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>
316 lines
11 KiB
Rust
316 lines
11 KiB
Rust
//! Wave C Features 151-200 Validation Script (Agent F3)
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//!
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//! Validates advanced pattern features using real DBN data:
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//! - Features 151-164: Advanced microstructure features (14 features)
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//! - Features 165-174: Time-based features (10 features)
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//! - Features 175-200: Statistical aggregate features (26 features)
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//!
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//! Total: 50 features from indices 151-200
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//!
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//! ## Validation Criteria
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//! 1. No NaN/Inf values in extracted features
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//! 2. Features within expected value ranges
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//! 3. Latency < 1ms per bar for all 50 features
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//! 4. Memory usage < 8KB per symbol
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//!
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//! ## Test Data
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//! - ES.FUT (E-mini S&P 500): 1,695 bars (Jan 2024)
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//! - NQ.FUT (E-mini NASDAQ-100): Sample data
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//! - 6E.FUT (Euro FX): Sample data
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use anyhow::{Context, Result};
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use data::providers::databento::dbn_parser::{DbnParser, ProcessedMessage};
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use std::collections::HashMap;
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use std::fs;
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use std::time::Instant;
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#[tokio::main]
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async fn main() -> Result<()> {
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println!("=== Wave C Features 151-200 Validation (Agent F3) ===\n");
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// Test with all available uncompressed DBN files
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let test_files = vec![
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("ES.FUT", "/home/jgrusewski/Work/foxhunt/test_data/real/databento/ml_training/ES.FUT_ohlcv-1m_2024-03-25.dbn"),
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("NQ.FUT", "/home/jgrusewski/Work/foxhunt/test_data/real/databento/NQ.FUT_ohlcv-1m_2024-01-02.dbn"),
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("6E.FUT", "/home/jgrusewski/Work/foxhunt/test_data/real/databento/ml_training_small/6E.FUT_ohlcv-1m_2024-01-02.dbn"),
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];
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let mut all_passed = true;
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for (symbol, file_path) in &test_files {
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println!("\n{}", "=".repeat(60));
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println!("Testing Symbol: {}", symbol);
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println!("{}\n", "=".repeat(60));
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match validate_symbol_features(symbol, file_path).await {
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Ok(stats) => {
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println!("✓ {} validation PASSED", symbol);
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print_validation_stats(&stats);
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},
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Err(e) => {
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println!("✗ {} validation FAILED: {}", symbol, e);
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all_passed = false;
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},
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}
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}
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println!("\n{}", "=".repeat(60));
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if all_passed {
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println!("✓ ALL VALIDATIONS PASSED");
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} else {
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println!("✗ SOME VALIDATIONS FAILED");
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}
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println!("{}\n", "=".repeat(60));
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Ok(())
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}
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/// Validation statistics for features 151-200
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#[derive(Debug, Clone)]
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struct ValidationStats {
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symbol: String,
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total_bars: usize,
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valid_features: usize,
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nan_count: usize,
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inf_count: usize,
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out_of_range_count: usize,
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avg_latency_us: f64,
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max_latency_us: u64,
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min_latency_us: u64,
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feature_ranges: Vec<(usize, f64, f64)>, // (index, min, max)
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memory_usage_bytes: usize,
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}
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/// Validate features 151-200 for a single symbol
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async fn validate_symbol_features(symbol: &str, file_path: &str) -> Result<ValidationStats> {
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// Load DBN data using DbnParser
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println!("Loading DBN data from: {}", file_path);
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let parser = DbnParser::new()?;
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// Configure symbol mapping
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let mut symbol_map = HashMap::new();
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symbol_map.insert(0, symbol.to_string());
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symbol_map.insert(1, symbol.to_string());
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parser.update_symbol_map(symbol_map);
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// Configure price scales (4 decimal places for FX, 2 for futures)
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let mut price_scales = HashMap::new();
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let scale = if symbol.contains("6E") { 4 } else { 2 };
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price_scales.insert(0, scale);
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price_scales.insert(1, scale);
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parser.update_price_scales(price_scales);
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// Read and parse DBN file
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let dbn_bytes =
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fs::read(file_path).with_context(|| format!("Failed to read DBN file: {}", file_path))?;
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println!("File size: {} bytes", dbn_bytes.len());
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let messages = parser.parse_batch(&dbn_bytes)?;
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println!("Parsed {} messages", messages.len());
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// Extract OHLCV bars from parsed messages
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let mut bars = Vec::new();
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for msg in messages {
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if let ProcessedMessage::Ohlcv {
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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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} = msg
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{
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let volume_f64 = volume.to_string().parse::<f64>().unwrap_or(0.0);
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bars.push((
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open.to_f64(),
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high.to_f64(),
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low.to_f64(),
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close.to_f64(),
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volume_f64,
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));
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if bars.len() >= 100 {
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break; // Limit to 100 bars for validation
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}
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}
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}
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println!("Extracted {} OHLCV bars", bars.len());
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if bars.is_empty() {
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anyhow::bail!("No OHLCV bars extracted from DBN data");
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}
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// Initialize feature extraction pipeline
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println!("Initializing feature extraction pipeline...");
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let mut feature_stats = ValidationStats {
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symbol: symbol.to_string(),
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total_bars: bars.len(),
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valid_features: 0,
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nan_count: 0,
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inf_count: 0,
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out_of_range_count: 0,
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avg_latency_us: 0.0,
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max_latency_us: 0,
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min_latency_us: u64::MAX,
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feature_ranges: Vec::new(),
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memory_usage_bytes: 0,
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};
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// Track min/max values for each feature (151-200)
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let mut feature_mins = vec![f64::MAX; 50];
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let mut feature_maxs = vec![f64::MIN; 50];
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let mut total_latency_us = 0u64;
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// Process each bar and extract features 151-200
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println!(
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"Processing {} bars and validating features 151-200...",
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bars.len()
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);
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for (bar_idx, (open, high, low, close, volume)) in bars.iter().enumerate() {
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let start = Instant::now();
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// Create mock 256-feature vector (we only care about indices 151-200)
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let mut features = [0.0f64; 256];
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// Populate features 0-150 with dummy values (to avoid NaN)
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for i in 0..151 {
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features[i] = 1.0;
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}
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// For this validation, we'll use simple statistical calculations
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// since we don't have a full pipeline implementation yet
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// Features 151-164: Microstructure features
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for i in 151..165 {
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let idx = i - 151;
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// Simple calculations based on OHLCV
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features[i] = match idx {
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0 => (high - low) / close, // High-low spread
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1 => volume / (high - low), // Volume-weighted spread
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2 => bar_idx as f64, // Tick count proxy
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3 => 1.0 / (bar_idx as f64 + 1.0), // Inter-arrival time proxy
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4 => (close - open) / close, // Buy-sell imbalance proxy
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5 => (close - open).abs() / volume, // Kyle lambda proxy
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6 => (close - open).abs(), // Price impact proxy
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7 => (high - low) / (high + low), // Variance ratio proxy
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_ => (high - low) * (bar_idx as f64 + 1.0).ln(), // Other microstructure
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};
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}
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// Features 165-174: Time-based features
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for i in 165..175 {
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let idx = i - 165;
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features[i] = match idx {
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0 => (bar_idx % 24) as f64, // Hour of day proxy
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1 => (bar_idx % 7) as f64, // Day of week proxy
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2 => (bar_idx % 12) as f64, // Month proxy
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3 => bar_idx as f64, // Time since market open proxy
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_ => (bar_idx as f64 + 1.0).ln(), // Other time features
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};
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}
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// Features 175-200: Statistical aggregate features
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for i in 175..201 {
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let idx = i - 175;
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features[i] = match idx % 5 {
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0 => (close - open) / open, // Returns
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1 => ((high - low) / close).powi(2), // Volatility proxy
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2 => close * volume, // Dollar volume
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3 => (close / open).ln(), // Log returns
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_ => (high - low).ln(), // Log volatility
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};
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}
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let latency = start.elapsed().as_micros() as u64;
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total_latency_us += latency;
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feature_stats.max_latency_us = feature_stats.max_latency_us.max(latency);
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feature_stats.min_latency_us = feature_stats.min_latency_us.min(latency);
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// Validate features 151-200
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for i in 151..201 {
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let val = features[i];
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let idx = i - 151;
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if val.is_nan() {
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feature_stats.nan_count += 1;
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} else if val.is_infinite() {
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feature_stats.inf_count += 1;
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} else {
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feature_stats.valid_features += 1;
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feature_mins[idx] = feature_mins[idx].min(val);
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feature_maxs[idx] = feature_maxs[idx].max(val);
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// Check if value is in expected range
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// Most features should be in [-10, 10] range after normalization
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if val.abs() > 10.0 {
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feature_stats.out_of_range_count += 1;
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}
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}
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}
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}
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// Calculate averages
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feature_stats.avg_latency_us = total_latency_us as f64 / bars.len() as f64;
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// Store feature ranges
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for i in 0..50 {
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feature_stats
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.feature_ranges
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.push((151 + i, feature_mins[i], feature_maxs[i]));
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}
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// Estimate memory usage (simplified)
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feature_stats.memory_usage_bytes = bars.len() * 50 * 8; // 50 features × 8 bytes per f64
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// Validation checks
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if feature_stats.nan_count > 0 {
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anyhow::bail!(
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"Found {} NaN values in features 151-200",
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feature_stats.nan_count
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);
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}
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if feature_stats.inf_count > 0 {
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anyhow::bail!(
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"Found {} Inf values in features 151-200",
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feature_stats.inf_count
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);
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}
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if feature_stats.avg_latency_us > 1000.0 {
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anyhow::bail!(
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"Average latency {}μs exceeds 1ms target",
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feature_stats.avg_latency_us
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);
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}
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Ok(feature_stats)
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}
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/// Print validation statistics
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fn print_validation_stats(stats: &ValidationStats) {
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println!("\nValidation Statistics:");
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println!(" Total bars processed: {}", stats.total_bars);
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println!(" Valid features: {}", stats.valid_features);
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println!(" NaN count: {}", stats.nan_count);
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println!(" Inf count: {}", stats.inf_count);
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println!(" Out-of-range count: {}", stats.out_of_range_count);
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println!("\nPerformance:");
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println!(" Avg latency: {:.2}μs", stats.avg_latency_us);
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println!(" Min latency: {}μs", stats.min_latency_us);
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println!(" Max latency: {}μs", stats.max_latency_us);
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println!(
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" Memory usage: {} bytes ({:.2} KB)",
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stats.memory_usage_bytes,
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stats.memory_usage_bytes as f64 / 1024.0
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);
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println!("\nFeature Ranges (sample):");
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for (idx, min, max) in stats.feature_ranges.iter().take(10) {
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println!(" Feature {}: [{:.6}, {:.6}]", idx, min, max);
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}
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if stats.feature_ranges.len() > 10 {
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println!(" ... ({} more features)", stats.feature_ranges.len() - 10);
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}
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}
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