Files
foxhunt/backtesting/tests/test_ml_integration.rs
jgrusewski 3bae23d814 🎯 MAJOR SUCCESS: 12 Parallel Agents Complete Type System Cleanup
ACHIEVEMENTS:
- Agent 1-4: Successfully moved OrderSide/OrderStatus/OrderType/Currency/TimeInForce to common
- Agent 5-6: Consolidated MarketDataEvent and Timestamp types to common
- Agent 7-8: Updated ALL imports from trading_engine::types to common::types
- Agent 9-11: Eliminated 50+ duplicates, cleaned modules, removed re-exports
- Agent 12: CRITICAL DISCOVERY - Root cause identified

ROOT CAUSE FOUND:
- Common crate missing canonical Order struct definition
- Forces all 8+ services to create duplicate Order definitions
- Architectural violation causing compilation chaos

NEXT: Implement canonical Order struct in common crate with parallel agents

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-09-26 16:51:08 +02:00

117 lines
4.0 KiB
Rust

//! Integration tests for ML models in backtesting framework
use backtesting::{
create_adaptive_strategy_with_config, AdaptiveStrategyConfig, AdaptiveStrategyRunner,
BacktestConfig, BacktestEngine, FeatureSettings, RiskSettings, Strategy,
};
use common::types::*;
#[tokio::test]
async fn test_dqn_strategy_integration() {
// Create backtesting engine
let config = BacktestConfig {
initial_capital: Decimal::from(100000),
..Default::default()
};
let mut engine = BacktestEngine::new(config).await.unwrap();
// Set adaptive strategy with DQN model
let adaptive_config = AdaptiveStrategyConfig {
active_models: vec!["DQN".to_string()],
..AdaptiveStrategyConfig::default()
};
let dqn_strategy = Box::new(create_adaptive_strategy_with_config(adaptive_config));
engine.set_strategy(dqn_strategy).await.unwrap();
// Verify strategy is set
let state = engine.get_state().await;
assert!(!state.is_running);
// Note: Actual backtesting would require market data loading
// This test validates the integration is working
}
#[tokio::test]
async fn test_ppo_strategy_integration() {
let config = BacktestConfig::default();
let mut engine = BacktestEngine::new(config).await.unwrap();
// Set adaptive strategy with PPO model
let adaptive_config = AdaptiveStrategyConfig {
active_models: vec!["PPO".to_string()],
..AdaptiveStrategyConfig::default()
};
let ppo_strategy = Box::new(create_adaptive_strategy_with_config(adaptive_config));
engine.set_strategy(ppo_strategy).await.unwrap();
let state = engine.get_state().await;
assert!(!state.is_running);
}
#[tokio::test]
async fn test_tlob_strategy_integration() {
let config = BacktestConfig::default();
let mut engine = BacktestEngine::new(config).await.unwrap();
// Set adaptive strategy with TLOB model
let adaptive_config = AdaptiveStrategyConfig {
active_models: vec!["TLOB".to_string()],
..AdaptiveStrategyConfig::default()
};
let tlob_strategy = Box::new(create_adaptive_strategy_with_config(adaptive_config));
engine.set_strategy(tlob_strategy).await.unwrap();
let state = engine.get_state().await;
assert!(!state.is_running);
}
#[tokio::test]
async fn test_ensemble_strategy_integration() {
let config = BacktestConfig::default();
let mut engine = BacktestEngine::new(config).await.unwrap();
// Set adaptive strategy with multiple ML models (ensemble)
let adaptive_config = AdaptiveStrategyConfig {
active_models: vec!["DQN".to_string(), "PPO".to_string(), "TLOB".to_string()],
..AdaptiveStrategyConfig::default()
};
let ensemble_strategy = Box::new(create_adaptive_strategy_with_config(adaptive_config));
engine.set_strategy(ensemble_strategy).await.unwrap();
let state = engine.get_state().await;
assert!(!state.is_running);
assert_eq!(state.portfolio_value, Decimal::ZERO); // Not yet initialized
}
#[tokio::test]
async fn test_adaptive_strategy_configuration() {
// Create custom adaptive strategy configuration
let config = AdaptiveStrategyConfig {
active_models: vec!["DQN".to_string(), "PPO".to_string(), "TLOB".to_string()],
min_confidence: 0.7, // Higher confidence requirement
max_position_size: 0.05, // 5% position size
lookback_period: 20,
model_update_frequency: 100,
risk_settings: RiskSettings {
max_drawdown: 0.15, // 15% max drawdown
stop_loss: 0.05,
take_profit: 0.10,
kelly_fraction: 0.25,
},
feature_settings: FeatureSettings::default(),
};
let adaptive_strategy = create_adaptive_strategy_with_config(config.clone());
// Test as a Strategy trait object to verify it implements the trait
let strategy: Box<dyn backtesting::Strategy> = Box::new(adaptive_strategy);
// Verify strategy has a name (strategy trait method)
let strategy_name = strategy.name();
assert!(
!strategy_name.is_empty(),
"Strategy should have a non-empty name"
);
}