mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
a523e62d94
Moved 13 scripts from root to scripts/: - create_strategy.py - debug_backtest.py - predix_add_risk_management.py - predix_batch_backtest.py - predix_full_eval.py - predix_gen_strategies_real_bt.py - predix_parallel.py - predix_quick_daytrading.py - predix_rebacktest_strategies.py - predix_simple_eval.py - predix_smart_strategy_gen.py - predix_strategy_report.py - watchdog_generator.sh Kept in root (intentional): - predix.py (main entry point) - start_llama.sh (convenience startup) - start_strategy_loop.sh (convenience startup) Root directory: 44 files → 31 files
59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
import json
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import pandas as pd
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import numpy as np
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# Strategy parameters
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factors_used = ["daily_ret", "daily_close_return_96", "daily_cc_return", "momentum_1d", "london_mom"]
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strategy_name = "ActiveDayMultiFactorScalper"
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description = "Daytrading-Strategie mit 5 niedrig-korrelierten Faktoren und niedrigen Schwellenwerten für 50+ Trades"
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# Python code for signal generation
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code = '''import numpy as np
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import pandas as pd
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# Rolling Z-Scores mit kurzen Fenstern für schnelle Signale
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z_daily_ret = (factors["daily_ret"] - factors["daily_ret"].rolling(15).mean()) / factors["daily_ret"].rolling(15).std()
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z_close_ret = (factors["daily_close_return_96"] - factors["daily_close_return_96"].rolling(20).mean()) / factors["daily_close_return_96"].rolling(20).std()
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z_cc_ret = (factors["daily_cc_return"] - factors["daily_cc_return"].rolling(15).mean()) / factors["daily_cc_return"].rolling(15).std()
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z_mom = (factors["momentum_1d"] - factors["momentum_1d"].rolling(25).mean()) / factors["momentum_1d"].rolling(25).std()
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z_london = (factors["london_mom"] - factors["london_mom"].rolling(30).mean()) / factors["london_mom"].rolling(30).std()
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# Kombiniere alle Z-Scores mit Gewichtung
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composite_signal = (
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0.25 * z_close_ret + # Höchste IC (0.255) - stärkstes Gewicht
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0.20 * z_london + # Zweithöchste IC (0.1857)
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0.20 * z_daily_ret + # IC 0.1291
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0.20 * z_cc_ret + # IC 0.1291
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0.15 * z_mom # IC 0.1291
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)
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# Niedrige Schwellenwerte für häufigere Signale (0.2-0.3)
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threshold_long = 0.25
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threshold_short = -0.25
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# Signal generieren
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signal = pd.Series(0, index=close.index, name="signal")
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signal[composite_signal > threshold_long] = 1
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signal[composite_signal < threshold_short] = -1
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# NaN behandeln (am Anfang durch rolling window)
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signal = signal.fillna(0).astype(int)
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'''
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# Create strategy dict
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strategy = {
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"strategy_name": strategy_name,
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"factor_names": factors_used,
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"description": description,
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"code": code
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}
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# Save to JSON
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output_file = f"{strategy_name}_strategy.json"
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with open(output_file, "w") as f:
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json.dump(strategy, f, indent=2)
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print(f"✅ Strategie gespeichert: {output_file}")
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print(f"📊 Faktoren: {', '.join(factors_used)}")
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print(f"🎯 Ziel: 50+ Trades mit niedrigen Schwellenwerten (±0.25)")
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