Humans and machines have found it extremely difficult to predict asset prices. Low signal to noise ratio and a continually changing market environment are the two main reasons for this low level of success of both humans and machines.
We have successfully tackled this intractable prediction problem by combining the expertise of humans and machines. This decade long effort has given us invaluable insights and experiences into the theory and application of AI/ML technologies.
We welcome hard prediction problems in any industry. These projects lead to deeper insights into the applications of AI/ML algorithms to different industries, increasing our edge in trading the financial markets.
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"I of dice possess the science and in numbers thus am skilled." - King Rituparna, in the Mahabharata, on estimating the leaves on a tree from a randomly selected branch.
Our AI trading algorithms have freed up a significant portion of our time. We launched our AI Advisory Program in the summer of 2018 to make the most of this time.
We are honored that Google's TensorFlow Probability (TFP) Group has chosen our team to develop educational materials about the application of TFP to financial machine learning. Our first blog post and TFP model is about the 'Trinity Of Errors' inherent in all financial models and the urgent need to use probabilistic modeling and machine learning technologies to mitigate them. We are delighted that this blog post has received glowing reviews in the AI/data science communities.
Google's Josh Dillon, co-inventor of TFP, and Deepak recently spoke about Financial Machine Learning With TensorFlow Probability at the Trading Show West conference in San Francisco. You can download the presentation here.
We are also excited about partnering with O'Reilly Media to develop educational content and cutting edge research in machine learning and computational finance. Our first online training course is about rethinking machine learning using the probabilistic models of TFP. The second course is about designing and automating algorithmic trading strategies.