In this project, we will build a smart beta portfolio and compare it to a benchmark index.
To find out how well the smart beta portfolio did, we will calculate the tracking error against the index.
Then we will build a portfolio by using quadratic programming to optimize the weights.
Our code will rebalance this portfolio and calculate turn over to evaluate the performance. Finally we will use this metric to find the optimal rebalancing Frequency.
For the dataset, we'll be using the end of day from Quotemedia.
This proyect is the third one in the Artificial Intelligence for Trading Nanodegree from Udacity.
All the documentation for this proyect is included in Lessons 1 to 7 of the Artificial Intelligence for Trading Nanodegree from Udacity.
Clone the repository in your local machine, type in terminal:
$git clone https://github.com/PachiCartelle/AIforTradingND_P03_Smart_Beta_and_Portfolio_Optimization
$cd AIforTradingND_P03_Smart_Beta_and_Portfolio_Optimization
Create an environment in Anaconda with the command:
$conda create --name=yourNewEnvironment python=3 anaconda
$source activate yourNewEnvironment
Fire the Jupyter Notebook:
$jupyter notebook
And you are ready to start with the Notebook.
All dependencies needed are incorporated in the requirements.txt file.
Please see CONTRIBUTING.md.
The content of this repository is licensed under a Creative Commons Attribution License