What will we cover in this tutorial?
Step 1: Read some Financial Historic Time Series Stock Prices
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
Step 2: Calculate the Simple Moving Average with Python and Pandas
To calculate the Simple Moving Average (MA) of the data can be done using the rolling and mean methods.
data['MA10'] = data['Close'].rolling(10).mean()
Where here we calculate the Simple Moving Average of 10 days. You can change it to fit your needs.
Step 3: Calculate the Exponential Moving Average with Python and Pandas
It is a bit more involved to calculate the Exponential Moving Average.
data['EMA10'] = data['Close'].ewm(span=10, adjust=False).mean()
There you need to set the span and adjust to False. This is needed to get the same numbers as on Yahoo! Finance.
Want to learn more?
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