refine design

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

@@ -12,9 +12,9 @@ The system follows a modular architecture with clear separation of concerns:
 
 ```mermaid
 graph TD
-    A[Data Provider] --> B[Data Processor]
+    A[Data Provider] --> B[Data Processor] (calculates pivot points)
     B --> C[CNN Model]
-    B --> D[RL Model]
+    B --> D[RL(DQN) Model]
     C --> E[Orchestrator]
     D --> E
     E --> F[Trading Executor]
@@ -67,6 +67,13 @@ Based on the existing implementation in `core/data_provider.py`, we'll enhance i
 - Enhance real-time data streaming
 - Implement better error handling and fallback mechanisms
 
+### BASE FOR ALL MODELS ###
+- ***INPUTS***: COB+OHCLV data frame as described: 
+   - OHCLV: 300 frames of (1s, 1m, 1h, 1d) ETH + 300s of 1s BTC
+   - COB: for each 1s OHCLV we have  +- 20 buckets of COB ammounts in USD
+   - 1,5,15 and 60s MA of the COB imbalance counting +- 5 COB buckets
+- ***OUTPUTS***: suggested trade action (BUY/SELL)
+
 ### 2. CNN Model
 
 The CNN Model is responsible for analyzing patterns in market data and predicting pivot points across multiple timeframes.
@@ -76,6 +83,8 @@ The CNN Model is responsible for analyzing patterns in market data and predictin
 - **CNNModel**: Main class for the CNN model.
 - **PivotPointPredictor**: Interface for predicting pivot points.
 - **CNNTrainer**: Class for training the CNN model.
+- ***INPUTS***: COB+OHCLV+Old Pivots (5 levels of pivots)
+- ***OUTPUTS***: next pivot point for each level as price-time vector. (can be plotted as trend line) + suggested trade action (BUY/SELL)
 
 #### Implementation Details
 
@@ -111,13 +120,13 @@ The RL Model is responsible for learning optimal trading strategies based on mar
 #### Implementation Details
 
 The RL Model will:
-- Accept market data, CNN predictions, and CNN hidden layer states as input
+- Accept market data, CNN model predictions (output), and CNN hidden layer states as input
 - Output trading action recommendations (buy/sell)
 - Provide confidence scores for each action
 - Learn from past experiences to adapt to the current market environment
 
 Architecture:
-- State representation: Market data, CNN predictions, CNN hidden layer states
+- State representation: Market data, CNN model predictions (output), CNN hidden layer states
 - Action space: Buy, Sell
 - Reward function: PnL, risk-adjusted returns
 - Policy network: Deep neural network
@@ -231,6 +240,11 @@ The Orchestrator coordinates training for all models by managing the prediction-
 - Monitors model performance degradation and triggers retraining
 - Maintains performance metrics for model comparison and selection
 
+**Training progress and checkpoints persistance**
+- it uses the checkpoint manager to store check points of each model over time as training progresses and we have improvements 
+- checkpoint manager has capability to ensure only top 5 to 10 best checkpoints are stored for each model deleting the least performant ones. it stores metadata along the CPs to decide the performance
+- we automatically load the best CP at startup if we have stored ones
+
 ##### 5. Decision Making and Trading Actions
 
 Beyond coordination, the Orchestrator makes final trading decisions: