Prediction Market Algorithm: From Estimate to Order

Define the Prediction Market Algorithm's Target

The algorithm begins with the venue, contract identifier, outcome selection and exact settlement condition. A model estimating a broad event is insufficient if the contract uses a particular source, threshold or revision policy. The probability must refer to the actual instrument being considered.

Write the target in a form that can be evaluated later. For the hypothetical example, assume the named first release must exceed a specified threshold and the simple payout is explicitly $1 or $0. Any exceptional cases are excluded by the example's definition, not assumed absent from real contracts.

The older guide to prediction market trading algorithm provides a broader design framework. A useful algorithm cannot repair an undefined target through sophisticated sizing. Contract meaning is a prerequisite because it determines what the forecast and payout calculations are about.

Preserve the Forecast and Its Evidence Time

Assume the model provides a central Yes estimate of 0.64. Record its issue time, source evidence, model version and evaluation scope. The number should be a probability estimate for the defined target, not a ranking score or internal confidence value relabeled as probability.

The estimate remains uncertain. For this teaching example, the analyst also examines a lower scenario of 0.58 to see how the proposal behaves under a less favorable assumption. That scenario is not a statistically established confidence bound unless a separate method supports such an interpretation.

Preserve the forecast before looking at later outcomes. A historical replay must use information available at the decision time, including missing inputs and delayed publications. The older guide to prediction market algorithmic trading explains why later corrections should not silently improve the apparent knowledge of the earlier algorithm.

Define an Algorithm Abstention Condition

An algorithm abstention condition states when no proposal should be produced. Relevant cases include unresolved contract mapping, expired forecast evidence, incomplete market data or an account state that cannot be reconciled. Each condition should produce a readable reason rather than a numeric output that looks like a decision.

Abstention is not a forecast of zero probability. It means the decision process lacks a required input or falls outside its authorized scope. Keep the last valid forecast as historical evidence if useful, but do not pass it forward as current simply because no replacement is available.

The algorithm should also distinguish research eligibility from trading eligibility. A contract can remain interesting to study while current account or market conditions prevent action. This separation helps reviewers understand whether the system rejected the idea, deferred it or lacked the evidence needed to decide.

Read the Relevant Market Side and Quantity

A proposal to buy Yes needs current offers and available quantities under the venue's actual representation. A displayed midpoint or last trade is not enough. The algorithm should preserve the book timestamp and estimate the cost for the intended size rather than assume every unit can trade at the best level.

In the hypothetical book, eight contracts are offered at $0.50 and ten more at $0.52. Those 18 contracts are the available quantity within the proposed price bound at the observed moment. Other offers, if any, lie outside the scenario's allowed price and are excluded.

The guide to event contract trading explains the underlying cost and payout mechanics. The algorithm uses those mechanics explicitly. A quote is an input to a proposal, while actual fills later establish the acquired quantity and cost. The two should remain separate in the record.

Include Costs Under a Named Assumption

For teaching, assume an invented fee of $0.01 per acquired contract and no other charges in the simplified scenario. At the highest allowed purchase price of $0.52, the maximum opening cost per contract is therefore $0.53. This is a modeling assumption, not a current fee schedule for any venue.

Actual charges must be checked under the relevant product and market conditions. Kalshi's fee guidance notes that fees can differ among markets. An algorithm should use current applicable rules and preserve the version or source used for its cost estimate.

If costs cannot be estimated with sufficient support for the strategy, the proposal should remain unavailable. A small apparent advantage can disappear under a modest fee or execution difference. The algorithm should not treat unknown costs as zero merely because doing so allows the calculation to proceed.

Calculate the Per-Contract Scenario

Under the simple $1-or-$0 payoff, a 0.64 personal probability implies an expected payout of $0.64 per contract according to that estimate. Against the maximum modeled cost of $0.53, the expected difference is $0.11. Under the lower 0.58 scenario, it is $0.05.

These are expectations under assumptions, not guaranteed gains. A contract can settle No and lose its opening cost in the example. The forecast may also be wrong, and the execution conditions may change. The calculation's purpose is to expose the relationship among probability, payout and cost.

A decision policy can require a margin appropriate to its own evidence and design. This article does not prescribe one. The important requirement is that the policy be written before inspecting outcomes and that uncertainty in the forecast or costs not be hidden behind a precise-looking expected-value figure.

Apply Cash Capacity Before Choosing Size

Assume the hypothetical workflow permits at most $13.25 of new opening cost for this proposal. Using the conservative per-contract cost of $0.53, that budget supports at most 25 contracts because 25 multiplied by $0.53 equals $13.25. Quantity must respect the actual venue's supported increments.

This is a capacity calculation, not a recommended budget or sizing rule. The algorithm should use the actual authorized amount and account state. Funds already committed elsewhere cannot be counted twice, and an unknown order status can make apparent available capacity unreliable.

Preserve the budget source and its time. If the account changes before authorization, recalculate or invalidate the proposal under the defined policy. A forecast remaining unchanged does not mean the account still has the same ability to act. Market analysis and resource authorization are different layers.

Apply Existing Exposure and Open Commitments

Now assume the workflow has a hypothetical cap of 40 Yes contracts exposed to the relevant event grouping. It already holds 20 and has two more in another open commitment. The remaining capacity is 18 contracts: 40 minus 20 minus two. This constraint is tighter than the cash capacity of 25.

The event grouping must have a defined meaning. Several contracts can share a risk driver even if their names differ. A real system may use a more suitable risk measure than contract count, especially when payouts or structures differ. The simple count limit here is chosen only to make the arithmetic visible.

The guide to prediction market algorithms compares methods and their assumptions. This example shows why a favorable standalone forecast cannot determine size alone. Existing exposure and commitments can bound the proposal independently of the model's confidence.

Use the Smallest Supported Capacity

The hypothetical limits are now 25 contracts from cash, 18 from the event cap and 18 from the observed book within the price bound. The bounded proposal is therefore at most 18 contracts. A different policy might impose another smaller limit, and a missing prerequisite would produce no proposal at all.

The maximum reserved opening cost under the conservative bound is 18 multiplied by $0.53, or $9.54. The observed book suggests a lower pre-fee cost: eight at $0.50 cost $4, and ten at $0.52 cost $5.20. The total is $9.20 before the invented $0.18 fee, or $9.38 all in.

Keep the maximum bound and observed estimate separate. The former supports authorization under the stated limit, while the latter describes the current book scenario. Neither is an actual fill. The distinction prevents a later execution difference from being mistaken for a broken accounting identity.

Check the Aggregate Expected Scenario Without Overclaiming

At the central 0.64 estimate, 18 simple $1 contracts have a modeled expected payout of $11.52. Subtracting the observed all-in estimate of $9.38 gives an expected difference of $2.14. Under the lower 0.58 scenario, expected payout is $10.44 and the difference is $1.06.

The possible realized payout in the simple example remains either $18 or zero if all 18 are held to resolution. The expectation is an average implied by a probability model, not a third settlement outcome. This distinction is essential when presenting calculations to readers who might mistake expected value for promised cash.

Sensitivity review should include assumptions beyond the probability. Costs can change, rules can be misunderstood and only part of the proposal may fill. The algorithm should preserve these possibilities and avoid presenting the aggregate expectation as a complete measure of every relevant risk.

Authorize a Specific Proposal Version

Authorization should apply to a defined contract, side, quantity, price bound and evidence version. If those inputs change materially, the system needs a policy for refreshing or rejecting the proposal. An earlier approval should not become unlimited permission to act under any later market conditions.

The guide to prediction market automation software develops permissions and exception handling. A forecast-producing component can remain separate from the component that checks account authority. That separation makes it easier to identify which decision permitted the external action.

Record an expiration condition. A proposal can become stale because of elapsed time, a changed book, a contract-state transition or new account evidence. Expiration should not erase the research record; it means the specific action is no longer supported under the conditions that originally authorized it.

Distinguish Submission From Completion

Submitting the authorized proposal creates an attempt, not necessarily a completed position. Preserve the attempt reference and the actual venue response. The system should distinguish accepted, open, partially filled, canceled, rejected and unknown states according to the current implementation rather than forcing them into a single success flag.

If the response is unknown, reconcile the existing attempt before creating another. A fresh order can duplicate exposure if the original already filled or remains live. A repeated forecast or a repeated notification is not new authorization to ignore that uncertainty.

The older guide to prediction market bot API explains why these boundaries matter. An algorithm should produce an inspectable chain from forecast to authorized proposal to attempt. It should not treat every retry as a new economic decision merely because the software function can be called again.

Recalculate After a Partial Fill

Suppose the hypothetical order fills eight contracts at $0.50 and four at $0.52. The pre-fee cost is $4 plus $2.08, or $6.08. With the invented $0.01 fee on each of 12 acquired contracts, total opening cost is $6.20. Six of the proposed 18 remain unfilled.

The account now holds 32 contracts in the simplified event grouping: the original 20 plus the new 12. It also has two contracts in the earlier separate commitment and six remaining from this proposal. Together they still total the 40-contract cap if all open quantities fill.

The algorithm must decide what happens to the remaining six under its current policy. It cannot reason as though all 18 were acquired, nor can it ignore the remaining commitment when considering a new proposal. Actual fills and live requests must both be reflected in the capacity calculation.

Reconcile Costs and Position State

After the order reaches a known state, compare the fill record, fees, position quantity and available capacity. Use actual charged amounts rather than the hypothetical preview when reviewing a real account. Preserve any discrepancy and its source instead of adjusting numbers silently until the totals look plausible.

If the remaining quantity is canceled, wait for the relevant confirmed state before releasing its commitment in the internal ledger. A cancellation request can race with fills under actual venue behavior. The final quantity must come from the reconciled evidence.

The algorithm's decision record should retain both the forecast scenario and the realized account path. This makes later evaluation possible. A useful forecast can encounter poor execution, and a weak forecast can produce a favorable isolated outcome. The record should allow those cases to be distinguished rather than reduced to one profit number.

Test the Forecast to Order Calculation

A forecast to order calculation should be checked with cases that exercise real boundaries. Use an expired forecast, an ambiguous contract, a stale book, insufficient cash capacity and an unknown prior attempt. Each should produce the defined abstention or reduced-size behavior before any submission is considered.

Test the arithmetic with the hypothetical values in this guide. Confirm the 18-contract cap, the $9.54 conservative bound, the $9.38 book-based estimate and the $6.20 cost of the 12-contract partial fill. These checks verify meaningful relationships rather than mirroring arbitrary implementation details.

Also test a case in which the correct output is no action despite a high forecast. For example, a zero remaining exposure capacity should block the proposal. This demonstrates that the algorithm respects independent constraints instead of allowing confidence to override the account policy.

Examine Which Proposals Actually Receive Fills

A forecast evaluated across all proposed trades can behave differently on the subset that actually fills. A resting purchase order may be more likely to execute after information makes the contract less attractive to other participants. The algorithm should therefore examine the conditions around fills rather than assume that completed orders are a random sample of its proposals.

In a historical study, a price touching the limit does not by itself establish that the algorithm's full quantity would have filled. Available size, timing and the venue's actual matching rules matter. Use a supported execution model and identify its limitations. An unrealistically generous fill assumption can make a careful forecast-to-order calculation look more effective than a real workflow could be.

Preserve unfilled and canceled proposals as well as completed ones. Compare the evidence state at authorization with the state near actual fills where the data allows. If the forecast expires while an order remains open, the policy needs to specify whether that commitment should be reviewed or withdrawn. The original favorable estimate should not remain a permanent justification for any later fill.

This review connects forecasting and execution without collapsing them. The model may remain well calibrated on its intended target while the order policy selects an unfavorable subset of moments. Conversely, execution may be accurate while the forecast is weak. A useful record lets the reviewer identify which assumption needs revision instead of attributing every result to the probability model alone.

A Prediction Market Algorithm Should Expose Every Assumption

The completed algorithm can explain why it considered the contract, which probability estimate it used, how costs were modeled and which limit determined the final size. It can also explain why a proposal expired or why an attempt remains unresolved. Those explanations are part of the design, not optional commentary added afterward.

The hypothetical example produced at most 18 contracts because cash, exposure and depth were evaluated separately. Its expected-value calculations depended on an uncertain forecast and explicit costs. Its actual position depended on fills. Keeping those distinctions visible prevents a neat formula from being mistaken for a guaranteed trading result.

Build the process as a chain of supported decisions with clear stopping conditions. Preserve the evidence and reconcile what actually happens. That creates an algorithm whose behavior can be tested, reviewed and revised without hiding its assumptions inside one opaque score or treating an uncertain submission as permission to start again.