augment_ppo

augment_ppo(
    data,
    date_column,
    close_column,
    fast_period=12,
    slow_period=26,
    reduce_memory=False,
    engine='auto',
)

Calculate the Percentage Price Oscillator (PPO) for pandas or polars data.

Parameters

Name	Type	Description	Default
data	DataFrame or GroupBy(pandas or polars)	Financial data to augment with PPO values. 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 closing price column.	required
fast_period	int	Lookback window for the fast EMA.	`12`
slow_period	int	Lookback window for the slow EMA.	`26`
reduce_memory	bool	Attempt to reduce memory usage when operating on pandas data.	`False`
engine	(auto, pandas, polars)	Execution engine. Defaults to inferring from the input data type.	`"auto"`

Returns

Name	Type	Description
	DataFrame	DataFrame with PPO values appended. Matches the backend of the input data.

Notes

The PPO is computed as the percentage difference between a fast and a slow exponential moving average (EMA):

PPO = (EMA_fast - EMA_slow) / EMA_slow * 100

The implementation follows the common convention of using min_periods=0 on the EMA calculations to accumulate values from the beginning of the series. Division-by-zero scenarios yield NaN to align with 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)
ppo_pd = (
    df.groupby("symbol")
    .augment_ppo(
        date_column="date",
        close_column="close",
        fast_period=12,
        slow_period=26,
    )
)

# Polars example using the tk accessor
ppo_pl = (
    pl.from_pandas(df.query("symbol == 'AAPL'"))
    .tk.augment_ppo(
        date_column="date",
        close_column="close",
        fast_period=12,
        slow_period=26,
    )
)