LivaNova PLC - EMA - MarketXLS
Data
EMA
Live48.78
Returns the Exponential Moving Average (EMA) based on historical average crossovers.
How calculated | EMA(today) = Value(today)*(Smoothing/(1+days)) + EMA(yesterday)*(1-(Smoothing/(1+days))
where Smoothing is generally kept 2 |
Example usage | =ExponentialMovingAverage("MSFT")- Returns ExponentialMovingAverage value for 30 day period =ExponentialMovingAverage("MSFT",20) - Returns ExponentialMovingAverage value for 20 day period =ExponentialMovingAverage("MSFT",20,"9/1/2022") - Returns ExponentialMovingAverage value for 20 day period with start date 1 September 2022 |
Notes | EMA is more reactive to recent price changes and hence the results are more timely and preferred more than SMA by many traders |
Assets | Stocks, ETFs, Mutual Funds, Currencies, Cryptocurrencies |
| Stock | EMA |
|---|---|
LIVN | 48.78 |
NUVA | 39.75 |
NURO | 3.94 |
LivaNova PLC
LIVN 
NGS
Sector: Healthcare
Industry: Medical Devices
47.06
USD
0.31
(0.66%)
Previous close: 46.75 Open: 46.66 Bid: 46.4 Ask: 47.78
52 week range
43.15 64.48
Mkt Cap: 2,539 M Avg Vol (90 Days): 576,245
Last updated: Friday 27th December 2024
Call: 1-877-778-8358 
Welcome! I'm Ankur, the founder and CEO of MarketXLS. With more than ten years of experience, I have assisted over 2,500 customers in developing personalized investment research strategies and monitoring systems using Excel.
I invite you to book a demo with me or my team to save time, enhance your investment research, and streamline your workflows.
I invite you to book a demo with me or my team to save time, enhance your investment research, and streamline your workflows.
Implement “your own” investment strategies in Excel with thousands of MarketXLS functions and templates.
MarketXLS is a complete Excel stock solution
I have used lots of stock and option information services. This is the only one which gives me what I need inside Excel
MarketXLS is a data junkie’s dream. It gives me the flexibility to mine for hidden treasures.
I like to access historical closing prices on a particular date. That makes tracking performance easy.