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

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Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

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Reinforcement Learning for Portfolio Management

Related tags

Overview

qtrader

Why Reinforcement Learning?

Setup

macOS

Documentation

Owner

Angelos Filos

Train emoji embeddings based on emoji descriptions.

This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing described in paper Discontinuous Grammar as a Foreign Language.

Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Object tracking using YOLO and a tracker(KCF, MOSSE, CSRT) in openCV

A minimal TPU compatible Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

COIN the currently largest dataset for comprehensive instruction video analysis.

Drone detection using YOLOv5

Direct Multi-view Multi-person 3D Human Pose Estimation

Histocartography is a framework bringing together AI and Digital Pathology

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

Code of the paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodner and Joachim Denzler

CMSC320 - Introduction to Data Science - Fall 2021

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

A framework to train language models to learn invariant representations.

Reproducing code of hair style replacement method from Barbershorp.

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Combining Diverse Feature Priors

Source code for the plant extraction workflow introduced in the paper “Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision”