187243bfdb
Replace hardcoded localhost:5000 with mlflow:5000 in pipeline.yaml and main.py health check fallback so containers can reach MLflow over Docker's internal network. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
152 lines
3.6 KiB
YAML
152 lines
3.6 KiB
YAML
# ML Pipeline Configuration
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# Full config for feature engineering, annotation ingestion, training, and inference
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data:
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raw_path: "data/raw/OHLCV.csv"
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enriched_path: "data/enriched/features.csv"
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labeled_path: "data/labeled/dataset.csv"
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annotations_path: "data/annotations/export.json"
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stages:
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feature_engineering:
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enabled: true
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# TA-Lib technical indicators
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talib_indicators:
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- name: "RSI"
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params:
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timeperiod: 14
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- name: "EMA"
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params:
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timeperiod: 20
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- name: "EMA"
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params:
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timeperiod: 50
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- name: "MACD"
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params:
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fastperiod: 12
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slowperiod: 26
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signalperiod: 9
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- name: "BBANDS"
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params:
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timeperiod: 20
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nbdevup: 2
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nbdevdn: 2
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- name: "ATR"
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params:
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timeperiod: 14
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- name: "ADX"
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params:
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timeperiod: 14
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- name: "CCI"
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params:
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timeperiod: 14
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- name: "MFI"
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params:
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timeperiod: 14
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- name: "STOCH"
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params:
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fastk_period: 14
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slowk_period: 3
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slowd_period: 3
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# Candle-derived features
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candle_features: true
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# Custom feature functions (module paths)
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custom_features: []
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annotation_ingestion:
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enabled: true
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# Label encoding: "window" or "bio"
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label_encoding: "window"
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# For windowed classification
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window_size: 30
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# Context padding (candles before/after)
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context_padding: 20
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# Minimum confidence for human annotations
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min_confidence: 1
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# Programmatic TA-Lib pattern labels
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programmatic_labels:
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enabled: true
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talib_patterns:
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- "CDLENGULFING"
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- "CDLHAMMER"
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- "CDLINVERTEDHAMMER"
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- "CDLSHOOTINGSTAR"
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- "CDLDOJI"
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- "CDLDOJISTAR"
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- "CDLMORNINGSTAR"
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- "CDLEVENINGSTAR"
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- "CDLHARAMI"
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- "CDLPIERCING"
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- "CDLDARKCLOUDCOVER"
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- "CDL3WHITESOLDIERS"
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- "CDL3BLACKCROWS"
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# Label merge strategy: "human_priority", "programmatic_priority", "both"
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merge_strategy: "human_priority"
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training:
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enabled: true
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# Model type: "random_forest", "xgboost"
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model_type: "random_forest"
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# Train/test split
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split_method: "temporal" # "temporal" or "random"
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test_split: 0.2
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validation_split: 0.1
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# Class balancing
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class_weights: "balanced" # "balanced" or null
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# Hyperparameters (model-specific)
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hyperparameters:
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# RandomForest
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n_estimators: 200
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max_depth: 15
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min_samples_split: 5
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min_samples_leaf: 2
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random_state: 42
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n_jobs: -1
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# XGBoost (when model_type is "xgboost")
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# n_estimators: 500
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# max_depth: 6
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# learning_rate: 0.01
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# subsample: 0.8
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# colsample_bytree: 0.8
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# random_state: 42
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# n_jobs: -1
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# MLflow settings
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mlflow:
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tracking_uri: "http://mlflow:5000"
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experiment_name: "candlestick_patterns"
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log_artifacts: true
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register_model: false # Set to true to register in model registry
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inference:
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enabled: true
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# Model source: "mlflow" or "local"
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model_source: "local"
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# For MLflow source
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mlflow_model_name: "candlestick_pattern_v1"
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mlflow_model_stage: "Production" # "Production", "Staging", "None"
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# For local source
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local_model_path: "models/best_model.pkl"
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# Batch processing
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batch_size: 1000
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# Preprocessing config loaded from MLflow artifact or use current config
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use_training_config: true
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