均值回归策略

%matplotlib inline

import matplotlib.pyplot as plt

import 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

策略思想

均值回归策略应用了股市投资中经典的高抛低吸思想，该类型策略一般在震荡市中表现优异； 但是在单边趋势行情中一般表现糟糕，往往会大幅跑输市场；

data = ts.get_k_data('hs300', start = '2010-01-01', end='2016-06-30')[['date','close']]

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

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

data['price'].plot(figsize = (10,8))

2. 策略开发思路

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

SMA = 50

data['SMA'] = data['price'].rolling(SMA).mean()

threshold = 250 #阈值；

data['distance'] = data['price'] - data['SMA']

data['distance'].dropna().plot(figsize=(10, 6), legend=True)

plt.axhline(threshold, color='r')

plt.axhline(-threshold, color='r')

plt.axhline(0, color='r')

data['position'] = np.where(data['distance'] > threshold, -1, np.nan) #核心精髓；

data['position'] = np.where(data['distance'] < -threshold, 1, data['position'])

data['position'] = np.where(data['distance'] *

            data['distance'].shift(1) < 0, 0, data['position'])

data['position'] = data['position'].ffill().fillna(0)

data['position'].ix[SMA:].plot(ylim=[-1.1, 1.1], figsize=(10, 6))

3. 计算策略年化收益并可视化

data['strategy'] = data['position'].shift(1) * data['returns']

data[['returns', 'strategy']].dropna().cumsum().apply(np.exp).plot(figsize=(10, 6))