In this series we discuss the economics of developing, deploying, and maintaining trading signals based on Artificial Intelligence (AI) and Machine Learning (ML). When should you build and when should you buy? Read on!
Introducing Octane, Signum's newest long-short index strategy built off reserve order activity data. Learn more about Signum's strategies
An alpha strategy based on Signum’s Liquidity Lamp end-of-day summary can outperform the S&P 500 4-to-1. It’s an example of the signal’s power.
In part 2 of the blog series learn about alpha cloning and popular strategies for replicating hedge funds’ portfolios and gaining traction with today’s innovations in machine learning technology.
Predictive trading signals can help achieve better execution. Learn how AI-powered signals can enhance execution algos.
Alpha Cloning, once known as Coattail investing, is a popular strategy used to replicate hedge funds. Part 1 details copycat methods that use forms 4 and 13F.
As AI is more widely used for predictive trading signals, firms face potential alpha decay—lost alpha from crowded trades. We offer ways to avoid that risk.
This article details key considerations for firms looking to develop AI-powered trading signals, either in-house or through a vendor.
Millisecond and even nanosecond trading speeds have become table stakes. Now, firms look for their next competitive edge: predictive signals driven by AI.
Iceberg orders, also known as reserve orders, are sources of significant hidden liquidity that can be uncovered using order-level market data.
In this article, we address those firms with existing investments in trading signals based on Artificial Intelligence (AI) and Machine Learning (ML) technology.
Welcome to our new “Signum cents” series of articles on the economics of developing and maintaining trading signals (a.k.a. predictive market data and real-time analytics).