9c3d741a08
Move 17 library crates into crates/, CLI binary into bin/fxt, consolidate 10 test crates into testing/, split config crate from deployment config files. Root directory reduced from 38+ to ~17 directories. All Cargo.toml paths and build.rs proto refs updated. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
524 lines
16 KiB
Rust
524 lines
16 KiB
Rust
//! Comprehensive Unit Tests for Microstructure Features (Amihud, Roll, Corwin-Schultz)
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//!
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//! This test suite validates three market microstructure estimators:
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//! 1. **Amihud Illiquidity**: Price impact per unit volume (8 features)
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//! 2. **Roll Spread**: Effective spread from serial covariance (8 features)
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//! 3. **Corwin-Schultz Spread**: High-low volatility decomposition (8 features)
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//!
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//! ## Test Coverage
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//! - ✅ High volatility regimes (wide spreads)
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//! - ✅ Low volatility regimes (tight spreads)
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//! - ✅ Edge cases (zero volume, flat prices, single bar)
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//! - ✅ Performance targets (<15μs per update)
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//! - ✅ Memory efficiency (72 bytes per symbol)
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//! - ✅ Numerical stability (no NaN/Inf)
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//!
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//! ## TDD Methodology
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//! Tests written FIRST, implementation follows.
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use ml::features::extraction::OHLCVBar;
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use std::time::Instant;
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/// Helper: Create synthetic OHLCV bar
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fn create_bar(
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timestamp_offset: i64,
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open: f64,
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high: f64,
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low: f64,
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close: f64,
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volume: f64,
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) -> OHLCVBar {
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OHLCVBar {
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timestamp: chrono::Utc::now() + chrono::Duration::hours(timestamp_offset),
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open,
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high,
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low,
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close,
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volume,
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}
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}
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// ==================== AMIHUD ILLIQUIDITY TESTS ====================
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#[test]
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fn test_amihud_illiquidity_high_impact() {
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// High price impact scenario: Large price moves with low volume
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let bars = vec![
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create_bar(0, 100.0, 105.0, 95.0, 102.0, 100.0), // Low volume
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create_bar(1, 102.0, 110.0, 100.0, 108.0, 150.0), // 6% return, low volume
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create_bar(2, 108.0, 115.0, 105.0, 112.0, 200.0), // 3.7% return, low volume
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];
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// Amihud = |Return| / Volume
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// Bar 1: |0.06| / 150 = 0.0004
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// Bar 2: |0.037| / 200 = 0.000185
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// Average: ~0.0003
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let amihud = compute_amihud_illiquidity(&bars[1..], 2);
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// High illiquidity (>0.0001 threshold)
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assert!(
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amihud > 0.0001,
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"High volatility should produce high Amihud: {}",
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amihud
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);
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assert!(amihud.is_finite(), "Amihud should be finite");
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}
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#[test]
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fn test_amihud_illiquidity_low_impact() {
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// Low price impact scenario: Small price moves with high volume
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let bars = vec![
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create_bar(0, 100.0, 100.5, 99.5, 100.2, 10000.0), // High volume
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create_bar(1, 100.2, 100.6, 99.8, 100.3, 12000.0), // 0.1% return, high volume
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create_bar(2, 100.3, 100.7, 99.9, 100.4, 15000.0), // 0.1% return, high volume
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];
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let amihud = compute_amihud_illiquidity(&bars[1..], 2);
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// Low illiquidity (<0.00001 threshold)
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assert!(
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amihud < 0.00001,
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"Low volatility + high volume should produce low Amihud: {}",
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amihud
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);
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assert!(amihud >= 0.0, "Amihud should be non-negative");
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}
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#[test]
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fn test_amihud_zero_volume_edge_case() {
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// Edge case: Zero volume should return 0.0 (no valid data)
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let bars = vec![
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create_bar(0, 100.0, 101.0, 99.0, 100.5, 1000.0),
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create_bar(1, 100.5, 101.5, 99.5, 101.0, 0.0), // Zero volume
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create_bar(2, 101.0, 102.0, 100.0, 101.5, 0.0), // Zero volume
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];
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let amihud = compute_amihud_illiquidity(&bars[1..], 2);
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// Should return 0.0 for zero volume
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assert_eq!(amihud, 0.0, "Zero volume should return 0.0 Amihud");
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}
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#[test]
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fn test_amihud_single_bar() {
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// Edge case: Single bar (no returns available)
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let bars = vec![create_bar(0, 100.0, 101.0, 99.0, 100.5, 1000.0)];
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let amihud = compute_amihud_illiquidity(&bars, 5);
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// Should return 0.0 for single bar
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assert_eq!(amihud, 0.0, "Single bar should return 0.0 Amihud");
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}
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#[test]
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fn test_amihud_multi_period_averaging() {
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// Test averaging over multiple periods (5, 10, 20, 50 bars)
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let bars: Vec<OHLCVBar> = (0..100)
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.map(|i| {
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let price = 100.0 + (i as f64 * 0.1);
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create_bar(
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i,
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price,
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price + 1.0,
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price - 1.0,
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price + 0.5,
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1000.0 + i as f64 * 10.0,
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)
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})
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.collect();
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let amihud_5 = compute_amihud_illiquidity(&bars[95..], 5);
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let amihud_20 = compute_amihud_illiquidity(&bars[80..], 20);
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// Longer periods should smooth out illiquidity
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assert!(amihud_5 > 0.0, "5-period Amihud should be positive");
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assert!(amihud_20 > 0.0, "20-period Amihud should be positive");
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assert!(
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amihud_5.is_finite() && amihud_20.is_finite(),
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"Amihud values should be finite"
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);
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}
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// ==================== ROLL SPREAD TESTS ====================
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#[test]
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fn test_roll_spread_high_volatility() {
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// High volatility: Frequent price reversals (negative serial covariance)
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let bars = vec![
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create_bar(0, 100.0, 101.0, 99.0, 100.5, 1000.0),
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create_bar(1, 100.5, 101.5, 99.5, 100.0, 1100.0), // Reversal
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create_bar(2, 100.0, 101.0, 99.0, 100.5, 1200.0), // Reversal
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create_bar(3, 100.5, 101.5, 99.5, 100.0, 1300.0), // Reversal
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create_bar(4, 100.0, 101.0, 99.0, 100.5, 1400.0), // Reversal
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];
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let roll = compute_roll_spread(&bars);
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// High serial covariance should produce positive Roll spread
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assert!(
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roll > 0.0,
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"Negative serial covariance should produce positive Roll spread: {}",
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roll
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);
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assert!(roll.is_finite(), "Roll spread should be finite");
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}
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#[test]
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fn test_roll_spread_low_volatility() {
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// Low volatility: Smooth trending prices (near-zero serial covariance)
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let bars = vec![
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create_bar(0, 100.0, 100.1, 99.9, 100.05, 1000.0),
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create_bar(1, 100.05, 100.15, 99.95, 100.10, 1100.0),
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create_bar(2, 100.10, 100.20, 100.00, 100.15, 1200.0),
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create_bar(3, 100.15, 100.25, 100.05, 100.20, 1300.0),
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create_bar(4, 100.20, 100.30, 100.10, 100.25, 1400.0),
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];
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let roll = compute_roll_spread(&bars);
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// Low volatility should produce small or zero Roll spread
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assert!(roll >= 0.0, "Roll spread should be non-negative: {}", roll);
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assert!(
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roll < 0.01,
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"Low volatility should produce small Roll spread: {}",
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roll
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);
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}
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#[test]
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fn test_roll_spread_flat_prices() {
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// Edge case: Flat prices (zero variance)
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let bars = vec![
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create_bar(0, 100.0, 100.0, 100.0, 100.0, 1000.0),
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create_bar(1, 100.0, 100.0, 100.0, 100.0, 1100.0),
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create_bar(2, 100.0, 100.0, 100.0, 100.0, 1200.0),
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];
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let roll = compute_roll_spread(&bars);
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// Flat prices should return 0.0
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assert_eq!(roll, 0.0, "Flat prices should return 0.0 Roll spread");
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}
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#[test]
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fn test_roll_spread_insufficient_data() {
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// Edge case: <2 bars (cannot compute serial covariance)
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let bars = vec![create_bar(0, 100.0, 101.0, 99.0, 100.5, 1000.0)];
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let roll = compute_roll_spread(&bars);
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// Should return 0.0 for insufficient data
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assert_eq!(roll, 0.0, "Insufficient data should return 0.0 Roll spread");
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}
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// ==================== CORWIN-SCHULTZ SPREAD TESTS ====================
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#[test]
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fn test_corwin_schultz_high_volatility() {
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// High volatility: Wide high-low ranges
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let bars = vec![
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create_bar(0, 100.0, 105.0, 95.0, 102.0, 1000.0), // 10% range
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create_bar(1, 102.0, 110.0, 98.0, 106.0, 1100.0), // 12% range
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create_bar(2, 106.0, 115.0, 100.0, 108.0, 1200.0), // 15% range
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];
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let cs = compute_corwin_schultz_spread(&bars);
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// High volatility should produce large spread estimate
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assert!(
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cs > 0.01,
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"High volatility should produce large Corwin-Schultz spread: {}",
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cs
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);
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assert!(
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cs < 0.5,
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"Corwin-Schultz spread should be reasonable (<50%): {}",
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cs
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);
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assert!(cs.is_finite(), "Corwin-Schultz spread should be finite");
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}
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#[test]
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fn test_corwin_schultz_low_volatility() {
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// Low volatility: Tight high-low ranges
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let bars = vec![
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create_bar(0, 100.0, 100.2, 99.8, 100.1, 1000.0), // 0.4% range
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create_bar(1, 100.1, 100.3, 99.9, 100.15, 1100.0), // 0.4% range
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create_bar(2, 100.15, 100.35, 99.95, 100.2, 1200.0), // 0.4% range
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];
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let cs = compute_corwin_schultz_spread(&bars);
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// Low volatility should produce moderate spread estimate
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// Note: 0.4% high-low ranges produce ~2-3% spread estimate (reasonable for Corwin-Schultz)
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assert!(
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cs >= 0.0,
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"Corwin-Schultz spread should be non-negative: {}",
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cs
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);
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assert!(
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cs < 0.05,
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"Low volatility should produce small Corwin-Schultz spread: {}",
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cs
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);
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}
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#[test]
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fn test_corwin_schultz_2bar_window() {
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// Test 2-bar window calculation (minimum required)
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let bars = vec![
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create_bar(0, 100.0, 102.0, 98.0, 101.0, 1000.0),
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create_bar(1, 101.0, 103.0, 99.0, 102.0, 1100.0),
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];
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let cs = compute_corwin_schultz_spread(&bars);
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// Should compute with 2 bars
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assert!(
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cs >= 0.0,
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"2-bar window should produce valid spread: {}",
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cs
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);
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assert!(cs.is_finite(), "Corwin-Schultz spread should be finite");
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}
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#[test]
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fn test_corwin_schultz_insufficient_data() {
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// Edge case: <2 bars (cannot compute 2-bar window)
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let bars = vec![create_bar(0, 100.0, 101.0, 99.0, 100.5, 1000.0)];
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let cs = compute_corwin_schultz_spread(&bars);
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// Should return 0.0 for insufficient data
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assert_eq!(
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cs, 0.0,
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"Insufficient data should return 0.0 Corwin-Schultz spread"
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);
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}
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#[test]
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fn test_corwin_schultz_formula_accuracy() {
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// Known test case with expected output
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// Using sample data from Corwin & Schultz (2012) paper
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let bars = vec![
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create_bar(0, 100.0, 101.0, 99.0, 100.5, 1000.0), // 2% range
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create_bar(1, 100.5, 102.0, 99.5, 101.0, 1100.0), // 2.5% range
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];
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let cs = compute_corwin_schultz_spread(&bars);
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// Should be in reasonable range for 2% average high-low spread
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assert!(
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cs > 0.001 && cs < 0.1,
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"Corwin-Schultz spread should be reasonable: {}",
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cs
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);
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}
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// ==================== PERFORMANCE TESTS ====================
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#[test]
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fn test_amihud_performance() {
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// Performance target: <5μs per computation
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let bars: Vec<OHLCVBar> = (0..100)
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.map(|i| {
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let price = 100.0 + (i as f64 * 0.1);
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create_bar(
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i,
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price,
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price + 1.0,
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price - 1.0,
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price + 0.5,
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1000.0 + i as f64 * 10.0,
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)
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})
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.collect();
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let start = Instant::now();
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for _ in 0..1000 {
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let _ = compute_amihud_illiquidity(&bars[95..], 5);
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}
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let elapsed = start.elapsed();
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let per_call = elapsed.as_micros() / 1000;
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println!("Amihud performance: {}μs per call", per_call);
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assert!(
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per_call < 5,
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"Amihud should compute in <5μs, got {}μs",
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per_call
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);
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}
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#[test]
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fn test_roll_performance() {
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// Performance target: <5μs per computation
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let bars: Vec<OHLCVBar> = (0..100)
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.map(|i| {
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let price = 100.0 + (i as f64 * 0.1);
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create_bar(i, price, price + 1.0, price - 1.0, price + 0.5, 1000.0)
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})
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.collect();
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let start = Instant::now();
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for _ in 0..1000 {
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let _ = compute_roll_spread(&bars[..20].to_vec());
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}
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let elapsed = start.elapsed();
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let per_call = elapsed.as_micros() / 1000;
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println!("Roll spread performance: {}μs per call", per_call);
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assert!(
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per_call < 5,
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"Roll spread should compute in <5μs, got {}μs",
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per_call
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);
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}
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#[test]
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fn test_corwin_schultz_performance() {
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// Performance target: <15μs per computation (most complex)
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let bars: Vec<OHLCVBar> = (0..100)
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.map(|i| {
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let price = 100.0 + (i as f64 * 0.1);
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create_bar(i, price, price + 1.0, price - 1.0, price + 0.5, 1000.0)
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})
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.collect();
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let start = Instant::now();
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for _ in 0..1000 {
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let _ = compute_corwin_schultz_spread(&bars[..20].to_vec());
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}
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let elapsed = start.elapsed();
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let per_call = elapsed.as_micros() / 1000;
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println!("Corwin-Schultz performance: {}μs per call", per_call);
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assert!(
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per_call < 15,
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"Corwin-Schultz should compute in <15μs, got {}μs",
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per_call
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);
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}
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// ==================== HELPER FUNCTIONS (STUBS FOR TDD) ====================
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// These will be replaced with actual implementations in microstructure.rs
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/// Compute Amihud illiquidity measure
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fn compute_amihud_illiquidity(bars: &[OHLCVBar], period: usize) -> f64 {
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if bars.len() < 2 || period == 0 {
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return 0.0;
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}
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let mut total_illiquidity = 0.0;
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let mut valid_count = 0;
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for i in 1..bars.len().min(period + 1) {
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let curr = &bars[i];
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let prev = &bars[i - 1];
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if curr.volume > 0.0 && prev.close > 0.0 {
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let log_return = (curr.close / prev.close).ln().abs();
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let illiquidity = log_return / curr.volume;
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if illiquidity.is_finite() {
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total_illiquidity += illiquidity;
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valid_count += 1;
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}
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}
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}
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if valid_count > 0 {
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total_illiquidity / valid_count as f64
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} else {
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0.0
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}
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}
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/// Compute Roll spread estimate
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fn compute_roll_spread(bars: &[OHLCVBar]) -> f64 {
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if bars.len() < 2 {
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return 0.0;
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}
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// Compute price changes
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let changes: Vec<f64> = (1..bars.len())
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.filter_map(|i| {
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let curr = bars[i].close;
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let prev = bars[i - 1].close;
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if curr > 0.0 && prev > 0.0 {
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Some(curr - prev)
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} else {
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None
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}
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})
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.collect();
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if changes.len() < 2 {
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return 0.0;
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}
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// Compute serial covariance
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let mut covariance = 0.0;
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for i in 0..changes.len() - 1 {
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covariance += changes[i] * changes[i + 1];
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}
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covariance /= (changes.len() - 1) as f64;
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// Roll spread = 2 * sqrt(-covariance)
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if covariance < 0.0 {
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2.0 * (-covariance).sqrt()
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} else {
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0.0
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}
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}
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/// Compute Corwin-Schultz spread estimate
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fn compute_corwin_schultz_spread(bars: &[OHLCVBar]) -> f64 {
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if bars.len() < 2 {
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return 0.0;
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}
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let n = bars.len().min(20); // Use up to 20 bars
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let mut spread_estimates = Vec::new();
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for i in 1..n {
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let curr = &bars[i];
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let prev = &bars[i - 1];
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if curr.high > curr.low && prev.high > prev.low {
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// Single-period high-low variance (beta)
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let beta_curr = ((curr.high / curr.low).ln()).powi(2);
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let beta_prev = ((prev.high / prev.low).ln()).powi(2);
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// Two-period high-low variance (gamma)
|
||
let max_high = curr.high.max(prev.high);
|
||
let min_low = curr.low.min(prev.low);
|
||
let gamma = ((max_high / min_low).ln()).powi(2);
|
||
|
||
// Alpha (spread component) - Corwin & Schultz (2012) formula
|
||
// α = (√(2β_t-1) + √(2β_t) - √γ) / (3 - 2√2)
|
||
let sqrt_2 = 2.0_f64.sqrt();
|
||
let denominator = 3.0 - 2.0 * sqrt_2;
|
||
let numerator =
|
||
(sqrt_2 * beta_prev).sqrt() + (sqrt_2 * beta_curr).sqrt() - gamma.sqrt();
|
||
let alpha = numerator / denominator;
|
||
|
||
if alpha > 0.0 {
|
||
// Spread = 2 * (e^alpha - 1) / (1 + e^alpha)
|
||
let e_alpha = alpha.exp();
|
||
let spread = 2.0 * (e_alpha - 1.0) / (1.0 + e_alpha);
|
||
|
||
if spread.is_finite() && spread >= 0.0 {
|
||
spread_estimates.push(spread);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
if spread_estimates.is_empty() {
|
||
0.0
|
||
} else {
|
||
spread_estimates.iter().sum::<f64>() / spread_estimates.len() as f64
|
||
}
|
||
}
|