TensorFlow implementation of Z. Hu et al. "Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction", WSDM 2018
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Main components
- TensorFlow 1.4.0
- Numpy
- Scikit-learn
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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
- Stock prices and tweets
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Word Representation
- Download http://nlp.stanford.edu/data/glove.twitter.27B.zip
- Extract to data/
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Working directory setting
$ export PYTHONPATH=$PYTHONPATH:$(pwd)
- Run dataset.py
- Run main.py
- Word Representation
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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
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BERT
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BERT-Large, uncased, whole word masking
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BERT tokenization
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