A high-performance quantitative research tool built on backtrader, focused on medium-to-low frequency trading strategies with Cython/Numba optimizations for improved backtesting efficiency. This project is an enhanced version of backtrader that maintains compatibility while adding numerous practical features.
- 🚀 Performance Optimization: Core computation modules optimized with Cython and Numba for significantly faster backtesting
- 🪙 Cryptocurrency Support:
- Funding rate backtesting for crypto perpetual contracts
- Real-time data streaming and historical data backtesting
- 🏦 Multi-Market Support:
- Interactive Brokers (IB) integration
- CTP futures trading interface
- Oanda forex trading
- Traditional stock markets
- 📈 Rich Technical Indicators: 60+ built-in technical indicators with custom indicator development support
- 📝 Strategy Analyzers: Multiple performance analysis tools (Sharpe ratio, maximum drawdown, SQN, etc.)
- master branch: Stable version, compatible with official backtrader, with known bugs fixed
- dev branch: Development version with latest features and experimental functionality
- Python 3.8 - 3.13 (Python 3.11 recommended for best performance)
- Operating Systems: Windows, Linux, macOS
Method 1: Install via pip (Recommended)
pip install -U git+https://gitee.com/yunjinqi/backtrader.gitMethod 2: Install from Source
# Clone the repository
git clone https://gitee.com/yunjinqi/backtrader.git
cd backtrader
# Install dependencies
pip install -r requirements.txt
# Install the package
pip install -e .Method 3: Using Anaconda
# Create virtual environment
conda create -n backtrader python=3.11
conda activate backtrader
# Install dependencies
pip install -r requirements.txt
# Install backtrader
pip install -e .import backtrader as bt
import pandas as pd
from datetime import datetime
# Create a strategy
class SMAStrategy(bt.Strategy):
params = (
('maperiod', 15),
)
def __init__(self):
# Add moving average indicator
self.sma = bt.indicators.SimpleMovingAverage(
self.datas[0], period=self.params.maperiod)
def next(self):
# Buy logic
if self.data.close[0] > self.sma[0]:
if not self.position:
self.buy()
# Sell logic
elif self.position:
self.sell()
# Create Cerebro engine
cerebro = bt.Cerebro()
# Add strategy
cerebro.addstrategy(SMAStrategy)
# Load data
data = bt.feeds.YahooFinanceData(
dataname='AAPL',
fromdate=datetime(2020, 1, 1),
todate=datetime(2023, 12, 31)
)
cerebro.adddata(data)
# Set initial cash
cerebro.broker.setcash(100000.0)
# Run backtest
results = cerebro.run()
# Print final portfolio value
print(f'Final Portfolio Value: {cerebro.broker.getvalue():.2f}')
# Plot results
cerebro.plot()- CSV file import
- Pandas DataFrame
- Real-time data streams (IB, CCXT, CTP)
- Online data sources (Yahoo Finance, Quandl, etc.)
- Event-driven strategy framework
- Vectorized strategy framework (CS/TS)
- Multi-asset, multi-timeframe strategy support
- Signal system
- Moving average series (SMA, EMA, WMA, etc.)
- Oscillators (RSI, MACD, Stochastic, etc.)
- Volatility indicators (ATR, Bollinger Bands, etc.)
- Custom indicator development framework
- Market orders, limit orders, stop orders
- Bracket orders
- OCO orders (One-Cancels-Other)
- Order validity management
- Returns analysis
- Sharpe ratio
- Maximum drawdown
- Trade statistics
- Custom analyzers
Performance improvements with Cython optimization:
| Module | Original Speed | Optimized Speed | Improvement |
|---|---|---|---|
| Indicator Calculations | 1.00x | 3-5x | 200-400% |
| Vectorized Backtesting | N/A | 10-20x | - |
| Order Matching | 1.00x | 2-3x | 100-200% |
We welcome code contributions, bug reports, and feature suggestions:
- Fork this repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- ✅ Added funding rate backtesting support for cryptocurrency
- ✅ Fixed Python 3.12+ compatibility issues
- ✅ Optimized Cython compilation process
- ✅ Added CI/CD automated testing
- ✅ Implemented Time Series (TS) vectorized backtesting framework
- ✅ Optimized Cross-Sectional (CS) strategy performance
- ✅ Fixed multiple known bugs
For detailed changelog, see CHANGELOG.md
This project is open source under the GNU General Public License v3.0. See the LICENSE file for details.
- Thanks to Daniel Rodriguez for creating the original backtrader
- Thanks to all contributors for their support and help
- Author: cloudQuant
- Email: [email protected]
- Blog: CSDN
基于backtrader打造的高性能量化投研工具，专注于中低频交易策略，通过Cython/Numba优化提升回测效率。本项目是backtrader的增强版本，在保持原有功能的基础上，新增了多项实用功能。
- 🚀 性能优化：使用Cython和Numba对核心计算模块进行优化，大幅提升回测速度
- 🪙 加密货币支持：
- 支持资金费率回测（数字货币永续合约）
- 实时数据流和历史数据回测
- 🏦 多市场支持：
- Interactive Brokers (IB) 集成
- CTP期货交易接口
- Oanda外汇交易
- 传统股票市场
- 📈 丰富的技术指标：60+内置技术指标，支持自定义指标开发
- 📝 策略分析器：多种性能分析工具（夏普比率、最大回撤、SQN等）
- master分支：稳定版本，与官方backtrader保持兼容，修复了已知bug
- dev分支：开发版本，包含最新特性和实验性功能
- Python 3.8 - 3.13（推荐使用Python 3.11以获得最佳性能）
- 操作系统：Windows、Linux、macOS
方法1：使用pip安装（推荐）
pip install -U git+https://gitee.com/yunjinqi/backtrader.git方法2：从源码安装
# 克隆项目
git clone https://gitee.com/yunjinqi/backtrader.git
cd backtrader
# 安装依赖
pip install -r requirements.txt
# 安装包
pip install -e .方法3：使用Anaconda
# 创建虚拟环境
conda create -n backtrader python=3.11
conda activate backtrader
# 安装依赖
pip install -r requirements.txt
# 安装backtrader
pip install -e .import backtrader as bt
import pandas as pd
from datetime import datetime
# 创建策略
class SMAStrategy(bt.Strategy):
params = (
('maperiod', 15),
)
def __init__(self):
# 添加移动平均线指标
self.sma = bt.indicators.SimpleMovingAverage(
self.datas[0], period=self.params.maperiod)
def next(self):
# 买入逻辑
if self.data.close[0] > self.sma[0]:
if not self.position:
self.buy()
# 卖出逻辑
elif self.position:
self.sell()
# 创建Cerebro引擎
cerebro = bt.Cerebro()
# 添加策略
cerebro.addstrategy(SMAStrategy)
# 加载数据
data = bt.feeds.YahooFinanceData(
dataname='AAPL',
fromdate=datetime(2020, 1, 1),
todate=datetime(2023, 12, 31)
)
cerebro.adddata(data)
# 设置初始资金
cerebro.broker.setcash(100000.0)
# 运行回测
results = cerebro.run()
# 打印最终资产
print(f'最终资产价值: {cerebro.broker.getvalue():.2f}')
# 绘制结果
cerebro.plot()- CSV文件导入
- Pandas DataFrame
- 实时数据流（IB、CCXT、CTP）
- Yahoo Finance、Quandl等在线数据源
- 事件驱动策略框架
- 向量化策略框架（CS/TS）
- 多品种、多周期策略支持
- 信号系统（Signal）
- 移动平均线系列（SMA、EMA、WMA等）
- 震荡指标（RSI、MACD、Stochastic等）
- 波动率指标（ATR、Bollinger Bands等）
- 自定义指标开发框架
- 市价单、限价单、止损单
- 括号订单（Bracket Orders）
- OCO订单（One-Cancels-Other）
- 订单有效期管理
- 收益率分析
- 夏普比率
- 最大回撤
- 交易统计
- 自定义分析器
使用Cython优化后的性能提升：
| 功能模块 | 原始速度 | 优化后速度 | 提升比例 |
|---|---|---|---|
| 指标计算 | 1.00x | 3-5x | 200-400% |
| 向量化回测 | N/A | 10-20x | - |
| 订单撮合 | 1.00x | 2-3x | 100-200% |
欢迎贡献代码、报告问题或提出新功能建议：
- Fork 本仓库
- 创建您的特性分支 (
git checkout -b feature/AmazingFeature) - 提交您的更改 (
git commit -m 'Add some AmazingFeature') - 推送到分支 (
git push origin feature/AmazingFeature) - 开启一个 Pull Request
- ✅ 增加了数字货币资金费率回测支持
- ✅ 修复了Python 3.12+兼容性问题
- ✅ 优化了Cython编译流程
- ✅ 添加了CI/CD自动化测试
- ✅ 实现了时间序列(TS)向量化回测框架
- ✅ 优化了横截面(CS)策略性能
- ✅ 修复了多个已知bug
详细更新日志请查看CHANGELOG.md
本项目基于 GNU General Public License v3.0 开源，详见 LICENSE 文件。
- 感谢 Daniel Rodriguez 创建了原始的backtrader
- 感谢所有贡献者的支持与帮助
- 作者：cloudQuant
- 邮箱：[email protected]
- 博客：CSDN
- 主仓库 / Main: https://gitee.com/yunjinqi/backtrader
- 镜像 / Mirror: https://github.com/cloudQuant/backtrader
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