import QUANTAXIS as QA
try:
    assert QA.__version__>='1.1.0'
except AssertionError:
    print('pip install QUANTAXIS >= 1.1.0 请升级QUANTAXIS后再运行此示例')

print('首先确定你已经完成了对于QUANTAXIS的基础认知,以及在本地存储完毕了QUANTAXIS的数据库')

首先确定你已经完成了对于QUANTAXIS的基础认知,以及在本地存储完毕了QUANTAXIS的数据库

QUANTAXIS 回测的一些基础知识

QA回测的核心是两个类

QA_BacktestBroker
QA_Account

回测数据的引入/迭代

QA.QA_fetch_stock_day_adv
QA.QA_fetch_stock_min_adv

指标的计算

DataStruct.add_func

对于账户的灵活运用

QA_Account
QA_Risk
QA_Portfolio
QA_PortfolioView
QA_User

STEP1 初始化账户,初始化回测broker

Account=QA.QA_Account()
Broker=QA.QA_BacktestBroker()

import warnings

# # 打印账户的信息
# try:
#     from pprint import  pprint as print
# except:
#     pass
# print(Account.message)

首先讲解Account类:

QA_Account在初始化的时候,可以自己指定很多信息:


    QA_Account(
        strategy_name=None, user_cookie=None, portfolio_cookie=None, account_cookie=None,
        market_type=MARKET_TYPE.STOCK_CN, frequence=FREQUENCE.DAY, broker=BROKER_TYPE.BACKETEST,
        init_hold={}, init_cash=1000000, commission_coeff=0.00025, tax_coeff=0.0015,
        margin_level=False, allow_t0=False, allow_sellopen=False,
        running_environment=RUNNING_ENVIRONMENT.BACKETEST)

        :param [str] strategy_name:  策略名称
        :param [str] user_cookie:   用户cookie
        :param [str] portfolio_cookie: 组合cookie
        :param [str] account_cookie:   账户cookie

        :param [dict] init_hold         初始化时的股票资产
        :param [float] init_cash:         初始化资金
        :param [float] commission_coeff:  交易佣金 :默认 万2.5   float 类型
        :param [float] tax_coeff:         印花税   :默认 千1.5   float 类型

        :param [Bool] margin_level:      保证金比例 默认False
        :param [Bool] allow_t0:          是否允许t+0交易  默认False
        :param [Bool] allow_sellopen:    是否允许卖空开仓  默认False

        :param [QA.PARAM] market_type:   市场类别 默认QA.MARKET_TYPE.STOCK_CN A股股票
        :param [QA.PARAM] frequence:     账户级别 默认日线QA.FREQUENCE.DAY
        :param [QA.PARAM] broker:        BROEKR类 默认回测 QA.BROKER_TYPE.BACKTEST
        :param [QA.PARAM] running_environment 当前运行环境 默认Backtest

        # 2018/06/11 init_assets 从float变为dict,并且不作为输入,作为只读属性
        #  :param [float] init_assets:       初始资产  默认 1000000 元 （100万）
        init_assets:{
            cash: xxx,
            stock: {'000001':2000},
            init_date: '2018-02-05',
            init_datetime: '2018-02-05 15:00:00'
        }
        # 2018/06/11 取消在初始化的时候的cash和history输入
        # :param [list] cash:              可用现金  默认 是 初始资产  list 类型
        # :param [list] history:           交易历史

# 重设账户初始资金

Account.reset_assets(200000)
Account.account_cookie='JCSC_EXAMPLE'

Account.init_assets

{'cash': 200000, 'hold': {}}

Account 有很多方法,暂时不详细展开,我们先直接进入下一步

SETP2:引入回测的市场数据

引入方法非常简单,直接使用QA_fetch_stock_day_adv系列即可

code 可以是多种多样的选取方式

1. QA.QA_fetch_stock_list_adv().code.tolist() # 获取全市场的股票代码
2. QA.QA_fetch_stock_block_adv().get_block('云计算').code  # 按版块选取
3. code= ['000001','000002'] # 自己指定

数据获取后,to_qfq() 即可获得前复权数据

data=DataSturct.to_qfq()

# QA.QA_fetch_stock_list_adv().code.tolist()
# QA.QA_fetch_stock_block_adv().get_block('云计算').code
#codelist=QA.QA_fetch_stock_block_adv().get_block('云计算').code
codelist=['000001']

data=QA.QA_fetch_stock_day_adv(codelist,'2017-09-01','2018-05-20')

data

< QA_DataStruct_Stock_day with 1 securities >

data=data.to_qfq()

# data.data

STEP3:计算一些指标

指标的计算可以在回测前,也可以在回测中进行

回测前的计算则是批量计算,效率较高

回测中的计算,效率略低,但代码量较小,易于理解

PS: 指标的相关介绍参见 QUANTAXIS的指标系统

import numpy as np
import pandas as pd
def MACD_JCSC(dataframe,SHORT=12,LONG=26,M=9):
    """
    1.DIF向上突破DEA，买入信号参考。
    
    2.DIF向下跌破DEA，卖出信号参考。
    """
    CLOSE=dataframe.close
    DIFF =QA.EMA(CLOSE,SHORT) - QA.EMA(CLOSE,LONG)
    DEA = QA.EMA(DIFF,M)
    MACD =2*(DIFF-DEA)

    CROSS_JC=QA.CROSS(DIFF,DEA)
    CROSS_SC=QA.CROSS(DEA,DIFF)
    ZERO=0
    return pd.DataFrame({'DIFF':DIFF,'DEA':DEA,'MACD':MACD,'CROSS_JC':CROSS_JC,'CROSS_SC':CROSS_SC,'ZERO':ZERO})

ind=data.add_func(MACD_JCSC)

ind.xs(codelist[0],level=1)['2018-01'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1fb23cb9f98>

ind.xs(codelist[0],level=1)['2018-01']

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	DIFF	DEA	MACD	CROSS_JC	CROSS_SC	ZERO
date
2018-01-02	0.110686	0.103225	0.014921	1	0	0
2018-01-03	0.100387	0.102657	-0.004541	0	1	0
2018-01-04	0.084801	0.099086	-0.028570	0	0	0
2018-01-05	0.075607	0.094390	-0.037566	0	0	0
2018-01-08	0.040453	0.083603	-0.086299	0	0	0
2018-01-09	0.022012	0.071285	-0.098545	0	0	0
2018-01-10	0.038391	0.064706	-0.052629	0	0	0
2018-01-11	0.045208	0.060806	-0.031197	0	0	0
2018-01-12	0.061988	0.061043	0.001890	1	0	0
2018-01-15	0.126236	0.074081	0.104309	0	0	0
2018-01-16	0.175134	0.094292	0.161683	0	0	0
2018-01-17	0.213839	0.118201	0.191275	0	0	0
2018-01-18	0.280788	0.150718	0.260139	0	0	0
2018-01-19	0.336418	0.187858	0.297119	0	0	0
2018-01-22	0.347467	0.219780	0.255374	0	0	0
2018-01-23	0.368907	0.249605	0.238603	0	0	0
2018-01-24	0.380702	0.275825	0.209755	0	0	0
2018-01-25	0.350521	0.290764	0.119515	0	0	0
2018-01-26	0.310920	0.294795	0.032249	0	0	0
2018-01-29	0.251630	0.286162	-0.069064	0	1	0
2018-01-30	0.195134	0.267957	-0.145646	0	0	0
2018-01-31	0.180544	0.250474	-0.139859	0	0	0

ind.loc['2018-01',slice(None)]

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		DIFF	DEA	MACD	CROSS_JC	CROSS_SC	ZERO
date	code
2018-01-02	000001	0.110686	0.103225	0.014921	1	0	0
2018-01-03	000001	0.100387	0.102657	-0.004541	0	1	0
2018-01-04	000001	0.084801	0.099086	-0.028570	0	0	0
2018-01-05	000001	0.075607	0.094390	-0.037566	0	0	0
2018-01-08	000001	0.040453	0.083603	-0.086299	0	0	0
2018-01-09	000001	0.022012	0.071285	-0.098545	0	0	0
2018-01-10	000001	0.038391	0.064706	-0.052629	0	0	0
2018-01-11	000001	0.045208	0.060806	-0.031197	0	0	0
2018-01-12	000001	0.061988	0.061043	0.001890	1	0	0
2018-01-15	000001	0.126236	0.074081	0.104309	0	0	0
2018-01-16	000001	0.175134	0.094292	0.161683	0	0	0
2018-01-17	000001	0.213839	0.118201	0.191275	0	0	0
2018-01-18	000001	0.280788	0.150718	0.260139	0	0	0
2018-01-19	000001	0.336418	0.187858	0.297119	0	0	0
2018-01-22	000001	0.347467	0.219780	0.255374	0	0	0
2018-01-23	000001	0.368907	0.249605	0.238603	0	0	0
2018-01-24	000001	0.380702	0.275825	0.209755	0	0	0
2018-01-25	000001	0.350521	0.290764	0.119515	0	0	0
2018-01-26	000001	0.310920	0.294795	0.032249	0	0	0
2018-01-29	000001	0.251630	0.286162	-0.069064	0	1	0
2018-01-30	000001	0.195134	0.267957	-0.145646	0	0	0
2018-01-31	000001	0.180544	0.250474	-0.139859	0	0	0

SETP4:选取回测的开始和结束日期,构建回测

data_forbacktest=data.select_time('2018-01-01','2018-05-01')


for items in data_forbacktest.panel_gen:
    for item in items.security_gen:
        daily_ind=ind.loc[item.index]
        if daily_ind.CROSS_JC.iloc[0]>0:
            order=Account.send_order(
                code=item.code[0], 
                time=item.date[0], 
                amount=1000, 
                towards=QA.ORDER_DIRECTION.BUY, 
                price=0, 
                order_model=QA.ORDER_MODEL.CLOSE, 
                amount_model=QA.AMOUNT_MODEL.BY_AMOUNT
                )
            #print(item.to_json()[0])
            Broker.receive_order(QA.QA_Event(order=order,market_data=item))
            
            
            trade_mes=Broker.query_orders(Account.account_cookie,'filled')
            res=trade_mes.loc[order.account_cookie,order.realorder_id]
            order.trade(res.trade_id,res.trade_price,res.trade_amount,res.trade_time)
        elif daily_ind.CROSS_SC.iloc[0]>0:
            if Account.sell_available.get(item.code[0], 0)>0:
                order=Account.send_order(
                    code=item.code[0], 
                    time=item.date[0], 
                    amount=Account.sell_available.get(item.code[0], 0), 
                    towards=QA.ORDER_DIRECTION.SELL, 
                    price=0, 
                    order_model=QA.ORDER_MODEL.MARKET, 
                    amount_model=QA.AMOUNT_MODEL.BY_AMOUNT
                    )
                Broker.receive_order(QA.QA_Event(order=order,market_data=item))


                trade_mes=Broker.query_orders(Account.account_cookie,'filled')
                res=trade_mes.loc[order.account_cookie,order.realorder_id]
                order.trade(res.trade_id,res.trade_price,res.trade_amount,res.trade_time)
    Account.settle()
            
        #break

receive deal
receive deal
receive deal
receive deal
receive deal
receive deal
receive deal

STEP5: 分析账户

Account.history

[['2018-01-02 00:00:00',
  '000001',
  13.7,
  1000,
  186295.0,
  'Order_FemEzpZ0',
  'Order_FemEzpZ0',
  'Trade_TQzDbd0g',
  'JCSC_EXAMPLE',
  5,
  0,
  None],
 ['2018-01-03 00:00:00',
  '000001',
  13.53,
  -1000,
  199806.47,
  'Order_mnKfLY0M',
  'Order_mnKfLY0M',
  'Trade_ldhSrMYa',
  'JCSC_EXAMPLE',
  5,
  13.530000000000001,
  None],
 ['2018-01-12 00:00:00',
  '000001',
  13.55,
  1000,
  186251.47,
  'Order_nWrOyhV4',
  'Order_nWrOyhV4',
  'Trade_AkSzOM7i',
  'JCSC_EXAMPLE',
  5,
  0,
  None],
 ['2018-01-29 00:00:00',
  '000001',
  13.93,
  -1000,
  200162.54,
  'Order_ygQNXbEJ',
  'Order_ygQNXbEJ',
  'Trade_HW4C7STK',
  'JCSC_EXAMPLE',
  5,
  13.93,
  None],
 ['2018-03-08 00:00:00',
  '000001',
  12.11,
  1000,
  188047.54,
  'Order_osmdlg2w',
  'Order_osmdlg2w',
  'Trade_UpRMLDi9',
  'JCSC_EXAMPLE',
  5,
  0,
  None],
 ['2018-03-26 00:00:00',
  '000001',
  11.03,
  -1000,
  199061.51,
  'Order_07LT6nZE',
  'Order_07LT6nZE',
  'Trade_kQDMnIJH',
  'JCSC_EXAMPLE',
  5,
  11.03,
  None],
 ['2018-04-10 00:00:00',
  '000001',
  11.42,
  1000,
  187636.51,
  'Order_r9Mdl61s',
  'Order_r9Mdl61s',
  'Trade_zA7EdDj0',
  'JCSC_EXAMPLE',
  5,
  0,
  None]]

Account.history_table

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	datetime	code	price	amount	cash	order_id	realorder_id	trade_id	account_cookie	commission	tax	message
0	2018-01-02 00:00:00	000001	13.70	1000	186295.00	Order_FemEzpZ0	Order_FemEzpZ0	Trade_TQzDbd0g	JCSC_EXAMPLE	5	0.00	None
1	2018-01-03 00:00:00	000001	13.53	-1000	199806.47	Order_mnKfLY0M	Order_mnKfLY0M	Trade_ldhSrMYa	JCSC_EXAMPLE	5	13.53	None
2	2018-01-12 00:00:00	000001	13.55	1000	186251.47	Order_nWrOyhV4	Order_nWrOyhV4	Trade_AkSzOM7i	JCSC_EXAMPLE	5	0.00	None
3	2018-01-29 00:00:00	000001	13.93	-1000	200162.54	Order_ygQNXbEJ	Order_ygQNXbEJ	Trade_HW4C7STK	JCSC_EXAMPLE	5	13.93	None
4	2018-03-08 00:00:00	000001	12.11	1000	188047.54	Order_osmdlg2w	Order_osmdlg2w	Trade_UpRMLDi9	JCSC_EXAMPLE	5	0.00	None
5	2018-03-26 00:00:00	000001	11.03	-1000	199061.51	Order_07LT6nZE	Order_07LT6nZE	Trade_kQDMnIJH	JCSC_EXAMPLE	5	11.03	None
6	2018-04-10 00:00:00	000001	11.42	1000	187636.51	Order_r9Mdl61s	Order_r9Mdl61s	Trade_zA7EdDj0	JCSC_EXAMPLE	5	0.00	None

#pd.concat([Account.daily_hold.reset_index().set_index('date'),pd.Series(data=None,index=pd.to_datetime(Account.trade_range).set_names('date'),name='predrop')],axis=1).ffill().drop(['predrop'],axis=1).reset_index()

Account.daily_cash.cash.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1fb242d8c88>

Account.daily_hold

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		000001
date	account_cookie
2018-01-02	JCSC_EXAMPLE	1000.0
2018-01-03	JCSC_EXAMPLE	0.0
2018-01-04	JCSC_EXAMPLE	0.0
2018-01-05	JCSC_EXAMPLE	0.0
2018-01-08	JCSC_EXAMPLE	0.0
2018-01-09	JCSC_EXAMPLE	0.0
2018-01-10	JCSC_EXAMPLE	0.0
2018-01-11	JCSC_EXAMPLE	0.0
2018-01-12	JCSC_EXAMPLE	1000.0
2018-01-15	JCSC_EXAMPLE	1000.0
2018-01-16	JCSC_EXAMPLE	1000.0
2018-01-17	JCSC_EXAMPLE	1000.0
2018-01-18	JCSC_EXAMPLE	1000.0
2018-01-19	JCSC_EXAMPLE	1000.0
2018-01-22	JCSC_EXAMPLE	1000.0
2018-01-23	JCSC_EXAMPLE	1000.0
2018-01-24	JCSC_EXAMPLE	1000.0
2018-01-25	JCSC_EXAMPLE	1000.0
2018-01-26	JCSC_EXAMPLE	1000.0
2018-01-29	JCSC_EXAMPLE	0.0
2018-01-30	JCSC_EXAMPLE	0.0
2018-01-31	JCSC_EXAMPLE	0.0
2018-02-01	JCSC_EXAMPLE	0.0
2018-02-02	JCSC_EXAMPLE	0.0
2018-02-05	JCSC_EXAMPLE	0.0
2018-02-06	JCSC_EXAMPLE	0.0
2018-02-07	JCSC_EXAMPLE	0.0
2018-02-08	JCSC_EXAMPLE	0.0
2018-02-09	JCSC_EXAMPLE	0.0
2018-02-12	JCSC_EXAMPLE	0.0
...	...	...
2018-02-26	JCSC_EXAMPLE	0.0
2018-02-27	JCSC_EXAMPLE	0.0
2018-02-28	JCSC_EXAMPLE	0.0
2018-03-01	JCSC_EXAMPLE	0.0
2018-03-02	JCSC_EXAMPLE	0.0
2018-03-05	JCSC_EXAMPLE	0.0
2018-03-06	JCSC_EXAMPLE	0.0
2018-03-07	JCSC_EXAMPLE	0.0
2018-03-08	JCSC_EXAMPLE	1000.0
2018-03-09	JCSC_EXAMPLE	1000.0
2018-03-12	JCSC_EXAMPLE	1000.0
2018-03-13	JCSC_EXAMPLE	1000.0
2018-03-14	JCSC_EXAMPLE	1000.0
2018-03-15	JCSC_EXAMPLE	1000.0
2018-03-16	JCSC_EXAMPLE	1000.0
2018-03-19	JCSC_EXAMPLE	1000.0
2018-03-20	JCSC_EXAMPLE	1000.0
2018-03-21	JCSC_EXAMPLE	1000.0
2018-03-22	JCSC_EXAMPLE	1000.0
2018-03-23	JCSC_EXAMPLE	1000.0
2018-03-26	JCSC_EXAMPLE	0.0
2018-03-27	JCSC_EXAMPLE	0.0
2018-03-28	JCSC_EXAMPLE	0.0
2018-03-29	JCSC_EXAMPLE	0.0
2018-03-30	JCSC_EXAMPLE	0.0
2018-04-02	JCSC_EXAMPLE	0.0
2018-04-03	JCSC_EXAMPLE	0.0
2018-04-04	JCSC_EXAMPLE	0.0
2018-04-09	JCSC_EXAMPLE	0.0
2018-04-10	JCSC_EXAMPLE	1000.0

64 rows × 1 columns

Risk=QA.QA_Risk(Account)

Risk.message

{'account_cookie': 'JCSC_EXAMPLE',
 'portfolio_cookie': None,
 'user_cookie': None,
 'annualize_return': -0.02,
 'profit': -0.0,
 'max_dropback': 0.01,
 'time_gap': 64,
 'volatility': 0.01,
 'benchmark_code': '000300',
 'bm_annualizereturn': -0.15,
 'bm_profit': -0.03,
 'beta': 1.0,
 'alpha': 0.13,
 'sharpe': -7.0,
 'init_cash': '200000.00',
 'last_assets': '199056.51',
 'total_tax': -38.49,
 'total_commission': -35.0,
 'profit_money': -943.49,
 'assets': [199995.0,
  199806.47,
  199806.47,
  199806.47,
  199806.47,
  199806.47,
  199806.47,
  199806.47,
  199801.47,
  200451.47,
  200451.47,
  200481.47,
  200971.47,
  201051.47,
  200691.47,
  200901.47,
  200891.47,
  200451.47,
  200301.47,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200162.54,
  200157.54,
  200137.54,
  200077.54,
  200067.54,
  199967.54,
  199757.54,
  199687.54,
  199877.54,
  199867.54,
  199947.54,
  199707.54,
  199387.54,
  199061.51,
  199061.51,
  199061.51,
  199061.51,
  199061.51,
  199061.51,
  199061.51,
  199061.51,
  199061.51,
  199056.51],
 'benchmark_assets': [202085.92384573357,
  203272.01801636998,
  204133.28356253446,
  204624.72900047217,
  205683.26489848486,
  207123.98120245922,
  208039.13764674764,
  207929.37820286216,
  208889.03171899606,
  208900.89760482148,
  210543.82837973803,
  210032.11205351513,
  211184.09180240333,
  211875.27965173626,
  214406.66862783392,
  216681.45782295603,
  217041.38969299494,
  215814.7537457882,
  216616.6898628254,
  212696.99224514925,
  210426.65275721162,
  211405.58833781187,
  209922.35260962966,
  211174.69797612482,
  211319.06625366793,
  205126.0626765978,
  200261.54390006946,
  198360.53010844925,
  189886.30998143484,
  192331.17687338853,
  194582.23429685974,
  196131.22680899137,
  200371.79775586433,
  201279.53802151186,
  203619.58958867402,
  200680.80519923565,
  198933.55351143697,
  200191.83182084493,
  198578.56576049206,
  198659.64931363266,
  201055.56942655632,
  199576.78340555867,
  201601.4001745274,
  203147.42621520266,
  204076.92060486352,
  202276.2724308503,
  201390.78070112554,
  202519.02867836278,
  200554.23575043076,
  201435.7721848804,
  201606.34429362134,
  200783.1484644802,
  198770.89199324633,
  193064.88414692934,
  191826.38231389716,
  193476.7292674546,
  189988.6532466794,
  192526.4695775992,
  192746.48287727954,
  192173.95388620123,
  190965.61117964212,
  190588.86930468382,
  190493.44780617073,
  194163.96182151238],
 'timeindex': ['2018-01-02',
  '2018-01-03',
  '2018-01-12',
  '2018-01-29',
  '2018-03-08',
  '2018-03-26',
  '2018-04-10'],
 'totaltimeindex': ['2018-01-02',
  '2018-01-03',
  '2018-01-04',
  '2018-01-05',
  '2018-01-08',
  '2018-01-09',
  '2018-01-10',
  '2018-01-11',
  '2018-01-12',
  '2018-01-15',
  '2018-01-16',
  '2018-01-17',
  '2018-01-18',
  '2018-01-19',
  '2018-01-22',
  '2018-01-23',
  '2018-01-24',
  '2018-01-25',
  '2018-01-26',
  '2018-01-29',
  '2018-01-30',
  '2018-01-31',
  '2018-02-01',
  '2018-02-02',
  '2018-02-05',
  '2018-02-06',
  '2018-02-07',
  '2018-02-08',
  '2018-02-09',
  '2018-02-12',
  '2018-02-13',
  '2018-02-14',
  '2018-02-22',
  '2018-02-23',
  '2018-02-26',
  '2018-02-27',
  '2018-02-28',
  '2018-03-01',
  '2018-03-02',
  '2018-03-05',
  '2018-03-06',
  '2018-03-07',
  '2018-03-08',
  '2018-03-09',
  '2018-03-12',
  '2018-03-13',
  '2018-03-14',
  '2018-03-15',
  '2018-03-16',
  '2018-03-19',
  '2018-03-20',
  '2018-03-21',
  '2018-03-22',
  '2018-03-23',
  '2018-03-26',
  '2018-03-27',
  '2018-03-28',
  '2018-03-29',
  '2018-03-30',
  '2018-04-02',
  '2018-04-03',
  '2018-04-04',
  '2018-04-09',
  '2018-04-10'],
 'ir': -2.0}

Risk.market_value.diff().iloc[-1]

code
000001    11420.0
Name: (2018-04-10 00:00:00, JCSC_EXAMPLE), dtype: float64

Risk.account.cash_table

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

		cash	datetime	date	account_cookie
datetime	account_cookie
2018-01-02 00:00:00	JCSC_EXAMPLE	186295	2018-01-02 00:00:00	2018-01-02	JCSC_EXAMPLE
2018-01-03 00:00:00	JCSC_EXAMPLE	199806	2018-01-03 00:00:00	2018-01-03	JCSC_EXAMPLE
2018-01-12 00:00:00	JCSC_EXAMPLE	186251	2018-01-12 00:00:00	2018-01-12	JCSC_EXAMPLE
2018-01-29 00:00:00	JCSC_EXAMPLE	200163	2018-01-29 00:00:00	2018-01-29	JCSC_EXAMPLE
2018-03-08 00:00:00	JCSC_EXAMPLE	188048	2018-03-08 00:00:00	2018-03-08	JCSC_EXAMPLE
2018-03-26 00:00:00	JCSC_EXAMPLE	199062	2018-03-26 00:00:00	2018-03-26	JCSC_EXAMPLE
2018-04-10 00:00:00	JCSC_EXAMPLE	187637	2018-04-10 00:00:00	2018-04-10	JCSC_EXAMPLE

z=Account.cash_table.drop_duplicates(subset='date', keep='last').set_index(['date', 'account_cookie'], drop=False)

res=Account.cash_table.drop_duplicates(subset='date', keep='last')

zx=pd.concat([res.set_index('date'),pd.Series(data=None,index=pd.to_datetime(Account.trade_range).dt.tz_localize(None).dt.tz_localize('Asia/Shanghai'),name='predrop')],axis=1).ffill().drop(['predrop'],axis=1)

zxx=pd.to_datetime(Account.trade_range).dt.tz_localize(None).dt.tz_localize('Asia/Shanghai')

zxx.set_names('date')

DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',
               '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',
               '2018-01-12', '2018-01-15', '2018-01-16', '2018-01-17',
               '2018-01-18', '2018-01-19', '2018-01-22', '2018-01-23',
               '2018-01-24', '2018-01-25', '2018-01-26', '2018-01-29',
               '2018-01-30', '2018-01-31', '2018-02-01', '2018-02-02',
               '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',
               '2018-02-09', '2018-02-12', '2018-02-13', '2018-02-14',
               '2018-02-22', '2018-02-23', '2018-02-26', '2018-02-27',
               '2018-02-28', '2018-03-01', '2018-03-02', '2018-03-05',
               '2018-03-06', '2018-03-07', '2018-03-08', '2018-03-09',
               '2018-03-12', '2018-03-13', '2018-03-14', '2018-03-15',
               '2018-03-16', '2018-03-19', '2018-03-20', '2018-03-21',
               '2018-03-22', '2018-03-23', '2018-03-26', '2018-03-27',
               '2018-03-28', '2018-03-29', '2018-03-30', '2018-04-02',
               '2018-04-03', '2018-04-04', '2018-04-09', '2018-04-10'],
              dtype='datetime64[ns]', name='date', freq=None)

Account.daily_hold.index.levels[0]

DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',
               '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',
               '2018-01-12', '2018-01-15', '2018-01-16', '2018-01-17',
               '2018-01-18', '2018-01-19', '2018-01-22', '2018-01-23',
               '2018-01-24', '2018-01-25', '2018-01-26', '2018-01-29',
               '2018-01-30', '2018-01-31', '2018-02-01', '2018-02-02',
               '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',
               '2018-02-09', '2018-02-12', '2018-02-13', '2018-02-14',
               '2018-02-22', '2018-02-23', '2018-02-26', '2018-02-27',
               '2018-02-28', '2018-03-01', '2018-03-02', '2018-03-05',
               '2018-03-06', '2018-03-07', '2018-03-08', '2018-03-09',
               '2018-03-12', '2018-03-13', '2018-03-14', '2018-03-15',
               '2018-03-16', '2018-03-19', '2018-03-20', '2018-03-21',
               '2018-03-22', '2018-03-23', '2018-03-26', '2018-03-27',
               '2018-03-28', '2018-03-29', '2018-03-30', '2018-04-02',
               '2018-04-03', '2018-04-04', '2018-04-09', '2018-04-10'],
              dtype='datetime64[ns]', name='date', freq=None)

#pd.Series(data=None,index=Account.trade_range,name='date')

Risk.market_value.sum(axis=1)

date        account_cookie
2018-01-02  JCSC_EXAMPLE      13700.0
2018-01-03  JCSC_EXAMPLE          0.0
2018-01-04  JCSC_EXAMPLE          0.0
2018-01-05  JCSC_EXAMPLE          0.0
2018-01-08  JCSC_EXAMPLE          0.0
2018-01-09  JCSC_EXAMPLE          0.0
2018-01-10  JCSC_EXAMPLE          0.0
2018-01-11  JCSC_EXAMPLE          0.0
2018-01-12  JCSC_EXAMPLE      13550.0
2018-01-15  JCSC_EXAMPLE      14200.0
2018-01-16  JCSC_EXAMPLE      14200.0
2018-01-17  JCSC_EXAMPLE      14230.0
2018-01-18  JCSC_EXAMPLE      14720.0
2018-01-19  JCSC_EXAMPLE      14800.0
2018-01-22  JCSC_EXAMPLE      14440.0
2018-01-23  JCSC_EXAMPLE      14650.0
2018-01-24  JCSC_EXAMPLE      14640.0
2018-01-25  JCSC_EXAMPLE      14200.0
2018-01-26  JCSC_EXAMPLE      14050.0
2018-01-29  JCSC_EXAMPLE          0.0
2018-01-30  JCSC_EXAMPLE          0.0
2018-01-31  JCSC_EXAMPLE          0.0
2018-02-01  JCSC_EXAMPLE          0.0
2018-02-02  JCSC_EXAMPLE          0.0
2018-02-05  JCSC_EXAMPLE          0.0
2018-02-06  JCSC_EXAMPLE          0.0
2018-02-07  JCSC_EXAMPLE          0.0
2018-02-08  JCSC_EXAMPLE          0.0
2018-02-09  JCSC_EXAMPLE          0.0
2018-02-12  JCSC_EXAMPLE          0.0
                               ...   
2018-02-26  JCSC_EXAMPLE          0.0
2018-02-27  JCSC_EXAMPLE          0.0
2018-02-28  JCSC_EXAMPLE          0.0
2018-03-01  JCSC_EXAMPLE          0.0
2018-03-02  JCSC_EXAMPLE          0.0
2018-03-05  JCSC_EXAMPLE          0.0
2018-03-06  JCSC_EXAMPLE          0.0
2018-03-07  JCSC_EXAMPLE          0.0
2018-03-08  JCSC_EXAMPLE      12110.0
2018-03-09  JCSC_EXAMPLE      12090.0
2018-03-12  JCSC_EXAMPLE      12030.0
2018-03-13  JCSC_EXAMPLE      12020.0
2018-03-14  JCSC_EXAMPLE      11920.0
2018-03-15  JCSC_EXAMPLE      11710.0
2018-03-16  JCSC_EXAMPLE      11640.0
2018-03-19  JCSC_EXAMPLE      11830.0
2018-03-20  JCSC_EXAMPLE      11820.0
2018-03-21  JCSC_EXAMPLE      11900.0
2018-03-22  JCSC_EXAMPLE      11660.0
2018-03-23  JCSC_EXAMPLE      11340.0
2018-03-26  JCSC_EXAMPLE          0.0
2018-03-27  JCSC_EXAMPLE          0.0
2018-03-28  JCSC_EXAMPLE          0.0
2018-03-29  JCSC_EXAMPLE          0.0
2018-03-30  JCSC_EXAMPLE          0.0
2018-04-02  JCSC_EXAMPLE          0.0
2018-04-03  JCSC_EXAMPLE          0.0
2018-04-04  JCSC_EXAMPLE          0.0
2018-04-09  JCSC_EXAMPLE          0.0
2018-04-10  JCSC_EXAMPLE      11420.0
Length: 64, dtype: float64

Account.history_table

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	datetime	code	price	amount	cash	order_id	realorder_id	trade_id	account_cookie	commission	tax	message
0	2018-01-02 00:00:00	000001	13.70	1000	186295.00	Order_FemEzpZ0	Order_FemEzpZ0	Trade_TQzDbd0g	JCSC_EXAMPLE	5	0.00	None
1	2018-01-03 00:00:00	000001	13.53	-1000	199806.47	Order_mnKfLY0M	Order_mnKfLY0M	Trade_ldhSrMYa	JCSC_EXAMPLE	5	13.53	None
2	2018-01-12 00:00:00	000001	13.55	1000	186251.47	Order_nWrOyhV4	Order_nWrOyhV4	Trade_AkSzOM7i	JCSC_EXAMPLE	5	0.00	None
3	2018-01-29 00:00:00	000001	13.93	-1000	200162.54	Order_ygQNXbEJ	Order_ygQNXbEJ	Trade_HW4C7STK	JCSC_EXAMPLE	5	13.93	None
4	2018-03-08 00:00:00	000001	12.11	1000	188047.54	Order_osmdlg2w	Order_osmdlg2w	Trade_UpRMLDi9	JCSC_EXAMPLE	5	0.00	None
5	2018-03-26 00:00:00	000001	11.03	-1000	199061.51	Order_07LT6nZE	Order_07LT6nZE	Trade_kQDMnIJH	JCSC_EXAMPLE	5	11.03	None
6	2018-04-10 00:00:00	000001	11.42	1000	187636.51	Order_r9Mdl61s	Order_r9Mdl61s	Trade_zA7EdDj0	JCSC_EXAMPLE	5	0.00	None

Account.cash_table

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

		cash	datetime	date	account_cookie
datetime	account_cookie
2018-01-02 00:00:00	JCSC_EXAMPLE	186295	2018-01-02 00:00:00	2018-01-02	JCSC_EXAMPLE
2018-01-03 00:00:00	JCSC_EXAMPLE	199806	2018-01-03 00:00:00	2018-01-03	JCSC_EXAMPLE
2018-01-12 00:00:00	JCSC_EXAMPLE	186251	2018-01-12 00:00:00	2018-01-12	JCSC_EXAMPLE
2018-01-29 00:00:00	JCSC_EXAMPLE	200163	2018-01-29 00:00:00	2018-01-29	JCSC_EXAMPLE
2018-03-08 00:00:00	JCSC_EXAMPLE	188048	2018-03-08 00:00:00	2018-03-08	JCSC_EXAMPLE
2018-03-26 00:00:00	JCSC_EXAMPLE	199062	2018-03-26 00:00:00	2018-03-26	JCSC_EXAMPLE
2018-04-10 00:00:00	JCSC_EXAMPLE	187637	2018-04-10 00:00:00	2018-04-10	JCSC_EXAMPLE

Risk.assets

date
2018-01-02    199995.00
2018-01-03    199806.47
2018-01-04    199806.47
2018-01-05    199806.47
2018-01-08    199806.47
2018-01-09    199806.47
2018-01-10    199806.47
2018-01-11    199806.47
2018-01-12    199801.47
2018-01-15    200451.47
2018-01-16    200451.47
2018-01-17    200481.47
2018-01-18    200971.47
2018-01-19    201051.47
2018-01-22    200691.47
2018-01-23    200901.47
2018-01-24    200891.47
2018-01-25    200451.47
2018-01-26    200301.47
2018-01-29    200162.54
2018-01-30    200162.54
2018-01-31    200162.54
2018-02-01    200162.54
2018-02-02    200162.54
2018-02-05    200162.54
2018-02-06    200162.54
2018-02-07    200162.54
2018-02-08    200162.54
2018-02-09    200162.54
2018-02-12    200162.54
                ...    
2018-02-26    200162.54
2018-02-27    200162.54
2018-02-28    200162.54
2018-03-01    200162.54
2018-03-02    200162.54
2018-03-05    200162.54
2018-03-06    200162.54
2018-03-07    200162.54
2018-03-08    200157.54
2018-03-09    200137.54
2018-03-12    200077.54
2018-03-13    200067.54
2018-03-14    199967.54
2018-03-15    199757.54
2018-03-16    199687.54
2018-03-19    199877.54
2018-03-20    199867.54
2018-03-21    199947.54
2018-03-22    199707.54
2018-03-23    199387.54
2018-03-26    199061.51
2018-03-27    199061.51
2018-03-28    199061.51
2018-03-29    199061.51
2018-03-30    199061.51
2018-04-02    199061.51
2018-04-03    199061.51
2018-04-04    199061.51
2018-04-09    199061.51
2018-04-10    199056.51
Name: 0, Length: 64, dtype: float64

Risk.assets.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1fb25356160>

Risk.benchmark_assets.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1fb257f74a8>

Risk.plot_assets_curve()

<module 'matplotlib.pyplot' from 'C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py'>

Risk.plot_dailyhold()

<module 'matplotlib.pyplot' from 'C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py'>

Risk.plot_signal()

<module 'matplotlib.pyplot' from 'C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py'>

Risk.profit_construct

{'total_buyandsell': -870.0,
 'total_tax': -38.49,
 'total_commission': -35.0,
 'total_profit': -943.49}

Performance=QA.QA_Performance(Account)

Performance.pnl_fifo

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    vertical-align: top;
}

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}

</style>

	sell_date	buy_date	amount	sell_price	buy_price	pnl_ratio	pnl_money
code
000001	2018-01-03	2018-01-02	1000	13.53	13.70	-0.012409	-170.0
000001	2018-01-29	2018-01-12	1000	13.93	13.55	0.028044	380.0
000001	2018-03-26	2018-03-08	1000	11.03	12.11	-0.089182	-1080.0

Performance.plot_pnlmoney(Performance.pnl_fifo)

<module 'matplotlib.pyplot' from 'C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py'>

STEP6: 存储结果

Account.save()
Risk.save()

STEP7: 查看存储的结果

account_info=QA.QA_fetch_account({'account_cookie':'JCSC_EXAMPLE'})

account=QA.QA_Account().from_message(account_info[0])

account

< QA_Account JCSC_EXAMPLE>

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QUANTAXIS回测分析全过程讲解.md

QUANTAXIS回测分析全过程讲解.md

QUANTAXIS 回测的一些基础知识

QA回测的核心是两个类

回测数据的引入/迭代

指标的计算

对于账户的灵活运用

STEP1 初始化账户,初始化回测broker

首先讲解Account类:

SETP2:引入回测的市场数据

STEP3:计算一些指标

SETP4:选取回测的开始和结束日期,构建回测

STEP5: 分析账户

STEP6: 存储结果

STEP7: 查看存储的结果

Files

QUANTAXIS回测分析全过程讲解.md

Latest commit

History

QUANTAXIS回测分析全过程讲解.md

File metadata and controls

QUANTAXIS 回测的一些基础知识

QA回测的核心是两个类

回测数据的引入/迭代

指标的计算

对于账户的灵活运用

STEP1 初始化账户,初始化回测broker

首先讲解Account类:

SETP2:引入回测的市场数据

STEP3:计算一些指标

SETP4:选取回测的开始和结束日期,构建回测

STEP5: 分析账户

STEP6: 存储结果

STEP7: 查看存储的结果