Ethereum EMA Calculation: Comparison with Trading Image
As a cryptocurrency enthusiast and merchant, understanding of technical analysis is very important in making reasonable decisions on investment. One aspect of technical analysis, which can be particularly complicated, is relatively different EMA (exponential sliding mean) calculations.
In this article, we will explore two popular EMA computing methods used in the cryptocurrency market: EMA from Binance Connector API and EMA. We will compare these calculations to provide insights to which one may fall behind.
Introduction
EMA calculations are widely used in technical analysis to determine the changes, sequels and potential support and resistance of the trends. There are several ways to calculate the EMA, but the two most popular methods are a simple sliding average (SMA) and the exponential sliding average (EMA).
Binance Connector API – Calculated EMA
Binance Connector API allows you to get real -time data from Binance Exchange. One such API is used in this article to calculate short, medium and long -term EMA 1 minute diagram.
Here’s a Python code passage showing how to calculate the estimated EMA using Binance Connector API:
`Python
Import inquiries
Def get_ema (data, period):
“” “
Calculate the calculated EMA based on the specified data and period.
Args:
Data (DICT): Dictionary containing chart data with key to “close”.
period (int): EMA computing period (in this case 1 minute).
Return:
List: List of calculated EMA values.
“” “
API_url = F ” {datažr .’Symbol’tsmt/Chart/{dataž.interval’ts/{dataratateframe ‘]}}”} “
Answer = Inquiries.get (api_url)
Data = JSON.LOADS (Response.Text)
EMA_VALUES = []
I am in the range (LEN (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]
EMA_VALUES.APPEND (EMA)
return ema_values
Get real -time data from Binance Api
symbol = ‘btcusdt’
Interval = ‘1m’
Time = ‘1m’
Data = Get_EMA ({‘symbol’: symbol, ‘interval’: interval, ‘time’: Time}, period = 120)
Draw the estimated EMA values in a 1 -minute diagram
Imported Matplotlib.Pyplot as PLT
plt.plot (data)
PLT.XLABEL (‘Time’)
PLT.YLABEL (“Price”)
plt.title (‘estimated EMA vs Trading View EMA))
plt.show ()
`
This code receives real -time data from Binance API, calculates short -term and long -term EMA using Get_ema and draws results in a 1 minute diagram.
Trading image EMA
There is another way to calculate EMA, which is often used in technical analysis. Here’s a Python code excerpt showing how to calculate a trade review EMA:
`Python
Import inquiries
Def Get_trarading_View_ema (Data):
“” “
Calculate the view of the data specified in the Trade EMA.
Args:
Data (DICT): Dictionary containing chart data with key to “close”.
Return:
List: EMA values Trading list.
“” “
API_url = F ” {Data [‘Symbol’t}/EMA”
header = {‘authorization’: ‘Carry your_api_key’}
Answer = Inquiries.get (api_url, headlines = headlines)
Parse json reply
EMA_VALUES = []
In the element reply.json () [‘data’]:
EMA_VALUE = (element [‘value’] + 1) / 2
EMA_VALUES.