feat: V9 advanced parameter sweep with MA gap filter (810K configs)
Parameter space expansion: - Original 15 params: 101K configurations - NEW: MA gap filter (3 dimensions) = 18× expansion - Total: ~810,000 configurations across 4 time profiles - Chunk size: 1,000 configs/chunk = ~810 chunks MA Gap Filter parameters: - use_ma_gap: True/False (2 values) - ma_gap_min_long: -5.0%, 0%, +5.0% (3 values) - ma_gap_min_short: -5.0%, 0%, +5.0% (3 values) Implementation: - money_line_v9.py: Full v9 indicator with MA gap logic - v9_advanced_worker.py: Chunk processor (1,000 configs) - v9_advanced_coordinator.py: Work distributor (2 EPYC workers) - run_v9_advanced_sweep.sh: Startup script (generates + launches) Infrastructure: - Uses existing EPYC cluster (64 cores total) - Worker1: bd-epyc-02 (32 threads) - Worker2: bd-host01 (32 threads via SSH hop) - Expected runtime: 70-80 hours - Database: SQLite (chunk tracking + results) Goal: Find optimal MA gap thresholds for filtering false breakouts during MA whipsaw zones while preserving trend entries.
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scripts/distributed_v9_advanced_worker.py
Executable file
157
scripts/distributed_v9_advanced_worker.py
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#!/usr/bin/env python3
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"""
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Distributed v9 advanced parameter sweep for cluster execution.
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This script is designed to run on worker nodes as part of distributed processing.
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The coordinator will split the 800K+ configurations into chunks and distribute
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them across the cluster.
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Expected per-worker throughput: ~300-500 configs/hour
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Total runtime: 40-80 hours on 2-worker cluster
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"""
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import argparse
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import sys
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from pathlib import Path
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# Add project root to path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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import pandas as pd
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from tqdm import tqdm
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from backtester.data_loader import load_data
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from backtester.indicators.money_line_v9 import MoneyLineV9Inputs, money_line_v9_signals
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from backtester.simulator import simulate_money_line
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def test_config(params):
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"""Test a single configuration."""
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# Load data once (cached)
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df = load_data("solusdt_5m.csv")
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# Create inputs with parameters
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inputs = MoneyLineV9Inputs(
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# Basic optimized params (FIXED from previous sweep)
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confirm_bars=0,
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flip_threshold_percent=0.5,
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cooldown_bars=3,
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adx_min=21,
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long_pos_max=75,
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short_pos_min=20,
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vol_min=1.0,
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# ADVANCED OPTIMIZATION PARAMETERS:
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atr_period=params['atr_period'],
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multiplier=params['multiplier'],
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adx_length=params['adx_length'],
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rsi_length=params['rsi_length'],
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rsi_long_min=params['rsi_long_min'],
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rsi_long_max=params['rsi_long_max'],
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rsi_short_min=params['rsi_short_min'],
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rsi_short_max=params['rsi_short_max'],
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vol_max=params['vol_max'],
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entry_buffer_atr=params['entry_buffer_atr'],
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use_heikin_ashi=params['use_heikin_ashi'],
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use_ma_gap_filter=params['use_ma_gap_filter'],
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ma_gap_long_min=params['ma_gap_long_min'],
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ma_gap_short_max=params['ma_gap_short_max'],
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)
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try:
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# Generate signals
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signals = money_line_v9_signals(df, inputs)
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# Simulate trades
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results = simulate_money_line(df, signals)
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return {
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'atr_period': params['atr_period'],
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'multiplier': params['multiplier'],
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'adx_length': params['adx_length'],
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'rsi_length': params['rsi_length'],
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'rsi_long_min': params['rsi_long_min'],
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'rsi_long_max': params['rsi_long_max'],
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'rsi_short_min': params['rsi_short_min'],
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'rsi_short_max': params['rsi_short_max'],
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'vol_max': params['vol_max'],
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'entry_buffer_atr': params['entry_buffer_atr'],
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'use_heikin_ashi': params['use_heikin_ashi'],
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'use_ma_gap_filter': params['use_ma_gap_filter'],
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'ma_gap_long_min': params['ma_gap_long_min'],
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'ma_gap_short_max': params['ma_gap_short_max'],
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'pnl': results['total_pnl'],
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'win_rate': results['win_rate'],
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'profit_factor': results['profit_factor'],
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'max_drawdown': results['max_drawdown'],
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'total_trades': results['total_trades'],
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}
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except Exception as e:
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print(f"Error testing config: {e}")
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return {
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'atr_period': params['atr_period'],
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'multiplier': params['multiplier'],
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'pnl': 0,
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'win_rate': 0,
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'profit_factor': 0,
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'max_drawdown': 0,
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'total_trades': 0,
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}
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def process_chunk(chunk_file: str, output_file: str):
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"""
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Process a chunk of parameter configurations.
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Args:
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chunk_file: CSV file with parameter combinations to test
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output_file: CSV file to save results
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"""
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print(f"Loading chunk: {chunk_file}")
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chunk_df = pd.read_csv(chunk_file)
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print(f"Chunk size: {len(chunk_df)} configurations")
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print()
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results = []
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for idx, row in tqdm(chunk_df.iterrows(), total=len(chunk_df), desc="Testing configs"):
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params = row.to_dict()
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result = test_config(params)
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results.append(result)
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# Save results
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results_df = pd.DataFrame(results)
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results_df.to_csv(output_file, index=False)
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print(f"\nResults saved to: {output_file}")
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# Print summary
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print()
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print("=" * 80)
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print("CHUNK COMPLETE")
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print("=" * 80)
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print()
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print(f"Configurations tested: {len(results_df)}")
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print(f"Best PnL: ${results_df['pnl'].max():.2f}")
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print(f"Mean PnL: ${results_df['pnl'].mean():.2f}")
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print(f"Configurations with trades: {(results_df['total_trades'] > 0).sum()}")
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print()
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def main():
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parser = argparse.ArgumentParser(description="Process v9 advanced sweep chunk")
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parser.add_argument("chunk_file", help="Input CSV file with parameter combinations")
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parser.add_argument("output_file", help="Output CSV file for results")
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args = parser.parse_args()
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print("=" * 80)
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print("v9 ADVANCED SWEEP - CHUNK PROCESSOR")
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print("=" * 80)
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print()
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process_chunk(args.chunk_file, args.output_file)
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if __name__ == "__main__":
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main()
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