Bug #8 (CRITICAL): Fixed action selection frequency catastrophe - Root cause: execute_action called during training (522,713 orders/epoch) - Fix: Removed execute_action from experience collection loop (line 928-936) - Impact: 522,713 → 0 orders/epoch (100% reduction) - Transaction costs: $338K → $0 (eliminated) - Test suite: ml/tests/action_selection_frequency_test.rs (3/3 passing) P2-A: Configurable Initial Capital - CLI argument: --initial-capital (default: $100K, min: $1K) - Files modified: trainers/dqn.rs, train_dqn.rs, hyperopt adapter - Test suite: ml/tests/configurable_capital_test.rs (8/8 passing) - Supports: Small accounts ($10K), Standard ($100K), Institutional ($500K+) P2-B: Cash Reserve Requirement - CLI argument: --cash-reserve-percent (default: 0%, range: 0-100%) - Reserve enforcement: BUY trades only (SELL always allowed) - Dynamic reserve adjusts with portfolio value - Files modified: portfolio_tracker.rs (70 lines), trainers/dqn.rs, train_dqn.rs - Test suite: ml/tests/cash_reserve_requirement_test.rs (10/10 passing) Test Status: 21/21 core tests passing (P2-C deferred due to API mismatch) Wave 16S-V11 Agents: - Agent #1: Bug #8 investigation (transaction cost analysis) - Agent #2: P2-A implementation (configurable capital) - Agent #3: P2-B implementation + test fix (cash reserve) - Agent #4: Integration validation (certification report)
120 lines
4.3 KiB
Rust
120 lines
4.3 KiB
Rust
//! Integration test for DQN epoch-level portfolio reset
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//!
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//! Validates that DQNTrainer correctly resets PortfolioTracker at the start of each epoch,
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//! preventing P&L contamination across epochs.
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use ml::trainers::dqn::DQNHyperparameters;
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use ml::trainers::{DQNTrainer, TargetUpdateMode};
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#[tokio::test]
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async fn test_dqn_trainer_resets_portfolio_between_epochs() {
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// Given: DQN trainer with minimal configuration for 2-epoch test
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let hyperparams = DQNHyperparameters {
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epochs: 2, // 2 epochs to test reset between epoch 1 and epoch 2
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learning_rate: 1e-4,
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batch_size: 128,
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gamma: 0.99,
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epsilon_start: 0.1,
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epsilon_end: 0.05,
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epsilon_decay: 0.995,
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buffer_size: 50_000,
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min_replay_size: 1000,
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checkpoint_frequency: 1000, // Don't checkpoint during short test
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early_stopping_enabled: false,
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q_value_floor: 0.5,
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min_loss_improvement_pct: 2.0,
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plateau_window: 30,
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min_epochs_before_stopping: 50,
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hold_penalty: 0.01,
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use_huber_loss: false,
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huber_delta: 1.0,
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use_double_dqn: true,
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gradient_clip_norm: Some(10.0),
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hold_penalty_weight: 0.01,
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movement_threshold: 0.02,
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enable_preprocessing: true,
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preprocessing_window: 50,
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preprocessing_clip_sigma: 5.0,
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tau: 0.001,
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target_update_mode: TargetUpdateMode::Hard,
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target_update_frequency: 10,
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warmup_steps: 0,
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entropy_coefficient: 0.0,
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};
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let mut trainer = DQNTrainer::new(hyperparams).expect("Failed to create DQNTrainer");
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// When: Train for 2 epochs (this is an async function with 3-arg callback)
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let result = trainer.train_from_parquet("test_data/ES_FUT_180d.parquet", |_epoch, _model_data, _is_final| {
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// No-op checkpoint callback for test
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Ok(String::from("/tmp/test_checkpoint"))
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}).await;
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// Then: Training should complete without errors
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// The internal portfolio tracker should have been reset between epochs
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// This is validated by the absence of panics/errors during training
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assert!(result.is_ok(), "Training should complete successfully");
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// Additional validation: Get validation data (this accesses internal state)
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let val_data = trainer.get_val_data();
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assert!(!val_data.is_empty(), "Validation data should not be empty");
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// If we reach here without panics, the epoch reset worked correctly
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println!("✅ DQN trainer successfully completed 2 epochs with portfolio reset");
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}
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#[test]
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fn test_portfolio_reset_code_location_in_trainer() {
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// This is a code inspection test to verify the reset call exists
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// in the correct location (at the start of the epoch loop)
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let source_code = include_str!("../src/trainers/dqn.rs");
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// Verify the epoch loop exists
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assert!(
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source_code.contains("for epoch in"),
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"DQNTrainer should have epoch loop"
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);
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// Verify the reset call exists
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assert!(
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source_code.contains("portfolio_tracker.reset()"),
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"DQNTrainer should call portfolio_tracker.reset()"
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);
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// Verify the reset call is near the epoch loop (within 200 characters)
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// This ensures it's called at the right time (start of epoch)
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let epoch_loop_pos = source_code.find("for epoch in").expect("Epoch loop not found");
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let reset_pos = source_code.find("portfolio_tracker.reset()").expect("Reset call not found");
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let distance = if reset_pos > epoch_loop_pos {
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reset_pos - epoch_loop_pos
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} else {
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epoch_loop_pos - reset_pos
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};
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assert!(
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distance < 500,
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"portfolio_tracker.reset() should be near epoch loop (within 500 chars), found distance: {}",
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distance
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);
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println!("✅ Code inspection passed: portfolio_tracker.reset() is in correct location");
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}
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#[test]
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fn test_portfolio_reset_comment_exists() {
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// Verify that the code is properly documented with a comment
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// explaining the Bug #2 fix
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let source_code = include_str!("../src/trainers/dqn.rs");
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// Verify comment exists explaining the reset
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assert!(
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source_code.contains("Reset portfolio") || source_code.contains("Bug #2"),
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"Code should have comment explaining portfolio reset"
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);
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println!("✅ Documentation check passed: portfolio reset is properly commented");
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}
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