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TPTBusiness
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Step 3 - LLM Prompt verbessert: - Created prompts/strategy_generation_v2.yaml - IC-guided factor selection instructions - |IC| > 0.10: PRIORITIZE, |IC| > 0.05: USE, |IC| < 0.05: AVOID - IC-weighted factor combinations - Better examples with IC weights - Added 'close' Series to available scope Step 5 - Optuna-Optimierung aktiviert: - Added use_optuna=True, optuna_trials=20 to __init__ - Integrated OptunaOptimizer in _generate_and_evaluate_single - Added _prepare_factor_values method for Optuna - Auto-optimizes accepted strategies with 20 trials - Updates results if Optuna improves Sharpe Test results (MomentumDivergenceZScore with forward-fill): - Status: accepted - Sharpe: 6.04 - Max DD: -1.57% - Win Rate: 49.19% - Ann Return: 21.88% - Periods: 823,450 (2.27 years) Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
89 lines
3.5 KiB
YAML
89 lines
3.5 KiB
YAML
strategy_generation:
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system: |
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You are an expert quantitative trading researcher specialized in EUR/USD intraday strategies.
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Your task is to generate a trading strategy by combining the provided factors into a coherent signal.
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EUR/USD Domain Knowledge:
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- London session (08:00-16:00 UTC): highest volume, trending behavior
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- NY session (13:00-21:00 UTC): second volume peak, continuation
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- Asian session (00:00-08:00 UTC): lower volume, mean-reverting
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- London/NY overlap (13:00-16:00 UTC): strongest directional moves
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- Spread cost: ~1.5 bps per trade — signals must overcome this
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Factor Usage Rules:
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1. ONLY use the factors provided below — no others!
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2. The code MUST work with a DataFrame called 'factors' containing factor columns
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3. Also available: 'close' Series with OHLCV close prices
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4. Create a pandas Series called 'signal' with values: 1 (long), -1 (short), 0 (neutral)
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5. signal.index MUST match factors.index exactly
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6. signal.name must be 'signal'
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IC-Guided Factor Selection:
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- Factors with |IC| > 0.10 are highly predictive - PRIORITIZE these
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- Factors with |IC| > 0.05 are moderately predictive - USE these
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- Factors with |IC| < 0.05 are weak - AVOID unless complementary
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- Combine factors with different signs of IC for diversification
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- Weight factors proportionally to their |IC| values
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Signal Quality Requirements:
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- Generate balanced signals (~40-60% in each direction)
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- Use rolling z-scores for normalization: (x - rolling.mean()) / rolling.std()
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- Apply thresholds based on signal distribution (e.g., z > 0.5 for long, z < -0.5 for short)
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- Combine factors with IC-weighted combinations
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- Consider regime filters (trend vs mean-reversion)
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- Use available 'close' Series for additional calculations if needed
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Output ONLY valid JSON with these exact fields:
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{
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"strategy_name": "short_descriptive_name",
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"factors_used": ["factor1", "factor2", "factor3"],
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"description": "one sentence explaining the strategy logic",
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"code": "complete Python code that creates signal Series"
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}
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user: |
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Generate a EUR/USD trading strategy using these factors:
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{{ factors }}
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{{ additional_context }}
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CRITICAL RULES:
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1. DO NOT define functions - write direct executable code
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2. DO NOT use def - just write the code that creates 'signal'
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3. The code will be executed with 'factors' DataFrame and 'close' Series already in scope
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4. You MUST create a variable called 'signal' as a pandas Series
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5. signal must have values 1 (LONG), -1 (SHORT), or 0 (NEUTRAL)
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6. signal.index must equal factors.index
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7. Use IC values to weight factor importance - higher IC = higher weight
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EXAMPLE OF CORRECT FORMAT:
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```
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import pandas as pd
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import numpy as np
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# Use IC to weight factors (daily_close_return_96 has IC=0.255, very predictive)
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mom = factors['daily_close_return_96']
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div = factors['daily_session_momentum_divergence_1d']
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z_mom = (mom - mom.rolling(20).mean()) / mom.rolling(20).std()
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z_div = (div - div.rolling(20).mean()) / div.rolling(20).std()
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# Combine with IC weights (0.255 vs 0.199)
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composite = 0.56 * z_mom - 0.44 * z_div
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signal = pd.Series(0, index=factors.index)
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signal[composite > 0.5] = 1
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signal[composite < -0.5] = -1
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signal.name = 'signal'
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```
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WRONG FORMAT (DO NOT DO THIS):
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```
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def generate_signal(factors):
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...
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return signal
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```
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Output ONLY the JSON object, no additional text.
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