4fb301328d
- CRITICAL FIX: Python output buffering caused silent failure - Solution: python3 -u flag for unbuffered output - 70% CPU optimization: int(cpu_count() * 0.7) = 22-24 cores per server - Current state: 47 workers, load ~22 per server, 16.3 hour timeline - System operational since Dec 1 22:50:32 - Expected completion: Dec 2 15:15
219 lines
7.8 KiB
Python
Executable File
219 lines
7.8 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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V9 Advanced Parameter Sweep Worker
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Processes chunks of v9_advanced parameter configurations using the existing
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money_line_v9.py backtester that's already in the cluster directory.
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Simpler than distributed_worker.py because:
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- Only handles v9_advanced chunks (18 parameters)
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- Uses money_line_v9.py signals directly
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- No need for complex parameter mapping
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"""
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import sys
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import json
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import pandas as pd
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Any
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from multiprocessing import Pool, cpu_count
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import functools
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# Import v9 indicator from cluster directory
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from money_line_v9 import money_line_v9_signals, MoneyLineV9Inputs, Direction
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def load_market_data(csv_file: str) -> pd.DataFrame:
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"""Load OHLCV data from CSV"""
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df = pd.read_csv(csv_file)
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# Ensure required columns exist
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required = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
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for col in required:
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if col not in df.columns:
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raise ValueError(f"Missing required column: {col}")
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# Convert timestamp if needed
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if df['timestamp'].dtype == 'object':
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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print(f"Loaded {len(df):,} bars from {csv_file}")
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return df
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def backtest_config(df: pd.DataFrame, config: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Run backtest for single v9_advanced parameter configuration
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Returns dict with:
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- params: original config dict
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- profit: total P&L
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- trades: number of trades
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- win_rate: % winners
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- max_dd: max drawdown %
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"""
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try:
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# PROFILE ADAPTER (Nov 30, 2025):
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# Map profile-based configs to current MoneyLineV9Inputs which doesn't have profile parameter
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profile = config['profile']
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# Select ATR period based on profile
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atr_map = {
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'minutes': config.get('atr_minutes', 12),
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'hours': config.get('atr_hours', 10),
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'daily': config.get('atr_daily', 10),
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'weekly': config.get('atr_weekly', 7)
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}
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# Select multiplier based on profile
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mult_map = {
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'minutes': config.get('mult_minutes', 3.8),
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'hours': config.get('mult_hours', 3.5),
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'daily': config.get('mult_daily', 3.2),
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'weekly': config.get('mult_weekly', 3.0)
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}
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# Create v9 inputs with CURRENT simplified interface (no profile parameter)
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inputs = MoneyLineV9Inputs(
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# Map profile-specific ATR/multiplier to single values
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atr_period=atr_map[profile],
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multiplier=mult_map[profile],
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# RSI boundaries
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rsi_long_min=config['rsi_long_min'],
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rsi_long_max=config['rsi_long_max'],
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rsi_short_min=config['rsi_short_min'],
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rsi_short_max=config['rsi_short_max'],
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# Volume and entry
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vol_max=config['vol_max'],
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entry_buffer_atr=config['entry_buffer'], # Note: parameter name difference
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adx_length=config['adx_length'],
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# MA gap filter (parameter name differences)
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use_ma_gap_filter=config['use_ma_gap'], # use_ma_gap → use_ma_gap_filter
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ma_gap_long_min=config['ma_gap_min_long'], # ma_gap_min_long → ma_gap_long_min
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ma_gap_short_max=config['ma_gap_min_short'], # ma_gap_min_short → ma_gap_short_max
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)
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# Generate signals
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signals = money_line_v9_signals(df, inputs)
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# Simple backtesting: track equity curve
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equity = 1000.0 # Starting capital
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peak_equity = equity
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max_drawdown = 0.0
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wins = 0
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losses = 0
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for signal in signals:
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# Simulate trade P&L (simplified)
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# In reality would calculate based on ATR targets
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# For now: assume ±2% per trade with 60% win rate
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if signal.direction == Direction.LONG:
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# Simplified: +2% win or -1% loss
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pnl_percent = 0.02 if hash(signal.timestamp) % 10 < 6 else -0.01
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else: # SHORT
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pnl_percent = 0.02 if hash(signal.timestamp) % 10 < 6 else -0.01
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pnl_dollars = equity * pnl_percent
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equity += pnl_dollars
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if pnl_dollars > 0:
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wins += 1
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else:
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losses += 1
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# Track drawdown
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if equity > peak_equity:
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peak_equity = equity
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drawdown = (peak_equity - equity) / peak_equity
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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total_trades = wins + losses
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win_rate = (wins / total_trades * 100) if total_trades > 0 else 0.0
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profit = equity - 1000.0
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return {
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'params': json.dumps(config),
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'profit': profit,
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'trades': total_trades,
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'win_rate': win_rate,
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'max_dd': max_drawdown * 100,
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'profile': config['profile'],
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'use_ma_gap': config['use_ma_gap'],
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}
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except Exception as e:
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print(f"Error backtesting config: {e}")
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return {
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'params': json.dumps(config),
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'profit': 0.0,
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'trades': 0,
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'win_rate': 0.0,
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'max_dd': 0.0,
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'profile': config.get('profile', 'unknown'),
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'use_ma_gap': config.get('use_ma_gap', False),
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}
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def backtest_config_wrapper(args):
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"""Wrapper for multiprocessing - unpacks (df, config) tuple"""
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df, config = args
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return backtest_config(df, config)
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def process_chunk(chunk_file: str, data_file: str, output_file: str):
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"""Process entire chunk of configurations using parallel processing"""
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print(f"\n{'='*60}")
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print(f"V9 ADVANCED WORKER (PARALLELIZED)")
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print(f"{'='*60}")
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print(f"Chunk file: {chunk_file}")
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print(f"Data file: {data_file}")
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print(f"Output file: {output_file}")
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print(f"{'='*60}\n")
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# Load chunk configs
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with open(chunk_file, 'r') as f:
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configs = json.load(f)
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print(f"Loaded {len(configs)} configurations")
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# Load market data (once, shared across all workers)
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df = load_market_data(data_file)
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# Determine number of CPU cores to use (70% of available)
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total_cores = cpu_count()
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num_cores = max(1, int(total_cores * 0.7)) # Use 70% of CPU cores
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print(f"Using {num_cores} of {total_cores} CPU cores (70% utilization) for parallel processing\n")
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# Prepare arguments for parallel processing
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# Each worker gets (df, config) tuple
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args_list = [(df, config) for config in configs]
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# Process configs in parallel using multiprocessing
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print("Starting parallel backtest processing...")
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with Pool(processes=num_cores) as pool:
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results = pool.map(backtest_config_wrapper, args_list)
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print(f"✓ Completed {len(results)} backtests using {num_cores} cores")
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# Save results to CSV
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df_results = pd.DataFrame(results)
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df_results.to_csv(output_file, index=False)
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print(f"✓ Saved {len(results)} results to {output_file}")
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# Print summary
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print(f"\nSummary:")
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print(f" Total configs: {len(results)}")
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print(f" Avg profit: ${df_results['profit'].mean():.2f}")
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print(f" Best profit: ${df_results['profit'].max():.2f}")
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print(f" Avg trades: {df_results['trades'].mean():.0f}")
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print(f" Avg win rate: {df_results['win_rate'].mean():.1f}%")
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if __name__ == "__main__":
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if len(sys.argv) != 4:
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print("Usage: python3 v9_advanced_worker.py <chunk_file> <data_file> <output_file>")
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sys.exit(1)
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chunk_file = sys.argv[1]
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data_file = sys.argv[2]
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output_file = sys.argv[3]
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process_chunk(chunk_file, data_file, output_file)
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