Prediction Market Strategies: A Venue-Neutral Research and Risk Framework

The core of most prediction market strategies is simple: estimate the probability of the exact contract outcome, compare that estimate with the all-in executable price, and participate only when the difference is large enough to justify uncertainty and risk.

Good research can still produce a bad trade when price already reflects it, costs consume the edge, or the contract resolves differently from the story in your head. A correct outcome does not prove the process was sound: buying a 20% event at an all-in cost equivalent to 40% is poor value even when it occurs.

This article is venue-neutral. Contract design, fees, matching, order types, resolution, and access vary, so consult official documentation before adapting it. For platform-specific mechanics, use the separate Kalshi strategy guide or Polymarket strategy guide.

Rewrite the contract as a testable sentence before researching it. Include the subject, threshold, time zone, deadline, resolution source, and treatment of revisions or ambiguous outcomes. Then list what would make YES and NO settle. This prevents being right about the news but wrong about the contract.

For example, “Will inflation fall?” is not interchangeable with “Will Agency X report year-over-year Measure Y below threshold Z for Month M in its first release?” A later revision or downward trend may have no bearing on that settlement.

Use a structured forecast

1. Decompose the event. Identify necessary steps and failure points rather than forecasting the outcome as one opaque question.
2. Separate observations from implications. “A meeting is scheduled” is a fact; “approval is 70% likely” is a modeled inference.
3. Record an interval. Pair a point estimate with a plausible range and the assumptions responsible for its width.
4. Set update triggers. Decide which primary releases or official actions would change the estimate.
5. Seek disconfirmation. State the strongest opposing case and evidence that would invalidate yours.

Timestamp the forecast before checking your eventual result. An editable number with no history cannot distinguish learning from hindsight.

A base rate is the outcome frequency for a relevant reference class. Before analyzing a particular merger deadline, ask how often comparable deals close by similar deadlines. Before forecasting an incumbent, examine comparable races and conditions. The class will be imperfect, but writing it down makes the starting assumption auditable.

Move away from that base rate only for case-specific evidence. To avoid double counting, map the causal path: two articles citing one poll are one observation, and a pundit reacting to a market price is not independent of that price. Prefer primary releases.

Calibration asks whether events assigned a probability occur at about that frequency over many forecasts. If 60% forecasts resolve YES only 40% of the time, the model may be overconfident or the sample distorted. Compare forecast buckets with outcomes only when the sample is meaningful.

A Brier score is one compact measure: square the difference between the forecast probability and the outcome coded as 1 for YES or 0 for NO. A 70% YES forecast that resolves YES scores (0.70 − 1)² = 0.09; if it resolves NO, it scores 0.49. Lower is better, but a forecast score does not measure trade profitability because it ignores price, costs, and size.

In a simplified binary YES contract that settles at $1 for YES and $0 for NO, let p be your probability of YES and c the total cost per contract, including the executable price and any modeled costs. Expected profit per contract is p − c: at 0.64 versus $0.59, that is $0.05.

This is not a guaranteed return. Stress the low end of the forecast range, worse execution, and possible resolution error. If a modest adjustment removes the edge, pass.

Price reflects available orders at a moment in time, not objective truth. Your forecast is uncertain too; the decision exists in the gap between two imperfect estimates.

Inspect the bid, ask, spread, depth at multiple price levels, recent activity, and time remaining. The last trade may be stale, and a narrow spread may cover only one contract. Estimate the volume-weighted price for the intended size and recalculate value.

A limit order controls the worst acceptable price but may never fill. A marketable order prioritizes execution but can cross the spread and depth. Partial fills can alter intended exposure. Official venue rules define order types, priority, and fees; simulation rules define how they are modeled.

• Before entry: define maximum all-in price, size, useful fill, and expiry.
• During entry: do not chase; recompute value at the new price.
• After entry: define hold and exit conditions in advance.
• In simulation: verify spread, depth, delay, partial fills, and fee treatment.

An exit is a new trade. Compare the current position with the best use of risk now, not with the entry price you wish you had.

Read the complete market rules before entry and save a copy or link in the journal. Identify the named resolution source, observation window, time zone, precision and rounding, treatment of corrections, and fallback procedure if the source is unavailable. Look for differences between preliminary and final data and between an announcement, enactment, occurrence, and official certification.

Resolution analysis can reverse an apparent edge. Suppose news strongly suggests that a policy will be announced before a deadline, but the contract requires publication in a specified official register. An announcement without publication is not enough. Your forecast needs the probability of every required step, not only the politically interesting one.

When wording remains ambiguous, do not fill the gap with the outcome that seems fair. Consult the venue’s official clarification channel and size for the possibility that the adjudicator applies a literal reading different from yours. A large price discrepancy sometimes compensates for ambiguity rather than representing overlooked information.

Group positions by common drivers: the same candidate, team, court ruling, economic release, weather system, or policy decision. Include logical relationships and hidden data dependence, such as several forecasts based on the same poll or model.

Calculate both contract-level maximum loss and scenario loss across the group. Ask: “If my shared assumption is wrong, what can these positions lose together?” Small positions can combine into a large loss when they depend on one premise.

Position sizing should begin with a predefined portfolio risk budget, not confidence language such as “high conviction.” Cap maximum loss per market and per correlation bucket. Kelly-style formulas are highly sensitive to probability error; no sizing formula rescues an uncalibrated forecast.

• Set maximum loss per contract, event, and correlated group before entry.
• Reserve headroom for worse fills, fees, and corrections.
• Reduce size for wide forecast ranges, thin liquidity, or ambiguous resolution.
• Never increase size merely to recover a loss or hit a target.

Imagine a hypothetical contract that pays $1 if a public agency reports a metric above a defined threshold by a deadline, using an unambiguous first-release rule. Comparable periods exceeded it 40% of the time, which becomes the base rate.

Current primary data support a 62% estimate with a 56%–68% range. The book is 53-cent bid and 56-cent ask. Depth implies a 56-cent average entry; modeled costs add two cents, for 58 cents all-in.

1. Point-estimate EV: 0.62 − 0.58 = $0.04 per contract.
2. Conservative EV: 0.56 is below the 0.58 all-in cost, so the edge does not survive ordinary forecast error.
3. Resolution: the first-release rule removes revision ambiguity.
4. Correlation: existing positions share the same release, increasing scenario loss.
5. Decision: pass, wait for a lower price, or use less than the normal size.

If the ask later falls to 52 cents without new information, a 54-cent all-in cost leaves a two-cent cushion even at the low forecast. A small position may qualify, but can still lose its full entry cost. Set maximum price and size before attempting an order.

This educational example omits venue-specific mechanics and is not a claim about an available market or return.

For every considered market—not only trades—record:

• □ Exact proposition, deadline, resolution source, and rule ambiguities.
• □ Reference class, base rate, primary evidence, counterevidence, and source timestamps.
• □ Point probability, plausible range, confidence reasons, and planned update triggers.
• □ Bid, ask, visible depth, intended order type, maximum price, costs, and expected value.
• □ Position maximum loss, correlation bucket, scenario loss, and portfolio headroom.
• □ Entry, exit, and invalidation rules—including a reason to pass.
• □ Actual fill, slippage, fees, partial-fill behavior, and any deviation from the plan.
• □ Final resolution, forecast score, trade P&L, execution quality, and process errors.

Review forecasts and trades separately. Forecast review covers calibration and evidence; trade review covers price, costs, size, and execution. A good forecast can still produce a poor trade.

Tag errors—base-rate neglect, duplicate evidence, stale data, resolution misread, price chasing, oversizing, missed correlation, or outcome bias. Recurring tags should produce a new checklist or limit.

Test the journal first in a prediction-market simulator. Platform-specific practice is also available through the guides to Kalshi paper trading and Polymarket paper trading. Simulation can test discipline, but does not predict real results.

This material is educational, not investment advice. No strategy or hypothetical example guarantees a profit. Trading, balances, positions, and fills on Refract Funding are simulated.

Refract Funding is independent of Kalshi and Polymarket. It uses their published market data as a simulation reference and does not send user orders to either venue.

Passing an Evaluation does not guarantee a funded account or payout. Funded access and payouts depend on the published program rules, eligibility, identity and fair-play review, regional availability, and approval.

Refract is not a sportsbook and does not place bets or route venue orders. Its market activity, balances, fills, Evaluations, and funded accounts are simulated. Use official venue documentation for current contract, fee, order, access, and resolution mechanics, and use Refract’s current rulebook for the simulation and evaluation mechanics that apply inside Refract.