da7fbd1b49
- Added accountType='UNIFIED' to wallet balance requests - Updated balance retrieval to support totalWalletBalance for UTA - Replaced rich.Live with standard logging for better Docker compatibility - Added PYTHONUNBUFFERED=1 to ensure real-time logs in containers - Updated docker-compose to point to NAS database (20.20.20.20) - Created GEMINI.md with comprehensive project context
66 lines
3.3 KiB
Markdown
66 lines
3.3 KiB
Markdown
# Gemini Context: BTC Trading Dashboard
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This project is a Bitcoin trading platform and automated bot system. It features a FastAPI backend, a real-time data collector, a PostgreSQL (TimescaleDB) database, and an interactive HTML/JS dashboard for technical analysis and strategy visualization.
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## Project Overview
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- **Purpose**: Real-time BTC data collection, technical indicator computation, and trading strategy execution/backtesting.
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- **Core Technologies**:
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- **Backend**: Python 3.9+ with FastAPI.
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- **Frontend**: Vanilla HTML/JS with `lightweight-charts`.
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- **Database**: PostgreSQL with TimescaleDB extension for time-series optimization.
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- **Infrastructure**: Docker & Docker Compose.
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- **Architecture**:
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- `data_collector`: Handles WebSocket data ingestion and custom timeframe generation.
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- `api_server`: Serves the dashboard and REST API for candle/indicator data.
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- `indicator_engine`: Computes SMA, EMA, and specialized HTS indicators.
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- `strategies`: Contains trading logic (e.g., Ping Pong bot, HTS strategy).
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## Building and Running
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### Local Setup
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1. **Environment**:
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```bash
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python -m venv venv
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source venv/bin/activate # venv\Scripts\activate on Windows
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pip install -r requirements.txt
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```
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2. **Configuration**: Create a `.env` file based on the project's requirements (see `README.md`).
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3. **Database Test**: `python test_db.py`
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4. **Run API Server**: `uvicorn src.api.server:app --reload --host 0.0.0.0 --port 8000`
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### Docker Deployment
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- **Commands**:
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- `docker-compose up -d` (from the `docker/` directory or root depending on setup).
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- **Services**: `timescaledb`, `data_collector`, `api_server`, `ping_pong_bot`.
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## Key Files and Directories
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- `src/api/server.py`: FastAPI entry point and REST endpoints.
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- `src/data_collector/main.py`: Data collection service logic.
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- `src/data_collector/indicator_engine.py`: Technical indicator calculations (stateless math).
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- `src/api/dashboard/static/`: Frontend assets (HTML, CSS, JS).
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- `src/strategies/`: Directory for trading strategy implementations.
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- `HTS_STRATEGY.md`: Detailed documentation for the "Higher Timeframe Trend System" strategy.
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- `AGENTS.md`: Specific coding guidelines and standards for AI agents.
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## Development Conventions
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### Python Standards
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- **Style**: Follow PEP 8; use Type Hints consistently.
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- **Documentation**: Use Google-style docstrings for all public functions and classes.
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- **Asynchrony**: Use `async`/`await` for all database (via `asyncpg`) and network operations.
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- **Validation**: Use Pydantic models for data validation and settings.
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### Frontend Standards
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- **Tech**: Vanilla CSS (Avoid Tailwind unless requested) and Vanilla JS.
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- **Location**: Static files reside in `src/api/dashboard/static/`.
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### AI Coding Guidelines (from `AGENTS.md`)
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- **Organization**: Place new code in corresponding modules (`api`, `data_collector`, `strategies`).
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- **Error Handling**: Use explicit exceptions; log errors with context; never suppress silently.
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- **Security**: Protect credentials; use environment variables; validate all inputs.
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## Strategy: HTS (Higher Timeframe Trend System)
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The project emphasizes the **HTS strategy**, which uses fast (33) and slow (144) RMA channels to identify trends. Key rules include price position relative to Red (Slow) and Aqua (Fast) channels, and a 1H Red Zone filter for long trades. Refer to `HTS_STRATEGY.md` for full logic.
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