Broad scanning means evaluating every active market against a single rule set rather than specializing in one category. A well-tuned scanner produces 5-15 viable trades per day across 12,000+ Kalshi markets — and the cross-category diversification reduces correlation between losses, which compounds the Sharpe ratio relative to a single-category strategy at the same per-trade edge. The discipline is in the scanner config, not the trade selection: get the rules right and the market opportunities surface themselves.
The logic is straightforward: if a trader only watches weather markets, they will miss a mispriced political contract. If they only watch high-volume markets, they will miss a wide-spread opportunity in a niche category. Breadth ensures the best risk-adjusted trades surface regardless of category.
Harry Markowitz's Modern Portfolio Theory (1952) established that diversification reduces portfolio variance without necessarily reducing expected return. The key insight: combining uncorrelated assets produces a better risk-return profile than concentrating in any single asset.
In prediction markets, this translates directly. A portfolio of trades across weather, politics, sports, crypto, and economics is less correlated than a portfolio concentrated in one category. A weather forecast bust doesn't affect political market outcomes; an election surprise doesn't affect temperature brackets.
Within-category correlation is often high:
A broad scanner naturally mitigates this by distributing trades across categories with low cross-correlation.
A broad scan can be discretionary (manually browsing Kalshi's market list) or systematic (applying quantitative filters programmatically). The systematic approach has several advantages:
Manski (2006) noted that aggregating information across prediction markets reveals patterns not visible in individual markets — a principle that applies equally to a trader scanning across categories.
A broad scan generates a large candidate set. The challenge is ranking these opportunities effectively. Common ranking dimensions include:
The relative weighting of these dimensions reflects the trader's priorities. A broad scanner typically uses balanced weights rather than heavily favoring any single factor.
One pitfall of broad scanning is over-concentration in a single event. For example, a weather event in Dallas might generate 10 different bracket markets that all look attractive. Without deduplication, a broad scanner might recommend filling the entire portfolio with Dallas temperature brackets — defeating the purpose of diversification.
Effective broad scanners limit the number of positions per event family (e.g., at most 2 brackets from the same event) and per category (e.g., at most 5 weather markets total).
Prediction Pilot's Broad Scanner template evaluates every active Kalshi market with balanced filters and ranking weights.
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