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

Multi-Scale Aligned Distillation for Low-Resolution Detection (CVPR2021)

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Reinforcement Learning for Portfolio Management

Related tags

Overview

qtrader

Why Reinforcement Learning?

Setup

macOS

Documentation

Owner

Angelos Filos

Multi-Scale Aligned Distillation for Low-Resolution Detection (CVPR2021)

Bridging Composite and Real: Towards End-to-end Deep Image Matting

🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos

Implementation of Fast Transformer in Pytorch

High level network definitions with pre-trained weights in TensorFlow

STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)

tf2onnx - Convert TensorFlow, Keras and Tflite models to ONNX.

Code for the Paper "Diffusion Models for Handwriting Generation"

Hepsiburada - Hepsiburada Urun Bilgisi Cekme

CoMoGAN: continuous model-guided image-to-image translation. CVPR 2021 oral.

The easiest tool for extracting radiomics features and training ML models on them.

Code release for Local Light Field Fusion at SIGGRAPH 2019

SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

Text Extraction Formulation + Feedback Loop for state-of-the-art WSD (EMNLP 2021)

Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

A Python framework for developing parallelized Computational Fluid Dynamics software to solve the hyperbolic 2D Euler equations on distributed, multi-block structured grids.

UIUCTF 2021 Public Challenge Repository

Drone Task1 - Drone Task1 With Python

CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energy Management, 2020, PikaPika team