augment_rsi

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

Calculate the Relative Strength Index (RSI) for pandas or polars data.

Parameters

Name	Type	Description	Default
data	DataFrame or GroupBy(pandas or polars)	Financial data to augment. Grouped inputs are processed per group before RSI columns are appended.	required
date_column	str	Name of the column containing date information.	required
close_column	str or list[str]	Column name(s) containing the closing prices used to compute RSI. When a list is supplied an RSI is generated for each column.	required
periods	int, tuple, or list	Lookback window(s) used when computing RSI. Accepts an integer, an inclusive tuple range, or a list of explicit periods. 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, cudf)	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}_rsi_{period}` columns appended. The return type matches the input backend.

Notes

RSI follows Wilder’s formulation, separating gains and losses before computing smoothed averages and forming the ratio. Values range from 0 to 100, with extreme readings typically interpreted as overbought or oversold. Division-by-zero cases yield NaN which mirrors pandas behaviour.

Examples

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


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

# Pandas example (engine inferred)
rsi_pd = (
    df.groupby("symbol")
    .augment_rsi(
        date_column="date",
        close_column="close",
        periods=[14, 28],
    )
)

# Polars example using the tk accessor
rsi_pl = (
    pl.from_pandas(df.query("symbol == 'AAPL'"))
    .tk.augment_rsi(
        date_column="date",
        close_column=["close"],
        periods=14,
    )
)