Loading
Preparing this view
Retrieving the current information and preserving your place. No action is required.
Loading current information
Forecast AlphaForecast AlphaLoading
Retrieving the current information and preserving your place. No action is required.
Loading
Retrieving the current information and preserving your place. No action is required.
Market 65.8% against model 64.2%. Resolves in 27d 6h, data updated 6h ago.
The model can still be informative here, but one or more gates blocks a trade call.
Decision layer
The model may still be informative, but at least one gate blocks an action-style signal.
Expected value after costs, not raw probability spread.
How much support the model sees across available inputs.
Thin markets can erase apparent edge through spread and slippage.
Resolution ambiguity, timing, and data quality pressure the decision.
usable feature coverage.
Why / why not trade
This public box mirrors the internal diagnostic style without exposing execution controls: decision, probability gap, cost-adjusted edge, blocker, and next thing to monitor.
no side selected
64.2% model / 65.8% market
fees, spread, slippage, risk
No edge after fees and slippage — the market price is fair within costs.
No edge after fees and slippage — the market price is fair within costs.
Model 64.2% vs market 65.8%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
No trade
The model may disagree with price, but the gates say the disagreement is not actionable right now.
Sign in to return to this exact question, review governed evidence, and record an append-only probability without exposing private thesis text.
Volume $389,893
Declining to trade is a feature: most markets are priced fairly within costs, and the risk gates run before any edge is considered.
The model estimates a 2-point lower probability than the market, primarily driven by historical base rate and recent price momentum.
| FACTOR | SIGNAL | WEIGHT | LOG-ODDS ΔLog-odds contribution measures how much each factor shifted the model's probability estimate in log-odds space — the mathematically correct way to stack independent evidence. Formula: Δlog-odds = weight × signal. Positive values push the probability up; negative values push it down. Log-odds are converted back to probability via the logistic function at the end. | DIRECTION | DESCRIPTION |
|---|---|---|---|---|---|
| Historical base rate | 63% | — | +0.518 | Bullish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
No comparable events matched for this market.
This market will resolve according to the company which owns the model which has the highest arena rank based on the arena.ai Text Arena (Overall) when the table under the "Leaderboard" tab is checked on October 31, 2026, 12:00 PM ET. Results from the "Rank" column under the "Text Arena | Overall" Leaderboard tab at https://arena.ai/leaderboard/text/overall-no-style-control with style control off (Adjustments: None) and filtered for "Models" will be used to resolve this market. Note: Models marked “AutoEval” at the applicable check time will not be considered, regardless of whether they display a rank or score. Models will be ordered primarily by their leaderboard rank at the market’s check time. If two or more models are tied on rank, they will be ordered by their Arena score, including any underlying, unrounded, granular values reflected in the data below the leaderboard. If a tie still remains, alphabetical order of company names as listed in this market group will be used as a final tiebreaker (e.g., if the two models are tied by exact arena score, “Google” would be ranked ahead of “SpaceXAI”). This market will resolve based on the company that occupies first place under this ranking. The resolution source for this market is the arena.ai Text Arena (Overall). If this resolution source is unavailable at check time, this market will remain open until the leaderboard comes back online and will resolve based on the first check after it becomes available. If it becomes permanently unavailable, this market will resolve to "Other".
analyzed by heuristicThis market will resolve according to the company which owns the model which has the highest arena rank based on the arena.ai Text Arena (Overall) when the table under the "Leaderboard" tab is checked on October 31, 2026, 12:00 PM ET. Results from the "Rank" column under the "Text Arena | Overall" Leaderboard tab at https://arena.ai/leaderboard/text/overall-no-style-control with style control off (Adjustments: None) and filtered for "Models" will be used to resolve this market. Note: Models marked “AutoEval” at the applicable check time will not be considered, regardless of whether they display a rank or score. Models will be ordered primarily by their leaderboard rank at the market’s check time. If two or more models are tied on rank, they will be ordered by their Arena score, including any underlying, unrounded, granular values reflected in the data below the leaderboard. If a tie still remains, alphabetical order of company names as listed in this market group will be used as a final tiebreaker (e.g., if the two models are tied by exact arena score, “Google” would be ranked ahead of “SpaceXAI”). This market will resolve based on the company that occupies first place under this ranking. The resolution source for this market is the arena.ai Text Arena (Overall). If this resolution source is unavailable at check time, this market will remain open until the leaderboard comes back online and will resolve based on the first check after it becomes available. If it becomes permanently unavailable, this market will resolve to "Other".
- Oracle dependency Resolution depends on a single named source continuing to publish the metric.
Resolves Sat, 31 Oct 2026 16:00:00 GMT. The contract pays on these exact criteria, not on the thesis.
The engine sizes NO TRADE markets to zero. Sizing never overrides the risk gates.
Paper position only. No real-money execution
| —This factor was not available for this market. No approved link prices the same event on another venue for this market. |
| 0.20 |
| — |
| — |
| Whether the same event is priced differently on another venue. A gap may signal an opportunity or a structural difference. |
| Recent price momentum | +0.15 | 0.35 | +0.052 | Bullish | Drift of the market's own implied probability over its last 8 price updates (typically hours; the key's "7d" is historical) — recent moves carry information. |
| BTC/ETH 7-day momentum | —This factor was not available for this market. This factor applies to crypto markets only. | 0.20 | — | — | 7-day Bitcoin or Ethereum return, normalized. Applied to crypto-category markets only. |
| Rate surprise | —This factor was not available for this market. This factor applies to Fed, CPI, and macro markets only. | 0.25 | — | — | 2-year Treasury yield reaction in the 48 hours after the most recent scheduled release — a proxy for how markets interpreted the data versus expectations. |
| Yield curve shift | —This factor was not available for this market. This factor applies to Fed, CPI, and macro markets only. | 0.15 | — | — | 30-day change in the 10-year minus 2-year Treasury spread. A flattening curve signals tightening expectations; steepening signals easing. |
| News signal | —This factor was not available for this market. No news signal available for this market in the past 14 days. | 0.25 | — | — | Reliability-weighted direction of relevant news from the past 14 days. Official sources (filings, agency statements) carry more weight than commentary. |
| Crowd forecast | —This factor was not available for this market. Insufficient forecasters to compute crowd signal. Requires at least 5 calibration-weighted estimates. | 0.20 | — | — | Calibration-weighted average of user probability estimates. Only applied when 5 or more weighted forecasters have submitted estimates. |
| Model probability | 63.9% | Prior: 63% · Market: 65.8% | |||
| Confidence (λ)Confidence λ (lambda) controls how much weight to give the model vs. the market. Formula: p_final = λ·p_model + (1−λ)·p_market. λ is derived from data quality, factor agreement, and liquidity. When inputs are weak, the model shrinks toward the market — not toward 50%. | 0.84 | Final: 64.2% = λ·model + (1−λ)·market | |||
Since the first stored model read on 2026-09-30, the market has moved from 71.1% to 65.8%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Missing: Cross-market divergence, News signal, Crowd forecast
When features are unavailable, the model increases uncertainty and weights the final estimate closer to the market price. Lower data quality does not mean the market is wrong. It means the model is being appropriately humble.
| Inverse liquidity | 25 | |
| Price volatility | 20 | |
| Resolution proximity | 0 | |
| Data quality | 65 | |
| Category base risk | 40 | |
| Resolution ambiguity | 18 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 20/100, higher = riskier.
| Market | Mkt | Delta |
|---|---|---|
| Category context | ||
| Will Eduardo Bolsonaro win the 2026 Brazilian presidential election? category context: wording overlap | 0.1% | -- |
| Will Eduardo Leite win the 2026 Brazilian presidential election? category context: wording overlap | 0.1% | -- |
| Will Aldo Rebelo win the 2026 Brazilian presidential election? category context: wording overlap | 0.1% | -- |
| Will Michelle Bolsonaro win the 2026 Brazilian presidential election? category context: wording overlap | 0.1% | -- |
| Will Pablo Marçal win the 2026 Brazilian presidential election? category context: wording overlap | 0.1% | +18pt |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.