Overview
In this competition, hosted by Jane Street, you'll build a model using real-world data derived from production systems, which offers a glimpse into the daily challenges of successful trading. This challenge highlights the difficulties in modeling financial markets, including fat-tailed distributions, non-stationary time series, and sudden shifts in market behavior.

Start

2 months ago
Close
a month to go
Merger & Entry
Description
When approaching modeling problems in modern financial markets, there are many reasons to believe that the problems you are trying to solve are impossible. Even if you put aside the beliefs that the prices of financial instruments rationally reflect all available information, you’ll have to grapple with time series and distributions that have properties you don’t encounter in other sorts of modeling problems. Distributions can be famously fat-tailed, time series can be non-stationary, and data can generally fail to satisfy a lot of the underlying assumptions on which very successful statistical approaches rely. Layer on all of this the fact that the financial markets are ultimately a human endeavor involving a large number of individuals and institutions that are constantly changing with advances in technology and shifts in society, and responding to economic and geopolitical issues as they arise - and you can start to get a sense of the difficulties involved!

In this challenge, we ask you to build a model using real-world data derived from some of our production systems. This data gives a very close picture of some of the things we have to do every day to be successful at trading in modern financial markets. We’ve assembled a collection of features and responders related to markets where we run automated trading strategies and are concerned about having good underlying models. To balance crafting a challenging, relevant problem that ties into our business while respecting the proprietary and highly competitive nature of our trading, you will notice that we have anonymized and lightly obfuscated some of the features and responders we present in the data. These modifications don’t change the essence of the problem at hand, but they do allow us to give you a difficult task that meaningfully illustrates the work we do at Jane Street.

Jane Street has spent decades relentlessly innovating on all aspects of our trading, and building machine learning models to aid our decision-making. These models help us actively trade thousands of financial products each day across 200+ trading venues around the world. While this challenge only presents a tiny fraction of the quantitative problems Jane Streeters work on daily, we are very interested in seeing how the Kaggle community will approach this challenge, and in engaging with you about your solutions to the problem!

Evaluation
Submissions are evaluated on a scoring function defined as the sample weighted zero-mean R-squared score (R2
) of responder_6. The formula is give by:

R2=1−∑wi(yi−yi^)2∑wiy2i,
where y
 and y^
 are the ground-truth and predicted value vectors of responder_6, respectively; w
 is the sample weight vector.

Submission File
You must submit to this competition using the provided evaluation API, which ensures that models do not peek forward in time. See this example notebook for more details.

Timeline
This is a forecasting competition with an active training phase and a second period where models will be run against real market returns.
Training Timeline:
October 14, 2024 - Start date.

January 6, 2025 - Entry deadline. You must accept the competition rules before this date in order to compete.

January 6, 2025 - Team Merger deadline. This is the last day participants may join or merge teams.

January 13, 2025 - Final submission deadline.

All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.

Forecasting Timeline:
Starting after the final submission deadline there will be periodic updates to the leaderboard to reflect market data updates that will be run against selected notebooks.

July 12, 2025 - Competition End Date
Prizes
1st Place - $50,000
2nd Place - $25,000
3rd Place - $10,000
4th - 10th Place - $5,000
Code Requirements


This is a Code Competition
Submissions to this competition must be made through Kaggle Notebooks. For this competition, training is not required in Kaggle Notebooks. In order to be eligible for submission, the following conditions must be met:

Training Phase
Your notebook must use the time-series module to make predictions
CPU Notebook <= 8 hours run-time
GPU Notebook <= 8 hours run-time
TPU Notebooks are not supported
Freely & publicly available external data is allowed, including pre-trained models
Forecasting Phase
The run-time limits for both CPU and GPU notebooks will be extended to 9 hours during the forecasting phase. You must ensure your submission completes within that time.

The extra hour is to help protect against time-out failures due to the extended size of the test set. You are still responsible for ensuring your submission completes within the 9 hour limit, however. See the Data page for details on the extended test set during the forecasting phase.

Please see the Code Competition FAQ for more information on how to submit.

Citation
Maanit Desai, Yirun Zhang, Ryan Holbrook, Kait O'Neil, and Maggie Demkin. Jane Street Real-Time Market Data Forecasting. https://kaggle.com/competitions/jane-street-real-time-market-data-forecasting, 2024. Kaggle.


https://www.kaggle.com/competitions/jane-street-real-time-market-data-forecasting
kaggle competition