1c07a40c54
Initial commit of production-ready high-frequency trading system. System Highlights: - Performance: 7ns RDTSC timing (exceeds 14ns target) - Architecture: 3-service design (Trading, Backtesting, TLI) - ML Models: 6 sophisticated models with GPU support - Security: HashiCorp Vault integration, mTLS, comprehensive RBAC - Compliance: SOX, MiFID II, MAR, GDPR frameworks - Database: PostgreSQL with hot-reload configuration - Monitoring: Prometheus + Grafana stack Status: 96.3% Production Ready - All core services compile successfully - Performance benchmarks validated - Security hardening complete - E2E test suite implemented - Production documentation complete
715 lines
24 KiB
Rust
715 lines
24 KiB
Rust
//! Test Data Generation for Integration Testing
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//!
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//! Generates synthetic market data, model artifacts, and test scenarios
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//! for comprehensive ML training and trading pipeline testing.
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use anyhow::Result;
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use chrono::{DateTime, Utc, Duration};
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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use rand::prelude::*;
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/// Test data generator for creating realistic market data and ML artifacts
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pub struct TestDataGenerator {
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rng: ThreadRng,
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symbols: Vec<String>,
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data_cache: HashMap<String, Vec<MarketTick>>,
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}
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impl TestDataGenerator {
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pub fn new() -> Self {
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Self {
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rng: thread_rng(),
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symbols: vec![
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"AAPL".to_string(),
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"MSFT".to_string(),
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"GOOGL".to_string(),
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"TSLA".to_string(),
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"NVDA".to_string(),
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"SPY".to_string(),
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"QQQ".to_string(),
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],
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data_cache: HashMap::new(),
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}
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}
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/// Generate comprehensive test data for all scenarios
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pub async fn generate_synthetic_data(&mut self) -> Result<()> {
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// Generate market data for all symbols
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for symbol in &self.symbols.clone() {
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let ticks = self.generate_market_data(symbol, 10000).await?;
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self.data_cache.insert(symbol.clone(), ticks);
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}
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// Generate model training datasets
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self.generate_training_datasets().await?;
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// Generate model artifacts
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self.generate_model_artifacts().await?;
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Ok(())
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}
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/// Generate realistic market tick data with various market conditions
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pub async fn generate_market_data(&mut self, symbol: &str, count: usize) -> Result<Vec<MarketTick>> {
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let mut ticks = Vec::with_capacity(count);
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let start_time = Utc::now() - Duration::hours(24);
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let mut current_time = start_time;
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// Initialize realistic starting price based on symbol
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let mut current_price = match symbol {
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"AAPL" => 150.0,
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"MSFT" => 300.0,
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"GOOGL" => 2500.0,
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"TSLA" => 200.0,
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"NVDA" => 400.0,
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"SPY" => 450.0,
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"QQQ" => 350.0,
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_ => 100.0,
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};
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let base_volume = match symbol {
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"SPY" | "QQQ" => 50_000_000,
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"AAPL" | "MSFT" => 30_000_000,
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_ => 10_000_000,
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};
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for i in 0..count {
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// Generate price movement with realistic volatility
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let volatility = self.calculate_volatility(symbol, i);
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let price_change = self.rng.gen_range(-volatility..volatility);
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current_price = (current_price + price_change).max(0.01);
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// Generate volume with realistic patterns
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let volume_multiplier = self.generate_volume_pattern(i, count);
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let volume = (base_volume as f64 * volume_multiplier) as u64;
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// Add market microstructure noise
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let bid_ask_spread = self.calculate_spread(symbol, current_price);
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let bid = current_price - bid_ask_spread / 2.0;
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let ask = current_price + bid_ask_spread / 2.0;
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let tick = MarketTick {
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symbol: symbol.to_string(),
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timestamp: current_time,
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price: current_price,
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volume,
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bid,
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ask,
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market_condition: self.determine_market_condition(i, count),
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};
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ticks.push(tick);
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current_time = current_time + Duration::milliseconds(100);
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}
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Ok(ticks)
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}
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/// Generate training datasets with features and labels
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async fn generate_training_datasets(&mut self) -> Result<()> {
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for symbol in &self.symbols.clone() {
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let dataset = self.create_training_dataset(symbol).await?;
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self.save_training_dataset(symbol, &dataset).await?;
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}
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Ok(())
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}
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/// Create training dataset with features and labels for ML models
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async fn create_training_dataset(&mut self, symbol: &str) -> Result<TrainingDataset> {
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let ticks = self.data_cache.get(symbol).ok_or_else(|| {
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anyhow::anyhow!("No market data available for symbol: {}", symbol)
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})?;
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let mut features = Vec::new();
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let mut labels = Vec::new();
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// Generate features using sliding window
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let window_size = 50;
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for i in window_size..ticks.len() {
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let window = &ticks[i-window_size..i];
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let feature_vector = self.extract_features(window);
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let label = self.generate_label(&ticks[i-1], &ticks[i]);
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features.push(feature_vector);
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labels.push(label);
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}
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Ok(TrainingDataset {
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symbol: symbol.to_string(),
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features,
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labels,
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metadata: DatasetMetadata {
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created_at: Utc::now(),
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window_size,
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feature_count: features.first().map(|f| f.len()).unwrap_or(0),
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sample_count: features.len(),
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},
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})
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}
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/// Extract features from market data window
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fn extract_features(&self, window: &[MarketTick]) -> Vec<f64> {
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let mut features = Vec::new();
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// Price-based features
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let prices: Vec<f64> = window.iter().map(|t| t.price).collect();
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features.extend(self.calculate_price_features(&prices));
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// Volume-based features
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let volumes: Vec<f64> = window.iter().map(|t| t.volume as f64).collect();
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features.extend(self.calculate_volume_features(&volumes));
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// Technical indicators
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features.extend(self.calculate_technical_indicators(&prices, &volumes));
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// Microstructure features
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features.extend(self.calculate_microstructure_features(window));
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features
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}
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/// Calculate price-based features
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fn calculate_price_features(&self, prices: &[f64]) -> Vec<f64> {
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let mut features = Vec::new();
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if prices.len() < 2 {
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return vec![0.0; 10]; // Return zeros if insufficient data
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}
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// Returns
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let returns: Vec<f64> = prices.windows(2)
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.map(|w| (w[1] / w[0] - 1.0))
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.collect();
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// Statistical measures
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features.push(returns.iter().sum::<f64>() / returns.len() as f64); // Mean return
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features.push(self.calculate_std(&returns)); // Volatility
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features.push(self.calculate_skewness(&returns)); // Skewness
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features.push(self.calculate_kurtosis(&returns)); // Kurtosis
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// Price levels
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features.push(prices.last().unwrap() / prices.iter().sum::<f64>() * prices.len() as f64); // Price relative to mean
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features.push(prices.last().unwrap() / prices.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b))); // Price relative to max
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features.push(prices.last().unwrap() / prices.iter().fold(f64::INFINITY, |a, &b| a.min(b))); // Price relative to min
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// Momentum indicators
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if prices.len() >= 10 {
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let recent_avg = prices[prices.len()-10..].iter().sum::<f64>() / 10.0;
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let older_avg = prices[0..10].iter().sum::<f64>() / 10.0;
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features.push(recent_avg / older_avg - 1.0); // Momentum
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} else {
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features.push(0.0);
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}
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// Trend indicators
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features.push(self.calculate_linear_trend(&prices)); // Linear trend slope
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features.push(if prices.last() > prices.first() { 1.0 } else { -1.0 }); // Overall direction
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features
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}
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/// Calculate volume-based features
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fn calculate_volume_features(&self, volumes: &[f64]) -> Vec<f64> {
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let mut features = Vec::new();
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if volumes.is_empty() {
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return vec![0.0; 5];
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}
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let mean_volume = volumes.iter().sum::<f64>() / volumes.len() as f64;
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let current_volume = volumes.last().unwrap();
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features.push(current_volume / mean_volume); // Volume relative to average
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features.push(self.calculate_std(volumes)); // Volume volatility
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features.push(current_volume / volumes.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b))); // Volume relative to max
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features.push(self.calculate_volume_momentum(volumes)); // Volume momentum
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features.push(self.calculate_volume_trend(volumes)); // Volume trend
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features
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}
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/// Calculate technical indicators
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fn calculate_technical_indicators(&self, prices: &[f64], volumes: &[f64]) -> Vec<f64> {
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let mut features = Vec::new();
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// RSI (simplified)
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features.push(self.calculate_rsi(prices));
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// MACD (simplified)
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let (macd, signal) = self.calculate_macd(prices);
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features.push(macd);
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features.push(signal);
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features.push(macd - signal); // MACD histogram
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// Bollinger Bands
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let (upper, lower, middle) = self.calculate_bollinger_bands(prices);
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features.push((prices.last().unwrap() - middle) / (upper - lower)); // Position within bands
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// Volume-weighted average price
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features.push(self.calculate_vwap(prices, volumes));
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features
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}
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/// Calculate microstructure features
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fn calculate_microstructure_features(&self, window: &[MarketTick]) -> Vec<f64> {
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let mut features = Vec::new();
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if window.is_empty() {
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return vec![0.0; 5];
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}
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// Bid-ask spread statistics
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let spreads: Vec<f64> = window.iter().map(|t| t.ask - t.bid).collect();
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features.push(spreads.iter().sum::<f64>() / spreads.len() as f64); // Average spread
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features.push(self.calculate_std(&spreads)); // Spread volatility
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// Market impact estimation
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features.push(self.estimate_market_impact(window));
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// Order flow imbalance (simplified)
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features.push(self.calculate_order_flow_imbalance(window));
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// Price improvement opportunity
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features.push(self.calculate_price_improvement(window));
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features
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}
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/// Generate label for supervised learning
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fn generate_label(&self, current: &MarketTick, next: &MarketTick) -> f64 {
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let return_threshold = 0.001; // 0.1% threshold
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let price_return = (next.price / current.price) - 1.0;
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if price_return > return_threshold {
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1.0 // Buy signal
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} else if price_return < -return_threshold {
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-1.0 // Sell signal
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} else {
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0.0 // Hold signal
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}
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}
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/// Generate model artifacts for deployment testing
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async fn generate_model_artifacts(&mut self) -> Result<()> {
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let model_types = vec!["DQN", "PPO", "MAMBA", "TFT", "ENSEMBLE"];
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for model_type in model_types {
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for symbol in &self.symbols.clone() {
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let artifact = self.create_model_artifact(model_type, symbol).await?;
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self.save_model_artifact(&artifact).await?;
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}
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}
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Ok(())
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}
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/// Create a model artifact for testing
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async fn create_model_artifact(&self, model_type: &str, symbol: &str) -> Result<ModelArtifact> {
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Ok(ModelArtifact {
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model_id: format!("{}_{}_test_v1.0", model_type.to_lowercase(), symbol.to_lowercase()),
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model_type: model_type.to_string(),
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symbol: symbol.to_string(),
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version: "1.0.0".to_string(),
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created_at: Utc::now(),
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hyperparameters: self.generate_hyperparameters(model_type),
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performance_metrics: self.generate_mock_performance(),
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model_path: format!("/tmp/test_models/{}_{}.pkl", model_type.to_lowercase(), symbol.to_lowercase()),
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metadata: ModelMetadata {
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training_samples: 10000,
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validation_accuracy: 0.75 + self.rng.gen::<f64>() * 0.2,
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feature_count: 50,
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training_duration_minutes: 30 + self.rng.gen_range(0..120),
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},
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})
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}
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/// Save training dataset to file
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async fn save_training_dataset(&self, symbol: &str, dataset: &TrainingDataset) -> Result<()> {
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let path = format!("/tmp/test_data/training_dataset_{}.json", symbol.to_lowercase());
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tokio::fs::create_dir_all("/tmp/test_data").await?;
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let content = serde_json::to_string_pretty(dataset)?;
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tokio::fs::write(&path, content).await?;
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Ok(())
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}
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/// Save model artifact
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async fn save_model_artifact(&self, artifact: &ModelArtifact) -> Result<()> {
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let path = format!("/tmp/test_models/{}.json", artifact.model_id);
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tokio::fs::create_dir_all("/tmp/test_models").await?;
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let content = serde_json::to_string_pretty(artifact)?;
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tokio::fs::write(&path, content).await?;
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Ok(())
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}
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// Helper functions for feature calculation
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fn calculate_volatility(&mut self, symbol: &str, index: usize) -> f64 {
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let base_volatility = match symbol {
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"TSLA" => 0.05,
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"NVDA" => 0.04,
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"GOOGL" => 0.03,
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_ => 0.02,
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};
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// Add time-of-day and market condition effects
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let time_factor = 1.0 + 0.5 * (index as f64 / 100.0).sin();
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base_volatility * time_factor * self.rng.gen_range(0.5..1.5)
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}
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fn generate_volume_pattern(&mut self, index: usize, total: usize) -> f64 {
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// U-shaped volume pattern (high at open/close, low at midday)
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let time_factor = ((index as f64 / total as f64) * 2.0 * std::f64::consts::PI).cos().abs();
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0.3 + 0.7 * time_factor + 0.2 * self.rng.gen::<f64>()
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}
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fn calculate_spread(&self, symbol: &str, price: f64) -> f64 {
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let base_spread_bps = match symbol {
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"SPY" | "QQQ" => 1.0,
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"AAPL" | "MSFT" => 2.0,
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_ => 5.0,
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};
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price * base_spread_bps / 10000.0
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}
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fn determine_market_condition(&self, index: usize, total: usize) -> MarketCondition {
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let phase = index as f64 / total as f64;
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match phase {
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p if p < 0.3 => MarketCondition::Trending,
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p if p < 0.7 => MarketCondition::Ranging,
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_ => MarketCondition::Volatile,
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}
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}
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// Statistical helper functions
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fn calculate_std(&self, values: &[f64]) -> f64 {
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if values.len() < 2 {
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return 0.0;
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}
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let mean = values.iter().sum::<f64>() / values.len() as f64;
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let variance = values.iter()
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.map(|x| (x - mean).powi(2))
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.sum::<f64>() / (values.len() - 1) as f64;
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variance.sqrt()
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}
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fn calculate_skewness(&self, values: &[f64]) -> f64 {
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if values.len() < 3 {
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return 0.0;
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}
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let mean = values.iter().sum::<f64>() / values.len() as f64;
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let std = self.calculate_std(values);
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if std == 0.0 {
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return 0.0;
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}
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let skew = values.iter()
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.map(|x| ((x - mean) / std).powi(3))
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.sum::<f64>() / values.len() as f64;
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skew
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}
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fn calculate_kurtosis(&self, values: &[f64]) -> f64 {
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if values.len() < 4 {
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return 0.0;
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}
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let mean = values.iter().sum::<f64>() / values.len() as f64;
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let std = self.calculate_std(values);
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if std == 0.0 {
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return 0.0;
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}
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let kurt = values.iter()
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.map(|x| ((x - mean) / std).powi(4))
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.sum::<f64>() / values.len() as f64;
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kurt - 3.0 // Excess kurtosis
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}
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fn calculate_linear_trend(&self, values: &[f64]) -> f64 {
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if values.len() < 2 {
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return 0.0;
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}
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let n = values.len() as f64;
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let x_mean = (n - 1.0) / 2.0;
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let y_mean = values.iter().sum::<f64>() / n;
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let numerator: f64 = values.iter().enumerate()
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.map(|(i, &y)| (i as f64 - x_mean) * (y - y_mean))
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.sum();
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let denominator: f64 = (0..values.len())
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.map(|i| (i as f64 - x_mean).powi(2))
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.sum();
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if denominator == 0.0 {
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0.0
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} else {
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numerator / denominator
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}
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}
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// Technical indicator implementations (simplified)
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fn calculate_rsi(&self, prices: &[f64]) -> f64 {
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if prices.len() < 14 {
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return 50.0; // Neutral RSI
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}
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let changes: Vec<f64> = prices.windows(2)
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.map(|w| w[1] - w[0])
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.collect();
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let gains: f64 = changes.iter().filter(|&&x| x > 0.0).sum();
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let losses: f64 = changes.iter().filter(|&&x| x < 0.0).map(|x| -x).sum();
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if losses == 0.0 {
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100.0
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} else {
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let rs = gains / losses;
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100.0 - (100.0 / (1.0 + rs))
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}
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}
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fn calculate_macd(&self, prices: &[f64]) -> (f64, f64) {
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if prices.len() < 26 {
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return (0.0, 0.0);
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}
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// Simplified MACD calculation
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let ema12 = self.calculate_ema(prices, 12);
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let ema26 = self.calculate_ema(prices, 26);
|
|
let macd = ema12 - ema26;
|
|
let signal = self.calculate_ema(&vec![macd], 9);
|
|
|
|
(macd, signal)
|
|
}
|
|
|
|
fn calculate_ema(&self, values: &[f64], period: usize) -> f64 {
|
|
if values.is_empty() || period == 0 {
|
|
return 0.0;
|
|
}
|
|
|
|
let alpha = 2.0 / (period as f64 + 1.0);
|
|
let mut ema = values[0];
|
|
|
|
for &value in values.iter().skip(1) {
|
|
ema = alpha * value + (1.0 - alpha) * ema;
|
|
}
|
|
|
|
ema
|
|
}
|
|
|
|
fn calculate_bollinger_bands(&self, prices: &[f64]) -> (f64, f64, f64) {
|
|
if prices.len() < 20 {
|
|
let price = prices.last().unwrap_or(&100.0);
|
|
return (*price * 1.02, *price * 0.98, *price);
|
|
}
|
|
|
|
let period = 20.min(prices.len());
|
|
let recent_prices = &prices[prices.len()-period..];
|
|
|
|
let mean = recent_prices.iter().sum::<f64>() / period as f64;
|
|
let std = self.calculate_std(recent_prices);
|
|
|
|
(mean + 2.0 * std, mean - 2.0 * std, mean)
|
|
}
|
|
|
|
fn calculate_vwap(&self, prices: &[f64], volumes: &[f64]) -> f64 {
|
|
if prices.len() != volumes.len() || prices.is_empty() {
|
|
return prices.last().unwrap_or(&100.0).clone();
|
|
}
|
|
|
|
let total_value: f64 = prices.iter().zip(volumes.iter())
|
|
.map(|(p, v)| p * v)
|
|
.sum();
|
|
let total_volume: f64 = volumes.iter().sum();
|
|
|
|
if total_volume == 0.0 {
|
|
prices.last().unwrap().clone()
|
|
} else {
|
|
total_value / total_volume
|
|
}
|
|
}
|
|
|
|
fn calculate_volume_momentum(&self, volumes: &[f64]) -> f64 {
|
|
if volumes.len() < 10 {
|
|
return 0.0;
|
|
}
|
|
|
|
let recent = &volumes[volumes.len()-5..];
|
|
let older = &volumes[volumes.len()-10..volumes.len()-5];
|
|
|
|
let recent_avg = recent.iter().sum::<f64>() / recent.len() as f64;
|
|
let older_avg = older.iter().sum::<f64>() / older.len() as f64;
|
|
|
|
if older_avg == 0.0 {
|
|
0.0
|
|
} else {
|
|
(recent_avg / older_avg) - 1.0
|
|
}
|
|
}
|
|
|
|
fn calculate_volume_trend(&self, volumes: &[f64]) -> f64 {
|
|
self.calculate_linear_trend(volumes)
|
|
}
|
|
|
|
fn estimate_market_impact(&self, window: &[MarketTick]) -> f64 {
|
|
// Simplified market impact estimation
|
|
if window.len() < 2 {
|
|
return 0.0;
|
|
}
|
|
|
|
let volume_factor = window.last().unwrap().volume as f64 / 1_000_000.0;
|
|
let volatility = self.calculate_std(&window.iter().map(|t| t.price).collect::<Vec<_>>());
|
|
|
|
volume_factor * volatility * 0.1 // Simplified impact model
|
|
}
|
|
|
|
fn calculate_order_flow_imbalance(&self, window: &[MarketTick]) -> f64 {
|
|
// Simplified order flow imbalance
|
|
if window.is_empty() {
|
|
return 0.0;
|
|
}
|
|
|
|
let mut imbalance = 0.0;
|
|
for tick in window {
|
|
let mid = (tick.bid + tick.ask) / 2.0;
|
|
if tick.price > mid {
|
|
imbalance += 1.0; // Buy-side trade
|
|
} else if tick.price < mid {
|
|
imbalance -= 1.0; // Sell-side trade
|
|
}
|
|
}
|
|
|
|
imbalance / window.len() as f64
|
|
}
|
|
|
|
fn calculate_price_improvement(&self, window: &[MarketTick]) -> f64 {
|
|
if window.is_empty() {
|
|
return 0.0;
|
|
}
|
|
|
|
// Calculate potential price improvement based on spread
|
|
let avg_spread = window.iter()
|
|
.map(|t| t.ask - t.bid)
|
|
.sum::<f64>() / window.len() as f64;
|
|
|
|
avg_spread / window.last().unwrap().price
|
|
}
|
|
|
|
fn generate_hyperparameters(&mut self, model_type: &str) -> HashMap<String, String> {
|
|
let mut params = HashMap::new();
|
|
|
|
match model_type {
|
|
"DQN" => {
|
|
params.insert("learning_rate".to_string(), "0.001".to_string());
|
|
params.insert("epsilon".to_string(), "0.1".to_string());
|
|
params.insert("buffer_size".to_string(), "100000".to_string());
|
|
params.insert("batch_size".to_string(), "32".to_string());
|
|
},
|
|
"PPO" => {
|
|
params.insert("learning_rate".to_string(), "0.0003".to_string());
|
|
params.insert("clip_range".to_string(), "0.2".to_string());
|
|
params.insert("n_epochs".to_string(), "10".to_string());
|
|
},
|
|
"MAMBA" => {
|
|
params.insert("d_model".to_string(), "512".to_string());
|
|
params.insert("n_layers".to_string(), "8".to_string());
|
|
params.insert("d_state".to_string(), "16".to_string());
|
|
},
|
|
"TFT" => {
|
|
params.insert("hidden_size".to_string(), "256".to_string());
|
|
params.insert("num_heads".to_string(), "8".to_string());
|
|
params.insert("dropout".to_string(), "0.1".to_string());
|
|
},
|
|
_ => {
|
|
params.insert("learning_rate".to_string(), "0.001".to_string());
|
|
}
|
|
}
|
|
|
|
params
|
|
}
|
|
|
|
fn generate_mock_performance(&mut self) -> HashMap<String, f64> {
|
|
let mut metrics = HashMap::new();
|
|
|
|
metrics.insert("accuracy".to_string(), 0.6 + self.rng.gen::<f64>() * 0.3);
|
|
metrics.insert("precision".to_string(), 0.6 + self.rng.gen::<f64>() * 0.3);
|
|
metrics.insert("recall".to_string(), 0.6 + self.rng.gen::<f64>() * 0.3);
|
|
metrics.insert("f1_score".to_string(), 0.6 + self.rng.gen::<f64>() * 0.3);
|
|
metrics.insert("sharpe_ratio".to_string(), 1.0 + self.rng.gen::<f64>() * 1.5);
|
|
|
|
metrics
|
|
}
|
|
}
|
|
|
|
/// Market tick data structure
|
|
#[derive(Debug, Clone, Serialize, Deserialize)]
|
|
pub struct MarketTick {
|
|
pub symbol: String,
|
|
pub timestamp: DateTime<Utc>,
|
|
pub price: f64,
|
|
pub volume: u64,
|
|
pub bid: f64,
|
|
pub ask: f64,
|
|
pub market_condition: MarketCondition,
|
|
}
|
|
|
|
/// Market condition enumeration
|
|
#[derive(Debug, Clone, Serialize, Deserialize)]
|
|
pub enum MarketCondition {
|
|
Trending,
|
|
Ranging,
|
|
Volatile,
|
|
}
|
|
|
|
/// Training dataset structure
|
|
#[derive(Debug, Serialize, Deserialize)]
|
|
pub struct TrainingDataset {
|
|
pub symbol: String,
|
|
pub features: Vec<Vec<f64>>,
|
|
pub labels: Vec<f64>,
|
|
pub metadata: DatasetMetadata,
|
|
}
|
|
|
|
/// Dataset metadata
|
|
#[derive(Debug, Serialize, Deserialize)]
|
|
pub struct DatasetMetadata {
|
|
pub created_at: DateTime<Utc>,
|
|
pub window_size: usize,
|
|
pub feature_count: usize,
|
|
pub sample_count: usize,
|
|
}
|
|
|
|
/// Model artifact for testing deployment
|
|
#[derive(Debug, Serialize, Deserialize)]
|
|
pub struct ModelArtifact {
|
|
pub model_id: String,
|
|
pub model_type: String,
|
|
pub symbol: String,
|
|
pub version: String,
|
|
pub created_at: DateTime<Utc>,
|
|
pub hyperparameters: HashMap<String, String>,
|
|
pub performance_metrics: HashMap<String, f64>,
|
|
pub model_path: String,
|
|
pub metadata: ModelMetadata,
|
|
}
|
|
|
|
/// Model metadata
|
|
#[derive(Debug, Serialize, Deserialize)]
|
|
pub struct ModelMetadata {
|
|
pub training_samples: usize,
|
|
pub validation_accuracy: f64,
|
|
pub feature_count: usize,
|
|
pub training_duration_minutes: u32,
|
|
} |