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Optimize investment portfolio for equities based on sharpe rate theory.

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Portfolio analysis based on sharpe value.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

You need to install all necessary modules on your environment

pip install pipenv
pipenv shell 

Open notebook

After installing the necessary modules, you need to install jupyter and open ipynb file on browser.

jupyter notebook --port8888

Execute notebook

You can execute first cell to install necessary module on your session like below. This part should be executed without any customisation.

Relevant modules

  • Jupyter notebook - Interactive and replicable programming environment
  • Scikit learn - Machine Learning module
  • keras - Deep Learning Library in python working on Tensorflow and Theano
  • pandas - Libarary for data transformation etc
  • numpy - use numpy when matrix type calculation is necessary

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Optimize investment portfolio for equities based on sharpe rate theory.

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