移动平均策略（MA）
2019-07-30 本文已影响0人
跨界师
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移动平均策略是趋势追踪思想的经典策略之一。
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策略使用的方法分为两种：
第一种：
1. 当上升并且交叉穿过X天的移动平均线时买入
2. 当下降并且交叉穿过X天的移动平均线时卖出
第二种：
1. 当x天的移动平均线上升并交叉穿过y天移动平均线时买入；
2. 当x天的移动平均线下降并交叉穿过y天移动平均线时卖出；
代码：
# 语言环境： python3
import tushare as ts
import matplotlib.pyplot as plt
import seaborn
import matplotlib as mpl
mpl.rcParams['font.family'] = 'serif' # 解决中文乱码问题
import numpy as np
import pandas as pd
# 获取代码为600030的股票
data = ts.get_k_data('600030',start='2010-01-01',end='2017-06-30')
data.head()
# 显示的结果
date open close high low volume code
0 2010-01-04 17.016 16.639 17.176 16.612 1106207.58 600030
1 2010-01-05 16.777 17.452 17.601 16.383 2093915.41 600030
2 2010-01-06 17.548 17.250 17.628 17.229 1437889.30 600030
3 2010-01-07 17.239 16.830 17.484 16.697 1235592.34 600030
4 2010-01-08 16.718 17.154 17.208 16.644 1040929.92 600030
# 将时间列作为索引列
data.set_index('date',inplace=True)
data.head()
open close high low volume code
date
2010-01-04 17.016 16.639 17.176 16.612 1106207.58 600030
2010-01-05 16.777 17.452 17.601 16.383 2093915.41 600030
2010-01-06 17.548 17.250 17.628 17.229 1437889.30 600030
2010-01-07 17.239 16.830 17.484 16.697 1235592.34 600030
2010-01-08 16.718 17.154 17.208 16.644 1040929.92 600030
# 平移数据
# 移动平均
data['SMA_20'] = data['close'].rolling(20).mean()
data['SMA_60'] = data['close'].rolling(60).mean()
data.head()
# 画出收盘价、移动20天周期的平均线和移动60天周期的平均线
data[['close','SMA_20','SMA_60']].plot(figsize=(10,6))
20天周期和60天周期
上图可以看出20天和60天的区别，另外下图是将上图中的三条曲线分开得到的；
三条曲线分开展示
# 下面需要计算收益率和策略
data['returns'] = np.log(data['close']/data['close'].shift(1))
data['position'] = np.where(data['SMA_20']>data['SMA_60'],1,-1)
data['returns_dis2'] = data['close']/data['close'].shift(1) - 1
data['returns_dis'] = data['close'].pct_change()
data.head()
open close high low volume code SMA1 SMA2 SMA_20 SMA_60 return yes_close position returns returns_dis2 returns_dis
date
2010-01-04 17.016 16.639 17.176 16.612 1106207.58 600030 NaN NaN NaN NaN NaN NaN -1 NaN NaN NaN
2010-01-05 16.777 17.452 17.601 16.383 2093915.41 600030 NaN NaN NaN NaN 0.047705 16.639 -1 0.047705 0.048861 0.048861
2010-01-06 17.548 17.250 17.628 17.229 1437889.30 600030 NaN NaN NaN NaN -0.011642 17.452 -1 -0.011642 -0.011575 -0.011575
2010-01-07 17.239 16.830 17.484 16.697 1235592.34 600030 NaN NaN NaN NaN -0.024649 17.250 -1 -0.024649 -0.024348 -0.024348
2010-01-08 16.718 17.154 17.208 16.644 1040929.92 600030 NaN NaN NaN NaN 0.019068 16.830 -1 0.019068 0.019251 0.019251
# 计算累计收益率
data['returns'].cumsum().apply(np.exp).plot(figsize=(10,6)) # 累计收益
累计收益率
