
if 'basic' in analysis_type:
analysis['trades'] = analyze_trades(result['trades'])
analysis['drawdown'] = analyze_drawdown(result['equity_curve'])
analysis['returns'] = analyze_returns(result['equity_curve'])
if 'advanced' in analysis_type:
analysis['risk_metrics'] = calculate_risk_metrics(result['equity_curve'])
analysis['correlation'] = calculate_correlation(result, benchmarks)
analysis['consecutive'] = analyze_consecutive_wins(result['trades'])
if 'deep' in analysis_type:
analysis['monte_carlo'] = monte_carlo_analysis(result)
analysis['pattern'] = detect_patterns(result['trades'])
analysis['ml_insights'] = ml_analysis(result)
return analysis
```
## Dependency Status
### Required Libraries (Already in jesse/requirements.txt)
- ✅ `optuna~=4.2.0` - Hyperparameter optimization
- ✅ `numpy`, `pandas` - Data analysis
- ✅ `scipy` - Statistical analysis
- ✅ `scikit-learn` - Optional ML patterns (not in current reqs, may need to add)
### Integration Points
1. All tools depend on the existing `backtest()` tool from Phase 2
2. Tools should use the `JesseWrapper` class for consistency
3. Error handling should be consistent with Phase 2 implementation
## Implementation Timeline
### Week 1: Foundation
- [ ] Set up Optuna integration framework
- [ ] Implement `optimize()` with single-metric support
- [ ] Add parallel execution support
- [ ] Create integration tests
### Week 2: Walk-Forward & Batch
- [ ] Implement `walk_forward()` analysis
- [ ] Add window management and period handling
- [ ] Implement `backtest_batch()` with async support
- [ ] Create comparison utilities
### Week 3: Analysis & Polish
- [ ] Implement `analyze_results()` with basic analysis
- [ ] Add advanced trade analysis metrics
- [ ] Create visualization data generators
- [ ] Comprehensive testing and documentation
## Design Patterns
### Pattern 1: Result Chaining
```python
# Allows tools to work together
result = mcp_client.backtest(strategy, symbol, ...)
optimized = mcp_client.optimize(strategy, symbol, ..., initial_result=result)
analysis = mcp_client.analyze_results(optimized)
```
### Pattern 2: Progressive Analysis
```python
# Depth control for different use cases
basic = analyze_results(result, 'basic') # Fast, lightweight
advanced = analyze_results(result, 'advanced') # Medium detail
deep = analyze_results(result, 'deep') # Slow, comprehensive
```
### Pattern 3: Batch Processing
```python
# Efficient multi-test runs
batch_result = backtest_batch(
strategy='my_strat',
symbols=['BTC-USDT', 'ETH-USDT'],
timeframes=['1h', '4h'],
hyperparameters=[params1, params2, params3]
)
# Results organized by symbol/timeframe/params
```
## Technical Challenges & Solutions
### Challenge 1: Jesse Installation Complexity
**Issue**: Jesse has many complex dependencies (Redis, PostgreSQL, Rust, etc.)
**Solution**:
- Create mock `JesseWrapper` for development/testing
- Use containerized Jesse for production
- Abstract away Jesse specifics in integration layer
### Challenge 2: Long-Running Operations
**Issue**: Optimize/Walk-Forward can take hours
**Solution**:
- Implement proper timeout handling
- Add progress callbacks
- Store intermediate results
- Allow resumption of interrupted operations
### Challenge 3: Memory Management
**Issue**: Loading 1000s of candles for parallel backtests uses lots of RAM
**Solution**:
- Implement data caching at Jesse level
- Use generators for streaming data
- Implement memory pooling for parallel jobs
## Testing Strategy
### Unit Tests
- Mock Jesse for all tools
- Test parameter validation
- Test edge cases (no trades, 100% win rate, etc.)
### Integration Tests
- Use containerized Jesse with small datasets
- Test tool chaining
- Test error scenarios
### Performance Tests
- Benchmark optimization convergence
- Measure parallel execution speedup
- Profile memory usage
## Phase 3 Success Criteria
✅ All 4 tools fully implemented
✅ Tests > 90% coverage
✅ Documentation complete
✅ Works with containerized Jesse
✅ Can optimize strategy in < 5 minutes (small dataset)
✅ Can run walk-forward analysis < 30 minutes
✅ Batch processing 10+ backtests in parallel
## Next Steps (If Phase 3 Complete)
### Phase 4: Monte Carlo & Risk Analysis (4 tools)
- `monte_carlo()` - Generate random walks
- `var_calculation()` - Value at Risk
- `stress_test()` - Black swan scenarios
- `risk_report()` - Comprehensive risk analysis
### Phase 5: Pairs Trading & Advanced (4 tools)
- `correlation_matrix()` - Cross-asset analysis
- `pairs_backtest()` - Pairs trading strategies
- `factor_analysis()` - Factor decomposition
- `regime_detector()` - Market regime analysis
## Resources & References
- **Optuna Docs**: https://optuna.readthedocs.io/
- **Jesse Docs**: https://docs.jesse.trade/
- **Walk-Forward Analysis**: https://www.investopedia.com/terms/w/walkforward.asp
- **Performance Analysis**: https://pmorissette.github.io/bt/
