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>
159 lines
3.6 KiB
YAML
159 lines
3.6 KiB
YAML
# Model Configuration
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# XGBoost Settings
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xgboost:
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base:
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n_estimators: 200
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max_depth: 5
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learning_rate: 0.05
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subsample: 0.8
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colsample_bytree: 0.8
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gamma: 0.1
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reg_alpha: 0.1
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reg_lambda: 1.0
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min_child_weight: 3
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tree_method: "hist"
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device: "cuda"
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random_state: 42
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hyperparameter_search:
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n_estimators: [100, 200, 300, 500]
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max_depth: [3, 5, 7]
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learning_rate: [0.01, 0.05, 0.1]
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subsample: [0.7, 0.8, 0.9]
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colsample_bytree: [0.7, 0.8, 0.9]
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gpu:
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max_bin: 512
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predictor: "gpu_predictor"
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# GRU Settings
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gru:
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architecture:
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hidden_size: 128
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num_layers: 2
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dropout: 0.2
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recurrent_dropout: 0.1
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use_attention: true
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attention_heads: 8
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attention_units: 128
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training:
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epochs: 100
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batch_size: 256
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learning_rate: 0.001
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optimizer: "adamw"
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loss: "mse"
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early_stopping_patience: 15
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reduce_lr_patience: 5
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reduce_lr_factor: 0.5
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min_lr: 1.0e-7
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gradient_clip: 1.0
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sequence:
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length: 32
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step: 1
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mixed_precision:
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enabled: true
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dtype: "bfloat16"
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# Transformer Settings
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transformer:
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architecture:
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d_model: 512
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nhead: 8
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num_encoder_layers: 4
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num_decoder_layers: 2
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dim_feedforward: 2048
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dropout: 0.1
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use_flash_attention: true
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training:
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epochs: 100
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batch_size: 512
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learning_rate: 0.0001
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warmup_steps: 4000
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gradient_accumulation_steps: 2
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sequence:
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max_length: 128
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# Meta-Model Settings
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meta_model:
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type: "xgboost" # Default: xgboost, Options: xgboost, linear, ridge, neural
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xgboost:
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n_estimators: 100
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max_depth: 3
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learning_rate: 0.1
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subsample: 0.8
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colsample_bytree: 0.8
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neural:
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hidden_layers: [64, 32]
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activation: "relu"
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dropout: 0.2
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features:
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use_original: true
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use_statistics: true
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max_original_features: 10
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levels:
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use_level_2: true
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use_level_3: true # Meta-metamodel
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# Metamodel Selection per Symbol (FASE 3 Results - 2026-01-18)
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# Based on Neural vs XGBoost comparison:
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# - XAUUSD: Neural wins (better R2, confidence accuracy)
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# - EURUSD: XGBoost wins (Neural had very negative R2)
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metamodel_selection:
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default: "xgboost" # Fallback for symbols not listed
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neural_gating_path: "models/metamodels_neural"
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xgboost_path: "models/metamodels"
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per_symbol:
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XAUUSD: "neural_gating" # Neural wins: R2 0.14 vs 0.11, 100% conf accuracy
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EURUSD: "xgboost" # XGBoost wins: Neural R2 was -157
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BTCUSD: "xgboost" # Not yet trained with Neural
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GBPUSD: "xgboost" # Not yet trained with Neural
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USDJPY: "xgboost" # Not yet trained with Neural
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# AMD Strategy Models
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amd:
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accumulation:
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focus_features: ["volume", "obv", "support_levels", "rsi"]
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model_type: "lstm"
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hidden_size: 64
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manipulation:
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focus_features: ["volatility", "volume_spikes", "false_breakouts"]
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model_type: "gru"
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hidden_size: 128
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distribution:
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focus_features: ["momentum", "divergences", "resistance_levels"]
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model_type: "transformer"
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d_model: 256
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# Output Configuration
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output:
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horizons:
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- name: "scalping"
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id: 0
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range: [1, 6] # 5-30 minutes
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- name: "intraday"
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id: 1
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range: [7, 18] # 35-90 minutes
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- name: "swing"
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id: 2
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range: [19, 36] # 95-180 minutes
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- name: "position"
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id: 3
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range: [37, 72] # 3-6 hours
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targets:
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- "high"
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- "low"
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- "close"
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- "direction" |