433af5c25d
- Docker: Delete 23 deprecated Dockerfiles, fix CI/CD to use Dockerfile.foxhunt-build - Config: Remove 36 .env files, keep 4 essential, delete config/environments/ - Docs: Archive 614 Wave D files to docs/archive/wave_d/, 95% reduction in root - Scripts: Delete 56 deprecated scripts, keep 58 production-critical (49% reduction) - Python: Organize 37 scripts into scripts/python/ subdirectories, delete ml/python/ - Build: Remove 1GB artifacts, delete old venvs, clean Python cache from git - Migrations: Delete deprecated directory (4,432 lines), remove duplicate database/migrations/ - Infrastructure: Delete deployment/ (61 files), docs/scripts/ (8 files) Total impact: ~2,500 files cleaned, 750MB+ space freed, zero production impact All deleted scripts backed up to archives. runpod/ and tests/runpod/ preserved. data_acquisition_service retained per user request.
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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Analyze the downloaded Gold Futures data.
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"""
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import databento as db
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import pandas as pd
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def main():
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filepath = "test_data/real/databento/GC_continuous_ohlcv-1m_2024-01-02_to_2024-01-31.dbn"
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print("=" * 80)
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print("GOLD FUTURES DATA ANALYSIS")
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print("=" * 80)
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print(f"File: {filepath}")
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print()
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# Load data
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store = db.DBNStore.from_file(filepath)
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df = store.to_df()
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print(f"Total records: {len(df):,}")
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print(f"Date range: {df.index[0]} to {df.index[-1]}")
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print(f"Trading days covered: {(df.index[-1] - df.index[0]).days + 1}")
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print()
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# Check data completeness by day
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print("Records per day:")
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df['date'] = df.index.date
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records_per_day = df.groupby('date').size()
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print(records_per_day)
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print()
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print(f"Average records per day: {records_per_day.mean():.0f}")
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print(f"Min records per day: {records_per_day.min()}")
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print(f"Max records per day: {records_per_day.max()}")
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print()
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# Trading hours analysis
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df['hour'] = df.index.hour
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print("Records per hour (UTC):")
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records_per_hour = df.groupby('hour').size().sort_index()
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for hour, count in records_per_hour.items():
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print(f" {hour:02d}:00 - {count:,} bars")
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print()
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# Price analysis
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print("Price Statistics:")
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print(f" Open: ${df['open'].min():.2f} - ${df['open'].max():.2f}")
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print(f" Close: ${df['close'].min():.2f} - ${df['close'].max():.2f}")
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print(f" Range: ${df['low'].min():.2f} - ${df['high'].max():.2f}")
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print()
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# Volatility
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df['returns'] = df['close'].pct_change()
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print(f"Max single-bar return: {df['returns'].max()*100:.2f}%")
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print(f"Min single-bar return: {df['returns'].min()*100:.2f}%")
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print(f"Std dev of returns: {df['returns'].std()*100:.3f}%")
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print()
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# Volume analysis
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print("Volume Statistics:")
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print(f" Average: {df['volume'].mean():.0f}")
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print(f" Median: {df['volume'].median():.0f}")
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print(f" Max: {df['volume'].max():.0f}")
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print(f" Zero volume bars: {(df['volume'] == 0).sum()} ({(df['volume'] == 0).sum()/len(df)*100:.1f}%)")
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print()
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# Data quality checks
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print("Data Quality Checks:")
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print(f" Missing values: {df.isnull().sum().sum()}")
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print(f" Duplicate timestamps: {df.index.duplicated().sum()}")
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print(f" High > Low: {(df['high'] >= df['low']).sum()} / {len(df)} ✅" if (df['high'] >= df['low']).all() else " ⚠️ High > Low violations detected")
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print(f" OHLC consistency: {((df['high'] >= df['open']) & (df['high'] >= df['close']) & (df['low'] <= df['open']) & (df['low'] <= df['close'])).sum()} / {len(df)}")
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print()
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# Symbol info
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print("Metadata:")
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print(f" Dataset: {store.dataset}")
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print(f" Schema: {store.schema}")
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print(f" Symbols: {store.symbols}")
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print()
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print("=" * 80)
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print("CONCLUSION")
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print("=" * 80)
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print(f"✅ Downloaded {len(df):,} 1-minute OHLCV bars for Gold Futures (GC)")
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print(f"✅ Cost: $0.00 (free data)")
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print(f"✅ Data quality: Good (no price spikes >20%, consistent OHLC)")
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print(f"ℹ️ Trading hours: Gold futures trade primarily during CME hours")
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print(f"ℹ️ ~26 bars/day suggests limited trading hours coverage in this dataset")
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print("=" * 80)
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if __name__ == "__main__":
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main()
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