import QUANTAXIS as QA
try:
    assert QA.__version__>='1.0.45'
except AssertionError:
    print('pip install QUANTAXIS >= 1.0.45 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()

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

{'account_cookie': 'Acc_CgcMzSZD',
 'allow_sellopen': False,
 'allow_t0': False,
 'broker': 'backtest',
 'cash': [1000000],
 'commission_coeff': 0.00025,
 'current_time': None,
 'history': [],
 'init_assets': 1000000,
 'margin_level': False,
 'market_type': 'stock_cn',
 'portfolio_cookie': None,
 'running_time': datetime.datetime(2018, 6, 6, 13, 57, 58, 389135),
 'source': 'account',
 'strategy_name': None,
 'tax_coeff': 0.0015,
 'trade_index': [],
 'user_cookie': None}

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, market_type=MARKET_TYPE.STOCK_CN, frequence=FREQUENCE.DAY,
                 broker=BROKER_TYPE.BACKETEST, portfolio_cookie=None, account_cookie=None,
                 sell_available={}, init_assets=None, cash=None, history=None, commission_coeff=0.00025, tax_coeff=0.0015,
                 margin_level=False, allow_t0=False, allow_sellopen=False)
                 
        :param strategy_name:  策略名称
        :param user_cookie:   用户cookie
        :param market_type:   市场类别 默认QA.MARKET_TYPE.STOCK_CN A股股票
        :param frequence:     账户级别 默认日线QA.FREQUENCE.DAY
        :param broker:        BROEKR类 默认回测 QA.BROKER_TYPE.BACKTEST
        :param portfolio_cookie: 组合cookie
        :param account_cookie:   账户cookie
        :param sell_available:   可卖股票数
        :param init_assets:       初始资产  默认 1000000 元 （100万）
        :param cash:              可用现金  默认 是 初始资产  list 类型
        :param history:           交易历史
        :param commission_coeff:  交易佣金 :默认 万2.5   float 类型
        :param tax_coeff:         印花税   :默认 千1.5   float 类型
        :param margin_level:      保证金比例 默认False
        :param allow_t0:          是否允许t+0交易  默认False
        :param allow_sellopen:    是否允许卖空开仓  默认False

# 重设账户初始资金

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

Account.init_assets

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

data=QA.QA_fetch_stock_day_adv(['000001','000002','000004','600000'],'2017-09-01','2018-05-20')

data

< QA_DataStruct_Stock_day with 4 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('000001',level=1)['2018-01'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1cf0d591d68>

ind.xs('000001',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
	000002	0.404554	0.189272	0.430564	0	0	0
	000004	-0.717479	-0.746157	0.057357	0	0	0
	600000	-0.062653	-0.054428	-0.016450	0	0	0
2018-01-03	000001	0.100387	0.102657	-0.004541	0	1	0
	000002	0.511649	0.253747	0.515803	0	0	0
	000004	-0.567604	-0.710447	0.285686	0	0	0
	600000	-0.058690	-0.055280	-0.006820	0	0	0
2018-01-04	000001	0.084801	0.099086	-0.028570	0	0	0
	000002	0.652650	0.333528	0.638244	0	0	0
	000004	-0.488320	-0.666021	0.355402	0	0	0
	600000	-0.054916	-0.055207	0.000582	1	0	0
2018-01-05	000001	0.075607	0.094390	-0.037566	0	0	0
	000002	0.886331	0.444089	0.884484	0	0	0
	000004	-0.425418	-0.617901	0.384965	0	0	0
	600000	-0.048944	-0.053955	0.010022	0	0	0
2018-01-08	000001	0.040453	0.083603	-0.086299	0	0	0
	000002	1.157306	0.586732	1.141148	0	0	0
	000004	-0.398377	-0.573996	0.351238	0	0	0
	600000	-0.044503	-0.052064	0.015122	0	0	0
2018-01-09	000001	0.022012	0.071285	-0.098545	0	0	0
	000002	1.344462	0.738278	1.212368	0	0	0
	000004	-0.358308	-0.530858	0.345101	0	0	0
	600000	-0.038924	-0.049436	0.021025	0	0	0
2018-01-10	000001	0.038391	0.064706	-0.052629	0	0	0
	000002	1.462222	0.883067	1.158309	0	0	0
	000004	-0.372237	-0.499134	0.253794	0	0	0
	600000	-0.008609	-0.041271	0.065324	0	0	0
2018-01-11	000001	0.045208	0.060806	-0.031197	0	0	0
2018-01-11	000002	1.496376	1.005729	0.981293	0	0	0
...	...	...	...	...	...	...	...
2018-01-22	000004	-0.541106	-0.511879	-0.058454	0	0	0
2018-01-22	600000	0.078336	0.049190	0.058291	0	0	0
2018-01-23	000001	0.368907	0.249605	0.238603	0	0	0
	000002	2.535276	2.079891	0.910770	0	0	0
	000004	-0.526304	-0.514764	-0.023080	0	0	0
	600000	0.068875	0.053127	0.031496	0	0	0
2018-01-24	000001	0.380702	0.275825	0.209755	0	0	0
	000002	2.600984	2.184110	0.833749	0	0	0
	000004	-0.474423	-0.506696	0.064544	1	0	0
	600000	0.114099	0.065322	0.097555	0	0	0
2018-01-25	000001	0.350521	0.290764	0.119515	0	0	0
	000002	2.548667	2.257021	0.583292	0	0	0
	000004	-0.453885	-0.496134	0.084496	0	0	0
	600000	0.154610	0.083179	0.142861	0	0	0
2018-01-26	000001	0.310920	0.294795	0.032249	0	0	0
	000002	2.465873	2.298792	0.334164	0	0	0
	000004	-0.442190	-0.485345	0.086310	0	0	0
	600000	0.165449	0.099633	0.131632	0	0	0
2018-01-29	000001	0.251630	0.286162	-0.069064	0	1	0
	000002	2.255685	2.290170	-0.068970	0	1	0
	000004	-0.448720	-0.478020	0.058599	0	0	0
	600000	0.176043	0.114915	0.122255	0	0	0
2018-01-30	000001	0.195134	0.267957	-0.145646	0	0	0
	000002	1.889068	2.209950	-0.641763	0	0	0
	000004	-0.448723	-0.472160	0.046876	0	0	0
	600000	0.154426	0.122817	0.063218	0	0	0
2018-01-31	000001	0.180544	0.250474	-0.139859	0	0	0
	000002	1.676000	2.103160	-0.854320	0	0	0
	000004	-0.537710	-0.485270	-0.104879	0	1	0
	600000	0.140515	0.126357	0.028316	0	0	0

88 rows × 6 columns

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.data.code[0], 
                time=item.data.date[0], 
                amount=1000, 
                towards=QA.ORDER_DIRECTION.BUY, 
                price=0, 
                order_model=QA.ORDER_MODEL.CLOSE, 
                amount_model=QA.AMOUNT_MODEL.BY_AMOUNT
                )
            Account.receive_deal(Broker.receive_order(QA.QA_Event(order=order,market_data=item)))
        elif daily_ind.CROSS_SC.iloc[0]>0:
            if Account.sell_available.get(item.code[0], 0)>0:
                order=Account.send_order(
                    code=item.data.code[0], 
                    time=item.data.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
                    )
                Account.receive_deal(Broker.receive_order(QA.QA_Event(order=order,market_data=item)))
    Account.settle()
            
        #break

STEP5: 分析账户

Account.history

[['2018-01-02 15:00:00',
  '000001',
  13.7,
  1000.0,
  'Order_eJFI3mwU',
  'Trade_4rcWX3PS',
  'JCSC_EXAMPLE',
  5.0,
  0.0],
 ['2018-01-03 09:30:00',
  '000001',
  13.53,
  -1000.0,
  'Order_Qxc4Mboq',
  'Trade_yVUrBc4g',
  'JCSC_EXAMPLE',
  5.0,
  20.295],
 ['2018-01-04 15:00:00',
  '600000',
  12.66,
  1000.0,
  'Order_ETydAzu6',
  'Trade_8y9dKuJc',
  'JCSC_EXAMPLE',
  5.0,
  0.0],
 ['2018-01-12 15:00:00',
  '000001',
  13.55,
  1000.0,
  'Order_Qgns5xvq',
  'Trade_tUu08RE2',
  'JCSC_EXAMPLE',
  5.0,
  0.0],
 ['2018-01-24 15:00:00',
  '000004',
  22.08,
  1000.0,
  'Order_xDeQcwGY',
  'Trade_fuE3mx4g',
  'JCSC_EXAMPLE',
  5.52,
  0.0],
 ['2018-01-29 09:30:00',
  '000001',
  13.93,
  -1000.0,
  'Order_EVhsUR0D',
  'Trade_G3UQJiSx',
  'JCSC_EXAMPLE',
  5.0,
  20.8875],
 ['2018-01-31 09:30:00',
  '000004',
  20.73,
  -1000.0,
  'Order_dKPcMH5R',
  'Trade_MyRfepw0',
  'JCSC_EXAMPLE',
  5.18375,
  31.1025],
 ['2018-02-02 09:30:00',
  '600000',
  13.1,
  -1000.0,
  'Order_RE4l1ZvW',
  'Trade_18MjFeYl',
  'JCSC_EXAMPLE',
  5.0,
  19.650000000000002],
 ['2018-02-05 15:00:00',
  '600000',
  13.49,
  1000.0,
  'Order_crPzt1EF',
  'Trade_3g1vTaoK',
  'JCSC_EXAMPLE',
  5.0,
  0.0],
 ['2018-02-08 09:30:00',
  '600000',
  13.18,
  -1000.0,
  'Order_KU6dzHiW',
  'Trade_bQmzWVvx',
  'JCSC_EXAMPLE',
  5.0,
  19.762500000000003],
 ['2018-02-13 15:00:00',
  '000004',
  20.1,
  1000.0,
  'Order_paEsc3nM',
  'Trade_5Tfcn4NK',
  'JCSC_EXAMPLE',
  5.025000000000001,
  0.0],
 ['2018-03-08 15:00:00',
  '000001',
  12.11,
  1000.0,
  'Order_POiBVAKq',
  'Trade_jiyxQFkf',
  'JCSC_EXAMPLE',
  5.0,
  0.0],
 ['2018-03-08 15:00:00',
  '000002',
  33.64,
  1000.0,
  'Order_Lt1FEkS8',
  'Trade_E4U1r3jB',
  'JCSC_EXAMPLE',
  8.41,
  0.0],
 ['2018-03-19 09:30:00',
  '000002',
  32.05,
  -1000.0,
  'Order_FRkfdiPC',
  'Trade_a7hFtQqY',
  'JCSC_EXAMPLE',
  8.0125,
  48.075],
 ['2018-03-26 09:30:00',
  '000001',
  11.03,
  -1000.0,
  'Order_WvZc9Sig',
  'Trade_Mw6Ch1aY',
  'JCSC_EXAMPLE',
  5.0,
  16.545],
 ['2018-03-29 15:00:00',
  '000002',
  34.16,
  1000.0,
  'Order_Afvx0KSs',
  'Trade_BD1L6zpm',
  'JCSC_EXAMPLE',
  8.54,
  0.0],
 ['2018-04-02 09:30:00',
  '000004',
  22.7,
  -1000.0,
  'Order_CKbxZqWv',
  'Trade_CwnGxfQU',
  'JCSC_EXAMPLE',
  5.67625,
  34.0575],
 ['2018-04-10 15:00:00',
  '000001',
  11.42,
  1000.0,
  'Order_MJfXHLSj',
  'Trade_QdgUj8WT',
  'JCSC_EXAMPLE',
  5.0,
  0.0],
 ['2018-04-11 15:00:00',
  '600000',
  11.91,
  1000.0,
  'Order_M3SDGLW7',
  'Trade_GdcCbXa2',
  'JCSC_EXAMPLE',
  5.0,
  0.0],
 ['2018-04-16 09:30:00',
  '000002',
  30.39,
  -1000.0,
  'Order_k2YQsO5h',
  'Trade_twRnvpfL',
  'JCSC_EXAMPLE',
  7.598750000000001,
  45.5925]]

Account.history_table

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	datetime	code	price	amount	order_id	trade_id	account_cookie	commission	tax
0	2018-01-02 15:00:00	000001	13.70	1000.0	Order_eJFI3mwU	Trade_4rcWX3PS	JCSC_EXAMPLE	5.00000	0.0000
1	2018-01-03 09:30:00	000001	13.53	-1000.0	Order_Qxc4Mboq	Trade_yVUrBc4g	JCSC_EXAMPLE	5.00000	20.2950
2	2018-01-04 15:00:00	600000	12.66	1000.0	Order_ETydAzu6	Trade_8y9dKuJc	JCSC_EXAMPLE	5.00000	0.0000
3	2018-01-12 15:00:00	000001	13.55	1000.0	Order_Qgns5xvq	Trade_tUu08RE2	JCSC_EXAMPLE	5.00000	0.0000
4	2018-01-24 15:00:00	000004	22.08	1000.0	Order_xDeQcwGY	Trade_fuE3mx4g	JCSC_EXAMPLE	5.52000	0.0000
5	2018-01-29 09:30:00	000001	13.93	-1000.0	Order_EVhsUR0D	Trade_G3UQJiSx	JCSC_EXAMPLE	5.00000	20.8875
6	2018-01-31 09:30:00	000004	20.73	-1000.0	Order_dKPcMH5R	Trade_MyRfepw0	JCSC_EXAMPLE	5.18375	31.1025
7	2018-02-02 09:30:00	600000	13.10	-1000.0	Order_RE4l1ZvW	Trade_18MjFeYl	JCSC_EXAMPLE	5.00000	19.6500
8	2018-02-05 15:00:00	600000	13.49	1000.0	Order_crPzt1EF	Trade_3g1vTaoK	JCSC_EXAMPLE	5.00000	0.0000
9	2018-02-08 09:30:00	600000	13.18	-1000.0	Order_KU6dzHiW	Trade_bQmzWVvx	JCSC_EXAMPLE	5.00000	19.7625
10	2018-02-13 15:00:00	000004	20.10	1000.0	Order_paEsc3nM	Trade_5Tfcn4NK	JCSC_EXAMPLE	5.02500	0.0000
11	2018-03-08 15:00:00	000001	12.11	1000.0	Order_POiBVAKq	Trade_jiyxQFkf	JCSC_EXAMPLE	5.00000	0.0000
12	2018-03-08 15:00:00	000002	33.64	1000.0	Order_Lt1FEkS8	Trade_E4U1r3jB	JCSC_EXAMPLE	8.41000	0.0000
13	2018-03-19 09:30:00	000002	32.05	-1000.0	Order_FRkfdiPC	Trade_a7hFtQqY	JCSC_EXAMPLE	8.01250	48.0750
14	2018-03-26 09:30:00	000001	11.03	-1000.0	Order_WvZc9Sig	Trade_Mw6Ch1aY	JCSC_EXAMPLE	5.00000	16.5450
15	2018-03-29 15:00:00	000002	34.16	1000.0	Order_Afvx0KSs	Trade_BD1L6zpm	JCSC_EXAMPLE	8.54000	0.0000
16	2018-04-02 09:30:00	000004	22.70	-1000.0	Order_CKbxZqWv	Trade_CwnGxfQU	JCSC_EXAMPLE	5.67625	34.0575
17	2018-04-10 15:00:00	000001	11.42	1000.0	Order_MJfXHLSj	Trade_QdgUj8WT	JCSC_EXAMPLE	5.00000	0.0000
18	2018-04-11 15:00:00	600000	11.91	1000.0	Order_M3SDGLW7	Trade_GdcCbXa2	JCSC_EXAMPLE	5.00000	0.0000
19	2018-04-16 09:30:00	000002	30.39	-1000.0	Order_k2YQsO5h	Trade_twRnvpfL	JCSC_EXAMPLE	7.59875	45.5925

Account.daily_hold

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    vertical-align: top;
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    text-align: right;
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	code	000001	000002	000004	600000
date	account_cookie
2018-01-02	JCSC_EXAMPLE	1000.0	0.0	0.0	0.0
2018-01-03	JCSC_EXAMPLE	0.0	0.0	0.0	0.0
2018-01-04	JCSC_EXAMPLE	0.0	0.0	0.0	1000.0
2018-01-12	JCSC_EXAMPLE	1000.0	0.0	0.0	1000.0
2018-01-24	JCSC_EXAMPLE	1000.0	0.0	1000.0	1000.0
2018-01-29	JCSC_EXAMPLE	0.0	0.0	1000.0	1000.0
2018-01-31	JCSC_EXAMPLE	0.0	0.0	0.0	1000.0
2018-02-02	JCSC_EXAMPLE	0.0	0.0	0.0	0.0
2018-02-05	JCSC_EXAMPLE	0.0	0.0	0.0	1000.0
2018-02-08	JCSC_EXAMPLE	0.0	0.0	0.0	0.0
2018-02-13	JCSC_EXAMPLE	0.0	0.0	1000.0	0.0
2018-03-08	JCSC_EXAMPLE	1000.0	1000.0	1000.0	0.0
2018-03-19	JCSC_EXAMPLE	1000.0	0.0	1000.0	0.0
2018-03-26	JCSC_EXAMPLE	0.0	0.0	1000.0	0.0
2018-03-29	JCSC_EXAMPLE	0.0	1000.0	1000.0	0.0
2018-04-02	JCSC_EXAMPLE	0.0	1000.0	0.0	0.0
2018-04-10	JCSC_EXAMPLE	1000.0	1000.0	0.0	0.0
2018-04-11	JCSC_EXAMPLE	1000.0	1000.0	0.0	1000.0
2018-04-16	JCSC_EXAMPLE	1000.0	0.0	0.0	1000.0

Risk=QA.QA_Risk(Account)

Risk.message

{'account_cookie': 'JCSC_EXAMPLE',
 'portfolio_cookie': None,
 'user_cookie': None,
 'annualize_return': -0.11,
 'profit': -0.03,
 'max_dropback': 0.04,
 'time_gap': 68,
 'volatility': 0.09,
 'benchmark_code': '000300',
 'bm_annualizereturn': -0.17,
 'bn_profit': -0.05,
 'beta': 1.0,
 'alpha': 0.06,
 'sharpe': -1.78,
 'init_assets': '200000.00',
 'last_assets': '194100.07'}

Risk.assets

date
2018-01-02    199995.00000
2018-01-03    199799.70500
2018-01-04    199794.70500
2018-01-12    200039.70500
2018-01-24    201784.18500
2018-01-29    200238.29750
2018-01-31    199262.01125
2018-02-02    199167.36125
2018-02-05    199162.36125
2018-02-08    198827.59875
2018-02-13    198822.57375
2018-03-08    199709.16375
2018-03-19    199583.07625
2018-03-26    198761.53125
2018-03-29    198712.99125
2018-04-02    197943.25750
2018-04-10    196628.25750
2018-04-11    197463.25750
2018-04-16    194100.06625
Name: 0, dtype: float64

Risk.assets.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1cf0d8fe898>

Risk.benchmark_assets.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1cf0fffb7b8>

Risk.plot_assets_curve()

Risk.plot_dailyhold()

Risk.plot_signal()

Performance=QA.QA_Performance(Account)

Performance.plot_pnlmoney(Performance.pnl_fifo)

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

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QUANTAXIS回测分析全过程讲解.md

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QUANTAXIS回测分析全过程讲解.md

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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