%matplotlib inline

import matplotlib.pyplot as plt

plt.style.use('seaborn')

import matplotlib as mpl

mpl.rcParams['font.family'] = 'serif'

import warnings; warnings.simplefilter('ignore')      #忽略警告信息；

import numpy as np

import pandas as pd

import tushare as ts

1. 数据准备 & 回测准备

data = ts.get_k_data('hs300', start = '2010-01-01', end='2017-06-30')[['date','close']]

data.rename(columns={'close': 'price'}, inplace=True)

data.set_index('date', inplace = True)

2. 策略开发思路

data['returns'] = np.log(data['price'] / data['price'].shift(1))

data['position'] = np.sign(data['returns'])

#关键语句,np.sign()很多地方用到；向量化；

data['s