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

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, 28.46999931335449, 28.9 ...
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           [27.70216560363769, 27.782365 ...
high:                    float64           [27.92034721374512, 27.839401 ...
low:                     float64           [27.553224563598636, 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           [36.513639518933225, 35.71389 ...
close_qsmom_5_252_126:   float64           [35.16871574164793, 36.369633 ...
close_qsmom_21_252_126:  float64           [26.25608616038381, 26.452917 ...

# 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           [27.70216560363769, 27.782365 ...
high:                    float64           [27.92034721374512, 27.839401 ...
low:                     float64           [27.553224563598636, 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           [36.36845548662661, 35.571893 ...
close_qsmom_5_252_126:   float64           [35.02887933997205, 36.225021 ...
close_qsmom_21_252_126:  float64           [26.15168779003297, 26.347736 ...