updated project adr

Author: Stevic-Nenad

docs/adr_project_foundation.md

@@ -15,107 +15,191 @@
     - All trading pairs available on Kraken
     - Focus on USDT pairs (e.g., BTC/USDT, ETH/USDT)
     - Include all market cap sizes for comprehensive testing
+    - Filter out stablecoin pairs (USDT, USDC, DAI, etc.)
 
 - [x] Choose data provider/API
     - Selected Kraken API
     - Using REST API for historical data
     - Using WebSocket for real-time data
 
 - [x] Define data structure
-    -  Using standard OHLCV format:
+    - Using standard OHLCV format:
         - timestamp: int (Unix timestamp)
         - open: float
         - high: float
         - low: float
         - close: float
         - volume: float
-    - Additional data:
-        - trade_count: int
-        - vwap: float (Volume Weighted Average Price)
-- [ ] Implement data fetching
-- [ ] Setup data storage
+    - Additional data for real-time updates:
+        - price: Decimal (current price)
+        - volume: Decimal (24h volume)
+        - timestamp: int (millisecond timestamp)
+
+- [x] Implement data fetching
+    - Implemented WebSocket connection for real-time data
+    - Implemented REST API calls for historical OHLCV data
+    - Created asset mapping for Kraken's symbol format (BTC→XBT)
+
+- [x] Setup data storage
+    - In-memory storage for real-time price data
+    - Rolling window storage for technical analysis (50 data points)
+    - Trade execution history for performance analysis
 
 ## 3. Trading Bot Architecture
+### ADR 2: Trading Modes
+
+- [x] Define trading modes
+    - Single-coin mode: Focus on a single trading pair (BTC/USDT)
+    - Multi-coin mode: Dynamically switch between coins based on opportunity scores
+
+- [x] Design allocation strategy
+    - Single-coin: Allocate entire portfolio to one symbol
+    - Multi-coin: 80% allocation to best opportunity, able to switch between coins
+
 ### ADR 3: Core Components
-- [ ] Define bot architecture
-- [ ] Implement basic trading logic
-- [ ] Setup risk management
-- [ ] Create position sizing logic
-- [ ] Implement order execution
-
-### ADR 4: Machine Learning Implementation
-- [ ] Choose TensorFlow models
-- [ ] Define feature engineering
-- [ ] Create training pipeline
-- [ ] Implement prediction logic
-- [ ] Setup model evaluation
-
-## 4. Testing Strategy
+
+- [x] Define bot architecture
+    - Modular design with separate client and trading logic
+    - Event-driven price updates via callbacks
+    - Asynchronous execution using asyncio
+
+- [x] Implement basic trading logic
+    - Technical analysis based signal generation
+    - RSI, moving averages, and volume analysis
+    - Opportunity scoring system (0-100) for coin selection
+
+- [x] Setup risk management
+    - Position size limits (95% of allocated balance)
+    - Minimum order size checks
+    - Error handling in trading and analysis loops
+
+- [x] Create position sizing logic
+    - Dynamic sizing based on allocated balance
+    - Support for partial position exits
+    - Automatic balance reallocation on coin switching
+
+- [x] Implement order execution
+    - Buy execution with price validation
+    - Sell execution with P&L tracking
+    - Order execution history tracking
+
+## 4. Technical Analysis Implementation
+### ADR 4: Analysis Strategy
+
+- [x] Choose technical indicators
+    - RSI (Relative Strength Index) for overbought/oversold conditions
+    - Short and long moving averages for trend detection
+    - Volume analysis for momentum confirmation
+
+- [x] Implement signal generation
+    - Buy signals: RSI < 35 or (short MA > long MA and RSI < 65)
+    - Sell signals: RSI > 65 or (short MA < long MA and RSI > 35)
+    - Neutral signals when conditions aren't met
+
+- [x] Create opportunity scoring system
+    - Base score of 50 (neutral)
+    - Adjust based on recent price changes
+    - Adjust based on RSI values
+    - Adjust based on MA trends
+    - Adjust based on volume spikes
+    - Clamped to 0-100 range
+
+## 5. Testing Strategy
 ### ADR 5: Testing Framework
-- [ ] Unit tests
+
+- [x] Unit tests
+    - Created test fixtures for client and bot
+    - Test individual methods in isolation
+    - Mocking of external dependencies
+
 - [ ] Integration tests
+    - Test interaction between client and trading bot
+    - Validate end-to-end functionality
+
 - [ ] Backtesting framework
-- [ ] Performance metrics
+    - Historical data processing
+    - Strategy performance evaluation
+
+- [x] Performance metrics
+    - Implemented tracking of:
+      - Total trades
+      - Win rate
+      - Profit/loss tracking
+      - Position P&L calculations
+      - Portfolio value calculation
+
 - [ ] Paper trading tests
+    - Live testing without real money
+
+## 6. User Interface
+### ADR 6: Command Line Interface
 
-## 5. Documentation
-- [ ] Setup documentation structure
-- [ ] Write technical documentation
-- [ ] Create API documentation
-- [ ] Document trading strategies
-- [ ] Create user guide
-
-## 6. Performance Monitoring
-### ADR 6: Monitoring System
-- [ ] Define KPIs
-- [ ] Setup logging
-- [ ] Implement monitoring dashboard
-- [ ] Create alert system
-- [ ] Performance reporting
-
-## 7. Project Report Components
-- [ ] Abstract
-- [ ] Introduction
-  - [ ] Problem statement
-  - [ ] Project goals
-- [ ] Main section
-  - [ ] Methodology
-  - [ ] Implementation
-  - [ ] Results
-- [ ] Discussion
-- [ ] Conclusion and outlook
-- [ ] Create presentation
-- [ ] Prepare handout
+- [x] User configuration
+    - Initial balance input
+    - Trading mode selection
+    - Symbol selection (automatic or manual)
+
+- [x] Status display
+    - Real-time portfolio status updates
+    - Position tracking with P&L
+    - Trading opportunity scores
+    - Session duration tracking
+
+- [x] Performance reporting
+    - Session summary on exit
+    - Comprehensive P&L breakdown
+    - Win rate calculation
+    - Remaining position reporting
+
+## 7. Project Structure
+- [x] Implemented code organization
+    - `traid/trading_bot.py`: Core trading bot implementation
+    - `traid/kraken_client.py`: Kraken API client implementation
+    - `traid/main.py`: User interface and entry point
+    - `tests/`: Test directory for pytest test files
 
 ## Directory Structure
 ```
 TRAID/
-├── docs/
-├── src/
-│   ├── data/         # Data handling
-│   ├── models/       # ML models
-│   ├── trading/      # Trading logic
-│   └── utils/        # Utilities
+├── traid/
+│   ├── __init__.py
+│   ├── trading_bot.py    # Trading bot implementation
+│   ├── kraken_client.py  # Kraken API client
+│   └── main.py           # Entry point and CLI
 ├── tests/
-│   ├── unit/
-│   └── integration/
-├── notebooks/        # Jupyter notebooks for analysis
-├── configs/          # Configuration files
-└── results/          # Trading results and analysis
+│   ├── __init__.py
+│   ├── test_trading_bot.py
+│   └── test_kraken_client.py
+├── requirements.txt
+└── README.md
 ```
 
-## Important Deadlines
-- [ ] Project proposal submission
-- [ ] Midterm review
-- [ ] Testing phase completion
-- [ ] Documentation completion
-- [ ] Final presentation
-- [ ] Project submission
-
-## Notes
-- Remember to update this checklist regularly
-- Each ADR should be documented separately with:
-  - Context
-  - Decision
-  - Consequences
-  - Status
+## Implementation Details
+
+### main.py:
+- Command-line interface for user configuration
+- Fetches available trading pairs from Kraken
+- Handles startup, execution, and graceful shutdown
+- Error handling and user interruption
+
+### trading_bot.py:
+- Core trading logic implementation
+- Technical analysis and signal generation
+- Trade execution and position management
+- Portfolio tracking and performance metrics
+- Asynchronous operation with two main loops:
+  - Analysis loop (5-minute interval)
+  - Trading loop (1-second interval)
+
+### kraken_client.py:
+- WebSocket connection for real-time data
+- REST API for historical data
+- Symbol formatting and mapping
+- Message handling and callback system
+
+## Future Enhancements
+- [ ] Implement machine learning models for improved signal generation
+- [ ] Add database storage for historical trades and performance
+- [ ] Create web dashboard for monitoring
+- [ ] Implement additional trading strategies
+- [ ] Add support for more exchanges