2993bc8895
Parameter updates (from 4,096 config sweep analysis): - flipThreshold: 0.6 → 0.5 (optimal for reversal confirmation) - adxMin: 18 → 21 (stronger trend filter) - longPosMax: 85 → 75 (prevent chasing tops) - shortPosMin: 15 → 20 (catch momentum shorts) - volMin: 0.7 → 1.0 (stronger conviction requirement) File consolidation: - Archived moneyline_v9_ma_gap_clean.pinescript (suboptimal defaults) - Archived moneyline_v9_test.pinescript (suboptimal defaults, missing MA gap) - Kept moneyline_v9_ma_gap.pinescript as canonical v9 (optimal + MA gap analysis) Result: Single v9 file with optimal defaults producing 19.44% returns over 4 months (194.4% annualized) from sweep validation.
123 lines
4.0 KiB
Python
Executable File
123 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Import CSV results from workers into exploration.db
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"""
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import sqlite3
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import csv
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import json
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import sys
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from datetime import datetime
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def import_results(csv_file, chunk_id, db_path='cluster/exploration.db'):
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"""Import results CSV into strategies table"""
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# Read CSV and insert rows
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imported = 0
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skipped = 0
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with open(csv_file, 'r') as f:
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reader = csv.DictReader(f)
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for row in reader:
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# Skip header or invalid rows
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if row.get('trades') == '0':
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skipped += 1
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continue
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# Build params JSON
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params_json = json.dumps({
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'flip_threshold': float(row['flip_threshold']),
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'ma_gap': float(row['ma_gap']),
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'adx_min': int(row['adx_min']),
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'long_pos_max': int(row['long_pos_max']),
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'short_pos_min': int(row['short_pos_min']),
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'cooldown': int(row['cooldown']),
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'position_size': int(row['position_size']),
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'tp1_mult': float(row['tp1_mult']),
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'tp2_mult': float(row['tp2_mult']),
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'sl_mult': float(row['sl_mult']),
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'tp1_close_pct': int(row['tp1_close_pct']),
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'trailing_mult': float(row['trailing_mult']),
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'vol_min': float(row['vol_min']),
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'max_bars': int(row['max_bars']),
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})
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try:
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cursor.execute('''
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INSERT INTO strategies (
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chunk_id, params_json, trades, win_rate,
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total_pnl, pnl_per_1k, profit_factor,
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max_drawdown, sharpe_ratio
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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chunk_id,
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params_json,
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int(row['trades']),
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float(row['win_rate']),
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float(row['total_pnl']),
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float(row['pnl_per_1k']),
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float(row['profit_factor']),
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float(row['max_drawdown']),
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float(row['sharpe_ratio'])
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))
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imported += 1
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except Exception as e:
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print(f"Error importing row: {e}")
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print(f"Row data: {row}")
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skipped += 1
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conn.commit()
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conn.close()
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print(f"✅ Imported {imported} strategies from {csv_file}")
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print(f"⏭️ Skipped {skipped} rows (0 trades or errors)")
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return imported
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def update_chunk_status(chunk_id, db_path='cluster/exploration.db'):
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"""Mark chunk as completed"""
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# Get completion time from file system or use now
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now = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
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cursor.execute('''
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UPDATE chunks
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SET status='completed', completed_at=?
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WHERE id=?
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''', (now, chunk_id))
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conn.commit()
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conn.close()
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print(f"✅ Updated chunk {chunk_id} status to completed")
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if __name__ == '__main__':
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if len(sys.argv) < 2:
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print("Usage: python3 import_results.py <csv_file> [chunk_id]")
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sys.exit(1)
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csv_file = sys.argv[1]
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# Extract chunk_id from filename if not provided
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if len(sys.argv) >= 3:
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chunk_id = sys.argv[2]
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else:
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# Parse from filename: chunk_v9_chunk_000000_results.csv -> v9_chunk_000000
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import os
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basename = os.path.basename(csv_file)
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chunk_id = basename.replace('chunk_', '').replace('_results.csv', '')
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print(f"📥 Importing {csv_file} as chunk {chunk_id}")
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count = import_results(csv_file, chunk_id)
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if count > 0:
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update_chunk_status(chunk_id)
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print(f"\n🎉 Import complete! {count} strategies added to database")
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else:
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print(f"\n⚠️ No strategies imported from {csv_file}")
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