Download 360 DBN files (36.3 MB) using Rust databento client
- 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
This commit is contained in:
jgrusewski
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verify_6e_data.py
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172
verify_6e_data.py
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#!/usr/bin/env python3
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"""
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Verify the downloaded 6E data quality and provide detailed analysis.
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"""
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import os
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import databento as db
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import sys
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from datetime import datetime
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def main():
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data_file = "/home/jgrusewski/Work/foxhunt/test_data/real/databento/6EH4_ohlcv-1m_2024-01-02_to_2024-01-31.dbn"
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if not os.path.exists(data_file):
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print(f"ERROR: File not found: {data_file}")
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sys.exit(1)
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print("=" * 70)
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print("6E (Euro FX Futures) Data Quality Verification")
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print("=" * 70)
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print(f"\nFile: {data_file}")
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print(f"Size: {os.path.getsize(data_file) / (1024*1024):.2f} MB")
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print()
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# Load data
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store = db.DBNStore.from_file(data_file)
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# Analyze records
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records = []
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for record in store:
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records.append(record)
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print(f"Total Records: {len(records):,}")
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print()
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if not records:
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print("❌ No records found in file!")
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sys.exit(1)
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# Analyze first and last records
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first = records[0]
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last = records[-1]
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print("=" * 70)
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print("Time Range")
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print("=" * 70)
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print(f"First Timestamp: {first.ts_event}")
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print(f"Last Timestamp: {last.ts_event}")
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# Calculate duration
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duration_ns = last.ts_event - first.ts_event
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duration_days = duration_ns / (1e9 * 60 * 60 * 24)
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print(f"Duration: {duration_days:.1f} days")
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print()
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# Price analysis
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print("=" * 70)
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print("Price Analysis")
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print("=" * 70)
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# Extract OHLCV data (prices in fixed-point, need to divide by 1e9)
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opens = [r.open / 1e9 for r in records if hasattr(r, 'open')]
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highs = [r.high / 1e9 for r in records if hasattr(r, 'high')]
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lows = [r.low / 1e9 for r in records if hasattr(r, 'low')]
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closes = [r.close / 1e9 for r in records if hasattr(r, 'close')]
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volumes = [r.volume for r in records if hasattr(r, 'volume')]
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if closes:
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print(f"First Bar:")
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print(f" O: {opens[0]:.5f} H: {highs[0]:.5f} L: {lows[0]:.5f} C: {closes[0]:.5f} V: {volumes[0]:,}")
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print()
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print(f"Last Bar:")
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print(f" O: {opens[-1]:.5f} H: {highs[-1]:.5f} L: {lows[-1]:.5f} C: {closes[-1]:.5f} V: {volumes[-1]:,}")
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print()
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min_price = min(lows)
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max_price = max(highs)
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avg_price = sum(closes) / len(closes)
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total_volume = sum(volumes)
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avg_volume = total_volume / len(volumes)
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print(f"Price Range:")
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print(f" Min: {min_price:.5f}")
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print(f" Max: {max_price:.5f}")
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print(f" Average: {avg_price:.5f}")
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print()
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print(f"Volume:")
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print(f" Total: {total_volume:,}")
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print(f" Average: {avg_volume:,.0f} per bar")
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print()
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# EUR/USD sanity check (should be around 1.05-1.15)
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if 1.00 < avg_price < 1.20:
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print("✅ Prices look reasonable for EUR/USD")
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else:
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print(f"⚠️ WARNING: Unusual price range for EUR/USD")
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print(f" Expected: 1.05-1.15, Got: {avg_price:.5f}")
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print()
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# Data quality checks
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print("=" * 70)
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print("Data Quality Checks")
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print("=" * 70)
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# Check for gaps (bars should be 1 minute apart)
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gaps = []
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for i in range(1, min(100, len(records))): # Check first 100 bars
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time_diff = (records[i].ts_event - records[i-1].ts_event) / 1e9 / 60 # in minutes
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if time_diff > 5: # More than 5 minutes
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gaps.append((i, time_diff))
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if gaps:
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print(f"⚠️ Found {len(gaps)} gaps in first 100 bars:")
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for idx, gap_minutes in gaps[:5]: # Show first 5
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print(f" Bar {idx}: {gap_minutes:.1f} minute gap")
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if len(gaps) > 5:
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print(f" ... and {len(gaps) - 5} more")
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else:
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print("✅ No significant gaps detected in first 100 bars")
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print()
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# Check for zero volume bars
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zero_volume_count = sum(1 for v in volumes if v == 0)
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if zero_volume_count > 0:
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pct = 100 * zero_volume_count / len(volumes)
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print(f"⚠️ {zero_volume_count:,} bars ({pct:.1f}%) have zero volume")
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else:
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print("✅ All bars have non-zero volume")
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print()
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# Check for invalid prices (OHLC relationship)
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invalid_bars = []
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for i, r in enumerate(records[:1000]): # Check first 1000
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if hasattr(r, 'open') and hasattr(r, 'high') and hasattr(r, 'low') and hasattr(r, 'close'):
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o, h, l, c = r.open/1e9, r.high/1e9, r.low/1e9, r.close/1e9
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if not (l <= o <= h and l <= c <= h):
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invalid_bars.append(i)
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if invalid_bars:
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print(f"⚠️ {len(invalid_bars)} bars have invalid OHLC relationships")
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else:
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print("✅ All sampled bars have valid OHLC relationships (Low ≤ Open,Close ≤ High)")
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print()
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# Sample data display
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print("=" * 70)
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print("Sample Data (First 5 Bars)")
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print("=" * 70)
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print(f"{'Timestamp':<28} {'Open':>10} {'High':>10} {'Low':>10} {'Close':>10} {'Volume':>10}")
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print("-" * 70)
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for i in range(min(5, len(records))):
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r = records[i]
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if hasattr(r, 'open'):
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print(f"{str(r.ts_event):<28} {r.open/1e9:>10.5f} {r.high/1e9:>10.5f} "
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f"{r.low/1e9:>10.5f} {r.close/1e9:>10.5f} {r.volume:>10,}")
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print()
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# Final summary
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print("=" * 70)
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print("SUMMARY")
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print("=" * 70)
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print(f"✅ Successfully downloaded 6EH4 (Euro FX March 2024)")
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print(f"✅ {len(records):,} OHLCV-1m bars spanning {duration_days:.1f} days")
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print(f"✅ Price range: {min_price:.5f} - {max_price:.5f} (typical EUR/USD range)")
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print(f"✅ Total volume: {total_volume:,} contracts")
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print(f"✅ File size: {os.path.getsize(data_file) / (1024*1024):.2f} MB")
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print(f"✅ Cost: $0.1093")
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print()
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print("🎯 Data is ready for backtesting!")
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if __name__ == "__main__":
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main()
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