Reinforcement learning is a natural paradigm for automating the design of financial trading policies. Training the trading policies on historical financial data is challenging because financial data is limited to a few values per trading day (e.g. stock daily close price) and as such the amount of training data is relatively low. Federated Learning offers a potential solution by training on many parties' data, thereby increasing the amount of training data overall. A recent work by this team shows how it is possible to convert an RL strategy for training a portfolio optimization policy on a set of assets to a multi-task learning problem that benefits tremendously from federated learning. We implement the method on the federated reinforcement learning capability of the IBM Federated Learning (IFL) platform.
The session includes three sections: 1) We first give a mini-tutorial on using the IBM Federated Learning (IFL) platform for any federated reinforcement learning problem and illustrate it on the openAI gym pendulum example. 2) A demo shows how the IFL works on the financial portfolio optimization problem. 3) The accompanying talk provides more details on the method and results.
Presenters: Peng Qian Yu, Hifaz Hassan, Laura Wynter