stock-market-ai
Stock Market Prediction & Trading Bot using AI with a Web Interface
Stock Market Prediction using an LSTM Network
Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following work. They work tremendously well on a large variety of problems, and are now widely used. LSTMs are explicitly designed to avoid the vanishing gradient problem.
All recurrent neural networks have the form of a chain of repeating modules of neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer. LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of having a single neural network layer, there are four, interacting in a very special way.
For more info check out this article
Stock Market Agent using Evolution Strategy Agent
Even though the name sounds fancy but under the hood, it’s perhaps the simplest algorithm you can devise for exploring a landscape. Consider an agent in an environment (like Pong) that’s implemented via a neural network. It takes pixels in the input layer and outputs probabilities of actions available to it (move the paddle up, down or do nothing).
Our task in reinforcement learning is to find the parameters (weights and biases) of the neural network (weights and biases) that make the agent win more often and hence get more rewards.
For more info check out this article
Requirements
- Python 3.6.2 (https://www.python.org/downloads/release/python-362/)
- Django (https://www.djangoproject.com/)
- Numpy (https://pypi.org/project/numpy/)
- Tensorflow (https://pypi.org/project/tensorflow/)
- Keras (https://pypi.org/project/Keras/)
- Seaborn (https://pypi.org/project/seaborn/)
- Yahoo-Finance (https://pypi.org/project/yahoo-finance/)
- Pandas (https://pypi.org/project/pandas/)
- Matplotlib (https://pypi.org/project/matplotlib/)
Usage
First start the django server using the following line,
python manage.py runserver
Main Page
The main page gives you three options to choose from: