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Anbima Data

intraday_ettj()

Retrieves and processes the intraday Brazilian yield curve data from ANBIMA.

This function fetches the most recent intraday yield curve data published by ANBIMA, containing real rates (IPCA-indexed), nominal rates, and implied inflation at various vertices (time points). The curve is published at around 12:30 PM BRT.

Returns:

Type Description
DataFrame

pd.DataFrame: A DataFrame containing the intraday ETTJ data.
DataFrame columns
  • date: Reference date of the yield curve
  • vertex: Time point in business days
  • nominal_rate: Zero-coupon nominal interest rate
  • real_rate: Zero-coupon real interest rate (IPCA-indexed)
  • implied_inflation: Implied inflation rate (break-even inflation)
Note

All rates are expressed in decimal format (e.g., 0.12 for 12%).

Source code in pyield/anbima/ettj.py 
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def intraday_ettj() -> pd.DataFrame:
    """
    Retrieves and processes the intraday Brazilian yield curve data from ANBIMA.

    This function fetches the most recent intraday yield curve data published by ANBIMA,
    containing real rates (IPCA-indexed), nominal rates, and implied inflation
    at various vertices (time points). The curve is published at around 12:30 PM BRT.

    Returns:
        pd.DataFrame: A DataFrame containing the intraday ETTJ data.

    DataFrame columns:
        - date: Reference date of the yield curve
        - vertex: Time point in business days
        - nominal_rate: Zero-coupon nominal interest rate
        - real_rate: Zero-coupon real interest rate (IPCA-indexed)
        - implied_inflation: Implied inflation rate (break-even inflation)

    Note:
        All rates are expressed in decimal format (e.g., 0.12 for 12%).
    """

    request_payload = {"Dt_Ref": "", "saida": "csv"}
    response = requests.post(INTRADAY_ETTJ_URL, data=request_payload)
    text_parts = response.text.split("ETTJ IPCA (%a.a./252)")

    nominal_text = text_parts[0]
    lines = nominal_text.splitlines()
    date_str = lines[1]
    date = pd.to_datetime(date_str, format="%d/%m/%Y")
    df_text = "\n".join(lines[2:])
    df_nominal = pd.read_csv(StringIO(df_text), sep=";", decimal=",", thousands=".")
    df_nominal = df_nominal.drop(columns=["Fechamento D -1"])
    df_nominal = df_nominal.rename(columns={"D0": "nominal_rate"})

    real_text = text_parts[1]
    lines = real_text.splitlines()
    df_text = "\n".join(lines[2:])
    df_real = pd.read_csv(StringIO(df_text), sep=";", decimal=",", thousands=".")
    df_real = df_real.drop(columns=["Fechamento D -1"])
    df_real = df_real.rename(columns={"D0": "real_rate"})

    df = pd.merge(df_nominal, df_real, on="Vertices", how="right")
    df = df.rename(columns={"Vertices": "vertex"})

    # Divide float columns by 100 and round to 6 decimal places
    df["real_rate"] = (df["real_rate"] / 100).round(ROUND_DIGITS)
    df["nominal_rate"] = (df["nominal_rate"] / 100).round(ROUND_DIGITS)
    df["implied_inflation"] = (df["nominal_rate"] + 1) / (df["real_rate"] + 1) - 1
    df["implied_inflation"] = df["implied_inflation"].round(ROUND_DIGITS)

    df["date"] = date
    column_order = ["date", "vertex", "nominal_rate", "real_rate", "implied_inflation"]
    return df[column_order].copy()

ipca_projection()

Retrieves the current IPCA projection from the ANBIMA website.

This function makes an HTTP request to the ANBIMA website, extracts HTML tables containing economic indicators, and specifically processes the IPCA projection data.

Process
  1. Accesses the ANBIMA indicators webpage
  2. Extracts the third table that contains the IPCA projection
  3. Locates the row labeled as "IPCA1"
  4. Extracts the projection value and converts it to decimal format
  5. Extracts and formats the reference month of the projection
  6. Extracts the date and time of the last update

Returns:

Name Type Description
IndicatorProjection IndicatorProjection

An object containing: - reference_period (pd.Period): Reference period of the projection - projected_value (float): Projected IPCA value as a decimal number - last_updated (pd.Timestamp): Date and time of the last data update

Raises:

Type Description
RequestException

If there are connection issues with the ANBIMA site
ValueError

If the expected data is not found in the page structure
Error

If the 'pt_BR.UTF-8' locale is not available on the system
Notes
  • The function requires internet connection to access the ANBIMA website
  • The structure of the ANBIMA page may change, which could affect the function
  • Temporarily changes the locale to pt_BR.UTF-8 to process dates in Portuguese
Source code in pyield/anbima/ipca.py 
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def ipca_projection() -> IndicatorProjection:
    """
    Retrieves the current IPCA projection from the ANBIMA website.

    This function makes an HTTP request to the ANBIMA website, extracts HTML tables
    containing economic indicators, and specifically processes the IPCA projection data.

    Process:
        1. Accesses the ANBIMA indicators webpage
        2. Extracts the third table that contains the IPCA projection
        3. Locates the row labeled as "IPCA1"
        4. Extracts the projection value and converts it to decimal format
        5. Extracts and formats the reference month of the projection
        6. Extracts the date and time of the last update

    Returns:
        IndicatorProjection: An object containing:
            - reference_period (pd.Period): Reference period of the projection
            - projected_value (float): Projected IPCA value as a decimal number
            - last_updated (pd.Timestamp): Date and time of the last data update

    Raises:
        requests.RequestException: If there are connection issues with the ANBIMA site
        ValueError: If the expected data is not found in the page structure
        locale.Error: If the 'pt_BR.UTF-8' locale is not available on the system

    Notes:
        - The function requires internet connection to access the ANBIMA website
        - The structure of the ANBIMA page may change, which could affect the function
        - Temporarily changes the locale to pt_BR.UTF-8 to process dates in Portuguese
    """
    url = "https://www.anbima.com.br/informacoes/indicadores/"
    r = requests.get(url)
    r.encoding = "latin1"
    dfs = pd.read_html(
        io.StringIO(r.text),
        flavor="html5lib",
        decimal=",",
        thousands=".",
        dtype_backend="numpy_nullable",
    )
    # The IPCA projection is in the third table
    df = dfs[2]

    last_update_str = df.iat[0, 0].split("Atualização:")[-1].strip()
    last_update = pd.to_datetime(last_update_str, format="%d/%m/%Y - %H:%M h")

    ipca_row = df.loc[df[0] == "IPCA1"]
    ipca_value = ipca_row.iloc[0, 2]
    ipca_value = pd.to_numeric(ipca_value, errors="coerce")
    ipca_value = round(float(ipca_value) / 100, 4)

    # Extract and format the reference month
    ipca_date = ipca_row.iloc[0, 1]
    ipca_date = str(ipca_date)
    ipca_date = ipca_date.split("(")[-1].split(")")[0]
    locale.setlocale(locale.LC_TIME, "pt_BR.UTF-8")
    ipca_date = pd.to_datetime(ipca_date, format="%b/%y")
    reference_period = ipca_date.to_period("M")
    locale.setlocale(locale.LC_TIME, "")  # Reset locale to default

    return IndicatorProjection(
        last_updated=last_update,
        reference_period=reference_period,
        projected_value=ipca_value,
    )

last_ettj()

Retrieves and processes the latest Brazilian yield curve data from ANBIMA.

This function fetches the most recent yield curve data published by ANBIMA, containing real rates (IPCA-indexed), nominal rates, and implied inflation at various vertices (time points).

Returns:

Type Description
DataFrame

pd.DataFrame: A DataFrame containing the latest ETTJ data.
DataFrame columns
  • date: Reference date of the yield curve
  • vertex: Time point in business days
  • nominal_rate: Zero-coupon nominal interest rate
  • real_rate: Zero-coupon real interest rate (IPCA-indexed)
  • implied_inflation: Implied inflation rate (break-even inflation)
Note

All rates are expressed in decimal format (e.g., 0.12 for 12%).

Source code in pyield/anbima/ettj.py 
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def last_ettj() -> pd.DataFrame:
    """
    Retrieves and processes the latest Brazilian yield curve data from ANBIMA.

    This function fetches the most recent yield curve data published by ANBIMA,
    containing real rates (IPCA-indexed), nominal rates, and implied inflation
    at various vertices (time points).

    Returns:
        pd.DataFrame: A DataFrame containing the latest ETTJ data.

    DataFrame columns:
        - date: Reference date of the yield curve
        - vertex: Time point in business days
        - nominal_rate: Zero-coupon nominal interest rate
        - real_rate: Zero-coupon real interest rate (IPCA-indexed)
        - implied_inflation: Implied inflation rate (break-even inflation)

    Note:
        All rates are expressed in decimal format (e.g., 0.12 for 12%).
    """
    text = _get_last_content()
    df = _convert_text_to_df(text)
    return _process_df(df)

last_ima(ima_type=None)

Fetch and process the last IMA market data available from ANBIMA.

This function processes the data into a structured DataFrame. It handles conversion of date formats, renames columns to English, and converts certain numeric columns to integer types. In the event of an error during data fetching or processing, an empty DataFrame is returned.

Parameters:

Name Type Description Default
ima_type str

Type of IMA index to filter the data. If None, all IMA indexes are returned. Defaults to None.

 None

Returns:

Type Description
DataFrame

pd.DataFrame: A DataFrame containing the IMA data.
DataFrame columns
  • Date: reference date of the data.
  • IMAType: type of IMA index.
  • BondType: type of bond.
  • Maturity: bond maturity date.
  • SelicCode: bond code in the SELIC system.
  • ISIN: international Securities Identification Number.
  • BDToMat: business days to maturity.
  • Duration: duration of the bond in business years (252 days/year).
  • IndicativeRate: indicative rate.
  • Price: bond price.
  • InterestPrice: interest price.
  • DV01: DV01 in R$.
  • PMR: average repurchase term.
  • Weight: weight of the bond in the index.
  • Convexity: convexity of the bond.
  • TheoreticalQuantity: theoretical quantity.
  • NumberOfOperations: number of operations.
  • NegotiatedQuantity: negotiated quantity.
  • NegotiatedValue: negotiated value.
  • MarketQuantity: market quantity.
  • MarketDV01: market DV01 in R$.
  • MarketValue: market value in R$.

Raises:

Type Description
Exception

Logs error and returns an empty DataFrame if any error occurs during fetching or processing.
Source code in pyield/anbima/ima.py 
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def last_ima(ima_type: ima_types | None = None) -> pd.DataFrame:
    """
    Fetch and process the last IMA market data available from ANBIMA.

    This function processes the data into a structured DataFrame.
    It handles conversion of date formats, renames columns to English, and converts
    certain numeric columns to integer types. In the event of an error during data
    fetching or processing, an empty DataFrame is returned.

    Args:
        ima_type (str, optional): Type of IMA index to filter the data. If None, all
            IMA indexes are returned. Defaults to None.

    Returns:
        pd.DataFrame: A DataFrame containing the IMA data.

    DataFrame columns:
        - Date: reference date of the data.
        - IMAType: type of IMA index.
        - BondType: type of bond.
        - Maturity: bond maturity date.
        - SelicCode: bond code in the SELIC system.
        - ISIN: international Securities Identification Number.
        - BDToMat: business days to maturity.
        - Duration: duration of the bond in business years (252 days/year).
        - IndicativeRate: indicative rate.
        - Price: bond price.
        - InterestPrice: interest price.
        - DV01: DV01 in R$.
        - PMR: average repurchase term.
        - Weight: weight of the bond in the index.
        - Convexity: convexity of the bond.
        - TheoreticalQuantity: theoretical quantity.
        - NumberOfOperations: number of operations.
        - NegotiatedQuantity: negotiated quantity.
        - NegotiatedValue: negotiated value.
        - MarketQuantity: market quantity.
        - MarketDV01: market DV01 in R$.
        - MarketValue: market value in R$.

    Raises:
        Exception: Logs error and returns an empty DataFrame if any error occurs during
            fetching or processing.
    """
    try:
        df = _fetch_last_ima()
        df = _process_last_ima(df)
        df = _reorder_last_ima(df)
        if ima_type is not None:
            df = df.query("IMAType == @ima_type").reset_index(drop=True)
        df = df.sort_values(["IMAType", "BondType", "Maturity"]).reset_index(drop=True)
        return df
    except Exception as e:
        logger.exception(f"Error fetching or processing the last IMA data: {e}")
        return pd.DataFrame()

tpf_data(date, bond_type=None, fetch_from_source=False)

Retrieve TPF secondary market data from ANBIMA.

This function fetches indicative rates and other data for Brazilian government bonds from ANBIMA. By default, it attempts to retrieve data from a local cache for performance. If fetch_from_source is True, it tries to fetch the data directly from the ANBIMA website.

Parameters:

Name Type Description Default
date DateScalar

The reference date for the data (e.g., '2024-06-14').

 required
bond_type str

Filters results by a specific bond type (e.g., 'LTN', 'NTN-B'). Defaults to None, returning all bond types.

 None
fetch_from_source bool

If True, forces the function to bypass the cache and fetch data directly from the source. Defaults to False.

 False

Returns:

Type Description
DataFrame

pd.DataFrame: A DataFrame containing the requested bond data. An empty
DataFrame

DataFrame is returned if no data is available for the specified date
DataFrame

(e.g., weekends, holidays, or future dates).

Examples:

>>> from pyield import anbima
>>> anbima.tpf_data(date="22-08-2025")
   ReferenceDate BondType MaturityDate  IndicativeRate         Price
0     2025-08-22      LFT   2025-09-01        0.000165  17200.957952
1     2025-08-22      LFT   2026-03-01       -0.000116  17202.058818
2     2025-08-22      LFT   2026-09-01       -0.000107  17202.901668
3     2025-08-22      LFT   2027-03-01        0.000302  17193.200289
4     2025-08-22      LFT   2027-09-01        0.000411  17186.767105
...
DataFrame columns
  • BondType: Tipo do título público (e.g., 'LTN', 'NTN-B').
  • ReferenceDate: Data de referência dos dados.
  • SelicCode: Código do título no SELIC.
  • IssueBaseDate: Data base ou de emissão do título.
  • MaturityDate: Data de vencimento do título.
  • BDToMat: Número de dias úteis entre a data de referência e o vencimento.
  • Duration: Macaulay Duration do título em anos.
  • DV01: Variação financeira no preço do título (em BRL) para uma mudança de 1 basis point (0,01%) na taxa de juros.
  • DV01USD: O mesmo que DV01, mas convertido para USD pela PTAX do dia.
  • Price: Preço Unitário (PU) do título na data de referência.
  • BidRate: Taxa de compra em formato decimal (e.g., 0.10 para 10%).
  • AskRate: Taxa de venda em formato decimal.
  • IndicativeRate: Taxa indicativa em formato decimal.
  • DIRate: Taxa DI interpolada para o vencimento do título.
  • StdDev: Desvio padrão da taxa indicativa.
  • LowerBoundRateD0: Limite inferior do intervalo indicativo para D+0.
  • UpperBoundRateD0: Limite superior do intervalo indicativo para D+0.
  • LowerBoundRateD1: Limite inferior do intervalo indicativo para D+1.
  • UpperBoundRateD1: Limite superior do intervalo indicativo para D+1.
  • Criteria: Critério utilizado pela ANBIMA para o cálculo.
Notes
  • Data for the last 5 business days is available on the public ANBIMA website.
  • Historical data (older than 5 business days) is only available via the RTM network. If you are not connected to RTM, requests for older dates will return an empty DataFrame.
Source code in pyield/anbima/tpf.py 
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def tpf_data(
    date: DateScalar,
    bond_type: str | None = None,
    fetch_from_source: bool = False,
) -> pd.DataFrame:
    """Retrieve TPF secondary market data from ANBIMA.

    This function fetches indicative rates and other data for Brazilian government
    bonds from ANBIMA. By default, it attempts to retrieve data from a local
    cache for performance. If `fetch_from_source` is True, it tries to fetch the data
    directly from the ANBIMA website.

    Args:
        date (DateScalar): The reference date for the data (e.g., '2024-06-14').
        bond_type (str, optional): Filters results by a specific bond type
            (e.g., 'LTN', 'NTN-B'). Defaults to None, returning all bond types.
        fetch_from_source (bool, optional): If True, forces the function to
            bypass the cache and fetch data directly from the source.
            Defaults to False.

    Returns:
        pd.DataFrame: A DataFrame containing the requested bond data. An empty
        DataFrame is returned if no data is available for the specified date
        (e.g., weekends, holidays, or future dates).

    Examples:
        >>> from pyield import anbima
        >>> anbima.tpf_data(date="22-08-2025")
           ReferenceDate BondType MaturityDate  IndicativeRate         Price
        0     2025-08-22      LFT   2025-09-01        0.000165  17200.957952
        1     2025-08-22      LFT   2026-03-01       -0.000116  17202.058818
        2     2025-08-22      LFT   2026-09-01       -0.000107  17202.901668
        3     2025-08-22      LFT   2027-03-01        0.000302  17193.200289
        4     2025-08-22      LFT   2027-09-01        0.000411  17186.767105
        ...

    DataFrame columns:
        - BondType: Tipo do título público (e.g., 'LTN', 'NTN-B').
        - ReferenceDate: Data de referência dos dados.
        - SelicCode: Código do título no SELIC.
        - IssueBaseDate: Data base ou de emissão do título.
        - MaturityDate: Data de vencimento do título.
        - BDToMat: Número de dias úteis entre a data de referência e o vencimento.
        - Duration: Macaulay Duration do título em anos.
        - DV01: Variação financeira no preço do título (em BRL) para uma
            mudança de 1 basis point (0,01%) na taxa de juros.
        - DV01USD: O mesmo que DV01, mas convertido para USD pela PTAX do dia.
        - Price: Preço Unitário (PU) do título na data de referência.
        - BidRate: Taxa de compra em formato decimal (e.g., 0.10 para 10%).
        - AskRate: Taxa de venda em formato decimal.
        - IndicativeRate: Taxa indicativa em formato decimal.
        - DIRate: Taxa DI interpolada para o vencimento do título.
        - StdDev: Desvio padrão da taxa indicativa.
        - LowerBoundRateD0: Limite inferior do intervalo indicativo para D+0.
        - UpperBoundRateD0: Limite superior do intervalo indicativo para D+0.
        - LowerBoundRateD1: Limite inferior do intervalo indicativo para D+1.
        - UpperBoundRateD1: Limite superior do intervalo indicativo para D+1.
        - Criteria: Critério utilizado pela ANBIMA para o cálculo.

    Notes:
        - Data for the last 5 business days is available on the public ANBIMA
          website.
        - Historical data (older than 5 business days) is only available via
          the RTM network. If you are not connected to RTM, requests for
          older dates will return an empty DataFrame.
    """
    date = convert_input_dates(date)

    date_log = date.strftime("%d/%m/%Y")
    today = dt.datetime.now(BZ_TIMEZONE).date()
    if date.date() > today:
        logger.info(
            f"Cannot fetch data for a future date ({date_log}). "
            "Returning empty DataFrame."
        )
        return pd.DataFrame()

    if fetch_from_source:
        # Try to fetch the data from the Anbima website
        df = _fetch_tpf_data(date)
    else:
        df = (
            get_cached_dataset("tpf")
            .query("ReferenceDate == @date")
            .reset_index(drop=True)
        )

    if df.empty:
        # If the data is still empty, return an empty DataFrame
        return pd.DataFrame()

    if bond_type:
        norm_bond_type = _bond_type_mapping(bond_type)  # noqa
        df = df.query("BondType == @norm_bond_type").reset_index(drop=True)

    return df.sort_values(["BondType", "MaturityDate"]).reset_index(drop=True)

tpf_fixed_rate_maturities(date)

Retrieve pre-defined maturity dates for LTN and NTN-F bonds.

This function fetches pre-defined maturity dates for 'LTN' (prefixadas) and 'NTN-F' (indexadas ao CDI) bond types from the cached ANBIMA dataset for a given reference date.

Parameters:

Name Type Description Default
date DateScalar

The reference date for maturity dates.

 required

Returns:

Type Description
Series

pd.Series: A Series containing unique maturity dates for 'LTN' and 'NTN-F' bonds, sorted in ascending order.
Source code in pyield/anbima/tpf.py 
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def tpf_fixed_rate_maturities(date: DateScalar) -> pd.Series:
    """Retrieve pre-defined maturity dates for LTN and NTN-F bonds.

    This function fetches pre-defined maturity dates for 'LTN' (prefixadas) and
    'NTN-F' (indexadas ao CDI) bond types from the cached ANBIMA dataset
    for a given reference date.

    Args:
        date (DateScalar): The reference date for maturity dates.

    Returns:
        pd.Series: A Series containing unique maturity dates for 'LTN' and
            'NTN-F' bonds, sorted in ascending order.
    """
    maturity_dates = (
        tpf_data(date)
        .query("BondType in ['LTN', 'NTN-F']")["MaturityDate"]
        .drop_duplicates()
        .sort_values(ignore_index=True)
        .reset_index(drop=True)
    )
    return maturity_dates