560863fa0d
Replace day-trading bot with long-term accumulation signal model. Predicts optimal BUY times using forward return analysis at 7d/30d/90d horizons, scoring each candle 0-100. Primary metric is now cost_basis_improvement_pct (model buy price vs DCA). - train_and_backtest.py: regression models (XGBoost/LSTM hybrid), accumulation-focused features (price position, momentum, volatility, volume, cycle), forward return targets, signal quality backtesting - orchestrator.py: cost improvement scoring, signal count validation - analyzer.py: accumulation-focused LLM system prompt - dashboard: cost improvement display, signal metrics table - config: new accumulation-focused parameters Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
53 lines
1.2 KiB
JSON
53 lines
1.2 KiB
JSON
{
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"model_type": "hybrid",
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"features": {
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"use_price_position": true,
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"use_momentum": true,
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"use_volatility": true,
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"use_volume": true,
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"use_cycle": true,
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"use_pca": true,
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"pca_variance": 0.95,
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"use_scaler": true
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},
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"target": {
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"type": "regression",
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"forward_periods_1h": [168, 720, 2160],
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"forward_periods_4h": [42, 180, 540],
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"weights": [0.2, 0.3, 0.5],
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"score_range": [0, 100]
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},
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"hyperparameters": {
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"learning_rate": 0.01,
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"max_depth": 5,
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"n_estimators": 500,
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"subsample": 0.8,
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"colsample_bytree": 0.8,
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"min_child_weight": 10,
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"gamma": 0.3,
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"reg_alpha": 0.5,
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"reg_lambda": 3.0,
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"lstm_hidden_size": 128,
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"lstm_num_layers": 2,
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"lstm_dropout": 0.3,
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"lstm_epochs": 100,
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"lstm_batch_size": 64,
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"lstm_sequence_length": 30,
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"lstm_patience": 10
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},
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"strategy": {
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"strong_buy_threshold": 80,
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"good_buy_threshold": 70,
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"poor_threshold": 30
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},
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"training": {
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"rolling_window": true,
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"rolling_train_size": 2500,
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"rolling_test_size": 300,
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"walk_forward_windows": 5,
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"train_pct": 0.7,
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"validation_pct": 0.15,
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"test_pct": 0.15
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},
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"timeframe": "4h"
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} |