
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib notebook


data = pd.read_csv("AAPL.csv", index_col=0, parse_dates=True)


data.head()


data['%-chg'] = data['Close'].pct_change()


data.head()


(79.422501 - 77.237503)/77.237503

0.028289340218572226


import numpy as np


data['Log returns'] = np.log(data['Close']/data['Close'].shift())


data.head()


data['Log returns'].std()

0.02976380454385509


volatility = data['Log returns'].std()*252**.5


volatility

0.47248574936504895


str_vol = str(round(volatility, 4)*100)


str_vol

'47.25'


fig, ax = plt.subplots()
data['Log returns'].hist(ax=ax, bins=50, alpha=0.6, color='b')
ax.set_xlabel("Log return")
ax.set_ylabel("Freq of log return")
ax.set_title("AAPL volatility: " + str_vol + "%")

Text(0.5, 1.0, 'AAPL volatility: 47.25%')


data.head()


data['MA10'] = data['Close'].rolling(10).mean()


data.tail()


data['EMA10'] = data['Close'].ewm(span=10, adjust=False).mean()


data.tail()


fig, ax = plt.subplots()
data[['MA10', 'EMA10']].loc['2020-12-01':].plot(ax=ax)
data['Close'].loc['2020-12-01':].plot(ax=ax, alpha=0.25)

<AxesSubplot:xlabel='Date'>

	Open	High	Low	Close	Adj Close	Volume
Date
2020-01-27	77.514999	77.942497	76.220001	77.237503	76.576187	161940000
2020-01-28	78.150002	79.599998	78.047501	79.422501	78.742477	162234000
2020-01-29	81.112503	81.962502	80.345001	81.084999	80.390747	216229200
2020-01-30	80.135002	81.022499	79.687500	80.967499	80.274246	126743200
2020-01-31	80.232498	80.669998	77.072502	77.377502	76.714989	199588400

	Open	High	Low	Close	Adj Close	Volume	%-chg
Date
2020-01-27	77.514999	77.942497	76.220001	77.237503	76.576187	161940000	NaN
2020-01-28	78.150002	79.599998	78.047501	79.422501	78.742477	162234000	0.028289
2020-01-29	81.112503	81.962502	80.345001	81.084999	80.390747	216229200	0.020932
2020-01-30	80.135002	81.022499	79.687500	80.967499	80.274246	126743200	-0.001449
2020-01-31	80.232498	80.669998	77.072502	77.377502	76.714989	199588400	-0.044339

	Open	High	Low	Close	Adj Close	Volume	%-chg	Log returns
Date
2020-01-27	77.514999	77.942497	76.220001	77.237503	76.576187	161940000	NaN	NaN
2020-01-28	78.150002	79.599998	78.047501	79.422501	78.742477	162234000	0.028289	0.027897
2020-01-29	81.112503	81.962502	80.345001	81.084999	80.390747	216229200	0.020932	0.020716
2020-01-30	80.135002	81.022499	79.687500	80.967499	80.274246	126743200	-0.001449	-0.001450
2020-01-31	80.232498	80.669998	77.072502	77.377502	76.714989	199588400	-0.044339	-0.045352

	Open	High	Low	Close	Adj Close	Volume	%-chg	Log returns
Date
2020-01-27	77.514999	77.942497	76.220001	77.237503	76.576187	161940000	NaN	NaN
2020-01-28	78.150002	79.599998	78.047501	79.422501	78.742477	162234000	0.028289	0.027897
2020-01-29	81.112503	81.962502	80.345001	81.084999	80.390747	216229200	0.020932	0.020716
2020-01-30	80.135002	81.022499	79.687500	80.967499	80.274246	126743200	-0.001449	-0.001450
2020-01-31	80.232498	80.669998	77.072502	77.377502	76.714989	199588400	-0.044339	-0.045352

	Open	High	Low	Close	Adj Close	Volume	%-chg	Log returns	MA10
Date
2021-01-20	128.660004	132.490005	128.550003	132.029999	132.029999	104319500	0.032856	0.032328	129.415000
2021-01-21	133.800003	139.669998	133.589996	136.869995	136.869995	120529500	0.036658	0.036002	130.442000
2021-01-22	136.279999	139.850006	135.020004	139.070007	139.070007	114459400	0.016074	0.015946	131.257001
2021-01-25	143.070007	145.089996	136.539993	142.919998	142.919998	157282400	0.027684	0.027308	132.344000
2021-01-26	143.600006	144.300003	141.369995	142.080002	142.080002	50388565	-0.005877	-0.005895	133.654001

Calculating simple¶

Standard deviation¶