Hyunjoon Kim's repositories
pykan
Kolmogorov Arnold Networks
efficient-kan
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
MASTER
This is the official code and supplementary materials for our AAAI-2024 paper: MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. MASTER is a stock transformer for stock price forecasting, which models the momentary and cross-time stock correlation and guide feature selection with market information.
PatchTST
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
qlib
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
stock-prediction-deep-neural-learning
Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting
ta
Technical Analysis Library using Pandas and Numpy
HIST
The source code and data of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information".
Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Datascience-Interview-Questions
Datascience-Interview-Questions for Korean
Adv-ALSTM
Code for paper "Enhancing Stock Movement Prediction with Adversarial Training" IJCAI 2019
stocknet-code
Code for stock movement prediction from tweets and historical stock prices.
stocknet-dataset
A comprehensive dataset for stock movement prediction from tweets and historical stock prices.
Stock-Price-Prediction-Model
Stock Price prediction using news data. The datasets used consists news and stock price data from 2008 to 2016. The polarity(Subjectivity, Objectivity, Positive, Negative, Neutral) data is gathered from the news data and further used to predict stock prices. Achieved an accuracy of 94% using XGBoost.