Stock closing and opening forecasting using Deep neural network and LSTM(technical indicators included)

Details about the indicators are here *https://github.com/saradindusengupta/technical_indicators_stock-market

Data :

*https://www.kaggle.com/souravroy1/stock-market-data/data The two py files

stock-forecast-lstm.py
stock-forecast-tweet.py

are for forecasting stock opening and closing prices from twitter and NYtimes using deep neural network and lstm. The notebook directory contains the results and ipynb files.

Results

![Alt text](

*Train Score: 0.00006 MSE (0.01 RMSE)

*Test Score: 0.00029 MSE (0.02 RMSE)

Future Improvements :

1. Include more technical indicators from *https://github.com/saradindusengupta/technical_indicators_stock-market
2. Use tweets for sentiment analysis more effectively
3. More data
4. More Indexes and better optimized hyperparameter

References:

1. Bernal, A., Fok, S., & Pidaparthi, R. (2012). Financial Market Time Series Prediction with Recurrent Neural Networks.

2. A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting CL Huang, CY Tsai - Expert Systems with Applications, 2009 - Elsevier

3. Twitter mood predicts the stock market J Bollen, H Mao, X Zeng - Journal of computational science, 2011 - Elsevier

4. Evaluating the impact of technical indicators on stock forecasting IEEE

5. A hybrid stock trading framework integrating technical analysis with machine learning techniques Rajashree Dash Pradipta Kishore Dash

Dependencies :

Language - Python 3.5

keras : https://keras.io/

tensorflow : https://www.tensorflow.org/

sklearn: http://scikit-learn.org/stable

numpy : http://www.numpy.org/

pickle: https://docs.python.org/2/library/pickle.html