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Market Competitiveness

来源：https://uqer.io/community/share/54b5c2f1f9f06c276f651a17

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>

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