The project aims to predict the stock prices of a given company using LSTM networks. It can be beneficial / it can come handy in following ways-
- Predicting the future performance of a certain stock thereby allowing you to decide the positions you want to take ie to sell, buy or hold.
- Selecting the industry for investment by studying the expected industry-wise market trends.
- Acting as a tool to confirm your analysis about the movement of stock prices.
The results should not be treated as conclusive for investment and do not ensure a 100% accuracy.
However, the minimum accuracy of the model is 86% which is taken to be acceptable considering the unpredictable nature of the stock market.
You will need Python (>3.6), TensorFlow2 to run this.
Installation of required packages is covered under installation
To get a local copy up and running follow these simple example steps.
- Make sure you have python3 setup on your system
- Clone the repo
git clone https://github.com/radioactive11/Project-Stonks- Install requirements
pip install -r requirements.txt- Open terminal and enter
python3 main.py
Enter the company ticker you want the prediction for.
For example
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Arijit Roy - @this_is_radioactive11
Project Link: https://github.com/radioactive11/Project-Stonks