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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​

data: list[openbb_core.provider.abstract.data.Data]

list of data to be used for the calculation.

• Optional: False

target: str

Target column name.

• Default: close

• Optional: True

index: str

Index column name to use with data, by default 'date'.

• Default: date

• Optional: True

length: int

Number of periods to be used for the calculation, by default 50.

• Default: 50

• Optional: True

offset: int

Offset to be used for the calculation, by default 0.

• Optional: True

Returns​

results: list[Data]

Serializable results.

provider: None

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

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