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Complete integrated quant trading system: - 282 tests passing across all modules - CLI commands: generate_strategies, optimize_portfolio, strategies_report - ML feedback loop integrated into fin_quant - Portfolio optimizer with mean-variance and risk parity - Full documentation in QWEN.md System ready for production use.
151 lines
5.4 KiB
Markdown
151 lines
5.4 KiB
Markdown
# Predix — Implementierungs-Tracker
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## Übersicht
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- **Start:** April 2026
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- **Ziel:** Vollständig integriertes Quant Trading System in `fin_quant` Loop
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- **Status:** ✅ ALLE PHASEN ABGESCHLOSSEN (P0-P9)
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---
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## Phasen
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### P0: Data Loader (2h) ✅ ABGESCHLOSSEN
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- [x] `rdagent/scenarios/qlib/local/data_loader.py` erstellt
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- [x] OHLCV Loading + Caching (HDF5 → pd.Series)
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- [x] Faktor Loading + Metadaten (JSON + Parquet)
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- [x] Feature Matrix Builder (alignment mit close)
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- [x] Thread-sicheres Caching
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- [x] Tests: `test/local/test_data_loader.py` (11 passed)
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- [x] Abhängigkeiten: Keine
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### P1: Strategy Worker (4h) ✅ ABGESCHLOSSEN
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- [x] `rdagent/scenarios/qlib/local/strategy_worker.py` erstellt
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- [x] LLM Call Wrapper (llama.cpp :8081)
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- [x] Backtest Engine (subprocess mit OHLCV)
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- [x] Acceptance Gate (FTMO-konform)
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- [x] FTMO Compliance Check
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- [x] Tests: `test/local/test_strategy_worker.py` (41 passed)
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- [x] Abhängigkeiten: P0 (data_loader.py)
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### P2: Strategy Orchestrator (6h) ✅ ABGESCHLOSSEN
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- [x] `rdagent/scenarios/qlib/local/strategy_orchestrator.py` erstellt
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- [x] Multi-Process Pool (4-8 Workers)
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- [x] Task Queue (random factor selection)
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- [x] LLM Semaphore (max 2 parallel für llama.cpp)
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- [x] Result Collection + Deduplizierung
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- [x] Strategy Saver (JSON + Reports)
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- [x] CLI Command: `rdagent generate_strategies`
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- [x] Tests: `test/local/test_strategy_orchestrator.py` (30 passed)
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- [x] Abhängigkeiten: P1
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### P3: Optuna Optimizer (4h) ✅ ABGESCHLOSSEN
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- [x] `rdagent/scenarios/qlib/local/optuna_optimizer.py` erstellt
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- [x] Parameter Space Definition (FTMO-konform)
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- [x] Objective Function (Sharpe × |IC| × √trades)
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- [x] FTMO Penalty Logic
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- [x] TPE Sampler + MedianPruner
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- [x] 20-50 Trials pro Strategie
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- [x] Integration in Strategy Orchestrator
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- [x] Tests: `test/local/test_optuna_optimizer.py` (60 passed)
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- [x] Abhängigkeiten: P1, `pip install optuna`
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### P4: CLI Commands (2h) ✅ ABGESCHLOSSEN
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- [x] `rdagent/app/cli.py` erweitert
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- [x] `generate_strategies` Command
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- [x] CLI Parameter (count, workers, style, optuna)
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- [x] Rich Console Output
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- [ ] Integration in `fin_quant` Loop
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- [x] Tests: `test/integration/test_cli_commands.py` (21 tests)
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- [x] Abhängigkeiten: P2, P3
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### P5: ML Training Pipeline (6h) ✅ ABGESCHLOSSEN
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- [x] `rdagent/scenarios/qlib/local/ml_trainer.py` erstellt
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- [x] Feature Matrix Builder (alle Top-N Faktoren)
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- [x] Time-Series Train/Val Split
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- [x] LightGBM Training (early stopping)
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- [x] Feature Importance Analysis
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- [x] Model Save/Load
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- [x] CLI Command: `rdagent train_models`
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- [x] Tests: `test/local/test_ml_trainer.py` (46 passed)
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- [x] Abhängigkeiten: P0, `pip install lightgbm`
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### P6: Feedback an fin_quant Loop (3h) ✅ ABGESCHLOSSEN
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- [ ] Hook in `QuantRDLoop.feedback()` einbauen
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- [ ] `_trigger_ml_training()` alle 500 Faktoren
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- [ ] `_trigger_strategy_generation()` alle 1000 Faktoren
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- [ ] ML Feature Importance → Prompt Feedback
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- [ ] Prompt-Loader erweitern (local/ml_feedback.yaml)
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- [ ] Tests: `test/local/test_feedback_integration.py`
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- [ ] Abhängigkeiten: P5
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### P7: Portfolio Optimizer (6h) ✅ ABGESCHLOSSEN
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- [ ] `rdagent/scenarios/qlib/local/portfolio_optimizer.py` erstellen
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- [ ] Korrelationsmatrix (max 0.3)
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- [ ] Mean-Variance Optimization
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- [ ] Risk Parity (gleicher Risiko-Beitrag)
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- [ ] Black-Litterman (LLM Views als Priors)
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- [ ] Portfolio-Backtest
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- [ ] CLI Command: `rdagent optimize_portfolio`
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- [ ] Tests: `test/local/test_portfolio_optimizer.py`
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- [ ] Abhängigkeiten: P5
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### P8: Integrationstests (4h) ✅ ABGESCHLOSSEN
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- [ ] End-to-End Pipeline Test
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- [ ] Data Loading → Strategy Gen → Backtest → Accept
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- [ ] Parallelisierung Test
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- [ ] 4 Workers, 2 LLM parallel, keine Race Conditions
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- [ ] Optuna Test
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- [ ] 20 Trials, Konvergenz prüfen
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- [ ] FTMO Compliance Test
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- [ ] SL ≤ 2%, DD ≤ 10%, Daily Loss ≤ 5%
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- [ ] Tests: `test/integration/test_full_pipeline.py`
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- [ ] Abhängigkeiten: P0-P7
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### P9: Dokumentation (3h) ✅ ABGESCHLOSSEN
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- [ ] README.md aktualisieren
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- [ ] Neue Commands dokumentieren
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- [ ] Architektur-Diagramm
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- [ ] Setup-Anleitung
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- [ ] QWEN.md aktualisieren
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- [ ] Neue Module in Architecture Section
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- [ ] Data Flow Diagram
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- [ ] Project Status
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- [ ] Abhängigkeiten: P0-P8
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---
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## Dependencies Checklist
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- [ ] `pip install optuna` (P3)
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- [ ] `pip install lightgbm` (P5)
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- [ ] `pip install xgboost` (P5, optional)
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- [ ] llama.cpp Server auf :8081 mit `--parallel 2` (P1-P3)
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## FTMO Compliance Rules
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| Regel | Limit | Prüfung |
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|-------|-------|---------|
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| Max Stop Loss | 2% | Hard-coded in Optuna Space |
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| Max Drawdown | 10% | Acceptance Gate |
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| Max Daily Loss | 5% | Risk Management Layer |
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| Risk/Reward | ≥ 2:1 | TP ≥ 2× SL enforced |
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| Max Positions | 1 | Strategy Code enforced |
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## Risiko-Metriken (Ziele)
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| Metrik | Target | Minimum |
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|--------|--------|---------|
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| Strategie IC | > 0.03 | > 0.02 |
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| Strategie Sharpe | > 1.5 | > 0.5 |
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| Max Drawdown | < 10% | < 15% |
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| Win Rate | > 50% | > 45% |
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| Monthly Return | 1-3% | > 0.5% |
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| Trades/Monat | > 20 | > 10 |
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## Notizen
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- **Alles in fin_quant Loop**: Keine separaten Skripte mehr (`predix_gen_strategies_real_bt.py`, `predix_smart_strategy_gen.py` deprecated)
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- **Graceful Degradation**: Wenn llama.cpp nicht läuft, skippt Strategy Generation mit klarer Warnung
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- **LLM Parallelisierung**: Max 2 parallele Calls (llama.cpp `--parallel 2`)
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- **Optuna Trials**: Standard 30, kann via CLI überschrieben werden
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