Reinforcement Learning for Portfolio Management

Last update: Jan 01, 2023

Overview

qtrader

Reinforcement Learning for Portfolio Management

Why Reinforcement Learning?

Learns the optimal action, rather than models the market.
Adaptive to temporary changes of the market, due to its online training.
Optimizes the long-term (cumulative) reward, rather than the instantaneous benefit.

Setup

Exclusively Python 3 compatible, because of typings

macOS

source scripts/setup.sh

Documentation

Interim Report: Introduction to qtrader
Papers Review: Motivation, pros & cons of existing methods
Resources: List of relevant resources
Final Report: Master's Thesis
Presentation: 15 minutes project presentation

Owner

Angelos Filos

Machine Learning at the University of Oxford.

GitHub Repository

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

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Reinforcement Learning for Portfolio Management

Related tags

Overview

qtrader

Why Reinforcement Learning?

Setup

macOS

Documentation

Owner

Angelos Filos

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

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FreeSOLO for unsupervised instance segmentation, CVPR 2022

Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

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A python-image-classification web application project, written in Python and served through the Flask Microframework

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