Awesome financial time series forecasting papers and codes

Final project repo for UCLA CS 267A

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas…

A Hypergraph-based Framework for Stock Selection via Hidden Concept Mining Representation in Hypergraph Attention Network

Implementation of AAAI-24 CI-STHPAN: Pre-Trained Attention Network for Stock Selection with Channel-Independent Spatio-Temporal Hypergraph

Code for Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting

Code for paper "Temporal Relational Ranking for Stock Prediction"

The source code and data of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information".

TD-HCN: A Trend-Driven Hypergraph Convolutional Network for Stock Recommendation

The code for paper "Enhanced stock price forecasting through a regularized ensemble framework with graph convolutional networks".

Price prediction for Crypto, Stock, and Index using Hybrid Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) models on PyTorch.

该项目是我在HKU量化交易课程期末的项目，旨在复现他人的交易策略。 我复现的是一篇关于加密货币的交易策略的论文，论文原作者为YUKUN LIU,ALEH TSYVINSKI,XI WU，于2022年首次发刊，通过规模、动量、交易量以及波动性这四类主题因子，分析不同特征下加密货币的市场表现。

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

Simplified implementations of deep learning related works

Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction

GCNET: graph-based prediction of stock price movement using graph convolutional network

翻墙-科学上网

PyTorch implementation for Paper "StockFormer: Learning Hybrid Trading Machines with Predictive Coding".

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

This is the project for deep learning in stock market prediction.

An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.

Reproduce the result of the paper "Deep Learning with Long Short-Term Memory Networks for Financial Market Prediction"