HAN

TensorFlow implementation of Z. Hu et al. "Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction", WSDM 2018

Main components
- TensorFlow 1.4.0
- Numpy
- Scikit-learn
Dataset
- Stock prices and tweets
  - Yumo Xu and Shay B. Cohen "Stock Movement Prediction from Tweets and Historical Prices", ACL 2018.
  - Copy https://github.com/yumoxu/stocknet-dataset/tree/master/price/preprocessed/* files to {PROJECT_PATH}/data/price/preprocessed/
  - Copy https://github.com/yumoxu/stocknet-dataset/tree/master/tweet/preprocessed/* files to {PROJECT_PATH}/data/tweet/preprocessed/
  - 87 stocks (S & P 500)
  - 31 Dec 2013 ~ 31 Dec 2015
Word Representation
- Download http://nlp.stanford.edu/data/glove.twitter.27B.zip
- Extract to data/
Working directory setting

$ export PYTHONPATH=$PYTHONPATH:$(pwd)

Experiment

Run dataset.py
Run main.py

Future Reference

Word Representation
- fastText (ref. https://github.com/facebookresearch/fastText#building-fasttext-for-python)
  - Installation
```
$ git clone https://github.com/facebookresearch/fastText.git
$ cd fastText
$ pip3 install .
```
  - wiki english folder set
    - ~/common/fasttext/wiki.en.bin
    - Download - https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.en.zip
- BERT
  - BERT-Large, uncased, whole word masking
    - https://storage.googleapis.com/bert_models/2019_05_30/wwm_uncased_L-24_H-1024_A-16.zip
  - BERT tokenization
    - https://github.com/google-research/bert#tokenization