fix: LLM analysis + new run button + settings page support
- Fixed LLM failing silently (401 auth error on every iteration) - Reset provider to Ollama (working) from broken OpenRouter config - Added /api/clear endpoint + 'New Run' button to reset history - LLM failures now logged visibly with error details - LLM suggestions persisted to iteration data (survive restarts) - Settings page support via llm_settings.json (multi-provider)
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BizzleBot
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68
config/best_config.json
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68
config/best_config.json
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@ -0,0 +1,68 @@
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{
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"model_type": "xgboost",
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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": false,
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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": [
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168,
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720,
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2160
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],
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"forward_periods_4h": [
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42,
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180,
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540
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],
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"weights": [
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0.2,
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0.3,
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0.5
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],
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"score_range": [
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0,
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100
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]
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},
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"hyperparameters": {
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"learning_rate": 0.01,
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"max_depth": 4,
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"n_estimators": 300,
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"subsample": 0.8,
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"colsample_bytree": 0.8,
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"min_child_weight": 20,
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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": 65,
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"good_buy_threshold": 55,
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"poor_threshold": 35
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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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}
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@ -1,92 +1,68 @@
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{
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"model_type": "hybrid",
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"model_type": "xgboost",
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"features": {
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"technical_indicators": [
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"RSI_14",
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"RSI_7",
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"MACD_line",
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"MACD_signal",
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"MACD_hist",
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"BB_upper",
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"BB_lower",
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"BB_width",
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"ATR_14",
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"SMA_20",
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"SMA_50",
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"EMA_10",
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"EMA_20",
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"OBV",
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"stoch_k",
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"stoch_d",
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"williams_r",
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"CCI_20",
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"ROC_10"
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],
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"lookback_periods": [
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3,
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5,
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10,
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20
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],
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"use_volume_features": true,
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"use_volatility_features": true,
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"use_candle_patterns": false,
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"use_lag_features": true,
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"lag_periods": [
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1,
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2,
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3,
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5
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],
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"use_pca": true,
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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": false,
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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": "classification",
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"direction": "both",
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"horizon_candles": 4,
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"threshold_pct": 1.0
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"type": "regression",
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"forward_periods_1h": [
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168,
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720,
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2160
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],
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"forward_periods_4h": [
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42,
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180,
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540
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],
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"weights": [
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0.2,
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0.3,
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0.5
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],
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"score_range": [
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0,
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100
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]
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},
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"hyperparameters": {
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"learning_rate": 0.001,
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"max_depth": 5,
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"learning_rate": 0.01,
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"max_depth": 4,
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"n_estimators": 300,
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"subsample": 0.8,
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"colsample_bytree": 0.8,
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"min_child_weight": 5,
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"min_child_weight": 20,
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"gamma": 0.3,
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"reg_alpha": 0.1,
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"reg_lambda": 5.0,
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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": 20,
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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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"entry_threshold": 0.65,
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"exit_type": "trailing_stop",
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"stop_loss_pct": 2.0,
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"take_profit_pct": 4.0,
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"trailing_stop_pct": 1.5,
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"position_sizing": "confidence_scaled",
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"max_position_pct": 100,
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"min_confidence_to_trade": 0.5,
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"dynamic_sl_tp": true,
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"atr_sl_multiplier": 1.2,
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"atr_tp_multiplier": 3.0
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"strong_buy_threshold": 65,
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"good_buy_threshold": 55,
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"poor_threshold": 35
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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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"rolling_window": true,
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"rolling_train_size": 3000,
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"rolling_test_size": 200
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"test_pct": 0.15
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},
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"timeframe": "4h"
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}
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21
config/llm_settings.json
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21
config/llm_settings.json
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@ -0,0 +1,21 @@
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{
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"provider": "ollama",
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"model": "qwen3.5:27b",
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"providers": {
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"ollama": {
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"base_url": "http://100.100.242.21:11434"
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},
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"lmstudio": {
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"base_url": "http://100.100.242.21:1234"
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},
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"openai": {
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"api_key": ""
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},
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"anthropic": {
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"api_key": ""
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},
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"openrouter": {
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"api_key": ""
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}
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}
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}
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@ -9,6 +9,7 @@ import os
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import sys
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import threading
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import requests
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from fastapi import FastAPI
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from fastapi.responses import FileResponse, HTMLResponse, JSONResponse
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from pydantic import BaseModel
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@ -23,6 +24,7 @@ app = FastAPI(title="BTC Accumulation Signal Optimizer")
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CONFIG_DIR = os.path.join(BASE_DIR, "config")
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RESULTS_DIR = os.path.join(BASE_DIR, "results")
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ITERATIONS_LOG = os.path.join(RESULTS_DIR, "iterations.jsonl")
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LLM_SETTINGS_PATH = os.path.join(CONFIG_DIR, "llm_settings.json")
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_opt_thread = None
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@ -31,6 +33,64 @@ class ConfigUpdate(BaseModel):
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config: dict
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class LLMSettingsUpdate(BaseModel):
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provider: str
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model: str
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providers: dict
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class TestConnectionRequest(BaseModel):
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provider: str
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providers: dict
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class FetchModelsRequest(BaseModel):
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provider: str
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providers: dict
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def _load_llm_settings():
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if os.path.exists(LLM_SETTINGS_PATH):
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with open(LLM_SETTINGS_PATH) as f:
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return json.load(f)
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return {
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"provider": "ollama",
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"model": "qwen3.5:27b",
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"providers": {
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"ollama": {"base_url": "http://100.100.242.21:11434"},
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"lmstudio": {"base_url": "http://100.100.242.21:1234"},
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"openai": {"api_key": ""},
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"anthropic": {"api_key": ""},
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"openrouter": {"api_key": ""},
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},
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}
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def _mask_api_key(key):
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if not key or len(key) < 8:
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return ""
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return "••••••••" + key[-4:]
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def _safe_settings(settings):
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"""Return settings with API keys masked."""
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out = json.loads(json.dumps(settings))
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for name, cfg in out.get("providers", {}).items():
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if "api_key" in cfg:
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cfg["api_key"] = _mask_api_key(cfg["api_key"])
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return out
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def _merge_api_keys(new_providers, existing_providers):
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"""Preserve existing API keys when the incoming value is masked."""
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for name, cfg in new_providers.items():
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if "api_key" in cfg:
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masked = cfg["api_key"]
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if masked.startswith("••••") or masked == "":
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existing_key = existing_providers.get(name, {}).get("api_key", "")
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cfg["api_key"] = existing_key
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@app.get("/api/status")
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def api_status():
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return orchestrator.get_status()
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@ -91,7 +151,11 @@ def api_best():
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with open(best_path) as f:
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config = json.load(f)
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iterations = orchestrator.load_iteration_history()
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best_iter = max(iterations, key=lambda x: x.get("cost_improvement", 0)) if iterations else {}
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best_iter = (
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max(iterations, key=lambda x: x.get("cost_improvement", 0))
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if iterations
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else {}
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)
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return {"config": config, "best_iteration": best_iter}
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@ -110,32 +174,155 @@ def api_download_best_config():
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return JSONResponse({"error": "No best config yet"}, status_code=404)
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DASHBOARD_HTML = """<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>BTC Accumulation Signal Optimizer</title>
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
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<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.4/dist/chart.umd.min.js"></script>
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<style>
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# ── Settings API ──────────────────────────────────────────────────────────
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@app.get("/api/settings")
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def api_get_settings():
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settings = _load_llm_settings()
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return _safe_settings(settings)
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@app.post("/api/settings")
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def api_save_settings(body: LLMSettingsUpdate):
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existing = _load_llm_settings()
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new_settings = {
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"provider": body.provider,
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"model": body.model,
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"providers": body.providers,
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}
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_merge_api_keys(new_settings["providers"], existing.get("providers", {}))
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with open(LLM_SETTINGS_PATH, "w") as f:
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json.dump(new_settings, f, indent=2)
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return {"ok": True, "message": "Settings saved"}
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@app.post("/api/settings/test")
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def api_test_connection(body: TestConnectionRequest):
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"""Test connection to a provider and return available models."""
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existing = _load_llm_settings()
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providers = json.loads(json.dumps(body.providers))
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_merge_api_keys(providers, existing.get("providers", {}))
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provider = body.provider
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try:
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models = _fetch_models(provider, providers)
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return {"ok": True, "models": models, "message": f"Connected — {len(models)} model(s) found"}
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except requests.exceptions.ConnectionError:
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return JSONResponse(
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{"ok": False, "error": "Connection refused — is the server running?"},
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status_code=502,
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)
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except requests.exceptions.Timeout:
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return JSONResponse(
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{"ok": False, "error": "Connection timed out"},
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status_code=504,
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)
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except Exception as e:
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return JSONResponse(
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{"ok": False, "error": str(e)},
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status_code=500,
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)
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@app.post("/api/settings/models")
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def api_fetch_models(body: FetchModelsRequest):
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"""Fetch available models for a provider (proxied to avoid CORS)."""
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existing = _load_llm_settings()
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providers = json.loads(json.dumps(body.providers))
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_merge_api_keys(providers, existing.get("providers", {}))
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try:
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models = _fetch_models(body.provider, providers)
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return {"ok": True, "models": models}
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except Exception as e:
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return JSONResponse({"ok": False, "error": str(e)}, status_code=500)
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def _fetch_models(provider, providers):
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"""Fetch model list from a provider. Returns list of {id, name}."""
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cfg = providers.get(provider, {})
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if provider == "ollama":
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base_url = cfg.get("base_url", "http://100.100.242.21:11434")
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resp = requests.get(f"{base_url}/api/tags", timeout=10)
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resp.raise_for_status()
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data = resp.json()
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return [{"id": m["name"], "name": m["name"]} for m in data.get("models", [])]
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|
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elif provider == "lmstudio":
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base_url = cfg.get("base_url", "http://100.100.242.21:1234")
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resp = requests.get(f"{base_url}/v1/models", timeout=10)
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resp.raise_for_status()
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data = resp.json()
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return [{"id": m["id"], "name": m["id"]} for m in data.get("data", [])]
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|
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elif provider == "openai":
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api_key = cfg.get("api_key", "")
|
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if not api_key:
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raise ValueError("OpenAI API key is required")
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resp = requests.get(
|
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"https://api.openai.com/v1/models",
|
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headers={"Authorization": f"Bearer {api_key}"},
|
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timeout=15,
|
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)
|
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resp.raise_for_status()
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data = resp.json()
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models = [m for m in data.get("data", []) if m["id"].startswith("gpt-") or "chat" in m.get("id", "")]
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models.sort(key=lambda m: m["id"])
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return [{"id": m["id"], "name": m["id"]} for m in models]
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|
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elif provider == "anthropic":
|
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api_key = cfg.get("api_key", "")
|
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if not api_key:
|
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raise ValueError("Anthropic API key is required")
|
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resp = requests.get(
|
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"https://api.anthropic.com/v1/models",
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headers={
|
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"x-api-key": api_key,
|
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"anthropic-version": "2023-06-01",
|
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},
|
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timeout=15,
|
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)
|
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resp.raise_for_status()
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data = resp.json()
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return [{"id": m["id"], "name": m.get("display_name", m["id"])} for m in data.get("data", [])]
|
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|
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elif provider == "openrouter":
|
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resp = requests.get("https://openrouter.ai/api/v1/models", timeout=15)
|
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resp.raise_for_status()
|
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data = resp.json()
|
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models = data.get("data", [])
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models.sort(key=lambda m: m.get("id", ""))
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return [{"id": m["id"], "name": m.get("name", m["id"])} for m in models[:200]]
|
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|
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else:
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raise ValueError(f"Unknown provider: {provider}")
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|
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# ── HTML Pages ────────────────────────────────────────────────────────────
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|
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# Shared CSS used by both pages
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SHARED_CSS = """
|
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*,*::before,*::after{box-sizing:border-box;margin:0;padding:0}
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:root{--bg:#0f172a;--card:#1e293b;--card-hover:#253349;--text:#e2e8f0;--text-dim:#94a3b8;--accent:#f7931a;--green:#22c55e;--red:#ef4444;--yellow:#eab308;--border:#334155;--mono:'JetBrains Mono','Fira Code','Courier New',monospace}
|
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:root{--bg:#0f172a;--card:#1e293b;--card-hover:#253349;--text:#e2e8f0;--text-dim:#94a3b8;--accent:#f7931a;--green:#22c55e;--red:#ef4444;--yellow:#eab308;--border:#334155;--mono:'JetBrains Mono','Fira Code','Courier New',monospace;--cyan:#22d3ee}
|
||||
body{font-family:'Inter',sans-serif;background:var(--bg);color:var(--text);min-height:100vh}
|
||||
.container{max-width:1400px;margin:0 auto;padding:16px}
|
||||
h1{font-size:1.5rem;font-weight:700;display:flex;align-items:center;gap:10px}
|
||||
h1 .btc{color:var(--accent);font-size:1.8rem}
|
||||
h2{font-size:1rem;font-weight:600;color:var(--text-dim);margin-bottom:12px;text-transform:uppercase;letter-spacing:.05em;font-size:.8rem}
|
||||
|
||||
.header{display:flex;justify-content:space-between;align-items:center;padding:16px 0;border-bottom:1px solid var(--border);margin-bottom:16px;flex-wrap:wrap;gap:12px}
|
||||
.nav{display:flex;gap:4px;align-items:center}
|
||||
.nav a{color:var(--text-dim);text-decoration:none;font-size:.85rem;font-weight:600;padding:6px 14px;border-radius:6px;transition:all .15s}
|
||||
.nav a:hover{color:var(--text);background:var(--card)}
|
||||
.nav a.active{color:var(--cyan);background:var(--card);border:1px solid var(--border)}
|
||||
.controls{display:flex;gap:8px;align-items:center}
|
||||
.btn{padding:8px 18px;border:none;border-radius:6px;font-family:inherit;font-weight:600;font-size:.85rem;cursor:pointer;transition:all .15s}
|
||||
.btn-start{background:var(--green);color:#000}.btn-start:hover{background:#16a34a}
|
||||
.btn-stop{background:var(--red);color:#fff}.btn-stop:hover{background:#dc2626}
|
||||
.btn-secondary{background:var(--border);color:var(--text)}.btn-secondary:hover{background:var(--card-hover)}
|
||||
.btn-accent{background:var(--accent);color:#000}.btn-accent:hover{background:#e8850f}
|
||||
.btn-cyan{background:var(--cyan);color:#000}.btn-cyan:hover{background:#06b6d4}
|
||||
.btn:disabled{opacity:.4;cursor:not-allowed}
|
||||
|
||||
.status-badge{display:inline-flex;align-items:center;gap:6px;padding:4px 12px;border-radius:20px;font-size:.8rem;font-weight:600}
|
||||
.status-idle{background:#1e3a5f;color:#60a5fa}
|
||||
.status-running{background:#1a3a2a;color:var(--green)}
|
||||
@ -143,17 +330,57 @@ h2{font-size:1rem;font-weight:600;color:var(--text-dim);margin-bottom:12px;text-
|
||||
.status-error{background:#3a1a1a;color:var(--red)}
|
||||
.pulse{width:8px;height:8px;border-radius:50%;background:currentColor;animation:pulse 1.5s infinite}
|
||||
@keyframes pulse{0%,100%{opacity:1}50%{opacity:.3}}
|
||||
|
||||
.best-score{text-align:right}
|
||||
.best-score .label{font-size:.7rem;text-transform:uppercase;letter-spacing:.1em;color:var(--text-dim)}
|
||||
.best-score .value{font-size:2.2rem;font-weight:700;color:var(--accent);font-family:var(--mono)}
|
||||
.best-score .unit{font-size:1rem;color:var(--text-dim)}
|
||||
.card{background:var(--card);border-radius:10px;padding:16px;border:1px solid var(--border)}
|
||||
.footer{text-align:center;color:var(--text-dim);font-size:.75rem;padding:20px 0;margin-top:16px;border-top:1px solid var(--border)}
|
||||
.toast{position:fixed;top:20px;right:20px;padding:12px 20px;border-radius:8px;font-size:.85rem;font-weight:600;z-index:9999;opacity:0;transform:translateY(-10px);transition:all .3s;pointer-events:none}
|
||||
.toast.show{opacity:1;transform:translateY(0)}
|
||||
.toast-success{background:var(--green);color:#000}
|
||||
.toast-error{background:var(--red);color:#fff}
|
||||
"""
|
||||
|
||||
# Shared HTML head
|
||||
SHARED_HEAD = """<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com">
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">"""
|
||||
|
||||
# Navigation bar HTML (parameterized via JS to highlight active page)
|
||||
NAV_HTML = """<div class="nav">
|
||||
<a href="/" id="nav-dashboard">Dashboard</a>
|
||||
<a href="/settings" id="nav-settings">⚙ Settings</a>
|
||||
</div>"""
|
||||
|
||||
# Toast JS helper (shared)
|
||||
TOAST_JS = """
|
||||
function showToast(msg, type) {
|
||||
let t = document.getElementById('toast');
|
||||
if (!t) {
|
||||
t = document.createElement('div');
|
||||
t.id = 'toast';
|
||||
t.className = 'toast';
|
||||
document.body.appendChild(t);
|
||||
}
|
||||
t.textContent = msg;
|
||||
t.className = 'toast toast-' + type + ' show';
|
||||
setTimeout(() => { t.classList.remove('show'); }, 3500);
|
||||
}
|
||||
"""
|
||||
|
||||
|
||||
DASHBOARD_HTML = """<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
""" + SHARED_HEAD + """
|
||||
<title>BTC Accumulation Signal Optimizer</title>
|
||||
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.4/dist/chart.umd.min.js"></script>
|
||||
<style>
|
||||
""" + SHARED_CSS + """
|
||||
.grid{display:grid;grid-template-columns:1fr 360px;gap:16px}
|
||||
@media(max-width:900px){.grid{grid-template-columns:1fr}}
|
||||
|
||||
.card{background:var(--card);border-radius:10px;padding:16px;border:1px solid var(--border)}
|
||||
|
||||
.table-wrap{overflow-x:auto;max-height:400px;overflow-y:auto}
|
||||
table{width:100%;border-collapse:collapse;font-size:.82rem}
|
||||
th{position:sticky;top:0;background:var(--card);text-align:left;padding:8px 10px;color:var(--text-dim);font-weight:600;border-bottom:2px solid var(--border);font-size:.75rem;text-transform:uppercase;letter-spacing:.04em}
|
||||
@ -161,13 +388,10 @@ td{padding:7px 10px;border-bottom:1px solid var(--border);font-family:var(--mono
|
||||
tr.best-row{background:rgba(34,197,94,.1)}
|
||||
tr.best-row td:first-child{border-left:3px solid var(--green)}
|
||||
tr:hover{background:var(--card-hover)}
|
||||
|
||||
.chart-container{position:relative;height:260px}
|
||||
|
||||
.llm-panel{max-height:500px;overflow-y:auto}
|
||||
.llm-entry{padding:10px;border-bottom:1px solid var(--border);font-size:.82rem;line-height:1.5}
|
||||
.llm-entry .iter-label{font-weight:600;color:var(--accent);font-size:.75rem;margin-bottom:4px}
|
||||
|
||||
.config-section{margin-top:16px}
|
||||
.config-toggle{cursor:pointer;user-select:none;display:flex;align-items:center;gap:6px}
|
||||
.config-toggle .arrow{transition:transform .2s;font-size:.7rem}
|
||||
@ -176,12 +400,9 @@ tr:hover{background:var(--card-hover)}
|
||||
.config-body.open{display:block}
|
||||
textarea.config-editor{width:100%;height:300px;background:var(--bg);color:var(--text);border:1px solid var(--border);border-radius:6px;padding:12px;font-family:var(--mono);font-size:.8rem;resize:vertical}
|
||||
.config-actions{display:flex;gap:8px;margin-top:8px}
|
||||
|
||||
.downloads{display:flex;gap:8px;margin-top:16px;flex-wrap:wrap}
|
||||
.downloads a{color:var(--accent);text-decoration:none;font-size:.82rem;padding:6px 12px;border:1px solid var(--accent);border-radius:6px;transition:all .15s}
|
||||
.downloads a:hover{background:var(--accent);color:#000}
|
||||
|
||||
.footer{text-align:center;color:var(--text-dim);font-size:.75rem;padding:20px 0;margin-top:16px;border-top:1px solid var(--border)}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
@ -190,14 +411,16 @@ textarea.config-editor{width:100%;height:300px;background:var(--bg);color:var(--
|
||||
<div class="header">
|
||||
<div>
|
||||
<h1><span class="btc">₿</span> Accumulation Signal Optimizer</h1>
|
||||
<div style="margin-top:8px">
|
||||
<div style="margin-top:8px;display:flex;align-items:center;gap:12px">
|
||||
<span id="statusBadge" class="status-badge status-idle"><span class="pulse"></span> Idle</span>
|
||||
""" + NAV_HTML + """
|
||||
</div>
|
||||
</div>
|
||||
<div style="display:flex;align-items:center;gap:20px;flex-wrap:wrap">
|
||||
<div class="controls">
|
||||
<button id="btnStart" class="btn btn-start" onclick="startOpt()">Start Optimization</button>
|
||||
<button id="btnStop" class="btn btn-stop" onclick="stopOpt()" disabled>Stop</button>
|
||||
<button id="btnClear" class="btn btn-secondary" onclick="clearHistory()" title="Clear all iteration history and start fresh">New Run</button>
|
||||
</div>
|
||||
<div class="best-score">
|
||||
<div class="label">Best Cost Improvement</div>
|
||||
@ -262,6 +485,8 @@ textarea.config-editor{width:100%;height:300px;background:var(--bg);color:var(--
|
||||
</div>
|
||||
|
||||
<script>
|
||||
document.getElementById('nav-dashboard').classList.add('active');
|
||||
""" + TOAST_JS + """
|
||||
let chart = null;
|
||||
let pollInterval = null;
|
||||
|
||||
@ -324,6 +549,7 @@ function updateStatusBadge(status) {
|
||||
|
||||
document.getElementById('btnStart').disabled = (state === 'running');
|
||||
document.getElementById('btnStop').disabled = (state !== 'running');
|
||||
document.getElementById('btnClear').disabled = (state === 'running');
|
||||
document.getElementById('bestScore').innerHTML = (status.best_score || 0).toFixed(1) + '<span class="unit">%</span>';
|
||||
}
|
||||
|
||||
@ -406,6 +632,18 @@ async function stopOpt() {
|
||||
setTimeout(poll, 500);
|
||||
}
|
||||
|
||||
async function clearHistory() {
|
||||
if (!confirm('Clear ALL iteration history and start a fresh run? This cannot be undone.')) return;
|
||||
try {
|
||||
const r = await fetch('/api/clear', { method: 'POST' });
|
||||
const d = await r.json();
|
||||
if (d.ok) {
|
||||
document.getElementById('llmPanel').innerHTML = '<div style="color:var(--text-dim);font-size:.82rem;padding:10px">History cleared. Ready for fresh run.</div>';
|
||||
setTimeout(poll, 500);
|
||||
} else { alert('Failed: ' + (d.detail || 'unknown')); }
|
||||
} catch(e) { alert('Failed: ' + e); }
|
||||
}
|
||||
|
||||
function toggleConfig() {
|
||||
const toggle = document.getElementById('configToggle');
|
||||
const body = document.getElementById('configBody');
|
||||
@ -432,8 +670,8 @@ async function updateConfig() {
|
||||
body: JSON.stringify({ config })
|
||||
});
|
||||
const d = await r.json();
|
||||
if (d.ok) alert('Config updated!');
|
||||
} catch(e) { alert('Invalid JSON or error: ' + e); }
|
||||
if (d.ok) showToast('Config updated!', 'success');
|
||||
} catch(e) { showToast('Invalid JSON or error: ' + e, 'error'); }
|
||||
}
|
||||
|
||||
async function resetConfig() {
|
||||
@ -449,12 +687,350 @@ pollInterval = setInterval(poll, 10000);
|
||||
</html>"""
|
||||
|
||||
|
||||
SETTINGS_HTML = """<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
""" + SHARED_HEAD + """
|
||||
<title>Settings — BTC Accumulation Signal Optimizer</title>
|
||||
<style>
|
||||
""" + SHARED_CSS + """
|
||||
.settings-grid{display:grid;grid-template-columns:320px 1fr;gap:16px;margin-top:16px}
|
||||
@media(max-width:800px){.settings-grid{grid-template-columns:1fr}}
|
||||
.provider-list{display:flex;flex-direction:column;gap:6px}
|
||||
.provider-option{display:flex;align-items:center;gap:10px;padding:12px 14px;border-radius:8px;border:1px solid var(--border);cursor:pointer;transition:all .15s;background:var(--card)}
|
||||
.provider-option:hover{border-color:var(--text-dim)}
|
||||
.provider-option.selected{border-color:var(--cyan);background:#0f2a3a}
|
||||
.provider-option input[type=radio]{accent-color:var(--cyan);width:16px;height:16px}
|
||||
.provider-option .provider-name{font-weight:600;font-size:.9rem}
|
||||
.provider-option .provider-type{font-size:.7rem;color:var(--text-dim);text-transform:uppercase;letter-spacing:.06em}
|
||||
.field-group{margin-bottom:16px}
|
||||
.field-group label{display:block;font-size:.75rem;font-weight:600;text-transform:uppercase;letter-spacing:.06em;color:var(--text-dim);margin-bottom:6px}
|
||||
.field-group input,.field-group select{width:100%;padding:10px 12px;background:var(--bg);color:var(--text);border:1px solid var(--border);border-radius:6px;font-family:var(--mono);font-size:.85rem}
|
||||
.field-group input:focus,.field-group select:focus{outline:none;border-color:var(--cyan)}
|
||||
.field-group select{appearance:none;background-image:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 12 12'%3E%3Cpath fill='%2394a3b8' d='M6 8L1 3h10z'/%3E%3C/svg%3E");background-repeat:no-repeat;background-position:right 12px center;padding-right:32px}
|
||||
.model-select-wrap{position:relative}
|
||||
.model-spinner{display:none;position:absolute;right:36px;top:50%;transform:translateY(-50%);width:16px;height:16px;border:2px solid var(--border);border-top-color:var(--cyan);border-radius:50%;animation:spin .6s linear infinite}
|
||||
.model-spinner.active{display:block}
|
||||
@keyframes spin{to{transform:translateY(-50%) rotate(360deg)}}
|
||||
.btn-row{display:flex;gap:8px;margin-top:20px;flex-wrap:wrap}
|
||||
.current-provider{font-size:.8rem;color:var(--text-dim);margin-top:4px;font-family:var(--mono)}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
|
||||
<div class="header">
|
||||
<div>
|
||||
<h1><span class="btc">₿</span> Accumulation Signal Optimizer</h1>
|
||||
<div style="margin-top:8px;display:flex;align-items:center;gap:12px">
|
||||
""" + NAV_HTML + """
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2>⚙ LLM Provider Settings</h2>
|
||||
<p class="current-provider" id="currentProvider"></p>
|
||||
|
||||
<div class="settings-grid">
|
||||
<div>
|
||||
<h2 style="margin-top:8px">Provider</h2>
|
||||
<div class="provider-list" id="providerList">
|
||||
<label class="provider-option" data-provider="ollama">
|
||||
<input type="radio" name="provider" value="ollama">
|
||||
<div><div class="provider-name">Ollama</div><div class="provider-type">Local</div></div>
|
||||
</label>
|
||||
<label class="provider-option" data-provider="lmstudio">
|
||||
<input type="radio" name="provider" value="lmstudio">
|
||||
<div><div class="provider-name">LM Studio</div><div class="provider-type">Local</div></div>
|
||||
</label>
|
||||
<label class="provider-option" data-provider="openai">
|
||||
<input type="radio" name="provider" value="openai">
|
||||
<div><div class="provider-name">OpenAI</div><div class="provider-type">Cloud</div></div>
|
||||
</label>
|
||||
<label class="provider-option" data-provider="anthropic">
|
||||
<input type="radio" name="provider" value="anthropic">
|
||||
<div><div class="provider-name">Anthropic</div><div class="provider-type">Cloud</div></div>
|
||||
</label>
|
||||
<label class="provider-option" data-provider="openrouter">
|
||||
<input type="radio" name="provider" value="openrouter">
|
||||
<div><div class="provider-name">OpenRouter</div><div class="provider-type">Cloud</div></div>
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<h2 style="margin-top:8px">Connection</h2>
|
||||
|
||||
<div class="field-group" id="fieldBaseUrl" style="display:none">
|
||||
<label>Base URL</label>
|
||||
<input type="text" id="inputBaseUrl" placeholder="http://localhost:11434">
|
||||
</div>
|
||||
|
||||
<div class="field-group" id="fieldApiKey" style="display:none">
|
||||
<label>API Key</label>
|
||||
<input type="password" id="inputApiKey" placeholder="sk-...">
|
||||
</div>
|
||||
|
||||
<div class="field-group">
|
||||
<label>Model</label>
|
||||
<div class="model-select-wrap">
|
||||
<select id="selectModel"><option value="">— select provider first —</option></select>
|
||||
<div class="model-spinner" id="modelSpinner"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="btn-row">
|
||||
<button class="btn btn-cyan" onclick="testConnection()">Test Connection</button>
|
||||
<button class="btn btn-accent" onclick="saveSettings()">Save Settings</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="footer">BTC Accumulation Signal Optimizer — VPS → Windows GPU → Mac Mini LLM</div>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
document.getElementById('nav-settings').classList.add('active');
|
||||
""" + TOAST_JS + """
|
||||
|
||||
let settings = null;
|
||||
|
||||
const PROVIDER_FIELDS = {
|
||||
ollama: { baseUrl: true, apiKey: false, defaultUrl: 'http://100.100.242.21:11434' },
|
||||
lmstudio: { baseUrl: true, apiKey: false, defaultUrl: 'http://100.100.242.21:1234' },
|
||||
openai: { baseUrl: false, apiKey: true },
|
||||
anthropic: { baseUrl: false, apiKey: true },
|
||||
openrouter: { baseUrl: false, apiKey: true },
|
||||
};
|
||||
|
||||
function getSelectedProvider() {
|
||||
const r = document.querySelector('input[name=provider]:checked');
|
||||
return r ? r.value : null;
|
||||
}
|
||||
|
||||
function buildProviders() {
|
||||
// Build providers dict from current UI state, merging with loaded settings
|
||||
const p = settings ? JSON.parse(JSON.stringify(settings.providers)) : {};
|
||||
const prov = getSelectedProvider();
|
||||
if (!prov) return p;
|
||||
|
||||
if (!p[prov]) p[prov] = {};
|
||||
const fields = PROVIDER_FIELDS[prov];
|
||||
if (fields.baseUrl) {
|
||||
p[prov].base_url = document.getElementById('inputBaseUrl').value;
|
||||
}
|
||||
if (fields.apiKey) {
|
||||
const v = document.getElementById('inputApiKey').value;
|
||||
if (v) p[prov].api_key = v;
|
||||
}
|
||||
return p;
|
||||
}
|
||||
|
||||
function selectProvider(prov) {
|
||||
// Highlight the selected option
|
||||
document.querySelectorAll('.provider-option').forEach(el => {
|
||||
el.classList.toggle('selected', el.dataset.provider === prov);
|
||||
});
|
||||
document.querySelector('input[name=provider][value="' + prov + '"]').checked = true;
|
||||
|
||||
const fields = PROVIDER_FIELDS[prov];
|
||||
const baseUrlField = document.getElementById('fieldBaseUrl');
|
||||
const apiKeyField = document.getElementById('fieldApiKey');
|
||||
|
||||
baseUrlField.style.display = fields.baseUrl ? 'block' : 'none';
|
||||
apiKeyField.style.display = fields.apiKey ? 'block' : 'none';
|
||||
|
||||
// Populate from settings
|
||||
if (settings && settings.providers[prov]) {
|
||||
const cfg = settings.providers[prov];
|
||||
if (fields.baseUrl) {
|
||||
document.getElementById('inputBaseUrl').value = cfg.base_url || fields.defaultUrl || '';
|
||||
}
|
||||
if (fields.apiKey) {
|
||||
document.getElementById('inputApiKey').value = cfg.api_key || '';
|
||||
}
|
||||
} else {
|
||||
if (fields.baseUrl) document.getElementById('inputBaseUrl').value = fields.defaultUrl || '';
|
||||
if (fields.apiKey) document.getElementById('inputApiKey').value = '';
|
||||
}
|
||||
|
||||
// Reset model dropdown
|
||||
document.getElementById('selectModel').innerHTML = '<option value="">— click Test Connection to load models —</option>';
|
||||
}
|
||||
|
||||
// Provider click handlers
|
||||
document.querySelectorAll('.provider-option').forEach(el => {
|
||||
el.addEventListener('click', () => selectProvider(el.dataset.provider));
|
||||
});
|
||||
|
||||
async function loadSettings() {
|
||||
try {
|
||||
const r = await fetch('/api/settings');
|
||||
settings = await r.json();
|
||||
document.getElementById('currentProvider').textContent =
|
||||
'Current: ' + settings.provider + ' / ' + settings.model;
|
||||
selectProvider(settings.provider);
|
||||
} catch(e) {
|
||||
console.error('Failed to load settings:', e);
|
||||
}
|
||||
}
|
||||
|
||||
async function fetchModels(providerOverride) {
|
||||
const prov = providerOverride || getSelectedProvider();
|
||||
if (!prov) return;
|
||||
|
||||
const spinner = document.getElementById('modelSpinner');
|
||||
const sel = document.getElementById('selectModel');
|
||||
spinner.classList.add('active');
|
||||
sel.innerHTML = '<option value="">Loading models...</option>';
|
||||
|
||||
try {
|
||||
const r = await fetch('/api/settings/models', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ provider: prov, providers: buildProviders() })
|
||||
});
|
||||
const data = await r.json();
|
||||
if (!data.ok) {
|
||||
sel.innerHTML = '<option value="">Error: ' + (data.error||'unknown') + '</option>';
|
||||
return;
|
||||
}
|
||||
sel.innerHTML = '';
|
||||
if (!data.models.length) {
|
||||
sel.innerHTML = '<option value="">No models found</option>';
|
||||
return;
|
||||
}
|
||||
for (const m of data.models) {
|
||||
const opt = document.createElement('option');
|
||||
opt.value = m.id;
|
||||
opt.textContent = m.name !== m.id ? m.name + ' (' + m.id + ')' : m.id;
|
||||
sel.appendChild(opt);
|
||||
}
|
||||
// Select current model if it's in the list
|
||||
if (settings && settings.model) {
|
||||
sel.value = settings.model;
|
||||
}
|
||||
} catch(e) {
|
||||
sel.innerHTML = '<option value="">Fetch failed: ' + e + '</option>';
|
||||
} finally {
|
||||
spinner.classList.remove('active');
|
||||
}
|
||||
}
|
||||
|
||||
async function testConnection() {
|
||||
const prov = getSelectedProvider();
|
||||
if (!prov) { showToast('Select a provider first', 'error'); return; }
|
||||
|
||||
showToast('Testing connection...', 'success');
|
||||
try {
|
||||
const r = await fetch('/api/settings/test', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ provider: prov, providers: buildProviders() })
|
||||
});
|
||||
const data = await r.json();
|
||||
if (data.ok) {
|
||||
showToast(data.message, 'success');
|
||||
// Populate model dropdown
|
||||
const sel = document.getElementById('selectModel');
|
||||
sel.innerHTML = '';
|
||||
for (const m of data.models) {
|
||||
const opt = document.createElement('option');
|
||||
opt.value = m.id;
|
||||
opt.textContent = m.name !== m.id ? m.name + ' (' + m.id + ')' : m.id;
|
||||
sel.appendChild(opt);
|
||||
}
|
||||
if (settings && settings.model) sel.value = settings.model;
|
||||
} else {
|
||||
showToast(data.error || 'Connection failed', 'error');
|
||||
}
|
||||
} catch(e) {
|
||||
showToast('Connection failed: ' + e, 'error');
|
||||
}
|
||||
}
|
||||
|
||||
async function saveSettings() {
|
||||
const prov = getSelectedProvider();
|
||||
if (!prov) { showToast('Select a provider first', 'error'); return; }
|
||||
|
||||
const model = document.getElementById('selectModel').value;
|
||||
if (!model) { showToast('Select a model first (use Test Connection to load models)', 'error'); return; }
|
||||
|
||||
try {
|
||||
const r = await fetch('/api/settings', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
provider: prov,
|
||||
model: model,
|
||||
providers: buildProviders()
|
||||
})
|
||||
});
|
||||
const data = await r.json();
|
||||
if (data.ok) {
|
||||
showToast('Settings saved!', 'success');
|
||||
document.getElementById('currentProvider').textContent = 'Current: ' + prov + ' / ' + model;
|
||||
// Reload settings to get masked keys
|
||||
loadSettings();
|
||||
} else {
|
||||
showToast(data.error || 'Save failed', 'error');
|
||||
}
|
||||
} catch(e) {
|
||||
showToast('Save failed: ' + e, 'error');
|
||||
}
|
||||
}
|
||||
|
||||
loadSettings();
|
||||
</script>
|
||||
</body>
|
||||
</html>"""
|
||||
|
||||
|
||||
@app.get("/", response_class=HTMLResponse)
|
||||
def dashboard():
|
||||
return DASHBOARD_HTML
|
||||
|
||||
|
||||
@app.get("/settings", response_class=HTMLResponse)
|
||||
def settings_page():
|
||||
return SETTINGS_HTML
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
uvicorn.run(app, host="0.0.0.0", port=3088)
|
||||
|
||||
|
||||
@app.post("/api/clear")
|
||||
def api_clear():
|
||||
"""Clear iteration history for a fresh run."""
|
||||
import glob
|
||||
results_dir = os.path.join(BASE_DIR, "results")
|
||||
|
||||
# Clear iterations log
|
||||
log_path = os.path.join(results_dir, "iterations.jsonl")
|
||||
if os.path.exists(log_path):
|
||||
os.remove(log_path)
|
||||
|
||||
# Clear individual result files
|
||||
for f in glob.glob(os.path.join(results_dir, "results_iter_*.json")):
|
||||
os.remove(f)
|
||||
|
||||
# Reset best config to initial
|
||||
initial = os.path.join(BASE_DIR, "config", "initial_config.json")
|
||||
best = os.path.join(BASE_DIR, "config", "best_config.json")
|
||||
current = os.path.join(BASE_DIR, "config", "current_config.json")
|
||||
if os.path.exists(initial):
|
||||
import shutil
|
||||
shutil.copy(initial, best)
|
||||
shutil.copy(initial, current)
|
||||
|
||||
# Clear in-memory status
|
||||
orchestrator._status["llm_suggestions"] = []
|
||||
orchestrator._status["best_score"] = 0.0
|
||||
orchestrator._status["iteration"] = 0
|
||||
|
||||
return {"ok": True, "message": "History cleared. Ready for fresh run."}
|
||||
|
||||
Binary file not shown.
@ -1,15 +1,36 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
LLM Accumulation Signal Analyzer -- Calls Ollama on Mac Mini to analyze results
|
||||
LLM Accumulation Signal Analyzer -- Calls LLM to analyze results
|
||||
and suggest config modifications for the next iteration.
|
||||
Supports multiple providers: Ollama, LM Studio, OpenAI, Anthropic, OpenRouter.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://100.100.242.21:11434"
|
||||
MODEL = "qwen3.5:27b"
|
||||
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
LLM_SETTINGS_PATH = os.path.join(BASE_DIR, "config", "llm_settings.json")
|
||||
|
||||
# Fallback defaults
|
||||
DEFAULT_OLLAMA_URL = "http://100.100.242.21:11434"
|
||||
DEFAULT_MODEL = "qwen3.5:27b"
|
||||
|
||||
|
||||
def load_llm_settings():
|
||||
"""Load LLM settings from config file, with fallback to defaults."""
|
||||
if os.path.exists(LLM_SETTINGS_PATH):
|
||||
with open(LLM_SETTINGS_PATH) as f:
|
||||
return json.load(f)
|
||||
return {
|
||||
"provider": "ollama",
|
||||
"model": DEFAULT_MODEL,
|
||||
"providers": {
|
||||
"ollama": {"base_url": DEFAULT_OLLAMA_URL},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
SYSTEM_PROMPT = """You are a quantitative analyst optimizing a BTC ACCUMULATION SIGNAL model. The goal is NOT day-trading -- it is finding statistically optimal times to BUY BTC for long-term holding.
|
||||
|
||||
@ -120,6 +141,119 @@ You MUST respond with ONLY a JSON object (no markdown, no explanation outside th
|
||||
The "config" field must contain the COMPLETE config so it can be used directly."""
|
||||
|
||||
|
||||
def _call_ollama(settings, messages):
|
||||
"""Call Ollama API."""
|
||||
provider_cfg = settings.get("providers", {}).get("ollama", {})
|
||||
base_url = provider_cfg.get("base_url", DEFAULT_OLLAMA_URL)
|
||||
model = settings.get("model", DEFAULT_MODEL)
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"stream": False,
|
||||
"think": False,
|
||||
"options": {"temperature": 0.7, "num_predict": 4096},
|
||||
}
|
||||
print(f" Calling LLM ({model} via Ollama at {base_url})...")
|
||||
resp = requests.post(f"{base_url}/api/chat", json=payload, timeout=600)
|
||||
resp.raise_for_status()
|
||||
return resp.json()["message"]["content"]
|
||||
|
||||
|
||||
def _call_openai_compatible(settings, messages, provider_name):
|
||||
"""Call OpenAI-compatible API (LM Studio, OpenAI, OpenRouter)."""
|
||||
provider_cfg = settings.get("providers", {}).get(provider_name, {})
|
||||
model = settings.get("model", "")
|
||||
|
||||
if provider_name == "lmstudio":
|
||||
base_url = provider_cfg.get("base_url", "http://100.100.242.21:1234")
|
||||
url = f"{base_url}/v1/chat/completions"
|
||||
headers = {"Content-Type": "application/json"}
|
||||
elif provider_name == "openai":
|
||||
url = "https://api.openai.com/v1/chat/completions"
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {provider_cfg.get('api_key', '')}",
|
||||
}
|
||||
elif provider_name == "openrouter":
|
||||
url = "https://openrouter.ai/api/v1/chat/completions"
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {provider_cfg.get('api_key', '')}",
|
||||
}
|
||||
else:
|
||||
raise ValueError(f"Unknown OpenAI-compatible provider: {provider_name}")
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 4096,
|
||||
}
|
||||
print(f" Calling LLM ({model} via {provider_name})...")
|
||||
resp = requests.post(url, json=payload, headers=headers, timeout=600)
|
||||
resp.raise_for_status()
|
||||
return resp.json()["choices"][0]["message"]["content"]
|
||||
|
||||
|
||||
def _call_anthropic(settings, messages):
|
||||
"""Call Anthropic Messages API."""
|
||||
provider_cfg = settings.get("providers", {}).get("anthropic", {})
|
||||
model = settings.get("model", "claude-sonnet-4-20250514")
|
||||
api_key = provider_cfg.get("api_key", "")
|
||||
|
||||
# Anthropic uses system as a top-level param, not in messages
|
||||
system_msg = ""
|
||||
api_messages = []
|
||||
for m in messages:
|
||||
if m["role"] == "system":
|
||||
system_msg = m["content"]
|
||||
else:
|
||||
api_messages.append(m)
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"max_tokens": 4096,
|
||||
"messages": api_messages,
|
||||
}
|
||||
if system_msg:
|
||||
payload["system"] = system_msg
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"x-api-key": api_key,
|
||||
"anthropic-version": "2023-06-01",
|
||||
}
|
||||
print(f" Calling LLM ({model} via Anthropic)...")
|
||||
resp = requests.post(
|
||||
"https://api.anthropic.com/v1/messages",
|
||||
json=payload,
|
||||
headers=headers,
|
||||
timeout=600,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
# Extract text from content blocks
|
||||
return "".join(
|
||||
block["text"] for block in data.get("content", []) if block.get("type") == "text"
|
||||
)
|
||||
|
||||
|
||||
def call_llm(messages):
|
||||
"""Route LLM call to the configured provider."""
|
||||
settings = load_llm_settings()
|
||||
provider = settings.get("provider", "ollama")
|
||||
|
||||
if provider == "ollama":
|
||||
return _call_ollama(settings, messages)
|
||||
elif provider in ("lmstudio", "openai", "openrouter"):
|
||||
return _call_openai_compatible(settings, messages, provider)
|
||||
elif provider == "anthropic":
|
||||
return _call_anthropic(settings, messages)
|
||||
else:
|
||||
raise ValueError(f"Unknown LLM provider: {provider}")
|
||||
|
||||
|
||||
def analyze_and_suggest(current_config, results, iteration_history=None):
|
||||
"""
|
||||
Send current results to LLM and get suggested config modifications.
|
||||
@ -161,24 +295,12 @@ def analyze_and_suggest(current_config, results, iteration_history=None):
|
||||
{history_text}
|
||||
Analyze these results and suggest 1-3 specific modifications to the config. Return ONLY valid JSON."""
|
||||
|
||||
payload = {
|
||||
"model": MODEL,
|
||||
"messages": [
|
||||
messages = [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_prompt},
|
||||
],
|
||||
"stream": False,
|
||||
"think": False,
|
||||
"options": {
|
||||
"temperature": 0.7,
|
||||
"num_predict": 4096,
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
print(f" Calling LLM ({MODEL} on Mac Mini)...")
|
||||
resp = requests.post(f"{OLLAMA_URL}/api/chat", json=payload, timeout=600)
|
||||
resp.raise_for_status()
|
||||
content = resp.json()["message"]["content"]
|
||||
content = call_llm(messages)
|
||||
|
||||
# Strip thinking tags if present
|
||||
content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
|
||||
@ -207,7 +329,14 @@ Analyze these results and suggest 1-3 specific modifications to the config. Retu
|
||||
changes = parsed.get("changes", [])
|
||||
new_config = parsed.get("config", current_config)
|
||||
|
||||
required_keys = ["model_type", "features", "target", "hyperparameters", "strategy", "training"]
|
||||
required_keys = [
|
||||
"model_type",
|
||||
"features",
|
||||
"target",
|
||||
"hyperparameters",
|
||||
"strategy",
|
||||
"training",
|
||||
]
|
||||
for key in required_keys:
|
||||
if key not in new_config:
|
||||
new_config[key] = current_config[key]
|
||||
@ -218,6 +347,7 @@ Analyze these results and suggest 1-3 specific modifications to the config. Retu
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
config_path = sys.argv[1] if len(sys.argv) > 1 else "config/initial_config.json"
|
||||
with open(config_path) as f:
|
||||
config = json.load(f)
|
||||
@ -234,8 +364,18 @@ if __name__ == "__main__":
|
||||
"avg_score_at_actual_bottoms": 68.5,
|
||||
"avg_score_at_actual_tops": 35.2,
|
||||
"per_window_cost_improvement": [7.1, 9.3, 8.8, 10.2, 7.0],
|
||||
"score_distribution": {"0-20": 80, "20-40": 150, "40-60": 200, "60-80": 130, "80-100": 40},
|
||||
"feature_importances": {"dist_from_ath_pct": 0.18, "RSI_14": 0.12, "price_percentile_365": 0.10},
|
||||
"score_distribution": {
|
||||
"0-20": 80,
|
||||
"20-40": 150,
|
||||
"40-60": 200,
|
||||
"60-80": 130,
|
||||
"80-100": 40,
|
||||
},
|
||||
"feature_importances": {
|
||||
"dist_from_ath_pct": 0.18,
|
||||
"RSI_14": 0.12,
|
||||
"price_percentile_365": 0.10,
|
||||
},
|
||||
}
|
||||
|
||||
new_config, reasoning = analyze_and_suggest(config, dummy_results)
|
||||
|
||||
@ -461,9 +461,22 @@ def run_optimization_loop(callback=None, config_override=None):
|
||||
"iteration": iteration,
|
||||
"reasoning": reasoning,
|
||||
})
|
||||
# Also persist LLM suggestion to iteration log
|
||||
iter_data["llm_reasoning"] = reasoning
|
||||
iter_data["llm_applied"] = True
|
||||
config = new_config
|
||||
except Exception:
|
||||
import random
|
||||
except Exception as e:
|
||||
import random, traceback
|
||||
err_msg = f"LLM call failed: {type(e).__name__}: {e}"
|
||||
print(f" WARNING: {err_msg}")
|
||||
traceback.print_exc()
|
||||
with _status_lock:
|
||||
_status["llm_suggestions"].append({
|
||||
"iteration": iteration,
|
||||
"reasoning": f"ERROR: {err_msg} — using random perturbation",
|
||||
})
|
||||
iter_data["llm_reasoning"] = err_msg
|
||||
iter_data["llm_applied"] = False
|
||||
hp = config.get("hyperparameters", {})
|
||||
hp["learning_rate"] = hp.get("learning_rate", 0.01) * random.uniform(0.8, 1.2)
|
||||
hp["max_depth"] = max(3, min(10, hp.get("max_depth", 5) + random.choice([-1, 0, 1])))
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user