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

Last update: Dec 27, 2022

Related tags

Deep Learning HIST

Overview

The HIST framework for stock trend forecasting

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

Environment

Install python3.7, 3.8 or 3.9.
Install the requirements in requirements.txt.

Install the Qlib and download the data:

# install Qlib from source
pip install --upgrade  cython
git clone https://github.com/microsoft/qlib.git && cd qlib
python setup.py install

# Download the stock features of Alpha360
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn --version v2

Reproduce the stock trend forecasting results

git clone https://github.com/Wentao-Xu/HIST.git
cd HIST
mkdir output

Reproduce our HIST framework

# CSI 100
python learn.py --model_name HIST --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_HIST

# CSI 300
python learn.py --model_name HIST --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_HIST

Reproduce the baselines

# MLP on CSI 100
python learn.py --model_name MLP --data_set csi100 --hidden_size 512 --num_layers 3 --outdir ./output/csi100_MLP

# MLP on CSI 300
python learn.py --model_name MLP --data_set csi300 --hidden_size 512 --num_layers 3 --outdir ./output/csi300_MLP

LSTM

# LSTM on CSI 100
python learn.py --model_name LSTM --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_LSTM

# LSTM on CSI 300
python learn.py --model_name LSTM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_LSTM

# GRU on CSI 100
python learn.py --model_name GRU --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_GRU

# GRU on CSI 300
python learn.py --model_name GRU --data_set csi300 --hidden_size 64 --num_layers 2 --outdir ./output/csi300_GRU

# SFM on CSI 100
python learn.py --model_name SFM --data_set csi100 --hidden_size 64 --num_layers 2 --outdir ./output/csi100_SFM

# SFM on CSI 300
python learn.py --model_name SFM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_SFM

GATs

# GATs on CSI 100
python learn.py --model_name GATs --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_GATs

# GATs on CSI 300
python learn.py --model_name GATs --data_set csi300 --hidden_size 64 --num_layers 2 --outdir ./output/csi300_GATs

ALSTM

# ALSTM on CSI 100
python learn.py --model_name ALSTM --data_set csi100 --hidden_size 64 --num_layers 2 --outdir ./output/csi100_ALSTM

# ALSTM on CSI 300
python learn.py --model_name ALSTM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_ALSTM

Transformer

# Transformer on CSI 100
python learn.py --model_name Transformer --data_set csi100 --hidden_size 32 --num_layers 3 --outdir ./output/csi100_Transformer

# Transformer on CSI 300
python learn.py --model_name Transformer --data_set csi300 --hidden_size 32 --num_layers 3 --outdir ./output/csi300_Transformer

ALSTM+TRA

We reproduce the ALSTM+TRA with its source code.

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

Related tags

Overview

The HIST framework for stock trend forecasting

Environment

Reproduce the stock trend forecasting results

Reproduce our HIST framework

Reproduce the baselines

Owner

Wentao Xu

某学校选课系统GIF验证码数据集 + Baseline模型 + 上下游相关工具

Object detection, 3D detection, and pose estimation using center point detection:

PyTorch code for: Learning to Generate Grounded Visual Captions without Localization Supervision

Styled Handwritten Text Generation with Transformers (ICCV 21)

The code for our paper submitted to RAL/IROS 2022: OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition.

CVPR2022 paper "Dense Learning based Semi-Supervised Object Detection"

A modular application for performing anomaly detection in networks

Repo for EMNLP 2021 paper "Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression"

Least Square Calibration for Peer Reviews

CVPRW 2021: How to calibrate your event camera

Relative Positional Encoding for Transformers with Linear Complexity

hySLAM is a hybrid SLAM/SfM system designed for mapping

Pre-Training 3D Point Cloud Transformers with Masked Point Modeling

Predicting Tweet Sentiment Maching Learning and streamlit

User-friendly bulk RNAseq deconvolution using simulated annealing

Chainer Implementation of Semantic Segmentation using Adversarial Networks

An elaborate and exhaustive paper list for Named Entity Recognition (NER)

Neural Architecture Search Powered by Swarm Intelligence 🐜

Official Pytorch Implementation of GraphiT

PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds