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    Stochastic Oscillator with Pandas DataFrames

    What will we cover?

    In this tutorial we will show how to calculate the Stochastic Oscillator with Pandas DataFrames.

    Watch tutorial

    Step 1: Retrieve the Data from CSV file with Pandas DataFrames

    We first need to read the data from a CSV file into a DataFrame. You can get the CSV file from here or directly from Yahoo! Finance.

    Alternatively you can you PandasDataframes as described in this tutorial.

    import pandas as pd
    import matplotlib.pyplot as plt
    %matplotlib notebook
    data = pd.read_csv("AAPL.csv", index_col=0, parse_dates=True)

    Step 2: Calculate the Stochastic Oscillator with Pandas DataFrames

    The Stochastic Oscillator is defined as follows.

    • 14-high: Maximum of last 14 trading days
    • 14-low: Minimum of last 14 trading days
    • %K(Last Close – 14-low)*100 / (14-high – 14-low)
    • %D: Simple Moving Average of %K

    That can be done as follows.

    high14 = data['High'].rolling(14).max()
    low14 = data['Low'].rolling(14).min()
    data['%K'] = (data['Close'] - low14)*100/(high14 - low14)
    data['%D'] = data['%K'].rolling(3).mean()

    Notice, we only keep the %K and %D. The high14 and low14 are temporary variables to make our calculations easier to read.

    Step 3: Visualize the Stochastic Oscillator with Matplotlib

    To visualize it.

    fig, ax = plt.subplots()
    data[['%K', '%D']].loc['2020-11-01':].plot(ax=ax)
    ax.axhline(80, c='r', alpha=0.3)
    ax.axhline(20, c='r', alpha=0.3)
    data['Close'].loc['2020-11-01':].plot(ax=ax, alpha=0.3, secondary_y=True)

    Resulting in the following.

    Want to learn more?

    This is part of a 2-hour full video course in 8 parts about Technical Analysis with Python.

    In the next lesson you will learn how to Export DataFrames to Excel with Charts in Multiple Sheets.

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