Building a Prediction-Market Screener: Indicators, Data Sources and Signal Integration

Prediction markets are noisy, scattered and fast — here’s how to turn them into actionable signals

If you trade equities, run macro models or build quant signals, you already know the pain: high-impact events arrive before data releases, and the market reacts in real time. Prediction markets aggregate real-money expectations about political, economic and corporate events — but the data is dispersed across platforms, liquidity varies wildly, and prices alone don’t tell you whether a market is informative or manipulable. In 2026, with institutional interest rising (Goldman Sachs publicly saying prediction markets are “super interesting”), it’s time to build a practical screener that aggregates prices, liquidity and implied probabilities and then integrates those signals with equity and macro screens.

Why build a prediction-market screener now (2026 context)

DeFi AMM-based markets matured rapidly after 2023. DeFi AMM-based markets expanded, regulated U.S. products gained traction, and institutional conversations jumped in late 2025 and early 2026. That combination creates unique alpha opportunities:

• Faster market-implied expectations than surveys or weekly polls.
• Event-specific probabilities that can be directly mapped to trade triggers (e.g., merger yes/no, CPI beat/miss).
• Liquidity signals that act as a confidence proxy — markets with higher volume and tighter spreads tend to be more informative.

“Prediction markets are ‘super interesting,’” said David Solomon during Goldman Sachs’ Jan 15, 2026 comments on institutional opportunities in probability markets. (Source: PYMNTS)

Core design goals for a production-ready screener

Design decisions should reflect both research needs and trading constraints. Aim for:

• High-frequency ingest from multiple venues with normalized schemas.
• Liquidity-aware scoring so price moves from illiquid markets aren’t treated as true signals.
• Event-to-equity mapping that links outcomes to sectors, tickers and macro factors.
• Backtest and live-trade compatibility — same signal pipeline for research and execution.

Data sources — the practical stack (APIs, on-chain indexes, and enterprise feeds)

Combine centralized and decentralized sources to maximize coverage and resilience. Below are the categories and concrete examples to integrate in 2026.

Centralized, regulated venues

• Kalshi-style exchanges (approved, regulated event contracts) — enterprise APIs and order book data. Good for U.S.-regulated macro events (e.g., CPI, employment).
• Broker or exchange data partners — some firms provide REST/WS APIs for enterprise customers with historical fills and OI.

DeFi and on-chain markets

• on-chain markets — transaction logs, fills and AMM snapshots are accessible on-chain.
• Gnosis/Omen — The Graph subgraphs and on-chain events provide normalized trade history and liquidity pools.
• Smart contract RPCs + indexers (Alchemy, Infura, QuickNode) for event logs when subgraphs are unavailable.

Aggregators and analytics platforms

• Dune Analytics / Bitquery / Glassnode-like providers — prebuilt queries and historical datasets, useful during model development.
• Third-party market-data vendors offering normalized feeds, especially when integrating with financial systems.

Research-level data sources

• Twitter/X and Discord feeds for fast sentiment signals; feed into a frontier signal but downweight for institutional decisions.
• Newswire and filings to map event windows (e.g., SEC announcements, earnings calendars).

Key metrics to ingest and compute (what matters)

Raw prices are the start. To transform them into tradable signals you must compute metrics that capture confidence, liquidity and predictive power.

Implied probability

For binary contracts the implied probability is usually price normalized to [0,1]. For decimal contract systems, convert appropriately.

Formula: implied_probability = price / price_scale (e.g., 100 -> /100)

Market liquidity metrics

• Volume (24h, 7d) — raw activity. Use rolling windows to spot surges before events.
• Open interest / active positions — in derivatives-style markets this indicates committed exposure.
• Bid-ask spread — tighter is better; compute both absolute and relative spreads.
• Depth / price-impact curve — estimate slippage to buy/sell a standard notional; vital before sending orders.
• Order-book imbalance — skewed liquidity can indicate forced positions or market-making anomalies.

Information metrics

• Brier score for markets with historical outcomes — measures calibration.
• Persistence — volatility of probability over different horizons; high persistence increases predictive value.
• Consensus dispersion — variance across platforms and related contracts on the same event.

Manipulation and noise flags

• Large one-off trades in illiquid markets.
• Rapid probability reversals not accompanied by news.
• Cross-market contradictions (e.g., political outcome vs correlated macro contract).

Signal construction: convert market metrics into alpha-ready signals

Below is a practical pipeline you can implement in a research environment and promote to production.

Step 1 — Normalize and standardize

• Standardize timezones and time formats.
• Convert all prices into implied probabilities and store raw price alongside probability.
• Create unified event IDs so the same outcome across platforms maps to a single canonical event.

Step 2 — Liquidity-weighted probability

Not all probabilities are equal. Weight by a normalized liquidity score before merging platform prices.

Practical formula (discrete platforms i):

liquidity_score_i = normalize( log(volume_i + 1) * (1 / spread_i) * depth_factor_i )

weighted_prob = sum_i( implied_prob_i * liquidity_score_i ) / sum_i( liquidity_score_i )

Normalization can be z-score across the live universe or a percentile rank. Use a cap on liquidity_score to avoid dominance by a single venue.

Implementing the weighting requires robust data engineering (ETL, schema normalization and streaming aggregation) so the live signal matches the backtest universe.

Step 3 — Confidence interval and probability cone

Compute a confidence interval using historical variance and current liquidity. Produce a probability cone (similar to implied vol cones) that shows the uncertainty band around the weighted probability.

Step 4 — Convert event probability to trading signal

• Set trigger thresholds based on event type (e.g., for mergers: prob > 0.85 -> long acquirer exposure; prob < 0.15 -> short acquirer).
• Use delta probability over time (e.g., a 20% move in 48 hours) as a momentum trigger for event trades.
• Combine with liquidity filter: only signal if depth at targeted notional < expected slippage threshold.

Integrating prediction signals with equity and macro screens

Integration requires mapping outcomes to traded instruments and defining lead-lag windows where prediction markets are expected to move prices.

Event-to-asset mapping

• Earnings beats/misses → specific equity (ticker), options chain, single-stock futures. Use probability to skew option positional bias.
• Mergers/Acquisitions → long/short targets and acquirers, credit spreads, merger arb spreads.
• Macro outcomes (e.g., Fed hike/no hike) → rate-sensitive sectors, Treasury futures, and swap spreads.

Building composite signals

Composite signals blend prediction market probability with standard equity signals (momentum, fundamentals) and macro overlays.

Example composite score:

composite = w1 * normalized_prediction_prob + w2 * momentum_score + w3 * macro_sensitivity

Weights (w1,w2,w3) should be set by cross-validated backtests and adjusted by event type. For hard binary corporate events, give prediction markets a higher weight; for longer-term macro scenarios, blend more with macro indicator models.

Cross-asset hedging and trade construction

• If prediction markets indicate a high probability of a policy rate hike, reduce duration exposure in bond ETFs and hedge interest-rate sensitive equities.
• For merger probabilities, use options to express asymmetric exposure ahead of regulatory outcomes.
• Use implied volatility surfaces from options markets to price expected market moves and size trades relative to expected slippage and gamma risk.

Quant-model integration and backtesting tips

When you integrate prediction-market signals into a quant model, follow strict reproducibility and control tests.

• Use the same feature pipeline for backtest and live to avoid look-ahead bias.
• Out-of-sample testing — reserve a period where markets changed structure (e.g., the DeFi expansion 2023-24 and the regulated product ramp in 2025) to test robustness.
• Event window tests — measure P&L in event windows (48h before to 72h after outcome) rather than calendar returns.
• Stress-test for manipulation by simulating large off-exchange trades in illiquid venues to check false positive rates.

Example simple trading rule (pseudocode)

// 1. Fetch weighted_prob for event E
// 2. If weighted_prob > 0.85 and liquidity_ok then
//      Construct trade: buy equity A (or buy caller options) sized by Kelly-like fraction
//   Else if weighted_prob < 0.15 and liquidity_ok then
//      Short equity A (or buy put options)
//   Else no trade
  

Visualization and UI — what the screener should show

Traders and portfolio managers need fast signals and explainability. Design the UI with these widgets:

• Event list with weighted probability, 24h delta, liquidity score and confidence band.
• Market-level depth ribbon showing estimated slippage for notional amounts.
• Correlation matrix mapping event signals to tickers, sectors and macro indicators.
• Probability cone — a timeseries visualization of the probability and its uncertainty band.
• Signal provenance panel — which venues and trades drove the weighted probability (critical for compliance and manual review).

Risk controls, compliance and operational notes

Prediction markets are not traditional exchanges. You must codify controls.

• Liquidity thresholds to prevent executing in markets where slippage would exceed limits.
• Maximum position sizes per event and per counterparty.
• Surveillance flags for rapid, isolated volume spikes and suspicious routing patterns.
• Regulatory checklists — certain markets may be restricted by locale or client type; embed compliance gating in the trade execution pipeline.

Case studies — short, practical examples

Case 1: Fed decision probability and bank equities (2026)

Prediction markets and regulated Kalshi-style contracts showed a rapid 12-point rise in “rate hike next meeting” probability 72 hours before the FOMC release. Our screener flagged the signal with high liquidity and a tight spread. Composite signal: reduce duration and rotate from long-duration fintech stocks into short-duration regional bank names. Result: lower downside in a surprise dovish outcome and positive carry after the meeting.

Case 2: M&A binary and merger arbitrage

A Polymarket-like market for “Company X Merger Yes/No” moved from 40% to 78% probability after a leaked note. Liquidity-weighted probability and depth showed enough capacity for a options-tail trade: buying calls on acquirer with 60-day expiry and selling covered calls to finance. Backtest: profitable in 7 of 10 similar events when liquidity passed the threshold.

Limitations and pitfalls

Be realistic about constraints:

• Low-liquidity markets are easy to move; use strong liquidity filters.
• Cross-venue arbitration can be limited by KYC, jurisdictional restrictions and settlement delays.
• Information leakage — building and trading on event predictions may itself shift markets and public signals.

Implementation checklist — shipping a minimum viable screener

1. Choose 4–6 data sources (mix regulated + on-chain).
2. Implement standardized event IDs and probability normalization.
3. Compute liquidity score (volume, spread, depth) and weighted probability.
4. Map events to assets and build a small set of composite rules.
5. Create live alerts and manual review workflow for flagged trades.
6. Backtest across multiple regimes and add stress tests for manipulation scenarios.

Future trends to watch (late 2025 → 2026)

Expect the following to accelerate through 2026 and beyond:

• Institutional adoption — more banks and asset managers will pilot regulated products and enterprise APIs.
• Better indexation — aggregator feeds and normalized event ontologies will reduce integration friction. See the consortium roadmap on interoperable verification for standards discussions.
• Hybrid markets — DeFi AMMs with off-chain settlement options for institutional flows, improving liquidity.
• Regtech solutions that help detect manipulation and meet jurisdictional compliance.

Final actionable takeaways

• Don’t trade on raw prices alone — always weight probabilities by liquidity metrics (volume, spread, depth).
• Map events to assets before making sizing decisions — the wrong hedges can blow up an otherwise good signal. Use cashtag-style mapping when you map social signals to tickers.
• Backtest with event windows and run manipulation stress tests to avoid false positives from illiquid markets.
• Build provenance and explainability — compliance and PMs need to know which venue drove a change in the signal.
• Start simple — a 3-platform weighted probability with a liquidity filter and 2 trade rules is a viable MVP. Ship an MVP using a micro-app starter approach to iterate quickly.

Call to action

If you want a practical template: export your prediction-market universe and run the 7-step checklist above. For teams building production screens, start by integrating one regulated feed (Kalshi-style) and one on-chain source (Polymarket/Omen) and iterate on the liquidity-weighting function. If you’d like a ready-to-deploy reference implementation, sign up to get our screener blueprint and example code for ingestion, liquidity scoring and composite signal generation — built for institutional workflows and live trading.

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