We will calculate the volatility of historic stock prices with Python library Pandas.
We will use Pandas Datareader to read some historic stock prices. See this tutorial for details.
import pandas_datareader as pdr import datetime as dt ticker = "AAPL" start = dt.datetime(2019, 1, 1) end = dt.datetime(2020, 12, 31) data = pdr.get_data_yahoo(ticker, start, end) print(data.head())
Resulting in this.
High Low Open Close Volume Adj Close Date 2019-01-02 39.712502 38.557499 38.722500 39.480000 148158800.0 38.505024 2019-01-03 36.430000 35.500000 35.994999 35.547501 365248800.0 34.669640 2019-01-04 37.137501 35.950001 36.132500 37.064999 234428400.0 36.149662 2019-01-07 37.207500 36.474998 37.174999 36.982498 219111200.0 36.069202 2019-01-08 37.955002 37.130001 37.389999 37.687500 164101200.0 36.756794
Let’s explore the difference between daily simple returns and daily log returns. Shortly explained, the log returns have the advantage that you can add them together, while this is not the case for simple returns. Therefore the log returns are used in most financial analysis.
To calculate the daily log returns we need the NumPy library. For the purpose here, we will not explore the depths of NumPy, all we need is to apply the log-function on a full column in our DataFrame (see my other FREE course for more details on NumPy).
import numpy as np data['Log returns'] = np.log(data['Close']/data['Close'].shift())
This creates a column called Log returns with the daily log return of the Close price.
We need the standard deviation for the volatility of the stock.
This can be calculated from our Log returns as follows.
The above gives the daily standard deviation. The volatility is defined as the annualized standard deviation. Using the above formula we can calculate it as follows.
volatility = data['Log returns'].std()*252**.5
Notice that square root is the same as **.5, which is the power of 1/2.
This can be visualized with Matplotlib.
str_vol = str(round(volatility, 4)*100)
fig, ax = plt.subplots()
data[‘Log returns’].hist(ax=ax, bins=50, alpha=0.6, color=’b’)
ax.set_ylabel(“Freq of log return”)
ax.set_title(“AAPL volatility: ” + str_vol + “%”)
Resulting in the following output.
This is part of the course of Master Technical Analysis with pandas.
In the next lesson you will learn about Simple and Exponential Moving Average with Python and Pandas.
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Did Carter find a strategy that will consistently beat the market?
Actually, it is not that hard to use Python to validate his calculations. But we can do better than that. If you want to work smarter than traditional investors then continue to read here.
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If you face some errors when reading data from Yahoo, try this:
!pip install --upgrade pandas-datareader
!pip install --upgrade pandas
Helped me a lot.