Artificial Intelligence and Machine Learning in Investment

AI and ML reflect the natural evolution of technology as increased computing power enables computers to sort through large data sets and crunch numbers to identify patterns and outliers.

Today, AI and ML are being employed to improve the customer experience, increase the efficiency and accuracy of operational workflows, and enhance performance by
supporting multiple aspects of the investment process.

Hedge Fund A.I. Based

Our Hedge Fund  is one of the main tools of our business and is driven by our Artificial Intelligence (A.I.) technology that represent the state of the art in trading business.

A.I. is managing more that 50+ trading algorithms that are studied for trading specific assets like commodities, crypto-currencies, tokens, indexes, options and much more.

Our trading beliefs are based on our collective market experience, which gives us a very incomplete picture of the trading infrastructure world, and it’s easy to jump to false conclusions. 


Our A.I., on the other side, manages bias-free trading, as preconceived notions of the market are symbolic of human cognition, trading traits hardwired into the brain, creating biases over and beyond those that are based factually.With more than 50+ algorithms the A.I. is changing the way we trade, one quantitative trade at a time.

Central Bank activities have distorted market prices and price discovery up to a point where it is hard to find Economic Ratio in many areas of financial markets. We developed our A.I. to optimally trade through this period of extreme manipulation while setting up for the inevitable move back to economic fundamentals.
Trading via deep learning machine is the systematic execution of trading orders decided by quantitative market models. Some estimates have quantitative or algorithmic trading now accounting for over 75% of the trading volume in the United States alone. 

This inspired us to create our own Artificial Intelligence. Built upon comprehensive and accurate prediction models incorporating a strategy that has the capacity to absorb multiple billions of dollars without negatively impacting its returns. Deep Learning Machine’s core strategy is based on sound econometric or rational basis and time parameters, not random discovery of patterns and crucially few parameters that need to be fitted to past data.

Breaking down the trading strategy


History will tell if current central bank policies will have proved beneficial to the world’s economies. What we do know already is that they have brought about significant revaluations across all major assets classes and with that it has proved beneficial to the owners of such assets and borrowers. One obvious side effect of this policy is that market price discovery is significantly distorted and thus capital is no longer allocated through the economically most efficient process.  In a world where it makes an economic difference whether rates are positive or negative this is what one has to expect. 


The return distributions of daily fluctuations have to take this asymmetry into account.