Financial data indicators (historical moving volatility, mean)

1. Import functions

Import NumPy as NP Import Pandas as PD Import Matplotlib.pyplot as Plt Import Tushare as TS Import Math

2. Data acquisition

data = Ts.get_hist_data ('000012', start='2015-06-23', end= ' 2017-11-16 ')

3. Moving Average

# Use of scrolling windows
data[' 42d ']= pd.rolling_mean (data[' close '],window=42)
data[' 252d ' =pd.rolling_mean (data[' close '],window=252)
Print (data[[' close ', ' 42d ', ' 252d ']].tail ())

data[[' close ', ' 42d ', ' 252d ']].plot (figsize= (8,5))
Plt.show ()

4. Moving historical fluctuations

data['return']=np.log (data['Close']/data['Close'].shift (1)) data['Mov_vol'] = PD.ROLLING_STD (data['return'],window=252) *math.sqrt (252) data[['Close','return','Mov_vol']].plot (subplots=true,style='b', Figsize= (8,7) ) plt.show ()

Financial data indicators (historical moving volatility, mean)