zl1665 Goto Github PK

498project

498P

adgat

Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks

adjustautocorrelation

Adjusting for Autocorrelated Errors in Neural Networks for Time Series

awesome-of-time-series-prediction

awesome-quant-machine-learning-trading

Quant/Algorithm trading resources with an emphasis on Machine Learning

bht-arima

Code for paper: Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting (AAAI-20)

cryptobubbles-naacl

deep-finance

Datasets, papers and books on AI & Finance.

deep-reinforcement-learning-for-optimal-execution-of-portfolio-transactions-using-ddpg

Performing a trading strategy using deep deterministic policy gradients to know when to buy, hold or sell stocks in a virtual environment that simulates stock prices.

deeptrends

Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series (AAAI'20)

dl4stock

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

earningscall_dataset

The earnings conference call dataset of S&P 500 companies

f-hmtc

Source codes of F-HMTC (IJCAI'20)

finbert

A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097

gluonts-hierarchical-icml-2021

gts

Discrete Graph Structure Learning for Forecasting Multiple Time Series, ICLR 2021.

hats

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

html-hierarchical-transformer-based-multi-task-learning-for-volatility-prediction

Code for WWW-20 Paper: HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction

informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

man-sf

Reproduction of the paper "Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations"

man-sf-emnlp

mtdnn

Multi-scale Two-way Deep Neural Network

mtgnn

multi-gcgru

The code and datasets of "Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction"

overnight-stock-movement-prediction

reinforcement_learning_oe

The work aims to explore Value based, Deep Reinforcment Learning (Deep Q-Learning and Double Deep Q-Learning) for the problem of Optimal Trade Execution. The problem of Optimal Trade Execution aims to find the the optimal "path" of executing a stock order, or in other words the number of shares to be executed at different steps given a time constraint, such that the price impact from the market is minimised and consequently revenue from executing a stock order maximised.

rl_markets

Market Making via Reinforcement Learning

st-norm

stemgnn

Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

stocknet-code

Code for stock movement prediction from tweets and historical stock prices.