rckrdmrd
75c4d07690
Hierarchical ML Pipeline for trading predictions:
- Level 0: Attention Models (volatility/flow classification)
- Level 1: Base Models (XGBoost per symbol/timeframe)
- Level 2: Metamodels (XGBoost Stacking + Neural Gating)
Key components:
- src/pipelines/hierarchical_pipeline.py - Main prediction pipeline
- src/models/ - All ML model classes
- src/training/ - Training utilities
- src/api/ - FastAPI endpoints
- scripts/ - Training and evaluation scripts
- config/ - YAML configurations
Note: Trained models (*.joblib, *.pt) are gitignored.
Regenerate with training scripts.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
211 lines
4.1 KiB
YAML
211 lines
4.1 KiB
YAML
# Trading Configuration
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# Symbols to trade
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symbols:
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primary:
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- "XAUUSD"
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- "EURUSD"
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- "GBPUSD"
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- "BTCUSD"
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secondary:
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- "USDJPY"
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- "GBPJPY"
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- "AUDUSD"
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- "NZDUSD"
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# Timeframes
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timeframes:
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primary: 5 # 5 minutes
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aggregations:
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- 15
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- 30
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- 60
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- 240
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# Features Configuration
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features:
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# Minimal set (14 indicators) - optimized from analysis
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minimal:
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momentum:
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- "macd_signal"
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- "macd_histogram"
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- "rsi"
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trend:
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- "sma_10"
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- "sma_20"
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- "sar"
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volatility:
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- "atr"
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volume:
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- "obv"
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- "ad"
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- "cmf"
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- "mfi"
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patterns:
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- "fractals_high"
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- "fractals_low"
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- "volume_zscore"
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# Extended set for experimentation
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extended:
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momentum:
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- "stoch_k"
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- "stoch_d"
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- "cci"
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trend:
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- "ema_12"
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- "ema_26"
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- "adx"
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volatility:
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- "bollinger_upper"
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- "bollinger_lower"
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- "keltner_upper"
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- "keltner_lower"
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# Partial hour features (anti-repainting)
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partial_hour:
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enabled: true
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features:
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- "open_hr_partial"
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- "high_hr_partial"
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- "low_hr_partial"
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- "close_hr_partial"
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- "volume_hr_partial"
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# Scaling strategies
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scaling:
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strategy: "hybrid" # Options: unscaled, scaled, ratio, hybrid
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scaler_type: "robust" # Options: standard, robust, minmax
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winsorize:
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enabled: true
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lower: 0.01
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upper: 0.99
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# Walk-Forward Validation
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validation:
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strategy: "walk_forward"
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n_splits: 5
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test_size: 0.2
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gap: 0 # Gap between train and test
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walk_forward:
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step_pct: 0.1 # 10% step size
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min_train_size: 10000
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expanding_window: false # If true, training set grows
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metrics:
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- "mse"
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- "mae"
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- "directional_accuracy"
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- "ratio_accuracy"
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- "sharpe_ratio"
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# Backtesting Configuration
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backtesting:
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initial_capital: 100000
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leverage: 1.0
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costs:
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commission_pct: 0.001 # 0.1%
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slippage_pct: 0.0005 # 0.05%
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spread_pips: 2
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risk_management:
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max_position_size: 0.1 # 10% of capital
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stop_loss_pct: 0.02 # 2%
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take_profit_pct: 0.04 # 4%
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trailing_stop: true
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trailing_stop_pct: 0.01
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position_sizing:
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method: "kelly" # Options: fixed, kelly, risk_parity
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kelly_fraction: 0.25 # Conservative Kelly
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# AMD Strategy Configuration
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amd:
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enabled: true
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phases:
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accumulation:
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volume_percentile_max: 30
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price_volatility_max: 0.01
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rsi_range: [20, 40]
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obv_trend_min: 0
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manipulation:
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volume_zscore_min: 2.0
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price_whipsaw_range: [0.015, 0.03]
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false_breakout_threshold: 0.02
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distribution:
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volume_percentile_min: 70
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price_exhaustion_min: 0.02
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rsi_range: [60, 80]
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cmf_max: 0
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signals:
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confidence_threshold: 0.7
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confirmation_bars: 3
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# Thresholds
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thresholds:
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dynamic:
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enabled: true
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mode: "atr_std" # Options: fixed, atr_std, percentile
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factor: 4.0
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lookback: 20
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fixed:
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buy: -0.02
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sell: 0.02
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# Real-time Configuration
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realtime:
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enabled: true
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update_interval: 5 # seconds
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websocket_port: 8001
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streaming:
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buffer_size: 1000
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max_connections: 100
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cache:
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predictions_ttl: 60 # seconds
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features_ttl: 300 # seconds
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# Monitoring
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monitoring:
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wandb:
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enabled: true
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project: "trading-agent"
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entity: null # Your wandb username
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tensorboard:
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enabled: true
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log_dir: "logs/tensorboard"
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alerts:
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enabled: true
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channels:
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- "email"
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- "telegram"
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thresholds:
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drawdown_pct: 10
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loss_streak: 5
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# Performance Optimization
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optimization:
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gpu:
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memory_fraction: 0.8
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allow_growth: true
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data:
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num_workers: 4
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pin_memory: true
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persistent_workers: true
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prefetch_factor: 2
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cache:
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use_redis: true
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use_disk: true
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disk_path: "cache/" |