Reducing Latency in a Prediction-Market Trading Bot

Latency is the time between an event happening in the market and your bot acting on it. In a prediction-market bot on Polymarket or Kalshi, that number is the sum of several independent delays, and most people optimize the wrong one. Before you rent a box in a data center, figure out where your milliseconds actually go.

The latency budget: where the time goes

Break a single decision-to-fill cycle into stages. Each is a separate problem with a separate fix.

• Data staleness: how old the price is by the time your code sees it. This is dominated by polling interval, not network speed.
• Network transit: round-trip time between your host and the venue. Fixed by geography and routing.
• Compute: how long your strategy takes to decide once it has fresh data.
• Order round-trip: the time to sign, send, and get an acknowledgement for an order, including auth overhead.
• Venue-side processing: matching-engine time you do not control.

The single biggest, cheapest win for most bots is the first one, and it has nothing to do with your server hardware.

Stop polling. Use the websocket feed.

If your bot fetches the order book on a timer, your data is on average half your poll interval old, and you are burning rate limit doing it. Both venues offer streaming instead. On Polymarket, the CLOB exposes a market channel over websocket that pushes book updates, price changes, and trades as they happen. You subscribe with the tradable outcome token IDs (the CLOB token IDs), not the market or condition ID, which is the most common setup mistake.

Kalshi follows the same pattern. Its websocket API delivers order book changes, trade executions, and fills. You subscribe to channels like orderbook_delta and trade per market ticker. The key detail is delta: you get a full snapshot once, then incremental updates on top of it.

That leads to the second polling fix. Maintain a local order book and apply deltas to it, rather than re-fetching the whole book on every tick. Rebuilding state locally is both faster and lighter than repeatedly pulling full snapshots.

Streaming also sidesteps REST rate limits. Websocket connections for live data are not subject to the same per-second caps that apply to REST polling endpoints. If you are polling hard enough to worry about latency, you are likely close to that ceiling anyway.

Keep the connection alive and authenticated

A dropped socket that silently stops delivering messages is worse than slow polling, because your book goes stale without an error. Two habits prevent it.

• Send heartbeats on schedule. Polymarket's docs specify that clients should ping the market and user channels periodically, and the server may close connections that do not subscribe or ping in time. Check the current docs for the required interval, as it can change.
• Reconnect with exponential backoff and re-request a fresh snapshot on reconnect, since you will have missed deltas while disconnected. On Kalshi, the orderbook_delta channel uses sequence numbers; after a reconnect, request a fresh snapshot and resume from there rather than trying to replay missed deltas.

On the order path, avoid paying auth cost per request. Kalshi signs requests with an RSA-PSS signature over the timestamp, method, and path. The timestamp must be Unix time in milliseconds, not seconds, or the signature fails. Sign the path without query parameters. Cache and reuse what you can so signing is not on your hot path more than it must be.

Cut the order round-trip

Once data is fresh, the order round-trip is usually your next-largest controllable cost. Practical moves, in rough order of payoff:

• Reuse persistent connections instead of opening a new TLS session per order. Handshake cost adds up.
• Pre-compute and pre-sign as much of the order as you can before the trigger fires.
• Move your host closer to the venue. Deploying near the exchange's servers removes transit you cannot otherwise recover.
• For serious order-entry throughput, Kalshi supports the FIX protocol for institutional and high-frequency operations. It is more work to set up than REST or websocket, but it is the lowest-latency order-entry path the venue offers. Contact Kalshi directly to inquire about access.

When latency actually matters, and when it does not

Not every strategy needs any of this. Latency matters when your edge is contested by other fast actors: cross-venue arbitrage, market making where you must pull quotes before you get run over, and reacting to a known event (a sports goal, an economic print) where everyone is racing to the same price. There, being late means being filled at the wrong price or not at all.

Latency does not matter much for slow-moving theses. If you are taking a position on a market that resolves in weeks and your edge is a probability estimate, shaving 50 milliseconds off your order path changes nothing. Poll on a sane interval, size correctly, and spend your effort on the model instead. Chasing microseconds on a multi-day horizon is wasted engineering.

A useful test: if you replayed the last month and added 200 milliseconds of delay to every fill, would your P&L meaningfully change? If not, latency is not your bottleneck. Measure before you optimize.

Measure it, then prove the change

Instrument each stage separately. Timestamp when a message leaves the venue (if the feed carries a timestamp), when you receive it, when you decide, and when you get the fill ack. Log the deltas. You cannot fix what you have not attributed to a stage.

This is where paper trading against the live book earns its keep. Banger runs a strategy against the real order feed without sending orders, so you can watch how your logic behaves on live data, including how stale your view gets under load, before a cent is at risk. A strategy in Banger is a Python class you run with 'banger run strategy.py --paper', wrapped in a declarative risk envelope: per-trade cap, daily loss stop, max open positions, and a kill switch. Banger never holds funds; you connect your own venue keys.

pip install bangertrades
banger run strategy.py --paper   # live book, no orders sent

The order of operations that works: switch from polling to a streaming feed, maintain the book locally with deltas, keep the connection healthy, then trim the order round-trip. Verify each change against a measured baseline. Only reach for colocation and FIX once you have proven the cheaper fixes are not enough. As of July 2026, venue APIs, rate limits, and endpoints change often, so re-check the current docs before you build against a specific number.