7e1fe1cc30
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.
300 lines
10 KiB
Python
300 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Advanced v9 Money Line parameter sweep - AGGRESSIVE optimization.
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This explores ~100K-200K parameter combinations across:
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- ATR profiles (period + multiplier variations)
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- RSI boundaries (4 parameters)
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- Volume max threshold
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- Entry buffer size
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- ADX length
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- Source mode (Chart vs Heikin Ashi)
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- MA gap filter (optional)
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Expected runtime: 40-80 hours on 2-worker cluster
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Target: Beat baseline $194.43/1k (19.44% returns)
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"""
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import itertools
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import multiprocessing as mp
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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(args):
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"""Test a single configuration."""
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config_id, params = args
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# Load data
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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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'config_id': config_id,
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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 {config_id}: {e}")
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return {
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'config_id': config_id,
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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 generate_parameter_grid():
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"""
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Generate comprehensive parameter grid for advanced optimization.
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AGGRESSIVE SEARCH SPACE:
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- ATR periods: 5 values (10, 12, 14, 16, 18)
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- Multipliers: 6 values (3.0, 3.2, 3.5, 3.8, 4.0, 4.2)
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- ADX length: 4 values (14, 16, 18, 20)
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- RSI length: 3 values (12, 14, 16)
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- RSI long min: 4 values (30, 35, 40, 45)
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- RSI long max: 4 values (65, 70, 75, 80)
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- RSI short min: 4 values (25, 30, 35, 40)
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- RSI short max: 4 values (60, 65, 70, 75)
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- Volume max: 4 values (3.0, 3.5, 4.0, 4.5)
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- Entry buffer: 3 values (0.15, 0.20, 0.25)
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- Source mode: 2 values (Chart, Heikin Ashi)
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- MA gap filter: 3 modes (disabled, longs_only, both)
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Total: 5×6×4×3×4×4×4×4×4×3×2×3 = 829,440 combinations
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This will take 2-3 days on 2-worker cluster but will find optimal settings.
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"""
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# ATR profile variations (5 × 6 = 30 combos)
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atr_periods = [10, 12, 14, 16, 18]
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multipliers = [3.0, 3.2, 3.5, 3.8, 4.0, 4.2]
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# ADX length variations (4 values)
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adx_lengths = [14, 16, 18, 20]
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# RSI length (3 values)
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rsi_lengths = [12, 14, 16]
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# RSI boundaries (4×4×4×4 = 256 combos)
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rsi_long_mins = [30, 35, 40, 45]
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rsi_long_maxs = [65, 70, 75, 80]
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rsi_short_mins = [25, 30, 35, 40]
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rsi_short_maxs = [60, 65, 70, 75]
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# Volume max (4 values)
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vol_maxs = [3.0, 3.5, 4.0, 4.5]
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# Entry buffer (3 values)
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entry_buffers = [0.15, 0.20, 0.25]
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# Source mode (2 values)
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use_heikin_ashis = [False, True]
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# MA gap filter modes (3 modes = 3 parameter sets)
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# Mode 1: Disabled
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# Mode 2: Longs only (require ma50 > ma200)
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# Mode 3: Both directions (bull/bear confirmation)
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ma_gap_configs = [
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(False, 0.0, 0.0), # Disabled
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(True, 0.5, 0.0), # Longs only: require 0.5% gap
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(True, 0.5, -0.5), # Both: longs need +0.5%, shorts need -0.5%
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]
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configs = []
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config_id = 0
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for atr_period, multiplier, adx_length, rsi_length, \
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rsi_long_min, rsi_long_max, rsi_short_min, rsi_short_max, \
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vol_max, entry_buffer, use_ha, ma_gap_config in \
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itertools.product(
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atr_periods, multipliers, adx_lengths, rsi_lengths,
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rsi_long_mins, rsi_long_maxs, rsi_short_mins, rsi_short_maxs,
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vol_maxs, entry_buffers, use_heikin_ashis, ma_gap_configs
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):
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# Validity check: RSI min < max
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if rsi_long_min >= rsi_long_max:
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continue
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if rsi_short_min >= rsi_short_max:
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continue
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use_ma_gap, ma_gap_long_min, ma_gap_short_max = ma_gap_config
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configs.append((config_id, {
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'atr_period': atr_period,
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'multiplier': multiplier,
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'adx_length': adx_length,
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'rsi_length': rsi_length,
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'rsi_long_min': rsi_long_min,
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'rsi_long_max': rsi_long_max,
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'rsi_short_min': rsi_short_min,
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'rsi_short_max': rsi_short_max,
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'vol_max': vol_max,
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'entry_buffer_atr': entry_buffer,
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'use_heikin_ashi': use_ha,
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'use_ma_gap_filter': use_ma_gap,
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'ma_gap_long_min': ma_gap_long_min,
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'ma_gap_short_max': ma_gap_short_max,
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}))
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config_id += 1
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return configs
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def main():
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"""Run advanced parameter sweep."""
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print("=" * 80)
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print("v9 ADVANCED PARAMETER SWEEP - AGGRESSIVE OPTIMIZATION")
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print("=" * 80)
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print()
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print("This will explore ~800K parameter combinations across:")
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print(" - ATR profiles (5 periods × 6 multipliers)")
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print(" - RSI boundaries (4×4×4×4 = 256 combinations)")
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print(" - Volume max (4 values)")
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print(" - Entry buffer (3 values)")
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print(" - ADX length (4 values)")
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print(" - RSI length (3 values)")
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print(" - Source mode (Chart vs Heikin Ashi)")
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print(" - MA gap filter (3 modes)")
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print()
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print("Expected runtime: 40-80 hours on 2-worker cluster")
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print("Target: Beat baseline $194.43/1k (19.44% returns)")
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print()
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# Generate parameter grid
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print("Generating parameter combinations...")
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configs = generate_parameter_grid()
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print(f"Total configurations: {len(configs):,}")
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print()
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# Determine number of workers
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n_workers = mp.cpu_count()
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print(f"Using {n_workers} CPU cores")
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print()
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# Run sweep
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print("Starting parameter sweep...")
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with mp.Pool(n_workers) as pool:
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results = list(tqdm(
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pool.imap(test_config, configs),
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total=len(configs),
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desc="Testing configs"
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))
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# Convert to DataFrame
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results_df = pd.DataFrame(results)
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# Save full results
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output_file = "sweep_v9_advanced_full.csv"
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results_df.to_csv(output_file, index=False)
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print(f"Full results saved to: {output_file}")
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# Sort by PnL and save top 1000
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top_results = results_df.nlargest(1000, 'pnl')
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top_file = "sweep_v9_advanced_top1000.csv"
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top_results.to_csv(top_file, index=False)
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print(f"Top 1000 configurations saved to: {top_file}")
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# Print summary
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print()
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print("=" * 80)
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print("SWEEP COMPLETE")
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print("=" * 80)
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print()
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print(f"Best configuration:")
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best = top_results.iloc[0]
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print(f" PnL: ${best['pnl']:.2f}")
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print(f" Win Rate: {best['win_rate']:.1f}%")
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print(f" Profit Factor: {best['profit_factor']:.2f}")
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print(f" Max Drawdown: ${best['max_drawdown']:.2f}")
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print(f" Total Trades: {best['total_trades']}")
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print()
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print("Parameters:")
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print(f" ATR Period: {best['atr_period']}")
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print(f" Multiplier: {best['multiplier']}")
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print(f" ADX Length: {best['adx_length']}")
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print(f" RSI Length: {best['rsi_length']}")
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print(f" RSI Long: {best['rsi_long_min']}-{best['rsi_long_max']}")
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print(f" RSI Short: {best['rsi_short_min']}-{best['rsi_short_max']}")
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print(f" Volume Max: {best['vol_max']}")
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print(f" Entry Buffer: {best['entry_buffer_atr']}")
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print(f" Heikin Ashi: {best['use_heikin_ashi']}")
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print(f" MA Gap Filter: {best['use_ma_gap_filter']}")
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if best['use_ma_gap_filter']:
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print(f" Long Min: {best['ma_gap_long_min']:.1f}%")
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print(f" Short Max: {best['ma_gap_short_max']:.1f}%")
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print()
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print(f"Baseline to beat: $194.43 (19.44%)")
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improvement = ((best['pnl'] - 194.43) / 194.43) * 100
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print(f"Improvement: {improvement:+.1f}%")
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if __name__ == "__main__":
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main()
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