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[Enhancement] Fixed Income Enhancements (#6490)

Browse Source 
* improve effr and move sofr to fixedincome.rate

* federal_reserve default start date

* fred cassette

* commercial paper

* cp series map

* update sofr and add federal_reserve to endpoint

* examples

* update ameribor

* maturity_dict

* add overnight bank funding rate

* add federal reserve to obfr

* pop footnoteId

* central bank holdings descriptions

* move moody's bond indices to the bond_indices endpoint

* add it to the INDEX_CHOICES

* improve hqm

* handle none

* add treasury minus fed funds to yield curve

* static assets

* estr

* description

* missed file in commit

* second return statement
This commit is contained in:
Danglewood
2024-06-18 13:42:55 -07:00
committed by GitHub
parent d465f22fbd
commit 1a990ba7f0
62 changed files with 7508 additions and 3431 deletions
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17
openbb_platform/core/openbb_core/provider/standard_models/ameribor_rates.py → openbb_platform/core/openbb_core/provider/standard_models/ameribor.py
View File

@@ -13,7 +13,7 @@ from openbb_core.provider.utils.descriptions import (
)
class AMERIBORQueryParams(QueryParams):
class AmeriborQueryParams(QueryParams):
"""AMERIBOR Query."""
start_date: Optional[dateType] = Field(
@@ -26,8 +26,19 @@ class AMERIBORQueryParams(QueryParams):
)
class AMERIBORData(Data):
class AmeriborData(Data):
"""AMERIBOR Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: Optional[float] = Field(description="AMERIBOR rate.")
symbol: Optional[str] = Field(
default=None, description=DATA_DESCRIPTIONS.get("symbol", "")
)
maturity: str = Field(description="Maturity length of the item.")
rate: float = Field(
description="Interest rate.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
title: Optional[str] = Field(
default=None,
description="Title of the series.",
)

35
openbb_platform/core/openbb_core/provider/standard_models/cp.py → openbb_platform/core/openbb_core/provider/standard_models/commercial_paper.py
View File

@@ -3,9 +3,9 @@
from datetime import (
date as dateType,
)
from typing import Literal, Optional
from typing import Optional
from pydantic import Field, field_validator
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
@@ -26,28 +26,21 @@ class CommercialPaperParams(QueryParams):
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
maturity: Literal["overnight", "7d", "15d", "30d", "60d", "90d"] = Field(
default="30d",
description="The maturity.",
)
category: Literal["asset_backed", "financial", "nonfinancial"] = Field(
default="financial",
description="The category.",
)
grade: Literal["aa", "a2_p2"] = Field(
default="aa",
description="The grade.",
)
@field_validator("maturity", "category", "grade", mode="before", check_fields=False)
@classmethod
def to_lower(cls, v: Optional[str]) -> Optional[str]:
"""Convert field to lowercase."""
return v.lower() if v else v
class CommercialPaperData(Data):
"""Commercial Paper Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: Optional[float] = Field(description="Commercial Paper Rate.")
symbol: Optional[str] = Field(
default=None, description=DATA_DESCRIPTIONS.get("symbol", "")
)
maturity: str = Field(description="Maturity length of the item.")
rate: float = Field(
description="Interest rate.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
title: Optional[str] = Field(
default=None,
description="Title of the series.",
)

33
openbb_platform/core/openbb_core/provider/standard_models/estr_rates.py
View File

@@ -1,33 +0,0 @@
"""ESTR Standard Model."""
from datetime import date as dateType
from typing import Optional
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
class ESTRQueryParams(QueryParams):
"""ESTR Query."""
start_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
class ESTRData(Data):
"""ESTR Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: Optional[float] = Field(description="ESTR rate.")

67
openbb_platform/core/openbb_core/provider/standard_models/euro_short_term_rate.py Normal file
View File

@@ -0,0 +1,67 @@
"""Euro Short Term Rate Standard Model."""
from datetime import date as dateType
from typing import Optional
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
class EuroShortTermRateQueryParams(QueryParams):
"""Euro Short Term Rate Query."""
start_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
class EuroShortTermRateData(Data):
"""Euro Short Term Rate Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: float = Field(
description="Volume-weighted trimmed mean rate.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_25: Optional[float] = Field(
default=None,
description="Rate at 25th percentile of volume.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_75: Optional[float] = Field(
default=None,
description="Rate at 75th percentile of volume.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
volume: Optional[float] = Field(
default=None,
description=DATA_DESCRIPTIONS.get("volume", "")+ " (Millions of €EUR).",
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1e6,
},
)
transactions: Optional[int] = Field(
default=None,
description="Number of transactions.",
)
number_of_banks: Optional[int] = Field(
default=None,
description="Number of active banks.",
)
large_bank_share_of_volume: Optional[float] = Field(
default=None,
description="The percent of volume attributable to the 5 largest active banks.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)

33
openbb_platform/core/openbb_core/provider/standard_models/fed_rates.py
View File

@@ -1,33 +0,0 @@
"""FED Standard Model."""
from datetime import date as dateType
from typing import Optional
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
class FEDQueryParams(QueryParams):
"""FED Query."""
start_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
class FEDData(Data):
"""FED Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: Optional[float] = Field(description="FED rate.")

75
openbb_platform/core/openbb_core/provider/standard_models/federal_funds_rate.py Normal file
View File

@@ -0,0 +1,75 @@
"""Federal Funds Rate Standard Model."""
from datetime import date as dateType
from typing import Optional
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
class FederalFundsRateQueryParams(QueryParams):
"""Federal Funds Rate Query."""
start_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
class FederalFundsRateData(Data):
"""Federal Funds Rate Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: float = Field(
description="Effective federal funds rate.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
target_range_upper: Optional[float] = Field(
default=None,
description="Upper bound of the target range.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
target_range_lower: Optional[float] = Field(
default=None,
description="Lower bound of the target range.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_1: Optional[float] = Field(
default=None,
description="1st percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_25: Optional[float] = Field(
default=None,
description="25th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_75: Optional[float] = Field(
default=None,
description="75th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_99: Optional[float] = Field(
default=None,
description="99th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
volume: Optional[float] = Field(
default=None,
description=DATA_DESCRIPTIONS.get("volume", "")
+ "The notional volume of transactions (Billions of $).",
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1e9,
},
)

22
openbb_platform/core/openbb_core/provider/standard_models/hqm.py → openbb_platform/core/openbb_core/provider/standard_models/high_quality_market.py
View File

@@ -3,9 +3,9 @@
from datetime import (
date as dateType,
)
from typing import Literal, Optional
from typing import Optional, Union
from pydantic import Field, field_validator
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
@@ -18,26 +18,18 @@ from openbb_core.provider.utils.descriptions import (
class HighQualityMarketCorporateBondQueryParams(QueryParams):
"""High Quality Market Corporate Bond Query."""
date: Optional[dateType] = Field(
date: Optional[Union[dateType, str]] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("date", ""),
)
yield_curve: Literal["spot", "par"] = Field(
default="spot",
description="The yield curve type.",
)
@field_validator("yield_curve", mode="before", check_fields=False)
@classmethod
def to_lower(cls, v: Optional[str]) -> Optional[str]:
"""Convert field to lowercase."""
return v.lower() if v else v
class HighQualityMarketCorporateBondData(Data):
"""High Quality Market Corporate Bond Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: Optional[float] = Field(description="HighQualityMarketCorporateBond Rate.")
rate: float = Field(
description="Interest rate.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
maturity: str = Field(description="Maturity.")
yield_curve: Literal["spot", "par"] = Field(description="The yield curve type.")

65
openbb_platform/core/openbb_core/provider/standard_models/overnight_bank_funding_rate.py Normal file
View File

@@ -0,0 +1,65 @@
"""Overnight Bank Funding Rate Standard Model."""
from datetime import date as dateType
from typing import Optional
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
class OvernightBankFundingRateQueryParams(QueryParams):
"""Overnight Bank Funding Rate Query."""
start_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
class OvernightBankFundingRateData(Data):
"""Overnight Bank Funding Rate Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: float = Field(
description="Overnight Bank Funding Rate.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_1: Optional[float] = Field(
default=None,
description="1st percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_25: Optional[float] = Field(
default=None,
description="25th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_75: Optional[float] = Field(
default=None,
description="75th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_99: Optional[float] = Field(
default=None,
description="99th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
volume: Optional[float] = Field(
default=None,
description=DATA_DESCRIPTIONS.get("volume", "")
+ "The notional volume of transactions (Billions of $).",
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1e9,
},
)

65
openbb_platform/core/openbb_core/provider/standard_models/sofr.py Normal file
View File

@@ -0,0 +1,65 @@
"""Secured Overnight Financing Rate Standard Model."""
from datetime import date as dateType
from typing import Optional
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
class SOFRQueryParams(QueryParams):
"""Secured Overnight Financing Rate Query."""
start_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
class SOFRData(Data):
"""SOFR Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: float = Field(
description="Effective federal funds rate.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_1: Optional[float] = Field(
default=None,
description="1st percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_25: Optional[float] = Field(
default=None,
description="25th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_75: Optional[float] = Field(
default=None,
description="75th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
percentile_99: Optional[float] = Field(
default=None,
description="99th percentile of the distribution.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
volume: Optional[float] = Field(
default=None,
description=DATA_DESCRIPTIONS.get("volume", "")
+ "The notional volume of transactions (Billions of $).",
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1e9,
},
)

33
openbb_platform/core/openbb_core/provider/standard_models/sofr_rates.py
View File

@@ -1,33 +0,0 @@
"""SOFR Standard Model."""
from datetime import date as dateType
from typing import Optional
from pydantic import Field
from openbb_core.provider.abstract.data import Data
from openbb_core.provider.abstract.query_params import QueryParams
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
class SOFRQueryParams(QueryParams):
"""SOFR Query."""
start_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
class SOFRData(Data):
"""SOFR Data."""
date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
rate: Optional[float] = Field(description="SOFR rate.")

102
openbb_platform/extensions/fixedincome/integration/test_fixedincome_api.py
View File

@@ -60,10 +60,18 @@ def test_fixedincome_government_us_yield_curve(params, headers):
@parametrize(
"params",
[
({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
(
{
"period": "overnight",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"provider": "federal_reserve",
}
),
(
{
"frequency": None,
"transform": None,
"aggregation_method": None,
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
@@ -86,10 +94,18 @@ def test_fixedincome_sofr(params, headers):
@parametrize(
"params",
[
({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
(
{
"parameter": "volume_weighted_trimmed_mean_rate",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"provider": "federal_reserve",
}
),
(
{
"frequency": None,
"transform": None,
"aggregation_method": None,
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
@@ -98,6 +114,33 @@ def test_fixedincome_sofr(params, headers):
],
)
@pytest.mark.integration
def test_fixedincome_rate_sofr(params, headers):
"""Test the SOFR endpoint."""
params = {p: v for p, v in params.items() if v}
query_str = get_querystring(params, [])
url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/sofr?{query_str}"
result = requests.get(url, headers=headers, timeout=10)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@parametrize(
"params",
[
(
{
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"transform": None,
"aggregation_method": None,
"frequency": None,
}
),
],
)
@pytest.mark.integration
def test_fixedincome_rate_estr(params, headers):
"""Test the ESTR rate endpoint."""
params = {p: v for p, v in params.items() if v}
@@ -138,13 +181,15 @@ def test_fixedincome_rate_sonia(params, headers):
@parametrize(
"params",
[
({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
(
{
"parameter": "overnight",
"maturity": "overnight",
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"transform": None,
"aggregation_method": None,
"frequency": None,
}
),
],
@@ -166,7 +211,10 @@ def test_fixedincome_rate_ameribor(params, headers):
[
(
{
"parameter": "weekly",
"frequency": "w",
"transform": None,
"aggregation_method": "avg",
"effr_only": False,
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
@@ -328,9 +376,11 @@ def test_fixedincome_corporate_moody(params, headers):
{
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"maturity": "30d",
"maturity": "overnight",
"category": "financial",
"grade": "aa",
"transform": None,
"aggregation_method": None,
"frequency": None,
"provider": "fred",
}
)
@@ -720,3 +770,37 @@ def test_fixedincome_mortgage_indices(params, headers):
result = requests.get(url, headers=headers, timeout=10)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@parametrize(
"params",
[
(
{
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"transform": None,
"aggregation_method": None,
"frequency": None,
"provider": "fred",
}
),
(
{
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"provider": "federal_reserve",
}
),
],
)
@pytest.mark.integration
def test_fixedincome_rate_overnight_bank_funding(params, headers):
"""Test the Overnight Bank Funding Rate endpoint."""
params = {p: v for p, v in params.items() if v}
query_str = get_querystring(params, [])
url = f"http://0.0.0.0:8000/api/v1/fixedincome/rate/overnight_bank_funding?{query_str}"
result = requests.get(url, headers=headers, timeout=10)
assert isinstance(result, requests.Response)
assert result.status_code == 200

98
openbb_platform/extensions/fixedincome/integration/test_fixedincome_python.py
View File

@@ -51,10 +51,18 @@ def test_fixedincome_government_us_yield_curve(params, obb):
@parametrize(
"params",
[
({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
(
{
"period": "overnight",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"provider": "federal_reserve",
}
),
(
{
"frequency": None,
"transform": None,
"aggregation_method": None,
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
@@ -74,10 +82,18 @@ def test_fixedincome_sofr(params, obb):
@parametrize(
"params",
[
({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
(
{
"parameter": "volume_weighted_trimmed_mean_rate",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"provider": "federal_reserve",
}
),
(
{
"frequency": None,
"transform": None,
"aggregation_method": None,
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
@@ -86,6 +102,30 @@ def test_fixedincome_sofr(params, obb):
],
)
@pytest.mark.integration
def test_fixedincome_rate_sofr(params, obb):
"""Test the fixedincome rate sofr endpoint."""
result = obb.fixedincome.rate.sofr(**params)
assert result
assert isinstance(result, OBBject)
assert len(result.results) > 0
@parametrize(
"params",
[
(
{
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"transform": None,
"aggregation_method": None,
"frequency": None,
}
),
],
)
@pytest.mark.integration
def test_fixedincome_rate_estr(params, obb):
"""Test the ESTR endpoint."""
result = obb.fixedincome.rate.estr(**params)
@@ -120,13 +160,15 @@ def test_fixedincome_rate_sonia(params, obb):
@parametrize(
"params",
[
({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
(
{
"parameter": "overnight",
"maturity": "overnight",
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"transform": None,
"aggregation_method": None,
"frequency": None,
}
),
],
@@ -145,7 +187,10 @@ def test_fixedincome_rate_ameribor(params, obb):
[
(
{
"parameter": "weekly",
"frequency": "w",
"transform": None,
"aggregation_method": "avg",
"effr_only": False,
"provider": "fred",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
@@ -153,9 +198,9 @@ def test_fixedincome_rate_ameribor(params, obb):
),
(
{
"provider": "federal_reserve",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"provider": "federal_reserve",
}
),
],
@@ -299,9 +344,11 @@ def test_fixedincome_corporate_moody(params, obb):
{
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"maturity": "30d",
"maturity": "overnight",
"category": "financial",
"grade": "aa",
"transform": None,
"aggregation_method": None,
"frequency": None,
"provider": "fred",
}
)
@@ -672,3 +719,34 @@ def test_fixedincome_mortgage_indices(params, obb):
assert result
assert isinstance(result, OBBject)
assert len(result.results) > 0
@parametrize(
"params",
[
(
{
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"transform": None,
"aggregation_method": None,
"frequency": None,
"provider": "fred",
}
),
(
{
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"provider": "federal_reserve",
}
),
],
)
@pytest.mark.integration
def test_fixedincome_rate_overnight_bank_funding(params, obb):
"""Test the Overnight Bank Funding Rate endpoint."""
result = obb.fixedincome.rate.overnight_bank_funding(**params)
assert result
assert isinstance(result, OBBject)
assert len(result.results) > 0

9
openbb_platform/extensions/fixedincome/openbb_fixedincome/corporate/corporate_router.py
View File

@@ -48,6 +48,13 @@ async def ice_bofa(
@router.command(
deprecated=True,
deprecation=OpenBBDeprecationWarning(
message="This endpoint is deprecated; use `/fixedincome/bond_indices` instead."
+ " Set `category` to `us` and `index` to `seasoned_corporate`.",
since=(4, 2),
expected_removal=(4, 5),
),
model="MoodyCorporateBondIndex",
examples=[
APIEx(parameters={"provider": "fred"}),
@@ -120,7 +127,7 @@ async def spot_rates(
model="CommercialPaper",
examples=[
APIEx(parameters={"provider": "fred"}),
APIEx(parameters={"maturity": "15d", "provider": "fred"}),
APIEx(parameters={"category": "all", "maturity": "15d", "provider": "fred"}),
],
)
async def commercial_paper(

8
openbb_platform/extensions/fixedincome/openbb_fixedincome/fixedincome_router.py
View File

@@ -2,6 +2,7 @@
# pylint: disable=W0613:unused-argument
from openbb_core.app.deprecation import OpenBBDeprecationWarning
from openbb_core.app.model.command_context import CommandContext
from openbb_core.app.model.example import APIEx
from openbb_core.app.model.obbject import OBBject
@@ -29,8 +30,13 @@ router.include_router(corporate_router)
model="SOFR",
examples=[
APIEx(parameters={"provider": "fred"}),
APIEx(parameters={"period": "overnight", "provider": "fred"}),
],
deprecated=True,
deprecation=OpenBBDeprecationWarning(
message="This endpoint is deprecated; use `/fixedincome/rate/sofr` instead.",
since=(4, 2),
expected_removal=(4, 5),
),
)
async def sofr(
cc: CommandContext,

63
openbb_platform/extensions/fixedincome/openbb_fixedincome/rate/rate_router.py
View File

@@ -17,10 +17,13 @@ router = Router(prefix="/rate")
@router.command(
model="AMERIBOR",
model="Ameribor",
examples=[
APIEx(parameters={"provider": "fred"}),
APIEx(parameters={"parameter": "30_day_ma", "provider": "fred"}),
APIEx(
description="The change from one year ago is applied with the transform parameter.",
parameters={"maturity": "all", "transform": "pc1", "provider": "fred"},
),
],
)
async def ameribor(
@@ -29,9 +32,9 @@ async def ameribor(
standard_params: StandardParams,
extra_params: ExtraParams,
) -> OBBject: # type: ignore
"""Ameribor.
"""AMERIBOR.
Ameribor (short for the American interbank offered rate) is a benchmark interest rate that reflects the true cost of
AMERIBOR (short for the American interbank offered rate) is a benchmark interest rate that reflects the true cost of
short-term interbank borrowing. This rate is based on transactions in overnight unsecured loans conducted on the
American Financial Exchange (AFX).
"""
@@ -60,6 +63,26 @@ async def sonia(
return await OBBject.from_query(Query(**locals()))
@router.command(
model="SOFR",
examples=[
APIEx(parameters={"provider": "fred"}),
],
)
async def sofr(
cc: CommandContext,
provider_choices: ProviderChoices,
standard_params: StandardParams,
extra_params: ExtraParams,
) -> OBBject: # type: ignore
"""Secured Overnight Financing Rate.
The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of
borrowing cash overnight collateralizing by Treasury securities.
"""
return await OBBject.from_query(Query(**locals()))
@router.command(
model="IORB",
examples=[APIEx(parameters={"provider": "fred"})],
@@ -80,10 +103,10 @@ async def iorb(
@router.command(
model="FEDFUNDS",
model="FederalFundsRate",
examples=[
APIEx(parameters={"provider": "fred"}),
APIEx(parameters={"parameter": "daily", "provider": "fred"}),
APIEx(parameters={"effr_only": True, "provider": "fred"}),
],
)
async def effr(
@@ -95,8 +118,7 @@ async def 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. In the
United States, the Federal Reserve System's Board of Governors set the bank rate, also known as the discount rate.
domestic banks to borrow money. The rates central banks charge are set to stabilize the economy.
"""
return await OBBject.from_query(Query(**locals()))
@@ -125,10 +147,10 @@ async def effr_forecast(
@router.command(
model="ESTR",
model="EuroShortTermRate",
examples=[
APIEx(parameters={"provider": "fred"}),
APIEx(parameters={"parameter": "number_of_active_banks", "provider": "fred"}),
APIEx(parameters={"transform": "ch1", "provider": "fred"}),
],
)
async def estr(
@@ -142,7 +164,7 @@ async def estr(
The euro short-term rate (€STR) reflects the wholesale euro unsecured overnight borrowing costs of banks located in
the euro area. The €STR is published on each TARGET2 business day based on transactions conducted and settled on
the previous TARGET2 business day (the reporting date “T”) with a maturity date of T+1 which are deemed to have been
executed at arm’s length and thus reflect market rates in an unbiased way.
executed at arm's length and thus reflect market rates in an unbiased way.
"""
return await OBBject.from_query(Query(**locals()))
@@ -199,3 +221,22 @@ async def dpcredit(
also known as the discount rate.
"""
return await OBBject.from_query(Query(**locals()))
@router.command(
model="OvernightBankFundingRate",
examples=[APIEx(parameters={"provider": "fred"})],
)
async def overnight_bank_funding(
cc: CommandContext,
provider_choices: ProviderChoices,
standard_params: StandardParams,
extra_params: ExtraParams,
) -> OBBject: # type: ignore
"""Overnight Bank Funding.
For the United States, the overnight bank funding rate (OBFR) is calculated as a volume-weighted median of
overnight federal funds transactions and Eurodollar transactions reported in the
FR 2420 Report of Selected Money Market Rates.
"""
return await OBBject.from_query(Query(**locals()))

1165
openbb_platform/openbb/assets/reference.json vendored
View File

File diff suppressed because it is too large Load Diff

26
openbb_platform/openbb/package/economy.py
View File

@@ -440,9 +440,9 @@ class ROUTER_economy(Container):
term : Optional[str]
Term of the security. Only returned for Agency securities. (provider: federal_reserve)
face_value : Optional[float]
Current face value of the security (Thousands). Current face value of the securities, which is the remaining principal balance of the securities. (provider: federal_reserve)
Current face value of the security (Thousands of $USD). Current face value of the securities, which is the remaining principal balance of the securities. (provider: federal_reserve)
par_value : Optional[float]
Par value of the security (Thousands). Changes in par may reflect primary and secondary market transactions and/or custodial account activity. (provider: federal_reserve)
Par value of the security (Thousands of $USD). Changes in par may reflect primary and secondary market transactions and/or custodial account activity. (provider: federal_reserve)
coupon : Optional[float]
Coupon rate of the security. (provider: federal_reserve)
spread : Optional[float]
@@ -450,27 +450,27 @@ class ROUTER_economy(Container):
percent_outstanding : Optional[float]
Total percent of the outstanding CUSIP issuance. (provider: federal_reserve)
bills : Optional[float]
Treasury bills amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Treasury bills amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
frn : Optional[float]
Floating rate Treasury notes amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Floating rate Treasury notes amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
notes_and_bonds : Optional[float]
Treasuy Notes and bonds amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Treasuy Notes and bonds amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
tips : Optional[float]
Treasury inflation-protected securities amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Treasury inflation-protected securities amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
mbs : Optional[float]
Mortgage-backed securities amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Mortgage-backed securities amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
cmbs : Optional[float]
Commercial mortgage-backed securities amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Commercial mortgage-backed securities amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
agencies : Optional[float]
Agency securities amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Agency securities amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
total : Optional[float]
Total SOMA holdings amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Total SOMA holdings amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
tips_inflation_compensation : Optional[float]
Treasury inflation-protected securities inflation compensation amount (Thousands). Only returned when 'summary' is True. (provider: federal_reserve)
Treasury inflation-protected securities inflation compensation amount (Thousands of $USD). Only returned when 'summary' is True. (provider: federal_reserve)
change_prior_week : Optional[float]
Change in SOMA holdings from the prior week (Thousands). (provider: federal_reserve)
Change in SOMA holdings from the prior week (Thousands of $USD). (provider: federal_reserve)
change_prior_year : Optional[float]
Change in SOMA holdings from the prior year (Thousands). (provider: federal_reserve)
Change in SOMA holdings from the prior year (Thousands of $USD). (provider: federal_reserve)
Examples
--------

89
openbb_platform/openbb/package/fixedincome.py
View File

@@ -2,13 +2,15 @@
import datetime
from typing import Literal, Optional, Union
from warnings import simplefilter, warn
from openbb_core.app.deprecation import OpenBBDeprecationWarning
from openbb_core.app.model.field import OpenBBField
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.static.container import Container
from openbb_core.app.static.utils.decorators import exception_handler, validate
from openbb_core.app.static.utils.filters import filter_inputs
from typing_extensions import Annotated
from typing_extensions import Annotated, deprecated
class ROUTER_fixedincome(Container):
@@ -322,6 +324,10 @@ class ROUTER_fixedincome(Container):
@exception_handler
@validate
@deprecated(
"This endpoint is deprecated; use `/fixedincome/rate/sofr` instead. Deprecated in OpenBB Platform V4.2 to be removed in V4.5.",
category=OpenBBDeprecationWarning,
)
def sofr(
self,
start_date: Annotated[
@@ -333,9 +339,9 @@ class ROUTER_fixedincome(Container):
OpenBBField(description="End date of the data, in YYYY-MM-DD format."),
] = None,
provider: Annotated[
Optional[Literal["fred"]],
Optional[Literal["federal_reserve", "fred"]],
OpenBBField(
description="The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: fred."
description="The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: federal_reserve, fred."
),
] = None,
**kwargs
@@ -352,17 +358,52 @@ class ROUTER_fixedincome(Container):
Start date of the data, in YYYY-MM-DD format.
end_date : Union[datetime.date, None, str]
End date of the data, in YYYY-MM-DD format.
provider : Optional[Literal['fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: fred.
period : Literal['overnight', '30_day', '90_day', '180_day', 'index']
Period of SOFR rate. (provider: fred)
provider : Optional[Literal['federal_reserve', 'fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: federal_reserve, fred.
frequency : Optional[Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']]
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
(provider: fred)
aggregation_method : Optional[Literal['avg', 'sum', 'eop']]
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
(provider: fred)
transform : Optional[Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']]
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
(provider: fred)
Returns
-------
OBBject
results : List[SOFR]
Serializable results.
provider : Optional[Literal['fred']]
provider : Optional[Literal['federal_reserve', 'fred']]
Provider name.
warnings : Optional[List[Warning_]]
List of warnings.
@@ -375,16 +416,40 @@ class ROUTER_fixedincome(Container):
----
date : date
The date of the data.
rate : Optional[float]
SOFR rate.
rate : float
Effective federal funds rate.
percentile_1 : Optional[float]
1st percentile of the distribution.
percentile_25 : Optional[float]
25th percentile of the distribution.
percentile_75 : Optional[float]
75th percentile of the distribution.
percentile_99 : Optional[float]
99th percentile of the distribution.
volume : Optional[float]
The trading volume.The notional volume of transactions (Billions of $).
average_30d : Optional[float]
30-Day Average SOFR (provider: fred)
average_90d : Optional[float]
90-Day Average SOFR (provider: fred)
average_180d : Optional[float]
180-Day Average SOFR (provider: fred)
index : Optional[float]
SOFR index as 2018-04-02 = 1 (provider: fred)
Examples
--------
>>> from openbb import obb
>>> obb.fixedincome.sofr(provider='fred')
>>> obb.fixedincome.sofr(period='overnight', provider='fred')
""" # noqa: E501
simplefilter("always", DeprecationWarning)
warn(
"This endpoint is deprecated; use `/fixedincome/rate/sofr` instead. Deprecated in OpenBB Platform V4.2 to be removed in V4.5.",
category=DeprecationWarning,
stacklevel=2,
)
return self._run(
"/fixedincome/sofr",
**filter_inputs(
@@ -392,7 +457,7 @@ class ROUTER_fixedincome(Container):
"provider": self._get_provider(
provider,
"fixedincome.sofr",
("fred",),
("federal_reserve", "fred"),
)
},
standard_params={

117
openbb_platform/openbb/package/fixedincome_corporate.py
View File

@@ -37,17 +37,6 @@ class ROUTER_fixedincome_corporate(Container):
Union[datetime.date, None, str],
OpenBBField(description="End date of the data, in YYYY-MM-DD format."),
] = None,
maturity: Annotated[
Literal["overnight", "7d", "15d", "30d", "60d", "90d"],
OpenBBField(description="The maturity."),
] = "30d",
category: Annotated[
Literal["asset_backed", "financial", "nonfinancial"],
OpenBBField(description="The category."),
] = "financial",
grade: Annotated[
Literal["aa", "a2_p2"], OpenBBField(description="The grade.")
] = "aa",
provider: Annotated[
Optional[Literal["fred"]],
OpenBBField(
@@ -70,14 +59,49 @@ class ROUTER_fixedincome_corporate(Container):
Start date of the data, in YYYY-MM-DD format.
end_date : Union[datetime.date, None, str]
End date of the data, in YYYY-MM-DD format.
maturity : Literal['overnight', '7d', '15d', '30d', '60d', '90d']
The maturity.
category : Literal['asset_backed', 'financial', 'nonfinancial']
The category.
grade : Literal['aa', 'a2_p2']
The grade.
provider : Optional[Literal['fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: fred.
maturity : Union[str, Literal['all', 'overnight', '7d', '15d', '30d', '60d', '90d']]
A target maturity. Multiple comma separated items allowed. (provider: fred)
category : Union[str, Literal['all', 'asset_backed', 'financial', 'nonfinancial', 'a2p2']]
The category of asset. Multiple comma separated items allowed. (provider: fred)
frequency : Optional[Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']]
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
(provider: fred)
aggregation_method : Optional[Literal['avg', 'sum', 'eop']]
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
(provider: fred)
transform : Optional[Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']]
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
(provider: fred)
Returns
-------
@@ -97,14 +121,22 @@ class ROUTER_fixedincome_corporate(Container):
---------------
date : date
The date of the data.
rate : Optional[float]
Commercial Paper Rate.
symbol : Optional[str]
Symbol representing the entity requested in the data.
maturity : str
Maturity length of the item.
rate : float
Interest rate.
title : Optional[str]
Title of the series.
asset_type : Optional[Literal['asset_backed', 'financial', 'nonfinancial', 'a2p2']]
The category of asset. (provider: fred)
Examples
--------
>>> from openbb import obb
>>> obb.fixedincome.corporate.commercial_paper(provider='fred')
>>> obb.fixedincome.corporate.commercial_paper(maturity='15d', provider='fred')
>>> obb.fixedincome.corporate.commercial_paper(category='all', maturity='15d', provider='fred')
""" # noqa: E501
return self._run(
@@ -120,11 +152,12 @@ class ROUTER_fixedincome_corporate(Container):
standard_params={
"start_date": start_date,
"end_date": end_date,
"maturity": maturity,
"category": category,
"grade": grade,
},
extra_params=kwargs,
info={
"maturity": {"fred": {"multiple_items_allowed": True}},
"category": {"fred": {"multiple_items_allowed": True}},
},
)
)
@@ -133,12 +166,11 @@ class ROUTER_fixedincome_corporate(Container):
def hqm(
self,
date: Annotated[
Union[datetime.date, None, str],
OpenBBField(description="A specific date to get data for."),
Union[str, datetime.date, None, List[Union[str, datetime.date, None]]],
OpenBBField(
description="A specific date to get data for. Multiple comma separated items allowed for provider(s): fred."
),
] = None,
yield_curve: Annotated[
Literal["spot", "par"], OpenBBField(description="The yield curve type.")
] = "spot",
provider: Annotated[
Optional[Literal["fred"]],
OpenBBField(
@@ -157,12 +189,12 @@ class ROUTER_fixedincome_corporate(Container):
Parameters
----------
date : Union[datetime.date, None, str]
A specific date to get data for.
yield_curve : Literal['spot', 'par']
The yield curve type.
date : Union[str, datetime.date, None, List[Union[str, datetime.d...
A specific date to get data for. Multiple comma separated items allowed for provider(s): fred.
provider : Optional[Literal['fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: fred.
yield_curve : Literal['spot', 'par']
The yield curve type. (provider: fred)
Returns
-------
@@ -182,14 +214,10 @@ class ROUTER_fixedincome_corporate(Container):
------------------------------
date : date
The date of the data.
rate : Optional[float]
HighQualityMarketCorporateBond Rate.
rate : float
Interest rate.
maturity : str
Maturity.
yield_curve : Literal['spot', 'par']
The yield curve type.
series_id : Optional[str]
FRED series id. (provider: fred)
Examples
--------
@@ -210,9 +238,9 @@ class ROUTER_fixedincome_corporate(Container):
},
standard_params={
"date": date,
"yield_curve": yield_curve,
},
extra_params=kwargs,
info={"date": {"fred": {"multiple_items_allowed": True}}},
)
)
@@ -328,6 +356,10 @@ class ROUTER_fixedincome_corporate(Container):
@exception_handler
@validate
@deprecated(
"This endpoint is deprecated; use `/fixedincome/bond_indices` instead. Set `category` to `us` and `index` to `seasoned_corporate`. Deprecated in OpenBB Platform V4.2 to be removed in V4.5.",
category=OpenBBDeprecationWarning,
)
def moody(
self,
start_date: Annotated[
@@ -398,6 +430,13 @@ class ROUTER_fixedincome_corporate(Container):
>>> obb.fixedincome.corporate.moody(index_type='baa', provider='fred')
""" # noqa: E501
simplefilter("always", DeprecationWarning)
warn(
"This endpoint is deprecated; use `/fixedincome/bond_indices` instead. Set `category` to `us` and `index` to `seasoned_corporate`. Deprecated in OpenBB Platform V4.2 to be removed in V4.5.",
category=DeprecationWarning,
stacklevel=2,
)
return self._run(
"/fixedincome/corporate/moody",
**filter_inputs(

2
openbb_platform/openbb/package/fixedincome_government.py
View File

@@ -242,7 +242,7 @@ class ROUTER_fixedincome_government(Container):
The country to get data. New Zealand, Mexico, Singapore, and Thailand have only monthly data. The nearest date to the requested one will be used. (provider: econdb)
use_cache : bool
If true, cache the request for four hours. (provider: econdb)
yield_curve_type : Literal['nominal', 'real', 'breakeven', 'corporate_spot', 'corporate_par']
yield_curve_type : Literal['nominal', 'real', 'breakeven', 'treasury_minus_fed_funds', 'corporate_spot', 'corporate_par']
Yield curve type. Nominal and Real Rates are available daily, others are monthly. The closest date to the requested date will be returned. (provider: fred)
Returns

473
openbb_platform/openbb/package/fixedincome_rate.py
View File

@@ -20,6 +20,8 @@ class ROUTER_fixedincome_rate(Container):
effr_forecast
estr
iorb
overnight_bank_funding
sofr
sonia
"""
@@ -46,9 +48,9 @@ class ROUTER_fixedincome_rate(Container):
] = None,
**kwargs
) -> OBBject:
"""Ameribor.
"""AMERIBOR.
Ameribor (short for the American interbank offered rate) is a benchmark interest rate that reflects the true cost of
AMERIBOR (short for the American interbank offered rate) is a benchmark interest rate that reflects the true cost of
short-term interbank borrowing. This rate is based on transactions in overnight unsecured loans conducted on the
American Financial Exchange (AFX).
@@ -61,13 +63,50 @@ class ROUTER_fixedincome_rate(Container):
End date of the data, in YYYY-MM-DD format.
provider : Optional[Literal['fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: fred.
parameter : Literal['overnight', 'term_30', 'term_90', '1_week_term_structure', '1_month_term_structure', '3_month_term_structure', '6_month_term_structure', '1_year_term_structure', '2_year_term_structure', '30_day_ma', '90_day_ma']
Period of AMERIBOR rate. (provider: fred)
maturity : Union[Literal['all', 'overnight', 'average_30d', 'average_90d', 'term_30d', 'term_90d'], str]
Period of AMERIBOR rate. Multiple comma separated items allowed. (provider: fred)
frequency : Optional[Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']]
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
(provider: fred)
aggregation_method : Optional[Literal['avg', 'sum', 'eop']]
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
(provider: fred)
transform : Optional[Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']]
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
(provider: fred)
Returns
-------
OBBject
results : List[AMERIBOR]
results : List[Ameribor]
Serializable results.
provider : Optional[Literal['fred']]
Provider name.
@@ -78,18 +117,25 @@ class ROUTER_fixedincome_rate(Container):
extra : Dict[str, Any]
Extra info.
AMERIBOR
Ameribor
--------
date : date
The date of the data.
rate : Optional[float]
AMERIBOR rate.
symbol : Optional[str]
Symbol representing the entity requested in the data.
maturity : str
Maturity length of the item.
rate : float
Interest rate.
title : Optional[str]
Title of the series.
Examples
--------
>>> from openbb import obb
>>> obb.fixedincome.rate.ameribor(provider='fred')
>>> obb.fixedincome.rate.ameribor(parameter='30_day_ma', provider='fred')
>>> # The change from one year ago is applied with the transform parameter.
>>> obb.fixedincome.rate.ameribor(maturity='all', transform='pc1', provider='fred')
""" # noqa: E501
return self._run(
@@ -107,6 +153,7 @@ class ROUTER_fixedincome_rate(Container):
"end_date": end_date,
},
extra_params=kwargs,
info={"maturity": {"fred": {"multiple_items_allowed": True}}},
)
)
@@ -310,8 +357,7 @@ class ROUTER_fixedincome_rate(Container):
"""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. In the
United States, the Federal Reserve System's Board of Governors set the bank rate, also known as the discount rate.
domestic banks to borrow money. The rates central banks charge are set to stabilize the economy.
Parameters
@@ -322,13 +368,50 @@ class ROUTER_fixedincome_rate(Container):
End date of the data, in YYYY-MM-DD format.
provider : Optional[Literal['federal_reserve', 'fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: federal_reserve, fred.
parameter : Literal['monthly', 'daily', 'weekly', 'daily_excl_weekend', 'annual', 'biweekly', 'volume']
Period of FED rate. (provider: fred)
frequency : Optional[Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']]
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
(provider: fred)
aggregation_method : Optional[Literal['avg', 'sum', 'eop']]
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
(provider: fred)
transform : Optional[Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']]
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
(provider: fred)
effr_only : bool
Return data without quantiles, target ranges, and volume. (provider: fred)
Returns
-------
OBBject
results : List[FEDFUNDS]
results : List[FederalFundsRate]
Serializable results.
provider : Optional[Literal['federal_reserve', 'fred']]
Provider name.
@@ -339,18 +422,40 @@ class ROUTER_fixedincome_rate(Container):
extra : Dict[str, Any]
Extra info.
FEDFUNDS
--------
FederalFundsRate
----------------
date : date
The date of the data.
rate : Optional[float]
FED rate.
rate : float
Effective federal funds rate.
target_range_upper : Optional[float]
Upper bound of the target range.
target_range_lower : Optional[float]
Lower bound of the target range.
percentile_1 : Optional[float]
1st percentile of the distribution.
percentile_25 : Optional[float]
25th percentile of the distribution.
percentile_75 : Optional[float]
75th percentile of the distribution.
percentile_99 : Optional[float]
99th percentile of the distribution.
volume : Optional[float]
The trading volume.The notional volume of transactions (Billions of $).
intraday_low : Optional[float]
Intraday low. This field is only present for data before 2016. (provider: federal_reserve)
intraday_high : Optional[float]
Intraday high. This field is only present for data before 2016. (provider: federal_reserve)
standard_deviation : Optional[float]
Standard deviation. This field is only present for data before 2016. (provider: federal_reserve)
revision_indicator : Optional[str]
Indicates a revision of the data for that date. (provider: federal_reserve)
Examples
--------
>>> from openbb import obb
>>> obb.fixedincome.rate.effr(provider='fred')
>>> obb.fixedincome.rate.effr(parameter='daily', provider='fred')
>>> obb.fixedincome.rate.effr(effr_only=True, provider='fred')
""" # noqa: E501
return self._run(
@@ -478,7 +583,7 @@ class ROUTER_fixedincome_rate(Container):
The euro short-term rate (€STR) reflects the wholesale euro unsecured overnight borrowing costs of banks located in
the euro area. The €STR is published on each TARGET2 business day based on transactions conducted and settled on
the previous TARGET2 business day (the reporting date “T”) with a maturity date of T+1 which are deemed to have been
executed at arm’s length and thus reflect market rates in an unbiased way.
executed at arm's length and thus reflect market rates in an unbiased way.
Parameters
@@ -489,13 +594,48 @@ class ROUTER_fixedincome_rate(Container):
End date of the data, in YYYY-MM-DD format.
provider : Optional[Literal['fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: fred.
parameter : Literal['volume_weighted_trimmed_mean_rate', 'number_of_transactions', 'number_of_active_banks', 'total_volume', 'share_of_volume_of_the_5_largest_active_banks', 'rate_at_75th_percentile_of_volume', 'rate_at_25th_percentile_of_volume']
Period of ESTR rate. (provider: fred)
frequency : Optional[Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']]
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
(provider: fred)
aggregation_method : Optional[Literal['avg', 'sum', 'eop']]
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
(provider: fred)
transform : Optional[Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']]
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
(provider: fred)
Returns
-------
OBBject
results : List[ESTR]
results : List[EuroShortTermRate]
Serializable results.
provider : Optional[Literal['fred']]
Provider name.
@@ -506,18 +646,30 @@ class ROUTER_fixedincome_rate(Container):
extra : Dict[str, Any]
Extra info.
ESTR
----
EuroShortTermRate
-----------------
date : date
The date of the data.
rate : Optional[float]
ESTR rate.
rate : float
Volume-weighted trimmed mean rate.
percentile_25 : Optional[float]
Rate at 25th percentile of volume.
percentile_75 : Optional[float]
Rate at 75th percentile of volume.
volume : Optional[float]
Volume (Millions of €EUR).
transactions : Optional[int]
Number of transactions.
number_of_banks : Optional[int]
Number of active banks.
large_bank_share_of_volume : Optional[float]
The percent of volume attributable to the 5 largest active banks.
Examples
--------
>>> from openbb import obb
>>> obb.fixedincome.rate.estr(provider='fred')
>>> obb.fixedincome.rate.estr(parameter='number_of_active_banks', provider='fred')
>>> obb.fixedincome.rate.estr(transform='ch1', provider='fred')
""" # noqa: E501
return self._run(
@@ -619,6 +771,271 @@ class ROUTER_fixedincome_rate(Container):
)
)
@exception_handler
@validate
def overnight_bank_funding(
self,
start_date: Annotated[
Union[datetime.date, None, str],
OpenBBField(description="Start date of the data, in YYYY-MM-DD format."),
] = None,
end_date: Annotated[
Union[datetime.date, None, str],
OpenBBField(description="End date of the data, in YYYY-MM-DD format."),
] = None,
provider: Annotated[
Optional[Literal["federal_reserve", "fred"]],
OpenBBField(
description="The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: federal_reserve, fred."
),
] = None,
**kwargs
) -> OBBject:
"""Overnight Bank Funding.
For the United States, the overnight bank funding rate (OBFR) is calculated as a volume-weighted median of
overnight federal funds transactions and Eurodollar transactions reported in the
FR 2420 Report of Selected Money Market Rates.
Parameters
----------
start_date : Union[datetime.date, None, str]
Start date of the data, in YYYY-MM-DD format.
end_date : Union[datetime.date, None, str]
End date of the data, in YYYY-MM-DD format.
provider : Optional[Literal['federal_reserve', 'fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: federal_reserve, fred.
frequency : Optional[Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']]
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
(provider: fred)
aggregation_method : Optional[Literal['avg', 'sum', 'eop']]
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
(provider: fred)
transform : Optional[Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']]
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
(provider: fred)
Returns
-------
OBBject
results : List[OvernightBankFundingRate]
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.
OvernightBankFundingRate
------------------------
date : date
The date of the data.
rate : float
Overnight Bank Funding Rate.
percentile_1 : Optional[float]
1st percentile of the distribution.
percentile_25 : Optional[float]
25th percentile of the distribution.
percentile_75 : Optional[float]
75th percentile of the distribution.
percentile_99 : Optional[float]
99th percentile of the distribution.
volume : Optional[float]
The trading volume.The notional volume of transactions (Billions of $).
revision_indicator : Optional[str]
Indicates a revision of the data for that date. (provider: federal_reserve)
Examples
--------
>>> from openbb import obb
>>> obb.fixedincome.rate.overnight_bank_funding(provider='fred')
""" # noqa: E501
return self._run(
"/fixedincome/rate/overnight_bank_funding",
**filter_inputs(
provider_choices={
"provider": self._get_provider(
provider,
"fixedincome.rate.overnight_bank_funding",
("federal_reserve", "fred"),
)
},
standard_params={
"start_date": start_date,
"end_date": end_date,
},
extra_params=kwargs,
)
)
@exception_handler
@validate
def sofr(
self,
start_date: Annotated[
Union[datetime.date, None, str],
OpenBBField(description="Start date of the data, in YYYY-MM-DD format."),
] = None,
end_date: Annotated[
Union[datetime.date, None, str],
OpenBBField(description="End date of the data, in YYYY-MM-DD format."),
] = None,
provider: Annotated[
Optional[Literal["federal_reserve", "fred"]],
OpenBBField(
description="The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: federal_reserve, fred."
),
] = None,
**kwargs
) -> OBBject:
"""Secured Overnight Financing Rate.
The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of
borrowing cash overnight collateralizing by Treasury securities.
Parameters
----------
start_date : Union[datetime.date, None, str]
Start date of the data, in YYYY-MM-DD format.
end_date : Union[datetime.date, None, str]
End date of the data, in YYYY-MM-DD format.
provider : Optional[Literal['federal_reserve', 'fred']]
The provider to use, by default None. If None, the priority list configured in the settings is used. Default priority: federal_reserve, fred.
frequency : Optional[Literal['a', 'q', 'm', 'w', 'wef', 'weth', 'wew', 'wetu', 'wem', 'wesu', 'wesa', 'bwew', 'bwem']]
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
(provider: fred)
aggregation_method : Optional[Literal['avg', 'sum', 'eop']]
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
(provider: fred)
transform : Optional[Literal['chg', 'ch1', 'pch', 'pc1', 'pca', 'cch', 'cca', 'log']]
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
(provider: fred)
Returns
-------
OBBject
results : List[SOFR]
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.
SOFR
----
date : date
The date of the data.
rate : float
Effective federal funds rate.
percentile_1 : Optional[float]
1st percentile of the distribution.
percentile_25 : Optional[float]
25th percentile of the distribution.
percentile_75 : Optional[float]
75th percentile of the distribution.
percentile_99 : Optional[float]
99th percentile of the distribution.
volume : Optional[float]
The trading volume.The notional volume of transactions (Billions of $).
average_30d : Optional[float]
30-Day Average SOFR (provider: fred)
average_90d : Optional[float]
90-Day Average SOFR (provider: fred)
average_180d : Optional[float]
180-Day Average SOFR (provider: fred)
index : Optional[float]
SOFR index as 2018-04-02 = 1 (provider: fred)
Examples
--------
>>> from openbb import obb
>>> obb.fixedincome.rate.sofr(provider='fred')
""" # noqa: E501
return self._run(
"/fixedincome/rate/sofr",
**filter_inputs(
provider_choices={
"provider": self._get_provider(
provider,
"fixedincome.rate.sofr",
("federal_reserve", "fred"),
)
},
standard_params={
"start_date": start_date,
"end_date": end_date,
},
extra_params=kwargs,
)
)
@exception_handler
@validate
def sonia(

12
openbb_platform/providers/federal_reserve/openbb_federal_reserve/__init__.py
View File

@@ -4,10 +4,16 @@ from openbb_core.provider.abstract.provider import Provider
from openbb_federal_reserve.models.central_bank_holdings import (
FederalReserveCentralBankHoldingsFetcher,
)
from openbb_federal_reserve.models.fed_rates import FederalReserveFEDFetcher
from openbb_federal_reserve.models.federal_funds_rate import (
FederalReserveFederalFundsRateFetcher,
)
from openbb_federal_reserve.models.money_measures import (
FederalReserveMoneyMeasuresFetcher,
)
from openbb_federal_reserve.models.overnight_bank_funding_rate import (
FederalReserveOvernightBankFundingRateFetcher,
)
from openbb_federal_reserve.models.sofr import FederalReserveSOFRFetcher
from openbb_federal_reserve.models.treasury_rates import (
FederalReserveTreasuryRatesFetcher,
)
@@ -21,7 +27,9 @@ federal_reserve_provider = Provider(
"CentralBankHoldings": FederalReserveCentralBankHoldingsFetcher,
"TreasuryRates": FederalReserveTreasuryRatesFetcher,
"MoneyMeasures": FederalReserveMoneyMeasuresFetcher,
"FEDFUNDS": FederalReserveFEDFetcher,
"OvernightBankFundingRate": FederalReserveOvernightBankFundingRateFetcher,
"FederalFundsRate": FederalReserveFederalFundsRateFetcher,
"SOFR": FederalReserveSOFRFetcher,
"YieldCurve": FederalReserveYieldCurveFetcher,
},
repr_name="Federal Reserve (FED)",

91
openbb_platform/providers/federal_reserve/openbb_federal_reserve/models/central_bank_holdings.py
View File

@@ -111,15 +111,21 @@ class FederalReserveCentralBankHoldingsData(CentralBankHoldingsData):
)
face_value: Optional[float] = Field(
default=None,
description="Current face value of the security (Thousands)."
description="Current face value of the security (Thousands of $USD)."
+ " Current face value of the securities, which is the remaining principal balance of the securities.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
par_value: Optional[float] = Field(
default=None,
description="Par value of the security (Thousands)."
description="Par value of the security (Thousands of $USD)."
+ " Changes in par may reflect primary and secondary market transactions and/or custodial account activity.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
coupon: Optional[float] = Field(
default=None,
@@ -138,68 +144,101 @@ class FederalReserveCentralBankHoldingsData(CentralBankHoldingsData):
)
bills: Optional[float] = Field(
default=None,
description="Treasury bills amount (Thousands)."
description="Treasury bills amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
frn: Optional[float] = Field(
default=None,
description="Floating rate Treasury notes amount (Thousands)."
description="Floating rate Treasury notes amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
notes_and_bonds: Optional[float] = Field(
default=None,
description="Treasuy Notes and bonds amount (Thousands)."
description="Treasuy Notes and bonds amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
tips: Optional[float] = Field(
default=None,
description="Treasury inflation-protected securities amount (Thousands)."
description="Treasury inflation-protected securities amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
mbs: Optional[float] = Field(
default=None,
description="Mortgage-backed securities amount (Thousands)."
description="Mortgage-backed securities amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
cmbs: Optional[float] = Field(
default=None,
description="Commercial mortgage-backed securities amount (Thousands)."
description="Commercial mortgage-backed securities amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
agencies: Optional[float] = Field(
default=None,
description="Agency securities amount (Thousands)."
description="Agency securities amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
total: Optional[float] = Field(
default=None,
description="Total SOMA holdings amount (Thousands)."
description="Total SOMA holdings amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
tips_inflation_compensation: Optional[float] = Field(
default=None,
description="Treasury inflation-protected securities inflation compensation amount (Thousands)."
description="Treasury inflation-protected securities inflation compensation amount (Thousands of $USD)."
+ " Only returned when 'summary' is True.",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
alias="inflationCompensation",
)
change_prior_week: Optional[float] = Field(
default=None,
description="Change in SOMA holdings from the prior week (Thousands).",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
description="Change in SOMA holdings from the prior week (Thousands of $USD).",
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
change_prior_year: Optional[float] = Field(
default=None,
description="Change in SOMA holdings from the prior year (Thousands).",
json_schema_extra={"x-unit_measurement": "usd", "x-frontend_multiply": 1000},
description="Change in SOMA holdings from the prior year (Thousands of $USD).",
json_schema_extra={
"x-unit_measurement": "currency",
"x-frontend_multiply": 1000,
},
)
@field_validator("security_type", mode="before", check_fields=False)

83
openbb_platform/providers/federal_reserve/openbb_federal_reserve/models/fed_rates.py
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"""Federal Reserve FED Model."""
from datetime import datetime, timedelta
from io import BytesIO
from typing import Any, Dict, List, Optional
import pandas as pd
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.fed_rates import (
FEDData,
FEDQueryParams,
)
from openbb_core.provider.utils.helpers import make_request
from pydantic import field_validator
class FederalReserveFEDQueryParams(FEDQueryParams):
"""FederalReserve FED Query."""
class FederalReserveFEDData(FEDData):
"""FederalReserve FED Data."""
@field_validator("rate", mode="before", check_fields=False)
@classmethod
def value_validate(cls, v):
"""Validate rate."""
try:
return float(v)
except ValueError:
return None
class FederalReserveFEDFetcher(
Fetcher[FederalReserveFEDQueryParams, List[Dict[str, List[FederalReserveFEDData]]]]
):
"""FederalReserve FED Model."""
data_type = FederalReserveFEDData
@staticmethod
def transform_query(params: Dict[str, Any]) -> FederalReserveFEDQueryParams:
"""Transform query."""
transformed_params = params
now = datetime.now().date()
if params.get("start_date") is None:
transformed_params["start_date"] = now - timedelta(days=10 * 365)
if params.get("end_date") is None:
transformed_params["end_date"] = now
return FederalReserveFEDQueryParams(**transformed_params)
# pylint: disable=unused-argument
@staticmethod
def extract_data(
query: FederalReserveFEDQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any
) -> dict:
"""Extract data."""
url = (
"https://www.federalreserve.gov/datadownload/Output.aspx?rel=H15&series=c5025f4bbbed155a6f17c587772ed69e"
"&lastobs=&from=01/01/1938&to=12/31/3023&filetype=csv&label=include&layout=seriescolumn"
)
r = make_request(url, **kwargs)
df = pd.read_csv(BytesIO(r.content), header=5, index_col=None, parse_dates=True)
df.columns = ["date", "rate"]
df = df[
(pd.to_datetime(df.date) >= pd.to_datetime(query.start_date))
& (pd.to_datetime(df.date) <= pd.to_datetime(query.end_date))
]
return df.to_dict(orient="records")
# pylint: disable=unused-argument
@staticmethod
def transform_data(
query: FederalReserveFEDQueryParams, data: dict, **kwargs: Any
) -> List[Dict[str, List[FederalReserveFEDData]]]:
"""Transform data."""
return [FederalReserveFEDData.model_validate(d) for d in data]

139
openbb_platform/providers/federal_reserve/openbb_federal_reserve/models/federal_funds_rate.py Normal file
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"""Federal Reserve Federal Funds Rate Model."""
# pylint: disable=unused-argument
from datetime import datetime
from typing import Any, Dict, List, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.federal_funds_rate import (
FederalFundsRateData,
FederalFundsRateQueryParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_core.provider.utils.helpers import amake_request
from pydantic import Field, field_validator
class FederalReserveFederalFundsRateQueryParams(FederalFundsRateQueryParams):
"""FederalReserve FED Query."""
class FederalReserveFederalFundsRateData(FederalFundsRateData):
"""FederalReserve FED Data."""
__alias_dict__ = {
"date": "effectiveDate",
"rate": "percentRate",
"target_range_upper": "targetRateTo",
"target_range_lower": "targetRateFrom",
"percentile_1": "percentPercentile1",
"percentile_25": "percentPercentile25",
"percentile_75": "percentPercentile75",
"percentile_99": "percentPercentile99",
"volume": "volumeInBillions",
"intraday_low": "intraDayLow",
"intraday_high": "intraDayHigh",
"standard_deviation": "stdDeviation",
"revision_indicator": "revisionIndicator",
}
intraday_low: Optional[float] = Field(
default=None,
description="Intraday low. This field is only present for data before 2016.",
)
intraday_high: Optional[float] = Field(
default=None,
description="Intraday high. This field is only present for data before 2016.",
)
standard_deviation: Optional[float] = Field(
default=None,
description="Standard deviation. This field is only present for data before 2016.",
)
revision_indicator: Optional[str] = Field(
default=None,
description="Indicates a revision of the data for that date.",
)
@field_validator("revision_indicator", mode="before", check_fields=False)
@classmethod
def validate_revision_indicator(cls, v):
"""Validate revision indicator."""
return None if v in ("", "''") else v
@field_validator(
"rate",
"target_range_upper",
"target_range_lower",
"percentile_1",
"percentile_25",
"percentile_75",
"percentile_99",
"intraday_high",
"intraday_low",
mode="before",
check_fields=False,
)
@classmethod
def normalize_percent(cls, v):
"""Normalize percent."""
if v is not None:
return v / 100 if v != 0 else 0
return None
class FederalReserveFederalFundsRateFetcher(
Fetcher[
FederalReserveFederalFundsRateQueryParams,
List[FederalReserveFederalFundsRateData],
]
):
"""Federal Reserve Federal Funds Fetcher."""
@staticmethod
def transform_query(
params: Dict[str, Any]
) -> FederalReserveFederalFundsRateQueryParams:
"""Transform query."""
transformed_params = params.copy()
now = datetime.now().date()
if params.get("start_date") is None:
transformed_params["start_date"] = datetime(2016, 3, 1).date()
if params.get("end_date") is None:
transformed_params["end_date"] = now
return FederalReserveFederalFundsRateQueryParams(**transformed_params)
@staticmethod
async def aextract_data(
query: FederalReserveFederalFundsRateQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
"""Extract the raw data."""
url = (
"https://markets.newyorkfed.org/api/rates/unsecured/effr/search.json?"
+ f"startDate={query.start_date}&endDate={query.end_date}"
)
results: List[Dict] = []
response = await amake_request(url, **kwargs)
if response.get("refRates"):
results = response["refRates"]
if not results:
raise EmptyDataError()
return results
@staticmethod
def transform_data(
query: FederalReserveFederalFundsRateQueryParams,
data: List[Dict],
**kwargs: Any,
) -> List[FederalReserveFederalFundsRateData]:
"""Transform data."""
results: List[FederalReserveFederalFundsRateData] = []
for d in data.copy():
_ = d.pop("type", None)
_ = d.pop("footnoteId", None)
results.append(FederalReserveFederalFundsRateData.model_validate(d))
return results

120
openbb_platform/providers/federal_reserve/openbb_federal_reserve/models/overnight_bank_funding_rate.py Normal file
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"""Federal Reserve Federal Funds Rate Model."""
# pylint: disable=unused-argument
from datetime import datetime
from typing import Any, Dict, List, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.overnight_bank_funding_rate import (
OvernightBankFundingRateData,
OvernightBankFundingRateQueryParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_core.provider.utils.helpers import amake_request
from pydantic import Field, field_validator
class FederalReserveOvernightBankFundingRateQueryParams(
OvernightBankFundingRateQueryParams
):
"""FederalReserve Overnight Bank Funding Rate Query."""
class FederalReserveOvernightBankFundingRateData(OvernightBankFundingRateData):
"""FederalReserve Overnight Bank Funding Rate Data."""
__alias_dict__ = {
"date": "effectiveDate",
"rate": "percentRate",
"percentile_1": "percentPercentile1",
"percentile_25": "percentPercentile25",
"percentile_75": "percentPercentile75",
"percentile_99": "percentPercentile99",
"volume": "volumeInBillions",
}
revision_indicator: Optional[str] = Field(
default=None,
description="Indicates a revision of the data for that date.",
)
@field_validator("revision_indicator", mode="before", check_fields=False)
@classmethod
def validate_revision_indicator(cls, v):
"""Validate revision indicator."""
return None if v in ("", "''") else v
@field_validator(
"rate",
"percentile_1",
"percentile_25",
"percentile_75",
"percentile_99",
mode="before",
check_fields=False,
)
@classmethod
def normalize_percent(cls, v):
"""Normalize percent."""
if v is not None:
return v / 100 if v != 0 else 0
return None
class FederalReserveOvernightBankFundingRateFetcher(
Fetcher[
FederalReserveOvernightBankFundingRateQueryParams,
List[FederalReserveOvernightBankFundingRateData],
]
):
"""Federal Reserve Federal Funds Fetcher."""
@staticmethod
def transform_query(
params: Dict[str, Any]
) -> FederalReserveOvernightBankFundingRateQueryParams:
"""Transform query."""
transformed_params = params.copy()
now = datetime.now().date()
if params.get("start_date") is None:
transformed_params["start_date"] = datetime(2016, 3, 1).date()
if params.get("end_date") is None:
transformed_params["end_date"] = now
return FederalReserveOvernightBankFundingRateQueryParams(**transformed_params)
@staticmethod
async def aextract_data(
query: FederalReserveOvernightBankFundingRateQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
"""Extract the raw data."""
url = (
"https://markets.newyorkfed.org/api/rates/unsecured/obfr/search.json?"
+ f"startDate={query.start_date}&endDate={query.end_date}"
)
results: List[Dict] = []
response = await amake_request(url, **kwargs)
if response.get("refRates"):
results = response["refRates"]
if not results:
raise EmptyDataError()
return results
@staticmethod
def transform_data(
query: FederalReserveOvernightBankFundingRateQueryParams,
data: List[Dict],
**kwargs: Any,
) -> List[FederalReserveOvernightBankFundingRateData]:
"""Transform data."""
results: List[FederalReserveOvernightBankFundingRateData] = []
for d in data.copy():
_ = d.pop("type", None)
_ = d.pop("revisionIndicator", None)
_ = d.pop("footnoteId", None)
results.append(FederalReserveOvernightBankFundingRateData.model_validate(d))
return results

107
openbb_platform/providers/federal_reserve/openbb_federal_reserve/models/sofr.py Normal file
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"""Federal Reserve SOFR Model."""
# pylint: disable=unused-argument
from datetime import datetime
from typing import Any, Dict, List, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.sofr import (
SOFRData,
SOFRQueryParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_core.provider.utils.helpers import amake_request
from pydantic import field_validator
class FederalReserveSOFRQueryParams(SOFRQueryParams):
"""FederalReserve FED Query."""
class FederalReserveSOFRData(SOFRData):
"""FederalReserve FED Data."""
__alias_dict__ = {
"date": "effectiveDate",
"rate": "percentRate",
"percentile_1": "percentPercentile1",
"percentile_25": "percentPercentile25",
"percentile_75": "percentPercentile75",
"percentile_99": "percentPercentile99",
"volume": "volumeInBillions",
}
@field_validator(
"rate",
"target_range_upper",
"target_range_lower",
"percentile_1",
"percentile_25",
"percentile_75",
"percentile_99",
mode="before",
check_fields=False,
)
@classmethod
def normalize_percent(cls, v):
"""Normalize percent."""
if v not in (None, "", "''", "NA"):
return float(v) / 100 if v != 0 else 0
return None
class FederalReserveSOFRFetcher(
Fetcher[
FederalReserveSOFRQueryParams,
List[FederalReserveSOFRData],
]
):
"""Federal Reserve Federal Funds Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> FederalReserveSOFRQueryParams:
"""Transform query."""
transformed_params = params.copy()
now = datetime.now().date()
if params.get("start_date") is None:
transformed_params["start_date"] = datetime(2018, 4, 2).date()
if params.get("end_date") is None:
transformed_params["end_date"] = now
return FederalReserveSOFRQueryParams(**transformed_params)
@staticmethod
async def aextract_data(
query: FederalReserveSOFRQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
"""Extract the raw data."""
url = (
"https://markets.newyorkfed.org/api/rates/secured/sofr/search.json?"
+ f"startDate={query.start_date}&endDate={query.end_date}"
)
results: List[Dict] = []
response = await amake_request(url, **kwargs)
if response.get("refRates"):
results = response["refRates"]
if not results:
raise EmptyDataError()
return results
@staticmethod
def transform_data(
query: FederalReserveSOFRQueryParams,
data: List[Dict],
**kwargs: Any,
) -> List[FederalReserveSOFRData]:
"""Transform data."""
results: List[FederalReserveSOFRData] = []
for d in data.copy():
_ = d.pop("type", None)
_ = d.pop("footnoteId", None)
_ = d.pop("revisionIndicator", None)
results.append(FederalReserveSOFRData.model_validate(d))
return results

53
openbb_platform/providers/federal_reserve/tests/record/http/test_federal_reserve_fetchers/test_federal_reserve_fed_fetcher.yaml vendored
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openbb_platform/providers/federal_reserve/tests/record/http/test_federal_reserve_fetchers/test_federal_reserve_federal_funds_rate_fetcher.yaml vendored Normal file
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openbb_platform/providers/federal_reserve/tests/record/http/test_federal_reserve_fetchers/test_federal_reserve_overnight_bank_funding_rate_fetcher.yaml vendored Normal file
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openbb_platform/providers/federal_reserve/tests/record/http/test_federal_reserve_fetchers/test_federal_reserve_sofr_fetcher.yaml vendored Normal file
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36
openbb_platform/providers/federal_reserve/tests/test_federal_reserve_fetchers.py
View File

@@ -7,10 +7,16 @@ from openbb_core.app.service.user_service import UserService
from openbb_federal_reserve.models.central_bank_holdings import (
FederalReserveCentralBankHoldingsFetcher,
)
from openbb_federal_reserve.models.fed_rates import FederalReserveFEDFetcher
from openbb_federal_reserve.models.federal_funds_rate import (
FederalReserveFederalFundsRateFetcher,
)
from openbb_federal_reserve.models.money_measures import (
FederalReserveMoneyMeasuresFetcher,
)
from openbb_federal_reserve.models.overnight_bank_funding_rate import (
FederalReserveOvernightBankFundingRateFetcher,
)
from openbb_federal_reserve.models.sofr import FederalReserveSOFRFetcher
from openbb_federal_reserve.models.treasury_rates import (
FederalReserveTreasuryRatesFetcher,
)
@@ -51,11 +57,11 @@ def test_federal_reserve_money_measures_fetcher(credentials=test_credentials):
@pytest.mark.record_http
def test_federal_reserve_fed_fetcher(credentials=test_credentials):
"""Test the Federal Reserve FED fetcher."""
def test_federal_reserve_federal_funds_rate_fetcher(credentials=test_credentials):
"""Test the Federal Reserve Federal Funds Rate fetcher."""
params = {"start_date": date(2023, 1, 1), "end_date": date(2023, 6, 6)}
fetcher = FederalReserveFEDFetcher()
fetcher = FederalReserveFederalFundsRateFetcher()
result = fetcher.test(params, credentials)
assert result is None
@@ -78,3 +84,25 @@ def test_federal_reserve_central_bank_holdings_fetcher(credentials=test_credenti
fetcher = FederalReserveCentralBankHoldingsFetcher()
result = fetcher.test(params, credentials)
assert result is None
@pytest.mark.record_http
def test_federal_reserve_sofr_fetcher(credentials=test_credentials):
"""Test the Federal Reserve SOFR Fetcher."""
params = {"start_date": date(2024, 6, 1), "end_date": date(2024, 6, 6)}
fetcher = FederalReserveSOFRFetcher()
result = fetcher.test(params, credentials)
assert result is None
@pytest.mark.record_http
def test_federal_reserve_overnight_bank_funding_rate_fetcher(
credentials=test_credentials,
):
"""Test the Federal Reserve Overnight Bank Funding Rate Fetcher."""
params = {"start_date": date(2024, 6, 1), "end_date": date(2024, 6, 6)}
fetcher = FederalReserveOvernightBankFundingRateFetcher()
result = fetcher.test(params, credentials)
assert result is None

26
openbb_platform/providers/fred/openbb_fred/__init__.py
View File

@@ -1,20 +1,22 @@
"""FRED provider module."""
from openbb_core.provider.abstract.provider import Provider
from openbb_fred.models.ameribor_rates import FREDAMERIBORFetcher
from openbb_fred.models.ameribor import FredAmeriborFetcher
from openbb_fred.models.balance_of_payments import FredBalanceOfPaymentsFetcher
from openbb_fred.models.bond_indices import FredBondIndicesFetcher
from openbb_fred.models.commercial_paper import FREDCommercialPaperFetcher
from openbb_fred.models.consumer_price_index import FREDConsumerPriceIndexFetcher
from openbb_fred.models.cp import FREDCommercialPaperFetcher
from openbb_fred.models.dwpcr_rates import FREDDiscountWindowPrimaryCreditRateFetcher
from openbb_fred.models.ecb_interest_rates import (
FREDEuropeanCentralBankInterestRatesFetcher,
)
from openbb_fred.models.estr_rates import FREDESTRFetcher
from openbb_fred.models.euro_short_term_rate import FredEuroShortTermRateFetcher
from openbb_fred.models.fed_projections import FREDPROJECTIONFetcher
from openbb_fred.models.fed_rates import FREDFEDFetcher
from openbb_fred.models.federal_funds_rate import FredFederalFundsRateFetcher
from openbb_fred.models.ffrmc import FREDSelectedTreasuryConstantMaturityFetcher
from openbb_fred.models.hqm import FREDHighQualityMarketCorporateBondFetcher
from openbb_fred.models.high_quality_market import (
FredHighQualityMarketCorporateBondFetcher,
)
from openbb_fred.models.ice_bofa import FREDICEBofAFetcher
from openbb_fred.models.iorb_rates import FREDIORBFetcher
from openbb_fred.models.manufacturing_outlook_texas import (
@@ -22,6 +24,9 @@ from openbb_fred.models.manufacturing_outlook_texas import (
)
from openbb_fred.models.moody import FREDMoodyCorporateBondIndexFetcher
from openbb_fred.models.mortgage_indices import FredMortgageIndicesFetcher
from openbb_fred.models.overnight_bank_funding_rate import (
FredOvernightBankFundingRateFetcher,
)
from openbb_fred.models.regional import FredRegionalDataFetcher
from openbb_fred.models.retail_prices import FredRetailPricesFetcher
from openbb_fred.models.search import (
@@ -31,7 +36,7 @@ from openbb_fred.models.senior_loan_officer_survey import (
FredSeniorLoanOfficerSurveyFetcher,
)
from openbb_fred.models.series import FredSeriesFetcher
from openbb_fred.models.sofr_rates import FREDSOFRFetcher
from openbb_fred.models.sofr import FREDSOFRFetcher
from openbb_fred.models.sonia_rates import FREDSONIAFetcher
from openbb_fred.models.spot import FREDSpotRateFetcher
from openbb_fred.models.survey_of_economic_conditions_chicago import (
@@ -58,10 +63,10 @@ Research division of the Federal Reserve Bank of St. Louis that has more than
"ConsumerPriceIndex": FREDConsumerPriceIndexFetcher,
"USYieldCurve": FREDUSYieldCurveFetcher,
"SOFR": FREDSOFRFetcher,
"ESTR": FREDESTRFetcher,
"EuroShortTermRate": FredEuroShortTermRateFetcher,
"SONIA": FREDSONIAFetcher,
"AMERIBOR": FREDAMERIBORFetcher,
"FEDFUNDS": FREDFEDFetcher,
"Ameribor": FredAmeriborFetcher,
"FederalFundsRate": FredFederalFundsRateFetcher,
"PROJECTIONS": FREDPROJECTIONFetcher,
"IORB": FREDIORBFetcher,
"DiscountWindowPrimaryCreditRate": FREDDiscountWindowPrimaryCreditRateFetcher,
@@ -70,6 +75,7 @@ Research division of the Federal Reserve Bank of St. Louis that has more than
"ManufacturingOutlookTexas": FredManufacturingOutlookTexasFetcher,
"MoodyCorporateBondIndex": FREDMoodyCorporateBondIndexFetcher,
"MortgageIndices": FredMortgageIndicesFetcher,
"OvernightBankFundingRate": FredOvernightBankFundingRateFetcher,
"CommercialPaper": FREDCommercialPaperFetcher,
"FredSearch": FredSearchFetcher,
"FredSeries": FredSeriesFetcher,
@@ -77,7 +83,7 @@ Research division of the Federal Reserve Bank of St. Louis that has more than
"RetailPrices": FredRetailPricesFetcher,
"SeniorLoanOfficerSurvey": FredSeniorLoanOfficerSurveyFetcher,
"SpotRate": FREDSpotRateFetcher,
"HighQualityMarketCorporateBond": FREDHighQualityMarketCorporateBondFetcher,
"HighQualityMarketCorporateBond": FredHighQualityMarketCorporateBondFetcher,
"TreasuryConstantMaturity": FREDTreasuryConstantMaturityFetcher,
"SelectedTreasuryConstantMaturity": FREDSelectedTreasuryConstantMaturityFetcher,
"SelectedTreasuryBill": FREDSelectedTreasuryBillFetcher,

227
openbb_platform/providers/fred/openbb_fred/models/ameribor.py Normal file
View File

@@ -0,0 +1,227 @@
"""FRED AMERIBOR Model."""
# pylint: disable=unused-argument
from typing import Any, Dict, List, Literal, Optional, Union
from openbb_core.provider.abstract.annotated_result import AnnotatedResult
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.ameribor import (
AmeriborData,
AmeriborQueryParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_fred.models.series import FredSeriesFetcher
from pandas import Categorical, DataFrame
from pydantic import Field
MATURITY_TO_FRED_ID = {
"all": "AMERIBOR,AMBOR30,AMBOR90,AMBOR30T,AMBOR90T",
"overnight": "AMERIBOR",
"average_30d": "AMBOR30",
"average_90d": "AMBOR90",
"term_30d": "AMBOR30T",
"term_90d": "AMBOR90T",
}
class FredAmeriborQueryParams(AmeriborQueryParams):
"""FRED AMERIBOR Query."""
__json_schema_extra__ = {"maturity": {"multiple_items_allowed": True}}
maturity: Union[
Literal[
"all",
"overnight",
"average_30d",
"average_90d",
"term_30d",
"term_90d",
],
str,
] = Field(
default="all",
description="Period of AMERIBOR rate.",
json_schema_extra={
"choices": [
"all",
"overnight",
"average_30d",
"average_90d",
"term_30d",
"term_90d",
]
},
)
frequency: Union[
None,
Literal[
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
],
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": [
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
]
},
)
aggregation_method: Union[None, Literal["avg", "sum", "eop"]] = Field(
default=None,
description="""
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
""",
json_schema_extra={"choices": ["avg", "sum", "eop"]},
)
transform: Union[
None, Literal["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": ["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
},
)
class FredAmeriborData(AmeriborData):
"""FRED AMERIBOR Data."""
class FredAmeriborFetcher(Fetcher[FredAmeriborQueryParams, List[FredAmeriborData]]):
"""FRED Ameribor Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> FredAmeriborQueryParams:
"""Transform query."""
return FredAmeriborQueryParams(**params)
@staticmethod
async def aextract_data(
query: FredAmeriborQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> Dict:
"""Extract data."""
maturities = query.maturity.split(",")
ids = ""
if len(maturities) == 1 or "all" in maturities:
ids = MATURITY_TO_FRED_ID[query.maturity]
else:
ids = ",".join([MATURITY_TO_FRED_ID[m] for m in maturities])
try:
response = await FredSeriesFetcher.fetch_data(
dict(
symbol=ids,
start_date=query.start_date,
end_date=query.end_date,
frequency=query.frequency,
aggregation_method=query.aggregation_method,
transform=query.transform,
),
credentials,
)
except Exception as e:
raise e from e
return {
"metadata": response.metadata,
"data": [d.model_dump() for d in response.result],
}
@staticmethod
def transform_data(
query: FredAmeriborQueryParams,
data: Dict,
**kwargs: Any,
) -> AnnotatedResult[List[FredAmeriborData]]:
"""Transform data."""
if not data["data"]:
raise EmptyDataError("The request was returned with no data.")
metadata = data.get("metadata", {})
maturity_dict = {
"AMERIBOR": "overnight",
"AMBOR30": "day_30",
"AMBOR90": "day_90",
"AMBOR30T": "day_30",
"AMBOR90T": "day_90",
}
df = DataFrame(data.get("data", []))
# Flatten data
df = df.melt(id_vars="date", var_name="symbol", value_name="value").query(
"value.notnull()"
)
df = df.rename(columns={"value": "rate"}).sort_values(by="date")
# Normalize percent values
df["rate"] = df["rate"].astype(float) / 100
df["maturity"] = df["symbol"].apply(lambda x: maturity_dict.get(x, x))
df["title"] = df["symbol"].apply(lambda x: metadata.get(x, {}).get("title", x))
maturity_categories = ["overnight", "day_30", "day_90"]
df["maturity"] = Categorical(
df["maturity"], categories=maturity_categories, ordered=True
)
df.sort_values(by=["date", "maturity"], inplace=True)
records = df.to_dict(orient="records")
return AnnotatedResult(
result=[FredAmeriborData.model_validate(d) for d in records],
metadata=metadata,
)

92
openbb_platform/providers/fred/openbb_fred/models/ameribor_rates.py
View File

@@ -1,92 +0,0 @@
"""FRED AMERIBOR Model."""
from typing import Any, Dict, List, Literal, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.ameribor_rates import (
AMERIBORData,
AMERIBORQueryParams,
)
from openbb_fred.utils.fred_base import Fred
from pydantic import Field, field_validator
AMERIBOR_PARAMETER_TO_FRED_ID = {
"overnight": "AMERIBOR",
"term_30": "AMBOR30T",
"term_90": "AMBOR90T",
"1_week_term_structure": "AMBOR1W",
"1_month_term_structure": "AMBOR1M",
"3_month_term_structure": "AMBOR3M",
"6_month_term_structure": "AMBOR6M",
"1_year_term_structure": "AMBOR1Y",
"2_year_term_structure": "AMBOR2Y",
"30_day_ma": "AMBOR30",
"90_day_ma": "AMBOR90",
}
class FREDAMERIBORQueryParams(AMERIBORQueryParams):
"""FRED AMERIBOR Query."""
parameter: Literal[
"overnight",
"term_30",
"term_90",
"1_week_term_structure",
"1_month_term_structure",
"3_month_term_structure",
"6_month_term_structure",
"1_year_term_structure",
"2_year_term_structure",
"30_day_ma",
"90_day_ma",
] = Field(default="overnight", description="Period of AMERIBOR rate.")
class FREDAMERIBORData(AMERIBORData):
"""FRED AMERIBOR Data."""
__alias_dict__ = {"rate": "value"}
@field_validator("rate", mode="before", check_fields=False)
@classmethod
def value_validate(cls, v):
"""Validate rate."""
try:
return float(v)
except ValueError:
return None
class FREDAMERIBORFetcher(
Fetcher[FREDAMERIBORQueryParams, List[Dict[str, List[FREDAMERIBORData]]]]
):
"""Transform the query, extract and transform the data from the FRED endpoints."""
data_type = FREDAMERIBORData
@staticmethod
def transform_query(params: Dict[str, Any]) -> FREDAMERIBORQueryParams:
"""Transform query."""
return FREDAMERIBORQueryParams(**params)
@staticmethod
def extract_data(
query: FREDAMERIBORQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> dict:
"""Extract data."""
key = credentials.get("fred_api_key") if credentials else ""
fred_series = AMERIBOR_PARAMETER_TO_FRED_ID[query.parameter]
fred = Fred(key)
data = fred.get_series(fred_series, query.start_date, query.end_date, **kwargs)
return data
@staticmethod
def transform_data(
query: FREDAMERIBORQueryParams, data: dict, **kwargs: Any
) -> List[Dict[str, List[FREDAMERIBORData]]]:
"""Transform data."""
keys = ["date", "value"]
return [FREDAMERIBORData(**{k: x[k] for k in keys}) for x in data]

16
openbb_platform/providers/fred/openbb_fred/models/bond_indices.py
View File

@@ -45,6 +45,12 @@ BAML_CATEGORIES = {
"oas": "BAMLC0A0CM",
"yield_to_worst": "BAMLC0A0CMSYTW",
},
"seasoned_corporate": {
"total_return": "",
"yield": "DAAA,AAA10Y,AAAFF,DBAA,BAA10Y,BAAFF",
"oas": "",
"yield_to_worst": "",
},
"high_yield": {
"total_return": "BAMLHYH0A0HYM2TRIV",
"yield": "BAMLH0A0HYM2EY",
@@ -259,6 +265,7 @@ BAML_CATEGORIES = {
BamlCategories = Literal["high_yield", "us", "emerging_markets"]
INDEX_CHOICES = [
"corporate",
"seasoned_corporate",
"liquid_corporate",
"yield_curve",
"crossover",
@@ -449,6 +456,15 @@ class FredBondIndicesFetcher(
+ f" Must be one of {','.join(BAML_CATEGORIES.get('us'))}."
)
messages.append(message)
elif (
index == "seasoned_corporate"
and values["index_type"] != "yield"
):
message = (
"Invalid index_type for index: 'seasoned_corporate'."
+ " Must be 'yield'."
)
messages.append(message)
else:
new_index.append(index)
if values["category"] == "high_yield":

372
openbb_platform/providers/fred/openbb_fred/models/commercial_paper.py Normal file
View File

@@ -0,0 +1,372 @@
"""FRED Commercial Paper Model."""
# pylint: disable=unused-argument
from typing import Any, Dict, List, Literal, Optional, Union
from openbb_core.provider.abstract.annotated_result import AnnotatedResult
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.commercial_paper import (
CommercialPaperData,
CommercialPaperParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_fred.models.series import FredSeriesFetcher
from pandas import Categorical, DataFrame
from pydantic import Field
CP_SERIES_IDS = {
"RIFSPPAAAD01NB": {
"maturity": "overnight",
"asset": "asset_backed",
"title": "Overnight AA Asset-Backed Commercial Paper Interest Rate",
},
"RIFSPPAAAD07NB": {
"maturity": "day_7",
"asset": "asset_backed",
"title": "7-Day AA Asset-Backed Commercial Paper Interest Rate",
},
"RIFSPPAAAD15NB": {
"maturity": "day_15",
"asset": "asset_backed",
"title": "15-Day AA Asset-Backed Commercial Paper Interest Rate",
},
"RIFSPPAAAD30NB": {
"maturity": "day_30",
"asset": "asset_backed",
"title": "30-Day AA Asset-Backed Commercial Paper Interest Rate",
},
"RIFSPPAAAD60NB": {
"maturity": "day_60",
"asset": "asset_backed",
"title": "60-Day AA Asset-Backed Commercial Paper Interest Rate",
},
"RIFSPPAAAD90NB": {
"maturity": "day_90",
"asset": "asset_backed",
"title": "90-Day AA Asset-Backed Commercial Paper Interest Rate",
},
"RIFSPPFAAD01NB": {
"maturity": "overnight",
"asset": "financial",
"title": "Overnight AA Financial Commercial Paper Interest Rate",
},
"RIFSPPFAAD07NB": {
"maturity": "day_7",
"asset": "financial",
"title": "7-Day AA Financial Commercial Paper Interest Rate",
},
"RIFSPPFAAD15NB": {
"maturity": "day_15",
"asset": "financial",
"title": "15-Day AA Financial Commercial Paper Interest Rate",
},
"RIFSPPFAAD30NB": {
"maturity": "day_30",
"asset": "financial",
"title": "30-Day AA Financial Commercial Paper Interest Rate",
},
"RIFSPPFAAD60NB": {
"maturity": "day_60",
"asset": "financial",
"title": "60-Day AA Financial Commercial Paper Interest Rate",
},
"RIFSPPFAAD90NB": {
"maturity": "day_90",
"asset": "financial",
"title": "90-Day AA Financial Commercial Paper Interest Rate",
},
"RIFSPPNAAD01NB": {
"maturity": "overnight",
"asset": "nonfinancial",
"title": "Overnight AA Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNAAD07NB": {
"maturity": "day_7",
"asset": "nonfinancial",
"title": "7-Day AA Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNAAD15NB": {
"maturity": "day_15",
"asset": "nonfinancial",
"title": "15-Day AA Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNAAD30NB": {
"maturity": "day_30",
"asset": "nonfinancial",
"title": "30-Day AA Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNAAD60NB": {
"maturity": "day_60",
"asset": "nonfinancial",
"title": "60-Day AA Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNAAD90NB": {
"maturity": "day_90",
"asset": "nonfinancial",
"title": "90-Day AA Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNA2P2D01NB": {
"maturity": "overnight",
"asset": "a2p2",
"title": "Overnight A2/P2 Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNA2P2D07NB": {
"maturity": "day_7",
"asset": "a2p2",
"title": "7-Day A2/P2 Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNA2P2D15NB": {
"maturity": "day_15",
"asset": "a2p2",
"title": "15-Day A2/P2 Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNA2P2D30NB": {
"maturity": "day_30",
"asset": "a2p2",
"title": "30-Day A2/P2 Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNA2P2D60NB": {
"maturity": "day_60",
"asset": "a2p2",
"title": "60-Day A2/P2 Nonfinancial Commercial Paper Interest Rate",
},
"RIFSPPNA2P2D90NB": {
"maturity": "day_90",
"asset": "a2p2",
"title": "90-Day A2/P2 Nonfinancial Commercial Paper Interest Rate",
},
}
ALL_IDS = list(CP_SERIES_IDS)
class FREDCommercialPaperParams(CommercialPaperParams):
"""FRED Commercial Paper Query."""
__json_schema_extra__ = {
"maturity": {"multiple_items_allowed": True},
"category": {"multiple_items_allowed": True},
}
maturity: Union[
str, Literal["all", "overnight", "7d", "15d", "30d", "60d", "90d"]
] = Field(
default="all",
description="A target maturity.",
json_schema_extra={
"choices": ["all", "overnight", "7d", "15d", "30d", "60d", "90d"]
},
)
category: Union[
str, Literal["all", "asset_backed", "financial", "nonfinancial", "a2p2"]
] = Field(
default="all",
description="The category of asset.",
json_schema_extra={
"choices": ["all", "asset_backed", "financial", "nonfinancial", "a2p2"]
},
)
frequency: Union[
None,
Literal[
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
],
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": [
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
]
},
)
aggregation_method: Union[None, Literal["avg", "sum", "eop"]] = Field(
default=None,
description="""
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
""",
json_schema_extra={"choices": ["avg", "sum", "eop"]},
)
transform: Union[
None, Literal["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": ["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
},
)
class FREDCommercialPaperData(CommercialPaperData):
"""FRED Commercial Paper Data."""
asset_type: Literal["asset_backed", "financial", "nonfinancial", "a2p2"] = Field(
description="The category of asset."
)
class FREDCommercialPaperFetcher(
Fetcher[
FREDCommercialPaperParams,
List[FREDCommercialPaperData],
]
):
"""FRED Commercial Paper Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> FREDCommercialPaperParams:
"""Transform query."""
return FREDCommercialPaperParams(**params)
@staticmethod
async def aextract_data(
query: FREDCommercialPaperParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> Dict:
"""Extract data."""
ids: List[str] = []
if query.maturity == "all" and query.category == "all":
ids = ALL_IDS
else:
MAT_DICT = {
"overnight": "01",
"7d": "07",
"15d": "15",
"30d": "30",
"60d": "60",
"90d": "90",
}
CAT_DICT = {
"asset_backed": "AAAD",
"financial": "FAAD",
"nonfinancial": "NAAD",
"a2p2": "NA2P2D",
}
maturities = query.maturity.split(",")
categories = query.category.split(",")
if "all" in categories:
categories = list(CAT_DICT)
if "all" in maturities:
maturities = list(MAT_DICT)
for cat in categories:
for mat in maturities:
ids.append(f"RIFSPP{CAT_DICT.get(cat)}{MAT_DICT.get(mat)}NB")
try:
response = await FredSeriesFetcher.fetch_data(
dict(
symbol=",".join(ids),
start_date=query.start_date if query.start_date else "2019-01-01",
end_date=query.end_date,
frequency=query.frequency,
aggregation_method=query.aggregation_method,
transform=query.transform,
),
credentials,
)
except Exception as e:
raise e from e
return {
"metadata": response.metadata,
"data": [d.model_dump() for d in response.result],
}
@staticmethod
def transform_data(
query: FREDCommercialPaperParams,
data: Dict,
**kwargs: Any,
) -> List[FREDCommercialPaperData]:
"""Transform data."""
if not data:
raise EmptyDataError("The request was returned empty.")
df = DataFrame(data["data"])
metadata = data.get("metadata", {})
# Flatten data
df = df.melt(id_vars="date", var_name="symbol", value_name="value").query(
"value.notnull()"
)
df = df.rename(columns={"value": "rate"}).sort_values(by="date")
# Normalize percent values
df["rate"] = df["rate"].astype(float) / 100
# Add asset type, maturity, and title
df["asset_type"] = df["symbol"].apply(lambda x: CP_SERIES_IDS[x]["asset"])
df["title"] = df["symbol"].apply(lambda x: CP_SERIES_IDS[x]["title"])
df["maturity"] = df["symbol"].apply(lambda x: CP_SERIES_IDS[x]["maturity"])
# Categorize and order.
asset_type_categories = ["asset_backed", "financial", "nonfinancial", "a2p2"]
maturity_categories = [
"overnight",
"day_7",
"day_15",
"day_30",
"day_60",
"day_90",
]
df["asset_type"] = Categorical(
df["asset_type"], categories=asset_type_categories, ordered=True
)
df["maturity"] = Categorical(
df["maturity"], categories=maturity_categories, ordered=True
)
df.sort_values(by=["date", "asset_type", "maturity"], inplace=True)
records = df.to_dict(orient="records")
return AnnotatedResult(
result=[FREDCommercialPaperData.model_validate(d) for d in records],
metadata=metadata,
)

87
openbb_platform/providers/fred/openbb_fred/models/cp.py
View File

@@ -1,87 +0,0 @@
"""FRED Commercial Paper Model."""
from typing import Any, Dict, List, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.cp import (
CommercialPaperData,
CommercialPaperParams,
)
from openbb_fred.utils.fred_base import Fred
from openbb_fred.utils.fred_helpers import get_cp_series_id
from pydantic import field_validator
class FREDCommercialPaperParams(CommercialPaperParams):
"""FRED Commercial Paper Query."""
class FREDCommercialPaperData(CommercialPaperData):
"""FRED Commercial Paper Data."""
__alias_dict__ = {"rate": "value"}
@field_validator("rate", mode="before", check_fields=False)
@classmethod
def value_validate(cls, v):
"""Validate rate."""
try:
return float(v)
except ValueError:
return None
class FREDCommercialPaperFetcher(
Fetcher[
FREDCommercialPaperParams,
List[FREDCommercialPaperData],
]
):
"""Transform the query, extract and transform the data from the FRED endpoints."""
data_type = FREDCommercialPaperData
@staticmethod
def transform_query(params: Dict[str, Any]) -> FREDCommercialPaperParams:
"""Transform query."""
return FREDCommercialPaperParams(**params)
@staticmethod
def extract_data(
query: FREDCommercialPaperParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any
) -> list:
"""Extract data."""
key = credentials.get("fred_api_key") if credentials else ""
fred = Fred(key)
series = get_cp_series_id(
maturity=query.maturity,
category=query.category,
grade=query.grade,
)
data = []
for s in series:
id_ = s["FRED Series ID"]
title = s["Title"]
d = fred.get_series(
series_id=id_,
start_date=query.start_date,
end_date=query.end_date,
**kwargs,
)
for item in d:
item["title"] = title
data.extend(d)
return data
@staticmethod
def transform_data(
query: FREDCommercialPaperParams, data: list, **kwargs: Any
) -> List[FREDCommercialPaperData]:
"""Transform data."""
return [FREDCommercialPaperData.model_validate(d) for d in data]

81
openbb_platform/providers/fred/openbb_fred/models/estr_rates.py
View File

@@ -1,81 +0,0 @@
"""FRED ESTR Model."""
from typing import Any, Dict, List, Literal, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.estr_rates import ESTRData, ESTRQueryParams
from openbb_fred.utils.fred_base import Fred
from pydantic import Field, field_validator
ESTR_PARAMETER_TO_ID = {
"volume_weighted_trimmed_mean_rate": "ECBESTRVOLWGTTRMDMNRT",
"number_of_transactions": "ECBESTRNUMTRANS",
"number_of_active_banks": "ECBESTRNUMACTBANKS",
"total_volume": "ECBESTRTOTVOL",
"share_of_volume_of_the_5_largest_active_banks": "ECBESTRSHRVOL5LRGACTBNK",
"rate_at_75th_percentile_of_volume": "ECBESTRRT75THPCTVOL",
"rate_at_25th_percentile_of_volume": "ECBESTRRT25THPCTVOL",
}
class FREDESTRQueryParams(ESTRQueryParams):
"""FRED ESTR Query."""
parameter: Literal[
"volume_weighted_trimmed_mean_rate",
"number_of_transactions",
"number_of_active_banks",
"total_volume",
"share_of_volume_of_the_5_largest_active_banks",
"rate_at_75th_percentile_of_volume",
"rate_at_25th_percentile_of_volume",
] = Field(
default="volume_weighted_trimmed_mean_rate", description="Period of ESTR rate."
)
class FREDESTRData(ESTRData):
"""FRED ESTR Data."""
__alias_dict__ = {"rate": "value"}
@field_validator("rate", mode="before", check_fields=False)
@classmethod
def value_validate(cls, v):
"""Validate rate."""
try:
return float(v)
except ValueError:
return None
class FREDESTRFetcher(
Fetcher[FREDESTRQueryParams, List[Dict[str, List[FREDESTRData]]]]
):
"""Transform the query, extract and transform the data from the FRED endpoints."""
data_type = FREDESTRData
@staticmethod
def transform_query(params: Dict[str, Any]) -> FREDESTRQueryParams:
"""Transform query"""
return FREDESTRQueryParams(**params)
@staticmethod
def extract_data(
query: FREDESTRQueryParams, credentials: Optional[Dict[str, str]], **kwargs: Any
) -> dict:
"""Extract data"""
key = credentials.get("fred_api_key") if credentials else ""
fred_series = ESTR_PARAMETER_TO_ID[query.parameter]
fred = Fred(key)
data = fred.get_series(fred_series, query.start_date, query.end_date, **kwargs)
return data
@staticmethod
def transform_data(
query: FREDESTRQueryParams, data: dict, **kwargs: Any
) -> List[Dict[str, List[FREDESTRData]]]:
"""Transform data"""
keys = ["date", "value"]
return [FREDESTRData(**{k: x[k] for k in keys}) for x in data]

194
openbb_platform/providers/fred/openbb_fred/models/euro_short_term_rate.py Normal file
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@@ -0,0 +1,194 @@
"""FRED Euro Short Term Rate Standard Model."""
from typing import Any, Dict, List, Literal, Optional, Union
from openbb_core.provider.abstract.annotated_result import AnnotatedResult
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.euro_short_term_rate import (
EuroShortTermRateData,
EuroShortTermRateQueryParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_fred.models.series import FredSeriesFetcher
from pydantic import Field, field_validator
class FredEuroShortTermRateQueryParams(EuroShortTermRateQueryParams):
"""FRED Euro Short Term Rate Query."""
frequency: Union[
None,
Literal[
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
],
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": [
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
]
},
)
aggregation_method: Union[None, Literal["avg", "sum", "eop"]] = Field(
default=None,
description="""
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
""",
json_schema_extra={"choices": ["avg", "sum", "eop"]},
)
transform: Union[
None, Literal["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": ["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
},
)
class FredEuroShortTermRateData(EuroShortTermRateData):
"""FRED Euro Short Term Rate Data."""
__alias_dict__ = {
"rate": "ECBESTRVOLWGTTRMDMNRT",
"transactions": "ECBESTRNUMTRANS",
"number_of_banks": "ECBESTRNUMACTBANKS",
"volume": "ECBESTRTOTVOL",
"large_bank_share_of_volume": "ECBESTRSHRVOL5LRGACTBNK",
"percentile_75": "ECBESTRRT75THPCTVOL",
"percentile_25": "ECBESTRRT25THPCTVOL",
}
@field_validator(
"rate",
"percentile_25",
"percentile_75",
"large_bank_share_of_volume",
mode="before",
check_fields=False,
)
@classmethod
def normalize_percent(cls, v):
"""Normalize percent."""
return float(v) / 100 if v else None
class FredEuroShortTermRateFetcher(
Fetcher[FredEuroShortTermRateQueryParams, List[FredEuroShortTermRateData]]
):
"""FRED Euro Short Term Rate Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> FredEuroShortTermRateQueryParams:
"""Transform query"""
return FredEuroShortTermRateQueryParams(**params)
@staticmethod
async def aextract_data(
query: FredEuroShortTermRateQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> Dict:
"""Extract data"""
ids = [
"ECBESTRVOLWGTTRMDMNRT",
"ECBESTRNUMTRANS",
"ECBESTRNUMACTBANKS",
"ECBESTRTOTVOL",
"ECBESTRSHRVOL5LRGACTBNK",
"ECBESTRRT75THPCTVOL",
"ECBESTRRT25THPCTVOL",
]
try:
response = await FredSeriesFetcher.fetch_data(
dict(
symbol=",".join(ids),
start_date=query.start_date if query.start_date else "2019-10-02",
end_date=query.end_date,
frequency=query.frequency,
aggregation_method=query.aggregation_method,
transform=query.transform,
),
credentials,
)
except Exception as e:
raise e from e
return {
"metadata": response.metadata,
"data": [d.model_dump() for d in response.result],
}
@staticmethod
def transform_data(
query: FredEuroShortTermRateQueryParams,
data: Dict,
**kwargs: Any,
) -> List[List[FredEuroShortTermRateData]]:
"""Transform data"""
if not data:
raise EmptyDataError("The request was returned empty.")
return AnnotatedResult(
result=[
FredEuroShortTermRateData.model_validate(d)
for d in data.get("data", [])
if d.get("ECBESTRVOLWGTTRMDMNRT")
],
metadata=data.get("metadata", {}),
)

80
openbb_platform/providers/fred/openbb_fred/models/fed_rates.py
View File

@@ -1,80 +0,0 @@
"""FRED FED Model."""
from typing import Any, Dict, List, Literal, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.fed_rates import (
FEDData,
FEDQueryParams,
)
from openbb_fred.utils.fred_base import Fred
from pydantic import Field, field_validator
FED_PARAMETER_TO_FRED_ID = {
"monthly": "FEDFUNDS",
"daily": "DFF",
"weekly": "FF",
"daily_excl_weekend": "RIFSPFFNB",
"annual": "RIFSPFFNA",
"biweekly": "RIFSPFFNBWAW",
"volume": "EFFRVOL",
}
class FREDFEDQueryParams(FEDQueryParams):
"""FRED FED Query."""
parameter: Literal[
"monthly",
"daily",
"weekly",
"daily_excl_weekend",
"annual",
"biweekly",
"volume",
] = Field(default="weekly", description="Period of FED rate.")
class FREDFEDData(FEDData):
"""FRED FED Data."""
__alias_dict__ = {"rate": "value"}
@field_validator("rate", mode="before", check_fields=False)
@classmethod
def value_validate(cls, v):
"""Validate rate."""
try:
return float(v)
except ValueError:
return None
class FREDFEDFetcher(Fetcher[FREDFEDQueryParams, List[Dict[str, List[FREDFEDData]]]]):
"""FRED FED Model."""
data_type = FREDFEDData
@staticmethod
def transform_query(params: Dict[str, Any]) -> FREDFEDQueryParams:
"""Transform query."""
return FREDFEDQueryParams(**params)
@staticmethod
def extract_data(
query: FREDFEDQueryParams, credentials: Optional[Dict[str, str]], **kwargs: Any
) -> dict:
"""Extract data."""
key = credentials.get("fred_api_key") if credentials else ""
fred_series = FED_PARAMETER_TO_FRED_ID[query.parameter]
fred = Fred(key)
data = fred.get_series(fred_series, query.start_date, query.end_date, **kwargs)
return data
@staticmethod
def transform_data(
query: FREDFEDQueryParams, data: dict, **kwargs: Any
) -> List[Dict[str, List[FREDFEDData]]]:
"""Transform data."""
keys = ["date", "value"]
return [FREDFEDData(**{k: x[k] for k in keys}) for x in data]

203
openbb_platform/providers/fred/openbb_fred/models/federal_funds_rate.py Normal file
View File

@@ -0,0 +1,203 @@
"""FRED Federal Funds Rate Model."""
from datetime import datetime
from typing import Any, Dict, List, Literal, Optional, Union
from openbb_core.provider.abstract.annotated_result import AnnotatedResult
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.federal_funds_rate import (
FederalFundsRateData,
FederalFundsRateQueryParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_fred.models.series import FredSeriesFetcher
from pandas import DataFrame, to_datetime
from pydantic import Field
EFFR_SERIES_IDS = {
"rate": "DFF",
"target_range_upper": "DFEDTARU",
"target_range_lower": "DFEDTARL",
"percentile_1": "EFFR1",
"percentile_25": "EFFR25",
"percentile_75": "EFFR75",
"percentile_99": "EFFR99",
"volume": "EFFRVOL",
}
class FredFederalFundsRateQueryParams(FederalFundsRateQueryParams):
"""FRED Federal Funds Rate Query."""
frequency: Union[
None,
Literal[
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
],
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": [
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
]
},
)
aggregation_method: Union[None, Literal["avg", "sum", "eop"]] = Field(
default=None,
description="""
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
""",
json_schema_extra={"choices": ["avg", "sum", "eop"]},
)
transform: Union[
None, Literal["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": ["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
},
)
effr_only: bool = Field(
default=False,
description="Return data without quantiles, target ranges, and volume.",
)
class FredFederalFundsRateData(FederalFundsRateData):
"""FRED Federal Funds Rate Data."""
class FredFederalFundsRateFetcher(
Fetcher[FredFederalFundsRateQueryParams, List[FredFederalFundsRateData]]
):
"""FRED Federal Funds Rate Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> FredFederalFundsRateQueryParams:
"""Transform query."""
transformed_params = params.copy()
now = datetime.now().date()
if params.get("start_date") is None:
transformed_params["start_date"] = (
datetime(2016, 1, 1).date()
if params.get("effr_only") is False
else None
)
if params.get("end_date") is None:
transformed_params["end_date"] = now
return FredFederalFundsRateQueryParams(**transformed_params)
@staticmethod
async def aextract_data(
query: FredFederalFundsRateQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> Dict:
"""Extract the raw data."""
ids = (
"DFF"
if query.effr_only is True
else ",".join(list(EFFR_SERIES_IDS.values()))
)
try:
response = await FredSeriesFetcher.fetch_data(
dict(
symbol=ids,
start_date=query.start_date,
end_date=query.end_date,
frequency=query.frequency,
aggregation_method=query.aggregation_method,
transform=query.transform,
),
credentials,
)
except Exception as e:
raise e from e
return {
"metadata": response.metadata,
"data": [d.model_dump() for d in response.result],
}
@staticmethod
def transform_data(
query: FredFederalFundsRateQueryParams, data: dict, **kwargs: Any
) -> AnnotatedResult[List[FredFederalFundsRateData]]:
"""Transform and validate the data."""
metadata = data.get("metadata", {})
df = DataFrame(data.get("data", [])).dropna()
if df.empty:
raise EmptyDataError(
"There was an error with the request and it was returned empty."
)
col_map = {v: k for k, v in EFFR_SERIES_IDS.items()}
df.date = df.date.apply(to_datetime)
df = df.set_index("date")
df = df.rename(columns=col_map)
for col in df.columns:
df[col] = (
df[col].astype(float) / 100
if col != "volume"
else df[col].astype(float)
)
records = df.reset_index().to_dict(orient="records")
return AnnotatedResult(
result=[FredFederalFundsRateData.model_validate(d) for d in records],
metadata={col_map[k]: v for k, v in metadata.items()},
)

143
openbb_platform/providers/fred/openbb_fred/models/high_quality_market.py Normal file
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"""FRED High Quality Market Corporate Bond Model."""
import asyncio
from datetime import date as dateType
from typing import Any, Dict, List, Literal, Optional
from dateutil import parser
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.high_quality_market import (
HighQualityMarketCorporateBondData,
HighQualityMarketCorporateBondQueryParams,
)
from openbb_core.provider.utils.helpers import amake_request
from pandas import Categorical, DataFrame
from pydantic import Field, field_validator
class FredHighQualityMarketCorporateBondQueryParams(
HighQualityMarketCorporateBondQueryParams
):
"""FRED High Quality Market Corporate Bond Query."""
__json_schema_extra__ = {"date": {"multiple_items_allowed": True}}
yield_curve: Literal["spot", "par"] = Field(
default="spot",
description="The yield curve type.",
json_schema_extra={"choices": ["spot", "par"]},
)
@field_validator("date", mode="before", check_fields=False)
@classmethod
def validate_date(cls, v):
"""Validate the dates entered."""
if v is None:
return None
if isinstance(v, (list, dateType)):
return v
new_dates: List = []
date_param = v
if isinstance(date_param, str):
new_dates = date_param.split(",")
elif isinstance(date_param, dateType):
new_dates.append(date_param.strftime("%Y-%m-%d"))
elif isinstance(date_param, list) and isinstance(date_param[0], dateType):
new_dates = [d.strftime("%Y-%m-%d") for d in new_dates]
else:
new_dates = date_param
return ",".join(new_dates) if len(new_dates) > 1 else new_dates[0]
class FredHighQualityMarketCorporateBondData(HighQualityMarketCorporateBondData):
"""FRED High Quality Market Corporate Bond Data."""
class FredHighQualityMarketCorporateBondFetcher(
Fetcher[
FredHighQualityMarketCorporateBondQueryParams,
List[FredHighQualityMarketCorporateBondData],
]
):
"""FRED High Quality Market Corporate Bond Fetcher."""
@staticmethod
def transform_query(
params: Dict[str, Any]
) -> FredHighQualityMarketCorporateBondQueryParams:
"""Transform query."""
return FredHighQualityMarketCorporateBondQueryParams(**params)
@staticmethod
async def aextract_data(
query: FredHighQualityMarketCorporateBondQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
"""Extract data."""
api_key = credentials.get("fred_api_key") if credentials else ""
element_id = "219299" if query.yield_curve == "spot" else "219294"
dates: List = [""]
if query.date:
if query.date and isinstance(query.date, dateType):
query.date = query.date.strftime("%Y-%m-%d")
dates = query.date.split(",")
dates = [d.replace(d[-2:], "01") if len(d) == 10 else d for d in dates]
dates = list(set(dates))
dates = (
[f"&observation_date={date}" for date in dates if date] if dates else ""
)
URLS = [
f"https://api.stlouisfed.org/fred/release/tables?release_id=402&element_id={element_id}"
+ f"{date}&include_observation_values=true&api_key={api_key}"
+ "&file_type=json"
for date in dates
]
results = []
async def get_one(URL):
"""Get the observations for a single date."""
data = await amake_request(URL)
if data:
elements = {k: v for k, v in data["elements"].items()}
for k, v in elements.items():
value = v.get("observation_value")
if not value:
continue
maturity = v.get("name").lower().split("-")
results.append(
{
"date": parser.parse(
v.get("observation_date"),
).date(),
"rate": float(value) / 100,
"maturity": (maturity[1] + "_" + maturity[0]).replace(
" ", ""
),
}
)
await asyncio.gather(*[get_one(URL) for URL in URLS])
return results
@staticmethod
def transform_data(
query: FredHighQualityMarketCorporateBondQueryParams,
data: List[Dict],
**kwargs: Any,
) -> List[FredHighQualityMarketCorporateBondData]:
"""Transform data."""
df = DataFrame(data)
df["maturity_int"] = df["maturity"].str.replace("year_", "").astype(float)
maturity_categories = sorted(df.maturity_int.unique().tolist())
df["maturity_int"] = Categorical(
df["maturity_int"], categories=maturity_categories, ordered=True
)
df = df.sort_values(by=["date", "maturity_int"]).reset_index(drop=True)
df = df.drop(columns=["maturity_int"])
records = df.to_dict("records")
return [
FredHighQualityMarketCorporateBondData.model_validate(d) for d in records
]

110
openbb_platform/providers/fred/openbb_fred/models/hqm.py
View File

@@ -1,110 +0,0 @@
"""FRED High Quality Market Corporate Bond Model."""
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional
from openbb_core.app.model.abstract.error import OpenBBError
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.hqm import (
HighQualityMarketCorporateBondData,
HighQualityMarketCorporateBondQueryParams,
)
from openbb_fred.utils.fred_base import Fred
from openbb_fred.utils.fred_helpers import (
YIELD_CURVE_SERIES_CORPORATE_PAR,
YIELD_CURVE_SERIES_CORPORATE_SPOT,
)
from pydantic import Field, field_validator
class FREDHighQualityMarketCorporateBondQueryParams(
HighQualityMarketCorporateBondQueryParams
):
"""FRED High Quality Market Corporate Bond Query."""
class FREDHighQualityMarketCorporateBondData(HighQualityMarketCorporateBondData):
"""FRED High Quality Market Corporate Bond Data."""
__alias_dict__ = {"rate": "value"}
series_id: str = Field(description="FRED series id.")
@field_validator("rate", mode="before", check_fields=False)
@classmethod
def value_validate(cls, v):
"""Validate rate."""
try:
return float(v)
except ValueError:
return None
class FREDHighQualityMarketCorporateBondFetcher(
Fetcher[
FREDHighQualityMarketCorporateBondQueryParams,
List[FREDHighQualityMarketCorporateBondData],
]
):
"""Transform the query, extract and transform the data from the FRED endpoints."""
@staticmethod
def transform_query(
params: Dict[str, Any]
) -> FREDHighQualityMarketCorporateBondQueryParams:
"""Transform query."""
return FREDHighQualityMarketCorporateBondQueryParams(**params)
@staticmethod
def extract_data(
query: FREDHighQualityMarketCorporateBondQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any
) -> list:
"""Extract data."""
key = credentials.get("fred_api_key") if credentials else ""
fred = Fred(key)
data = []
today = datetime.today().date()
if query.date and query.date >= today:
raise OpenBBError("Date must be in the past.")
start_date = (
query.date - timedelta(days=50)
if query.date
else today - timedelta(days=50)
)
if query.yield_curve == "spot":
fred_series = YIELD_CURVE_SERIES_CORPORATE_SPOT
elif query.yield_curve == "par":
fred_series = YIELD_CURVE_SERIES_CORPORATE_PAR
else:
raise OpenBBError("Invalid yield curve type.")
for maturity, id_ in fred_series.items():
d = fred.get_series(
series_id=id_,
start_date=start_date,
**kwargs,
)
for observation in d:
series_data = {
"series_id": id_,
"maturity": maturity,
"yield_curve": query.yield_curve,
"date": observation["date"],
"value": observation["value"],
}
data.append(series_data)
return data
@staticmethod
def transform_data(
query: FREDHighQualityMarketCorporateBondQueryParams, data: list, **kwargs: Any
) -> List[FREDHighQualityMarketCorporateBondData]:
"""Transform data."""
return [FREDHighQualityMarketCorporateBondData.model_validate(d) for d in data]

196
openbb_platform/providers/fred/openbb_fred/models/overnight_bank_funding_rate.py Normal file
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@@ -0,0 +1,196 @@
"""FRED Overnight Bank Funding Rate Model."""
from typing import Any, Dict, List, Literal, Optional, Union
from openbb_core.provider.abstract.annotated_result import AnnotatedResult
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.overnight_bank_funding_rate import (
OvernightBankFundingRateData,
OvernightBankFundingRateQueryParams,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_fred.models.series import FredSeriesFetcher
from pydantic import Field, field_validator
OBFR_ID_TO_FIELD = {
"OBFR": "rate",
"OBFR1": "percentile_1",
"OBFR25": "percentile_25",
"OBFR75": "percentile_75",
"OBFR99": "percentile_99",
"OBFRVOL": "volume",
}
ALL_IDS = list(OBFR_ID_TO_FIELD)
class FredOvernightBankFundingRateQueryParams(OvernightBankFundingRateQueryParams):
"""FRED Overnight Bank Funding Rate Query Params."""
frequency: Union[
None,
Literal[
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
],
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": [
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
]
},
)
aggregation_method: Union[None, Literal["avg", "sum", "eop"]] = Field(
default=None,
description="""
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
""",
json_schema_extra={"choices": ["avg", "sum", "eop"]},
)
transform: Union[
None, Literal["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": ["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
},
)
class FredOvernightBankFundingRateData(OvernightBankFundingRateData):
"""Fred Overnight Bank Funding Rate Data."""
__alias_dict__ = {
"rate": "OBFR",
"percentile_1": "OBFR1",
"percentile_25": "OBFR25",
"percentile_75": "OBFR75",
"percentile_99": "OBFR99",
"volume": "OBFRVOL",
}
@field_validator(
"rate",
"percentile_1",
"percentile_25",
"percentile_75",
"percentile_99",
mode="before",
check_fields=False,
)
@classmethod
def normalize_percent(cls, v):
"""Normalize percent."""
return float(v) / 100 if v else None
class FredOvernightBankFundingRateFetcher(
Fetcher[
FredOvernightBankFundingRateQueryParams, List[FredOvernightBankFundingRateData]
]
):
"""Fred Overnight Bank Funding Rate Fetcher."""
@staticmethod
def transform_query(
params: Dict[str, Any]
) -> FredOvernightBankFundingRateQueryParams:
"""Transform query."""
return FredOvernightBankFundingRateQueryParams(**params)
@staticmethod
async def aextract_data(
query: FredOvernightBankFundingRateQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any
) -> Dict:
"""Extract data."""
try:
response = await FredSeriesFetcher.fetch_data(
dict(
symbol=",".join(ALL_IDS),
start_date=query.start_date,
end_date=query.end_date,
frequency=query.frequency,
aggregation_method=query.aggregation_method,
transform=query.transform,
),
credentials,
)
except Exception as e:
raise e from e
return {
"metadata": response.metadata,
"data": [d.model_dump() for d in response.result],
}
@staticmethod
def transform_data(
query: FredOvernightBankFundingRateQueryParams, data: dict, **kwargs: Any
) -> AnnotatedResult[List[FredOvernightBankFundingRateData]]:
"""Transform data"""
if not data:
raise EmptyDataError("The request was returned empty.")
return AnnotatedResult(
result=[
FredOvernightBankFundingRateData.model_validate(d)
for d in data.get("data", [])
if d.get("OBFR")
],
metadata=data.get("metadata", {}),
)

216
openbb_platform/providers/fred/openbb_fred/models/sofr.py Normal file
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@@ -0,0 +1,216 @@
"""FRED SOFR Model."""
from typing import Any, Dict, List, Literal, Optional, Union
from openbb_core.provider.abstract.annotated_result import AnnotatedResult
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.sofr import SOFRData, SOFRQueryParams
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_fred.models.series import FredSeriesFetcher
from pydantic import Field, field_validator
SOFR_ID_TO_FIELD = {
"SOFR": "rate",
"SOFR1": "percentile_1",
"SOFR25": "percentile_25",
"SOFR75": "percentile_75",
"SOFR99": "percentile_99",
"SOFRVOL": "volume",
"SOFR30DAYAVG": "average_30d",
"SOFR90DAYAVG": "average_90d",
"SOFR180DAYAVG": "average_180d",
"SOFRINDEX": "index",
}
class FREDSOFRQueryParams(SOFRQueryParams):
"""FRED SOFR Query."""
frequency: Union[
None,
Literal[
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
],
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": [
"a",
"q",
"m",
"w",
"wef",
"weth",
"wew",
"wetu",
"wem",
"wesu",
"wesa",
"bwew",
"bwem",
]
},
)
aggregation_method: Union[None, Literal["avg", "sum", "eop"]] = Field(
default=None,
description="""
A key that indicates the aggregation method used for frequency aggregation.
avg = Average
sum = Sum
eop = End of Period
""",
json_schema_extra={"choices": ["avg", "sum", "eop"]},
)
transform: Union[
None, Literal["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
] = Field(
default=None,
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
""",
json_schema_extra={
"choices": ["chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log"]
},
)
class FREDSOFRData(SOFRData):
"""FRED SOFR Data."""
__alias_dict__ = {
"rate": "SOFR",
"percentile_1": "SOFR1",
"percentile_25": "SOFR25",
"percentile_75": "SOFR75",
"percentile_99": "SOFR99",
"volume": "SOFRVOL",
"average_30d": "SOFR30DAYAVG",
"average_90d": "SOFR90DAYAVG",
"average_180d": "SOFR180DAYAVG",
"index": "SOFRINDEX",
}
average_30d: Optional[float] = Field(
default=None,
description="30-Day Average SOFR",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
average_90d: Optional[float] = Field(
default=None,
description="90-Day Average SOFR",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
average_180d: Optional[float] = Field(
default=None,
description="180-Day Average SOFR",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
index: Optional[float] = Field(
default=None,
description="SOFR index as 2018-04-02 = 1",
)
@field_validator(
"rate",
"percentile_1",
"percentile_25",
"percentile_75",
"percentile_99",
"average_30d",
"average_90d",
"average_180d",
mode="before",
check_fields=False,
)
@classmethod
def normalize_percent(cls, v):
"""Normalize percent."""
return float(v) / 100 if v else None
class FREDSOFRFetcher(Fetcher[FREDSOFRQueryParams, List[FREDSOFRData]]):
"""FRED SOFR Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> FREDSOFRQueryParams:
"""Transform query."""
return FREDSOFRQueryParams(**params)
@staticmethod
async def aextract_data(
query: FREDSOFRQueryParams, credentials: Optional[Dict[str, str]], **kwargs: Any
) -> Dict:
"""Extract data."""
ids = list(SOFR_ID_TO_FIELD)
try:
response = await FredSeriesFetcher.fetch_data(
dict(
symbol=",".join(ids),
start_date=query.start_date if query.start_date else "2018-04-02",
end_date=query.end_date,
frequency=query.frequency,
aggregation_method=query.aggregation_method,
transform=query.transform,
),
credentials,
)
except Exception as e:
raise e from e
return {
"metadata": response.metadata,
"data": [d.model_dump() for d in response.result],
}
@staticmethod
def transform_data(
query: FREDSOFRQueryParams, data: dict, **kwargs: Any
) -> AnnotatedResult[List[FREDSOFRData]]:
"""Transform data"""
if not data:
raise EmptyDataError("The request was returned empty.")
return AnnotatedResult(
result=[
FREDSOFRData.model_validate(d)
for d in data.get("data", [])
if d.get("SOFR")
],
metadata=data.get("metadata", {}),
)

71
openbb_platform/providers/fred/openbb_fred/models/sofr_rates.py
View File

@@ -1,71 +0,0 @@
"""FRED SOFR Model."""
from typing import Any, Dict, List, Literal, Optional
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.sofr_rates import SOFRData, SOFRQueryParams
from openbb_fred.utils.fred_base import Fred
from pydantic import Field, field_validator
SOFR_PARAMETER_TO_FRED_ID = {
"overnight": "SOFR",
"30_day": "SOFR30DAYAVG",
"90_day": "SOFR90DAYAVG",
"180_day": "SOFR180DAYAVG",
"index": "SOFRINDEX",
}
class FREDSOFRQueryParams(SOFRQueryParams):
"""FRED SOFR Query."""
period: Literal["overnight", "30_day", "90_day", "180_day", "index"] = Field(
default="overnight", description="Period of SOFR rate."
)
class FREDSOFRData(SOFRData):
"""FRED SOFR Data."""
__alias_dict__ = {"rate": "value"}
@field_validator("rate", mode="before", check_fields=False)
@classmethod
def value_validate(cls, v):
"""Validate rate."""
try:
return float(v)
except ValueError:
return None
class FREDSOFRFetcher(
Fetcher[FREDSOFRQueryParams, List[Dict[str, List[FREDSOFRData]]]]
):
"""Transform the query, extract and transform the data from the FRED endpoints."""
data_type = FREDSOFRData
@staticmethod
def transform_query(params: Dict[str, Any]) -> FREDSOFRQueryParams:
"""Transform query."""
return FREDSOFRQueryParams(**params)
@staticmethod
def extract_data(
query: FREDSOFRQueryParams, credentials: Optional[Dict[str, str]], **kwargs: Any
) -> dict:
"""Extract data."""
key = credentials.get("fred_api_key") if credentials else ""
fred_series = SOFR_PARAMETER_TO_FRED_ID[query.period]
fred = Fred(key)
data = fred.get_series(fred_series, query.start_date, query.end_date, **kwargs)
return data
@staticmethod
def transform_data(
query: FREDSOFRQueryParams, data: dict, **kwargs: Any
) -> List[Dict[str, List[FREDSOFRData]]]:
"""Transform data"""
keys = ["date", "value"]
return [FREDSOFRData(**{k: x[k] for k in keys}) for x in data]

17
openbb_platform/providers/fred/openbb_fred/models/yield_curve.py
View File

@@ -22,12 +22,27 @@ class FREDYieldCurveQueryParams(YieldCurveQueryParams):
__json_schema_extra__ = {"date": {"multiple_items_allowed": True}}
yield_curve_type: Literal[
"nominal", "real", "breakeven", "corporate_spot", "corporate_par"
"nominal",
"real",
"breakeven",
"treasury_minus_fed_funds",
"corporate_spot",
"corporate_par",
] = Field(
default="nominal",
description="Yield curve type."
+ " Nominal and Real Rates are available daily, others are monthly."
+ " The closest date to the requested date will be returned.",
json_schema_extra={
"choices": [
"nominal",
"real",
"breakeven",
"treasury_minus_fed_funds",
"corporate_spot",
"corporate_par",
]
},
)

241
openbb_platform/providers/fred/openbb_fred/utils/commercial_paper.csv vendored
View File

@@ -1,241 +0,0 @@
Maturity,Category,Grade,Title,Description,FRED Series ID
overnight,asset_backed,aa,Overnight AA Asset-Backed Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPAAAD01NB
7d,asset_backed,aa,7-Day AA Asset-Backed Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPAAAD07NB
15d,asset_backed,aa,15-Day AA Asset-Backed Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPAAAD15NB
30d,asset_backed,aa,30-Day AA Asset-Backed Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPAAAD30NB
60d,asset_backed,aa,60-Day AA Asset-Backed Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPAAAD60NB
90d,asset_backed,aa,90-Day AA Asset-Backed Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPAAAD90NB
overnight,financial,aa,Overnight AA Financial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPFAAD01NB
7d,financial,aa,7-Day AA Financial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPFAAD07NB
15d,financial,aa,15-Day AA Financial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPFAAD15NB
30d,financial,aa,30-Day AA Financial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPFAAD30NB
60d,financial,aa,60-Day AA Financial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPFAAD60NB
90d,financial,aa,90-Day AA Financial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPFAAD90NB
overnight,non_financial,aa,Overnight AA Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNAAD01NB
7d,non_financial,aa,7-Day AA Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNAAD07NB
15d,non_financial,aa,15-Day AA Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNAAD15NB
30d,non_financial,aa,30-Day AA Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNAAD30NB
60d,non_financial,aa,60-Day AA Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNAAD60NB
90d,non_financial,aa,90-Day AA Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNAAD90NB
overnight,non_financial,a2_p2,Overnight A2/P2 Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNA2P2D01NB
7d,non_financial,a2_p2,7-Day A2/P2 Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNA2P2D07NB
15d,non_financial,a2_p2,15-Day A2/P2 Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNA2P2D15NB
30d,non_financial,a2_p2,30-Day A2/P2 Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNA2P2D30NB
60d,non_financial,a2_p2,60-Day A2/P2 Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNA2P2D60NB
90d,non_financial,a2_p2,90-Day A2/P2 Nonfinancial Commercial Paper Interest Rate,"To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities.
Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates.
CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement.
For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, ""Robust Locally Weighted Regression and Smoothing Scatterplots,"" Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, ""Regression by Local Fitting,"" Journal of Econometrics, 37, 87-114.
Source: https://www.federalreserve.gov/releases/cp/about.htm
",RIFSPPNA2P2D90NB
1 Maturity Category Grade Title Description FRED Series ID
2 overnight asset_backed aa Overnight AA Asset-Backed Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPAAAD01NB
3 7d asset_backed aa 7-Day AA Asset-Backed Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPAAAD07NB
4 15d asset_backed aa 15-Day AA Asset-Backed Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPAAAD15NB
5 30d asset_backed aa 30-Day AA Asset-Backed Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPAAAD30NB
6 60d asset_backed aa 60-Day AA Asset-Backed Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPAAAD60NB
7 90d asset_backed aa 90-Day AA Asset-Backed Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPAAAD90NB
8 overnight financial aa Overnight AA Financial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPFAAD01NB
9 7d financial aa 7-Day AA Financial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPFAAD07NB
10 15d financial aa 15-Day AA Financial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPFAAD15NB
11 30d financial aa 30-Day AA Financial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPFAAD30NB
12 60d financial aa 60-Day AA Financial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPFAAD60NB
13 90d financial aa 90-Day AA Financial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPFAAD90NB
14 overnight non_financial aa Overnight AA Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNAAD01NB
15 7d non_financial aa 7-Day AA Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNAAD07NB
16 15d non_financial aa 15-Day AA Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNAAD15NB
17 30d non_financial aa 30-Day AA Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNAAD30NB
18 60d non_financial aa 60-Day AA Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNAAD60NB
19 90d non_financial aa 90-Day AA Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNAAD90NB
20 overnight non_financial a2_p2 Overnight A2/P2 Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNA2P2D01NB
21 7d non_financial a2_p2 7-Day A2/P2 Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNA2P2D07NB
22 15d non_financial a2_p2 15-Day A2/P2 Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNA2P2D15NB
23 30d non_financial a2_p2 30-Day A2/P2 Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNA2P2D30NB
24 60d non_financial a2_p2 60-Day A2/P2 Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNA2P2D60NB
25 90d non_financial a2_p2 90-Day A2/P2 Nonfinancial Commercial Paper Interest Rate To calculate CP interest rate indexes, the Federal Reserve Board uses DTCC's data for certain trades to estimate a relation between interest rates on the traded securities and their maturities. In this calculation, the trades represent sales of CP by dealers or direct issuers to investors (that is, the offer side) and are weighted according to the face value of the CP so that larger trades have a greater effect on the resulting index. With the relation between interest rates and maturities established, the reported interest rates represent the estimated interest rates for the specified maturities. Interest rates calculated through the process described above are a statistical aggregation of numerous data reflecting many trades for different issuers, maturities, and so forth. Accordingly, the reported interest rates purport to reflect activity in certain segments of the market, but they may not equal interest rates for any specific trade. As with other statistical processes, this one is designed to minimize the difference between the interest rates at which actual trades occur and the estimated interest rates. CP trades included in the calculation are chosen according to the specifications listed in the table below. Data to assess CP trades relative to these criteria are updated daily from numerous publicly available sources. Standard Industrial Classification (SIC) code classifications are taken from the Securities and Exchange Commission (SEC) Directory of Companies Required to File Annual Reports with the SEC. When an issuer's primary SIC code is not reported in the SEC directory, the primary SIC code reported in the issuer's financial reports is used; otherwise, SIC codes are determined upon consultation with the Office of Management and Budget's Standard Industrial Classification Manual or its Supplement. For a discussion of econometric techniques for fitting the term structure of interest rates, including bibliographic information, see, for example, William S. Cleveland, 1979, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, 74, 829-36, or William S. Cleveland, Susan J. Devlin, and Eric Grosse, 1988, "Regression by Local Fitting," Journal of Econometrics, 37, 87-114. Source: https://www.federalreserve.gov/releases/cp/about.htm RIFSPPNA2P2D90NB

7
openbb_platform/providers/fred/openbb_fred/utils/fred_helpers.py
View File

@@ -79,6 +79,13 @@ YIELD_CURVES = {
"T20YIEM": "year_20",
"T30YIEM": "year_30",
},
"treasury_minus_fed_funds": {
"T3MFF": "month_3",
"T6MFF": "month_6",
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"T5YFF": "year_5",
"T10YFF": "year_10",
},
"corporate_spot": {
"HQMCB6MT": "month_6",
"HQMCB1YR": "year_1",

130
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fred_ameribor_fetcher.yaml vendored Normal file
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109
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fred_commercial_paper_fetcher.yaml vendored
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785
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fred_euro_short_term_rate_fetcher.yaml vendored Normal file
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146
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fred_federal_funds_rate_fetcher.yaml vendored Normal file
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609
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fred_high_quality_market_corporate_bond_fetcher.yaml vendored
View File

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- Tue, 11 Jun 2024 20:43:18 GMT
Expires:
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- Tue, 11 Jun 2024 20:43:18 GMT
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684
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fred_overnight_bank_funding_rate_fetcher.yaml vendored Normal file
View File

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Set-Cookie:
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Domain=.stlouisfed.org; Path=/; Expires=Mon, 10 Jun 2024 20:19:47 GMT; Max-Age=7199
Strict-Transport-Security:
- max-age=86400
Vary:
- Accept-Encoding
status:
code: 200
message: OK
version: 1

1030
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fred_sofr_fetcher.yaml vendored Normal file
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538
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fredameribor_fetcher.yaml vendored
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@@ -1,538 +0,0 @@
interactions:
- request:
body: null
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate, br
Connection:
- keep-alive
method: GET
uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&file_type=json&series_id=AMERIBOR
response:
body:
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AUAtA1RAVAQA
headers:
Accept-Ranges:
- bytes
Connection:
- keep-alive
Content-Encoding:
- gzip
Content-Length:
- '28224'
Content-Type:
- application/json; charset=UTF-8
Date:
- Mon, 18 Sep 2023 09:59:52 GMT
Expires:
- Mon, 18 Sep 2023 10:00:51 GMT
Last-Modified:
- Fri, 15 Sep 2023 12:03:10 GMT
Vary:
- Accept-Encoding
x-rate-limit-limit:
- '120'
x-rate-limit-remaining:
- '104'
status:
code: 200
message: OK
version: 1

179
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fredestr_fetcher.yaml vendored
View File

@@ -1,179 +0,0 @@
interactions:
- request:
body: null
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate, br
Connection:
- keep-alive
method: GET
uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&file_type=json&series_id=ECBESTRVOLWGTTRMDMNRT
response:
body:
string: !!binary |
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0zJmMFpV1I4Z9AwEmFJtbC0AfauOfIeVAQA=
headers:
Accept-Ranges:
- bytes
Connection:
- keep-alive
Content-Encoding:
- gzip
Content-Length:
- '7550'
Content-Type:
- application/json; charset=UTF-8
Date:
- Mon, 18 Sep 2023 09:59:51 GMT
Expires:
- Mon, 18 Sep 2023 10:00:51 GMT
Last-Modified:
- Mon, 18 Sep 2023 08:31:03 GMT
Set-Cookie:
- ak_bmsc=6BB4D3D3A59B20D3818DD1E14A5E85C7~000000000000000000000000000000~YAAQFk4SAk8HZJOKAQAAUGa7pxUGEDWHb3oa/qFWKkiodi7FWgH58ieCCMVauAQl7DOYb3FAAEmFJtMW0Dh2ZjXVTjeDrQo2tUowbJwSuN1srdvQ0GqlozvQjows4fxAvMkJZ5wnXGbPE/9O3JFjGGt4UbSa8ru5z4nrFuO+6uusFQs9yJkCwUZL+5wfqG2YI9PEoIW3LbsMAinEFfT5Rwsp/qedwPskTOX2iuMrdksKnl8d9uUoNF1gxEyl9ywh/Kn6+ufh7PwgO8chH0KXZVWfxCIHMo0cj8lNzWW4WkB1NtFepZyEC1uSOHF7sHJGPVRy7mbdaRtg4GOr8QrH0P6uxqAxTEDJ1E9+/zA02N2E/210A1e+TWFDtkzczU53;
Domain=.stlouisfed.org; Path=/; Expires=Mon, 18 Sep 2023 11:59:50 GMT; Max-Age=7199;
HttpOnly
Vary:
- Accept-Encoding
x-rate-limit-limit:
- '120'
x-rate-limit-remaining:
- '106'
status:
code: 200
message: OK
version: 1

597
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fredfed_fetcher.yaml vendored
View File

@@ -1,597 +0,0 @@
interactions:
- request:
body: null
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate, br
Connection:
- keep-alive
method: GET
uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&file_type=json&series_id=FF
response:
body:
string: !!binary |
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HaKmvvEoQ9QQ4wGLrQUA3ab+KsY7BQA=
headers:
Accept-Ranges:
- bytes
Connection:
- keep-alive
Content-Encoding:
- gzip
Content-Length:
- '31601'
Content-Type:
- application/json; charset=UTF-8
Date:
- Mon, 18 Sep 2023 09:59:52 GMT
Expires:
- Mon, 18 Sep 2023 10:00:52 GMT
Last-Modified:
- Thu, 14 Sep 2023 20:19:06 GMT
Vary:
- Accept-Encoding
x-rate-limit-limit:
- '120'
x-rate-limit-remaining:
- '103'
status:
code: 200
message: OK
version: 1

212
openbb_platform/providers/fred/tests/record/http/test_fred_fetchers/test_fredsofr_fetcher.yaml vendored
View File

@@ -1,212 +0,0 @@
interactions:
- request:
body: null
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate, br
Connection:
- keep-alive
method: GET
uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&file_type=json&series_id=SOFR
response:
body:
string: !!binary |
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84
openbb_platform/providers/fred/tests/test_fred_fetchers.py
View File

@@ -4,20 +4,22 @@ import datetime
import pytest
from openbb_core.app.service.user_service import UserService
from openbb_fred.models.ameribor_rates import FREDAMERIBORFetcher
from openbb_fred.models.ameribor import FredAmeriborFetcher
from openbb_fred.models.balance_of_payments import FredBalanceOfPaymentsFetcher
from openbb_fred.models.bond_indices import FredBondIndicesFetcher
from openbb_fred.models.commercial_paper import FREDCommercialPaperFetcher
from openbb_fred.models.consumer_price_index import FREDConsumerPriceIndexFetcher
from openbb_fred.models.cp import FREDCommercialPaperFetcher
from openbb_fred.models.dwpcr_rates import FREDDiscountWindowPrimaryCreditRateFetcher
from openbb_fred.models.ecb_interest_rates import (
FREDEuropeanCentralBankInterestRatesFetcher,
)
from openbb_fred.models.estr_rates import FREDESTRFetcher
from openbb_fred.models.euro_short_term_rate import FredEuroShortTermRateFetcher
from openbb_fred.models.fed_projections import FREDPROJECTIONFetcher
from openbb_fred.models.fed_rates import FREDFEDFetcher
from openbb_fred.models.federal_funds_rate import FredFederalFundsRateFetcher
from openbb_fred.models.ffrmc import FREDSelectedTreasuryConstantMaturityFetcher
from openbb_fred.models.hqm import FREDHighQualityMarketCorporateBondFetcher
from openbb_fred.models.high_quality_market import (
FredHighQualityMarketCorporateBondFetcher,
)
from openbb_fred.models.ice_bofa import FREDICEBofAFetcher
from openbb_fred.models.iorb_rates import FREDIORBFetcher
from openbb_fred.models.manufacturing_outlook_texas import (
@@ -25,6 +27,9 @@ from openbb_fred.models.manufacturing_outlook_texas import (
)
from openbb_fred.models.moody import FREDMoodyCorporateBondIndexFetcher
from openbb_fred.models.mortgage_indices import FredMortgageIndicesFetcher
from openbb_fred.models.overnight_bank_funding_rate import (
FredOvernightBankFundingRateFetcher,
)
from openbb_fred.models.regional import FredRegionalDataFetcher
from openbb_fred.models.retail_prices import FredRetailPricesFetcher
from openbb_fred.models.search import (
@@ -34,7 +39,7 @@ from openbb_fred.models.senior_loan_officer_survey import (
FredSeniorLoanOfficerSurveyFetcher,
)
from openbb_fred.models.series import FredSeriesFetcher
from openbb_fred.models.sofr_rates import FREDSOFRFetcher
from openbb_fred.models.sofr import FREDSOFRFetcher
from openbb_fred.models.sonia_rates import FREDSONIAFetcher
from openbb_fred.models.spot import FREDSpotRateFetcher
from openbb_fred.models.survey_of_economic_conditions_chicago import (
@@ -85,9 +90,12 @@ def test_fred_us_yield_curve_fetcher(credentials=test_credentials):
@pytest.mark.record_http
def test_fredsofr_fetcher(credentials=test_credentials):
def test_fred_sofr_fetcher(credentials=test_credentials):
"""Test FREDSOFRFetcher."""
params = {}
params = {
"start_date": datetime.date(2024, 6, 1),
"end_date": datetime.date(2024, 6, 6),
}
fetcher = FREDSOFRFetcher()
result = fetcher.test(params, credentials)
@@ -95,11 +103,14 @@ def test_fredsofr_fetcher(credentials=test_credentials):
@pytest.mark.record_http
def test_fredestr_fetcher(credentials=test_credentials):
"""Test FREDESTRFetcher."""
params = {}
def test_fred_euro_short_term_rate_fetcher(credentials=test_credentials):
"""Test FREDEuroShortTermRateFetcher."""
params = {
"start_date": datetime.date(2024, 6, 1),
"end_date": datetime.date(2024, 6, 6),
}
fetcher = FREDESTRFetcher()
fetcher = FredEuroShortTermRateFetcher()
result = fetcher.test(params, credentials)
assert result is None
@@ -115,21 +126,29 @@ def test_fredsonia_fetcher(credentials=test_credentials):
@pytest.mark.record_http
def test_fredameribor_fetcher(credentials=test_credentials):
"""Test FREDAMERIBORFetcher."""
params = {}
def test_fred_ameribor_fetcher(credentials=test_credentials):
"""Test FredAmeriborFetcher."""
params = {
"start_date": datetime.date(2023, 1, 1),
"end_date": datetime.date(2023, 6, 6),
"maturity": "overnight",
}
fetcher = FREDAMERIBORFetcher()
fetcher = FredAmeriborFetcher()
result = fetcher.test(params, credentials)
assert result is None
@pytest.mark.record_http
def test_fredfed_fetcher(credentials=test_credentials):
"""Test FREDFEDFetcher."""
params = {}
def test_fred_federal_funds_rate_fetcher(credentials=test_credentials):
"""Test FRED Federal Funds Rate Fetcher."""
params = {
"start_date": datetime.date(2023, 1, 1),
"end_date": datetime.date(2023, 6, 6),
"effr_only": True,
}
fetcher = FREDFEDFetcher()
fetcher = FredFederalFundsRateFetcher()
result = fetcher.test(params, credentials)
assert result is None
@@ -212,8 +231,10 @@ def test_fred_moody_corporate_bond_index_fetcher(credentials=test_credentials):
def test_fred_commercial_paper_fetcher(credentials=test_credentials):
"""Test FREDCommercialPaperFetcher."""
params = {
"start_date": datetime.date(2023, 1, 1),
"end_date": datetime.date(2023, 6, 6),
"start_date": datetime.date(2024, 1, 1),
"end_date": datetime.date(2024, 2, 1),
"category": "asset_backed",
"maturity": "30d",
}
fetcher = FREDCommercialPaperFetcher()
@@ -236,10 +257,10 @@ def test_fred_spot_rate_fetcher(credentials=test_credentials):
@pytest.mark.record_http
def test_fred_high_quality_market_corporate_bond_fetcher(credentials=test_credentials):
"""Test FREDHighQualityMarketCorporateBondFetcher."""
params = {"date": datetime.date(2023, 1, 1)}
"""Test FredHighQualityMarketCorporateBondFetcher."""
params = {"date": "2023-01-01"}
fetcher = FREDHighQualityMarketCorporateBondFetcher()
fetcher = FredHighQualityMarketCorporateBondFetcher()
result = fetcher.test(params, credentials)
assert result is None
@@ -439,3 +460,16 @@ def test_fred_manufacturing_outlook_texas_fetcher(
fetcher = FredManufacturingOutlookTexasFetcher()
result = fetcher.test(params, credentials)
assert result is None
@pytest.mark.record_http
def test_fred_overnight_bank_funding_rate_fetcher(credentials=test_credentials):
"""Test FRED Overnight Bank Funding Rate Fetcher."""
params = {
"start_date": datetime.date(2024, 6, 1),
"end_date": datetime.date(2024, 6, 6),
}
fetcher = FredOvernightBankFundingRateFetcher()
result = fetcher.test(params, credentials)
assert result is None