Skill: zhenstaff/openclaw-quant-skill (version 0.1.0)

--- name: openclaw-quant description: Professional quantitative trading system for cryptocurrency - backtesting, paper trading, live trading, and strategy optimization tags: [quant, trading, backtest, crypto, strategy, optimization, bitcoin, trading-bot, algorithmic-trading] version: 0.1.0 --- # Quantitative Trading Skill Professional quantitative trading system designed for cryptocurrency markets, featuring backtesting, paper trading, live trading, and advanced strategy optimization. ## Installation ### Step 1: Install the Skill ```bash clawhub install openclaw-quant ``` ### Step 2: Clone & Setup the Project ```bash # Clone repository git clone https://github.com/ZhenRobotics/openclaw-quant.git ~/openclaw-quant cd ~/openclaw-quant # Install dependencies pip install -r requirements.txt # Set API keys (optional for backtesting) export BINANCE_API_KEY="your-key" export BINANCE_API_SECRET="your-secret" ``` ### Step 3: Verify Installation ```bash cd ~/openclaw-quant python -m openclaw_quant --help ``` --- ## Core Features - **Backtesting Engine**: Test strategies on historical data with realistic simulation - **Paper Trading**: Real-time simulation with live market data - **Live Trading**: Automated trading on real exchanges (Binance, OKX, etc.) - **Strategy Optimization**: Parameter optimization using Bayesian methods - **Technical Indicators**: 50+ built-in indicators (MA, RSI, MACD, Bollinger, etc.) - **Risk Management**: Position sizing, stop-loss, take-profit - **Performance Analytics**: Sharpe ratio, max drawdown, win rate, profit factor - **Multi-Exchange Support**: Via ccxt library (100+ exchanges) --- ## When to Use This Skill **AUTO-TRIGGER** when user's message contains: - Keywords: `backtest`, `trading strategy`, `quant`, `cryptocurrency trading`, `optimize strategy` - Requests like: "test this trading strategy", "backtest MA cross", "paper trade bitcoin" - Strategy descriptions: "buy when RSI < 30, sell when RSI > 70" - Performance questions: "what's the sharpe ratio", "calculate max drawdown" **TRIGGER EXAMPLES**: - "Backtest a moving average crossover strategy on Bitcoin" - "Optimize RSI parameters for the last 6 months" - "Start paper trading with my momentum strategy" - "What's the win rate of this strategy?" **DO NOT USE** when: - Only portfolio tracking (use portfolio-tracker skill) - Only price alerts (use price-alert skill) - General crypto news or information --- ## Quick Start Examples ### Example 1: Simple Moving Average Strategy ```python from openclaw_quant import Strategy, Backtest class MAStrategy(Strategy): # Parameters (can be optimized) fast_period = 10 slow_period = 30 def init(self): # Vectorized indicator calculation self.fast_ma = self.I(SMA, self.data.Close, self.fast_period) self.slow_ma = self.I(SMA, self.data.Close, self.slow_period) def next(self): # Event-driven logic if self.fast_ma[-1] > self.slow_ma[-1]: if not self.position: self.buy() else: if self.position: self.sell() # Backtest bt = Backtest(MAStrategy, data, cash=10000, commission=0.001) result = bt.run() print(result) result.plot() ``` ### Example 2: RSI Mean Reversion ```python class RSIStrategy(Strategy): rsi_period = 14 oversold = 30 overbought = 70 def init(self): self.rsi = self.I(RSI, self.data.Close, self.rsi_period) def next(self): if self.rsi[-1] < self.oversold: if not self.position: self.buy() elif self.rsi[-1] > self.overbought: if self.position: self.sell() bt = Backtest(RSIStrategy, data, cash=10000) result = bt.run() ``` ### Example 3: Parameter Optimization ```python # Optimize parameters automatically result = bt.optimize( fast_period=range(5, 20, 2), slow_period=range(20, 60, 5), maximize='sharpe_ratio' # or 'total_return', 'profit_factor' ) print(f"Best parameters: {result.best_params}") print(f"Sharpe Ratio: {result.sharpe_ratio:.2f}") ``` ### Example 4: Paper Trading ```python from openclaw_quant import LiveTrading # Paper trading with real-time data live = LiveTrading( strategy=MAStrategy, exchange='binance', symbol='BTC/USDT', paper=True # Simulation mode ) live.run() ``` ### Example 5: Live Trading ```python # Real trading (use with caution!) live = LiveTrading( strategy=MAStrategy, exchange='binance', symbol='BTC/USDT', paper=False, # Real mode [REDACTED_SECRET]'BINANCE_API_KEY'), api_secret=os.getenv('BINANCE_API_SECRET') ) live.run() ``` --- ## Agent Usage Guide ### Command-Line Interface ```bash # Backtest a strategy openclaw-quant backtest --strategy ma_cross --symbol BTCUSDT --days 365 # Optimize parameters openclaw-quant optimize --strategy rsi --symbol ETHUSDT --metric sharpe_ratio # Paper trading openclaw-quant paper --strategy ma_cross --symbol BTCUSDT # Live trading openclaw-quant live --strategy ma_cross --symbol BTCUSDT --confirm # View results openclaw-quant results --backtest-id abc123 ``` ### Natural Language (via OpenClaw Agent) Agent can understand requests like: - "Backtest a moving average crossover on Bitcoin for the last year" - "Optimize RSI parameters to maximize Sharpe ratio" - "Start paper trading with 10k USDT using my momentum strategy" - "Show me the performance metrics of the last backtest" - "What's the max drawdown of my live trading account?" --- ## Performance Metrics The system calculates comprehensive performance metrics: | Metric | Description | |--------|-------------| | **Total Return** | Overall profit/loss percentage | | **Annualized Return** | Return extrapolated to one year | | **Sharpe Ratio** | Risk-adjusted return (higher is better) | | **Sortino Ratio** | Downside risk-adjusted return | | **Max Drawdown** | Largest peak-to-trough decline | | **Win Rate** | Percentage of profitable trades | | **Profit Factor** | Gross profit / Gross loss | | **Calmar Ratio** | Return / Max drawdown | | **Average Win/Loss** | Mean profit/loss per trade | | **Expectancy** | Expected value per trade | --- ## Built-in Strategies The system includes several ready-to-use strategies: 1. **MA Cross**: Moving average crossover 2. **RSI Mean Reversion**: Buy oversold, sell overbought 3. **MACD Momentum**: MACD line and signal crossover 4. **Bollinger Bounce**: Trade Bollinger band touches 5. **Breakout**: Support/resistance breakouts 6. **Grid Trading**: Buy low, sell high in range 7. **DCA (Dollar Cost Average)**: Regular accumulation 8. **Mean Reversion**: Statistical arbitrage --- ## Technical Indicators 50+ indicators available via `self.I()` method: **Trend Indicators:** - SMA, EMA, WMA, DEMA, TEMA - MACD, ADX, Aroon, Supertrend **Momentum Indicators:** - RSI, Stochastic, CCI, Williams %R - ROC (Rate of Change), Momentum **Volatility Indicators:** - Bollinger Bands, ATR, Keltner Channels - Standard Deviation, Historical Volatility **Volume Indicators:** - OBV, Volume SMA, MFI, VWAP - Accumulation/Distribution, CMF --- ## Risk Management Built-in risk management features: ```python class MyStrategy(Strategy): def init(self): # Set risk parameters self.risk_per_trade = 0.02 # 2% of capital self.stop_loss = 0.05 # 5% stop loss self.take_profit = 0.10 # 10% take profit self.ma = self.I(SMA, self.data.Close, 20) def next(self): if self.ma[-1] > self.data.Close[-1]: if not self.position: # Calculate position size based on risk size = self.calculate_position_size( risk=self.risk_per_trade, stop_loss=self.stop_loss ) self.buy(size=size) self.set_stop_loss(self.stop_loss) self.set_take_profit(self.take_profit) ``` --- ## Data Sources Supports multiple data sources: 1. **Exchange APIs**: Binance, OKX, Bybit, etc. (via ccxt) 2. **CSV Files**: Load historical data from files 3. **Database**: PostgreSQL, SQLite for caching 4. **Real-time WebSocket**: Live market data ```python # Example: Load data from Binance from openclaw_quant import DataFetcher fetcher = DataFetcher('binance') data = fetcher.fetch_candles( symbol='BTC/USDT', timeframe='1h', since='2023-01-01', limit=1000 ) ``` --- ## Configuration Example configuration file (`config.yaml`): ```yaml backtest: initial_capital: 10000 commission: 0.001 # 0.1% slippage: 0.0005 # 0.05% strategy: name: ma_cross parameters: fast_period: 10 slow_period: 30 exchange: name: binance testnet: false risk: max_position_size: 0.1 # 10% of capital max_drawdown: 0.2 # Stop if 20% drawdown daily_loss_limit: 0.05 # Stop if 5% daily loss notification: telegram: enabled: true bot_token: "your-token" chat_id: "your-chat-id" ``` --- ## Project Structure ``` openclaw-quant/ ├── src/ │ ├── openclaw_quant/ │ │ ├── __init__.py │ │ ├── strategy.py # Strategy base class │ │ ├── backtest.py # Backtest engine │ │ ├── live.py # Live trading engine │ │ ├── broker.py # Order execution │ │ ├── data.py # Data fetching │ │ ├── indicators.py # Technical indicators │ │ ├── optimizer.py # Parameter optimization │ │ ├── metrics.py # Performance metrics │ │ └── risk.py # Risk management │ └── strategies/ │ ├── ma_cross.py │ ├── rsi.py │ └── ... ├── examples/ │ ├── backtest_example.py │ ├── optimization_example.py │ └── paper_trading_example.py ├── tests/ ├── docs/ ├── requirements.txt └── README.md ``` --- ## Requirements ``` Python >= 3.9 pandas >= 2.0.0 numpy >= 1.24.0 ccxt >= 4.0.0 optuna >= 3.0.0 matplotlib >= 3.7.0 pydantic >= 2.0.0 ``` --- ## Troubleshooting ### Issue 1: API Connection Error **Error**: `ccxt.NetworkError` or connection timeout **Solution**: ```bash # Check internet connection # Verify API keys are correct # Use testnet for testing: exchange = ccxt.binance({'enableRateLimit': True, 'options': {'defaultType': 'future', 'testnet': True}}) ``` ### Issue 2: Insufficient Data **Error**: Not enough candles for strategy **Solution**: ```python # Increase warmup period bt = Backtest(strategy, data, warmup=100) # Skip first 100 candles # Or fetch more historical data data = fetcher.fetch_candles(symbol='BTC/USDT', limit=5000) ``` ### Issue 3: Optimization Takes Too Long **Solution**: ```python # Reduce search space result = bt.optimize( fast_period=range(5, 20, 5), # Larger step slow_period=range(20, 60, 10), max_tries=50 # Limit iterations ) ``` --- ## Safety Guidelines ### Backtesting - Always test on historical data first - Use realistic commission and slippage - Beware of overfitting - Walk-forward validation recommended ### Paper Trading - Test for at least 1-2 weeks - Monitor slippage and fills - Check strategy behavior in different market conditions ### Live Trading - Start with small capital - Set strict risk limits - Monitor continuously for first week - Have emergency stop mechanism - Never risk more than you can afford to lose --- ## Performance Tips 1. **Vectorization**: Use `self.I()` for indicators (computed once) 2. **Data Caching**: Cache historical data to avoid repeated API calls 3. **Optimization**: Use Bayesian optimization, not grid search 4. **Parallel Backtesting**: Test multiple symbols simultaneously 5. **WebSocket**: Use WebSocket for real-time data (faster than REST) --- ## Documentation - **GitHub**: https://github.com/ZhenRobotics/openclaw-quant - **Quick Start**: `~/openclaw-quant/QUICKSTART.md` - **API Reference**: `~/openclaw-quant/docs/API.md` - **Strategy Guide**: `~/openclaw-quant/docs/STRATEGIES.md` - **Full README**: `~/openclaw-quant/README.md` --- ## Roadmap ### Version 0.1.0 (Current) - Basic backtesting engine - Simple strategies (MA, RSI) - Binance support ### Version 0.2.0 - Paper trading - Parameter optimization - More indicators ### Version 0.3.0 - Live trading - Multi-exchange support - Advanced risk management ### Version 1.0.0 - Production-ready - Web dashboard - Strategy marketplace --- ## Cost - **Development**: Free and open source (MIT License) - **Data**: Free (using exchange APIs) - **Trading Fees**: Varies by exchange (typically 0.1%) - **API Costs**: Free tier available on most exchanges --- ## License MIT License - Free for personal and commercial use --- ## Support - **Issues**: https://github.com/ZhenRobotics/openclaw-quant/issues - **Discussions**: https://github.com/ZhenRobotics/openclaw-quant/discussions - **ClawHub**: https://clawhub.ai/ZhenStaff/openclaw-quant --- ## Agent Behavior Guidelines When using this skill, agents should: **DO**: - Check if project is installed before use - Warn about risks in live trading mode - Validate strategy parameters - Show clear performance metrics - Explain optimization results **DON'T**: - Execute live trades without explicit confirmation - Recommend specific trading strategies - Guarantee profits or returns - Ignore risk warnings - Make financial advice --- **Status**: Under Development (Alpha) **Author**: @ZhenStaff **Last Updated**: 2026-03-05


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)