augment_cmo

augment_cmo(
    data,
    date_column,
    close_column,
    periods=14,
    reduce_memory=False,
    engine='auto',
)

Calculate the Chande Momentum Oscillator (CMO) using pandas or polars backends.

Parameters

Name	Type	Description	Default
data	DataFrame or GroupBy(pandas or polars)	Input financial data. Grouped inputs are processed per group before the indicator columns are appended.	required
date_column	str or ColumnSelector	Name or selector for the column containing date information.	required
close_column	str, ColumnSelector, or list	Column(s) containing closing prices. Selectors/lists must resolve to a single column.	required
periods	int, tuple, or list	Lookback window(s) applied to the CMO calculation. Accepts a single integer, an inclusive tuple range, or an explicit list. Defaults to `14`.	`14`
reduce_memory	bool	Attempt to reduce memory usage when operating on pandas data. If a polars input is supplied a warning is emitted and no conversion occurs.	`False`
engine	(auto, pandas, polars)	Execution engine. `"auto"` (default) infers the backend from the input data while allowing explicit overrides.	`"auto"`

Returns

Name	Type	Description
	DataFrame	DataFrame with `{close_column}_cmo_{period}` columns appended for every requested period. The return type matches the input backend.

Notes

The Chande Momentum Oscillator (CMO) compares the magnitude of recent gains to recent losses over the supplied lookback window. Values range from -100 (all losses) to +100 (all gains). Division-by-zero cases are guarded by returning NaN which matches the pandas behaviour.

Examples

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

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

df

	symbol	date	open	high	low	close	volume	adjusted
0	META	2013-01-02	27.440001	28.180000	27.420000	28.000000	69846400	28.000000
1	META	2013-01-03	27.879999	28.469999	27.590000	27.770000	63140600	27.770000
2	META	2013-01-04	28.010000	28.930000	27.830000	28.760000	72715400	28.760000
3	META	2013-01-07	28.690001	29.790001	28.650000	29.420000	83781800	29.420000
4	META	2013-01-08	29.510000	29.600000	28.860001	29.059999	45871300	29.059999
...	...	...	...	...	...	...	...	...
16189	GOOG	2023-09-15	138.800003	139.360001	137.179993	138.300003	48947600	138.300003
16190	GOOG	2023-09-18	137.630005	139.929993	137.630005	138.960007	16233600	138.960007
16191	GOOG	2023-09-19	138.250000	139.175003	137.500000	138.830002	15479100	138.830002
16192	GOOG	2023-09-20	138.830002	138.839996	134.520004	134.589996	21473500	134.589996
16193	GOOG	2023-09-21	132.389999	133.190002	131.089996	131.360001	22042700	131.360001

16194 rows × 8 columns

# Chande Momentum Oscillator - pandas engine
cmo_pd = (
    df
    .groupby("symbol")
    .augment_cmo(
        date_column="date",
        close_column="close",
        periods=[14, 28],
    )
)

cmo_pd.glimpse()

<class 'pandas.core.frame.DataFrame'>: 16194 rows of 10 columns
symbol:        object            ['META', 'META', 'META', 'META', 'META' ...
date:          datetime64[ns]    [Timestamp('2013-01-02 00:00:00'), Time ...
open:          float64           [27.440000534057617, 27.8799991607666,  ...
high:          float64           [28.18000030517578, 28.469999313354492, ...
low:           float64           [27.420000076293945, 27.59000015258789, ...
close:         float64           [28.0, 27.770000457763672, 28.760000228 ...
volume:        int64             [69846400, 63140600, 72715400, 83781800 ...
adjusted:      float64           [28.0, 27.770000457763672, 28.760000228 ...
close_cmo_14:  float64           [nan, nan, nan, nan, nan, nan, nan, nan ...
close_cmo_28:  float64           [nan, nan, nan, nan, nan, nan, nan, nan ...

# Chande Momentum Oscillator - polars engine
pl_df = pl.from_pandas(df.query("symbol == 'AAPL'"))
cmo_pl = (
    pl_df
    .tk.augment_cmo(
        date_column="date",
        close_column="close",
        periods=14,
    )
)

cmo_pl.glimpse()

Rows: 2699
Columns: 9
$ symbol                <str> 'AAPL', 'AAPL', 'AAPL', 'AAPL', 'AAPL', 'AAPL', 'AAPL', 'AAPL', 'AAPL', 'AAPL'
$ date         <datetime[ns]> 2013-01-02 00:00:00, 2013-01-03 00:00:00, 2013-01-04 00:00:00, 2013-01-07 00:00:00, 2013-01-08 00:00:00, 2013-01-09 00:00:00, 2013-01-10 00:00:00, 2013-01-11 00:00:00, 2013-01-14 00:00:00, 2013-01-15 00:00:00
$ open                  <f64> 19.779285430908203, 19.567142486572266, 19.177499771118164, 18.64285659790039, 18.90035629272461, 18.66071319580078, 18.876785278320312, 18.60714340209961, 17.952856063842773, 17.796428680419922
$ high                  <f64> 19.821428298950195, 19.63107109069824, 19.236785888671875, 18.9035701751709, 18.996070861816406, 18.750356674194336, 18.882856369018555, 18.761428833007812, 18.125, 17.82107162475586
$ low                   <f64> 19.343929290771484, 19.321428298950195, 18.77964210510254, 18.399999618530273, 18.616071701049805, 18.428213119506836, 18.41142845153809, 18.53642845153809, 17.80392837524414, 17.26357078552246
$ close                 <f64> 19.608213424682617, 19.36071395874023, 18.821428298950195, 18.71071434020996, 18.761070251464844, 18.467857360839844, 18.696786880493164, 18.582143783569336, 17.91964340209961, 17.354286193847656
$ volume                <i64> 560518000, 352965200, 594333600, 484156400, 458707200, 407604400, 601146000, 350506800, 734207600, 876772400
$ adjusted              <f64> 16.791179656982422, 16.579240798950195, 16.1174373626709, 16.02262306213379, 16.065746307373047, 15.814659118652344, 16.010698318481445, 15.912524223327637, 15.345203399658203, 14.86106777191162
$ close_cmo_14          <f64> None, None, None, None, None, None, None, None, None, None

from pytimetk.utils.selection import contains

selector_df = (
    df
    .augment_cmo(
        date_column=contains("dat"),
        close_column=contains("clos"),
        periods=14,
    )
)

selector_df.glimpse()

<class 'pandas.core.frame.DataFrame'>: 16194 rows of 9 columns
symbol:        object            ['META', 'META', 'META', 'META', 'META' ...
date:          datetime64[ns]    [Timestamp('2013-01-02 00:00:00'), Time ...
open:          float64           [27.440000534057617, 27.8799991607666,  ...
high:          float64           [28.18000030517578, 28.469999313354492, ...
low:           float64           [27.420000076293945, 27.59000015258789, ...
close:         float64           [28.0, 27.770000457763672, 28.760000228 ...
volume:        int64             [69846400, 63140600, 72715400, 83781800 ...
adjusted:      float64           [28.0, 27.770000457763672, 28.760000228 ...
close_cmo_14:  float64           [nan, nan, 0.7218692365814648, 10.04243 ...