zlma
Calculate the zero lag exponential moving average (ZLEMA).
Created by John Ehlers and Ric Way. The idea is do a regular exponential moving average (EMA) calculation but on a de-lagged data instead of doing it on the regular data. Data is de-lagged by removing the data from "lag" days ago thus removing (or attempting to) the cumulative effect of the moving average.
Examples
from openbb import obb
# Get the Chande Momentum Oscillator.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')
zlma_data = obb.technical.zlma(data=stock_data.results, target='close', length=50, offset=0)
obb.technical.zlma(length=2, data=[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}])
Parameters
- standard
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | List[Data] | List of data to be used for the calculation. | False | |
| target | str | Target column name. | False | |
| index | str, optional | Index column name to use with data, by default "date". | False | |
| length | int, optional | Number of periods to be used for the calculation, by default 50. | False | |
| offset | int, optional | Offset to be used for the calculation, by default 0. | False |
Returns
OBBject
results : List[Data]
The calculated data.