These forecasts are then used to power a search engine for probabilities, which can be found [here](https://metaforecast.org/) (try searching "Trump", "China" or "Semiconductors") (source code [here](https://github.com/QURIresearch/metaforecast-website-nextjs)). A json endpoint can be found [here](https://metaforecast.org/data/metaforecasts.json).
I also created a search engine using Elicit's IDE, 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".
The following environment variables are currently needed to run the `master` branch:
-`MONGODB_URL`, a string in the format `"mongodb+srv://<username>:<password>@<mongodburl>/?retryWrites=true&w=majority&useNewUrlParser=true&useUnifiedTopology=true"`
-`REBUIDNETLIFYHOOKURL`, a string in the format `"https://api.netlify.com/build_hooks/someprivatestring"`
-`CSETFORETELL_COOKIE`
-`GOODJUDGMENTOPENCOOKIE`
-`HYPERMINDCOOKIE`
The cookie formats can be found in `src/input/privatekeys_example.json`; these session cookies are necessary to query CSET-foretell, Good Judgment Open and Hypermind. You can get these cookies by creating an account in said platforms and then making and inspecting a request (e.g., by making a prediction, or browsing questions). After doing this, you should create the environment variables.
Alternatively, for fewer complications, have a look at the `commandlineinterface` branch, which instead of requiring environment variables only requires a `src/privatekeys.json`, in the same format as its `src/privatekeys_example.json`. Its disadvantages are that the command line tool in the `commandlineinterface` is more difficult to integrate with other services.
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 to assign [here](https://github.com/QURIresearch/metaforecasts/blob/master/src/stars.js)
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%.
- Right now, I'm fetching only a couple of common properties, such as the title, url, platform, whether a question is binary (yes/no), its percentage, and the number of forecasts. However, the code contains more fields commented out, such as trade volume, liquidity, etc.
