augment_qsmomentum

augment_qsmomentum(
    data,
    date_column,
    close_column,
    roc_fast_period=21,
    roc_slow_period=252,
    returns_period=126,
    reduce_memory=False,
    engine='pandas',
)

The function augment_qsmomentum calculates Quant Science Momentum for financial data.

Parameters

Name	Type	Description	Default
data	Union[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]	The `data` parameter in the `augment_qsmomentum` function is expected to be a pandas DataFrame or a pandas DataFrameGroupBy object. This parameter represents the input data on which the momentum calculations will be performed.	required
date_column	str	The `date_column` parameter in the `augment_qsmomentum` function refers to the column in your input data that contains the dates associated with the financial data. This column is used for time-based operations and calculations within the function.	required
close_column	str	The `close_column` parameter in the `augment_qsmomentum` function refers to the column in the input DataFrame that contains the closing prices of the financial instrument or asset for which you want to calculate the momentum.	required
roc_fast_period	Union[int, Tuple[int, int], List[int]]	The `roc_fast_period` parameter in the `augment_qsmomentum` function determines the period used for calculating the fast Rate of Change (ROC) momentum indicator.	`21`
roc_slow_period	Union[int, Tuple[int, int], List[int]]	The `roc_slow_period` parameter in the `augment_qsmomentum` function represents the period used for calculating the slow rate of change (ROC) in momentum analysis.	`252`
returns_period	Union[int, Tuple[int, int], List[int]]	The `returns_period` parameter in the `augment_qsmomentum` function determines the period over which the returns are calculated.	`126`
reduce_memory	bool	The `reduce_memory` parameter in the `augment_qsmomentum` function is a boolean flag that indicates whether memory reduction techniques should be applied to the input data before and after the momentum calculation process. If set to `True`, memory reduction methods will be used to optimize memory usage, potentially reducing	`False`
engine	str	The `engine` parameter in the `augment_qsmomentum` function specifies the computation engine to be sed for calculating momentum. It can have two possible values: “pandas” or “polars”.	`'pandas'`

Returns

Name	Type	Description
	The function `augment_qsmomentum` returns a pandas DataFrame that has been augmented with columns	representing the Quant Science Momentum (QSM) calculated based on the specified parameters such as roc_fast_period, roc_slow_period, and returns_period.

Notes

The Quant Science Momentum (QSM) is a momentum indicator that is calculated based on the Slow Rate of Change (ROC) usually over a 252-day period and the Fast Rate of Change (ROC) usually over a 21-day period.

The QSM is calculated as the difference between the slow and fast ROCs divided by the standard deviation of the returns over a specified period.

This provides a measure of momentum that is normalized by the rolling volatility of the returns.

Examples

import pandas as pd
import polars as pl
import pytimetk as tk

df = tk.load_dataset("stocks_daily", parse_dates = ['date'])

df.glimpse()

<class 'pandas.core.frame.DataFrame'>: 16194 rows of 8 columns
symbol:    object            ['META', 'META', 'META', 'META', 'META', 'M ...
date:      datetime64[ns]    [Timestamp('2013-01-02 00:00:00'), Timestam ...
open:      float64           [27.440000534057617, 27.8799991607666, 28.0 ...
high:      float64           [28.18000030517578, 28.469999313354492, 28. ...
low:       float64           [27.420000076293945, 27.59000015258789, 27. ...
close:     float64           [28.0, 27.770000457763672, 28.7600002288818 ...
volume:    int64             [69846400, 63140600, 72715400, 83781800, 45 ...
adjusted:  float64           [28.0, 27.770000457763672, 28.7600002288818 ...

# PANDAS QS MOMENTUM CALCULATION
df_qsmom = (
    df
        .query('symbol == "GOOG"')
        .augment_qsmomentum(
            date_column = 'date',
            close_column = 'close',
            roc_fast_period = [1, 5, 21],
            roc_slow_period = 252,
            returns_period = 126,
            engine = "pandas"
        )
)

df_qsmom.dropna().glimpse()

<class 'pandas.core.frame.DataFrame'>: 2448 rows of 11 columns
symbol:                  object            ['GOOG', 'GOOG', 'GOOG', 'GOO ...
date:                    datetime64[ns]    [Timestamp('2013-12-31 00:00: ...
open:                    float64           [27.702165603637695, 27.78236 ...
high:                    float64           [27.920347213745117, 27.83940 ...
low:                     float64           [27.553224563598633, 27.60303 ...
close:                   float64           [27.913124084472656, 27.72408 ...
volume:                  int64             [54519590, 73129082, 66917888 ...
adjusted:                float64           [27.913124084472656, 27.72408 ...
close_qsmom_1_252_126:   float64           [36.51363951893322, 35.713897 ...
close_qsmom_5_252_126:   float64           [35.16871574164789, 36.369633 ...
close_qsmom_21_252_126:  float64           [26.2560861603838, 26.4529175 ...

# POLARS QS MOMENTUM CALCULATION
df_qsmom = (
    df
        .query('symbol == "GOOG"')
        .augment_qsmomentum(
            date_column = 'date',
            close_column = 'close',
            roc_fast_period = [1, 5, 21],
            roc_slow_period = 252,
            returns_period = 126,
            engine = "polars"
        )
)

df_qsmom.dropna().glimpse()

<class 'pandas.core.frame.DataFrame'>: 2448 rows of 11 columns
symbol:                  object            ['GOOG', 'GOOG', 'GOOG', 'GOO ...
date:                    datetime64[ns]    [Timestamp('2013-12-31 00:00: ...
open:                    float64           [27.702165603637695, 27.78236 ...
high:                    float64           [27.920347213745117, 27.83940 ...
low:                     float64           [27.553224563598633, 27.60303 ...
close:                   float64           [27.913124084472656, 27.72408 ...
volume:                  int64             [54519590, 73129082, 66917888 ...
adjusted:                float64           [27.913124084472656, 27.72408 ...
close_qsmom_1_252_126:   float64           [36.3684554866266, 35.5718931 ...
close_qsmom_5_252_126:   float64           [35.028879339972015, 36.22502 ...
close_qsmom_21_252_126:  float64           [26.151687790032966, 26.34773 ...