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 supports multi-symbol simultaneous trading (XRP, TRX, DOGE etc.) 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 (all symbols)
bash scripts/train_and_deploy.sh

# Single symbol training
bash scripts/train_and_deploy.sh --symbol TRXUSDT

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

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

# Weekly strategy report (manual, skip data fetch)
python scripts/weekly_report.py --skip-fetch

# Weekly report with data refresh
python scripts/weekly_report.py

# Fetch historical data (single symbol with auto correlation)
python scripts/fetch_history.py --symbol TRXUSDT --interval 15m --days 365

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

# Deploy models to production
bash scripts/deploy_model.sh --symbol XRPUSDT

Architecture

Entry point: main.py → creates Config → shared RiskManager → per-symbol TradingBot instances → asyncio.gather()

Multi-symbol architecture: Each symbol gets its own TradingBot instance with independent Exchange, MLFilter, and DataStream. The RiskManager is shared as a singleton across all bots, enforcing global daily loss limits and same-direction position limits via asyncio.Lock.

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

1. src/data_stream.py — Combined WebSocket for primary+correlation symbols, 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 — 26-feature extraction (ADX + OI 파생 피처 포함), ONNX priority > LightGBM fallback, threshold ≥ 0.55
4. src/exchange.py + src/risk_manager.py — Dynamic margin, MARKET orders with SL/TP, daily loss limit (5%), same-direction limit
5. src/user_data_stream.py + src/notifier.py — Real-time TP/SL detection via WebSocket, Discord webhooks

Dual-layer kill switch (per-symbol, in src/bot.py): Fast Kill (8 consecutive net losses) + Slow Kill (last 15 trades PF < 0.75). Trade history persisted to data/trade_history/{symbol}.jsonl. Blocks new entries only; existing SL/TP exits work normally. Manual reset via RESET_KILL_SWITCH_{SYMBOL}=True env var + restart.

Parallel execution: Per-symbol bots run independently via asyncio.gather(). Each bot's user_data_stream also runs in parallel.

Model/data directories: models/{symbol}/ and data/{symbol}/ for per-symbol models. Falls back to models/ root if symbol dir doesn't exist.

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, SYMBOLS (comma-separated, currently XRPUSDT only — SOL/DOGE/TRX removed due to PF < 1.0), CORRELATION_SYMBOLS (default BTCUSDT,ETHUSDT), LEVERAGE, DISCORD_WEBHOOK_URL, MARGIN_MAX_RATIO, MARGIN_MIN_RATIO, MAX_SAME_DIRECTION (default 2), NO_ML_FILTER (default true — ML disabled due to insufficient feature alpha).

src/config.py uses @dataclass with __post_init__ to load and validate all env vars. Per-symbol strategy params supported via SymbolStrategyParams — override with ATR_SL_MULT_{SYMBOL}, ATR_TP_MULT_{SYMBOL}, SIGNAL_THRESHOLD_{SYMBOL}, ADX_THRESHOLD_{SYMBOL}, VOL_MULTIPLIER_{SYMBOL}. Access via config.get_symbol_params(symbol).

Deployment

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

Design & Implementation Plans

All design documents and implementation plans are stored in docs/plans/ with the naming convention YYYY-MM-DD-feature-name.md. Design docs (-design.md) describe architecture decisions; implementation plans (-plan.md) contain step-by-step tasks for Claude to execute.

Chronological plan history:

Date	Plan	Status
2026-03-01	xrp-futures-autotrader	Completed
2026-03-01	discord-notifier-and-position-recovery	Completed
2026-03-01	upload-to-gitea	Completed
2026-03-01	dockerfile-and-docker-compose	Completed
2026-03-01	fix-pandas-ta-python312	Completed
2026-03-01	jenkins-gitea-registry-cicd	Completed
2026-03-01	ml-filter-design / ml-filter-implementation	Completed
2026-03-01	train-on-mac-deploy-to-lxc	Completed
2026-03-01	m4-accelerated-training	Completed
2026-03-01	vectorized-dataset-builder	Completed
2026-03-01	btc-eth-correlation-features (design + plan)	Completed
2026-03-01	dynamic-margin-ratio (design + plan)	Completed
2026-03-01	lgbm-improvement	Completed
2026-03-01	15m-timeframe-upgrade	Completed
2026-03-01	oi-nan-epsilon-precision-threshold	Completed
2026-03-02	rs-divide-mlx-nan-fix	Completed
2026-03-02	reverse-signal-reenter (design + plan)	Completed
2026-03-02	realtime-oi-funding-features	Completed
2026-03-02	oi-funding-accumulation	Completed
2026-03-02	optuna-hyperparam-tuning (design + plan)	Completed
2026-03-02	user-data-stream-tp-sl-detection (design + plan)	Completed
2026-03-02	adx-filter-design	Completed
2026-03-02	hold-negative-sampling (design + plan)	Completed
2026-03-03	position-monitor-logging	Completed
2026-03-03	adx-ml-feature-migration (design + plan)	Completed
2026-03-03	optuna-precision-objective-plan	Completed
2026-03-03	demo-1m-125x (design + plan)	In Progress
2026-03-04	oi-derived-features (design + plan)	Completed
2026-03-05	multi-symbol-trading (design + plan)	Completed
2026-03-06	multi-symbol-dashboard (design + plan)	Completed
2026-03-06	strategy-parameter-sweep (plan)	Completed
2026-03-07	weekly-report (plan)	Completed
2026-03-07	code-review-improvements	Partial (#1,#2,#4,#5,#6,#8 완료)
2026-03-19	critical-bugfixes (C5,C1,C3,C8)	Completed
2026-03-21	dashboard-code-review-r2 (#14,#19)	Completed
2026-03-21	code-review-fixes-r2 (9 issues)	Completed
2026-03-21	ml-pipeline-fixes (C1,C3,I1,I3,I4,I5)	Completed
2026-03-21	training-threshold-relaxation (plan)	Completed
2026-03-21	purged-gap-and-ablation (plan)	Completed
2026-03-21	ml-validation-result	ML OFF > ML ON 확정, SOL/DOGE/TRX 제외, XRP 단독 운영
2026-03-21	ml-validation-pipeline (plan)	Completed
2026-03-22	backtest-market-context (design)	설계 완료, 구현 대기
2026-03-22	testnet-uds-verification (design)	설계 완료, 구현 대기