[x] create script to allow downloading of historical data from Yahoo or similar, and registering and ingesting of custom data bundle
[x] run zipline algorithm for backtesting within Python code, instead of at command line. This will allow us to invoke backtesting in Django later on more easily (using https://github.com/alpacahq/roboadvisor as a starting point but remove dependency on alpaca dataset)
[x] apply Modern Portfolio Theory to a given basket of assets
[x] add visualisation of backtest similar to Quantopian's
[ ] compare its output (performance stats such as Sharpe ratio, cumulative returns, max drawdown) with that from Quantopian to verify that results are the same/similar.
We will need to