openpredictionmarkets/socialpredict: Open-Source Prediction Market Engine in Go

openpredictionmarkets/socialpredict: Open-Source Prediction Market Engine in Go

Prediction markets aggregate collective intelligence to forecast outcomes—from election results to product launches. Yet most platforms are closed SaaS products with opaque pricing, limited customization, and no control over data or market mechanics. For developers, researchers, and organizations that need sovereign infrastructure, the choice has been build-from-scratch or accept vendor constraints.

openpredictionmarkets/socialpredict addresses this gap directly. It is a self-hostable, MIT-licensed prediction market platform written in Go, designed for individuals, classrooms, companies, and government use. With 212 GitHub stars, 68 forks, and active development through July 2026, the project offers concrete deployment paths, documented capacity testing, and governance features absent from many alternatives. This article examines what the project delivers, how to run it, and where it fits in the prediction market landscape.

What is openpredictionmarkets/socialpredict?

openpredictionmarkets/socialpredict is an open-source prediction market engine maintained by the OpenPredictionMarkets organization. The project is written primarily in Go and released under the MIT License, permitting commercial and academic use with minimal restrictions.

The platform's positioning is explicit: it targets anyone needing prediction market infrastructure they control themselves. The README lists users ranging from individuals to governments, with Kenyon College's Political Science department (course PSCI 303, Campaigns & Elections) documented as an actual adopter. This is not aspirational marketing—the syllabus is publicly linked.

Technically, the project follows a monolithic service architecture with a roadmap toward microservices. The 2025-2030 roadmap shows progression from service architecture refinement through microservices and math improvements, cloud and UX enhancements, and ultimately high-performance computing and analytics capabilities. This staged approach suggests maintainers prioritize operational stability before architectural complexity.

The project demonstrates production discipline unusual for its size: automated testing via GitHub Actions, container security scanning with Trivy, staged deployments to kconfs.com on main branch merges, and release-tracked deployments to brierfoxforecast.com. Load testing with k6 has produced documented capacity evidence—250 bets/sec sustained for 5 minutes on a single DigitalOcean Basic AMD 8 vCPU / 32 GiB instance—with dossiers published in the repository.

Key Features

Moderator-Driven Market Governance

SocialPredict implements a multi-role market lifecycle. Approved moderators propose markets; admins retain publishing authority. Markets transition through proposed, published, and rejected states, with cost accounting and refund behavior for rejected proposals. Suspended moderators lose creation and governance capabilities. This separation of proposal and approval powers supports organizational use cases requiring oversight.

Administrative Operational Controls

Admins access user governance queues for moderator eligibility review, market stewardship reassignment for operational continuity, and market discovery layout controls including tags, topic navigation, pinned markets, and search-optimized organization. A CMS layer manages OpenGraph metadata, social share images, and embedding preparation for public market sharing.

Performance-Optimized Read Models

The backend separates transaction-time math from display data. Display-oriented accounting snapshots cache volume, probability, participant counts, and chart data. Freshness messaging indicates cache state to users. Pagination applies to bets, positions, leaderboards, and market lists. Boundary tests enforce that snapshots do not participate in buy/sell/resolution transactions—an architectural guard against consistency bugs.

Documented Load Testing and Capacity Evidence

The loadtest/ directory contains a k6-based harness with smoke, baseline, hot-market burst, and soak scenarios. Remote fixture seeding, host telemetry capture (CPU, RAM, disk, network, Docker^{↗ Bright Coding Blog}, Postgres, Traefik), and profiled host recording enable reproducible capacity claims. The [INTERNAL_LINK: infrastructure-monitoring] approach here merits attention from teams building similar platforms.

Immutable Market Contracts with Amendment Workflows

Market titles are immutable; descriptions support additive amendments with admin review queues. Current stewards—not necessarily original creators—govern and resolve markets. Moderator work profits are thresholded and paid after successful resolution, with user-visible financial reporting.

Use Cases

Academic Research and Teaching

Kenyon College's documented adoption for a political science course demonstrates pedagogical application. Instructors can deploy instances for student prediction exercises, with moderator workflows enabling TA-proposed markets and professor-controlled publication. The MIT license permits institutional customization without legal review.

Internal Corporate Forecasting

Organizations can run private instances for product timeline predictions, sales forecasting, or risk assessment. The admin governance tools, market stewardship reassignment, and role-based permissions map to enterprise needs for oversight and operational continuity.

Community and Media Organizations

The OpenGraph metadata, social share images, and planned embeddable markets support public-facing prediction content. Topic navigation and pinned markets enable editorial curation—functionality suited to newsrooms or community platforms building engagement around forecastable events.

Government and Public Policy

The README explicitly includes governments in its target audience. The self-hosted model addresses data sovereignty requirements, while the documented capacity testing supports procurement and compliance processes that demand evidence-based infrastructure claims.

Installation & Setup

Local Development

The project provides dedicated documentation for local setup:

# Referenced paths from the repository:
# - /README/LOCAL_SETUP.md — environment configuration
# - /README/README-CONFIG.md — economic model customization
# - ./SocialPredict dev-bootstrap-users — local smoke testing fixtures

The dev-bootstrap-users command seeds test accounts for development validation. Economic customization is documented separately, indicating the platform supports configurable market mechanisms rather than hardcoded rules.

Web Deployment

Production deployment documentation spans multiple documents:

# Referenced deployment paths:
# - /README/STAGE_SETUP.md — website setup with HTTPS
# - /README/INFRA/README-INFRA-HOSTOPS.md — HostOps scaffold
# - /README/INFRA/README-INFRA-STAGING.md — staging deployment
# - /README/INFRA/README-INFRA-PRODUCTION.md — production deployment

Deployment conventions are established:

• main branch merges deploy to staging at kconfs.com
• Published GitHub releases deploy to brierfoxforecast.com
• GitHub Actions dispatch Ansible-based deploys
• Post-deploy verification checks /health and /readyz endpoints

HostOps serves as a local convenience wrapper for SSH access, environment discovery, disk monitoring, cleanup, and load-test telemetry support.

Real Code Examples

The README does not contain extensive inline code samples; the following reflects documented commands and configuration references. The project emphasizes operational documentation over copy-paste snippets.

Development Bootstrap

# Seed local development fixtures
./SocialPredict dev-bootstrap-users

This command populates local instances with test users for smoke testing. It is referenced in the Getting Started section without additional parameters, suggesting sensible defaults or interactive configuration.

Load Test Execution

The load testing framework is documented in /loadtest/README.md with CLI operations in /loadtest/cli/OPERATING.md. The harness supports:

# Referenced capabilities (exact commands in loadtest docs):
# - k6 smoke tests
# - k6 baseline scenario
# - k6 hot-market burst test
# - k6 soak test
# - Remote staging fixture seeding
# - Fixture CSV pull from remote
# - Host telemetry capture

The separation of load test code into a dedicated loadtest/ directory with its own README, CLI runbook, and dossier directory indicates mature operational documentation.

API Documentation Reference

Backend API documentation is canonicalized at:

# Canonical API reference location:
/backend/docs/README.md

The "canonical" designation suggests this is the maintained, authoritative reference rather than scattered inline comments.

The README's relative paucity of inline code reflects the project's stage—operational and deployment documentation is prioritized over quickstart snippets. Developers should expect to consult the referenced documents for implementation details.

Advanced Usage & Best Practices

Capacity planning: The load-test dossiers emphasize that results depend on exact release, host size, database topology, and rate-limit configuration. Before production sizing, consult the most recent dossier—currently the Capacity Forecast Dossier, 2026-06-02. The 250 bets/sec sustained figure is validated; 300 bets/sec is explicitly noted as not yet a clean target.

Staging discipline: The staging environment at kconfs.com may be reset, load-tested, or unavailable. Do not treat it as a stable demo. For release evaluation, track brierfoxforecast.com which follows published releases.

Moderator workflow design: The proposal-cost and refund behavior for rejected markets creates economic skin-in-the-game for moderators. Organizations adopting this should calibrate proposal costs to their moderation capacity—too low risks spam; too high discourages participation.

Read model awareness: Display widgets include freshness messaging. For applications requiring real-time accuracy (e.g., automated trading), verify whether cached read models meet latency requirements or if direct transaction-path queries are needed.

Comparison with Alternatives

Augur offers stronger decentralization guarantees but with blockchain complexity and throughput constraints. Polymarket provides lower friction for end users but requires trust in platform operators and offers no deployment control. openpredictionmarkets/socialpredict occupies a middle ground: sufficient decentralization of operation (self-hosted) with centralized governance features (moderator workflows, admin controls) that organizations typically require.

FAQ

What license covers openpredictionmarkets/socialpredict? MIT License. Commercial use, modification, and distribution are permitted with attribution.

What are the system requirements for deployment? The README does not specify minimum requirements. Capacity testing used DigitalOcean Basic AMD 8 vCPU / 32 GiB for the 250 bets/sec result.

Can I customize the prediction market economics? Yes. /README/README-CONFIG.md documents economic customization.

Is there a public API? Canonical backend API documentation exists at /backend/docs/README.md.

How current is the project? Last commit dated 2026-07-14. Active development with published release process.

What database does it use? Postgres is referenced in load-test telemetry; exact version requirements are in deployment docs.

Can I embed markets in external sites? Planned feature. OpenGraph metadata and security header updates are in place; iframe-friendly embeds are documented as future work.

Conclusion

openpredictionmarkets/socialpredict delivers concrete, documented infrastructure for prediction markets rather than aspirational promises. Its strengths are operational transparency—published load tests, staged deployment practices, governance tooling—and the permissive MIT license that removes adoption friction. The 212-star, 68-fork footprint reflects early-stage traction with meaningful validation (academic adoption, capacity evidence).

The platform suits organizations prioritizing control over convenience: universities running course markets, companies with internal forecasting needs, media organizations building public engagement, or any entity where data sovereignty and customizable economics matter. It does not suit users seeking zero-configuration SaaS or blockchain-native decentralization.

The roadmap through 2030 indicates sustained ambition; the staged architecture evolution suggests maintainers understand the operational cost of premature complexity. For teams evaluating prediction market infrastructure, the documented capacity evidence and deployment conventions provide uncommon decision-making material.

Explore the repository, review the load-test dossiers, and deploy to staging at kconfs.com to validate against your requirements. The project welcomes contributions—start with CONTRIBUTING.md and the development conventions.

Get started: https://github.com/openpredictionmarkets/socialpredict