Iqisa is a library for handling and comparing forecasting datasets from different platforms.

Iqisa: A Library For Handling Forecasting Datasets

The eventual success of my archives reinforced my view that public permission-less datasets are often a bottleneck to research: you cannot guarantee that people will use your dataset, but you can guarantee that they won’t use it.

—Gwern Branwen, “2019 News”, 2019

Iqisa is a collection of forecasting datasets and a simple library for handling those datasets. Code and data available here.

So far it contains data from:

The Good Judgment Project: ~790k market trades+~3.14m survey predictions≈3.9m forecasts
The Metaculus public API: ~210k forecasts
PredictionBook: ~64k forecasts

for a total of ~4.2m forecasts, as well as code for handling private Metaculus data (available to researchers on request to Metaculus), but I plan to also add data from various other sources.

The documentation can be found here, but a simple example for using the library is seeing whether traders with more than 100 trades have a better Brier score than traders in general:

import gjp
import iqisa as iqs
market_fcasts=gjp.load_markets()

def brier_score(probabilities, outcomes):
	return np.mean((probabilities-outcomes)**2)

def brier_score_user(user_forecasts):
	user_right=(user_forecasts['outcome']==user_forecasts['answer_option'])
	probabilities=user_forecasts['probability']
	return np.mean((probabilities-user_right)**2)

trader_scores=iqs.score(market_fcasts, brier_score, on=['user_id'])
filtered_trader_scores=iqs.score(market_fcasts.groupby(['user_id']).filter(lambda x: len(x)>100), brier_score, on=['user_id'])

And we can see:

>>> np.mean(trader_scores)
score    0.159194
dtype: float64
>>> np.mean(filtered_trader_scores)
score    0.159018
dtype: float64

Concluding that more experienced traders are only very slightly better at trading.

Usages

Range and Forecasting Accuracy (niplav, 2023)

Possible Projects

Take questions from different platforms that are close to each other on sentence2vec, and check which platform made the better predictions on that question.

Known Bugs

Since this is a project I’m now doing in my free time, it might not be as polished as it should be. Sorry :-/

If you decide to work with this library, feel free to contact me.

Issues with the time fields
- The native pandas datetime format is too restricted for some time ranges in these datasets, those values might be set to NaT.
- Not all time-related fields have timezone information attached to them.
Some predictions in the dataset have occurred after question resolution. There should be a way to filter those out programmatically.
The columns of the datasets are not sorted the same way for question DataFrames and forecast DataFrames.
I fear that despite my best efforts, not all data frome the GJP data has been transferred.
The default fields in the Metaculus & PredictionBook data should be NA more often than they are right now.
The documentation is still slightly spotty, and tests are mostly nonexistent.
Some variables shouldn’t be exposed, but are.

Feature Wishlist

Create a pip package
Add data from more platforms

Potential Additional Sources for Forecasting Data

Acknowledgements

Credits go to Arb Research for funding the first 85% of this work, and Misha Yagudin in particular for guidance and mentorship.