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CryptoQuant量化开源框架
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# CryptoQuant量化开源框架 TIP 你的支持是Rudy更新的动力 Python量化投资编程教学帮你快速入行 加入StudyQuant微信粉丝群：studyquant88
CryptoQuant is an algorithmic trading library for crypto-assets written in Python. It allows trading strategies to be easily expressed and backtested against historical data (with daily and minute resolution), providing analytics and insights regarding a particular strategy's performance. cryptoquant also supportslive-trading of crypto-assets starting with many exchanges (Okex,Binance,Bitmex etc) with more being added over time. CryptoQuant是一套基于Python的量化交易框架，帮助个人/机构量化人员进行数字货币量化交易。框架具有回测/实盘交易功能。 策略框架支持多个平台切换回测。 并提供交易所实盘交易接口（如OKEX) 。 全新的《Python数字货币量化投资实战》系列在线课程，已经在微信公众号[StudyQuant]上线，一整套数字货币量化解决方案。覆盖CTA等策略（已完成）等内容。 # 量化交易课程量化课程推荐 (opens new window) # Features 🎉Ease of Use: CryptoQuant tries to get out of your way so that you can focus on algorithm development. ✔️ 开箱即用 ： CryptoQuant提供一套量化框架帮助您专注策略开发 ✔️ 回测：回测框架支持数据导入，自定义交易订单号，多线程回测、遗传算法寻优等功能 ✔️ 实盘交易： 框架提供数字货币交易所接口DEMO ✔️ 文档支持:社区论坛 (opens new window) # 环境准备 支持的系统版本：Windows 7以上/Windows Server 2008以上/Ubuntu 18.04 LTS 支持的Python版本：Python 3.6 64位/ 3.7+ # InstallationWindows 使用要安装Python，激活环境，进入cryptoquant/install目录下的运行install.bat 安装依赖库 安装dependencies 中的依赖库 # Quickstart # 如何导入数据 from cryptoquant.trader.constant import Direction, Exchange, Interval, Offset, Status, Product, OptionType, OrderType import pandas as pd from cryptoquant.app.data_manage.data_manager import save_data_to_cryptoquant if __name__ == '__main__': df = pd.read_csv('IF9999.csv') symbol = 'IF9999' save_data_to_cryptoquant(symbol, df, Exchange.CFFEX) # 如何回测 from datetime import datetime from cryptoquant.app.cta_backtester.engine import BacktestingEngine, OptimizationSetting from cryptoquant.app.cta_strategy.strategies.atr_rsi_strategy import ( AtrRsiStrategy, ) #%% engine = BacktestingEngine() engine.set_parameters( vt_symbol="IF9999.CFFEX", interval="1m", start=datetime(2020, 1, 1), end=datetime(2020, 4, 30), rate=0.3/10000, slippage=0.5, size=300, pricetick=0.2, capital=1_000_0, ) setting = {} engine.add_strategy(AtrRsiStrategy,setting) # 导入数据 engine.load_data() # 开始回测 engine.run_backtesting() #计算收益 df = engine.calculate_result() # 开始统计 engine.calculate_statistics() # 开始画图 engine.show_chart() from cryptoquant.trader.constant import Direction, Exchange, Interval, Offset, Status, Product, OptionType, OrderType import pandas as pd from cryptoquant.app.data_manage.data_manager import save_data_to_cryptoquant if __name__ == '__main__': df = pd.read_csv('IF9999.csv') symbol = 'IF9999' save_data_to_cryptoquant(symbol, df, Exchange.CFFEX) # 实盘交易 from cryptoquant.api.okex.okex_spot_exchange import OkexSpotApi #导入交易所接口密钥 from cryptoquant.config.config import ok_api_key, ok_seceret_key, ok_passphrase from cryptoquant.api.okex.spot_api import SpotAPI from cryptoquant.api.api_gateway.apigateway import ApiGateway # 实例化OKEX接口的类 api = SpotAPI(ok_api_key, ok_seceret_key, ok_passphrase, True) # 实例化自己封装好接口类 api_gateway = OkexSpotApi(api) # 实例化策略与交易所接口之间的中间通道类 exchange = ApiGateway(api_gateway) kline_df = exchange.get_kline_data(symbol, minutes) print(kline_df) ticker = exchange.get_ticker(symbol) print(ticker) # 买单 order_data = exchange.buy(symbol,3,1) # 卖单 # order_data = exchange.sell(symbol, 6, 1) # 更多示例代码和维护的交易系统For more demo code and strategy demo, Please check the course, some homeworks may required to completed. 1.0 数字货币量化课程 (opens new window) 2.0 Python领域开发入门 (opens new window) # 捐助如果您觉得我们的开源软件对你有所帮助，请扫下方二维码购买课程支持。 课程链接 (opens new window)
QQ社群：1032965883 wechat: 82789754 如果无法解决请前往官方社区论坛 (opens new window)的 如果你有什么量化问题、python学习、课程咨询等问题，都可以咨询我。
非常希望大牛来贡献代码，完善项目功能。 在提交代码的时候，请遵守以下规则，以提高代码质量： 使用autopep8 (opens new window)格式化你的代码。运行autopep8 --in-place --recursive .即可。 使用flake8 (opens new window)检查你的代码，确保没有error和warning。在项目根目录下运行flake8即可。← Python 基础教学 4.0-量化课程 → |
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