Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. To read more about LSTM and RNN, visit this exceptional blog: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
In this Jupyter Notebook, I've applied LSTM RNN with Technical Indicators namely Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD) and Bollinger Bands to predict the price of Bank Nifty. We can improve the efficiency by adding more parameters to our model. Some of the ideas are adding sentiment analysis using NLP to process daily tweets, adding fundamental ratios, using NLP to analyze quarterly and annual income statements, etc. and check how it affects the outcome of our model. I bet the efficiency increases!
If anyone wants to brainstorm ideas or collab on a project, hit me up at:
LinkedIn: https://www.linkedin.com/in/tejas-linge/
Mail: tejas.linge101@gmail.com