Building a Prediction-Market Trading Bot with Claude Code

A Claude Code prediction-market trading bot needs more than generated strategy code: it needs secure credentials, market-rule validation, venue-specific order checks, persistent order state and failure recovery. Use Claude Code to help build and test those components, while keeping live execution behind explicit, deterministic controls.

If you are still comparing bot types, start with what to check before choosing a Polymarket trading bot. This guide goes deeper into implementation: what custom code must maintain, what reusable agent skills should contain, and when OddsFantasy’s no-code sports presets fit the task better.

What components does a reliable trading bot need?

Separate the strategy’s decision from the executor’s authority. A strategy should propose an action; a validator should decide whether that action is permitted; an executor should submit it and reconcile the result. Generated code is not evidence that these boundaries work.

• Market adapter: retrieves identifiers, rules, contract metadata and market data.
• Strategy engine: produces proposed entries or exits with explicit reasons.
• Risk validator: checks freshness, price, quantity, exposure and permitted trading windows.
• Execution adapter: authenticates requests and handles venue-specific submission and cancellation.
• State store and supervisor: persist intentions, orders and fills, reconcile discrepancies, and pause trading when required.

Keep venue adapters distinct. Gemini, Pascal and Polymarket documentation describe different systems; a safeguard documented for one is not automatically available on another. Treat the controls below as an engineering checklist, then confirm each endpoint, field and behavior against the venue you actually use.

How should you use Claude Code without giving it unrestricted trading authority?

According to the Claude Code CLI reference, Claude Code can run a prompt non-interactively with claude -p "query". A useful development request is: “Implement order validation against supplied contract metadata and write rejection tests. Do not connect to a live account.”

The CLI supports named permission modes, including plan and bypassPermissions, plus an API-call budget cap through --max-budget-usd in print mode. Choose permissions deliberately and review changes before deployment. The budget cap limits model API spending; it is not a trading stake cap, exposure limit or stop-loss.

What should reusable agent skills contain?

Design reusable skills as narrow development procedures, not broad instructions to “find profitable trades.” A skill can explain how to implement a venue adapter, validate an order or investigate a failed reconciliation. Keep live credentials outside its examples, and make its expected inputs, outputs and failure behavior explicit.

• Specify which venue and documentation the procedure applies to.
• Define accepted metadata, proposed-order fields and rejection reasons.
• Require tests for malformed inputs, partial fills and uncertain submission outcomes.
• Require human review for changes to signing, exposure limits or live-execution permissions.

How should credentials and request signing be handled?

Pascal’s documentation provides a Python quickstart with request-signing and order-placement examples, including a market-making script that can be adapted. Pascal also separates wallet custody and trading-key lifecycle from order placement and cancellation, which use trading keys. That distinction matters when designing access boundaries.

For a custom implementation, keep secrets out of prompts, repositories, fixtures and logs. Give the execution process only the access it needs, and document key rotation and revocation. Treat a quickstart as implementation scaffolding: review its assumptions rather than assuming the example contains your complete production security model.

Pascal signed requests include client_ts_ms and recv_window_ms for replay and staleness checks. Your adapter should construct these fields correctly and surface authentication failures. Monitor clock discrepancies and investigate rejected signatures instead of repeatedly resending a request whose timestamp or authorization may already be invalid.

Why must the bot validate market rules rather than titles?

The Polymarket Help Center states that markets resolve according to their market-page rules. The title describes the market; the rules determine resolution, including the resolution source, market end date and edge cases. Matching a title string is therefore not enough to authorize a trade.

• Bind the strategy to the exact market and outcome identifiers.
• Check the resolution source, end date and relevant edge-case language.
• For sports, explicitly review any treatment of postponements, abandonment or extra time rather than assuming it.
• Store the reviewed rules and pause new entries when a relevant change requires review.

Polymarket also says a clarification clears the order book and cancels resting orders; an advance announcement clears it at announcement time too. A bot should therefore reconcile orders after clarification rather than assuming its local “open” status remains correct. Do not automatically replace cancelled orders before reviewing the clarification.

What must be checked before an order is submitted?

The Gemini prediction-market trading documentation recommends WebSocket order placement for active trading and market making. It also requires checking terms-acceptance status before orders and validating quantity and price against instrument-specific increments and minimums. Those are execution requirements, not strategy signals.

Do not hard-code one quantity or price grid across instruments. Read the relevant quantityIncrement, quantityMinimum, priceIncrement and priceMinimum metadata. Reject invalid proposals, or apply an explicitly documented adjustment and revalidate exposure. Complete required terms acceptance through an authorized workflow rather than burying it in retry logic.

Order instructions must match your intent. Gemini documents good-til-cancel, immediate-or-cancel and fill-or-kill behavior, plus maker-only orders that cancel if they would immediately take liquidity. A signal appearing does not imply an order filled; cancellation can be the expected result of the instruction you selected.

How should the bot reserve exposure for unfilled orders?

Consider an illustrative football Under 2.5 goals buy order for 100 units at 52¢ per unit. Its limit-price commitment is $52 before fees. If 40 units fill at 52¢, the filled cost is $20.80, while the remaining 60 units still represent $31.20 of potential commitment.

For this fixed-price example, committed purchase amount = filled cost + remaining quantity × limit price. The calculation is $20.80 + 60 × $0.52 = $52. This is an exposure-accounting example, not live market data or a forecast of returns.

A validator that counts only filled positions could authorize another entry while the first order remains live. Reserve capacity for outstanding orders, account for applicable fees separately, and release that reservation only after the order’s remaining quantity is conclusively filled, cancelled or otherwise terminated.

How should order state survive disconnects and restarts?

Persist the proposed action before submission, then link it to the venue’s order identifier when available. Track cumulative fills and remaining quantity separately from status. A useful local state model distinguishes submitted, acknowledged, partially filled, cancel-pending and terminal orders, while preserving an explicit unknown state when evidence is incomplete.

1. Record a local action identifier, market, outcome, side, quantity, price and validation result.
2. Submit through the venue adapter and retain the acknowledgement or error.
3. Apply fill and status updates without counting the same event twice.
4. Reconcile against venue orders and positions after interruption.
5. Resume new entries only when unexplained differences are resolved.

Cancellation is a request, not proof that no further fill occurred. Likewise, an exit signal is not a closed position. Serialize competing exit actions and check remaining exposure before submitting another close. Where supported, reduce-only instructions can help: Pascal documents orders that reduce exposure without increasing it.

What should happen when execution fails?

Classify failures before retrying. A timeout can leave submission outcome uncertain; blindly submitting again risks duplicate exposure. Prefer venue-supported request deduplication where available, but do not assume it exists. Otherwise reconcile using the available order records before deciding whether another submission is justified.

• Rate limiting: Pascal requires handling HTTP 429 responses. Back off and avoid an immediate retry loop.
• Stale data or disconnected feed: pause new entries until freshness and state are restored.
• Validation rejection: correct the underlying input rather than resending the same invalid order.
• Uncertain acknowledgement: preserve the unknown state and reconcile before replacing the order.
• Process restart: restore persisted intentions, orders and fills before evaluating new signals.

Which tests matter before enabling live trading?

Test failures, not just the successful submission path. Start with deterministic fixtures and a local simulated venue adapter. A small live stake can limit the amount exposed during a test, but it does not prove correctness or eliminate risk. Keep a manual pause procedure independent of strategy decisions.

1. Reject off-grid prices, undersized quantities and stale market data.
2. Simulate partial fills followed by cancellation and a late fill update.
3. Simulate a submission timeout followed by discovery of an accepted order.
4. Restart during an open order and verify that exposure is reconstructed.
5. Trigger two exit conditions together and verify that they do not create duplicate closes.

When are OddsFantasy’s no-code sports presets a better fit?

If your objective is rule-based sports entry and exit automation rather than a custom execution system, OddsFantasy’s trading terminal and bot lets you create strategies without writing code. Entry presets combine odds, spread, traded volume, order-book liquidity and bullish, bearish or neutral chart market structure with a stake.

Presets use generic markets such as Money Line and Total, with outcomes including home, draw, away, over and under, so one preset can run across many matches. Before running, OddsFantasy previews which selected matches pass its rules. Starter presets include Football Under 2.5 and Money Line Home Favorite.

A preset can run immediately or on a schedule. Schedules start and stop at configured offsets from kickoff, re-check rules at a refresh interval, and can optionally continue entering after kickoff. This replaces writing that particular workflow yourself; it does not establish that the chosen conditions have a profitable edge.

Exit presets automatically manage positions opened by an entry preset. They can combine a maximum-drawdown market close, pre-match and live limit closes at target profit percentages, pre-match and live time closes, and a live momentum market close. See managing positions in OddsFantasy for the position-management workflow.

The drawdown close sells only while the bid-ask spread is at or below its configured maximum. The momentum close measures executable bid prices over a rolling five-second window. OddsFantasy claims each exit before execution to avoid closing the same position twice. A spread safeguard can also leave a position open when the spread is too wide.

The free plan includes one entry preset, one exit preset, 20 selected matches, three markets per preset and one running monitor of each type. Classic and Premium raise these limits; compare OddsFantasy plans with the number of workflows you intend to run.

Should you maintain custom code or use a preset?

Choose custom code when your requirements justify owning adapters, signing, state persistence, monitoring and recovery. Choose presets when their sports entry and exit rules cover your workflow. In either case, automation executes your assumptions; it does not make those assumptions correct or guarantee fills, wins or returns.