Reinforcement Learning for Finance

Abstract: 

Reinforcement Learning and related algorithms, such as Deep Q-Learning (DQL), have led to major breakthroughs in different fields. DQL, for example, is at the core of the AIs developed by DeepMind that achieved superhuman levels in such complex games as Chess, Shogi, and Go ("AlphaGo", "AlphaZero"). Reinforcement Learning can also be beneficially applied to typical problems in finance, such as algorithmic trading, dynamic hedging of option, or dynamic asset allocation. The workshop addresses the problem of limited data availability in finance and solutions to it, such as synthetic data generation through GANs. It also shows how to apply the DQL algorithm to typical financial problems. The workshop is based on my new O'Reilly book "Reinforcement Learning for Finance -- A Python-based Introduction".

Session Outline:

The workshop comprises the following modules:

* Basics of Deep Q-Learning: basic elements of Reinforcement Learning and the DQL algorithm are discussed and illustrated through code examples
* Synthetic Data Generation: using a Generative Adversarial Network (GAN), synthetic returns data is generated that shows statistical characteristics very close to those observed for real financial returns data
* Dynamic Asset Allocation: DQL is applied to the problem of dynamically allocate funds to different assets in order to maximize the realized Sharpe ratio

Background Knowledge:

Basic proficiency with Python and Finance is expected. Attendees who want to follow along should know how to set up a Python environment (e.g. with conda) and how to install the required packages.

Bio: 

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

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