I connected to a public WebSocket feed and found mispriced tokens on Polymarket

Source: Dev.to

Overview

I connected two WebSocket feeds: one to the Chainlink oracle that settles Polymarket’s 15‑minute crypto markets, and another to the Polymarket order book. Watching them side‑by‑side revealed a consistent lag: the oracle updates in under a second, while the order book takes about 55 seconds to reflect the same move. For almost a full minute, tokens sit on stale data. The settlement source is public, anyone can connect, and it consistently runs nearly a minute ahead of the market it settles. This isn’t a bug—just how the market works—but it’s rare to be able to measure it so cleanly.

Strategy

The core logic is simple:

1. On every oracle price tick, check three conditions:

   • price moved > 0.07 % from market open
   • time remaining > 5 minutes
   • token price < $0.62

2. If all three are true, buy the cheap side, wait for settlement, and collect $1 (or lose the stake).

The interesting part was not the strategy itself but making it run reliably.

Implementation

Architecture

A single process holds an open WebSocket receiving sub‑second updates, tracks 16 overlapping markets, evaluates signals on every tick, places orders without blocking the price feed, sends Telegram notifications, persists state for crash recovery, and monitors its own health—all concurrently.

asyncio was the obvious choice: almost zero CPU work, everything is I/O‑bound. Seven concurrent tasks run in one event loop:

tasks = [
    oracle.run(shutdown),
    market_lifecycle_loop(...),
    signal_evaluation_loop(...),
    telegram.run(shutdown),
    state_persist_loop(...),
    redeem_loop(...),
    sanity_check_loop(...),
]
await asyncio.gather(*tasks)

No threads, no locks, no multiprocessing.

Handling Zombie WebSocket Connections

The nastiest bug was a zombie WebSocket: the connection stayed alive, ping/pong worked, no exceptions were thrown, but price data silently stopped flowing. A simple recv timeout didn’t help because heartbeat frames still arrived.

Fix: Track the timestamp of the last real price update using a monotonic clock and check it on every timeout. If no real data has arrived within a threshold, kill the connection and force a reconnect.

Verifying External Credentials

Another subtle issue: the bot started, logs looked perfect, but after 20 minutes the Telegram chat ID was wrong, causing all notifications to fail silently.

Fix: Verify every external credential before entering the main loop:

# Verify Telegram
telegram.get_me()
telegram.send_message(test_chat_id, "test")
# Verify Polymarket API keys
polymarket.verify_keys()

If something is wrong, the bot exits within the first two seconds with a clear error, e.g.:

ERROR: Telegram chat_id=123456 is invalid.
Tip: send any message to @your_bot first, then use getUpdates to find your chat_id.

Backtest Results

• Markets evaluated: 8,876 resolved markets
• Price points: 146,000
• Trades flagged: 5,017
• Win rate: 61.4 % across BTC, ETH, XRP, and SOL

I tried seven different ways to break the system (date splits, parameter grid search, doubled fees, daily breakdown, etc.). None of them killed it, though the real‑world environment will be tougher: thinner order books, larger slippage, and shrinking lag over time. This is not a retirement plan.

Open Source

The entire project is open source and includes a demo mode that connects to live data and paper‑trades without any API keys.

https://github.com/JonathanPetersonn/oracle-lag-sniper

Call for Collaboration

If you have built similar real‑time asyncio systems, I’d love to hear how you structured them. This “many concurrent long‑lived tasks sharing state” pattern feels common, but I couldn’t find good open‑source references when building this.