Ethereum EMA calculation: a comparison with the commerce view
As an enthusiastic and cryptocurrency merchant, understanding technical analysis is crucial to make informed decisions about investments. An aspect of technical analysis that can be particularly challenging is to compare different EMA calculations (exponential mobile average).
In this article, we will explore two popular EMA calculation methods used in the cryptocurrency market: the EMA calculated from the Binance connector API and an EMA negotiation view. We will compare these calculations to provide information about which one could be lagging compared to others.
Introduction
EMA calculations are widely used in the technical analysis to identify reversions of trends, continuations and possible levels of support and resistance. There are several ways to calculate EMA, but two of the most popular methods are the simple mobile average (SMA) and the exponential mobile average (EMA).
Binance Connector API – Calculated Ema
The Binance connector API allows you to recover real -time data from the exchange of Binance. One of those API final points is used in this article to calculate EMA in the short, medium and long term in a 1 minute graph.
Here is an extract of the python code that demonstrates how to calculate the calculated EMA using the Binance connector API:
`Python
Import requests
Def get_ema (data, period):
""
Calculate the EMA calculated for data and period given.
ARGS:
Data (DICT): Dictionary that contains graph data with the key 'closing'.
Period (INT): The EMA calculation period (1 minute in this case).
Returns:
List: Calculated EMA Values List.
""
API_URL = F " Data='Symbol'font>tmonty/Chart/{Data Sorval'hongitude"
Answer = request.get (API_URL)
Data = Json.Loads (Answer.text)
Ema_values = []
For i in the range (Len (Data)):
Yes I> 0:
Calculate the EMA values using the calculated formula
EMA = (2 / (R&D 1)) * (data ['close'] [i] - data ['close'] [i -1]) + data ['close'] [i]
EMA_VALUS.APPEND (EMA)
Return Ema_values
Recover real -time data from the Binance API
symbol = 'btcusdt'
interval = '1m'
Time time = '1m'
data = get_ema ({'symbol': symbol, 'interval': interval, 'time frame': time time}, period = 120)
Trace the EMA values calculated in a 1 minute graph
Import Matpletlib.pyplet as PLT
PLT.LOT (Data)
PLT.XLABEL ('TIME')
Plt.ylabel ('price')
PLT.title ('EMA calculated vs
Plt.show ()
This code recovers real -time data from the Binance API, calculates EMA in the short and long term using the 'Get_Ama function and draws the results in a 1 minute graph.
Commercial view Ema
The API of the commercial view connector provides another way to calculate EMA, which is often used in the technical analysis. Here is an extract of the python code that demonstrates how to calculate the commercial view EMA:
“ Python
Import requests
Def get_trading_view_ema (data):
“”
Calculate the EMA negotiation view for given data.
ARGS:
Data (DICT): Dictionary that contains graphic data with the key ‘closing’.
Returns:
List: List of commercial views Ema values.
“”
API_URL = F “
Headers = {‘Authorization’: ‘Bearer Your_api_Key’}
Answer = request.get (API_url, headed = headers)
Analyze the json answer
Ema_values = []
For element in response. JSON () [‘Data’]:
Ema_value = (Item [‘value’] + 1) / 2
Ema_values.
