How a Prediction Market Trading Bot Operates
A detailed look at data ingestion, signal creation, risk limits, order flow and error handling for autonomous prediction market trading bots.
Produced with automation, then checked by deterministic quality rules and an independent source-grounded review before publication.
- 01The bot continuously ingests market data with source and timestamp metadata.
- 02It transforms data into probabilistic signals that guide order decisions.
- 03Risk controls are enforced by owner‑signed limits on order size, notional, and loss.
- 04Order execution follows a durable mutation pattern that tracks success and failure explicitly.
- 05Unexpected errors or timeouts require reconciliation rather than assuming order cancellation.
A prediction market trading bot operates by constantly receiving market data, converting that data into probabilistic signals, and then placing orders that respect a set of owner defined risk limits. Each step of the workflow is recorded with a durable mutation identifier that captures the intent of the order and the eventual outcome. This approach ensures that a timeout does not automatically imply a failed trade, because the bot will later query the market interface to reconcile the true status. For related context, see Understanding Cross‑Venue Trading and Execution Fragmentation.
What data does the bot need to operate?
The bot requires real time market data that includes three essential pieces of information: the source of the quote, a precise timestamp, and any freshness warnings supplied by the data provider. The source field allows the bot to prefer data from venues with higher reliability, while the timestamp lets the bot reject quotes that are older than a configurable freshness window. If a warning indicates that the data may be delayed or incomplete, the bot must flag the quote and exclude it from signal calculations. Missing or unverified data must never be treated as zero because that would create artificial price signals and could lead to unintended exposure.
How are trading signals generated?
Signal generation typically relies on statistical or machine learning models that estimate the probability of each possible outcome in a prediction market contract. The model consumes the cleaned market data and produces a probability distribution. The bot then compares the model probability to a predefined threshold that reflects the owner’s view of value. If the probability exceeds the threshold, the bot creates a buy signal; if it falls below a lower threshold, a sell signal is generated. Because models are imperfect, the bot adds an uncertainty buffer to the threshold and avoids placing multiple orders based on a single marginal signal. This reduces the risk of over‑trading on noisy predictions.
How does the bot enforce risk controls?
- The owner can set a daily notional ceiling to cap total exposure.
- A per order size limit prevents unusually large positions from being opened.
- Daily loss limits stop the bot from continuing after a predefined loss amount.
- Expiry dates on limits ensure they are reviewed and refreshed regularly.
What steps does the bot follow to place an order?
- The bot reads the latest market snapshot, confirming source and timestamp.
- It runs the signal model and checks the result against all risk limits.
- If the order passes all checks, the bot creates a durable mutation identifier and signs the order with its agent key.
- The signed order is sent to the market interface, which returns a status and a confirmation identifier.
- The bot records the outcome; if a timeout occurs, it queries the status to reconcile the actual result.
How does the bot manage position sizing and capital allocation?
Position sizing is driven by owner signed limits that define maximum order size, maximum daily notional, and maximum daily loss. The bot calculates the available capital for each trade by subtracting the sum of open positions from the daily notional ceiling. If the calculation shows that a new trade would exceed any limit, the bot rejects the signal and logs the reason. This deterministic approach ensures that the bot never exceeds the risk profile defined by the owner, even when market volatility spikes.
What monitoring and logging are required for safe operation?
Continuous monitoring is essential because autonomous agents operate without human intervention. The bot must emit structured logs for every market snapshot, signal evaluation, risk check, order submission, and status reconciliation. Logs should include the durable mutation identifier, timestamps, and any error codes returned by the market interface. A separate monitoring service can watch these logs for patterns such as repeated timeouts, limit breaches, or unexpected status codes and trigger an emergency stop by revoking the agent key. This layered safety net helps the owner intervene before capital is at risk.
Frequently asked questions
No. Trading can lose all allocated capital, and the bot’s performance depends on model accuracy, market conditions, and risk controls.
The owner key holds full authority, including withdrawals. The agent key is scoped for order placement only and respects the limits set by the owner.
Missing or unverified data is flagged and excluded from signal calculations; it is never treated as a zero price.
No. The emergency stop revokes the agent key to stop new activity, but existing positions must be reviewed and closed by the owner.
See the article [How to Build an AI Trading Bot with Robust Risk Controls](/blog/build-ai-trading-bot-risk-controls) for detailed guidance.
The post [What Is an Autonomous Trading Agent?](/blog/what-is-an-autonomous-trading-agent) provides a useful overview.
Sources and verification
Product claims in this article were checked against these first-party references. Runtime status remains authoritative for current availability.
- Felix documentationfirst party
- Felix machine referencefirst party
Build with Felix now.
Felix infrastructure is live through MCP and the API. The full trading app launches September 17.
Cross‑venue trading spreads orders across many markets, creating fragmented execution. This article explains the mechanics, challenges, and how traders can manage the uncertainty.
Algorithmic trade execution uses software to send orders based on predefined rules, balancing speed, cost, and market impact. This article explains the process, controls, and risks.