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    Visualize Why Long-term Investing is Less Risky – Pandas and Matplotlib

    What will we cover in this tutorial?

    We will look at how you can use Pandas Datareader (Pandas) and Matplotlib to create a visualization of why long-term investing is less risky.

    Here risk is simply meaning the risk of loosing money.

    Specifically, we will investigate how likely it is to loose money (and how much) if you invest for a 1 year perspective vs a 10 year perspective.

    Step 1: Establish the data for the investigation

    One of the most widely used index is the S&P 500 index. This index lists 500 large companies on the US market exchange and is one of the most commonly followed equity indices.

    We will use this index and retrieve data back from 1970 and up until today.

    This can be done as follow.

    import pandas_datareader as pdr
    from datetime import datetime
    data = pdr.get_data_yahoo('^GSPC', datetime(1970, 1, 1))

    Then the DataFrame data will contain all data from 1970 up until today. The ^GSPC is the ticker for the S&P 500 index.

    Step 2: Calculate the annual return from 1970 and forward using Pandas

    The annual return for a year is calculated by taking the last trading value of the divided by the first day and subtracting 1, then multiply that by 100 to get it in percentage.

    Calculating it for all years then you can visualize it with a histogram as follows.

    import pandas as pd
    import pandas_datareader as pdr
    from datetime import datetime
    import matplotlib.pyplot as plt
    data = pdr.get_data_yahoo('^GSPC', datetime(1970, 1, 1))
    years = []
    annual_return = []
    for year in range(1970, 2021):
        data_year = data.loc[f'{year}']['Adj Close']
        annual_return.append((data_year.iloc[-1] / data_year.iloc[0] - 1) * 100)
    df = pd.DataFrame(annual_return, index=years)
    bins = [i for i in range(-40, 45, 5)]
    df.plot.hist(bins=bins, title='1 year')

    Notice that we create a new DataFrame with all the annual returns for each of the years and use it to make a histogram.

    The result is as follows.

    What you see is a histogram indicating how many years a given annual return was occurring.

    Hence, a -40-35% (negative) return occurred once, while a 0-5% return happened 6 times in the span of years from 1970 to 2020 (inclusive).

    What does this tell us?

    Well, you can lose up to 40%, but you can also gain up to 35% in one year. It also shows you that it is more likely to gain (positive return) than lose.

    But what if we invested the money for 10 years.

    Step 3: Calculate the average annual return in 10 years spans starting from 1970 using Pandas

    This is actually quite similar, but with a few changes.

    First of all, the average return is calculated using the CAGR (Compound Annual Growth Rate) formula.

    This results in the following code.

    import pandas as pd
    import pandas_datareader as pdr
    from datetime import datetime
    import matplotlib.pyplot as plt
    data = pdr.get_data_yahoo('^GSPC', datetime(1970, 1, 1))
    years = []
    avg_annual_return = []
    for year in range(1970, 2011):
        data_year = data.loc[f'{year}':f'{year + 9}']['Adj Close']
        avg_annual_return.append(((data_year.iloc[-1] / data_year.iloc[0]) ** (1 / 10) - 1) * 100)
    df = pd.DataFrame(avg_annual_return, index=years)
    bins = [i for i in range(-40, 45, 5)]
    df.plot.hist(bins=bins, title='10 years')

    There are a few changes. One is the formula for the average annual return (as stated above) and the other is that we use 10 years of data. Notice, that we only add 9 to the year. This is because that both years are inclusive.

    This results in this histogram.

    As you see. One in 3 cases there was a negative return over the a 10 year span. Also, the loss was only in the range -5-0%. Otherwise, the return would be positive.

    Now is that nice?

    12% Investment Solution

    Would you like to get 12% in return of your investments?

    D. A. Carter promises and shows how his simple investment strategy will deliver that in the book The 12% Solution. The book shows how to test this statement by using backtesting.

    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.

    Python for Finance: Unlock Financial Freedom and Build Your Dream Life

    Discover the key to financial freedom and secure your dream life with Python for Finance!

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    Don’t let your dreams slip away. Master Python for Finance and pave your way to a profitable and fulfilling career. Start building the future you deserve today!

    Python for Finance a 21 hours course that teaches investing with Python.

    Learn pandas, NumPy, Matplotlib for Financial Analysis & learn how to Automate Value Investing.

    “Excellent course for anyone trying to learn coding and investing.” – Lorenzo B.

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