cointrader/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

CoinTrader is a Python asyncio-based automated cryptocurrency trading bot for Binance Futures. It trades XRPUSDT on 15-minute candles, using BTC/ETH as correlation features. The system has 5 layers: Data (WebSocket streams) → Signal (technical indicators) → ML Filter (ONNX/LightGBM) → Execution & Risk → Event/Alert (Discord).

Common Commands

# venv 
source .venv/bin/activate

# Run the bot
python main.py

# Run full test suite
bash scripts/run_tests.sh

# Run filtered tests
bash scripts/run_tests.sh -k "bot"

# Run pytest directly
pytest tests/ -v --tb=short

# ML training pipeline (LightGBM default)
bash scripts/train_and_deploy.sh

# MLX GPU training (macOS Apple Silicon)
bash scripts/train_and_deploy.sh mlx

# Hyperparameter tuning (50 trials, 5-fold walk-forward)
python scripts/tune_hyperparams.py

# Fetch historical data
python scripts/fetch_history.py --symbols XRPUSDT BTCUSDT ETHUSDT --interval 15m --days 365

# Deploy models to production
bash scripts/deploy_model.sh

Architecture

Entry point: main.py → creates Config (dataclass from env vars) → runs TradingBot

5-layer data flow on each 15m candle close:

1. src/data_stream.py — Combined WebSocket for XRP/BTC/ETH, deque buffers (200 candles each)
2. src/indicators.py — RSI, MACD, BB, EMA, StochRSI, ATR; weighted signal aggregation → LONG/SHORT/HOLD
3. src/ml_filter.py + src/ml_features.py — 23-feature extraction, ONNX priority > LightGBM fallback, threshold ≥ 0.60
4. src/exchange.py + src/risk_manager.py — Dynamic margin, MARKET orders with SL/TP, daily loss limit (5%)
5. src/user_data_stream.py + src/notifier.py — Real-time TP/SL detection via WebSocket, Discord webhooks

Parallel execution: user_data_stream runs independently via asyncio.gather() alongside candle processing.

Key Patterns

• Async-first: All I/O operations use async/await; parallel tasks via asyncio.gather()
• Reverse signal re-entry: While holding LONG, if SHORT signal appears → close position, cancel SL/TP, open SHORT. _is_reentering flag prevents race conditions with User Data Stream
• ML hot reload: ml_filter.check_and_reload() compares file mtime on every candle, reloads model without restart
• Active Config pattern: Best hyperparams stored in models/active_lgbm_params.json, must be manually approved before retraining
• Graceful degradation: Missing model → all signals pass; API failure → use fallback values (0.0 for OI/funding)
• Walk-forward validation: Time-series CV with undersampling (1:1 class balance, preserving time order)
• Label generation: Binary labels based on 24-candle (6h) lookahead — check SL hit first (conservative), then TP

Testing

• All external APIs (Binance, Discord) are mocked with unittest.mock.AsyncMock
• Async tests use @pytest.mark.asyncio
• 14 test files, 80+ test cases covering all layers
• Testing is done in actual terminal, not IDE sandbox

Configuration

Environment variables via .env file (see .env.example). Key vars: BINANCE_API_KEY, BINANCE_API_SECRET, SYMBOL (default XRPUSDT), LEVERAGE, DISCORD_WEBHOOK_URL, MARGIN_MAX_RATIO, MARGIN_MIN_RATIO, NO_ML_FILTER.

src/config.py uses @dataclass with __post_init__ to load and validate all env vars.

Deployment

• Docker: Dockerfile (Python 3.12-slim) + docker-compose.yml
• CI/CD: Jenkins pipeline (Gitea → Docker registry → LXC production server)
• Models stored in models/, data cache in data/, logs in logs/