I've been experimenting with automated trading infrastructure for Polymarket's short-duration crypto markets.
One problem I keep coming back to is risk management.
Finding an entry signal is relatively straightforward. The harder question is:
What happens when the market changes immediately after entering?
My bot combines Polymarket market data with a real-time Coinbase price feed.
Polymarket Data
+
Coinbase BTC Movement
↓
Risk Engine
↓
HOLD / REDUCE / EXIT
Polymarket provides information such as:
Coinbase provides another view of the underlying crypto market:
Coinbase isn't being used as a prediction oracle. It's an additional risk signal.
Suppose the bot holds an UP position.
Then BTC suddenly moves sharply lower on Coinbase.
Instead of blindly waiting for the market to finish, the risk engine can reassess:
BTC moves
↓
Risk detected
↓
Check position + liquidity
↓
HOLD / REDUCE / EXIT
The goal is to make the bot continuously evaluate whether the conditions behind the original trade are still valid.
The interesting engineering challenge isn't only the trading strategy.
It's everything around it:
The broader architecture is:
Observe
↓
Analyze
↓
Enter
↓
Monitor
↓
Detect Risk
↓
Manage Position
↓
Exit
I'm continuing to experiment with this approach and improve the trading infrastructure.
GitHub: Polymarket Trading Bot Python V2
Telegram: @BenjaminCup
If you're building trading bots, Web3 infrastructure, or real-time market systems, I'd be interested in hearing how you're approaching risk management.
Educational/research project. Automated trading involves significant risk and does not guarantee profits.