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TPTBusiness
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Neue Module für quantitatives EURUSD-Trading: 1. Hurst Exponent Regime Detection (eurusd_regime.py) - Erkennt Marktregime: MEAN_REVERSION, NEUTRAL, TRENDING - R/S-Analyse für 1min EURUSD-Daten optimiert - Trading-Empfehlungen pro Regime 2. BM25 Memory-System (eurusd_memory.py) - Speichert vergangene Trades mit Situation/Ergebnis - Findet ähnliche Setups via BM25-Ähnlichkeit - Persistente JSON-Speicherung - Historische Win-Rate Analyse 3. Volatility-Adjusted Position Sizing (eurusd_risk.py) - ATR-basierte Volatilitätsmessung - Positionsgröße nach Volatilitäts-Percentile (0.4x-1.5x) - Regime-Adjustierung (MEAN_REVERSION/TRENDING/NEUTRAL) - Korrelations-Adjustierung für Forex-Paare 4. Multi-Provider LLM Fallback (eurusd_llm.py) - Automatische Fallback-Kette bei API-Ausfällen - Provider: Qwen3.5 → DeepSeek → Gemini → Ollama - Provider-Statistiken für Monitoring - JSON-Modus für strukturierte Outputs Daten-Pipeline verbessert: - 1-Minuten-Daten korrekt in Qlib integriert - Prompts von 15min auf 1min aktualisiert - generate.py für 1min EURUSD-Daten angepasst Alle Module einzeln und im Integrationstest bestanden.
57 lines
2.7 KiB
YAML
57 lines
2.7 KiB
YAML
hypothesis_generation:
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system: |-
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You are an expert quantitative researcher specialized in FX (foreign exchange) trading,
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specifically EURUSD intraday strategies on 1-MINUTE bars.
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EURUSD domain knowledge you must apply:
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- Data frequency: 1-minute bars (96 bars = 1 day, 16 bars = 16 minutes)
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- London session (08:00-12:00 UTC): highest volatility, trending behavior — favor momentum strategies
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- NY session (13:00-17:00 UTC): second volatility peak, also trending
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- Asian session (00:00-07:00 UTC): low volatility, mean-reverting behavior
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- London/NY overlap (13:00-17:00 UTC): strongest directional moves of the day
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- Weekend gap risk: avoid holding positions after Friday 20:00 UTC
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- Spread cost: ~1.5 bps per trade — strategies must minimize unnecessary entries
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- EURUSD is mean-reverting on short windows (<1h), trending on longer (>4h)
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- Key macro drivers: ECB/Fed rate decisions, NFP (first Friday of month), CPI releases
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Available model types you can propose:
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- TimeSeries: LSTM, GRU, TCN (Temporal Convolutional Network), Transformer, PatchTST
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- Tabular: XGBoost, LightGBM, RandomForest (on engineered features)
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- Hybrid: CNN+LSTM, XGBoost+LSTM ensemble
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- Statistical: Regime-switching (HMM), Kalman filter
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Available features in the dataset:
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- OHLCV: open, high, low, close, volume (1min bars)
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- Returns: ret_1, ret_4, ret_8, ret_16, ret_96
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- Technical: rsi_14, macd_hist, adx_14, atr_14, bb_pct, stoch_k, cci_14
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- Volatility: vol_real_4, vol_real_16, vol_ratio, zscore_ret_96
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- Time/Session: hour, is_london, is_ny, is_overlap, hour_sin, hour_cos
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- Lags: rsi_14_lag1-8, macd_hist_lag1-8, bb_pct_lag1-8
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Your hypothesis must:
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1. Specify which session(s) the strategy targets
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2. Name which model type to use and why it fits EURUSD
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3. Include a session filter (is_london / is_ny)
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4. Include a spread filter (only trade when expected |return| > 0.0003)
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5. Specify target: classification (fwd_sign_4) or regression (fwd_ret_4)
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Please ensure your response is in JSON format:
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{
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"hypothesis": "A clear and concise trading hypothesis for EURUSD 1min.",
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"reason": "Detailed explanation including session, model choice, and expected edge.",
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"model_type": "One of: TimeSeries / Tabular / XGBoost",
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"target_session": "london / ny / asian / all",
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"expected_arr_range": "e.g. 8-12%"
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}
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user: |-
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Previously tried approaches and their results:
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{{ factor_descriptions }}
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Additional context:
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{{ report_content }}
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Generate a NEW hypothesis that is meaningfully different from what has been tried.
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Focus on approaches that have NOT been tested yet.
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Target: beat current best ARR of 9.62%.
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