strategy-lab/to_explore/pyquantnews/27_Decomposition.ipynb

This code retrieves and analyzes unemployment data, focusing on trends and seasonality. It uses the OpenBB SDK to fetch unemployment data from 2010 to 2019. The data is then processed to calculate rolling statistics and visualized. Seasonal decomposition and STL decomposition are applied to understand the seasonal and trend components. Additionally, the Hodrick-Prescott filter is used to separate the cyclical and trend components of the data.

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import pandas as pd

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from statsmodels.tsa.seasonal import seasonal_decompose, STL
from statsmodels.tsa.filters.hp_filter import hpfilter
from openbb_terminal.sdk import openbb

Retrieve unemployment data from OpenBB SDK for the period starting 2010

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df = openbb.economy.unemp(2010)

Set the index to the 'date' column, filter up to 2019-12-31, and sort by date

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df = df.set_index("date")[:"2019-12-31"].sort_index()

Calculate rolling mean and standard deviation with a 12-month window

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df["rolling_mean"] = df["unemp"].rolling(window=12).mean()
df["rolling_std"] = df["unemp"].rolling(window=12).std()

Plot the unemployment rate with rolling mean and standard deviation

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df.plot(title="Unemployment rate")

Perform seasonal decomposition of the unemployment data using an additive model and plot results

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decomposition_results = seasonal_decompose(
    df["unemp"], 
    model="additive"
).plot()

Apply STL decomposition to the unemployment data and plot the results

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stl_decomposition = STL(df[["unemp"]]).fit()
stl_decomposition.plot().suptitle("STL Decomposition");

Apply Hodrick-Prescott filter to decompose the unemployment data into cycle and trend components and plot results

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hp_df = df[["unemp"]].copy()
hp_df["cycle"], hp_df["trend"] = hpfilter(hp_df["unemp"], 129600)
hp_df.plot(subplots=True, title="Hodrick-Prescott filter");

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