import pytimetk as tk
import polars as pl
df = tk.load_dataset("stocks_daily", parse_dates=["date"])
# Pandas example (engine inferred)
cmo_pd = (
df.groupby("symbol")
.augment_cmo(
date_column="date",
close_column="close",
periods=[14, 28],
)
)
# Polars example using the tk accessor
cmo_pl = (
pl.from_pandas(df.query("symbol == 'AAPL'"))
.tk.augment_cmo(
date_column="date",
close_column="close",
periods=14,
)
)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 | Name of the column containing date information. | required |
| close_column | str | Name of the column containing closing prices. | 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.