Jesse MCP Server

# Phase 4: Monte Carlo & Risk Analysis Implementation ## Overview Implement 4 advanced risk analysis tools that provide sophisticated risk assessment and scenario testing capabilities. ## Status: Planning **Start Date**: 2025-11-27 **Estimated Duration**: 2-3 weeks ## Phase 4 Tools (4 of 16) ### 1. `monte_carlo()` - Advanced Monte Carlo Simulation **Purpose**: Generate random walks for comprehensive risk analysis **Input Parameters**: - `backtest_result`: Result from backtest() or backtest_batch() - `simulations`: Number of Monte Carlo runs (default: 10000) - `confidence_levels`: List of confidence levels (default: [0.95, 0.99]) - `resample_method`: "bootstrap", "block_bootstrap", "stationary_bootstrap" (default: "bootstrap") - `block_size`: Block size for block bootstrap (default: 20) - `include_drawdowns`: Include drawdown analysis (default: true) - `include_returns`: Include return distribution analysis (default: true) **Output**: - Final value distribution with percentiles - Confidence intervals for key metrics - Drawdown distribution analysis - Return distribution statistics - Probability of success metrics - Visual data for plotting **Implementation Approach**: ```python def monte_carlo(backtest_result, simulations=10000, confidence_levels=[0.95, 0.99], ...): # Extract equity curve and returns equity_curve = backtest_result["equity_curve"] returns = [point["return"] for point in equity_curve] # Generate Monte Carlo paths final_values = [] drawdowns = [] for i in range(simulations): if resample_method == "bootstrap": # Bootstrap resampling sampled_returns = np.random.choice(returns, size=len(returns), replace=True) elif resample_method == "block_bootstrap": # Block bootstrap for preserving autocorrelation sampled_returns = block_bootstrap(returns, block_size) elif resample_method == "stationary_bootstrap": # Stationary bootstrap for time series sampled_returns = stationary_bootstrap(returns) # Generate path path_value = 10000 # Starting value path_drawdown = 0 peak_value = 10000 for ret in sampled_returns: path_value *= (1 + ret) peak_value = max(peak_value, path_value) current_dd = (peak_value - path_value) / peak_value path_drawdown = max(path_drawdown, current_dd) final_values.append(path_value) drawdowns.append(path_drawdown) # Calculate statistics and confidence intervals return { "final_value_stats": calculate_distribution_stats(final_values), "drawdown_stats": calculate_distribution_stats(drawdowns), "confidence_intervals": calculate_confidence_intervals(final_values, confidence_levels), "probability_of_profit": len([v for v in final_values if v > 10000]) / simulations, "simulations": simulations, "method": resample_method } ``` ### 2. `var_calculation()` - Value at Risk Analysis **Purpose**: Calculate various VaR measures for risk quantification **Input Parameters**: - `backtest_result`: Result from backtest() or backtest_batch() - `confidence_levels`: List of confidence levels (default: [0.90, 0.95, 0.99]) - `time_horizons`: List of time horizons in days (default: [1, 5, 10, 30]) - `method`: "historical", "parametric", "monte_carlo" (default: "historical") - `monte_carlo_sims`: Number of simulations for Monte Carlo VaR (default: 10000) **Output**: - VaR values for all confidence levels and time horizons - Expected Shortfall (ES) values - Conditional VaR (CVaR) calculations - Historical VaR backtesting - VaR exceedance analysis - Risk metrics comparison **Implementation Approach**: ```python def var_calculation(backtest_result, confidence_levels=[0.90, 0.95, 0.99], time_horizons=[1, 5, 10, 30], method="historical"): # Extract returns equity_curve = backtest_result["equity_curve"] returns = [point["return"] for point in equity_curve] var_results = {} for horizon in time_horizons: # Aggregate returns for horizon horizon_returns = aggregate_returns(returns, horizon) for confidence in confidence_levels: if method == "historical": var = np.percentile(horizon_returns, (1 - confidence) * 100) es = np.mean(horizon_returns[horizon_returns <= var]) elif method == "parametric": # Assume normal distribution mean = np.mean(horizon_returns) std = np.std(horizon_returns) var = mean + norm.ppf(1 - confidence) * std es = mean - norm.pdf(norm.ppf(1 - confidence)) * std / (1 - confidence) elif method == "monte_carlo": # Monte Carlo simulation var, es = monte_carlo_var(horizon_returns, confidence, monte_carlo_sims) var_results[f"{horizon}d_{confidence*100}%"] = { "var": var, "expected_shortfall": es, "method": method } return { "var_results": var_results, "confidence_levels": confidence_levels, "time_horizons": time_horizons, "method": method, "backtest_stats": calculate_backtest_stats(returns) } ``` ### 3. `stress_test()` - Black Swan Scenario Testing **Purpose**: Test strategy performance under extreme market conditions **Input Parameters**: - `backtest_result`: Result from backtest() or backtest_batch() - `scenarios`: Predefined scenarios or custom (default: ["market_crash", "volatility_spike", "correlation_breakdown"]) - `custom_scenarios`: Custom scenario definitions - `shock_magnitude`: Shock magnitude for scenarios (default: -0.20 for -20%) - `volatility_multiplier`: Volatility increase factor (default: 3.0) - `correlation_shift`: Correlation regime shift (default: 0.8) - `recovery_periods`: Recovery period analysis in days (default: [5, 10, 20, 30]) **Output**: - Performance under each stress scenario - Recovery time analysis - Maximum drawdown under stress - Scenario comparison matrix - Stress test summary statistics - Risk factor sensitivity analysis **Implementation Approach**: ```python def stress_test(backtest_result, scenarios=["market_crash", "volatility_spike", "correlation_breakdown"], shock_magnitude=-0.20, volatility_multiplier=3.0): # Extract base data equity_curve = backtest_result["equity_curve"] returns = [point["return"] for point in equity_curve] stress_results = {} for scenario in scenarios: if scenario == "market_crash": # Apply sudden market crash stressed_returns = apply_market_crash(returns, shock_magnitude) elif scenario == "volatility_spike": # Increase volatility stressed_returns = apply_volatility_spike(returns, volatility_multiplier) elif scenario == "correlation_breakdown": # Break correlation assumptions stressed_returns = apply_correlation_breakdown(returns, correlation_shift) # Calculate stressed performance stressed_equity = generate_equity_curve(stressed_returns) max_dd = calculate_max_drawdown(stressed_equity) recovery_times = calculate_recovery_times(stressed_equity, recovery_periods) stress_results[scenario] = { "scenario": scenario, "shock_applied": shock_magnitude if scenario == "market_crash" else volatility_multiplier, "max_drawdown": max_dd, "final_return": stressed_returns[-1] if stressed_returns else 0, "recovery_analysis": recovery_times, "stressed_returns": stressed_returns[:100] # First 100 for analysis } return { "stress_results": stress_results, "base_performance": backtest_result, "scenario_comparison": compare_scenarios(stress_results), "risk_assessment": assess_stress_resilience(stress_results) } ``` ### 4. `risk_report()` - Comprehensive Risk Analysis **Purpose**: Generate professional risk assessment report **Input Parameters**: - `backtest_result`: Result from backtest() or backtest_batch() - `include_monte_carlo`: Include Monte Carlo analysis (default: true) - `include_var_analysis`: Include VaR calculations (default: true) - `include_stress_test`: Include stress testing (default: true) - `monte_carlo_sims`: Monte Carlo simulations (default: 5000) - `report_format`: "summary", "detailed", "executive" (default: "summary") **Output**: - Overall risk rating and score - Risk factor breakdown - Key risk metrics dashboard - Risk-adjusted performance measures - Recommendations for risk management - Professional report formatting **Implementation Approach**: ```python def risk_report(backtest_result, include_monte_carlo=True, include_var_analysis=True, include_stress_test=True, report_format="summary"): report = { "strategy": backtest_result.get("strategy"), "symbol": backtest_result.get("symbol"), "timeframe": backtest_result.get("timeframe"), "period": f"{backtest_result.get('start_date')} to {backtest_result.get('end_date')}", "generated_at": datetime.now().isoformat() } # Base risk metrics report["base_metrics"] = calculate_base_risk_metrics(backtest_result) # Monte Carlo analysis if include_monte_carlo: report["monte_carlo"] = monte_carlo(backtest_result, simulations=monte_carlo_sims) # VaR analysis if include_var_analysis: report["var_analysis"] = var_calculation(backtest_result) # Stress testing if include_stress_test: report["stress_test"] = stress_test(backtest_result) # Overall risk assessment report["risk_assessment"] = comprehensive_risk_assessment(report) # Recommendations report["recommendations"] = generate_risk_recommendations(report) # Format based on report type if report_format == "executive": return format_executive_report(report) elif report_format == "detailed": return format_detailed_report(report) else: return format_summary_report(report) ``` ## Dependency Status ### Required Libraries - ✅ `numpy`, `pandas` - Data analysis (already available) - ✅ `scipy` - Statistical analysis (already available) - ✅ `scipy.stats` - Statistical distributions (already available) - ✅ `matplotlib` - Optional for visualization (may need to add) - ✅ `seaborn` - Optional for advanced plots (may need to add) ### Integration Points 1. All tools depend on existing backtest results from Phase 1-3 2. Tools should use the `Phase3Optimizer` class for consistency 3. Error handling should be consistent with previous phases 4. Mock implementations should work alongside real calculations ## Implementation Timeline ### Week 1: Foundation - [ ] Set up statistical analysis framework - [ ] Implement `monte_carlo()` with bootstrap methods - [ ] Add confidence interval calculations - [ ] Create integration tests ### Week 2: Risk Metrics - [ ] Implement `var_calculation()` with multiple methods - [ ] Add Expected Shortfall calculations - [ ] Implement `stress_test()` with scenario framework - [ ] Create scenario comparison utilities ### Week 3: Reporting & Polish - [ ] Implement `risk_report()` with comprehensive analysis - [ ] Add professional report formatting - [ ] Create visualization data generators - [ ] Comprehensive testing and documentation ## Design Patterns ### Pattern 1: Progressive Analysis ```python # Allows different levels of risk analysis basic_risk = risk_report(result, include_monte_carlo=False) standard_risk = risk_report(result, include_var_analysis=True) comprehensive_risk = risk_report(result, include_all=True) ``` ### Pattern 2: Scenario Framework ```python # Extensible scenario testing framework scenarios = [ {"name": "market_crash", "params": {"shock": -0.20}}, {"name": "volatility_spike", "params": {"multiplier": 3.0}}, {"name": "custom", "params": custom_params} ] stress_results = stress_test(result, scenarios=scenarios) ``` ### Pattern 3: Confidence Intervals ```python # Consistent confidence interval calculations ci_95 = calculate_confidence_interval(data, 0.95) ci_99 = calculate_confidence_interval(data, 0.99) multiple_ci = calculate_confidence_intervals(data, [0.90, 0.95, 0.99]) ``` ## Technical Challenges & Solutions ### Challenge 1: Computational Complexity **Issue**: Monte Carlo with 10,000+ simulations can be slow **Solution**: - Implement efficient vectorized operations - Use numpy random number generation - Add progress callbacks for long operations - Implement early stopping for convergence ### Challenge 2: Statistical Assumptions **Issue**: Different VaR methods make different assumptions **Solution**: - Implement multiple methods (historical, parametric, Monte Carlo) - Document assumptions clearly - Provide method comparison - Allow user to choose appropriate method ### Challenge 3: Scenario Realism **Issue**: Stress scenarios need to be realistic **Solution**: - Base scenarios on historical events - Use realistic shock magnitudes - Provide scenario customization - Include recovery period analysis ## Testing Strategy ### Unit Tests - Test statistical calculations with known datasets - Test confidence interval calculations - Test scenario application - Test report generation ### Integration Tests - Test with real backtest results - Test tool chaining (backtest → risk_report) - Test performance with large datasets - Test error scenarios ### Performance Tests - Benchmark Monte Carlo simulation speed - Measure memory usage with large simulations - Test concurrent execution - Profile bottlenecks ## Phase 4 Success Criteria ✅ All 4 tools fully implemented ✅ Tests > 90% coverage ✅ Documentation complete ✅ Works with mock and real data ✅ Monte Carlo < 30 seconds (10,000 simulations) ✅ VaR calculations < 5 seconds ✅ Stress testing < 10 seconds ✅ Professional report formatting ## Next Steps (If Phase 4 Complete) ### 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 ### Phase 6: Autonomous Workflows (4 tools) - `auto_optimize()` - Fully autonomous optimization - `strategy_evolution()` - Strategy improvement over time - `portfolio_optimizer()` - Multi-strategy portfolio optimization - `risk_budget_allocator()` - Risk budget allocation ## Resources & References - **Monte Carlo Methods**: https://en.wikipedia.org/wiki/Monte_Carlo_method - **Value at Risk**: https://en.wikipedia.org/wiki/Value_at_risk - **Stress Testing**: https://www.investopedia.com/terms/s/stresstesting.asp - **Bootstrap Methods**: https://en.wikipedia.org/wiki/Bootstrapping_(statistics) - **Jesse Docs**: https://docs.jesse.trade/