effr
Fed Funds Rate.
Get Effective Federal Funds Rate data. A bank rate is the interest rate a nation's central bank charges to its domestic banks to borrow money. The rates central banks charge are set to stabilize the economy.
Examples
from openbb import obb
obb.fixedincome.rate.effr(provider='fred')
obb.fixedincome.rate.effr(effr_only=True, provider='fred')
Parameters
- standard
- federal_reserve
- fred
start_date: date | str
Start date of the data, in YYYY-MM-DD format.
end_date: date | str
End date of the data, in YYYY-MM-DD format.
start_date: date | str
Start date of the data, in YYYY-MM-DD format.
end_date: date | str
End date of the data, in YYYY-MM-DD format.
start_date: date | str
Start date of the data, in YYYY-MM-DD format.
end_date: date | str
End date of the data, in YYYY-MM-DD format.
frequency: Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']
Description
Frequency aggregation to convert daily data to lower frequency.
a = Annual
q = Quarterly
m = Monthly
w = Weekly
wef = Weekly, Ending Friday
weth = Weekly, Ending Thursday
wew = Weekly, Ending Wednesday
wetu = Weekly, Ending Tuesday
wem = Weekly, Ending Monday
wesu = Weekly, Ending Sunday
wesa = Weekly, Ending Saturday
bwew = Biweekly, Ending Wednesday
bwem = Biweekly, Ending Monday
aggregation_method: Literal['avg', 'sum', 'eop']
Description
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
transform: Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']
Description
Transformation type
None = No transformation
chg = Change
ch1 = Change from Year Ago
pch = Percent Change
pc1 = Percent Change from Year Ago
pca = Compounded Annual Rate of Change
cch = Continuously Compounded Rate of Change
cca = Continuously Compounded Annual Rate of Change
log = Natural Log
effr_only: bool
Default: False
Return data without quantiles, target ranges, and volume.
Returns
results: list[FederalFundsRate]
Serializable results.
provider: Optional[Literal['federal_reserve', 'fred']]
Provider name.
warnings: Optional[list[Warning_]]
list of warnings.
chart: Optional[Chart]
Chart object.
extra: dict[str, Any]
Extra info.
Data
- standard
- federal_reserve
- fred
date: date | str
The date of the data.
rate: float
Effective federal funds rate.
target_range_upper: float
Upper bound of the target range.
target_range_lower: float
Lower bound of the target range.
percentile_1: float
1st percentile of the distribution.
percentile_25: float
25th percentile of the distribution.
percentile_75: float
75th percentile of the distribution.
percentile_99: float
99th percentile of the distribution.
volume: float
The trading volume.The notional volume of transactions (Billions of $).
date: date | str
The date of the data.
rate: float
Effective federal funds rate.
target_range_upper: float
Upper bound of the target range.
target_range_lower: float
Lower bound of the target range.
percentile_1: float
1st percentile of the distribution.
percentile_25: float
25th percentile of the distribution.
percentile_75: float
75th percentile of the distribution.
percentile_99: float
99th percentile of the distribution.
volume: float
The trading volume.The notional volume of transactions (Billions of $).
intraday_low: float
Intraday low. This field is only present for data before 2016.
intraday_high: float
Intraday high. This field is only present for data before 2016.
standard_deviation: float
Standard deviation. This field is only present for data before 2016.
revision_indicator: str
Indicates a revision of the data for that date.
date: date | str
The date of the data.
rate: float
Effective federal funds rate.
target_range_upper: float
Upper bound of the target range.
target_range_lower: float
Lower bound of the target range.
percentile_1: float
1st percentile of the distribution.
percentile_25: float
25th percentile of the distribution.
percentile_75: float
75th percentile of the distribution.
percentile_99: float
99th percentile of the distribution.
volume: float
The trading volume.The notional volume of transactions (Billions of $).