Python量化交易框架介绍

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如切换回测。并提供交易所实盘交易接口（如OKEX) 。框架支持自定义交易订单号的。统计接口模板，实盘交易。

全新的《Python数字货币量化投资实战》系列课程，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等策略等内容。

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:官方社区论坛

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+

Installation

Windows
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)

github 链接

GITHUB代码仓库链接

cryptoquant :An Quantatitive trading library for crypto-assets 开源量化交易框架