Ethereum: Calculated EMA is lagging compared to Trading View EMA
Ethereum EMA Calculation – Comparison with Trading View
As a cryptocurrency enthusiast and trader, understanding technical analysis is essential to making informed investment decisions. One aspect of technical analysis that can be particularly challenging is comparing different EMA (exponential moving average) calculations.
In this article, we will explore two popular EMA calculation methods used in the cryptocurrency market: the EMA calculation from the Binance Connector API and the EMA for displaying trades. We will compare these calculations to get an idea of which one might be lagging behind the others.
Introduction
EMA calculations are widely used in technical analysis to identify trend reversals, continuations, and potential support and resistance levels. There are several ways to calculate the EMA, but the two most popular methods are the simple moving average (SMA) and the exponential moving average (EMA).
Binance Connector API: Calculate EMA
The Binance Connector API allows you to retrieve real-time data from the Binance exchange. This article uses one of these API endpoints to calculate short, medium and long term EMAs on a 1 minute chart.
Below is a Python code snippet showing how to calculate the EMA using the Binance Connector API:
`python
Import Requirements
def get_ema(data, period):
"""
Calculate the EMA calculated for the given data and period.
Arguments:
data (dict): A dictionary containing chart data with a 'close' key.
period (int): The period of the EMA calculation (in this case, 1 minute).
Return:
list: List of calculated EMA values.
"""
api_url = f" "
response = requests. get(api_url)
data = json. loads(response. text)
ema_values = []
for y in 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. add(ema)
return ema values
Retrieve real-time data from Binance API
symbol = 'BTCUSDT'
interval = '1m'
timeframe = '1m'
data = get_ema({'symbol': symbol, 'interval': interval, 'timeframe': timeframe}, period=120)
Plot the calculated EMA values on a 1-minute chart
import matplotlib.pyplot as plt
plt.plot(data)
plt.xlabel('Time')
plt.ylabel('Price')
plt.title('Calculate EMA relative to trading view EMA')
plt.show()
`
This code retrieves real-time data from the Binance API, calculates the short-term and long-term EMA using theget_emafunction, and plots the results on a 1-minute chart.
Trading View EMA
The Trading View Connector API provides another way to calculate the EMA, which is often used in technical analysis. Below is a Python code snippet showing how to calculate the Trading View EMA:
`python
Import Requirements
def get_trading_view_ema(data):
“””
Calculate the Trading View EMA for the given data.
Arguments:
data (dict): A dictionary containing chart data with a ‘close’ key.
Return:
list: List of Trading View EMA values.
“””
api_url = f”
headers = {‘Authorization’: ‘Bearer YOUR_API_KEY’}
response = requests. get(api_url, headers=headers)
Parse the JSON response
ema_values = []
for element in response.json()[‘data’]:
ema_value = (item[‘value’] + 1) / 2
ema_values.
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