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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA strategy is better than a string of lucky favorites only when its probabilities remain accurate across many comparable, resolved forecasts and its trades still make money after spreads, fees, liquidity limits, and settlement rules. Test forecast quality and trading performance as separate questions: use calibration and Brier Score for the first, and a costed trade ledger for the second.
Why picking the favorite is an incomplete test
Backing the contract with the highest probability can look sensible while telling you very little about whether a strategy is good. One event has one outcome. A 70% forecast can lose without being a bad forecast, and a 40% forecast can win without proving skill. The relevant question is whether probabilities are reliable over a large set of forecasts that share a definition, horizon, contract type, and settlement process.
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There are two distinct tests:
- Forecast quality: Did the stated probabilities match the frequencies of the outcomes that eventually occurred?
- Trading quality: After entry price, exit price, fees, spread, depth, sizing, and timing, did the positions produce positive net results?
A strategy can pass one test and fail the other. Treating a profitable short sample as proof of forecasting skill, or treating good calibration as proof of tradable profit, confuses different questions.
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Define the forecast before collecting results
Freeze the observation time
Record the probability or market-implied probability at a predeclared time before resolution. A price observed three months before an event is not equivalent to a price observed minutes before the result, when substantial information may already be public. If you use several horizons, report each horizon separately rather than averaging them together.
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Keep the event definition stable
Write down the exact contract, outcome definition, category, forecast timestamp, and expected resolution date. “Will inflation fall?” is not a reproducible event unless the contract specifies the measure, threshold, reference period, and source. Use the same event class and resolution convention when comparing strategies.
Predeclare inclusion and exclusion rules
Decide in advance how you will handle canceled markets, revised official data, markets that remain open, partial fills, and contracts with unusual settlement terms. Changing the rules after seeing outcomes creates selection bias.
Measure probability quality, not just winning picks
Calibration asks whether percentages mean what they say
Calibration is a frequency question. Collect forecasts that fall into a band, such as 65% to 75%, and compare the observed frequency of Yes outcomes with the assigned probabilities. A strategy that repeatedly says “70%” should produce approximately 70% Yes outcomes across a sufficiently large, comparable cohort. This test evaluates the probabilities themselves, not merely whether the top-ranked outcome won each time.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Use enough observations in each probability band to avoid drawing conclusions from a handful of events. Report the number of forecasts, the number of resolutions, the average assigned probability, and the observed outcome rate for every band. A strategy can be overconfident (its 80% forecasts occur less often than 80%), underconfident, or well calibrated while still having different levels of sharpness.
Brier Score rewards accurate probabilities
For a binary forecast, code the outcome as y = 1 when the event resolves Yes and y = 0 when it resolves No. If the forecast probability is p, the individual Brier score is (p - y)^2; average that value across the sample. Lower is better within the same setup.
Brier Score penalizes confident errors more heavily than cautious ones. A forecast of 0.90 on a No outcome scores 0.81, while a forecast of 0.55 on the same outcome scores 0.3025. Do not compare scores across different event mixes, horizons, or outcome definitions without explaining why the comparison is valid. Always state the sample, forecast time, contract class, and benchmark.
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Use a baseline that is evaluated on the same data
A score has meaning only relative to a stated baseline. Possible baselines include the market probability at the same timestamp, a historical base rate for the event class, or a simple constant probability. Evaluate the baseline on exactly the same resolved contracts and horizon. A strategy that beats a baseline on one horizon may not beat it earlier in the event lifecycle.
CFA Institute’s 2016 article Portfolio Structuring: The Science of Forecasting describes Brier Score as the metric Good Judgment selected to track forecasts against eventual outcomes and discusses forecasting platforms as a way to refine forecaster skill. That discussion supports repeated scoring and review; it does not establish that any particular prediction-market strategy will be profitable.
Separate the forecast record from the trading ledger
Probability record
Maintain a record even when you do not trade. At minimum, store:
- Contract identifier and full event wording.
- Probability, quoted price, or model output.
- Timestamp and time remaining to expected resolution.
- Outcome category and resolution source.
- Whether the forecast was generated before or after a material information release.
- Final settled outcome and the resulting Brier score.
This file answers, “Were the probabilities useful?” It should not be edited when a trade is later closed.
Trade ledger
For every order, record:
- Side, quantity, limit or execution price, and timestamp.
- Filled quantity versus requested quantity.
- Bid-ask spread and available depth when the order was placed.
- Entry and exit assumptions, including whether the position was held to settlement.
- Applicable fee schedule, fee amount, and any other transaction cost.
- Realized payout, mark-to-market value, and net profit or loss.
The ledger answers, “What happened to money under the stated execution assumptions?” Keep it separate from the probability record so a good forecast cannot be made to look profitable by ignoring an unfavorable fill.
An illustrative net-return calculation
Suppose, purely as an example, that a trader buys 100 Yes contracts at 0.65 and later sells them at 0.72. The gross price gain is 100 × (0.72 − 0.65) = 7.00 contract-dollar units before fees. If the position is held to settlement instead, the gross result is 100 × (1 − 0.65) = 35.00 when Yes occurs, or −65.00 when No occurs. The ledger must then subtract the actual entry, exit, and settlement-related charges under the schedule in force for that market. These arithmetic examples are not performance claims.
Put fees, spread, and liquidity into the test
An apparent probability edge is not automatically an executable edge. For each market, model the conditions under which an order could actually be filled.
| Friction | What to record | Why it changes the result |
|---|---|---|
| Fees | Market-specific schedule, effective date, side of trade, and quantity | A small gross edge can disappear after charges; schedules can change. |
| Bid-ask spread | Displayed bid, displayed ask, and the side used for entry and exit | Buying at the ask and selling at the bid creates an immediate cost. |
| Depth | Available contracts at each price level | A quoted price may apply to only a small portion of the intended order. |
| Position size | Order size relative to displayed liquidity | Larger orders can move the execution price and worsen the average fill. |
| Exit rule | Planned sale, mark-to-market, or settlement hold | Different exits create different cash flows and exposure to late information. |
Kalshi’s official fee schedule effective February 5, 2026 includes a formula for specified S&P 500 and Nasdaq-100 contracts. Those terms are specific to the named markets, not a universal charge for every event contract, and the schedule may be superseded. Use the schedule that applies to the exact contract and transaction date rather than importing a fee from another market.
When a strategy scales, run a sensitivity analysis: calculate net results at the observed spread, at a wider spread, with partial fills, and with a delayed exit. If the advantage disappears under a modestly less favorable fill, describe it as execution-sensitive rather than robust.
Read the resolution rules before labeling an outcome
Contract language controls the result
Market-specific rules define what is being forecast, the criteria for a Yes or No result, and the verification source. The Market Rules and Rules Summary help pages explain that the summary identifies the measured value and timeline, while the full rules contain additional terms. Read the complete contract before counting a forecast as settled.
“Make sure to read market rules before trading!”
Kalshi Help Center, “Market Rules,” March 17, 2026
Official data can be delayed or revised
The contract terms and named source determine the outcome. Official data may arrive after trading closes or be revised, and settlement timing depends on the market rules. A market that remains open after the apparent real-world event is not necessarily unresolved in error. Use the platform’s actual settlement status and source publication, not an informal interpretation of what happened.
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Keep an audit trail
Save the rule version, source, publication date, and settlement timestamp used in your evaluation. If a source revises a figure, note whether the contract rules use the first release, a later revision, or another specified value. This prevents a strategy from receiving credit or blame for an outcome it was never contractually designed to predict.
Interpret volume and horizon evidence carefully
Kalshi Research’s August 2026 study analyzed 2,243,741 resolved markets across eleven categories, excluding Exotics, from the platform’s launch in 2021 through mid-2026. In that dataset and analysis, reported Brier Scores fell from approximately 0.08–0.09 at a three-month horizon to roughly 0.02 at close.
| Study detail | Reported information |
|---|---|
| Publisher and date | Kalshi Research, August 2026 |
| Sample | 2,243,741 resolved markets |
| Categories | Eleven, excluding Exotics |
| Coverage | Platform launch in 2021 through mid-2026 |
| Brier Score pattern | Approximately 0.08–0.09 at three months; roughly 0.02 at close |
These are results from Kalshi’s dataset and analysis, not a universal prediction-market benchmark. The study reports that calibration improves as resolution approaches and is stronger with greater event-level volume and more unique traders. Those relationships are observational: they do not prove that volume causes better forecasts, that adding traders always improves accuracy, or that every low-volume market is unusable. No independent replication of the study or general market-wide profitability statistic is established here.
Use the findings as hypotheses for your own evaluation. Separate early-horizon and near-close results, record event-level volume and unique participation, and test whether an apparent relationship survives controls for category, contract type, and time to resolution.
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A consistent framework for comparing two strategies
When comparing strategies, publish the setup alongside the result. The following axes prevent a favorable comparison from being created by mismatched samples.
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| Axis | Required disclosure | Question answered |
|---|---|---|
| Probability quality | Calibration bands, Brier Score, sample size, and missing outcomes | Are the probabilities reliable? |
| Forecast timing | Exact time before resolution or separate horizon buckets | Were both strategies tested with the same information? |
| Benchmark | Baseline and its score on the same contracts | Does the strategy add information? |
| Tradability | Entry and exit prices, fills, spread, depth, and size | Could the quoted edge be executed? |
| Net results | Fees and transaction costs under the dated schedule | What remained after friction? |
| Resolution integrity | Rules, verification source, and settlement status | Was the outcome counted correctly? |
| Evidence strength | Category mix, sample size, and observational versus causal claims | How far can the conclusion be generalized? |
An end-to-end evaluation procedure
- Write the research question. Specify whether you are testing probability accuracy, net trading return, or both.
- Define the universe. List eligible contract classes, categories, resolution sources, and the date range. Exclude or separately label markets with incomparable rules.
- Choose observation times. For example, collect forecasts at three months, one month, one week, and close only if each horizon has enough observations. Do not pool them by default.
- Capture forecasts before resolution. Timestamp every probability and preserve the original value. Do not replace it with the final price.
- Record the contract rules. Save the measured value, threshold, source, timeline, and any revision or cancellation provisions.
- Wait for contractual settlement. Use the actual resolved outcome and settlement timestamp, not a news headline or personal judgment.
- Calculate calibration and Brier Score. Report probability bands, counts, observed rates, average score, and a same-sample baseline.
- Reconstruct executable trades. Apply realistic prices, partial fills, spread, depth, position limits, exit rules, and the fee schedule effective on each transaction date.
- Stress-test assumptions. Recalculate results with wider spreads, delayed exits, smaller fill rates, and alternative but plausible position sizes.
- Report uncertainty and limits. Show the sample, category mix, missing data, horizon, and whether any relationship is merely observational.
Common evaluation mistakes and their fixes
| Mistake | Why it misleads | Fix |
|---|---|---|
| Counting only favorite wins | Ignores the size and reliability of assigned probabilities | Use calibration and Brier Score across resolved forecasts. |
| Mixing early and near-close prices | They reflect different information sets | Score horizons separately. |
| Comparing unlike contracts | Different base rates and rules change difficulty | Match contract class, event definition, and settlement source. |
| Calling a profitable sample proof of skill | Short samples can be dominated by variance | Keep a long, predeclared record and compare with a baseline. |
| Ignoring fees and spread | Gross gains may not survive execution costs | Use actual dated fees and both sides of the spread. |
| Assuming quoted depth is executable | Large orders can receive worse fills | Model level-by-level depth and partial fills. |
| Settling by informal interpretation | Official sources and revisions can change the contractual result | Apply the full market rules and recorded settlement. |
| Treating volume as causal | Busy markets may differ in many other ways | Describe the relationship as observational unless a causal design supports more. |
Personal-finance safeguards for strategy testing
Prediction-market positions can lose their full stake when the event resolves against them. Keep experimental capital separate from emergency savings, debt payments, taxes, and near-term goals. Set a maximum position size before seeing the forecast, and include correlated contracts in the same exposure limit; several markets can depend on one underlying event.
Maintain cash-flow records that distinguish deposited funds, unsettled positions, realized gains, realized losses, fees, and withdrawals. A strategy that looks profitable on a mark-to-market screen may have capital tied up until settlement. Do not increase size merely because a recent run of favorites won.
How to report a credible conclusion
A useful report states exactly what was tested: the contracts, dates, horizons, probability source, settlement rules, baseline, number of resolved observations, Brier Score, calibration results, and net trading result after costs. Explain which conclusions are direct measurements and which are hypotheses. If the strategy has good calibration but weak net returns, say so plainly; that result identifies an execution problem rather than disproving the forecasts.
Likewise, a positive net result with poor calibration may reflect a favorable short sample, a pricing opportunity unrelated to probability skill, or unmodeled risk. Continue collecting out-of-sample forecasts and trades under the same definitions before treating either outcome as durable evidence.
Bottom line
Evaluate prediction-market strategies as probability systems and trading systems. Score repeated, comparable forecasts with calibration and Brier Score; compare them with a same-sample baseline; then reconstruct what could actually have been traded after fees, spread, depth, sizing, and settlement timing. The most likely outcome winning often is not enough evidence, while a carefully documented record can show whether a strategy is accurate, executable, both, or neither.
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