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'll calculate the tracking error against the index. We'll then build a portfolio by using quadratic programming to optimize the weights. The code will rebalance this portfolio and calculate turn over to evaluate the performance. We'll use this metric to find the optimal rebalancing Frequency. For the dataset, we'll be using the end of day from Quotemedia.
Smart beta strategies seek to passively follow indices, while also considering alternative weighting schemes such as volatility, liquidity, quality, value, size and momentum.
The goal of smart beta is to obtain alpha, lower risk or increase diversification at a cost lower than traditional active management and marginally higher than straight index investing. It seeks the best construction of an optimally diversified portfolio. In effect, smart beta is a combination of efficient-market hypothesis and value investing. The smart beta investment approach applies to popular asset classes, such as equities, fixed income, commodities and multi-asset classes.
The Jupyter nodebook must be run under python 3.6. Type the below commands in the Terminal to to create and activate a conda environment.
conda create -n py36 python=3.6 anaconda
activate py36 #if on Windows
source activate py36 #if on Linux or MacOS
After that, run the main notebook project_3.ipynb using the above local environment.
├── Part 1: calcuate the tracking error using divident yield against market cap weighted index
├── Simulate index weights
├── Simulate dividend weights
├── Generate close returns
├── Generate weighted returns
└── Compute the tracking error
└── Part 2: optimize portfolio by minimize the variance and distance from weights of the market cap weighted index
├── Compute the optimial weights using cvxpy
└── Rebalance Portfolio Over Time
Jinjin Liang authored the main functions in project_3.ipynb, and this README. All other project files were created by Udacity.