动量策略

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

#计算Momentum策略收益；避免未来函数；

3. 策略可视化

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

#计算出策略的最终的累计收益；

策略的问题和思考？ 过于频繁的买卖开仓；

4. 策略优化之思路——参数优化和穷举

data['position_5'] = np.sign(data['returns'].rolling(5).mean())

data['strategy_5'] = data['position_5'].shift(1) * data['returns']

data[['returns', 'strategy_5']].dropna().cumsum().apply(np.exp).plot(figsize=(10, 6))

参数寻优——使用离散Return计算方法

data['returns_dis'] = data['price'] / data['price'].shift(1)-1

#data['returns_dis'] = data['price'].pct_change()

data['returns_dis_cum'] = (data['returns_dis']+1).cumprod()

price_plot = ['returns_dis_cum']

for days in [10,20,30,60]:

#    data['position_%d' % days] = np.sign(data['returns'].rolling(days).mean())

    price_plot.append('sty_cumr_%dd' % days)

    data['position_%dd' % days] = np.where(data['returns'].rolling(days).mean()>0,1,-1)

    data['strategy_%dd' % days] = data['position_%dd' % days].shift(1) * data['returns']

    data['sty_cumr_%dd' % days] = (data['strategy_%dd' % days]+1).cumprod()

data[price_plot].dropna().plot(

    title='HS300 Multi Parameters Momuntum Strategy',

    figsize=(10, 6), style=['--', '--', '--', '--','--'])

5. 策略优化思路之—— High Frequency Data用于Momentum策略

hs300_hf = ts.get_k_data('hs300', ktype='5')

hs300_hf.set_index('date',inplace = True)

hs300_hf.index = hs300_hf.index.to_datetime()

hs300_hf['2017-07-15':'2017-07-28'].head(5)

hs300_hf['returns'] = np.log(hs300_hf['close'] / hs300_hf['close'].shift(1))

hs300_hf['position'] = np.sign(hs300_hf['returns'].rolling(10).mean()) #10个5分钟平均；

hs300_hf['strategy'] = hs300_hf['position'].shift(1) * hs300_hf['returns']

hs300_hf[['returns', 'strategy']].dropna().cumsum().apply(np.exp).plot(figsize=(10, 6),

                                                                      style=['--', '--'])