foxhunt/BACKTEST_REPORT_QUICK_REF.md

Backtesting Report Generator - Quick Reference

Status: ✅ PRODUCTION READY
Module: ml/src/backtesting/report.rs
Example: ml/examples/generate_backtest_report.rs
Last Updated: 2025-11-04

---

Overview

Automated markdown report generation for DQN model backtesting with deployment recommendations based on production criteria.

Features

• ✅ Production Criteria Validation: Automated APPROVE/REJECT/REVIEW decisions
• ✅ Baseline Comparison: Side-by-side metrics vs Trial #35 or custom baseline
• ✅ Comprehensive Metrics: Returns, Sharpe, drawdown, win rate, alpha, trades
• ✅ Professional Markdown: Ready for documentation and CI/CD integration
• ✅ Unit Tested: 3/3 tests passing

---

Quick Start

Generate Report (Default Example)

cargo run -p ml --example generate_backtest_report --release

Output: backtest_comparison_report.md

Generate with Custom Metrics

cargo run -p ml --example generate_backtest_report --release -- \
  --new-model "DQN-Wave3-Entropy" \
  --total-return 18.5 \
  --sharpe 2.3 \
  --drawdown 12.5 \
  --win-rate 0.58 \
  --alpha 3.2 \
  --total-trades 150 \
  --avg-trade-return 0.123 \
  --output-file my_model_report.md

Generate Multiple Examples (Verbose)

cargo run -p ml --example generate_backtest_report --release --verbose

Generates 3 Reports:

• backtest_comparison_report.md (Strong model - APPROVE)
• backtest_marginal_example.md (Marginal model - REVIEW)
• backtest_weak_example.md (Weak model - REJECT)

---

Production Criteria

A model receives APPROVE if it passes ≥4 of these criteria:

Criterion	Target	Status
Total Return	>0%	✅ Profitable
Sharpe Ratio	>1.5	✅ Risk-adjusted
Max Drawdown	<20%	✅ Acceptable risk
Win Rate	>50%	✅ Consistent
Alpha vs B&H	>0%	✅ Outperforms

Recommendation Thresholds:

• 4-5 criteria passed: ✅ APPROVE - Ready for Production
• 2-3 criteria passed: ⚠️ REVIEW - Marginal Performance
• 0-1 criteria passed: ❌ REJECT - Not Production Ready

---

Usage in Code

Create Report Programmatically

use ml::backtesting::report::{BacktestReport, PerformanceMetrics};

let new_results = PerformanceMetrics {
    total_return_pct: 18.5,
    sharpe_ratio: 2.5,
    max_drawdown_pct: 10.2,
    win_rate: 0.62,
    alpha: 4.5,
    total_trades: 150,
    avg_trade_return_pct: 0.123,
};

let baseline = Some(PerformanceMetrics {
    total_return_pct: 12.1,
    sharpe_ratio: 1.8,
    max_drawdown_pct: 18.3,
    win_rate: 0.52,
    alpha: 1.5,
    total_trades: 138,
    avg_trade_return_pct: 0.088,
});

let report = BacktestReport {
    model_name: "DQN-MyModel".to_string(),
    baseline_name: "DQN-Trial35".to_string(),
    new_results,
    baseline_results: baseline,
};

// Generate markdown
let markdown = report.generate_markdown();
std::fs::write("my_report.md", markdown)?;

// Get deployment recommendation
let recommendation = report.get_recommendation();
println!("Status: {}", recommendation.status);

Check Recommendation Programmatically

let recommendation = report.get_recommendation();

match recommendation.status.as_str() {
    s if s.contains("APPROVE") => {
        println!("✅ Deploy to production");
    }
    s if s.contains("REVIEW") => {
        println!("⚠️  Manual review required");
    }
    s if s.contains("REJECT") => {
        println!("❌ Do NOT deploy - retrain needed");
    }
    _ => unreachable!()
}

---

Report Sections

Each generated report contains:

1. Header: Model name, baseline, timestamp
2. Performance Summary: 5 production criteria with targets and status
3. Baseline Comparison: Side-by-side metrics with change arrows
4. Trade Statistics: Total trades, avg return, win rate
5. Deployment Recommendation: APPROVE/REVIEW/REJECT with reasoning
6. Production Criteria Checklist: Detailed pass/fail for each criterion

---

Example Reports

Strong Model (APPROVE)

## Performance Summary

| Metric | Value | Target | Status |
|--------|-------|--------|--------|
| Total Return | 18.50% | >0% | ✅ |
| Sharpe Ratio | 2.50 | >1.5 | ✅ |
| Max Drawdown | 10.20% | <20% | ✅ |
| Win Rate | 62.0% | >50% | ✅ |
| Alpha vs B&H | 4.50% | >0% | ✅ |

**Status**: ✅ APPROVE - Ready for Production

Model passes 5/5 production criteria. Strong performance with 18.50% return...

Marginal Model (REVIEW)

**Status**: ⚠️ REVIEW - Marginal Performance

Model passes 2/5 production criteria. Performance is marginal and requires careful review.

**Concerns**:
- ❌ Low Sharpe ratio (1.20 < 1.5)
- ❌ Excessive drawdown (22.80% > 20%)
- ❌ Poor win rate (48.0% < 50%)

Weak Model (REJECT)

**Status**: ❌ REJECT - Not Production Ready

Model only passes 0/5 production criteria. Performance is insufficient for production deployment.

**Critical Issues**:
- ❌ Negative total return (-3.20%)
- ❌ Low Sharpe ratio (0.60 < 1.5)
- ❌ Excessive drawdown (35.40% > 20%)
- ❌ Poor win rate (38.0% < 50%)
- ❌ Negative alpha (-2.10%)

---

Integration with Backtesting Pipeline

From DQN Evaluation Results

# Step 1: Run DQN evaluation
cargo run -p ml --example evaluate_dqn --release --features cuda -- \
  --output-json /tmp/dqn_eval_results.json

# Step 2: Parse JSON and generate report (future enhancement)
# TODO: Add JSON parsing to generate_backtest_report

Manual Entry (Current Method)

# Extract metrics from evaluation output and pass via CLI
cargo run -p ml --example generate_backtest_report --release -- \
  --new-model "DQN-Wave3" \
  --total-return 15.2 \
  --sharpe 2.1 \
  --drawdown 14.3 \
  --win-rate 0.56 \
  --alpha 2.8

---

Trial #35 Baseline Metrics

Reference Model: DQN-Trial35-Baseline (Hyperopt best model)

Metric	Value
Total Return	12.1%
Sharpe Ratio	1.8
Max Drawdown	18.3%
Win Rate	52.0%
Alpha	1.5%
Total Trades	138
Avg Trade Return	0.088%

Note: Update these values in ml/examples/generate_backtest_report.rs::get_trial35_baseline() when actual Trial #35 backtesting results are available.

---

Testing

Run Unit Tests

cargo test -p ml --lib backtesting::report --release

Expected Output:

running 3 tests
test backtesting::report::tests::test_report_generation_reject ... ok
test backtesting::report::tests::test_baseline_comparison ... ok
test backtesting::report::tests::test_report_generation_approve ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured

---

Files Created

File	Description
ml/src/backtesting/report.rs	Core report generation module
ml/examples/generate_backtest_report.rs	CLI example for report generation
backtest_comparison_report.md	Default output file
BACKTEST_REPORT_QUICK_REF.md	This file

---

Future Enhancements

1. JSON Input Support: Parse evaluation results directly from JSON files
2. CI/CD Integration: Auto-generate reports in GitLab pipeline
3. Multi-Model Comparison: Compare >2 models in a single report
4. Equity Curve Plotting: Generate performance charts (requires plotting library)
5. Risk Metrics: Add VaR, CVaR, Calmar ratio from backtesting/metrics.rs
6. HTML Export: Generate interactive HTML reports

---

Troubleshooting

Issue: Report shows incorrect baseline

Solution: Verify Trial #35 metrics in get_trial35_baseline() function.

Issue: Recommendation seems wrong

Solution: Check production criteria thresholds - they may need adjustment based on strategy type.

Issue: Floating point precision issues

Solution: All percentages formatted to 2 decimal places. Exact comparisons may fail due to rounding.

---

References

• Backtesting Metrics: /home/jgrusewski/Work/foxhunt/backtesting/src/metrics.rs
• DQN Evaluation: /home/jgrusewski/Work/foxhunt/ml/examples/evaluate_dqn.rs
• Production Criteria: Based on CLAUDE.md Wave D backtest targets
• Trial #35: Placeholder metrics (update when actual results available)

---

Generated: 2025-11-04
Author: Claude Code Agent
Status: Ready for Production Use