Everyone who builds a Polymarket bot for the first time focuses on the same things: probability estimation, Kelly criterion sizing, maybe some clever arbitrage logic across correlated markets. That's the interesting stuff. That's what gets written up in notebooks and shared in Discord servers.
It's also not where bots actually lose money.
After digging through analysis of tens of millions of executions across hundreds of automated market participants, the pattern is consistent enough to be almost boring: the bots that leak P&L aren't the ones with bad models. They're the ones with bad pipes.
What a "Simple" Prediction Market Bot Actually Ingests
A naive mental model of a Polymarket bot is something like: poll the market, estimate probability, place a bet if the price is wrong, repeat. That's roughly how the first generation of these bots worked, and it's roughly why they were mediocre.
A more realistic implementation is simultaneously tracking:
- The raw CLOB order book for the target market (bid/ask depth, not just mid)
- Price feeds for underlying assets when the market is correlated to something tradeable (think any market touching crypto prices, election polling aggregators, or weather APIs)
- Time-to-expiry curves, because a market at 80 cents with 6 hours left behaves completely differently than the same market with 3 weeks left
- Inventory across correlated markets to avoid accidental concentration
- Cross-market liquidity conditions, because your exit matters as much as your entry
Open-source implementations that have gotten serious about this problem (the ones using ensemble probability estimation across multiple LLM providers and vector search for historical market context) share a common architecture pattern: the trading logic is almost embarrassingly simple once you unwrap it. The complexity is entirely in keeping all those feeds synchronized and fresh.
Here's a simplified version of what that signal aggregation layer tends to look like:
class MarketSignalAggregator:
def __init__(self):
self.feeds = {
"orderbook": OrderBookFeed(),
"underlying": UnderlyingAssetFeed(),
"expiry": ExpiryCalcFeed(),
"inventory": InventoryStateFeed(),
}
self.last_updated = {}
def get_coherent_snapshot(self) -> dict | None:
snapshots = {}
now = time.monotonic()
for name, feed in self.feeds.items():
snapshot = feed.latest()
age = now - snapshot.timestamp
# Stale feed = poisoned decision
if age > MAX_FEED_AGE_SECONDS:
return None
snapshots[name] = snapshot
return snapshots
The return None on a stale feed is the important part. A bot that makes decisions on a 4-second-old order book snapshot in a fast-moving market isn't being cautious. It's guessing. The whole point of automation is that you're supposed to be faster and more disciplined than a human, not slower and less disciplined in a way that's harder to notice.
Why Latency Compounds
The edge in automated prediction market trading is repricing speed. When new information hits (a poll drops, a block is mined, a regulatory announcement lands), the market adjusts. Bots that see the update first and reprice accordingly capture the spread. Bots that see it second provide liquidity to the bots that saw it first.
This is not a subtle effect. It's the entire game.
What makes it tricky is that "seeing it first" isn't just about having a fast connection to the market. It's about having coherent, synchronized views across all your signal sources simultaneously. A bot that gets the underlying asset price update 200ms before it gets the corresponding order book update is not 200ms ahead. It's momentarily making decisions based on an inconsistent world state, which is often worse than making no decision at all.
This is the infrastructure problem that doesn't show up in backtests. Historical data is always coherent by the time you analyze it. Gaps have been filled, timestamps have been aligned, stale ticks have been filtered. Live data is messier. Feeds go quiet. WebSocket connections drop and reconnect with a gap. One source publishes an update 80ms before the correlated source catches up.
The bots that handle this gracefully (by pausing, widening spreads, or reducing size during periods of feed incoherence) tend to survive. The ones that don't notice have a habit of looking fine in aggregate stats right up until a volatile 10-minute window quietly destroys a week of gains.
The Infrastructure Is the Strategy
There's a tendency to treat data infrastructure as a solved problem, something you bolt on after you figure out the "real" strategy. For prediction market automation specifically, that framing is backwards.
The strategy is the infrastructure. Knowing when to trust your signal composite, how to detect and recover from feed degradation, how to maintain consistent latency across heterogeneous data sources including REST APIs, WebSockets, and on-chain event streams is what separates bots that are net profitable from bots that are scientifically interesting.
This is the class of problem that companies like Turboline are built around: coherent, low-latency ingestion of multiple live data streams simultaneously, which sounds like a commodity feature until you've spent a week debugging why your bot made a confident trade on a 3-second-old snapshot.
The trading logic is almost always the easy part. It fits in a notebook and makes for a good README. The hard part is the plumbing that ensures the trading logic never runs on a lie.
Build the pipes first. Model accuracy on stale data is just confidence in the wrong answer.
