How to Automate Prediction-Market Trading

Automation makes a specified procedure repeatable; it does not repair a bad hypothesis or prove that all necessary operations are handled. Start with a small paper-research question, delivered data and an inspectable version. This guide separates data, decisions, execution modeling and constraints before interpreting a result.

The four pieces of any automated strategy

The following four areas are a useful starting framework, not an exhaustive specification or a guarantee that a system works when each has a label:

• Data: exact instrument, quote/quantity units, history, timestamps and missing observations.
• Decision: a defined rule with permitted inputs and explicit intent.
• Execution model: admission, request state, fill assumptions and later reconciliation.
• Constraints: exposure, activity and resource limits with demonstrated scope and enforcement.

Keep the evidence for each area distinct. A correct feature calculation is not an accepted order; a simulated fill is not real execution; a passing fixture does not establish a production journey.

Data: read the book before you touch orders

For an ordinary $1/$0 binary contract, a dollar quote gives a price-implied break-even convention before costs. It does not reveal true probability. Read the actual payout; scalar or exceptional instruments need a different model.

Use each venue’s current documented endpoints and protocol. Public reads, streaming subscriptions and authenticated operations can have different prerequisites. Preserve snapshots/deltas, sequence and timestamps rather than assuming every connected socket delivers complete timely data.

Kalshi documents native market data and order interfaces, but rate limits and capabilities are account/product specific. Do not declare an entire strategy-frequency class impossible from an unspecified limit. Choose a bounded observation procedure and respect the applicable constraints.

Polymarket documents a prices-history endpoint. That returned timestamp/price series is not a complete historical order book or trade-count archive, but the earlier claim that no built-in history exists is false. Inspect the actual fields, requested window and returned coverage before choosing a data source.

Signal: keep the first one boring

Start with a calculation or observer whose expected behavior you can exercise offline. Cross-market relationships, mean reversion and news response are research hypotheses, not automatic legible or profitable entry rules. Preserve why the proposed input relates to the exact outcome.

Payout rules and executable-price conventions shape the model. A midpoint differs from an acquisition ask or reducing bid. Limit orders may execute immediately or remain unfilled under the specific rules; order form alone does not establish role, full quantity or fee treatment.

Execution: the gap between intent and fill

Trace intent, admission, request, acceptance, partial execution and terminal state separately. A requested marketable price is not a promise to sweep every available level or complete a quantity regardless of timing, status and order settings.

Concrete execution rules to build in from day one:

• Keep the exact action, side, price/quantity units and intended instrument with a request.
• Represent partial quantities and uncertain terminal states explicitly.
• Use the actual venue’s current authentication and idempotency contract when an authenticated path is required.
• Do not infer real execution or account eligibility from a public-data or paper fixture.

Constraints need a defined scope

Specify whether a constraint applies to an order, cumulative position, strategy context, account or shared infrastructure. A declared field needs tested enforcement. A loss-admission rule is distinct from cancellation, liquidation and stopping a worker.

A fully paid ordinary binary long has a bounded stake loss under that instrument model. Do not generalize that statement to every product or financed position, and do not call a price stop a guaranteed exit. Preserve costs and correlated-outcome exposure.

Build vs buy

Compare the actual capabilities and maintenance needed by the experiment. There is no universal few-hundred-line estimate for a complete integration. Data coverage, lifecycle, precision, recovery and capacity can each matter; a framework does not remove the need to inspect its model.

Banger’s public beta is paper-only. It provides a Strategy interface, versioned research and supported paper workflows. The actual 0.1.7 callback and RiskEnvelope fields differ from generic per_trade_cap/daily_loss_stop/kill_switch descriptions. Complete CLI account setup and use an existing strategy.py for a bounded local run:

banger run strategy.py --paper
# Omitting --paper still defaults to paper; explicit --live is blocked.

Choose the smallest procedure that answers the research question and record its model and limitations. A purpose-built runtime can supply useful components without guaranteeing exact current fees, depth replay, real fills or continuous uptime.

A sane rollout order

Inspect source and inputs, exercise fixtures, define a short supervised paper window, then verify delivered data, any simulated execution, history and completed shutdown. Banger’s forward-paper assumed passive fills and fee policy need their own interpretation. This sequence does not enable or authorize public live trading.

One note on where you can trade

Verify the exact product and current access separately from data availability. International and US venues with similar branding are not the same regulated entity or contract set. This implementation guide makes no nationwide eligibility determination and does not recommend funding or real order routing.