1. Introduction to open source address:
Github.com/zhanggao201…
AmazingQuant is an open source framework for quantitative backtest trading based on event-driven. The following figure shows the overall framework.
Trade_center mossion_engine Trade_center mossion_engine trade_center mossion_engine trade_center mossion_engine trade_center mossion_engine trade_center mossion_engine The event_order and event_Risk_management engine, Broker_engine performs pre-order check and risk control respectively. Broker_engine is divided into two parts: A Simulate broker (mainly event_deal) for back test and strategy_Center Bar_engine that connects CTP, xSpeed and other interfaces for real transaction In the back test or trading mode, execute the transaction logic of each bar in the k-line way, and run the Analysis_Center Analysis_Engine to analyze and visualize the transaction records formed by back test on the daily line, minute line and sub-line. Net worth, annualized return, alpha, beta, retracement and other indicators, brison, Fama and other classical models 2. Install the configuration
Directing a 3.4
It is recommended to use shard and configure the boot item example PyMongo
Python calls MongoDB talib
Technical index calculation library Anaconda
If python 3.5 is larger than 3.5, the CTP interface is temporarily unavailable due to compilation issues, and Linux Ubuntu can be resolved later
The development environment is Ubuntu, of course, can also be used under Windows, but the database configuration and CTP transaction interface need to be re-installed AmazingQuant
PIP install AmazingQuant Strategy to write
# -*- coding: utf-8 -*-
__author__ = "gao"
import numpy as np
import talib
# import strategy基类
from AmazingQuant.strategy_center.strategy import *
# import 交易模块
from AmazingQuant.trade_center.trade import Trade
# 继承strategy基类
class MaStrategy(StrategyBase):
def initialize(self):
# 设置运行模式，回测或者交易
self.run_mode = RunMode.BACKTESTING.value
# 设置回测资金账号
self.account = ["test0", "test1"]
# 设置回测资金账号资金量
self.capital = {"test0": 2000000, "test1": 1000}
# 设置回测基准
self.benchmark = "000300.SH"
# 设置复权方式
self.rights_adjustment = RightsAdjustment.NONE.value
# 设置回测起止时间
self.start = "2015-01-11"
self.end = "2016-01-16"
# 设置运行周期
self.period = "daily"
# 设置股票池
self.universe = ['000001.SZ', '000002.SZ', '000008.SZ', '000060.SZ', '000063.SZ', '000069.SZ', '000100.SZ',
'000157.SZ', '000166.SZ', '000333.SZ', '000338.SZ', '000402.SZ', '000413.SZ', '000415.SZ',
'000423.SZ', '000425.SZ', '000503.SZ', '000538.SZ', '000540.SZ', '000559.SZ', '000568.SZ',
'000623.SZ', '000625.SZ', '000627.SZ', '000630.SZ', '000651.SZ', '000671.SZ', '000686.SZ',
'000709.SZ', '000723.SZ', '000725.SZ', '000728.SZ', '000738.SZ', '000750.SZ', '000768.SZ',
'000776.SZ', '000783.SZ', '000792.SZ', '000826.SZ', '000839.SZ', '000858.SZ', '000876.SZ',
'000895.SZ', '000898.SZ', '000938.SZ', '000959.SZ', '000961.SZ', '000963.SZ', '000983.SZ',
'001979.SZ', '002007.SZ', '002008.SZ', '002024.SZ', '002027.SZ', '002044.SZ', '002065.SZ',
'002074.SZ', '002081.SZ', '002142.SZ', '002146.SZ', '002153.SZ', '002174.SZ', '002202.SZ',
'002230.SZ', '002236.SZ', '002241.SZ', '002252.SZ', '002292.SZ', '002294.SZ', '002304.SZ',
'002310.SZ', '002352.SZ', '002385.SZ', '002411.SZ', '002415.SZ', '002424.SZ', '002426.SZ',
'002450.SZ', '002456.SZ', '002460.SZ', '002465.SZ', '002466.SZ', '002468.SZ', '002470.SZ',
'002475.SZ', '002500.SZ', '002508.SZ', '002555.SZ', '002558.SZ', '002572.SZ', '002594.SZ',
'002601.SZ', '002602.SZ', '002608.SZ', '002624.SZ', '002673.SZ', '002714.SZ', '002736.SZ',
'002739.SZ', '002797.SZ', '002831.SZ', '002839.SZ', '002841.SZ', '300003.SZ', '300015.SZ',
'300017.SZ', '300024.SZ', '300027.SZ', '300033.SZ', '300059.SZ', '300070.SZ', '300072.SZ',
'300122.SZ', '300124.SZ', '300136.SZ', '300144.SZ', '300251.SZ', '300315.SZ', '600000.SH',
'600008.SH', '600009.SH', '600010.SH', '600011.SH', '600015.SH', '600016.SH', '600018.SH',
'600019.SH', '600021.SH', '600023.SH', '600028.SH', '600029.SH', '600030.SH', '600031.SH',
'600036.SH', '600038.SH', '600048.SH', '600050.SH', '600061.SH', '600066.SH', '600068.SH',
'600074.SH', '600085.SH', '600089.SH', '600100.SH', '600104.SH', '600109.SH', '600111.SH',
'600115.SH', '600118.SH', '600153.SH', '600157.SH', '600170.SH', '600177.SH', '600188.SH',
'600196.SH', '600208.SH', '600219.SH', '600221.SH', '600233.SH', '600271.SH', '600276.SH',
'600297.SH', '600309.SH', '600332.SH', '600340.SH', '600352.SH', '600362.SH', '600369.SH',
'600372.SH', '600373.SH', '600376.SH', '600383.SH', '600390.SH', '600406.SH', '600415.SH',
'600436.SH', '600482.SH', '600485.SH', '600489.SH', '600498.SH', '600518.SH', '600519.SH',
'600522.SH', '600535.SH', '600547.SH', '600549.SH', '600570.SH', '600583.SH', '600585.SH',
'600588.SH', '600606.SH', '600637.SH', '600649.SH', '600660.SH', '600663.SH', '600674.SH',
'600682.SH', '600685.SH', '600688.SH', '600690.SH', '600703.SH', '600704.SH', '600705.SH',
'600739.SH', '600741.SH', '600795.SH', '600804.SH', '600816.SH', '600820.SH', '600827.SH',
'600837.SH', '600871.SH', '600886.SH', '600887.SH', '600893.SH', '600895.SH', '600900.SH',
'600909.SH', '600919.SH', '600926.SH', '600958.SH', '600959.SH', '600977.SH', '600999.SH',
'601006.SH', '601009.SH', '601012.SH', '601018.SH', '601021.SH', '601088.SH', '601099.SH',
'601111.SH', '601117.SH', '601118.SH', '601155.SH', '601163.SH', '601166.SH', '601169.SH',
'601186.SH', '601198.SH', '601211.SH', '601212.SH', '601216.SH', '601225.SH', '601228.SH',
'601229.SH', '601288.SH', '601318.SH', '601328.SH', '601333.SH', '601336.SH', '601375.SH',
'601377.SH', '601390.SH', '601398.SH', '601555.SH', '601600.SH', '601601.SH', '601607.SH',
'601608.SH', '601611.SH', '601618.SH', '601628.SH', '601633.SH', '601668.SH', '601669.SH',
'601688.SH', '601718.SH', '601727.SH', '601766.SH', '601788.SH', '601800.SH', '601818.SH',
'601857.SH', '601866.SH', '601872.SH', '601877.SH', '601878.SH', '601881.SH', '601888.SH',
'601898.SH', '601899.SH', '601901.SH', '601919.SH', '601933.SH', '601939.SH', '601958.SH',
'601966.SH', '601985.SH', '601988.SH', '601989.SH', '601991.SH', '601992.SH', '601997.SH',
'601998.SH', '603160.SH', '603799.SH', '603833.SH', '603858.SH', '603993.SH']
# 设置在运行前是否缓存日线，分钟线等各个周期数据
self.daily_data_cache = True
print(self.universe)
# 回测滑点设置，按固定值0.01,20-0.01 = 19.99;百分比0.01,20*(1-0.01) = 19.98;平仓时用"+"
self.set_slippage(stock_type=StockType.STOCK.value, slippage_type=SlippageType.SLIPPAGE_FIX.value, value=0.01)
# 回测股票手续费和印花税，卖出印花税，千分之一；开仓手续费，万分之三；平仓手续费，万分之三，最低手续费，５元
# 沪市，卖出有万分之二的过户费，加入到卖出手续费
self.set_commission(stock_type=StockType.STOCK_SH.value, tax=0.001, open_commission=0.0003,
close_commission=0.0003,
close_today_commission=0, min_commission=5)
# 深市不加过户费
self.set_commission(stock_type=StockType.STOCK_SZ.value, tax=0.001, open_commission=0.0003,
close_commission=0.0005,
close_today_commission=0, min_commission=5)
def handle_bar(self, event):
# 取当前bar的持仓情况
available_position_dict = {}
for position in Environment.bar_position_data_list:
available_position_dict[position.instrument + "." + position.exchange] = position.position - position.frozen
# 当前bar的具体时间戳
current_date = data_transfer.millisecond_to_date(millisecond=self.timetag, format="%Y-%m-%d")
# 时间戳转换成int，方便后面取数据
current_date_int = data_transfer.date_str_to_int(current_date)
print(current_date)
# 取数据实例
data_class = GetData()
# 循环遍历股票池
for stock in self.universe:
# 取当前股票的收盘价
close_price = data_class.get_market_data(Environment.daily_data, stock_code=[stock], field=["close"],
end=current_date)
# print(self.start, current_date)
close_array = np.array(close_price)
if len(close_array) > 0:
# 利用talib计算MA
ma5 = talib.MA(np.array(close_price), timeperiod=5)
ma20 = talib.MA(np.array(close_price), timeperiod=20)
# print(type(close_price.keys()))
# 过滤因为停牌没有数据
if current_date_int in close_price.keys():
# 如果5日均线突破20日均线，并且没有持仓，则买入这只股票100股，以收盘价为指定价交易
if ma5[-1] > ma20[-1] and stock not in available_position_dict.keys():
Trade(self).order_shares(stock_code=stock, shares=100, price_type="fix",
order_price=close_price[current_date_int],
account=self.account[0])
print("buy", stock, 1, "fix", close_price[current_date_int], self.account)
# 如果20日均线突破5日均线，并且有持仓，则卖出这只股票100股，以收盘价为指定价交易
elif ma5[-1] < ma20[-1] and stock in available_position_dict.keys():
Trade(self).order_shares(stock_code=stock, shares=-100, price_type="fix",
order_price=close_price[current_date_int],
account=self.account[0])
print("sell", stock, -1, "fix", close_price[current_date_int], self.account)
if __name__ == "__main__":
# 测试运行完整个策略所需时间，目前没有做过多优化，
# 300只股票，日线数据，一年的时间区间，4000多次交易记录，在我的虚拟机大概80s，换个性能稍好点的机器，应该会快很多
from AmazingQuant.utils.performance_test import Timer
time_test = Timer(True)
with time_test:
# 运行策略，设置是否保存委托，成交，资金，持仓
MaStrategy().run(save_trade_record=True)
Copy the code4. Analysis of backtest results
Automatically generates test results
The generated commission, transaction, funds, positions CVS files are written to the folder where the policy is automatically generated back test report
The back test report is in HTML format, which can be opened and viewed in a browser. The result is shown as follows:
5. Firm trading
Python3.5 encapsulation of CTP’s C++ interface using boost is currently implemented in a vnpy way and will be implemented later with broker_engine
6. Implemented and upcoming features
Database construction read data strategy run back test back test transaction record storage and analysis Real disk CTP interface encapsulation will realize various data docking
Broker_engine is used for CTP trading interfaces, such as stock minute data, stock financials data, stock sector component data, futures minute data, and daily data
CTP, xSpeed and other visualized backtest analysis modules for trading and positions of each bar in the backtest range
Increase the analysis and visualization of brison, FAMA and other performance attribution models
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