| Stage 2 Simple Universal LSTM Model Architecture | |
| ============================================= | |
| Model Type: Sequential LSTM | |
| Training Date: 20250705_170829 | |
| Training Stocks: 20 stocks | |
| Features: 6 features | |
| Target: Close | |
| Architecture: | |
| 1. LSTM Layer 1: 50 units, return_sequences=True | |
| 2. Dropout: 0.2 | |
| 3. LSTM Layer 2: 50 units, return_sequences=False | |
| 4. Dropout: 0.2 | |
| 5. Dense Output: 1 unit | |
| Training Parameters: | |
| - Batch Size: 16 | |
| - Learning Rate: 0.001 | |
| - Epochs Trained: 88 | |
| - Early Stopping Patience: 12 | |
| Performance Metrics: | |
| - RMSE: $26.85 | |
| - MAE: $12.60 | |
| - R�: 0.9903 | |
| - MAPE: 2.68% | |
| Features Used: | |
| - Open | |
| - High | |
| - Low | |
| - Close | |
| - Volume | |
| - sentiment_10d_avg | |
| Stocks Trained On: | |
| - AAPL | |
| - AMZN | |
| - AVGO | |
| - BRK.B | |
| - COST | |
| - GOOG | |
| - JNJ | |
| - JPM | |
| - LLY | |
| - MA | |
| - META | |
| - MSFT | |
| - NFLX | |
| - NVDA | |
| - ORCL | |
| - PG | |
| - TSLA | |
| - V | |
| - WMT | |
| - XOM | |
| Usage Instructions: | |
| 1. Load model: model = tf.keras.models.load_model('../models\stage2_universal_lstm_20250705_170829.keras') | |
| 2. Load scalers: scalers = joblib.load('../models\stage2_scalers_20250705_170829.pkl') | |
| 3. Preprocess data using feature_scaler and target_scaler | |
| 4. Make predictions on sequences of shape (batch_size, 60, 6) | |
| 5. Inverse transform predictions using target_scaler | |