Follow these steps to implement the rolling three-factor model in Python.

1. Import the libraries:

import pandas as pd
import yfinance as yf
import statsmodels.formula.api as smf
import pandas_datareader.data as web

2. Define the parameters:

ASSETS = ['AMZN', 'GOOG', 'AAPL', 'MSFT']
WEIGHTS = [0.25, 0.25, 0.25, 0.25]
START_DATE = '2009-12-31'
END_DATE = '2018-12-31'

3. Download the factor-related data:

df_three_factor = web.DataReader('F-F_Research_Data_Factors', 
                                 'famafrench', start=START_DATE)[0]
df_three_factor = df_three_factor.div(100)
df_three_factor.index = df_three_factor.index.format()

4. Download the prices of risky assets from Yahoo Finance:

asset_df = yf.download(ASSETS,
                       start=START_DATE,
                       end=END_DATE,
                       adjusted=True,
                       progress=False)

5. Calculate the monthly returns on the risky assets:

asset_df = asset_df['Adj Close'].resample('M') 
                                .last() 
                                .pct_change() 
                                .dropna()
asset_df.index = asset_df.index.strftime('%Y-%m')

6. Calculate the portfolio returns:

asset_df['portfolio_returns'] = np.matmul(asset_df[ASSETS].values, 
                                          WEIGHTS)

7. Merge the datasets:

ff_data = asset_df.join(df_three_factor).drop(ASSETS, axis=1)
ff_data.columns = ['portf_rtn', 'mkt', 'smb', 'hml', 'rf']
ff_data['portf_ex_rtn'] = ff_data.portf_rtn - ff_data.rf

8. Define a function for the rolling n-factor model:

def rolling_factor_model(input_data, formula, window_size):
    coeffs = []

    for start_index in range(len(input_data) - window_size + 1): 
        end_index = start_index + window_size

        ff_model = smf.ols(
            formula=formula,
            data=input_data[start_index:end_index]
        ).fit()
   
        coeffs.append(ff_model.params)

    coeffs_df = pd.DataFrame(
        coeffs, 
        index=input_data.index[window_size - 1:]
    )

    return coeffs_df

9. Estimate the rolling three-factor model and plot the results:

MODEL_FORMULA = 'portf_ex_rtn ~ mkt + smb + hml'
results_df = rolling_factor_model(ff_data, 
                                  MODEL_FORMULA, 
                                  window_size=60)
results_df.plot(title = 'Rolling Fama-French Three-Factor model')

Executing the code results in the following plot:

By inspecting the preceding plot, we can see the following:

• The intercept is almost constant and very close to 0.
• There is some variability in the factors, but no sudden reversals or unexpected jumps.