Political prediction markets crash when news breaks. Most of these crashes recover. I built a bot that buys the crash and sells the recovery.
48 paper trades. 89.6 percent win rate. Running live on Polymarket.
The Edge
When a political market drops more than 15 percent in under 4 hours, it recovers to 90 percent of the pre-crash price about 70 percent of the time. The bot watches 200 markets every 15 minutes, detects crashes, and buys the dip.
How It Works
- Fetch 200 active political markets from the Gamma API
- Track 4-hour price highs for each market
- When current price drops more than 15 percent from the 4h high: buy
- Exit when price recovers to 90 percent of pre-crash high, or after 48 hours
Key Design Decisions
Position sizing: 5 dollars per trade. Small enough that losses do not matter, big enough to prove the edge.
Exit strategy: BUY NO to exit (not SELL YES). This is a quirk of Polymarket CLOB — selling YES tokens requires them as collateral, but buying the complementary NO token achieves the same economic result.
Kill switch: If win rate drops below 55 percent after 20 trades, the bot pauses automatically.
Results
After 48 closed paper trades:
- Win rate: 89.6 percent (43 wins, 5 losses)
- Average profit per trade: 0.70 dollars
- Total paper PnL: +33.72 dollars
- Most profitable: political qualification markets (FIFA World Cup, primaries)
- Most losses: timeout exits on slow-moving markets
What I Learned
Paper PnL is not real PnL. My live execution had bugs where exits could not fire due to a safety cap that was too low. Verify live round-trips before trusting paper numbers.
The CLOB minimum order is 5 shares. At 2 dollars per trade with prices above 40 cents, you get fewer than 5 shares and the order gets rejected. Position size must account for this.
Political markets have strong mean-reversion. Sports and crypto markets do not — they trend. Different market types need different strategies.
The bot runs autonomously via a Python process monitored by a Paperclip agent that restarts it if it crashes.
Building autonomous trading systems at github.com/LuciferForge
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