pivot: rewrite as BTC accumulation signal optimizer

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>

config/initial_config.json

@@ -1,62 +1,53 @@
 {
   "model_type": "hybrid",
   "features": {
-    "technical_indicators": ["RSI_14", "RSI_7", "MACD_line", "MACD_signal", "MACD_hist", "BB_upper", "BB_lower", "BB_width", "ATR_14", "SMA_20", "SMA_50", "EMA_10", "EMA_20", "OBV", "stoch_k", "stoch_d", "williams_r", "CCI_20", "ROC_10"],
-    "lookback_periods": [3, 5, 10, 20],
-    "use_volume_features": true,
-    "use_volatility_features": true,
-    "use_candle_patterns": false,
-    "use_lag_features": true,
-    "lag_periods": [1, 2, 3, 5],
+    "use_price_position": true,
+    "use_momentum": true,
+    "use_volatility": true,
+    "use_volume": true,
+    "use_cycle": true,
     "use_pca": true,
     "pca_variance": 0.95,
     "use_scaler": true
   },
   "target": {
-    "type": "classification",
-    "direction": "both",
-    "horizon_candles": 8,
-    "threshold_pct": 1.5
+    "type": "regression",
+    "forward_periods_1h": [168, 720, 2160],
+    "forward_periods_4h": [42, 180, 540],
+    "weights": [0.2, 0.3, 0.5],
+    "score_range": [0, 100]
   },
   "hyperparameters": {
-    "learning_rate": 0.001,
+    "learning_rate": 0.01,
     "max_depth": 5,
-    "n_estimators": 300,
+    "n_estimators": 500,
     "subsample": 0.8,
     "colsample_bytree": 0.8,
-    "min_child_weight": 5,
+    "min_child_weight": 10,
     "gamma": 0.3,
-    "reg_alpha": 0.1,
-    "reg_lambda": 2.0,
+    "reg_alpha": 0.5,
+    "reg_lambda": 3.0,
     "lstm_hidden_size": 128,
     "lstm_num_layers": 2,
     "lstm_dropout": 0.3,
     "lstm_epochs": 100,
     "lstm_batch_size": 64,
-    "lstm_sequence_length": 20,
+    "lstm_sequence_length": 30,
     "lstm_patience": 10
   },
   "strategy": {
-    "entry_threshold": 0.60,
-    "exit_type": "trailing_stop",
-    "stop_loss_pct": 2.0,
-    "take_profit_pct": 4.0,
-    "trailing_stop_pct": 1.5,
-    "position_sizing": "confidence_scaled",
-    "max_position_pct": 100,
-    "min_confidence_to_trade": 0.55,
-    "dynamic_sl_tp": true,
-    "atr_sl_multiplier": 1.5,
-    "atr_tp_multiplier": 3.0
+    "strong_buy_threshold": 80,
+    "good_buy_threshold": 70,
+    "poor_threshold": 30
   },
   "training": {
+    "rolling_window": true,
+    "rolling_train_size": 2500,
+    "rolling_test_size": 300,
     "walk_forward_windows": 5,
     "train_pct": 0.7,
     "validation_pct": 0.15,
-    "test_pct": 0.15,
-    "rolling_window": true,
-    "rolling_train_size": 2000,
-    "rolling_test_size": 200
+    "test_pct": 0.15
   },
   "timeframe": "4h"
-}
+}