e8a68ee39f
- Created data/examples/download_ml_training_data.rs using reqwest + Databento HTTP API - Downloaded 90 days × 4 symbols (ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT) - Files saved to test_data/real/databento/ml_training/ - Total: 360 files, 15 MB compressed DBN format - Used existing Rust pattern from download_nq_fut.rs - API key loaded from .env file - 100% success rate (360/360 files) - Ready for ML training benchmarks Next: Create simplified training benchmark for RTX 3050 Ti GPU measurements
468 lines
16 KiB
Rust
468 lines
16 KiB
Rust
//! Comprehensive tests for performance metrics calculation
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//!
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//! Target Coverage: 70%+ for Sharpe ratio, drawdown, win rate, and all performance metrics
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//!
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//! Uses real DBN market data (ES.FUT 2024-01-02) for realistic metric calculations.
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use anyhow::Result;
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use rust_decimal::Decimal;
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mod test_data_helpers;
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use backtesting_service::performance::PerformanceAnalyzer;
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use backtesting_service::strategy_engine::BacktestTrade;
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use config::structures::BacktestingPerformanceConfig;
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use test_data_helpers::*;
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/// Test basic performance metrics calculation with real data
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#[tokio::test]
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async fn test_basic_performance_metrics() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Generate trades from real ES.FUT data
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let trades = generate_real_trades(3).await?;
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let initial_capital = 100000.0;
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let metrics = analyzer.calculate_metrics(&trades, initial_capital);
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// Validate basic counts
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assert_eq!(metrics.total_trades, 3, "Should have 3 trades");
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// Win rate should be between 0-100%
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assert!(metrics.win_rate >= 0.0 && metrics.win_rate <= 100.0,
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"Win rate should be valid percentage: {}", metrics.win_rate);
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// Total winning + losing trades = total trades
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assert_eq!(
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metrics.winning_trades + metrics.losing_trades,
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metrics.total_trades,
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"Winning + losing should equal total trades"
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);
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// Real data should have realistic returns (not guaranteed profit)
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assert!(
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metrics.total_return.is_finite(),
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"Total return should be finite"
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);
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Ok(())
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}
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/// Test Sharpe ratio calculation with real data
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#[tokio::test]
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async fn test_sharpe_ratio_calculation() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Generate trades from real data
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let trades = generate_mixed_trades().await?;
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if trades.is_empty() {
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return Ok(()); // Skip if no data available
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}
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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// Get expected Sharpe range from real data analysis
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let (min_sharpe, max_sharpe) = get_real_sharpe_range().await?;
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// Sharpe ratio should be finite and within realistic bounds
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assert!(
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metrics.sharpe_ratio.is_finite(),
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"Sharpe ratio should be finite, got: {}",
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metrics.sharpe_ratio
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);
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// Real intraday data can have negative Sharpe (choppy markets)
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assert!(
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metrics.sharpe_ratio >= min_sharpe && metrics.sharpe_ratio <= max_sharpe,
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"Sharpe ratio {} outside expected range [{}, {}]",
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metrics.sharpe_ratio,
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min_sharpe,
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max_sharpe
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);
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Ok(())
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}
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/// Test Sortino ratio calculation
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#[tokio::test]
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async fn test_sortino_ratio_calculation() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Mix of wins and losses
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 110.0, 0, 5), // +10%
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 95.0, 5, 10), // -5%
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 108.0, 10, 15), // +8%
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create_trade(4, "AAPL", TradeSide::Buy, 100.0, 100.0, 92.0, 15, 20), // -8%
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create_trade(5, "AAPL", TradeSide::Buy, 100.0, 100.0, 112.0, 20, 25), // +12%
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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// Sortino ratio should be calculated (can be positive or negative depending on downside)
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assert!(metrics.sortino_ratio.is_finite(), "Sortino ratio should be finite");
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Ok(())
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}
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/// Test maximum drawdown calculation with real data
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#[tokio::test]
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async fn test_maximum_drawdown() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Generate trades from real data (includes natural drawdown patterns)
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let trades = generate_mixed_trades().await?;
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if trades.is_empty() {
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return Ok(()); // Skip if no data available
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}
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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// Get expected drawdown range from real data
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let (min_dd, max_dd) = get_real_drawdown_range().await?;
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// Max drawdown should be non-negative
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assert!(
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metrics.max_drawdown >= 0.0,
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"Max drawdown should be non-negative, got: {}",
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metrics.max_drawdown
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);
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// Real data should have realistic drawdown
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assert!(
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metrics.max_drawdown >= min_dd && metrics.max_drawdown <= max_dd,
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"Max drawdown {} outside expected range [{}, {}]",
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metrics.max_drawdown,
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min_dd,
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max_dd
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);
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Ok(())
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}
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/// Test win rate calculation with real data
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#[tokio::test]
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async fn test_win_rate_calculation() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Generate 10 trades from real data
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let trades = generate_real_trades(10).await?;
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert_eq!(metrics.total_trades, 10, "Should have 10 trades");
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// Win + loss should equal total
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assert_eq!(
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metrics.winning_trades + metrics.losing_trades,
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metrics.total_trades,
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"Winning + losing should equal total"
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);
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// Win rate should be valid percentage
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assert!(
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metrics.win_rate >= 0.0 && metrics.win_rate <= 100.0,
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"Win rate should be 0-100%, got: {}",
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metrics.win_rate
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);
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// Win rate calculation should match trade counts
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let expected_win_rate = (metrics.winning_trades as f64 / metrics.total_trades as f64) * 100.0;
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assert!(
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(metrics.win_rate - expected_win_rate).abs() < 0.1,
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"Win rate calculation mismatch: expected {}, got {}",
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expected_win_rate,
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metrics.win_rate
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);
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Ok(())
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}
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/// Test profit factor calculation
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#[tokio::test]
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async fn test_profit_factor() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Gross profit: $1000, Gross loss: $300 -> Profit factor: 3.33
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 110.0, 0, 5), // +$1000
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 97.0, 5, 10), // -$300
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert!(metrics.profit_factor > 3.0 && metrics.profit_factor < 3.5,
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"Profit factor should be ~3.33");
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Ok(())
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}
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/// Test average win and loss calculation
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#[tokio::test]
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async fn test_average_win_loss() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 110.0, 0, 1), // +$1000
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 106.0, 1, 2), // +$600
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 95.0, 2, 3), // -$500
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create_trade(4, "AAPL", TradeSide::Buy, 100.0, 100.0, 92.0, 3, 4), // -$800
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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// Average win: ($1000 + $600) / 2 = $800
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assert!((metrics.avg_win - 800.0).abs() < 1.0, "Average win should be $800");
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// Average loss: -($500 + $800) / 2 = -$650
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assert!((metrics.avg_loss + 650.0).abs() < 1.0, "Average loss should be -$650");
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Ok(())
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}
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/// Test largest win and loss tracking
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#[tokio::test]
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async fn test_largest_win_loss() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 105.0, 0, 1), // +$500
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 115.0, 1, 2), // +$1500 (largest win)
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 95.0, 2, 3), // -$500
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create_trade(4, "AAPL", TradeSide::Buy, 100.0, 100.0, 88.0, 3, 4), // -$1200 (largest loss)
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert!((metrics.largest_win - 1500.0).abs() < 1.0, "Largest win should be $1500");
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assert!((metrics.largest_loss + 1200.0).abs() < 1.0, "Largest loss should be -$1200");
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Ok(())
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}
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/// Test Calmar ratio calculation
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#[tokio::test]
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async fn test_calmar_ratio() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Create trades over a year with known drawdown
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 120.0, 0, 90), // +20%
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 120.0, 110.0, 90, 180), // -10%
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 110.0, 130.0, 180, 365), // +20%
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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// Calmar = Annualized Return / Max Drawdown
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assert!(metrics.calmar_ratio > 0.0, "Calmar ratio should be positive");
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Ok(())
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}
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/// Test VaR (Value at Risk) calculation
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#[tokio::test]
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async fn test_var_calculation() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Mix of returns for VaR calculation
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 105.0, 0, 1),
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 103.0, 1, 2),
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 98.0, 2, 3),
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create_trade(4, "AAPL", TradeSide::Buy, 100.0, 100.0, 107.0, 3, 4),
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create_trade(5, "AAPL", TradeSide::Buy, 100.0, 100.0, 95.0, 4, 5),
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert!(metrics.var_95.is_some(), "VaR should be calculated");
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let var = metrics.var_95.unwrap();
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assert!(var < 0.0, "VaR should be negative (potential loss)");
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Ok(())
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}
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/// Test expected shortfall calculation
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#[tokio::test]
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async fn test_expected_shortfall() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 105.0, 0, 1),
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 92.0, 1, 2),
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 108.0, 2, 3),
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create_trade(4, "AAPL", TradeSide::Buy, 100.0, 100.0, 90.0, 3, 4),
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert!(metrics.expected_shortfall.is_some(), "Expected shortfall should be calculated");
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Ok(())
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}
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/// Test annualized return calculation
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#[tokio::test]
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async fn test_annualized_return() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// Trades over 6 months with 10% total return
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 110.0, 0, 180), // +10% over 6 months
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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// Annualized return should be higher than 10% (compound effect)
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assert!(metrics.annualized_return > 10.0, "Annualized return should be > 10%");
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Ok(())
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}
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/// Test volatility calculation
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#[tokio::test]
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async fn test_volatility_calculation() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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// High volatility trades
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 120.0, 0, 1), // +20%
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 85.0, 1, 2), // -15%
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 115.0, 2, 3), // +15%
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create_trade(4, "AAPL", TradeSide::Buy, 100.0, 100.0, 90.0, 3, 4), // -10%
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert!(metrics.volatility > 0.0, "Volatility should be positive");
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Ok(())
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}
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/// Test edge case: no trades
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#[tokio::test]
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async fn test_no_trades() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades: Vec<BacktestTrade> = vec![];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert_eq!(metrics.total_trades, 0);
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assert_eq!(metrics.total_return, 0.0);
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assert_eq!(metrics.win_rate, 0.0);
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assert_eq!(metrics.sharpe_ratio, 0.0);
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Ok(())
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}
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/// Test edge case: all winning trades
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#[tokio::test]
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async fn test_all_winning_trades() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 105.0, 0, 1),
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 103.0, 1, 2),
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 107.0, 2, 3),
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert_eq!(metrics.win_rate, 100.0);
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assert_eq!(metrics.winning_trades, 3);
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assert_eq!(metrics.losing_trades, 0);
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assert!(metrics.profit_factor.is_infinite(), "Profit factor should be infinite with no losses");
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Ok(())
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}
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/// Test edge case: all losing trades
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#[tokio::test]
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async fn test_all_losing_trades() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 95.0, 0, 1),
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 93.0, 1, 2),
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 92.0, 2, 3),
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];
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let metrics = analyzer.calculate_metrics(&trades, 100000.0);
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assert_eq!(metrics.win_rate, 0.0);
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assert_eq!(metrics.winning_trades, 0);
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assert_eq!(metrics.losing_trades, 3);
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assert_eq!(metrics.profit_factor, 0.0, "Profit factor should be 0 with no wins");
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Ok(())
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}
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/// Test equity curve generation
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#[tokio::test]
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async fn test_equity_curve_generation() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 105.0, 0, 1),
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 110.0, 1, 2),
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 108.0, 2, 3),
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];
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let initial_capital = 100000.0;
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let equity_curve = analyzer.generate_equity_curve(&trades, initial_capital);
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// Should have points for each trade + initial
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assert!(equity_curve.len() >= 4, "Equity curve should have at least 4 points");
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// First point should be initial capital
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assert!((equity_curve[0].equity - initial_capital).abs() < 0.01);
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// Drawdown at start should be 0
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assert_eq!(equity_curve[0].drawdown, 0.0);
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Ok(())
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}
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/// Test rolling metrics calculation
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#[tokio::test]
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async fn test_rolling_metrics() -> Result<()> {
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let config = BacktestingPerformanceConfig::default();
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let analyzer = PerformanceAnalyzer::new(&config)?;
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let trades = vec![
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create_trade(1, "AAPL", TradeSide::Buy, 100.0, 100.0, 105.0, 0, 5),
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create_trade(2, "AAPL", TradeSide::Buy, 100.0, 100.0, 103.0, 5, 10),
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create_trade(3, "AAPL", TradeSide::Buy, 100.0, 100.0, 107.0, 10, 15),
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create_trade(4, "AAPL", TradeSide::Buy, 100.0, 100.0, 102.0, 15, 20),
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create_trade(5, "AAPL", TradeSide::Buy, 100.0, 100.0, 108.0, 20, 25),
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|
];
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|
|
|
let rolling = analyzer.calculate_rolling_metrics(&trades, 10);
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|
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assert!(!rolling.rolling_sharpe.is_empty(), "Rolling Sharpe should be calculated");
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|
assert!(!rolling.rolling_volatility.is_empty(), "Rolling volatility should be calculated");
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assert!(!rolling.rolling_returns.is_empty(), "Rolling returns should be calculated");
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|
|
|
Ok(())
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|
}
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