Nima Nooshi

Customer Success Engineer, Databricks

Past sessions

Democratisation of AI and invent of big data technologies has disrupted the quantitative finance practice. Various ML and DL models provide the next generation of nonlinear and non-intuitive time-series modelling compared to the traditional econometric. The same applies to optimisation problems. Reinforcement learning provides an alternative approach to the stochastic optimisation which has been traditionally used in the context of portfolio management.

In this talk I will showcase an end to end asset management pipeline based on recent AI developments. I will show how to build an autonomous portfolio manager which learns to rebalance the portfolio assets dynamically. Not only does the autonomous agent use AI (say an actor-critic type of network as in DDPG) to learn, it can usually use various other AI components to help with the learning. In particular, I discuss how a predictive AI component, such as a nonlinear-dynamic Boltzmann machine, can improve the learning of the agent. This component uses AI to improve on well known autoregressive models to predict the prices of the assets in the portfolio for the next time step. These can then be fed into the learning agent to essentially restrict the exploration of the whole action-state space. I also discuss the possibility of using a data generating component (using GANs) to learn the conditional distribution of asset prices and then generate synthetic data to overcome the problem of limited historical data.

With all this, I plan to provide:

  1. Conceptual understanding of how AI/Big Data change the traditional quantitative finance practice.
  2. How a real example of an end to end data pipeline looks like and how different components of a complex model work together in a unified platform architecture