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Anchor Prediction Market

Anchor Prediction Market is a Rust program built with Anchor for a decentralized cricket prediction market. It represents match outcomes as YES and NO shares, prices them with a logarithmic market scoring rule (LMSR), and describes USDC settlement after the event.

Independent OpenViST project profile, based on linked sources.

Anchor Prediction Market — OpenViST project profile in DeFi.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it as a reference for a binary cricket prediction-market mechanism when you need to understand LMSR pricing and the described USDC settlement model.

Who it is for

  • Developers exploring a Solana Anchor program for binary cricket prediction markets.
  • People designing interfaces to present outcome probabilities, estimated purchase costs and price impact.

Documented uses

  • Represent a cricket match outcome with YES and NO shares.
  • Calculate implied outcome prices from share quantities using LMSR.
  • Estimate how a trade changes share price and purchase cost.
  • Describe settlement values for winning and losing shares in USDC.
Before you choose

Requirements & limits

  • The documented event format is binary YES/NO.
  • LMSR price sensitivity depends on the liquidity parameter b.
  • The README notes price impact, so a displayed share price does not by itself establish the total cost of a purchase.
  • The documented settlement model values winning shares at 1 USDC and losing shares at 0 USDC.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Estimate YES/NO share prices with LMSR

Use the README’s LMSR equations and example share quantities to calculate implied outcome prices.

Before you start

  • A liquidity parameter b and YES/NO share quantities q_yes and q_no.
  • The LMSR price formulas: P_yes = e^(q_yes/b) / (e^(q_yes/b) + e^(q_no/b)); P_no = 1 - P_yes.

Steps

  1. Set b, q_yes, and q_no for the market state being evaluated.

  2. Calculate P_yes using the LMSR formula.

  3. Calculate P_no as 1 - P_yes.

  4. Interpret the resulting prices as implied outcome probabilities; the README’s equal-share example gives 50% for each outcome.

Expected outcome

An LMSR-based estimate of the YES and NO outcome prices for the chosen market state.

Guide limitations

  • The README provides formulas and illustrative quantities, not an implementation procedure or a trading interface.
  • Prices change as trades change share quantities.
Source-based practical guide

Interpret the documented YES/NO trade and settlement example

Understand the README’s example of buying outcome shares and how winning and losing shares are valued after the event concludes.

Before you start

  • A binary cricket-market outcome represented by YES and NO shares.
  • An event that has concluded to determine the winning outcome.

Steps

  1. Choose YES if predicting the event will occur, or NO if predicting it will not.

  2. Account for the README’s example that share prices adjust with trades and that purchase cost includes price impact.

  3. After the event concludes, value winning tokens at 1 USDC each and losing tokens at 0 USDC each.

Expected outcome

A documented model for interpreting a YES/NO position’s example purchase cost and post-event token value.

Guide limitations

  • The README gives an illustrative trade, not instructions for placing a trade or claiming settlement.
  • The example’s cost is approximate and excludes the unspecified amount of price impact.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
0xtkm/anchor-prediction-market · Official homepage title · retained in its source language