Standardize Market Titles With Proposition Canonicalization

by Alex Johnson 60 views

Introduction

In the dynamic world of prediction markets, clarity and consistency are paramount. Imagine trying to understand the nuances of different bets when each is phrased in a unique way. It's like trying to decipher a thousand different languages all at once! This is precisely the challenge that Proposition Canonicalization aims to solve. By converting unstructured market titles into structured propositions with standardized fields, we can enable deterministic relation inference, making it easier to analyze, compare, and act upon market data. This article delves into the 'why' and 'how' of this crucial process, outlining the technical specifications and acceptance criteria for its implementation.

The Challenge of Ambiguous Market Titles

Currently, prediction markets often store titles as free-text fields. This means that a single real-world event can be represented in numerous ways. For instance, consider a bet on Bitcoin's future price. You might see titles like: "Will Bitcoin exceed $100,000 by January 2025?" or perhaps "BTC price above 100K end of Q1 2025". While a human might easily grasp that these refer to the same underlying prediction, for a machine, especially a Large Language Model (LLM), these are distinct pieces of information. This ambiguity forces the LLM to guess the equivalence, introducing a layer of uncertainty and potential errors in relation inference. This problem is not unique to cryptocurrency; it spans across various prediction markets, from political events to economic indicators. The lack of a standardized format hinders efficient data processing, automated analysis, and the development of sophisticated trading strategies. Without a common ground, comparing market sentiment, identifying arbitrage opportunities, or even aggregating data becomes a complex and error-prone task. The very essence of a prediction market is to distill complex future possibilities into quantifiable outcomes, but this is undermined when the market's description itself is so open to interpretation. This is where the need for a structured approach becomes not just beneficial, but essential for the scalability and reliability of any prediction market platform.

The Solution: Structured Propositions

The core of our solution lies in transforming these ambiguous text titles into a structured Proposition schema. This schema breaks down the market's core meaning into distinct, standardized fields. Think of it like creating a universal translator for market bets. Each proposition will have clearly defined attributes, such as:

  • subject: The main entity or topic being predicted. For example, "bitcoin_price" or "trump_election" or "fed_rate". This identifies what is being bet on.
  • predicate: The action or relationship being asserted. This could be "exceeds", "equals", "contains", "wins", or "announces". This defines how the subject is related to the object.
  • object: The target value or entity. This might be a numerical value like 100000 for a price prediction, or a specific outcome like "win" for an election.

Beyond these core elements, we also introduce qualifiers to add necessary context and precision:

  • polarity: This boolean field indicates whether the market is a "yes" (TRUE) or "no" (FALSE) proposition. For example, "Will Bitcoin exceed $100,000?" is TRUE, while "Will Bitcoin not exceed $100,000?" would be FALSE.
  • threshold: If the predicate involves a comparison (like "exceeds" or "below"), this field captures the specific numeric threshold. In our Bitcoin example, this would be 100000.
  • unit: Specifies the unit of measurement for the object or threshold, such as "USD", "BTC", or "percentage".
  • time_window: This is crucial for time-sensitive predictions. It defines the deadline for the event's resolution, typically formatted as an ISO 8601 timestamp (e.g., 2025-01-31T23:59:59Z). This ensures we know when the prediction needs to be resolved.
  • jurisdiction: For predictions tied to specific geographic regions (e.g., political elections), this field specifies the relevant country or area.
  • resolution_source: Identifies the oracle or API that will be used to determine the outcome of the market, ensuring a reliable and verifiable resolution.

Finally, the structure includes metadata to track the parsing process itself:

  • confidence: A score between 0 and 1 indicating how confident the parser is in its extraction. This allows us to flag potentially ambiguous or poorly parsed titles.
  • raw_text: A copy of the original, unstructured market title, serving as a reference.

By adopting this structured approach, we move from a sea of ambiguous text to a clear, organized, and machine-readable representation of market predictions. This lays the foundation for more robust analysis, smarter automation, and ultimately, a more efficient prediction market ecosystem.

Technical Blueprint: Database, ORM, and Parsing Service

Implementing Proposition Canonicalization requires a robust technical foundation. Our approach involves defining a new database schema, creating corresponding ORM models, and developing a sophisticated parsing service powered by LLMs. This ensures seamless integration and efficient processing.

Database Schema (propositions table)

To store our structured propositions, we'll introduce a new table named propositions. This table will be linked to the existing markets table via a market_id. The schema is designed to capture all the fields discussed previously:

  • id: A unique identifier for each proposition (UUID).
  • market_id: A foreign key linking to the markets table, ensuring a one-to-one relationship.
  • Core Semantic Fields: subject, predicate, object as defined earlier.
  • Qualifiers: polarity (BOOLEAN), threshold (NUMERIC), unit (VARCHAR), time_window (TIMESTAMPTZ), jurisdiction (VARCHAR), resolution_source (VARCHAR).
  • Metadata: confidence (NUMERIC), raw_text (TEXT), created_at, and updated_at for auditing and tracking.

Appropriate indexes will be created on fields like subject, time_window, and market_id to optimize query performance. This robust schema ensures that all necessary information is captured and readily accessible for analysis.

ORM Model (Proposition class)

In our Python codebase, specifically within src/arbitrage/database/models.py, we'll define a Proposition class that maps directly to the propositions database table. This SQLAlchemy model will inherit from Base and TimestampMixin (for created_at and updated_at fields). Each attribute in the Python class will correspond to a column in the database table, using appropriate types (e.g., Mapped[str], Mapped[Decimal], Mapped[datetime]). Crucially, it will include a relationship definition linking a Proposition back to its Market object (`market: Mapped[