replay system

.kiro/specs/multi-modal-trading-system/tasks.md

@@ -48,11 +48,17 @@
   - Add confidence score calculation for predictions
   - _Requirements: 2.2, 2.3, 2.6_
 
-- [ ] 2.2. Implement CNN training pipeline
-  - Create a CNNTrainer class
+- [x] 2.2. Implement CNN training pipeline with comprehensive data storage
+
+
+
+  - Create a CNNTrainer class with training data persistence
   - Implement methods for training the model on historical data
   - Add mechanisms to trigger training when new pivot points are detected
-  - _Requirements: 2.4, 2.5, 5.2, 5.3_
+  - Store all training inputs, outputs, gradients, and loss values for replay
+  - Implement training episode storage with profitability metrics
+  - Add capability to replay and retrain on most profitable pivot predictions
+  - _Requirements: 2.4, 2.5, 5.2, 5.3, 5.7_
 
 - [ ] 2.3. Implement CNN inference pipeline
   - Create methods for real-time inference
@@ -78,13 +84,20 @@
   - Create a TradingActionGenerator class
   - Implement methods to generate buy/sell recommendations
   - Add confidence score calculation for actions
+
+
+
   - _Requirements: 3.2, 3.7_
 
-- [ ] 3.2. Implement RL training pipeline
-  - Create an RLTrainer class
+- [ ] 3.2. Implement RL training pipeline with comprehensive experience storage
+  - Create an RLTrainer class with advanced experience replay
   - Implement methods for training the model on historical data
-  - Add experience replay for improved sample efficiency
-  - _Requirements: 3.3, 3.5, 5.4_
+  - Store all training episodes with state-action-reward-next_state tuples
+  - Implement profitability-based experience prioritization
+  - Add capability to replay and retrain on most profitable trading sequences
+  - Store gradient information and model checkpoints for each profitable episode
+  - Implement experience buffer with profit-weighted sampling
+  - _Requirements: 3.3, 3.5, 5.4, 5.7_
 
 - [ ] 3.3. Implement RL inference pipeline
   - Create methods for real-time inference