I was working on a small crypto tool for my own use and noticed that checking multiple exchange pages manually was becoming inconvenient. When watching several coins at the same time, it is easy to miss short price movements or sudden changes in market activity.
I did not plan to build a full trading terminal. The original idea was much simpler: create a lightweight monitor that could collect real time market information and show the changes I actually cared about.
During the development process, I tested AllTick as the data source for receiving crypto quote updates. The first version was only a basic script that printed incoming data, but it helped me understand how real time market streams behave in practice.
The connection logic was simple:
import websocket
import json
def on_message(ws, message):
data = json.loads(message)
print("update:", data)
def on_open(ws):
request = {
"cmd": "subscribe",
"symbol": "BTCUSDT",
"type": "quote"
}
ws.send(json.dumps(request))
ws = websocket.WebSocketApp(
"wss://quote.alltick.co/ws",
on_open=on_open,
on_message=on_message
)
ws.run_forever()
At the beginning, I thought the hardest part would be receiving the data, but the actual problems appeared after the connection started running for a while.
One issue was handling frequent updates. Crypto markets can generate many price changes in a short period, and processing every single update immediately is not always necessary. I added some simple filtering logic to avoid doing unnecessary work.
Another thing I had to deal with was connection stability. A websocket connection may work perfectly during testing but still disconnect unexpectedly when running continuously. Adding reconnect handling became an important improvement for making the tool more reliable.
Timestamp handling was another small detail that became important later. When storing market data or comparing price movements, inconsistent timestamps can create confusing results. Normalizing time information before processing the data saved me from several debugging sessions.
This project is still a work in progress. I am mainly using it to understand how market data applications are built and where the practical problems appear when moving from a simple script to a continuously running service.
There are still areas I want to improve, especially data storage and better event filtering. If anyone here has built similar crypto monitoring tools, I would be interested in hearing how you handle data streams and connection issues.
