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LFT

data(date)

Fetch the LFT indicative rates for the given reference date from ANBIMA.

Parameters:

Name Type Description Default
date DateScalar

The reference date for fetching the data.

 required

Returns:

Type Description
DataFrame

pl.DataFrame: DataFrame containing the following columns: - ReferenceDate: The reference date for the data. - BondType: The type of bond. - MaturityDate: The maturity date of the LFT bond. - IndicativeRate: The Anbima indicative rate for the LFT bond. - Price: The price of the LFT bond.

Examples:

>>> from pyield import lft
>>> lft.data("23-08-2024")
shape: (14, 14)
┌───────────────┬──────────┬───────────┬───────────────┬───┬───────────┬───────────┬────────────────┬──────────┐
│ ReferenceDate ┆ BondType ┆ SelicCode ┆ IssueBaseDate ┆ … ┆ BidRate   ┆ AskRate   ┆ IndicativeRate ┆ DIRate   │
│ ---           ┆ ---      ┆ ---       ┆ ---           ┆   ┆ ---       ┆ ---       ┆ ---            ┆ ---      │
│ date          ┆ str      ┆ i64       ┆ date          ┆   ┆ f64       ┆ f64       ┆ f64            ┆ f64      │
╞═══════════════╪══════════╪═══════════╪═══════════════╪═══╪═══════════╪═══════════╪════════════════╪══════════╡
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.000306  ┆ 0.000226  ┆ 0.000272       ┆ 0.10408  │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ -0.000397 ┆ -0.000481 ┆ -0.000418      ┆ 0.11082  │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ -0.000205 ┆ -0.000258 ┆ -0.00023       ┆ 0.114315 │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.000085  ┆ 0.00006   ┆ 0.000075       ┆ 0.114982 │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.000124  ┆ 0.000097  ┆ 0.000114       ┆ 0.114955 │
│ …             ┆ …        ┆ …         ┆ …             ┆ … ┆ …         ┆ …         ┆ …              ┆ …        │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001501  ┆ 0.001476  ┆ 0.001491       ┆ 0.11564  │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001597  ┆ 0.001571  ┆ 0.001587       ┆ 0.115773 │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001601  ┆ 0.001574  ┆ 0.001591       ┆ 0.115904 │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001649  ┆ 0.001627  ┆ 0.001641       ┆ 0.115854 │
│ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001696  ┆ 0.00168   ┆ 0.001687       ┆ 0.115806 │
└───────────────┴──────────┴───────────┴───────────────┴───┴───────────┴───────────┴────────────────┴──────────┘
Source code in pyield/tn/lft.py 
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def data(date: DateScalar) -> pl.DataFrame:
    """
    Fetch the LFT indicative rates for the given reference date from ANBIMA.

    Args:
        date (DateScalar): The reference date for fetching the data.

    Returns:
        pl.DataFrame: DataFrame containing the following columns:
            - ReferenceDate: The reference date for the data.
            - BondType: The type of bond.
            - MaturityDate: The maturity date of the LFT bond.
            - IndicativeRate: The Anbima indicative rate for the LFT bond.
            - Price: The price of the LFT bond.

    Examples:
        >>> from pyield import lft
        >>> lft.data("23-08-2024")
        shape: (14, 14)
        ┌───────────────┬──────────┬───────────┬───────────────┬───┬───────────┬───────────┬────────────────┬──────────┐
        │ ReferenceDate ┆ BondType ┆ SelicCode ┆ IssueBaseDate ┆ … ┆ BidRate   ┆ AskRate   ┆ IndicativeRate ┆ DIRate   │
        │ ---           ┆ ---      ┆ ---       ┆ ---           ┆   ┆ ---       ┆ ---       ┆ ---            ┆ ---      │
        │ date          ┆ str      ┆ i64       ┆ date          ┆   ┆ f64       ┆ f64       ┆ f64            ┆ f64      │
        ╞═══════════════╪══════════╪═══════════╪═══════════════╪═══╪═══════════╪═══════════╪════════════════╪══════════╡
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.000306  ┆ 0.000226  ┆ 0.000272       ┆ 0.10408  │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ -0.000397 ┆ -0.000481 ┆ -0.000418      ┆ 0.11082  │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ -0.000205 ┆ -0.000258 ┆ -0.00023       ┆ 0.114315 │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.000085  ┆ 0.00006   ┆ 0.000075       ┆ 0.114982 │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.000124  ┆ 0.000097  ┆ 0.000114       ┆ 0.114955 │
        │ …             ┆ …        ┆ …         ┆ …             ┆ … ┆ …         ┆ …         ┆ …              ┆ …        │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001501  ┆ 0.001476  ┆ 0.001491       ┆ 0.11564  │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001597  ┆ 0.001571  ┆ 0.001587       ┆ 0.115773 │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001601  ┆ 0.001574  ┆ 0.001591       ┆ 0.115904 │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001649  ┆ 0.001627  ┆ 0.001641       ┆ 0.115854 │
        │ 2024-08-23    ┆ LFT      ┆ 210100    ┆ 2000-07-01    ┆ … ┆ 0.001696  ┆ 0.00168   ┆ 0.001687       ┆ 0.115806 │
        └───────────────┴──────────┴───────────┴───────────────┴───┴───────────┴───────────┴────────────────┴──────────┘
    """  # noqa: E501
    return anbima.tpf_data(date, "LFT")

maturities(date)

Fetch the bond maturities available for the given reference date.

Parameters:

Name Type Description Default
date DateScalar

The reference date for fetching the data.

 required

Returns:

Type Description
Series

pl.Series: A Series of bond maturities available for the reference date.

Examples:

>>> from pyield import lft
>>> lft.maturities("22-08-2024")
shape: (14,)
Series: 'MaturityDate' [date]
[
    2024-09-01
    2025-03-01
    2025-09-01
    2026-03-01
    2026-09-01
    …
    2029-03-01
    2029-09-01
    2030-03-01
    2030-06-01
    2030-09-01
]
Source code in pyield/tn/lft.py 
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def maturities(date: DateScalar) -> pl.Series:
    """
    Fetch the bond maturities available for the given reference date.

    Args:
        date (DateScalar): The reference date for fetching the data.

    Returns:
        pl.Series: A Series of bond maturities available for the reference date.

    Examples:
        >>> from pyield import lft
        >>> lft.maturities("22-08-2024")
        shape: (14,)
        Series: 'MaturityDate' [date]
        [
            2024-09-01
            2025-03-01
            2025-09-01
            2026-03-01
            2026-09-01
            …
            2029-03-01
            2029-09-01
            2030-03-01
            2030-06-01
            2030-09-01
        ]
    """
    df_rates = data(date)
    return df_rates["MaturityDate"]

premium(lft_rate, di_rate)

Calculate the premium of the LFT bond over the DI Futures rate.

Parameters:

Name Type Description Default
lft_rate float

The annualized trading rate over the selic rate for the bond.

 required
di_rate float

The DI Futures annualized yield rate (interpolated to the same maturity as the LFT).

 required

Returns:

Name Type Description
float float

The premium of the LFT bond over the DI Futures rate.

Examples:

Calculate the premium of a LFT in 28/04/2025

>>> from pyield import lft
>>> lft_rate = 0.001124  # 0.1124%
>>> di_rate = 0.13967670224373396  # 13.967670224373396%
>>> lft.premium(lft_rate, di_rate)
1.008594331960501
Source code in pyield/tn/lft.py 
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def premium(lft_rate: float, di_rate: float) -> float:
    """
    Calculate the premium of the LFT bond over the DI Futures rate.

    Args:
        lft_rate (float): The annualized trading rate over the selic rate for the bond.
        di_rate (float): The DI Futures annualized yield rate (interpolated to the same
            maturity as the LFT).

    Returns:
        float: The premium of the LFT bond over the DI Futures rate.

    Examples:
        Calculate the premium of a LFT in 28/04/2025
        >>> from pyield import lft
        >>> lft_rate = 0.001124  # 0.1124%
        >>> di_rate = 0.13967670224373396  # 13.967670224373396%
        >>> lft.premium(lft_rate, di_rate)
        1.008594331960501
    """
    if has_null_args(lft_rate, di_rate):
        return float("nan")
    # daily rate
    ltt_factor = (lft_rate + 1) ** (1 / 252)
    di_factor = (di_rate + 1) ** (1 / 252)
    return (ltt_factor * di_factor - 1) / (di_factor - 1)

quotation(settlement, maturity, rate)

Calculate the quotation of a LFT bond using Anbima rules.

Parameters:

Name Type Description Default
settlement DateScalar

The settlement date of the bond.

 required
maturity DateScalar

The maturity date of the bond.

 required
rate float

The annualized yield rate of the bond

 required

Returns:

Name Type Description
float float

The quotation of the bond.

Examples:

Calculate the quotation of a LFT bond with a 0.02 yield rate:

>>> from pyield import lft
>>> lft.quotation(
...     settlement="24-07-2024",
...     maturity="01-09-2030",
...     rate=0.001717,  # 0.1717%
... )
98.9645
Source code in pyield/tn/lft.py 
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def quotation(
    settlement: DateScalar,
    maturity: DateScalar,
    rate: float,
) -> float:
    """
    Calculate the quotation of a LFT bond using Anbima rules.

    Args:
        settlement (DateScalar): The settlement date of the bond.
        maturity (DateScalar): The maturity date of the bond.
        rate (float): The annualized yield rate of the bond

    Returns:
        float: The quotation of the bond.

    Examples:
        Calculate the quotation of a LFT bond with a 0.02 yield rate:
        >>> from pyield import lft
        >>> lft.quotation(
        ...     settlement="24-07-2024",
        ...     maturity="01-09-2030",
        ...     rate=0.001717,  # 0.1717%
        ... )
        98.9645
    """
    # Validate and normalize dates
    if has_null_args(settlement, maturity, rate):
        return float("nan")
    settlement = cv.convert_dates(settlement)
    maturity = cv.convert_dates(maturity)

    # The number of bdays between settlement (inclusive) and the maturity (exclusive)
    bdays = bday.count(settlement, maturity)

    # Calculate the number of periods truncated as per Anbima rules
    num_of_years = tools.truncate(bdays / 252, 14)

    discount_factor = 1 / (1 + rate) ** num_of_years

    return tools.truncate(100 * discount_factor, 4)