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

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Step 2: Clone & Setup the Project

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Step 3: Verify Installation

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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, INLINECODE4
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

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Example 2: RSI Mean Reversion

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Example 3: Parameter Optimization

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Example 4: Paper Trading

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Example 5: Live Trading

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Agent Usage Guide

Command-Line Interface

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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

Built-in Strategies

The system includes several ready-to-use strategies:

1. MA Cross: Moving average crossover
RSI Mean Reversion: Buy oversold, sell overbought
MACD Momentum: MACD line and signal crossover
Bollinger Bounce: Trade Bollinger band touches
Breakout: Support/resistance breakouts
Grid Trading: Buy low, sell high in range
DCA (Dollar Cost Average): Regular accumulation
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:

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Data Sources

Supports multiple data sources:

1. Exchange APIs: Binance, OKX, Bybit, etc. (via ccxt)
CSV Files: Load historical data from files
Database: PostgreSQL, SQLite for caching
Real-time WebSocket: Live market data

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Configuration

Example configuration file (config.yaml):

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Project Structure

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Requirements

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Troubleshooting

Issue 1: API Connection Error

Error: ccxt.NetworkError or connection timeout

Solution:
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Issue 2: Insufficient Data

Error: Not enough candles for strategy

Solution:
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Issue 3: Optimization Takes Too Long

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
)

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)
Data Caching: Cache historical data to avoid repeated API calls
Optimization: Use Bayesian optimization, not grid search
Parallel Backtesting: Test multiple symbols simultaneously
WebSocket: Use WebSocket for real-time data (faster than REST)

Documentation

- GitHub: https://github.com/ZhenRobotics/openclaw-quant
Quick Start: INLINECODE9
API Reference: INLINECODE10
Strategy Guide: INLINECODE11
Full README: INLINECODE12

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

量化交易技能

专为加密货币市场设计的专业量化交易系统，具备回测、模拟交易、实盘交易和高级策略优化功能。

安装

步骤1：安装技能

bash
clawhub install quant-analyst

步骤2：克隆并设置项目

bash

克隆仓库

git clone https://github.com/ZhenRobotics/openclaw-quant.git ~/openclaw-quant
cd ~/openclaw-quant

安装依赖

pip install -r requirements.txt

设置API密钥（回测时可选）

export BINANCEAPIKEY=your-key export BINANCEAPISECRET=your-secret

步骤3：验证安装

bash
cd ~/openclaw-quant
python -m openclaw_quant --help

核心功能

- 回测引擎：在历史数据上以真实模拟方式测试策略
模拟交易：使用实时市场数据进行实时模拟
实盘交易：在真实交易所（币安、OKX等）上自动交易
策略优化：使用贝叶斯方法进行参数优化
技术指标：50+内置指标（MA、RSI、MACD、布林带等）
风险管理：仓位管理、止损、止盈
绩效分析：夏普比率、最大回撤、胜率、盈亏比
多交易所支持：通过ccxt库（100+交易所）

何时使用此技能

当用户消息包含以下内容时自动触发：

- 关键词：回测、交易策略、量化、加密货币交易、优化策略
请求如：测试这个交易策略、回测MA交叉、模拟交易比特币
策略描述：当RSI < 30时买入，当RSI > 70时卖出
绩效问题：夏普比率是多少、计算最大回撤

触发示例：

- 在比特币上回测移动平均线交叉策略
优化过去6个月的RSI参数
用我的动量策略开始模拟交易
这个策略的胜率是多少？

不要使用当：

- 仅需投资组合跟踪（使用portfolio-tracker技能）
仅需价格提醒（使用price-alert技能）
一般加密货币新闻或信息

快速入门示例

示例1：简单移动平均线策略

python
from openclaw_quant import Strategy, Backtest

class MAStrategy(Strategy):
# 参数（可优化）
fast_period = 10
slow_period = 30

def init(self):
# 向量化指标计算
self.fastma = self.I(SMA, self.data.Close, self.fastperiod)
self.slowma = self.I(SMA, self.data.Close, self.slowperiod)

def next(self):
# 事件驱动逻辑
if self.fastma[-1] > self.slowma[-1]:
if not self.position:
self.buy()
else:
if self.position:
self.sell()

回测

bt = Backtest(MAStrategy, data, cash=10000, commission=0.001) result = bt.run() print(result) result.plot()

示例2：RSI均值回归

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()

示例3：参数优化

python

自动优化参数

result = bt.optimize(
fast_period=range(5, 20, 2),
slow_period=range(20, 60, 5),
maximize=sharperatio # 或 totalreturn, profit_factor
)

print(f最佳参数：{result.best_params})
print(f夏普比率：{result.sharpe_ratio:.2f})

示例4：模拟交易

python
from openclaw_quant import LiveTrading

使用实时数据进行模拟交易

live = LiveTrading( strategy=MAStrategy, exchange=binance, symbol=BTC/USDT, paper=True # 模拟模式 )

live.run()

示例5：实盘交易

python

真实交易（谨慎使用！）

live = LiveTrading(
strategy=MAStrategy,
exchange=binance,
symbol=BTC/USDT,
paper=False, # 真实模式
apikey=os.getenv(BINANCEAPI_KEY),
apisecret=os.getenv(BINANCEAPI_SECRET)
)

live.run()

代理使用指南

命令行界面

bash

回测策略

openclaw-quant backtest --strategy ma_cross --symbol BTCUSDT --days 365

优化参数

openclaw-quant optimize --strategy rsi --symbol ETHUSDT --metric sharpe_ratio

模拟交易

openclaw-quant paper --strategy ma_cross --symbol BTCUSDT

实盘交易

openclaw-quant live --strategy ma_cross --symbol BTCUSDT --confirm

查看结果

openclaw-quant results --backtest-id abc123

自然语言（通过OpenClaw代理）

代理可以理解如下请求：

- 回测比特币过去一年的移动平均线交叉策略
优化RSI参数以最大化夏普比率
用1万USDT开始模拟交易，使用我的动量策略
显示上次回测的绩效指标
我的实盘交易账户的最大回撤是多少？

绩效指标

系统计算全面的绩效指标：

指标	描述
总收益率	总体盈亏百分比
年化收益率

内置策略

系统包含多个即用型策略：

1. MA交叉：移动平均线交叉
RSI均值回归：超卖买入，超买卖出
MACD动量：MACD线和信号线交叉
布林带反弹：交易布林带触及
突破：支撑/阻力突破
网格交易：区间内低买高卖
DCA（定投）：定期积累
均值回归：统计套利

技术指标

通过self.I()方法提供50+指标：

趋势指标：

- SMA、EMA、WMA、DEMA、TEMA
MACD、ADX、Aroon、Supertrend

动量指标：

- RSI、随机指标、CCI、威廉%R
ROC（变化率）、动量

波动率指标：

- 布林带、ATR、凯尔特纳通道
标准差、历史波动率

成交量指标：

- OBV、成交量SMA、MFI、VWAP
累积/派发、CMF

风险管理

内置风险管理功能：

python
class MyStrategy(Strategy):
def init(self):
# 设置风险参数
self.riskpertrade = 0.02 # 资金的2%
self.stop_loss = 0.05 # 5%止损
self.take_profit = 0.10 # 10%止盈

self.ma = self.I(SMA, self.data.Close, 20)

def next(self):
if self.ma[-1] > self.data.Close[-1]:
if not self.position:
# 基于风险计算仓位大小
size = self.calculatepositionsize(
risk=self.riskpertrade,
stoploss=self.stoploss
)
self.buy(size=size)
self.setstoploss(self.stop_loss)
self.settakeprofit(self.take_profit)

数据源

支持多种数据

quant-analyst量化分析师

quant-analyst

Quantitative Trading Skill

Installation

Step 1: Install the Skill

Step 2: Clone & Setup the Project

Step 3: Verify Installation

Core Features

When to Use This Skill

Quick Start Examples

Example 1: Simple Moving Average Strategy

Example 2: RSI Mean Reversion

Example 3: Parameter Optimization

Example 4: Paper Trading

Example 5: Live Trading

Agent Usage Guide

Command-Line Interface

Natural Language (via OpenClaw Agent)

Performance Metrics

Built-in Strategies

Technical Indicators

Risk Management

Data Sources

Configuration

Project Structure

Requirements

Troubleshooting

Issue 1: API Connection Error

Issue 2: Insufficient Data

Issue 3: Optimization Takes Too Long

Safety Guidelines

Backtesting

Paper Trading

Live Trading

Performance Tips

Documentation

Roadmap

Version 0.1.0 (Current)

Version 0.2.0

Version 0.3.0

Version 1.0.0

Cost

License

Support

Agent Behavior Guidelines

量化交易技能

安装

步骤1：安装技能

步骤2：克隆并设置项目

克隆仓库

安装依赖

设置API密钥（回测时可选）

步骤3：验证安装

核心功能

何时使用此技能

快速入门示例

示例1：简单移动平均线策略

回测

示例2：RSI均值回归

示例3：参数优化

自动优化参数

示例4：模拟交易

使用实时数据进行模拟交易

示例5：实盘交易

真实交易（谨慎使用！）

代理使用指南

命令行界面

回测策略

优化参数

模拟交易

实盘交易

查看结果

自然语言（通过OpenClaw代理）

绩效指标

内置策略

技术指标

风险管理

数据源

标签

通过对话安装