e8a68ee39f
- 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
108 lines
3.2 KiB
Rust
108 lines
3.2 KiB
Rust
//! DBN to CSV Export Example
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//!
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//! Exports ES.FUT DBN data to CSV format for external analysis.
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use anyhow::Result;
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use backtesting_service::dbn_repository::DbnMarketDataRepository;
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use backtesting_service::repositories::MarketDataRepository;
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use std::collections::HashMap;
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use std::fs::File;
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use std::io::Write;
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#[tokio::main]
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async fn main() -> Result<()> {
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println!("ES.FUT DBN to CSV Exporter");
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println!("==========================\n");
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// Load data
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let mut file_mapping = HashMap::new();
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file_mapping.insert(
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"ES.FUT".to_string(),
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"test_data/real/databento/ES.FUT_ohlcv-1m_2024-01-02.dbn".to_string(),
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);
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let repo = DbnMarketDataRepository::new(file_mapping).await?;
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let symbols = vec!["ES.FUT".to_string()];
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let start_time = 1704153600_000_000_000i64; // 2024-01-02 00:00:00 UTC
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let end_time = 1704240000_000_000_000i64; // 2024-01-03 00:00:00 UTC
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println!("📂 Loading data from DBN file...");
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let data = repo
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.load_historical_data(&symbols, start_time, end_time)
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.await?;
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if data.is_empty() {
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println!("❌ ERROR: No data loaded!");
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return Ok(());
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}
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println!("✅ Loaded {} bars", data.len());
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// Export to CSV
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let output_path = "ES.FUT_2024-01-02.csv";
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let mut file = File::create(output_path)?;
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println!("\n📝 Writing CSV file: {}", output_path);
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// Write header with detailed column descriptions
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writeln!(
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file,
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"timestamp_utc,timestamp_unix_ns,symbol,open,high,low,close,volume"
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)?;
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// Write data
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for bar in &data {
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writeln!(
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file,
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"{},{},{},{},{},{},{},{}",
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bar.timestamp.format("%Y-%m-%d %H:%M:%S"),
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bar.timestamp.timestamp_nanos_opt().unwrap_or(0),
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bar.symbol,
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bar.open,
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bar.high,
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bar.low,
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bar.close,
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bar.volume
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)?;
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}
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println!("✅ Successfully exported {} bars to {}", data.len(), output_path);
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println!();
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// Print sample rows
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println!("📋 Sample Data (first 5 rows):");
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println!(
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"{:<20} {:<10} {:>8} {:>8} {:>8} {:>8} {:>10}",
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"Timestamp", "Symbol", "Open", "High", "Low", "Close", "Volume"
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);
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println!("{}", "-".repeat(85));
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for bar in data.iter().take(5) {
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println!(
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"{:<20} {:<10} {:>8.2} {:>8.2} {:>8.2} {:>8.2} {:>10.0}",
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bar.timestamp.format("%Y-%m-%d %H:%M:%S"),
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bar.symbol,
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bar.open,
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bar.high,
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bar.low,
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bar.close,
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bar.volume
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);
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}
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println!("\n💡 Usage Examples:");
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println!(" • Python pandas: df = pd.read_csv('{}') ", output_path);
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println!(" • R: data <- read.csv('{}') ", output_path);
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println!(" • Excel: Open file directly");
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println!(" • SQL: LOAD DATA INFILE '{}' INTO TABLE ...", output_path);
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println!();
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println!("📊 Analysis Tools:");
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println!(" • Calculate moving averages");
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println!(" • Plot candlestick charts");
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println!(" • Compute technical indicators (RSI, MACD, Bollinger Bands)");
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println!(" • Run statistical analysis");
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Ok(())
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}
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