القائمة
Professional quantitative trading system for cryptocurrency - backtesting, paper trading, live trading, and strategy optimization
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | quant-analyst |
| 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 |
Professional quantitative trading system designed for cryptocurrency markets, featuring backtesting, paper trading, live trading, and advanced strategy optimization.
clawhub install quant-analyst
# 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"
cd ~/openclaw-quant
python -m openclaw_quant --help
AUTO-TRIGGER when user's message contains:
backtest, trading strategy, quant, cryptocurrency trading, optimize strategyTRIGGER EXAMPLES:
DO NOT USE when:
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()
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()
# 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}")
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()
# Real trading (use with caution!)
live = LiveTrading(
strategy=MAStrategy,
exchange='binance',
symbol='BTC/USDT',
paper=False, # Real mode
api_key=os.getenv('BINANCE_API_KEY'),
api_secret=os.getenv('BINANCE_API_SECRET')
)
live.run()
# 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
Agent can understand requests like:
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 |
The system includes several ready-to-use strategies:
50+ indicators available via self.I() method:
Trend Indicators:
Momentum Indicators:
Volatility Indicators:
Volume Indicators:
Built-in risk management features:
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)
Supports multiple data sources:
# 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
)
Example configuration file (config.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"
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
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
Error: ccxt.NetworkError or connection timeout
Solution:
# Check internet connection
# Verify API keys are correct
# Use testnet for testing:
exchange = ccxt.binance({'enableRateLimit': True, 'options': {'defaultType': 'future', 'testnet': True}})
Error: Not enough candles for strategy
Solution:
# 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)
Solution:
# 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
)
self.I() for indicators (computed once)~/openclaw-quant/QUICKSTART.md~/openclaw-quant/docs/API.md~/openclaw-quant/docs/STRATEGIES.md~/openclaw-quant/README.mdMIT License - Free for personal and commercial use
When using this skill, agents should:
DO:
DON'T:
Status: Under Development (Alpha)
Author: @ZhenStaff
Last Updated: 2026-03-05