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
65 lines
1.6 KiB
Python
65 lines
1.6 KiB
Python
#!/usr/bin/env python3
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"""
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List available datasets and check symbology rules.
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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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def main():
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# Check for API key
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api_key = os.environ.get("DATABENTO_API_KEY")
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if not api_key:
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print("ERROR: DATABENTO_API_KEY environment variable not set")
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sys.exit(1)
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# Create client
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client = db.Historical(api_key)
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print("=" * 70)
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print("Available Datasets")
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print("=" * 70)
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# List datasets
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try:
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datasets = client.metadata.list_datasets()
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print(f"\nFound {len(datasets)} datasets:\n")
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for dataset in datasets:
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print(f"Dataset: {dataset}")
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print()
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print("=" * 70)
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print("CME Group Datasets (for futures)")
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print("=" * 70)
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cme_datasets = [d for d in datasets if 'CME' in str(d) or 'GLBX' in str(d)]
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if cme_datasets:
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for ds in cme_datasets:
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print(f" - {ds}")
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else:
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print(" (filtering by name, showing all)")
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for ds in datasets:
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print(f" - {ds}")
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except Exception as e:
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print(f"Error listing datasets: {e}")
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# Let's also try to get symbology info
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print()
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print("=" * 70)
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print("Dataset Details for GLBX.MDP3")
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print("=" * 70)
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try:
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# Get dataset info
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dataset_info = client.metadata.list_schemas("GLBX.MDP3")
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print(f"\nAvailable schemas for GLBX.MDP3:")
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for schema in dataset_info:
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print(f" - {schema}")
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except Exception as e:
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print(f"Error getting dataset info: {e}")
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if __name__ == "__main__":
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main()
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