How to Resolve "Queue Overflow" in Asynchronous Data Processing in Binance API

I am working on a binance trading bot using asyncio and the Binance API to stream and process cryptocurrency data. However, I frequently encounter a “Queue overflow” error which suggests that the internal queue handling incoming messages is full, resulting in data being processed too slowly. Here’s the error message I repeatedly see:


Queue overflow for coin (WIFUSDT) !
Sleeping for 30 seconds before retrying...
Unknown message type: {'e': 'error', 'm': 'Queue overflow. Message not filled'}, weight set to 5
This error occurs despite my attempts to manage the queue size and processing rate. 

Below is a simplified version of the part of the code that seems to be address the issue:

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  class RateLimiter:
        # initialization and request allowance logic

    class LOB:
        def __init__(self, symbols):
            # setup, including asyncio loop and Binance Socket Manager

        async def async_stream_data(self, symbol):
            while True:
                    async with self.bsm.futures_multiplex_socket(streams=[f"{symbol.lower()}@depth20@100ms", f"{symbol.lower()}@kline_4h", f"{symbol.lower()}@trade"]) as stream:
                        while True:
                            msg = await asyncio.wait_for(stream.recv(), timeout=60)
                            if < 100:
                                await self.process_message(msg, symbol)
                                await asyncio.sleep(10)  # sleep if queue is full
                except Exception as e:
                    # handle exceptions and retry logic

        async def process_message(self, msg, symbol):
            # message processing logic

    # Main execution flow setup and entry point

async def main():
    async_coins = ["BTCUSDT", "WIFUSDT"] 
    tasks = []
        for coin in async_coins:
            loop = asyncio.get_event_loop()
        # ---------------------------------------------------------------
        # await all asyns tasks
        return await asyncio.gather(*tasks, return_exceptions=True)
    except Exception as e:
        print(f"An Exception occured in main: {e}")

if __name__ == "__main__":


How can I optimize the message processing or manage the queue more efficiently to avoid overflows? Are there better strategies for handling high-frequency data in such an asynchronous environment?

Python 3.11
python-binance 1.0.19