Null as Signal

Name: SimpleFunctions
Author: SimpleFunctions

Null as Signal is the framework reframe that treats a missing indicator value not as a data defect but as a positive entry condition for a specific strategy. When LAS is null, when EE is null, when PIV is near zero, when CVR is null — each null state corresponds to a maker or first-mover strategy that other traders have not noticed because they are filtering the nulls out. The four named null patterns are the entry conditions for the four maker strategies in the SimpleFunctions playbook.

CLI:sf scan --las-null --tau-min 30 --tau-max 200

The Reframe

Most prediction-market dashboards (and most new traders) treat a null value as missing data. The default UX response is to filter the nulls out of the scan and only show the markets where the indicator was successfully computed. That is the wrong default for a maker.

Null does not mean "the system failed to compute LAS." Null means "no one has been looking at this market." The cron job that computes LAS runs only on the top 500 markets by 24-hour volume — so a null LAS is a market that is not in the top 500, which means it is not on anyone's radar. That is the entry condition for being the first quote on the book.

The same reframe applies to every other Tier B indicator. EE = null means the contract has no detected siblings, which means the thesis is uncontaminated. PIV ≈ 0 means the contract has had fewer than 2 1¢-delta events in the last week, which means it is in a stable range that a market maker can quote against. CVR = null means the contract is not part of a coordinated family, which means there is no maker holding the cross-prices together.

Each null state is an entry condition, not an exclusion.

The Four Named Patterns

The four nulls map to four strategies in the SimpleFunctions maker playbook:

1. LAS = null → virgin Polymarket strategy. The market has no orderbook history and no recent volume. A maker can post quotes 8-12 cents wide, sit on the book as the only resting orders, and earn the spread on whatever taker eventually wanders in. Sizing is small per market because most virgins stay virgin, but the cumulative coverage across 30,000+ null-LAS markets is meaningful.

2. EE = null → uncontaminated thesis strategy. The contract has no detected sibling outcomes, which means the thesis is unmediated by sibling overround. A trader can take a position based purely on the contract's own merits without disentangling spillover from neighboring markets. This is the cleanest case for a directional bet on a single isolated event.

3. PIV ≈ 0 → range maker strategy. The contract is in a stable price range with very few 1¢-delta events. A maker can post a quote on both sides of the current mid, expecting that any move will reverse before the wider range is exited. The strategy works because nobody is currently chewing on the contract — the lack of activity is itself the edge.

4. CVR = null → first-mover strategy. The contract has no detected sibling family, so a thesis that develops here will not have already been priced into related markets. A trader can take a position before the contagion wave that does not exist yet — and if a sibling family does emerge later, the early position is the seed.

Why It Works as a Framing

The framing inverts the default "maximize the number" instinct. You are not trying to find the highest LAS or the highest CRI. You are trying to find the combination of indicator values that matches the strategy you intend to run. For maker strategies, the combination usually includes one or more nulls. For taker strategies, the combination requires no nulls (because takers need executable edge, which requires both sides of the book to exist).

The same dataset feeds both kinds of trader. The difference is whether you filter the nulls out (taker) or filter the nulls in (maker). Most software in the prediction-market space does the first by default and never offers the second, which is why the maker side is undercrowded.

Example

A scan that intentionally surfaces null values, run on a Wednesday morning:

| Filter | Markets returned | Strategy |
|---|:---:|---|
| LAS IS NULL AND τ between 30 and 200 days | 28,400 | virgin Polymarket maker |
| EE IS NULL AND IY > 200% | 1,940 | uncontaminated thesis |
| PIV ≤ 2 (7d) AND τ > 60 days | 8,700 | range maker |
| CVR IS NULL AND CRI > 0.5 | 6,200 | first-mover |

Each row is a market list that no taker-oriented dashboard would surface, because every dashboard filters nulls out by default. The 28,400 virgin markets have no displayed liquidity, so they look "broken" to a taker — but they are exactly the universe a maker wants to put first quotes on. The 1,940 uncontaminated thesis candidates have no sibling overround signal, so they look "missing data" to a taker — but they are the cleanest possible directional bets.

The composite read: the same 47,000-market universe contains both a taker game and a maker game, and which game you are playing determines which markets are interesting. For a taker, nulls are noise. For a maker, nulls are the signal.

The CLI surfaces this with the inverted-filter flags: `sf scan --las-null --tau-min 30 --tau-max 200` returns the virgin-Polymarket universe directly. The default `sf scan` continues to filter nulls out, because the taker game is the more common starting point. Both games are first-class.

Related Terms

Market Maker

A market maker is a participant who continuously provides both buy (bid) and sell (ask) orders on a prediction market, earning the spread in exchange for providing liquidity to other traders.

Thesis

A thesis is a structured investment argument that combines a directional view, a causal model of why the view is correct, and specific market positions that express the view. In SimpleFunctions, a thesis is the core unit of analysis.

Event Overround (EE)

Event overround is the sum of YES prices across all mutually exclusive outcomes in a single prediction-market event, minus one: EE = Σpᵢ − 1. A clean market gives EE = 0. Positive EE means the outcomes collectively overstate certainty (sell-side arbitrage). Negative EE means the outcomes collectively understate it (buy-side arbitrage).

Liquidity Availability Score (LAS)

Liquidity Availability Score is a per-market scalar that captures whether a prediction-market orderbook is thick enough to actually trade against, computed as the sum of bid depth and ask depth scaled by the bid-ask spread. LAS is null on roughly 99% of markets, because the warm-regime cron only computes it for the top 500 by 24-hour volume — and that null is itself the most useful signal LAS produces.

Position-Implied Velocity (PIV)

Position-Implied Velocity is the rate at which positions are being added or removed on a prediction-market contract, derived from the count of 1¢ price-delta events recorded in the market_indicator_history table over a 7-day rolling window. PIV is the substrate metric that separates a contract that is being actively traded from one whose price moves come from a single posted quote and stale fills.

Contagion Velocity Rate (CVR)

Contagion Velocity Rate measures how quickly a thesis priced into one prediction-market contract propagates to its semantic neighbors, computed as the lag in hours between a 5¢ move on a parent contract and an equivalent move on a related contract from the same event family. Low CVR means a thesis is spreading fast; null CVR (the common case) means the contract has no detected siblings and the thesis is uncontaminated by neighboring price action.

Cycle Clustering (CYC)

Cycle Clustering is the process of grouping prediction-market contracts that belong to the same recurring event family using a fixed set of nine slug regex patterns. CYC turns the universe of 47,000 isolated contracts into ~2,500 connected event families, which is the prerequisite for computing yield curves, cross-sibling overround, and contagion velocity. Roughly 41.4% of markets get assigned to a family; the remaining 58.6% are events that do not fit any of the nine patterns and are handled separately.

Valuation Funnel

The valuation funnel is the three-stage hierarchy that turns the universe of 47,000 prediction-market contracts into a single trade: filter by indicator, then read the orderbook, then apply causal reasoning. Each stage feeds the next, each stage is hierarchical (you do not skip ahead), and each stage uses a different cognitive tool — computation at stage 1, mechanical orderbook reading at stage 2, judgment at stage 3.

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