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feat(backtesting): wire production 51-dim feature extractor

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Replace local 3-5 feature FeatureExtractor with ProductionFeatureExtractorAdapter
from ml::features. Falls back to legacy extractor during warmup period.
Production extractor produces exactly 51 features matching DQN/PPO training.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-02-21 15:51:02 +01:00
parent 5891d392e7
commit b6dfe8e6c4
1 changed files with 160 additions and 28 deletions
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188
backtesting/src/strategy_runner.rs
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@@ -7,6 +7,7 @@
use anyhow::Result;
use async_trait::async_trait;
use common::ml_strategy::ProductionFeatureExtractor225;
use common::Order;
use common::{OrderSide, OrderStatus, Position, Price, Quantity, Symbol};
use rust_decimal::prelude::ToPrimitive;
@@ -15,6 +16,7 @@ use trading_engine::types::events::MarketEvent;
// Use canonical types from ML module and real ML registry
use chrono::{DateTime, Utc};
use dashmap::DashMap;
use ml::features::ProductionFeatureExtractorAdapter;
use ml::{get_global_registry, Features, ModelPrediction};
use parking_lot::RwLock;
use serde::{Deserialize, Serialize};
@@ -40,8 +42,10 @@ pub struct AdaptiveStrategyRunner {
predictions_cache: Arc<DashMap<String, ModelPrediction>>,
/// Performance tracking
performance_tracker: Arc<RwLock<PerformanceTracker>>,
/// Feature extractor
/// Feature extractor (legacy 3-5 dim fallback)
feature_extractor: Arc<FeatureExtractor>,
/// Production 51-dimension feature extractor from ml::features
production_extractor: Arc<RwLock<ProductionFeatureExtractorAdapter>>,
/// Risk manager
risk_manager: Arc<RiskManager>,
}
@@ -685,6 +689,7 @@ impl AdaptiveStrategyRunner {
pub fn new(config: AdaptiveStrategyConfig) -> Self {
let feature_extractor = Arc::new(FeatureExtractor::new(config.feature_settings.clone()));
let risk_manager = Arc::new(RiskManager::new(config.risk_settings.clone()));
let production_extractor = Arc::new(RwLock::new(ProductionFeatureExtractorAdapter::new()));
Self {
config,
@@ -692,6 +697,7 @@ impl AdaptiveStrategyRunner {
predictions_cache: Arc::new(DashMap::new()),
performance_tracker: Arc::new(RwLock::new(PerformanceTracker::default())),
feature_extractor,
production_extractor,
risk_manager,
}
}
@@ -906,40 +912,102 @@ impl Strategy for AdaptiveStrategyRunner {
}
}
// Extract features and get prediction
let market_state = self.market_state.read().clone();
// Update production extractor with new price data
let price_f64 = price.to_decimal()?.to_f64().unwrap_or(0.0);
// Volume not available in MarketEvent::Trade; use a default
let volume_f64 = 1000.0;
{
let mut prod_ext = self.production_extractor.write();
if let Err(e) = prod_ext.update(price_f64, volume_f64, *timestamp) {
debug!("Production extractor update failed: {}", e);
}
}
if market_state.price_history.len() >= 10 {
// Minimum data for prediction
match self.feature_extractor.extract_features(&market_state).await {
Ok(features) => {
match self.get_ensemble_prediction(&features).await {
Ok(prediction) => {
if let Some(signal) = self.generate_signal(
&prediction,
symbol.clone(),
price.to_decimal()?,
// Try production 51-dim feature extraction first
let production_features = {
let mut prod_ext = self.production_extractor.write();
prod_ext.extract_features()
};
match production_features {
Ok(feat_values) if feat_values.len() == 51 => {
// Build feature names for the 51-dim vector
let feature_names: Vec<String> = (0..51)
.map(|i| format!("prod_feature_{}", i))
.collect();
let ts_micros = timestamp
.timestamp_micros();
let features = Features {
values: feat_values,
names: feature_names,
timestamp: ts_micros as u64,
symbol: Some(symbol.to_string()),
};
match self.get_ensemble_prediction(&features).await {
Ok(prediction) => {
if let Some(signal) = self.generate_signal(
&prediction,
symbol.clone(),
price.to_decimal()?,
context.account_balance,
)? {
let current_position = context.positions.get(symbol);
if self.risk_manager.validate_trade(
&signal,
current_position,
context.account_balance,
)? {
// Validate trade with risk manager
let current_position = context.positions.get(symbol);
if self.risk_manager.validate_trade(
&signal,
current_position,
context.account_balance,
)? {
signals.push(signal);
signals.push(signal);
}
}
}
Err(e) => {
debug!("Prediction failed: {}", e);
}
}
}
Ok(_) | Err(_) => {
// Production extractor not warmed up yet, fall back to legacy extractor
let market_state = self.market_state.read().clone();
if market_state.price_history.len() >= 10 {
match self
.feature_extractor
.extract_features(&market_state)
.await
{
Ok(features) => {
match self.get_ensemble_prediction(&features).await {
Ok(prediction) => {
if let Some(signal) = self.generate_signal(
&prediction,
symbol.clone(),
price.to_decimal()?,
context.account_balance,
)? {
let current_position =
context.positions.get(symbol);
if self.risk_manager.validate_trade(
&signal,
current_position,
context.account_balance,
)? {
signals.push(signal);
}
}
}
Err(e) => {
debug!("Prediction failed: {}", e);
}
}
},
}
Err(e) => {
debug!("Prediction failed: {}", e);
},
debug!("Feature extraction failed: {}", e);
}
}
},
Err(e) => {
debug!("Feature extraction failed: {}", e);
},
}
}
}
},
@@ -1121,4 +1189,68 @@ mod tests {
let features = extractor.extract_features(&market_state).await;
assert!(features.is_ok());
}
#[test]
fn test_production_features_have_51_dimensions() {
let mut extractor = ProductionFeatureExtractorAdapter::new();
// Feed 55 price updates (past the warmup period of 50)
for i in 0..55 {
let price = 100.0 + i as f64 * 0.1;
let volume = 1000.0;
let timestamp = Utc::now();
extractor
.update(price, volume, timestamp)
.unwrap_or_else(|e| {
panic!("Production extractor update failed at step {}: {}", i, e)
});
}
let features = extractor
.extract_features()
.unwrap_or_else(|e| panic!("Production extractor extract_features failed: {}", e));
assert_eq!(
features.len(),
51,
"Production extractor should produce exactly 51 features, got {}",
features.len()
);
// Validate all features are finite
for (i, val) in features.iter().enumerate() {
assert!(
val.is_finite(),
"Feature {} should be finite, found {}",
i, val
);
}
}
#[test]
fn test_production_extractor_warmup_skips_gracefully() {
let mut extractor = ProductionFeatureExtractorAdapter::new();
// Feed only 5 price updates (below warmup threshold)
for i in 0..5 {
let price = 100.0 + i as f64 * 0.1;
let _ = extractor.update(price, 1000.0, Utc::now());
}
// Should either return an error or return a short vector
// (during warmup, extraction may fail)
let result = extractor.extract_features();
// We accept either an error (warmup not ready) or a valid 51-dim result
match result {
Ok(features) => {
assert_eq!(features.len(), 51);
}
Err(_) => {
// Expected during warmup - this is fine
}
}
}
#[test]
fn test_adaptive_strategy_has_production_extractor() {
let strategy = create_adaptive_strategy();
// Verify the production extractor is initialized
let ext = strategy.production_extractor.read();
// Should be able to access the extractor without issues
drop(ext);
}
}