Numerai has released a new signal evaluation method called True Contribution. True Contribution is computed by treating Numerai as an end-to-end artificial intelligence system. By computing the gradient of optimized portfolio returns with respect to the NMR staked on a signal using differentiable convex optimization layers, Numerai can now surface and incentivize signals making the largest intelligence contributions to our hedge fund. Numerai’s performance is already good (our Sharpe Ratio* is 2.54 over the last 12 months) but True Contribution has the potential to make Numerai the first “Type IV” hedge fund.
If you’ve heard of Numerai and read any of our documentation you’ve likely heard of Numerai Compute and it’s associated tool, numerai-cli (if you haven’t heard of us, check us out). This was great if you wanted to create some resources in AWS, deploy your models to the cloud, and allow Numerai to trigger your models automatically, leaving you to sip your beverage of choice, put your feet up on your desk, and watch the daily scores roll in.
Currently, it’s supported anywhere where Python 3 and Docker are available including MacOS/OSX, Windows 10 (and 8 with some extra work), and Ubuntu 18+. We recently released the 0.2.* version series which provided some setup scripts to increase accessibility, allowing you to run just 5 commands to start automatically submitting from the cloud. Creating autonomous Numerai models has never been easier.
Despite this, our CLI (and the Compute Cluster you set up with it) has been feature poor; you could only deploy 1 code base to 1 cloud provider and the example Python code was mom’s spaghetti [code]. If a user wanted to parallelize their models, they would have to figure it out. If a user wanted to debug their deployed prediction node, they would have to figure it out. If a user wanted to modify the example Python code, they would have to figure it out. These issues make Compute inaccessible to the majority of current and new users.
So what are we doing about it? In Numerai CLI 0.3.0, the example Python code has been streamlined from 3 examples totaling over 500 lines of code, to 1 example under 100 lines. Our Cloud Provisioning code has been modularized to a plug-and-play design so we can continue adding new cloud providers like GCP. Finally, our CLI has been restructured to be centered on Prediction Nodes (more on those later); this means you can run your models however you want, deploy them wherever you want, and submit to any tournament you want on a per-model basis. Compute customization has never been easier.
Streamlined, easier to read, and no longer tied to complex, deprecated packages, we’ve taken steps towards easing the learning curve by using every basic concept you need in one file. This includes introducing the idea of multiple models in the first few lines as well as simple, atomic functions for retrieving data, fitting, predicting, and submitting; all of this in less than 100 lines of code. We’ve also included a new example model for Numerai Signals, which introduces the concepts of pulling in your own financial data and generating your own features. It’s certainly more complicated than the tournament example, clocking in at just under 400 lines in total, but each component is still logically separated into a functional paradigm to allow a novice data scientist to read and understand how to analyze market data.
In the future, these examples will serve as the stepping stones for both veteran data scientists and newbies alike by encouraging everyone to simply change a few lines to improve their models and continue making simple changes until they’ve become a Numerai Master.
A useful and simple first example is vital in our Master Plan. We can’t monopolize intelligence, data, or the market unless we diversify our users, bring in new ideas, and make our strategies better than the current hedge fund overlords. Being a beginner has never been easier.
We imagine a future in which Numerai can ping any AI on any infrastructure for any prediction at any time. This isn’t feasible unless we architect the tools to build this future; Rome wasn’t built in a day and it wasn’t built with a stone hammer. Numerai CLI 0.3.0 aims to give users a better way to build and deploy any model by re-architecting around the principle of plug-and-play modules.
