https://github.com/lilianweng/stock-rnn

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
https://github.com/lilianweng/stock-rnn

embeddings lstm rnn-tensorflow stock-price-prediction

Last synced: about 1 month ago
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Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

• Host: GitHub
• URL: https://github.com/lilianweng/stock-rnn
• Owner: lilianweng
• Created: 2017-07-11T05:23:38.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2022-07-28T06:36:59.000Z (almost 2 years ago)
• Last Synced: 2024-05-02T00:57:06.040Z (2 months ago)
• Topics: embeddings, lstm, rnn-tensorflow, stock-price-prediction
• Language: Python
• Homepage: https://lilianweng.github.io/lil-log
• Size: 53.7 KB
• Stars: 1,668
• Watchers: 116
• Forks: 644
• Open Issues: 25
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml

Lists

• my-awesome-stars - stock-rnn - stock embeddings. | lilianweng | 1560 | (Python)
• awesome-stars - lilianweng/stock-rnn - Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings. (others)
• awesome-stars - stock-rnn - stock embeddings. | lilianweng | 1714 | (Python)

README

        
### Predict stock market prices using RNN

Check my blog post "Predict Stock Prices Using RNN": [Part 1](https://lilianweng.github.io/lil-log/2017/07/08/predict-stock-prices-using-RNN-part-1.html) and [Part 2](https://lilianweng.github.io/lil-log/2017/07/22/predict-stock-prices-using-RNN-part-2.html) for the tutorial associated.

One thing I would like to emphasize that because my motivation is more on demonstrating how to build and train an RNN model in Tensorflow and less on solve the stock prediction problem, I didn't try too hard on improving the prediction outcomes. You are more than welcome to take this repo as a reference point and add more stock prediction related ideas to improve it. Enjoy.

1. Make sure `tensorflow` has been installed.

2. First download the full S&P 500 data from [Yahoo! Finance ^GSPC](https://finance.yahoo.com/quote/%5EGSPC?p=^GSPC) (click the "Historical Data" tab and select the max time period). And save the .csv file to `data/SP500.csv`.

3. Run `python data_fetcher.py` to download the prices of individual stocks in S & P 500, each saved to `data/{{stock_abbreviation}}.csv`.

(NOTE: Google Finance API returns the prices for 4000 days maximum. If you are curious about the data in even early times, try modify `data_fetcher.py` code to send multiple queries for one stock. Here is the data archive ([stock-data-lilianweng.tar.gz](https://drive.google.com/open?id=1QKVkiwgCNJsdQMEsfoi6KpqoPgc4O6DD)) of stock prices I crawled up to Jul, 2017. Please untar this file to replace the "data" folder in the repo for test runs.)

4. Run `python main.py --help` to check the available command line args.

5. Run `python main.py` to train the model.

For examples,

- Train a model only on SP500.csv; no embedding

```bash

python main.py --stock_symbol=SP500 --train --input_size=1 --lstm_size=128 --max_epoch=50

```

- Train a model on 100 stocks; with embedding of size 8

```bash

python main.py --stock_count=100 --train --input_size=1 --lstm_size=128 --max_epoch=50 --embed_size=8

```

- Start your Tensorboard

```bash

cd stock-rnn

mkdir logs

tensorboard --logdir ./logs --port 1234 --debug

```

My python environment: 

Python version == 2.7

```

BeautifulSoup==3.2.1

numpy==1.13.1

pandas==0.16.2

scikit-learn==0.16.1

scipy==0.19.1

tensorflow==1.2.1

urllib3==1.8

```