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TPTBusiness
4758de0eee
- Rename FTMO_* constants → generic names (RISK_PER_TRADE, MAX_DAILY_LOSS, etc.) - Rename backtest_signal_ftmo → backtest_signal_risk - Rename _apply_ftmo_mask → _apply_risk_mask - Clean all FTMO/riskMgmt mentions from commit messages via filter-branch - AGENTS.md: add non-negotiable rule — NEVER mention proprietary terms in commits/releases - Code variables and function names sanitized project-wide - Force-pushed rewritten history to remote
468 lines
17 KiB
Python
468 lines
17 KiB
Python
#!/usr/bin/env python
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"""
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Quick Daytrading Strategy Generator with CORRECT factor alignment.
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Uses forward-fill to align daily factors to 1-min frequency,
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then runs fast backtests without LLM calls.
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Usage:
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python nexquant_quick_daytrading.py 5
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python nexquant_quick_daytrading.py 10
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"""
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import json, time, subprocess, tempfile # nosec
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from rich.console import Console
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console = Console()
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STRATEGIES_DIR = Path('results/strategies_new')
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STRATEGIES_DIR.mkdir(parents=True, exist_ok=True)
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FACTOR_FILES = Path('results/factors')
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VALUE_FILES = FACTOR_FILES / 'values'
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OHLCV_PATH = Path('git_ignore_folder/factor_implementation_source_data/intraday_pv.h5')
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# Best daytrading strategies (12-min horizon, optimized for RiskMgmt)
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DAYTRADING_COMBOS = [
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{
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'name': 'MomentumDivergence12min',
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'factors': ['daily_close_return_96', 'daily_session_momentum_divergence_1d'],
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'code': '''mom = factors['daily_close_return_96']
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div = factors['daily_session_momentum_divergence_1d']
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w = 20
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mom_z = (mom - mom.rolling(w).mean()) / (mom.rolling(w).std() + 1e-8)
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div_z = (div - div.rolling(w).mean()) / (div.rolling(w).std() + 1e-8)
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composite = (mom_z - div_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.3] = 1
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signal[composite < -0.3] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'LondonSessionScalp',
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'factors': ['london_mom', 'daily_session_momentum_divergence_1d'],
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'code': '''mom = factors['london_mom']
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div = factors['daily_session_momentum_divergence_1d']
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w = 15
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mom_z = (mom - mom.rolling(w).mean()) / (mom.rolling(w).std() + 1e-8)
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div_z = (div - div.rolling(w).mean()) / (div.rolling(w).std() + 1e-8)
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composite = (mom_z - div_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.25] = 1
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signal[composite < -0.25] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'TrendReversionScalp',
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'factors': ['daily_ols_slope_96', 'daily_session_momentum_divergence_1d', 'DailyTrendStrength_Raw'],
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'code': '''slope = factors['daily_ols_slope_96']
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div = factors['daily_session_momentum_divergence_1d']
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trend = factors['DailyTrendStrength_Raw']
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w = 20
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slope_z = (slope - slope.rolling(w).mean()) / (slope.rolling(w).std() + 1e-8)
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div_z = (div - div.rolling(w).mean()) / (div.rolling(w).std() + 1e-8)
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trend_z = (trend - trend.rolling(w).mean()) / (trend.rolling(w).std() + 1e-8)
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composite = (0.5 * slope_z - 0.3 * div_z + 0.2 * trend_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.3] = 1
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signal[composite < -0.3] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'VolAdjMomentum12',
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'factors': ['daily_ret_vol_adj_1d', 'daily_session_momentum_divergence_1d', 'DCP'],
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'code': '''vol = factors['daily_ret_vol_adj_1d']
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div = factors['daily_session_momentum_divergence_1d']
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dcp = factors['DCP']
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w = 20
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vol_z = (vol - vol.rolling(w).mean()) / (vol.rolling(w).std() + 1e-8)
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div_z = (div - div.rolling(w).mean()) / (div.rolling(w).std() + 1e-8)
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dcp_z = (dcp - dcp.rolling(w).mean()) / (dcp.rolling(w).std() + 1e-8)
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composite = (0.5 * vol_z - 0.3 * div_z + 0.2 * dcp_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.35] = 1
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signal[composite < -0.35] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'SessionMeanReversion',
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'factors': ['session_momentum_diff', 'daily_norm_body', 'daily_c2c_return'],
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'code': '''session = factors['session_momentum_diff']
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body = factors['daily_norm_body']
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c2c = factors['daily_c2c_return']
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w = 15
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sess_z = (session - session.rolling(w).mean()) / (session.rolling(w).std() + 1e-8)
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body_z = (body - body.rolling(w).mean()) / (body.rolling(w).std() + 1e-8)
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c2c_z = (c2c - c2c.rolling(w).mean()) / (c2c.rolling(w).std() + 1e-8)
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composite = (0.5 * sess_z + 0.3 * body_z + 0.2 * c2c_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.4] = 1
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signal[composite < -0.4] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'MomentumContinuation',
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'factors': ['daily_mom', 'daily_ret_1d', 'momentum_1d'],
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'code': '''mom = factors['daily_mom']
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ret = factors['daily_ret_1d']
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mom2 = factors['momentum_1d']
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w = 12
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mom_z = (mom - mom.rolling(w).mean()) / (mom.rolling(w).std() + 1e-8)
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ret_z = (ret - ret.rolling(w).mean()) / (ret.rolling(w).std() + 1e-8)
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mom2_z = (mom2 - mom2.rolling(w).mean()) / (mom2.rolling(w).std() + 1e-8)
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composite = (0.4 * mom_z + 0.3 * ret_z + 0.3 * mom2_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.2] = 1
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signal[composite < -0.2] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'HighFreqScalper',
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'factors': ['daily_close_return_96', 'DCP', 'london_mom'],
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'code': '''close_ret = factors['daily_close_return_96']
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dcp = factors['DCP']
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london = factors['london_mom']
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w = 10
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cr_z = (close_ret - close_ret.rolling(w).mean()) / (close_ret.rolling(w).std() + 1e-8)
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dcp_z = (dcp - dcp.rolling(w).mean()) / (dcp.rolling(w).std() + 1e-8)
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lon_z = (london - london.rolling(w).mean()) / (london.rolling(w).std() + 1e-8)
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composite = (0.4 * cr_z + 0.3 * dcp_z + 0.3 * lon_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.25] = 1
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signal[composite < -0.25] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'AdaptiveMomentumMR',
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'factors': ['daily_close_return_96', 'daily_session_momentum_divergence_1d', 'daily_ols_slope_96'],
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'code': '''mom = factors['daily_close_return_96']
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div = factors['daily_session_momentum_divergence_1d']
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slope = factors['daily_ols_slope_96']
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w = 20
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mom_z = (mom - mom.rolling(w).mean()) / (mom.rolling(w).std() + 1e-8)
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div_z = (div - div.rolling(w).mean()) / (div.rolling(w).std() + 1e-8)
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slope_z = (slope - slope.rolling(w).mean()) / (slope.rolling(w).std() + 1e-8)
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# Regime detection: high momentum = trend, low = mean reversion
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regime = (mom_z.abs() > 1.0).astype(float)
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composite = (regime * mom_z + (1 - regime) * (-div_z) + 0.3 * slope_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.4] = 1
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signal[composite < -0.4] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'TrendPullbackScalp',
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'factors': ['daily_close_return_96', 'daily_session_momentum_divergence_1d', 'daily_norm_body'],
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'code': '''mom = factors['daily_close_return_96']
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div = factors['daily_session_momentum_divergence_1d']
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body = factors['daily_norm_body']
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w = 15
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mom_z = (mom - mom.rolling(w).mean()) / (mom.rolling(w).std() + 1e-8)
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div_z = (div - div.rolling(w).mean()) / (div.rolling(w).std() + 1e-8)
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body_z = (body - body.rolling(w).mean()) / (body.rolling(w).std() + 1e-8)
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# Enter on pullbacks (divergence against trend)
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composite = (mom_z - 0.5 * div_z * mom_z.sign() + 0.2 * body_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.35] = 1
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signal[composite < -0.35] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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{
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'name': 'IntradayMomentumBlend',
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'factors': ['daily_close_return_96', 'london_mom', 'daily_session_momentum_divergence_1d', 'DCP'],
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'code': '''mom = factors['daily_close_return_96']
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lon = factors['london_mom']
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div = factors['daily_session_momentum_divergence_1d']
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dcp = factors['DCP']
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w = 20
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mom_z = (mom - mom.rolling(w).mean()) / (mom.rolling(w).std() + 1e-8)
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lon_z = (lon - lon.rolling(w).mean()) / (lon.rolling(w).std() + 1e-8)
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div_z = (div - div.rolling(w).mean()) / (div.rolling(w).std() + 1e-8)
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dcp_z = (dcp - dcp.rolling(w).mean()) / (dcp.rolling(w).std() + 1e-8)
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composite = (0.3 * mom_z + 0.3 * lon_z - 0.2 * div_z + 0.2 * dcp_z).fillna(0)
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signal = pd.Series(0, index=close.index, name='signal')
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signal[composite > 0.3] = 1
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signal[composite < -0.3] = -1
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signal = signal.fillna(0).astype(int)''',
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},
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]
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def load_factor_series(name):
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"""Load factor parquet and return as Series with correct index."""
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safe = name.replace('/','_').replace('\\','_')[:150]
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pf = VALUE_FILES / f"{safe}.parquet"
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if not pf.exists():
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return None
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df = pd.read_parquet(str(pf))
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# Extract EURUSD
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if df.index.names == ['datetime', 'instrument']:
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df_reset = df.reset_index()
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if 'instrument' in df_reset.columns:
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df_eur = df_reset[df_reset['instrument'] == 'EURUSD'].copy()
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df_eur = df_eur.set_index('datetime')
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series = df_eur.iloc[:, -1] # Last column is the factor value
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series.name = name
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return series
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# If single index, just return first column
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series = df.iloc[:, 0]
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series.name = name
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return series
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def main(n_strategies=5):
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console.print("[bold cyan]🎯 Daytrading Strategy Generator (Quick Mode)[/bold cyan]\n")
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console.print(" Style: 12-minute forward returns")
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console.print(" Target: RiskMgmt compliant (IC>0.02, Sharpe>0.5, Trades>20, DD>-10%)\n")
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# Load OHLCV data
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if not OHLCV_PATH.exists():
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console.print(f"[red]✗ OHLCV data not found: {OHLCV_PATH}[/red]")
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return
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ohlcv = pd.read_hdf(str(OHLCV_PATH), key='data')
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# Extract close prices with datetime-only index (not MultiIndex)
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if '$close' in ohlcv.columns:
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close = ohlcv['$close'].dropna()
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elif 'close' in ohlcv.columns:
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close = ohlcv['close'].dropna()
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else:
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close = ohlcv.select_dtypes(include=[np.number]).iloc[:, 0].dropna()
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# Extract datetime from MultiIndex if present
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if isinstance(close.index, pd.MultiIndex):
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close_dt_idx = close.index.get_level_values('datetime')
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close_series = pd.Series(close.values, index=close_dt_idx, name='close')
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else:
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close_series = close
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close_series = close_series.dropna()
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console.print(f"[green]✓[/green] Loaded {len(close_series):,} OHLCV bars")
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# Load all factor series and align to close index
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all_factor_series = {}
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for combo in DAYTRADING_COMBOS:
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for factor_name in combo['factors']:
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if factor_name in all_factor_series:
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continue
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series = load_factor_series(factor_name)
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if series is not None:
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# Forward fill to match close frequency
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series_ff = series.reindex(close_series.index).ffill()
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all_factor_series[factor_name] = series_ff
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# Create factors DataFrame
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df_factors = pd.DataFrame(all_factor_series)
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df_factors = df_factors.dropna(how='all')
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console.print(f"[green]✓[/green] Loaded {len(df_factors.columns)} factor series")
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console.print(f"[green]✓[/green] Aligned to {len(df_factors):,} bars\n")
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accepted = []
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for i, combo in enumerate(DAYTRADING_COMBOS[:n_strategies]):
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console.print(f"[{i+1}/{n_strategies}] Testing {combo['name']}...")
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# Build factor dataframe
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valid_factors = [f for f in combo['factors'] if f in df_factors.columns]
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if len(valid_factors) < 2:
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console.print(f" ✗ Not enough valid factors")
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continue
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strat_factors = df_factors[valid_factors].dropna()
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if len(strat_factors) < 1000:
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console.print(f" ✗ Not enough data: {len(strat_factors)} bars")
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continue
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# Build backtest script
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forward_bars = 12
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strategy_code = combo['code']
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script = f"""
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import pandas as pd
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import numpy as np
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import json
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close = pd.read_pickle('close.pkl') # nosec
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factors = pd.read_pickle('factors.pkl') # nosec
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# Execute strategy
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try:
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{chr(10).join(' ' + l for l in strategy_code.split(chr(10)))}
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except Exception as e:
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print(f"ERROR: {{e}}")
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exit(1)
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if 'signal' not in dir():
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print("ERROR: No signal generated")
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exit(1)
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signal = signal.fillna(0)
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# Align
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common_idx = close.index.intersection(signal.index)
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close = close.loc[common_idx]
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signal = signal.loc[common_idx]
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# Forward returns (12-min horizon for daytrading)
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FORWARD_BARS = {forward_bars}
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returns_fwd = close.pct_change(FORWARD_BARS).shift(-FORWARD_BARS)
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signal_aligned = signal.loc[returns_fwd.dropna().index]
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fwd_returns = returns_fwd.loc[signal_aligned.index]
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if len(signal_aligned) < 100 or len(fwd_returns) < 100:
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print("ERROR: Not enough data")
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exit(1)
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# Metrics
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ic = signal_aligned.corr(fwd_returns)
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strategy_returns = signal_aligned * fwd_returns
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sharpe = strategy_returns.mean() / strategy_returns.std() * np.sqrt(252 * 1440 / {forward_bars}) if strategy_returns.std() > 0 else 0
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cum = (1 + strategy_returns).cumprod()
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running_max = cum.expanding().max()
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drawdown = (cum - running_max) / running_max.replace(0, np.nan)
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max_dd = drawdown.min() if len(drawdown) > 0 else 0
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win_rate = (strategy_returns > 0).sum() / len(strategy_returns) if len(strategy_returns) > 0 else 0
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n_trades = int((signal_aligned != signal_aligned.shift(1)).sum())
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total_return = cum.iloc[-1] - 1
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n_bars = len(strategy_returns)
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n_months = n_bars / (252 * 1440 / {forward_bars} / 12) if n_bars > 0 else 1
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monthly_return = (1 + total_return) ** (1 / n_months) - 1 if n_months > 0 and (1 + total_return) > 0 else total_return
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result = {{
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"status": "success",
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"sharpe": float(sharpe),
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"max_drawdown": float(max_dd) if not np.isnan(max_dd) else -0.20,
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"win_rate": float(win_rate),
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"ic": float(ic) if not np.isnan(ic) else 0,
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"n_trades": n_trades,
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"total_return": float(total_return),
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"monthly_return_pct": float(monthly_return * 100),
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"n_bars": int(n_bars),
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"n_months": float(n_months),
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"signal_long": int((signal_aligned == 1).sum()),
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"signal_short": int((signal_aligned == -1).sum()),
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"signal_neutral": int((signal_aligned == 0).sum()),
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}}
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print(json.dumps(result))
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"""
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# Run backtest
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import tempfile
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with tempfile.TemporaryDirectory() as td:
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tdp = Path(td)
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strat_close = close_series.loc[strat_factors.index]
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strat_close.to_pickle(str(tdp / 'close.pkl')) # nosec
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strat_factors.to_pickle(str(tdp / 'factors.pkl')) # nosec
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script_path = tdp / 'run.py'
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script_path.write_text(script)
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try:
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result_proc = subprocess.run( # nosec B603
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[sys.executable, str(script_path)],
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capture_output=True, text=True, timeout=60,
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cwd=str(tdp)
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)
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if result_proc.returncode != 0:
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console.print(f" ✗ Failed: {result_proc.stderr[:200]}")
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continue
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result = None
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for line in result_proc.stdout.strip().split('\n'):
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try:
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result = json.loads(line)
|
|
break
|
|
except:
|
|
continue
|
|
|
|
if not result or result.get('status') != 'success':
|
|
console.print(f" ✗ Invalid result")
|
|
continue
|
|
|
|
except subprocess.TimeoutExpired: # nosec
|
|
console.print(f" ✗ Timeout")
|
|
continue
|
|
except Exception as e:
|
|
console.print(f" ✗ Error: {e}")
|
|
continue
|
|
|
|
ic = result.get('ic', 0)
|
|
sharpe = result.get('sharpe', 0)
|
|
trades = result.get('n_trades', 0)
|
|
dd = result.get('max_drawdown', 0)
|
|
|
|
# RiskMgmt criteria
|
|
if abs(ic) > 0.02 and sharpe > 0.5 and trades > 20 and dd > -0.10:
|
|
strategy = {
|
|
'strategy_name': combo['name'],
|
|
'factor_names': combo['factors'],
|
|
'description': f"Daytrading strategy combining {', '.join(combo['factors'])}",
|
|
'code': combo['code'],
|
|
'real_backtest': result,
|
|
'metrics': result,
|
|
'summary': {
|
|
'sharpe': sharpe,
|
|
'max_drawdown': dd,
|
|
'win_rate': result.get('win_rate', 0),
|
|
'monthly_return_pct': result.get('monthly_return_pct', 0),
|
|
'real_ic': ic,
|
|
'real_n_trades': trades,
|
|
'forward_bars': 12,
|
|
'trading_style': 'daytrading',
|
|
}
|
|
}
|
|
|
|
fname = f"{int(time.time())}_{combo['name']}.json"
|
|
with open(STRATEGIES_DIR / fname, 'w') as f:
|
|
json.dump(strategy, f, indent=2, ensure_ascii=False)
|
|
|
|
accepted.append(strategy)
|
|
console.print(f" ✓ [green]ACCEPT[/green]: IC={ic:.4f}, Sharpe={sharpe:.2f}, Trades={trades}, DD={dd:.1%}")
|
|
else:
|
|
console.print(f" ✗ [red]REJECT[/red]: IC={ic:.4f}, Sharpe={sharpe:.2f}, Trades={trades}, DD={dd:.1%}")
|
|
|
|
console.print(f"\n[bold green]✓ {len(accepted)}/{n_strategies} strategies accepted[/bold green]\n")
|
|
|
|
if accepted:
|
|
console.print("[bold]Results:[/bold]")
|
|
for s in accepted:
|
|
bt = s['real_backtest']
|
|
console.print(f" • {s['strategy_name']:30s} IC={bt['ic']:.4f} Sharpe={bt['sharpe']:.2f} "
|
|
f"Monthly={bt['monthly_return_pct']:.2f}% Trades={bt['n_trades']}")
|
|
|
|
if __name__ == '__main__':
|
|
import sys
|
|
n = int(sys.argv[1]) if len(sys.argv) > 1 else 5
|
|
main(n)
|