cp series map

2 files changed, 331 insertions, 287 deletions

diff --git a/openbb_platform/providers/fred/openbb_fred/models/commercial_paper.py b/openbb_platform/providers/fred/openbb_fred/models/commercial_paper.py
index b957ff08495..d4e895217fc 100644
--- a/openbb_platform/providers/fred/openbb_fred/models/commercial_paper.py
+++ b/openbb_platform/providers/fred/openbb_fred/models/commercial_paper.py
@@ -1,34 +1,261 @@
 """FRED Commercial Paper Model."""
 
-from typing import Any, Dict, List, Optional
+# 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.cp import (
+from openbb_core.provider.standard_models.commercial_paper 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
+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."""
 
-    __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
+    asset_type: Literal["asset_backed", "financial", "nonfinancial", "a2p2"] = Field(
+        description="The category of asset."
+    )
 
 
 class FREDCommercialPaperFetcher(
@@ -37,9 +264,7 @@ class FREDCommercialPaperFetcher(
         List[FREDCommercialPaperData],
     ]
 ):
-    """Transform the query, extract and transform the data from the FRED endpoints."""
-
-    data_type = FREDCommercialPaperData
+    """FRED Commercial Paper Fetcher."""
 
     @staticmethod
     def transform_query(params: Dict[str, Any]) -> FREDCommercialPaperParams:
@@ -47,41 +272,101 @@ class FREDCommercialPaperFetcher(
         return FREDCommercialPaperParams(**params)
 
     @staticmethod
-    def extract_data(
+    async def aextract_data(
         query: FREDCommercialPaperParams,
         credentials: Optional[Dict[str, str]],
-        **kwargs: Any
-    ) -> list:
+        **kwargs: Any,
+    ) -> Dict:
         """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,
+        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,
             )
-            for item in d:
-                item["title"] = title
-            data.extend(d)
+        except Exception as e:
+            raise e from e
 
-        return data
+        return {
+            "metadata": response.metadata,
+            "data": [d.model_dump() for d in response.result],
+        }
 
     @staticmethod
     def transform_data(
-        query: FREDCommercialPaperParams, data: list, **kwargs: Any
+        query: FREDCommercialPaperParams,
+        data: Dict,
+        **kwargs: Any,
     ) -> List[FREDCommercialPaperData]:
         """Transform data."""
-        return [FREDCommercialPaperData.model_validate(d) for d in 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,
+        )
diff --git a/openbb_platform/providers/fred/openbb_fred/utils/commercial_paper.csv b/openbb_platform/providers/fred/openbb_fred/utils/commercial_paper.csv
deleted file mode 100644
index 8897cb334c5..00000000000
--- a/openbb_platform/providers/fred/openbb_fred/utils/commercial_paper.csv
+++ /dev/null
@@ -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 discu