57c2565e63
- Bug #73 recurrence: Position opened Dec 7 22:15 but PM never monitored - Root cause: Container running OLD code from BEFORE Dec 7 fix (2:46 AM start < 2:46 AM commit) - User lost 08 on unprotected SOL-PERP SHORT - Fix: Rebuilt and restarted container with 3-layer safety system - Status: VERIFIED deployed - all safety layers active - Prevention: Container timestamp MUST be AFTER commit timestamp
312 lines
11 KiB
Python
Executable File
312 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
|
||
"""
|
||
V11 Full Parameter Sweep Worker
|
||
|
||
Processes chunks of v11 parameter configurations (26,244 combinations total).
|
||
Uses all available cores for multiprocessing.
|
||
|
||
FULL EXHAUSTIVE SWEEP across all filter parameters.
|
||
"""
|
||
|
||
import sys
|
||
import csv
|
||
import pandas as pd
|
||
from pathlib import Path
|
||
from typing import Dict, List, Any
|
||
from multiprocessing import Pool
|
||
import functools
|
||
import itertools
|
||
|
||
# Add current directory to path for v11_moneyline_all_filters import
|
||
sys.path.insert(0, str(Path(__file__).parent))
|
||
|
||
from v11_moneyline_all_filters import (
|
||
money_line_v11_signals,
|
||
MoneyLineV11Inputs
|
||
)
|
||
from backtester.simulator import simulate_money_line
|
||
|
||
# CPU limit: Use all available cores (24 for worker1, 18 for worker2)
|
||
MAX_WORKERS = 24 # Default, overridden by --workers argument
|
||
|
||
# Global data file path (set by init_worker)
|
||
_DATA_FILE = None
|
||
|
||
def init_worker(data_file):
|
||
"""Initialize worker process with data file path"""
|
||
global _DATA_FILE
|
||
_DATA_FILE = data_file
|
||
|
||
# FULL EXHAUSTIVE Parameter grid (26,244 combinations)
|
||
PARAMETER_GRID = {
|
||
'flip_threshold': [0.25, 0.3, 0.35, 0.4, 0.45, 0.5], # 6 values
|
||
'adx_min': [0, 5, 10, 15, 20, 25], # 6 values
|
||
'long_pos_max': [90, 95, 100], # 3 values
|
||
'short_pos_min': [0, 5, 10], # 3 values
|
||
'vol_min': [0.0, 0.5, 1.0], # 3 values
|
||
'entry_buffer_atr': [0.0, 0.05, 0.10], # 3 values
|
||
'rsi_long_min': [20, 25, 30], # 3 values
|
||
'rsi_short_max': [70, 75, 80], # 3 values
|
||
}
|
||
# Total: 6×6×3×3×3×3×3×3 = 26,244 combos
|
||
|
||
|
||
def load_market_data(csv_file: str) -> pd.DataFrame:
|
||
"""Load OHLCV data from CSV"""
|
||
df = pd.read_csv(csv_file)
|
||
|
||
# Ensure required columns exist
|
||
required = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
|
||
for col in required:
|
||
if col not in df.columns:
|
||
raise ValueError(f"Missing required column: {col}")
|
||
|
||
# Convert timestamp if needed
|
||
if df['timestamp'].dtype == 'object':
|
||
df['timestamp'] = pd.to_datetime(df['timestamp'])
|
||
|
||
df = df.set_index('timestamp')
|
||
print(f"✓ Loaded {len(df):,} bars from {csv_file}")
|
||
return df
|
||
|
||
|
||
def backtest_config(config: Dict[str, Any]) -> Dict[str, Any]:
|
||
"""
|
||
Run backtest for single v11 test parameter configuration
|
||
|
||
Loads data from global _DATA_FILE path on first call.
|
||
|
||
Returns dict with:
|
||
- params: original config dict
|
||
- pnl: total P&L
|
||
- trades: number of trades
|
||
- win_rate: % winners
|
||
- profit_factor: wins/losses ratio
|
||
- max_drawdown: max drawdown $
|
||
"""
|
||
# Load data (cached per worker process)
|
||
global _DATA_FILE
|
||
df = pd.read_csv(_DATA_FILE)
|
||
df['timestamp'] = pd.to_datetime(df['timestamp'])
|
||
df = df.set_index('timestamp')
|
||
|
||
try:
|
||
# Create v11 inputs
|
||
inputs = MoneyLineV11Inputs(
|
||
use_quality_filters=True, # 🔧 FIX: Enable filters for progressive sweep
|
||
flip_threshold=config['flip_threshold'],
|
||
adx_min=config['adx_min'],
|
||
long_pos_max=config['long_pos_max'],
|
||
short_pos_min=config['short_pos_min'],
|
||
vol_min=config['vol_min'],
|
||
entry_buffer_atr=config['entry_buffer_atr'],
|
||
rsi_long_min=config['rsi_long_min'],
|
||
rsi_long_max=70, # 🔧 FIX: Add missing fixed parameter
|
||
rsi_short_min=30, # 🔧 FIX: Add missing fixed parameter
|
||
rsi_short_max=config['rsi_short_max'],
|
||
)
|
||
|
||
print(f" Generating signals...", flush=True)
|
||
# Generate signals
|
||
signals = money_line_v11_signals(df, inputs)
|
||
print(f" Got {len(signals)} signals, simulating...", flush=True)
|
||
|
||
if not signals:
|
||
return {
|
||
'params': config,
|
||
'pnl': 0.0,
|
||
'trades': 0,
|
||
'win_rate': 0.0,
|
||
'profit_factor': 0.0,
|
||
'max_drawdown': 0.0,
|
||
}
|
||
|
||
# Simple backtesting: track equity curve
|
||
equity = 1000.0 # Starting capital
|
||
peak_equity = equity
|
||
max_drawdown = 0.0
|
||
wins = 0
|
||
losses = 0
|
||
win_pnl = 0.0
|
||
loss_pnl = 0.0
|
||
|
||
for signal in signals:
|
||
# Simple trade simulation
|
||
# TP1 at +0.86%, SL at -1.29% (ATR-based defaults)
|
||
entry = signal.entry_price
|
||
|
||
# Look ahead in data to see if TP or SL hit
|
||
signal_idx = df.index.get_loc(signal.timestamp)
|
||
|
||
# Look ahead up to 100 bars
|
||
max_bars = min(100, len(df) - signal_idx - 1)
|
||
if max_bars <= 0:
|
||
continue
|
||
|
||
future_data = df.iloc[signal_idx+1:signal_idx+1+max_bars]
|
||
|
||
if signal.direction == "long":
|
||
tp_price = entry * 1.0086 # +0.86%
|
||
sl_price = entry * 0.9871 # -1.29%
|
||
|
||
# Check if TP or SL hit
|
||
hit_tp = (future_data['high'] >= tp_price).any()
|
||
hit_sl = (future_data['low'] <= sl_price).any()
|
||
|
||
if hit_tp:
|
||
pnl = 1000.0 * 0.0086 # $8.60 on $1000 position
|
||
equity += pnl
|
||
wins += 1
|
||
win_pnl += pnl
|
||
elif hit_sl:
|
||
pnl = -1000.0 * 0.0129 # -$12.90 on $1000 position
|
||
equity += pnl
|
||
losses += 1
|
||
loss_pnl += abs(pnl)
|
||
else: # short
|
||
tp_price = entry * 0.9914 # -0.86%
|
||
sl_price = entry * 1.0129 # +1.29%
|
||
|
||
# Check if TP or SL hit
|
||
hit_tp = (future_data['low'] <= tp_price).any()
|
||
hit_sl = (future_data['high'] >= sl_price).any()
|
||
|
||
if hit_tp:
|
||
pnl = 1000.0 * 0.0086 # $8.60 on $1000 position
|
||
equity += pnl
|
||
wins += 1
|
||
win_pnl += pnl
|
||
elif hit_sl:
|
||
pnl = -1000.0 * 0.0129 # -$12.90 on $1000 position
|
||
equity += pnl
|
||
losses += 1
|
||
loss_pnl += abs(pnl)
|
||
|
||
# Track drawdown
|
||
peak_equity = max(peak_equity, equity)
|
||
current_drawdown = peak_equity - equity
|
||
max_drawdown = max(max_drawdown, current_drawdown)
|
||
|
||
total_trades = wins + losses
|
||
win_rate = wins / total_trades if total_trades > 0 else 0.0
|
||
profit_factor = win_pnl / loss_pnl if loss_pnl > 0 else (float('inf') if win_pnl > 0 else 0.0)
|
||
total_pnl = equity - 1000.0
|
||
|
||
return {
|
||
'params': config,
|
||
'pnl': round(total_pnl, 2),
|
||
'trades': total_trades,
|
||
'win_rate': round(win_rate * 100, 1),
|
||
'profit_factor': round(profit_factor, 3) if profit_factor != float('inf') else 999.0,
|
||
'max_drawdown': round(max_drawdown, 2),
|
||
}
|
||
|
||
except Exception as e:
|
||
print(f"✗ Error backtesting config: {e}")
|
||
return {
|
||
'params': config,
|
||
'pnl': 0.0,
|
||
'trades': 0,
|
||
'win_rate': 0.0,
|
||
'profit_factor': 0.0,
|
||
'max_drawdown': 0.0,
|
||
}
|
||
|
||
|
||
def generate_parameter_combinations() -> List[Dict[str, Any]]:
|
||
"""Generate all 256 parameter combinations"""
|
||
keys = PARAMETER_GRID.keys()
|
||
values = PARAMETER_GRID.values()
|
||
|
||
combinations = []
|
||
for combo in itertools.product(*values):
|
||
config = dict(zip(keys, combo))
|
||
combinations.append(config)
|
||
|
||
return combinations
|
||
|
||
|
||
def process_chunk(data_file: str, chunk_id: str, start_idx: int, end_idx: int):
|
||
"""Process a chunk of parameter combinations"""
|
||
print(f"\n{'='*60}")
|
||
print(f"V11 Test Worker - {chunk_id}")
|
||
print(f"Processing combinations {start_idx} to {end_idx-1}")
|
||
print(f"{'='*60}\n")
|
||
|
||
# Load market data
|
||
df = load_market_data(data_file)
|
||
|
||
# Generate all combinations
|
||
all_combos = generate_parameter_combinations()
|
||
print(f"✓ Generated {len(all_combos)} total combinations")
|
||
|
||
# Get this chunk's combinations
|
||
chunk_combos = all_combos[start_idx:end_idx]
|
||
print(f"✓ Processing {len(chunk_combos)} combinations in this chunk\n")
|
||
|
||
# Backtest with multiprocessing (pass data file path instead of dataframe)
|
||
print(f"⚡ Starting {MAX_WORKERS}-core backtest...\n")
|
||
|
||
with Pool(processes=MAX_WORKERS, initializer=init_worker, initargs=(data_file,)) as pool:
|
||
results = pool.map(backtest_config, chunk_combos)
|
||
|
||
print(f"\n✓ Completed {len(results)} backtests")
|
||
|
||
# Write results to CSV
|
||
output_dir = Path('v11_test_results')
|
||
output_dir.mkdir(exist_ok=True)
|
||
|
||
csv_file = output_dir / f"{chunk_id}_results.csv"
|
||
|
||
with open(csv_file, 'w', newline='') as f:
|
||
writer = csv.writer(f)
|
||
|
||
# Header
|
||
writer.writerow([
|
||
'flip_threshold', 'adx_min', 'long_pos_max', 'short_pos_min',
|
||
'vol_min', 'entry_buffer_atr', 'rsi_long_min', 'rsi_short_max',
|
||
'pnl', 'win_rate', 'profit_factor', 'max_drawdown', 'total_trades'
|
||
])
|
||
|
||
# Data rows
|
||
for result in results:
|
||
params = result['params']
|
||
writer.writerow([
|
||
params['flip_threshold'],
|
||
params['adx_min'],
|
||
params['long_pos_max'],
|
||
params['short_pos_min'],
|
||
params['vol_min'],
|
||
params['entry_buffer_atr'],
|
||
params['rsi_long_min'],
|
||
params['rsi_short_max'],
|
||
result['pnl'],
|
||
result['win_rate'],
|
||
result['profit_factor'],
|
||
result['max_drawdown'],
|
||
result['trades'],
|
||
])
|
||
|
||
print(f"✓ Results saved to {csv_file}")
|
||
|
||
# Show top 5 results
|
||
sorted_results = sorted(results, key=lambda x: x['pnl'], reverse=True)
|
||
print(f"\n🏆 Top 5 Results:")
|
||
for i, r in enumerate(sorted_results[:5], 1):
|
||
print(f" {i}. PnL: ${r['pnl']:,.2f} | Trades: {r['trades']} | WR: {r['win_rate']}%")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
if len(sys.argv) != 4:
|
||
print("Usage: python v11_test_worker.py <data_file> <chunk_id> <start_idx>")
|
||
sys.exit(1)
|
||
|
||
data_file = sys.argv[1]
|
||
chunk_id = sys.argv[2]
|
||
start_idx = int(sys.argv[3])
|
||
|
||
# Calculate end index (256 combos per chunk)
|
||
end_idx = start_idx + 256
|
||
|
||
process_chunk(data_file, chunk_id, start_idx, end_idx)
|