Basic operations

class src.lib.analysis.basic.Basic

Bases: object

calc_EMA(dataframe: pandas.core.frame.DataFrame, source_column: str, length: int, result_column: str = '', value_prediction=nan)

Calculate the exponential moving average (EMA) for the specified column and length in a Pandas dataframe. Depending on the parameters, the result will be a new column on the same dataframe.

Parameters

  • source_column (string) – Name of the column in the Pandas Dataframe to be used for the calculation of the moving average.

  • length (int) – Number of samples to be used as rolling window for the average calculation.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: EMA length source_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

calc_MovingStdDev(dataframe: pandas.core.frame.DataFrame, source_column: str, length: int, minimum_length: Optional[int] = None, result_column: str = '', value_prediction=nan)

Calculate the simple moving standard deviation for the specified column and length in a Pandas dataframe. Depending on the parameters, the result will be a new column on the same dataframe.

Parameters

  • source_column (string) – Name of the column in the Pandas Dataframe to be used for the calculation of the moving average.

  • length (int) – Number of samples to be used as rolling window for the average calculation.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: SMA length source_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

calc_SMA(dataframe: pandas.core.frame.DataFrame, source_column: str, length: int, minimum_length: Optional[int] = None, result_column: str = '', value_prediction=nan)

Calculate the simple moving average (SMA) for the specified column and length in a Pandas dataframe. Depending on the parameters, the result will be a new column on the same dataframe.

Parameters

  • source_column (string) – Name of the column in the Pandas Dataframe to be used for the calculation of the moving average.

  • length (int) – Number of samples to be used as rolling window for the average calculation.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: SMA length source_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

calc_absolute(dataframe: pandas.core.frame.DataFrame, source_column: str, result_column: str = '', value_prediction=nan)

Calculate de delta value between the Close (Final) and the Open value for the ticker. It will add a column called “Delta” to the Pandas dataframe.

Parameters

none –

calc_change(dataframe: pandas.core.frame.DataFrame, source_column: str, shift: int, result_column: str = '', value_prediction=nan)

Calculate the difference between entries from a column. For example the closing price difference for every day.

Parameters

  • source_column (string) – Name of the column in the Pandas Dataframe to be used for the calculation of the change.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: SMA length source_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

calc_delta(dataframe: pandas.core.frame.DataFrame, value_prediction=nan)

Calculate de delta value between the Close (Final) and the Open value for the ticker. It will add a column called “Delta” to the Pandas dataframe.

Parameters

None –

The input for the calculation is based on the Pandas dataframe data which is already available. The expected column for this operation is:

  1. Open

  2. Close Final

Returns

The outcome from the calculation is not explicitly returned, but added to the Pandas dataframe as new columns. The new columns are:

  1. Delta: Result of the difference between Close Final and Open values for every sample.

Return type

None

calc_difference(dataframe: pandas.core.frame.DataFrame, minuend_column: str, subtrahend_column: str, result_column: str = '', value_prediction=nan)

Calculate the difference between 2 columns from a Pandas Dataframe.

Parameters

  • minuend_column (string) – Name of the column in the Pandas Dataframe to be used as the minuend of the differentiation operation.

  • subtrahend_column (string) – Name of the column in the Pandas Dataframe to be used as the subtrahend of the differentiation operation.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: minuend_column minus subtrahend_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

Returns

The outcome from the calculation is not explicitly returned, but added to the Pandas dataframe as new columns. The new columns are:

  1. result_column: Result of the difference operation.

Return type

None

calc_division(dataframe: pandas.core.frame.DataFrame, dividend_column: str, divisor_column: str, result_column: str = '', value_prediction=nan)

Calculate the division between 2 columns from a Pandas Dataframe.

Parameters

  • dividend_column (string) – Name of the column in the Pandas Dataframe to be used as the dividend of the division operation.

  • divisor_column (string) – Name of the column in the Pandas Dataframe to be used as the divisor of the division operation.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: minuend_column minus subtrahend_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

calc_integration(dataframe: pandas.core.frame.DataFrame, source_column: str, length: int = 0, result_column: str = '', value_prediction=nan)

Calculate the integration for the specified column for a window in a Pandas dataframe. For length equals 0, the complete series is used. Depending on the parameters, the result will be a new column on the same dataframe.

calc_maximum(dataframe: pandas.core.frame.DataFrame, source_column: str, length: int, result_column: str = '', value_prediction=nan)

Calculate the rolling maximum.

calc_minimum(dataframe: pandas.core.frame.DataFrame, source_column: str, length: int, result_column: str = '', value_prediction=nan)

Calculate the rolling minimum.

calc_multiplication(dataframe: pandas.core.frame.DataFrame, factor1_column: str, factor2_column: str, result_column: str = '', value_prediction=nan)

Calculate the division between 2 columns from a Pandas Dataframe.

Parameters

  • factor1_column (string) – Name of the column in the Pandas Dataframe to be used as the dividend of the division operation.

  • factor2_column (string) – Name of the column in the Pandas Dataframe to be used as the divisor of the division operation.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: minuend_column minus subtrahend_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

calc_scalar_multiplication(dataframe: pandas.core.frame.DataFrame, factor1_column: str, factor2: float, result_column: str = '', value_prediction=nan)

Calculate the multiplication between a column from a Pandas Dataframe and a number.

Parameters

  • factor1_column (string) – Name of the column in the Pandas Dataframe to be used as the dividend of the division operation.

  • factor2_column (float) – Value of the factor.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: minuend_column minus subtrahend_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

Returns

The outcome from the calculation is not explicitly returned, but added to the Pandas dataframe as new columns. The new columns are:

  1. result_column: Result of the scalar multiplication operation.

Return type

None

calc_threshold(dataframe: pandas.core.frame.DataFrame, source_column: str, threshold: float, comparison: str, replace_value: float, result_column: str = '', value_prediction=nan)

Replaces values below or above a threshold, replacing them by a new one.

Parameters

  • source_column (string) – Name of the column in the Pandas Dataframe to be used for the calculation of the change.

  • result_column (string, optional) – Name of the column in the Pandas Dataframe to be used as the result of the operation. If no information is passed, then the name will be in the format: SMA length source_column.

  • result_dataframe (Pandas Dataframe, optional) – If passed, the result of the operation will be stored in the new dataframe, otherwise, the original dataframe is used.

convert_numpy(dataframe: pandas.core.frame.DataFrame, source_column: str)
split_data(data, percetage_learning: float = - 1, sequence_length: int = 0)