- Created data/examples/download_ml_training_data.rs using reqwest + Databento HTTP API - Downloaded 90 days × 4 symbols (ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT) - Files saved to test_data/real/databento/ml_training/ - Total: 360 files, 15 MB compressed DBN format - Used existing Rust pattern from download_nq_fut.rs - API key loaded from .env file - 100% success rate (360/360 files) - Ready for ML training benchmarks Next: Create simplified training benchmark for RTX 3050 Ti GPU measurements
210 lines
7.8 KiB
Rust
210 lines
7.8 KiB
Rust
//! Download 90 days of real market data from Databento for ML training
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//!
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//! Downloads OHLCV-1m data for multiple futures symbols for ML model training.
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//!
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//! Symbols downloaded:
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//! - ES.FUT (E-mini S&P 500) - Stock index
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//! - NQ.FUT (E-mini NASDAQ) - Tech index
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//! - ZN.FUT (10-Year Treasury) - Fixed income
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//! - 6E.FUT (Euro FX) - Currency
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//!
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//! Usage:
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//! source .env && cargo run -p data --example download_ml_training_data
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use chrono::{Datelike, NaiveDate};
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use std::env;
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use std::time::Duration;
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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println!("================================================================================");
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println!("ML Training Data Download - Databento (Rust)");
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println!("================================================================================\n");
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// Load API key from environment
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let api_key = env::var("DATABENTO_API_KEY")
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.map_err(|_| "DATABENTO_API_KEY environment variable not set")?;
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println!("✅ API Key found: {}...{}", &api_key[0..10], &api_key[api_key.len()-10..]);
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println!();
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// Configuration
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let symbols = vec!["ES.FUT", "NQ.FUT", "ZN.FUT", "6E.FUT"];
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let dataset = "GLBX.MDP3";
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let schema = "ohlcv-1m";
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let start_date = NaiveDate::from_ymd_opt(2024, 1, 2).unwrap();
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let num_days = 90;
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// Generate trading dates (excluding weekends)
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let mut dates = Vec::new();
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let mut current = start_date;
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while dates.len() < num_days {
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if current.weekday().num_days_from_monday() < 5 {
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dates.push(current);
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}
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current = current.succ_opt().ok_or("Date overflow")?;
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}
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// Estimate cost ($0.12 per symbol per day)
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let estimated_cost = dates.len() as f64 * symbols.len() as f64 * 0.12;
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println!("📊 Download Configuration:");
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println!(" Start date: {}", start_date);
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println!(" Trading days: {}", dates.len());
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println!(" Symbols: {} ({})", symbols.len(), symbols.join(", "));
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println!(" Schema: {}", schema);
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println!(" Dataset: {}", dataset);
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println!(" Output: test_data/real/databento/ml_training/");
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println!();
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println!("📦 Total Downloads: {} files", dates.len() * symbols.len());
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println!("💰 Estimated Cost: ${:.2}", estimated_cost);
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println!();
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println!("⚠️ This will download data and incur costs (~${:.2})", estimated_cost);
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println!("Press Ctrl+C to cancel, or press Enter to continue...");
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let mut input = String::new();
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std::io::stdin().read_line(&mut input)?;
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println!();
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// Create output directory
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std::fs::create_dir_all("test_data/real/databento/ml_training")?;
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println!("📁 Created output directory: test_data/real/databento/ml_training");
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println!();
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// Create HTTP client
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let client = reqwest::Client::builder()
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.timeout(Duration::from_secs(60))
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.build()?;
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println!("✅ HTTP client initialized");
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println!();
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// Track statistics
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let mut successful = 0;
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let mut failed = 0;
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let mut skipped = 0;
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let mut total_bytes = 0u64;
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let total_files = dates.len() * symbols.len();
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// Download all combinations
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let mut current_file = 0;
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for symbol in &symbols {
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println!("{:-<80}", "");
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println!("📥 Downloading: {}", symbol);
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println!("{:-<80}", "");
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println!();
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for date in &dates {
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current_file += 1;
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let progress = (current_file as f64 / total_files as f64) * 100.0;
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let date_str = date.format("%Y-%m-%d").to_string();
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print!("[{}/{} - {:.1}%] {} @ {}... ",
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current_file, total_files, progress, symbol, date_str);
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std::io::Write::flush(&mut std::io::stdout())?;
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// Check if file already exists
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let output_path = format!(
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"test_data/real/databento/ml_training/{}_ohlcv-1m_{}.dbn",
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symbol.replace("/", "_"), date_str
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);
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if std::path::Path::new(&output_path).exists() {
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let size = std::fs::metadata(&output_path)?.len();
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successful += 1;
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total_bytes += size;
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println!("✅ {} KB (exists)", size / 1024);
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skipped += 1;
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continue;
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}
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// Build request URL
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let url = format!(
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"https://hist.databento.com/v0/timeseries.get_range?dataset={}&symbols={}&schema={}&start={}T00:00:00Z&end={}T23:59:59Z&encoding=dbn&stype_in=parent",
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dataset, symbol, schema, date_str, date_str
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);
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// Make request
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match client
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.get(&url)
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.basic_auth(&api_key, Some(""))
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.send()
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.await
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{
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Ok(response) if response.status().is_success() => {
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match response.bytes().await {
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Ok(body) => {
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let size = body.len() as u64;
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std::fs::write(&output_path, &body)?;
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successful += 1;
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total_bytes += size;
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println!("✅ {} KB", size / 1024);
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}
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Err(e) => {
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failed += 1;
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println!("❌ Error: {}", e);
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}
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}
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}
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Ok(response) => {
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let status = response.status();
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if status.as_u16() == 404 {
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failed += 1;
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println!("⚠️ No data (holiday/no trading)");
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} else {
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let error_text = response.text().await.unwrap_or_default();
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failed += 1;
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println!("❌ Error {}: {}", status, error_text);
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}
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}
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Err(e) => {
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failed += 1;
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println!("❌ Network error: {}", e);
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}
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}
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// Small delay to avoid rate limits
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tokio::time::sleep(Duration::from_millis(100)).await;
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}
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println!();
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}
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// Summary
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println!();
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println!("================================================================================");
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println!("📊 DOWNLOAD SUMMARY");
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println!("================================================================================");
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println!();
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println!("✅ Successful: {}/{}", successful, total_files);
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println!("⏭️ Skipped: {}/{}", skipped, total_files);
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println!("❌ Failed: {}/{}", failed, total_files);
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println!();
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println!("💾 Total Size: {:.1} MB", total_bytes as f64 / 1_048_576.0);
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println!("💰 Estimated Cost: ${:.2}", estimated_cost);
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println!();
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let success_rate = (successful as f64 / total_files as f64) * 100.0;
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println!("📋 NEXT STEPS:");
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println!("1. Run ML readiness validation with new data:");
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println!(" cargo test -p ml --test ml_readiness_validation_tests");
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println!();
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println!("2. Run training time benchmarks:");
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println!(" cargo run -p ml --example benchmark_training_time --release");
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println!();
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if success_rate >= 80.0 {
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println!("✅ SUCCESS: Downloaded {:.1}% of requested data!", success_rate);
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println!(" Ready for ML training benchmarks on RTX 3050 Ti");
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} else if success_rate >= 50.0 {
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println!("⚠️ PARTIAL SUCCESS: Downloaded {:.1}% of data", success_rate);
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println!(" May be sufficient for benchmarking, but consider re-downloading missing files");
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} else {
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println!("❌ ERROR: Only downloaded {:.1}% of data", success_rate);
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println!(" Check errors above and retry");
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}
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Ok(())
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}
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