Market Competitiveness

发布于 2022-02-20 22:26:18 字数 15743 浏览 827 评论 0 收藏 0

来一个奇葩无厘头的市场竞争策略

策略思路

某一行业的几大龙头股票，在稳定时期此消彼长

策略实现

股票池：选择一行业内的流动性比较好的龙头股票；例如三家自助品牌汽车，长安、比亚迪和长城，以下按照三只股票情况讨论
观察某一天时，股票价格和该股票在过去几天内平均值的关系
如果两只股票下跌，则预测另一只股票上涨；如果两只股票上涨，则预测另一只股票下跌
如果某天三只股票中的两只较其平均值有较大幅度下跌，而另一只股票较其平均值比较稳定不变，则买入后面这只比较稳定的股票
如果某天三只股票中的两只较其平均值有较大幅度上涨，而另一只股票较其平均值比较稳定不变，则卖出后面这只比较稳定的股票

import quartz
import quartz.backtest    as qb
import quartz.performance as qp
from   quartz.api         import *

import pandas as pd
import numpy  as np
from datetime   import datetime
from matplotlib import pylab

start = datetime(2012, 1, 1)
end = datetime(2014, 12, 1)
benchmark = 'HS300'
universe = ['000625.XSHE', # 长安汽车
            '002594.XSHE', # 比亚迪汽车
            '601633.XSHG'  # 长城汽车
            ]

capital_base = 1000000
refresh_rate = 5
window = 10

def initialize(account):
    account.amount = 100000
    account.universe = universe
    add_history('hist', window)

def handle_data(account):

    stk_0 = universe[0]
    stk_1 = universe[1]
    stk_2 = universe[2]

    prices_0 = account.hist[stk_0]['closePrice']
    prices_1 = account.hist[stk_1]['closePrice']
    prices_2 = account.hist[stk_2]['closePrice']

    mu_0 = prices_0.mean()
    mu_1 = prices_1.mean()
    mu_2 = prices_2.mean()

    # 两只下跌较大幅度，一只较稳定，买入较稳定这只股票
    if prices_0[-1] > mu_0 and prices_1[-1] < 0.975 * mu_1 and prices_2[-1] < 0.975 * mu_2:
        order(stk_0, account.amount)
    if prices_1[-1] > mu_1 and prices_2[-1] < 0.975 * mu_2 and prices_0[-1] < 0.975 * mu_0:
        order(stk_1, account.amount)
    if prices_2[-1] > mu_2 and prices_0[-1] < 0.975 * mu_0 and prices_1[-1] < 0.975 * mu_1:
        order(stk_2, account.amount)

    # 两只上涨较大幅度，一只较稳定，卖出较稳定这只股票
    if prices_0[-1] < mu_0 and prices_1[-1] > 1.025 * mu_1 and prices_2[-1] > 1.025 * mu_2:
        order_to(stk_0, 0)
    if prices_1[-1] < mu_1 and prices_0[-1] > 1.025 * mu_0 and prices_2[-1] > 1.025 * mu_2:
        order_to(stk_1, 0)
    if prices_2[-1] < mu_2 and prices_0[-1] > 1.025 * mu_0 and prices_1[-1] > 1.025 * mu_1:
        order_to(stk_2, 0)

bt

	tradeDate	cash	stock_position	portfolio_value	benchmark_return	blotter
0	2012-01-18	1000000.00000	{}	1000000.00000	0.000000	[]
1	2012-01-19	1000000.00000	{}	1000000.00000	0.019058	[]
2	2012-01-20	1000000.00000	{}	1000000.00000	0.014478	[]
3	2012-01-30	1000000.00000	{}	1000000.00000	-0.017318	[]
4	2012-01-31	1000000.00000	{}	1000000.00000	0.001439	[]
5	2012-02-01	1000000.00000	{}	1000000.00000	-0.014311	[]
6	2012-02-02	1000000.00000	{}	1000000.00000	0.023567	[]
7	2012-02-03	1000000.00000	{}	1000000.00000	0.007985	[]
8	2012-02-06	1000000.00000	{}	1000000.00000	-0.000705	[]
9	2012-02-07	1000000.00000	{}	1000000.00000	-0.018515	[]
10	2012-02-08	1000000.00000	{}	1000000.00000	0.028594	[]
11	2012-02-09	1000000.00000	{}	1000000.00000	0.000394	[]
12	2012-02-10	1000000.00000	{}	1000000.00000	0.001737	[]
13	2012-02-13	1000000.00000	{}	1000000.00000	-0.000648	[]
14	2012-02-14	1000000.00000	{}	1000000.00000	-0.003900	[]
15	2012-02-15	1000000.00000	{}	1000000.00000	0.010904	[]
16	2012-02-16	1000000.00000	{}	1000000.00000	-0.005308	[]
17	2012-02-17	1000000.00000	{}	1000000.00000	0.000399	[]
18	2012-02-20	1000000.00000	{}	1000000.00000	0.001427	[]
19	2012-02-21	1000000.00000	{}	1000000.00000	0.008559	[]
20	2012-02-22	1000000.00000	{}	1000000.00000	0.013668	[]
21	2012-02-23	1000000.00000	{}	1000000.00000	0.003380	[]
22	2012-02-24	1000000.00000	{}	1000000.00000	0.016023	[]
23	2012-02-27	1000000.00000	{}	1000000.00000	0.003231	[]
24	2012-02-28	1000000.00000	{}	1000000.00000	0.002217	[]
25	2012-02-29	1000000.00000	{}	1000000.00000	-0.010637	[]
26	2012-03-01	1000000.00000	{}	1000000.00000	-0.000303	[]
27	2012-03-02	1000000.00000	{}	1000000.00000	0.017692	[]
28	2012-03-05	1000000.00000	{}	1000000.00000	-0.006432	[]
29	2012-03-06	1000000.00000	{}	1000000.00000	-0.015641	[]
...	...	...	...	...	...	...
664	2014-10-21	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1913031.23401	-0.008685	[]
665	2014-10-22	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1933648.47401	-0.006062	[]
666	2014-10-23	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1953640.67401	-0.009385	[]
667	2014-10-24	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1823065.79401	-0.002183	[]
668	2014-10-27	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1859302.04401	-0.009152	[]
669	2014-10-28	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1863675.44401	0.020187	[]
670	2014-10-29	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1871797.24401	0.014371	[]
671	2014-10-30	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1883042.97401	0.007156	[]
672	2014-10-31	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1913656.09401	0.015958	[]
673	2014-11-03	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1902410.64401	0.001682	[]
674	2014-11-04	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1964886.42401	0.000247	[]
675	2014-11-05	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2049228.79401	-0.003869	[]
676	2014-11-06	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2020489.70401	0.001047	[]
677	2014-11-07	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2027362.02401	-0.001564	[]
678	2014-11-10	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2043606.06401	0.025410	[]
679	2014-11-11	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2022988.64401	-0.002775	[]
680	2014-11-12	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2049228.91401	0.013957	[]
681	2014-11-13	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2054226.61401	-0.005616	[]
682	2014-11-14	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1987377.18401	0.000519	[]
683	2014-11-17	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1988626.85401	-0.005420	[]
684	2014-11-18	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2006744.97401	-0.010004	[]
685	2014-11-19	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2014241.95401	-0.001653	[]
686	2014-11-20	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1980504.81401	-0.000047	[]
687	2014-11-21	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	1989251.55401	0.018273	[]
688	2014-11-24	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2085464.99401	0.025470	[]
689	2014-11-25	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2156687.78401	0.013702	[]
690	2014-11-26	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2142942.92401	0.013949	[]
691	2014-11-27	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2146691.26401	0.011557	[]
692	2014-11-28	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2276016.94401	0.019724	[]
693	2014-12-01	1.56401	{u'000625.XSHE': 1.0, u'601633.XSHG': 62476.0}	2245404.03401	0.003913	[]

694 rows × 6 columns

perf = qp.perf_parse(bt)
out_keys = ['annualized_return', 'volatility', 'information',
            'sharpe', 'max_drawdown', 'alpha', 'beta']

for k in out_keys:
    print '%s: %s' % (k, perf[k])

annualized_return: 0.448632577093
volatility: 0.397466535866
information: 0.825863671828
sharpe: 1.04326663926
max_drawdown: 0.518092986656
alpha: 0.392363999248
beta: 0.886220585368

perf['cumulative_return'].plot()
perf['benchmark_cumulative_return'].plot()
pylab.legend(['current_strategy','HS300'])

<matplotlib.legend.Legend at 0x4e27c50>