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

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>