main.py

import pandas as pd
from read_input import read_model_input
from term_structure import calculate_zero_coupon_price
from black_sholes import set_up_black_sholes
from vasicek import set_up_vasicek
from hull_white import set_up_hull_white

param_raw = pd.read_csv("Parameters.csv", sep=',', index_col=0)

combined_run = []

for run_id in param_raw.index:
    [modeling_parameters, curve_parameters] = read_model_input(run_id)
    
    zero_coupon_price = lambda t: calculate_zero_coupon_price(t, curve_parameters["target_maturities"], curve_parameters["calibration_vector"], curve_parameters["ultimate_forward_rate"], curve_parameters["convergence_speed"] )
    if modeling_parameters["run_type"] == "HW":
        run = set_up_hull_white(run_id, modeling_parameters, zero_coupon_price)        
    elif modeling_parameters["run_type"] == "BS":
        run = set_up_black_sholes(run_id, modeling_parameters, zero_coupon_price)
    elif modeling_parameters["run_type"] == "V":
        run = set_up_vasicek(run_id, modeling_parameters, zero_coupon_price)
    else:
        raise ValueError("Model type not available")

    if isinstance(combined_run,pd.DataFrame):
        combined_run = pd.concat([combined_run,run])
    else:
        combined_run = run

combined_run.to_csv("Output/run.csv")