Targets

Collection of target calculators

QuarterlyTarget

class ml_investment.targets.QuarterlyTarget(data_key: str, col: str, quarter_shift: int = 0, n_jobs: int = 2)[source]

Bases: object

Calculator of target represented as column in quarter-based data. Work with quarterly slices of company.

Parameters

  • data_key – key of dataloader in data argument during calculate()

  • col – column name for target calculation(like marketcap, revenue)

  • quarter_shift – number of quarters to shift. e.g. if quarter_shift = 0 than value for current quarter will be returned. If quarter_shift = 1 than value for next quarter will be returned. If quarter_shift = -1 than value for previous quarter will be returned.

calculate(data: Dict, index: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Interface to calculate targets for dates and tickers in index parameter based on data

Parameters

  • data – dict having field named as value in data_key param of __init__() This field should contain class implementing load(index) -> pd.DataFrame interface

  • index – pd.DataFrame containing information of tickers and dates to calculate targets for. Should have columns: ["ticker", "date"]

Returns

targets having ‘y’ column. Index of this dataframe has the same values as index param. Each row contains target for ticker company at date quarter

Return type

pd.DataFrame

QuarterlyDiffTarget

class ml_investment.targets.QuarterlyDiffTarget(data_key: str, col: str, norm: bool = True, n_jobs: int = 2)[source]

Bases: object

Calculator of target represented as difference between column values in current and previous quarter. Work with quarterly slices of company.

Parameters

  • data_key – key of dataloader in data argument during calculate()

  • col – column name for target calculation(like marketcap, revenue)

  • norm – normalize difference to previous quarter or not

  • n_jobs – number of threads for calculation

calculate(data: Dict, index: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Interface to calculate targets for dates and tickers in index parameter based on data

Parameters

  • data – dict having field named as value in data_key param of __init__() This field should contain class implementing load(index) -> pd.DataFrame interface

  • index – pd.DataFrame containing information of tickers and dates to calculate targets for. Should have columns: ["ticker", "date"]

Returns

targets having ‘y’ column. Index of this dataframe has the same values as index param. Each row contains target for ticker company at date quarter

Return type

pd.DataFrame

QuarterlyBinDiffTarget

class ml_investment.targets.QuarterlyBinDiffTarget(data_key: str, col: str, n_jobs: int = 2)[source]

Bases: object

Calculator of target represented as binary difference between column values in current and previous quarter. Work with quarterly slices of company.

Parameters

  • data_key – key of dataloader in data argument during calculate()

  • col – column name for target calculation(like marketcap, revenue)

  • n_jobs – number of threads for calculation

calculate(data: Dict, index: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Interface to calculate targets for dates and tickers in index parameter based on data

Parameters

  • data – dict having field named as value in data_key param of __init__() This field should contain class implementing load(index) -> pd.DataFrame interface

  • index – pd.DataFrame containing information of tickers and dates to calculate targets for. Should have columns: ["ticker", "date"]

Returns

targets having ‘y’ column. Index of this dataframe has the same values as index param. Each row contains target for ticker company at date quarter

Return type

pd.DataFrame

DailyAggTarget

class ml_investment.targets.DailyAggTarget(data_key: str, col: str, horizon: int = 100, foo: typing.Callable = <function mean>, n_jobs: int = 2)[source]

Bases: object

Calculator of target represented as aggregation function of daily values. Work with daily slices of company.

Parameters

  • data_key – key of dataloader in data argument during calculate()

  • col – column name for target calculation(like marketcap, pe)

  • horizon – number of days for target calculation. If horizon > 0 than values will be get from the future of current date. If horizon < 0 than values will be get from the past of current date

  • foo – function processing target aggregation

  • n_jobs – number of threads for calculation

calculate(data: Dict, index: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Interface to calculate targets for dates and tickers in index parameter based on data

Parameters

  • data – dict having field named as value in data_key param of __init__() This field should contain class implementing load(index) -> pd.DataFrame interface

  • index – pd.DataFrame containing information of tickers and dates to calculate targets for. Should have columns: ["ticker", "date"]

Returns

targets having ‘y’ column. Index of this dataframe has the same values as index param. Each row contains target for ticker company at date day

Return type

pd.DataFrame

DailySmoothedQuarterlyDiffTarget

class ml_investment.targets.DailySmoothedQuarterlyDiffTarget(daily_data_key: str, quarterly_data_key: str, col: str, smooth_horizon: int = 30, norm: bool = True, n_jobs: int = 2)[source]

Bases: object

Feature calculator getting difference between current and last quarter smoothed daily column values. Work with company quarter slices.

Parameters

  • daily_data_key – key of dataloader in data argument during calculate() for daily data loading

  • quarterly_data_key – key of dataloader in data argument during calculate() for quarterly data loading

  • col – column name for target calculation(like marketcap, pe)

  • smooth_horizon – number of days for target calculation. If smooth_horizon > 0 than values for smoothing wiil be get from future of quarter date. If smooth_horizon < 0 than values for smoothing will be get from the past of quarter date

  • norm – normalize result or not

  • n_jobs – number of threads for calculation

calculate(data: Dict, index: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Interface to calculate targets for dates and tickers in index parameter based on data

Parameters

  • data – dict having field named as value in data_key param of __init__() This field should contain class implementing load(index) -> pd.DataFrame interface

  • index – pd.DataFrame containing information of tickers and dates to calculate targets for. Should have columns: ["ticker", "date"]

Returns

targets having ‘y’ column. Index of this dataframe has the same values as index param. Each row contains target for ticker company at date quarter

Return type

pd.DataFrame

ReportGapTarget

class ml_investment.targets.ReportGapTarget(data_key: str, col: str, smooth_horizon: int = 1, norm: bool = True, n_jobs: int = 2)[source]

Bases: object

Calculator of target represented as smoothed gap at some date(i.e. report date). Work with daily slices of company.

Parameters

  • data_key – key of dataloader in data argument during calculate()

  • col – column name for target calculation(like marketcap, pe)

  • smooth_horizon – number of days for column smoothing

  • norm – normalize gap value or not

  • n_jobs – number of threads for calculation

calculate(data: Dict, index: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Interface to calculate targets for dates and tickers in index parameter based on data

Parameters

  • data – dict having field named as value in data_key param of __init__() This field should contain class implementing load(index) -> pd.DataFrame interface

  • index – pd.DataFrame containing information of tickers and dates to calculate targets for. Should have columns: ["ticker", "date"]

Returns

targets having ‘y’ column. Index of this dataframe has the same values as index param. Each row contains target for ticker company at date time

Return type

pd.DataFrame

BaseInfoTarget

class ml_investment.targets.BaseInfoTarget(data_key: str, col: str)[source]

Bases: object

Calculator of target represented by base company information

Parameters

  • data_key – key of dataloader in data argument during calculate()

  • col – column name for target calculation(like sector, industry)

calculate(data, index: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]

Interface to calculate targets for tickers in index parameter based on data

Parameters

  • data – dict having field named as value in data_key param of __init__() This field should contain class implementing load(index) -> pd.DataFrame interface

  • index – pd.DataFrame containing information of tickers to calculate targets for. Should have columns: ["ticker"]

Returns

targets having ‘y’ column. Index of this dataframe has the same values as index param. Each row contains target for ticker company

Return type

pd.DataFrame