Data source page: "Daily Treasury Par Yield Curve Rates"
CSV file (1990-present): daily UST yields
import pandas as pd
url = "https://raw.githubusercontent.com/epogrebnyak/data-ust/master/ust.csv"
df = pd.read_csv(df, parse_dates=["date"]).set_index("date")Build a local CSV file with US Treasuries yields by maturity:
import os
import pandas as pd
from ust import save_xml, read_rates, available_years
# save UST yield rates to local folder for selected years
for year in available_years():
save_xml(year, folder="./xml")
# run later - force update last year (overwrites existing file)
save_xml(2022, folder="./xml", overwrite=True)
# read UST yield rates as pandas dataframe
df = read_rates(start_year=2020, end_year=2022, folder="./xml")
print(df)
# save as single CSV file
df.to_csv("rates.csv")
# read back later
df = pd.read_csv("rates.csv", parse_dates=["date"]).set_index("date")
"""
BC_1MONTH BC_3MONTH BC_6MONTH BC_1YEAR ... BC_10YEAR BC_20YEAR BC_30YEAR BC_30YEARDISPLAY
date ...
2020-01-02 1.53 1.54 1.57 1.56 ... 1.88 2.19 2.33 2.33
2020-01-03 1.52 1.52 1.55 1.55 ... 1.80 2.11 2.26 2.26
2020-01-06 1.54 1.56 1.56 1.54 ... 1.81 2.13 2.28 2.28
2020-01-07 1.52 1.54 1.56 1.53 ... 1.83 2.16 2.31 2.31
2020-01-08 1.50 1.54 1.56 1.55 ... 1.87 2.21 2.35 2.35
... ... ... ... ... ... ... ... ... ...
2022-03-07 0.17 0.38 0.75 1.07 ... 1.78 2.29 2.19 2.19
2022-03-08 0.16 0.36 0.72 1.12 ... 1.86 2.34 2.24 2.24
2022-03-09 0.18 0.38 0.75 1.15 ... 1.94 2.38 2.29 2.29
2022-03-10 0.19 0.39 0.75 1.19 ... 1.98 2.45 2.38 2.38
2022-03-11 0.17 0.40 0.78 1.22 ... 2.00 2.45 2.36 2.36
[550 rows x 12 columns]
start_yearcan go back to 1990, but even longer dataset is avilable at FRED- API changed in February 2022
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