Skip to content

Federal Reserve

You can import the module via

from quantflow.data.fed import FederalReserve

quantflow.data.fed.FederalReserve dataclass 

FederalReserve(
    url="https://www.federalreserve.gov/datadownload/Output.aspx",
)

Bases: AioHttpClient

Federal Reserve API client.

This class is used to fetch yield curves from the Federal Reserve data download

url class-attribute instance-attribute 

url = "https://www.federalreserve.gov/datadownload/Output.aspx"

Base URL for the Federal Reserve data download API.

yield_curves async 

yield_curves(**params)

Treasury constant maturity par yields indexed by observation date.

Columns are the tenors in MATURITIES and rates are returned as decimals (for example 0.0372 for 3.72%).

Source code in quantflow/data/fed.py 
51
52
53
54
55
56
57
58
59
60
61
62
63
64
async def yield_curves(self, **params: Any) -> pd.DataFrame:
    """Treasury constant maturity par yields indexed by observation date.

    Columns are the tenors in ``MATURITIES`` and rates are returned as
    decimals (for example ``0.0372`` for 3.72%).
    """
    params.update(series="bf17364827e38702b42a58cf8eaa3f78", rel="H15")
    data = await self._get_text(params)
    df = pd.read_csv(data, header=5, index_col=None, parse_dates=True)
    df.columns = list(("date",) + MATURITIES)  # type: ignore
    df["date"] = pd.to_datetime(df["date"])
    df = df.set_index("date").replace("ND", np.nan)
    df = df.apply(pd.to_numeric, errors="coerce") / 100.0
    return df.dropna(axis=0, how="all")

ref_rates async 

ref_rates(**params)

Get a pandas dataframe of policy rates

Prior to 2021-07-08 it is the rate on excess reserves (IOER rate) After 2021-07-08 it is the rate on reserve balances (IORB rate)

The IOER rate was the primary tool used by the Federal Reserve to set a floor on the federal funds rate. While the Interest rate on required reserves (IORR rate) existed, the IOER rate had a more direct impact on market rates, as banks typically held far more excess reserves than required reserves. Therefore, the IOER rate was more influential in the Fed's monetary policy implementation.

Source code in quantflow/data/fed.py 
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
async def ref_rates(self, **params: Any) -> pd.DataFrame:
    """Get a pandas dataframe of policy rates

    Prior to 2021-07-08 it is the rate on excess reserves (IOER rate)
    After 2021-07-08 it is the rate on reserve balances (IORB rate)

    The IOER rate was the primary tool used by the Federal Reserve to set
    a floor on the federal funds rate.
    While the Interest rate on required reserves (IORR rate) existed,
    the IOER rate had a more direct impact on market rates,
    as banks typically held far more excess reserves than required reserves.
    Therefore, the IOER rate was more influential
    in the Fed's monetary policy implementation.
    """
    params.update(series="c27939ee810cb2e929a920a6bd77d9f6", rel="PRATES")
    data = await self._get_text(params)
    df = pd.read_csv(data, header=5, index_col=None, parse_dates=True)
    ioer = df["RESBME_N.D"]
    iorb = df["RESBM_N.D"]
    rate = iorb.combine_first(ioer)
    return pd.DataFrame(
        {
            "date": df["Time Period"],
            "rate": rate,
        }
    )