双均线

技术/策略/双均线策略.md

+# 双均线策略
+
+首先导入所需的库。
+
+```py
+# coding: utf-8
+
+import numpy as np
+import pandas as pd
+import tushare as ts
+import matplotlib.pyplot as plt
+```
+
+我使用“华夏上证50ETF”来试验。取 2011.1.1 到 2017.12.31 之间的数据。多往前取点数据，因为需要计算 MA120。
+
+```py
+df = ts.get_k_data('510050', start='20100701', end='20171231')
+df.set_index(pd.DatetimeIndex(df.date), inplace=True)
+df.drop('date', axis=1, inplace=True)
+
+df.head()
+'''
+             open  close   high    low     volume    code
+date
+2011-01-04  1.987  2.009  2.018  1.978  3270052.0  510050
+2011-01-05  1.997  1.993  2.006  1.989  2975378.0  510050
+2011-01-06  1.995  1.980  2.005  1.973  2769546.0  510050
+2011-01-07  1.980  2.000  2.031  1.966  5507308.0  510050
+2011-01-10  1.998  1.973  2.014  1.971  3589159.0  510050
+'''
+```
+
+计算 ROC。
+
+```py
+close_m1 = df.close.shift(1)
+df['roc'] = (df.close - close_m1) / close_m1
+
+df.head()
+'''
+             open  close   high    low     volume    code       roc
+date
+2011-01-04  1.987  2.009  2.018  1.978  3270052.0  510050       NaN
+2011-01-05  1.997  1.993  2.006  1.989  2975378.0  510050 -0.007964
+2011-01-06  1.995  1.980  2.005  1.973  2769546.0  510050 -0.006523
+2011-01-07  1.980  2.000  2.031  1.966  5507308.0  510050  0.010101
+2011-01-10  1.998  1.973  2.014  1.971  3589159.0  510050 -0.013500
+'''
+```
+
+计算 MA20 和 MS120，你也可以试验其它的窗口。SMA 定义请参照指标部分。
+
+```py
+ma20 = sma(df.close, 20)
+ma20 = np.append([np.nan] * 19, ma20)
+df['ma20'] = ma20
+
+ma120 = sma(df.close, 120)
+ma120 = np.append([np.nan] * 119, ma120)
+df['ma120'] = ma120
+
+df.dropna(inplace=True)
+
+df.head()
+'''
+             open  close   high    low     volume    code       roc     ma20  \
+date
+2011-07-01  2.001  1.995  2.011  1.988  1882240.0  510050 -0.001502  1.95860
+2011-07-04  2.007  2.048  2.049  2.007  4154892.0  510050  0.026566  1.96270
+2011-07-05  2.046  2.039  2.050  2.031  2152954.0  510050 -0.004395  1.96600
+2011-07-06  2.036  2.026  2.036  2.011  2145618.0  510050 -0.006376  1.96875
+2011-07-07  2.040  2.018  2.050  2.016  2740925.0  510050 -0.003949  1.97255
+
+               ma120
+date
+2011-07-01  2.022592
+2011-07-04  2.022917
+2011-07-05  2.023300
+2011-07-06  2.023683
+2011-07-07  2.023833
+'''
+```
+
+绘制收盘价、MA20 和 MA120，大家可以看出点规律了。
+
+```py
+df[['close', 'ma20', 'ma120']].plot()
+plt.show()
+```
+
+![](img/stg-2ma-1.png)
+
+下面是我们的策略。如果 MA20 比 MA120 大 5% 或以上，就买入，否则卖出。5% 是阈值，防止频繁买卖，你也可以试验其它的。
+
+
+```py
+signal = (df.ma20 - df.ma120 > df.ma120 * 0.05).astype(int)
+```
+
+`signal`是买入或者卖出信号，它是连续的 01 数组。由于我们拿到某一天的收盘价，判断之后，下一天才可以操作，所以后移一天。
+
+```py
+df['signal'] = signal.shift(1)
+df.dropna(inplace=True)
+
+df.head()
+'''
+             open  close   high    low     volume    code       roc     ma20  \
+date
+2011-07-04  2.007  2.048  2.049  2.007  4154892.0  510050  0.026566  1.96270
+2011-07-05  2.046  2.039  2.050  2.031  2152954.0  510050 -0.004395  1.96600
+2011-07-06  2.036  2.026  2.036  2.011  2145618.0  510050 -0.006376  1.96875
+2011-07-07  2.040  2.018  2.050  2.016  2740925.0  510050 -0.003949  1.97255
+2011-07-08  2.020  2.027  2.037  2.014  2566631.0  510050  0.004460  1.97655
+
+               ma120  signal
+date
+2011-07-04  2.022917     0.0
+2011-07-05  2.023300     0.0
+2011-07-06  2.023683     0.0
+2011-07-07  2.023833     0.0
+2011-07-08  2.024283     0.0
+'''
+```
+
+然后计算基准收益和策略收益：
+
+```py
+df['benchmark'] = (df.roc + 1).cumprod() - 1
+df['strategy'] = (df.roc * df.signal + 1).cumprod() - 1
+
+df.head()
+'''
+             open  close   high    low     volume    code       roc     ma20  \
+date
+2011-07-04  2.007  2.048  2.049  2.007  4154892.0  510050  0.026566  1.96270
+2011-07-05  2.046  2.039  2.050  2.031  2152954.0  510050 -0.004395  1.96600
+2011-07-06  2.036  2.026  2.036  2.011  2145618.0  510050 -0.006376  1.96875
+2011-07-07  2.040  2.018  2.050  2.016  2740925.0  510050 -0.003949  1.97255
+2011-07-08  2.020  2.027  2.037  2.014  2566631.0  510050  0.004460  1.97655
+
+               ma120  signal  benchmark  strategy
+date
+2011-07-04  2.022917     0.0   0.026566       0.0
+2011-07-05  2.023300     0.0   0.022055       0.0
+2011-07-06  2.023683     0.0   0.015539       0.0
+2011-07-07  2.023833     0.0   0.011529       0.0
+2011-07-08  2.024283     0.0   0.016040       0.0
+'''
+```
+
+最后绘制两个受益的曲线。
+
+```py
+df[['benchmark', 'strategy']].plot()
+plt.show()
+```
+
+![](img/stg-2ma-2.png)