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AI forecasting on live prediction markets
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FinancePython · TypeScriptGitHub @PrakharGoel7OnlineOwner unconfirmed
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Agentic forecasting and prediction-market portfolio platform with calibrated probabilities, Kalshi integration, and social thesis sharing.
Taken from the repository README
Loading the full README.
Read the full README ↗A social forecasting platform for turning beliefs about the future into calibrated probabilities and shareable prediction-market portfolios.
Prism combines a multi-agent forecasting engine with live Kalshi market data. Users can research a binary question, express a broader thesis as a weighted basket of contracts, and publish that thesis for others to discover and follow.
ENABLE_LIVE_TRADING=true on the API service.flowchart LR
UI[Next.js web app] --> API[FastAPI service]
API --> Forecast[Multi-agent forecaster]
API --> Basket[Trading companion]
Forecast --> Router[OpenRouter]
Basket --> Router
Forecast --> Search[Web research]
Basket --> Kalshi[Kalshi market data]
API --> DB[(PostgreSQL / SQLite)]
UI --> Auth[Supabase Auth]
The forecasting pipeline separates outside-view reasoning from inside-view reasoning. Three base-rate agents build independent reference classes, a reconciler establishes a prior, inside-view agents update it, and a supervisor produces the final ensemble estimate. Platt scaling is applied before the result is returned.
| Layer | Technology |
|---|---|
| Web | Next.js 16, React 19, TypeScript, Framer Motion |
| API | FastAPI, Pydantic, Uvicorn |
| AI | OpenRouter, structured multi-agent workflows |
| Markets | Kalshi API and local market caches |
| Data and auth | PostgreSQL with SQLite fallback, Supabase Auth |
| Deployment | Vercel and Railway |
forecaster/ Core forecasting engine and CLI
trading_companion/ Belief analysis and basket-construction agents
prism/api/ FastAPI application and persistence layer
prism/frontend/ Next.js product experience
tests/ Deterministic forecasting unit tests
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
uvicorn prism.api.main:app --reload --port 8000
Fill in the credentials you need in .env. Without DATABASE_URL, the API uses a local SQLite database.
cd prism/frontend
npm ci
cp .env.example .env.local
npm run dev
Open http://localhost:3000. The local API runs at http://localhost:8000.
The forecasting engine can also run independently:
python -m forecaster forecast \
"Will the Federal Reserve cut rates before July 2027?" \
--runs 3 \
--output forecast.json
| Variable | Purpose |
|---|---|
OPENROUTER_API_KEY | Model access for forecasting and basket agents |
KALSHI_API_KEY | Kalshi API identifier |
KALSHI_PRIVATE_KEY_FILE | Path to a Kalshi private key file |
DATABASE_URL | PostgreSQL connection string; optional locally |
SUPABASE_URL | Supabase project URL used by the API |
SUPABASE_JWT_SECRET | Validates authenticated API requests |
KALSHI_CREDENTIALS_ENCRYPTION_KEY | Fernet key used to encrypt each user's Kalshi key ID and private key at rest; keep server only and retain for existing connections |
ENABLE_LIVE_TRADING | Set to true after deployment checks to allow confirmed Kalshi order submission |
PRISM_ADMIN_API_KEY | Optional secret for diagnostic and maintenance endpoints; if unset, they are unavailable |
ALLOWED_ORIGINS | Comma-separated CORS allowlist |
NEXT_PUBLIC_API_URL | Browser-facing API base URL |
NEXT_PUBLIC_SUPABASE_URL | Supabase project URL used by the frontend |
NEXT_PUBLIC_SUPABASE_ANON_KEY | Supabase browser client key |
See the checked-in .env.example files for the full local templates.
python -m unittest discover -s tests -v
python -m compileall -q forecaster trading_companion prism/api
cd prism/frontend
npm run lint
npm run build
Prism is a research and portfolio-construction project. Its forecasts and market views are not financial advice.