[Metaforecast](https://metaforecast.org) is a search engine for probabilities from various prediction markes and forecasting platforms. Try searching "Trump", "China" or "Semiconductors".
This repository includes the source code for both the website and the library that fetches forecasts needed to replace them. We also aim to provide tooling to integrate metaforecast with other services.
You'll need a PostgreSQL instance, either local (see https://www.postgresql.org/download/) or in the cloud (for example, you can spin one up on https://www.digitalocean.com/products/managed-databases-postgresql or https://supabase.com/).
`npm run cli` starts a local CLI which presents the user with choices. If you would like to skip that step, use the option name instead, e.g., `npm run cli wildeford`.
- Twitter, using our [@metaforecast](https://twitter.com/metaforecast) bot
- [Global Guessing](https://globalguessing.com/russia-ukraine-forecasts/), which integrates our dashboards
- [Fletcher](https://fletcher.fun/), a popular Discord bot. You can invoke metaforecast with `!metaforecast search-term`
- [Elicit](https://elicit.org/), which uses GPT-3 to deliver vastly superior semantic search (as opposed to fuzzy word matching). If you have access to the Elicit IDE, you can use the action "Search Metaforecast database. This is not being updated regularly.
We also provide a public database, which can be accessed with a script similar to [this one](src/backend/manual/manualDownload.ts). We are also open to integrating our Algolia search instance with other trusted services (in addition to Fletcher.)
- frontend code is in [src/pages/](./src/pages/), [src/web/](./src/web/) and in a few other places which are required by Next.js (e.g. root-level configs in postcss.config.js and tailwind.config.js)
- various backend code is in [src/backend/](./src/backend/)
- fetching libraries for various platforms is in [src/backend/platforms/](./src/backend/platforms/)
Star ratings—e.g. ★★★☆☆—are an indicator of the quality of an aggregate forecast for a question. These ratings currently try to reflect my own best judgment and the best judgment of forecasting experts I've asked, based on our collective experience forecasting on these platforms. Thus, stars have a strong subjective component which could be formalized and refined in the future. You can see the code used to decide how many stars a forecast should get by looking at the function `calculateStars()` in the files for every platform [here](./src/backend/platforms).
With regards the quality, I am most uncertain about Smarkets, Hypermind, Ladbrokes and WilliamHill, as I haven't used them as much. Also note that, whatever other redeeming features they might have, prediction markets rarely go above 95% or below 5%.
