Winning Quantopian Morningstar data Fundamentals algorithm


In late 2014, Quantopian – the pythonic trading platform - made available programmatic access to fundamental data from Morningstar.  Python trading algorithms can use this data in trading logic.  To introduce the community to the use of this data, developers were invited to submit algorithms exploring this new avenue.


My python trading algorithm submission “Using #Fundamentals to identify uptrending volatile small caps”  was one of seven selected by Quantopian as best showing use of this data.


The premise of the algorithm is to use the Morningstar fundamentals data to screen for volatile small cap stocks (market cap<$2B) that are trending up, and have both positive free cash flow as well as basic eps, and are trading in a specific range.  This combination of stock screening coupled with recent price activity analysis would be much more difficult to perform on other trading platforms, if even possible.


The algorithm also showcases many of the trading features possible on Quantopian, such as monitoring cash balance to limit purchases to what can be afforded, setting a trading commission model, using trailing stop orders and profit taking limit orders to improve performance while taking prudent risk management actions.  The algo stays cash positive at all times as shown in the custom signals portion of the trading summary.


Using the Quantopian backtester for the last five years shows a Sharpe ratio of 1.44 and a positive alpha of 0.09.


UPDATE 5-Feb-2015 : In Quantopian Tutorial 3 - Basics of Fundamentals - this algorithm was on of two used as an example with a walkthru of the code by Quantopian trainer Seong Lee:


If you need tutoring, troubleshooting, code walk throughs, consulting or programming service for your automated trading algorithm to run either in Quantopian's python trading engine or for validation work using the python open source zipline back tester facility please  Contact Me with your quantatitive finance requirements.