ODDS connects Elyra to decentralized prediction markets, scanning for mispriced events and structural arbitrage opportunities that manual browsing cannot surface at scale. Rather than scrolling through hundreds of open markets looking for edges, you query Elyra and receive a ranked list of opportunities ordered by edge size, with the reasoning behind each one.Documentation Index
Fetch the complete documentation index at: https://docs.getelyra.xyz/llms.txt
Use this file to discover all available pages before exploring further.
Supported platforms
Polymarket
The largest decentralized prediction market by volume. ODDS scans event lines, computes implied probabilities, and applies semantic clustering to identify related markets with divergent pricing.
DFlow
Solana-native prediction market with on-chain orderbooks. ODDS analyzes taker flow and available liquidity to surface opportunities where order flow and quoted odds are misaligned.
What ODDS detects
Cross-book line shopping
ODDS queries both venues simultaneously and identifies where the best available odds on the same event differ between books. You get the optimal entry point without comparing venues manually.
Semantic clustering and mispricing
On Polymarket, ODDS groups semantically related markets (events that are logically correlated) and flags cases where the implied probabilities are inconsistent across the cluster. A cluster where the sum of implied probabilities deviates materially from 100% is a structural signal.
Structural arbitrage identification
When correlated markets are priced divergently — for example, two markets on the same underlying outcome trading at odds that imply a riskless spread — ODDS surfaces that pair ranked by profit potential percentage.
Natural language examples
Scanning parameters
You can control the scope of an ODDS scan through natural language or direct flags when running the trade research pipeline.| Parameter | Default | Description |
|---|---|---|
--max-markets | 600 | Maximum number of markets to fetch and analyze |
--top | 5 | Number of top opportunities to surface per table |
--json | off | Return raw JSON output instead of formatted tables |
Polymarket semantic clustering uses sentence-transformers for embedding-based grouping. On first run, a Hugging Face model is downloaded to
.cache/huggingface/. If the library is unavailable, ODDS falls back to TF-IDF clustering automatically — no configuration required.