Calculating Ethereum Ema: Comparison with trading view

As an enthusiast cryptocurrency and trader, the understanding of technical analysis is decisive for informed investment decisions. One aspects of technical analysis, which may be particularly demanding, is to compare various EMA calculations (exponential sliding diameter).

In this article, we will examine two popular methods of calculating EMA used in the crypto market: calculated EMA from the Binance Connector API and the EMA business point of view. Let’s compare these calculations to provide knowledge that could be lagged compared to others.

Introduction

EMA calculations are widely used in technical analysis to identify the reversal of trends, continuations and potential support and resistance levels. There are several ways to calculate EMA, but two of the most popular methods are a simple sliding diameter (SMA) and an exponential sliding diameter (EMA).

API Binance Connector – Calculated Ema

API Binance Connector API allows you to load real -time data from Binance Exchange exchange. One such end point of API is used in this article to calculate short, medium and long-term EMA on a 1-minute chart.

Here’s the excerpt of the Python code that shows how to calculate the calculated EMA using the API Binance Connector API:

`Python

Import requirements

Def Get_ema (data, period):

“” “”

Calculate the calculated EMA for the data and period.

Arg:

Data (DICT): a dictionary containing a chart data with the key “Close”.

Period (int): EMA calculation period (in this case 1-minute).

Returns:

List: List of calculated EMA values.

“” “”

API_URL = F ” {data [symbol

Response = Requests.get (API_URL)

Data = Json.loads (Response.Text)

EMA_VALues = []

For i in the extent (only (data)):

If I> 0:

Calculate EMA values ​​using the calculated formula

Ema = (2 / (i + 1)) * (data [‘close’] [i] – data [‘close’] [i -1]) + data [“Close ‘] [i] [i]

Ema_Values.append (Ema)

Return Ema_Values

#Load data in real time from Binance API

symbol = ‘btcusdt’

interval = ‘1m’

Timeframe = ‘1m’

Data = Get_ema ({‘symbol’

Pay the calculated EMA values ​​per 1-minute table

Import Matplolib.PyPlot as PLT

PLT.PLOT (data)

PLT.XLABEL (“Time”)

PLT.YLABEL (“PRICE”)

PLT.TITLE (“Calculated EMA vs EMA trafficking”)

PLT.SHOW ()

`

This code loads real -time data from the Binance API, calculates the short and long -term EMA using the Get_ema function, and the results portray the results for 1 minute of the graph.

View trading EMA

The API connector of the business display provides another way of calculating EMA, which is often used in technical analysis. Here is the excerpt of the Python code that shows how to calculate the business view of EMA:

`Python

Import requirements

Def Get_trading_view_ema (data):

“” “”

Calculate EMA Trading View EMA for the data.

Arg:

DATA (DICT): Dictionary containing chart data with key “Close”.

Returns:

List: List of stores View EMA values.

“” “”

API_URL = F ” {data [symbol’cien”

headers = {‘authorization’: ‘youp_ap_Api_key’ bearer}

Response = Requests.get (API_URL, Headers = Headers)

BARME JSON REATES

EMA_VALues = []

For an item in reply.Json () [‘Data’]:

EMA_VALUE = (Item [‘Value’] + 1) / 2

EMA_VALues.

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