Prof. Marcos Lopez de Prado
Global Head of Quant R&D at ADIA, Cornell Professor, and bestselling author on financial machine learning.
Prof. Caroline Uhler
MIT Professor and Co-Director at the Broad Institute, expert in machine learning, statistics, and causal inference.
Prof. Guido Imbens
2021 Nobel Laureate in Economics, Stanford Professor, and leading authority on causal inference.
Crowdsourcing has a very important role to play in investing. Firms turn investing problems into forecasting problems, then outsource to global researchers.
Prof. Marcos Lopez de Prado
Global Head of Quantitative R&D at ADIA
Client: Broad Institute of MIT & Harvard
The Schmidt Center needed to enhance computer vision algorithms for cell discovery without compromising patient data privacy. The Result: The Crunch network generalized financial modeling architectures to solve biomedical vision tasks, achieving 76.7% accuracy in identifying causal roles and outperforming internal benchmarks by 2x.
Client: ADIA Lab
Distinguishing true cause-and-effect from simple correlation is the hardest challenge in big data. ADIA Lab challenged the network to find causal drivers in high-dimensional datasets. The Result: The network successfully identified true causal graphs, outperforming the client’s internal best models by 17%. This framework is now used to detect "false positives" in massive datasets.
The Problem: Your quant team is limited by headcount, budget, and recruitment timelines. Hiring senior talent takes months. Outsourcing means risking your IP.
The Solution: Crunch runs encrypted ML competitions on your prediction problems. Thousands of global ML engineers and PhDs compete to build superior models. You get breakthrough alpha. They never see your raw data.
Seamless path from competition to deployment with enterprise-grade infrastructure
10,000+ ML engineers and 1,200+ PhDs specializing in ML and quantitative finance competing to solve your hardest problems
Competitions designed by leading researchers from Stanford, Cornell, and Abu Dhabi Investment Authority Lab
Access models through simple APIs. Deploy like any data provider. Works with your existing infrastructure
TEE-based privacy, models run encrypted, deploy in your cloud or on-premise with full data control
Sub-60 microsecond predictions for high-frequency trading and real-time alpha generation
We mitigate overfitting through competitive regularization. Aggregating thousands of independent models neutralizes noise to ensure robust, generalizable signals.
Set up your ML quant challenge on Crunch Hub and open to global talent.
Run from 6-month to never closing competitions on Crunch. Collect diverse solutions from elite researchers while Crunch handles the compute and GPUs hassles.
From day one of your Crunch competition, Crunch is orchestrating all the participants models behind a simple API.
Organizers can use Python to infer, retrain and test any model at any time. They can also deploy model containers in the cloud or on-premises to easily access the models.
Winners are always identified on out-of-sample or live data hold out . Sometime on a period as long as 6 months of live data such as for the ADIA Lab Market Prediction Competition. Crunch handles the payment of the reward, including complete KYC of all winners.
Deploy the model selected to production with institutional proof and ultra-low-latency infrastructure.