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Double EMA API

The FMP DEMA endpoint provides the double exponential moving average of a security's closing price over a specified period of time.

About Double EMA API

The FMP DEMA endpoint provides the double exponential moving average of a security's closing price over a specified period of time. DEMAs are calculated by taking an exponential moving average of an exponential moving average. DEMAs are a type of technical indicator that is even more responsive to price changes than EMAs. This makes them even more useful for identifying short-term trends and reversals. DEMAs can also be used to identify support and resistance levels in the same way that SMAs, EMAs, and WMAs are used. The FMP DEMA endpoint is a valuable tool for investors who need to:
  • Identify even shorter-term trends and reversals in a security's price than EMAs
  • Develop trading strategies based on technical analysis
  • How it works

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    Related Double EMA APIs

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    Relative Strength Index

    The FMP RSI endpoint provides the Relative Strength Index (RSI) for a security over a specified period of time. Read more

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    Simple Moving Average

    The FMP SMA endpoint provides the simple moving average of a security's closing price over a specified period of time. Read more

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    Exponential Moving Average

    The FMP EMA endpoint provides the exponential moving average of a security's closing price over a specified period of time. Read more

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    Technical Indicators Daily

    Daily technical indicators such as the SMA, EMA, and RSI are available, all options are displayed below. Use them to make advanced charts that will assist you in analyzing price changes or trading. Read more

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    Standard Deviation

    The FMP Standard Deviation endpoint provides the standard deviation of a security's closing price over a specified period of time Read more

    Double EMA API FAQs

    How do you calculate the Exponential Moving Average (EMA) data on the Technical Indicators endpoint?

    For the Exponential Moving Average (EMA), the period calculates the ema over the 13 most recent daily intervals. It aligns with the indicator period. For example, if you used the daily technical indicator and set the period to 13, it would calculate EMA using data from the last 13 days.

    Is the Intraday price adjusted?

    Yes, the intraday data is adjusted for split.

    How many years of intraday data I can get in one query?

    Due to the sheer size and volume of the historical intraday price data there are limits to the number of records that can be returned in a given query. Contact our support team for more information.

    How can I retrieve historical intraday price data for more than 1 month?

    To retrieve historical intraday price data for more than 1 month, you will need to implement a loop that iterates over day intervals covering the entire desired time range. This approach ensures a suitable response speed for the data being returned. For example, if you require more than 1 month of 30-minute data, you can make queries over day intervals to cover the entire desired time range. Here is an example link for retrieving 1-minute data: https://financialmodelingprep.com/api/v3/historical-chart/1min/AAPL?from=2024-12-04&to=2024-12-06&apikey=YOUR-API-KEY

    Which Exponential Moving Average (EMA) do you use?

    The "EMA" corresponds to the period you set for the technical indicator.

    Does the intraday historical market cover pre-market and after hours?

    Yes, the historical market data for pre-market and post-market data can be found here https://financialmodelingprep.com/api/v4/historical-price/AAPL/1/minute/2021-06-11/2021-06-11?apikey=your_api_key or https://financialmodelingprep.com/api/v3/historical-chart/1min/AAPL?from=2021-06-11&to=2021-06-11&extended=true&apikey=your_api_key

    How does your company handle discrepancies in stock price data?

    Our company takes discrepancies in stock price data seriously. When discrepancies are reported, our development team investigates and fixes the issues promptly. We also continuously work on improving our data quality by running reliable daily verification loops on our dataset.

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