main.py

import smithwilson as sw
import pandas as pd

# Input - Switzerland EIOPA spot rates with LLP 25 years and extrapolation period of 150 years
# Source: https://eiopa.europa.eu/Publications/Standards/EIOPA_RFR_20190531.zip
#         EIOPA_RFR_20190531_Term_Structures.xlsx; Tab: RFR_spot_no_VA
rates = [
    -0.00803, -0.00814, -0.00778, -0.00725, -0.00652, -0.00565, -0.0048, -0.00391, -0.00313, -0.00214, -0.0014, -0.00067,
    -0.00008, 0.00051, 0.00108, 0.00157, 0.00197, 0.00228, 0.0025, 0.00264, 0.00271, 0.00274, 0.0028, 0.00291, 0.00309
]
terms = [float(y + 1) for y in range(len(rates))]     # 1.0, 2.0, ..., 25.0
ufr = 0.029
alpha = 0.128562

# Target - Extrapolate to 150 years
terms_target = [float(y + 1) for y in range(150)]     # 1.0, 2.0, ..., 150.0

# Calculate fitted rates based on actual observations and two parametes alpha & UFR
fitted_rates = sw.fit_smithwilson_rates(rates_obs=rates, t_obs=terms, t_target=terms_target, alpha=alpha, ufr=ufr)

# Display Outputs
# Create dictionary with maturity as key and rate as value
extrapolated = dict(zip(terms_target, fitted_rates.flatten()))
print(extrapolated)

# Create dataframe
observed_df = pd.DataFrame(data=rates, index=terms, columns=["observed"])
extrapolated_df = pd.DataFrame(data=fitted_rates, index=terms_target, columns=["extrapolated"])

# Combie and print dataframe
print(observed_df.join(extrapolated_df, how="outer"))