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I can help you pull live data from the Binance API and at the same time graph it using Matplotlib. However, I need to clarify that there are a few issues with your current code.

First, the createFrame function is not defined anywhere in your code. You will need to define this function or replace it with an existing one that retrieves data from Binance frames.

Second, the socket object appears to be used for WebSocket connections, but Matplotlib does not have a built-in API for real-time graphing. However, you can use the matplotlib.ticker module to create automated tickers for your chart. This will allow you to display live data without needing to poll the Binance API constantly.

Here is an example of how you can modify your code to accomplish this:

import socket
import time
import matplotlib.pyplot as plt









Define the function to retrieve Binance frame data
def create_frame(msg):

Assuming msg is a bytes object, decode it first
frame = msg. decode('utf-8')
return frame
class BinanceClient:
def __init__(self):
self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
async def connect_to_binance(self, url):
await self.socket.connect((url, 443))
self.socket.settimeout(1) 
Set a timeout for the connection
async def send_message(self, msg):
await self.socket.recv()
frame = create_frame(msg)
return frame.decode('utf-8')

Create a BinanceClient instance
client = BinanceClient()
async def main():
while True:

Connect to Binance API and get the latest frame data
url = "
client.connect_to_binance(url)
msg = await client.send_message("GET ORDERBOOK")

Plot the received frame data using Matplotlib
frames = []
while True:
frame = create_frame(msg)
frames.append(frame.decode('utf-8'))
time.sleep(0.01) 
Wait 10 milliseconds
plt.figure(figsize=(20, 10))
plt.plot(frames)
plt.title("Binance Frame Data")
plt.xlabel("Time")
plt.ylabel("Frame Data")
plt.show()

Get the latest Binance frame data
msg = await client.send_message("GET ORDERBOOK")
if __name__ == "__main__":
main()

This code creates an instance of BinanceClient and uses it to connect to the Binance API. It then plots the received frame data using Matplotlib.

Note that this is a simplified example and you may need to adjust the connection timeout and plot interval to suit your specific needs. Additionally, you will need to replace the create_frame function with an existing one that retrieves frame data from Binance.

Also, keep in mind that graphing real-time data requires periodic requests to the server, which can be resource intensive. You may want to consider using a more efficient data streaming API if you plan on handling large volumes of data.

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