Reducing Latency in a Prediction-Market Trading Bot

Latency is an interval with a defined start and end. A feed receipt delay, a callback duration and an order acknowledgement measure different parts of a system. Optimizing an unlabeled average can make a graph smaller without improving the experiment or revealing what actually caused a slow observation.

Map the path you can observe

List the stages: source event, network receipt, parsing, queueing, decision calculation and any request/response. Some source times are unavailable or come from a different clock. Keep that missing information explicit. Local elapsed-time measurements should use a monotonic clock; wall-clock UTC timestamps are useful for records but do not guarantee synchronized machines.

The helper below measures an awaited local operation. It does not estimate exchange event time, network transit or real order acknowledgement. The example performs only an in-process coroutine, so its result demonstrates the measurement path rather than a venue benchmark.

import asyncio
from time import perf_counter_ns

async def measure_local(awaitable):
    start = perf_counter_ns()
    result = await awaitable
    return result, (perf_counter_ns() - start) / 1_000_000

async def local_example():
    await asyncio.sleep(0)
    return "observation parsed"

async def demonstrate():
    result, elapsed_ms = await measure_local(local_example())
    assert result == "observation parsed" and elapsed_ms >= 0
    return elapsed_ms

Record distributions and failure counts under a stated workload, not just successful averages. Changes in instrument activity, payload size or queue pressure can make two runs incomparable. A timeout belongs in the result rather than being discarded because no duration was returned normally.

Examine polling and streaming assumptions

Polling cadence affects observation opportunities, but the familiar half-an-interval waiting estimate requires assumptions about event timing and the polling process. Network delay, request duration, caching and failures also matter. A shorter interval can increase load and rate-limit failures without producing a correspondingly fresher view.

WebSockets can deliver updates without repeatedly requesting a snapshot. They still have authentication, subscription, buffering, heartbeat and reconnect behavior. A connection can be open while the relevant state is stale or incomplete. Measure receipt gaps and rebuilding behavior instead of assuming the transport removes all observation delay.

Preserve request authentication

Kalshi's current documentation supports RSA-PSS and Ed25519 API key types. The client must support the chosen type and sign the correct timestamp, method and path. Its path convention excludes query parameters. Do not reuse a timestamp-specific signature indefinitely as a speed optimization or infer key type solely from a generic PEM header.

Loading an authorized key once into an appropriately controlled process is different from caching a completed request signature. Preserve the signing and clock requirements when optimizing. This article does not create, rotate or transmit any credential, and a public paper observation does not necessarily require a trading credential.

Optimize the demonstrated bottleneck

Only after measuring consider parser work, redundant requests, avoidable blocking operations or infrastructure placement. A closer region may change some network paths, but it is not a universal latency guarantee. A different protocol or server should justify its complexity with a comparable measurement and an actual experiment requirement.

Avoid doing unbounded computation or blocking I/O inside a callback. Bound queues and record what happens when work cannot keep up. A faster calculation using the wrong outcome, stale data or incomplete state is not an improvement in research validity.

Interpret Banger measurements honestly

The public Kalshi adapter has an authenticated ticker-stream path and a public REST fallback. The public Polymarket adapter streams market observations. A strategy's on_market_tick(market, tick, ctx) measures delivered callbacks, not every venue event or a reconstructed real order queue.

Banger's public product is paper research. The packaged simulator's fill assumptions do not provide a real order acknowledgement benchmark. Hypothetical delayed-decision scenarios can reveal dependence on an assumption, but they cannot validate microsecond or millisecond execution claims using coarse data. Preserve the environment, feed path, version and measurement boundaries so a later comparison answers the same question.