Test Prediction Market Strategies Before Real Money

Manual traders belong here too

You do not need to build a bot to use PredArena. Search live Kalshi and Polymarket markets, choose YES or NO, place an order, and then review fills, fee drag, P&L, open positions, and trade history. The same live-depth fill model used by the API powers dashboard trades.

The point is simple: learn the market, test your sizing, and build a shareable record before deciding whether a real-money account deserves the risk.

What makes agent trading different?

A human trader can pause when an order looks wrong. A prediction market agent often reacts automatically to market data, social signals, model outputs, or LLM reasoning chains. That speed is useful, but it also means bugs can compound quickly: a malformed request, an incorrect side selection, an infinite retry loop, or a rate-limit failure can turn into real losses.

The key question for builders is not only whether an agent has a good forecast. It is whether the full trading system can survive live execution conditions: thin liquidity, changing spreads, fees, quota limits, and noisy telemetry.

How PredArena validates prediction market agents

PredArena gives AI agents and quant bots a Kalshi-style order API. Your agent can read live market data, submit supported orders to PredArena, and receive realistic fills based on live order book depth. No real assets, fiat currency, or prediction contracts move through PredArena.

1. Create a portfolio in the PredArena dashboard.
2. Trade manually in the dashboard, or generate an API key for automation.
3. Point supported order requests to https://predarena.org/api/v2/portfolio/orders.
4. Submit market, limit, stop-loss, take-profit, or bracket orders with ticker, side, and contract count.
5. Review average price, total cost, venue fees, balance changes, and P&L.
6. Fix agent behavior before connecting live trading credentials.

Execution checks every agent should pass

Why live order book depth matters

Many trading results look strong because fills are assumed at the midpoint or at a single quote. Prediction markets can be much thinner than large liquid exchanges, especially around niche events. If an agent tries to buy more contracts than are available at the best price, real execution walks into worse levels or fails.

PredArena models that reality by walking live order book levels for supported orders. The result is a more credible estimate of whether a manual trader or automated agent can actually trade the intended size under current market conditions.

Who uses prediction market agent trading sandboxes?

PredArena is built for manual traders, developers working on autonomous AI trading agents, LLM-based market researchers, quantitative strategy scripts, tokenized AI agent projects, and teams that need a public or private record of prediction market performance. The common need is simple: validate the idea or system before live capital is at risk.

Current scope

PredArena supports dashboard trading, API-based trading, Kalshi and Polymarket market data, market and limit orders, stop-loss, take-profit, bracket orders, cancellation, CSV exports, portfolio analytics, watchlists, alerts, and signed webhooks. It does not provide investment advice, route real orders, or offer historical replay/backtesting.