augment_ppo

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

Calculate PPO for a given financial instrument using either pandas or polars engine.

Parameters

Name	Type	Description	Default
`data`	Union[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]	Pandas DataFrame or GroupBy object containing financial data.	required
`date_column`	str	Name of the column containing date information.	required
`close_column`	str	Name of the column containing closing price data.	required
`fast_period`	int	Number of periods for the fast EMA in PPO calculation.	`12`
`slow_period`	int	Number of periods for the slow EMA in PPO calculation.	`26`
`reduce_memory`	bool	Whether to reduce memory usage of the data before performing the calculation.	`False`
`engine`	str	Computation engine to use (‘pandas’ or ‘polars’).	`'pandas'`

Returns

Type	Description
pd.DataFrame	DataFrame with PPO values added.

Notes

The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average. The PPO is best used to confirm the direction of the price trend and gauge its momentum.

The PPO is calculated by subtracting a long-term EMA from a short-term EMA, then dividing the result by the long-term EMA, and finally multiplying by 100.

Advantages Over MACD: The PPO’s percentage-based calculation allows for easier comparisons between different securities, regardless of their price levels. This is a distinct advantage over the MACD, which provides absolute values and can be less meaningful when comparing stocks with significantly different prices.

Examples

import pandas as pd
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

# PPO pandas engine
df_ppo = (
    df
        .groupby('symbol')
        .augment_ppo(
            date_column = 'date', 
            close_column = 'close', 
            fast_period = 12, 
            slow_period = 26, 
            engine = "pandas"
        )
)

df_ppo.glimpse()

<class 'pandas.core.frame.DataFrame'>: 16194 rows of 9 columns
symbol:                object            ['AAPL', 'AAPL', 'AAPL', 'AAPL' ...
date:                  datetime64[ns]    [Timestamp('2013-01-02 00:00:00 ...
open:                  float64           [19.779285430908203, 19.5671424 ...
high:                  float64           [19.821428298950195, 19.6310710 ...
low:                   float64           [19.343929290771484, 19.3214282 ...
close:                 float64           [19.608213424682617, 19.3607139 ...
volume:                int64             [560518000, 352965200, 59433360 ...
adjusted:              float64           [16.791179656982422, 16.5792407 ...
close_ppo_line_12_26:  float64           [0.0, -0.10078442036791524, -0. ...

# PPO polars engine
df_ppo = (
    df
        .groupby('symbol')
        .augment_ppo(
            date_column = 'date', 
            close_column = 'close', 
            fast_period = 12, 
            slow_period = 26, 
            engine = "polars"
        )
)

df_ppo.glimpse()

<class 'pandas.core.frame.DataFrame'>: 16194 rows of 9 columns
symbol:                object            ['META', 'META', 'META', 'META' ...
date:                  datetime64[ns]    [Timestamp('2013-01-02 00:00:00 ...
open:                  float64           [27.440000534057617, 27.8799991 ...
high:                  float64           [28.18000030517578, 28.46999931 ...
low:                   float64           [27.420000076293945, 27.5900001 ...
close:                 float64           [28.0, 27.770000457763672, 28.7 ...
volume:                int64             [69846400, 63140600, 72715400,  ...
adjusted:              float64           [28.0, 27.770000457763672, 28.7 ...
close_ppo_line_12_26:  float64           [0.0, -0.06556683019189882, 0.1 ...