Alan Coppola

Alan Coppola
Having fun learning about quantitative financial market anallysis through the Quantopian platform for the past 5 years. Now applying signal processing and machine learning based artificial intelligence techniques to practical problems through the use of mathematics, statistics and programming in the areas of algorithmic trading.

Introduction

The best way to algorithmically invest is to find a strategy that works, yet cannot be easily reproduced by you or your broker by hand. One of our clients led us to the Software as a Service (SaaS) sector ( https://saasy.blog/). His thesis that he has successfully been using for a few years is that certain assets in this sector are undervalued due to investors not understanding these types of software companies. He outlined the specific features that make the asset undervalued, and from this, together, we produced a universe of approximately 40 stocks from which to research, backtest , and trade various strategies on. In what follows, we show the workflow that culminates in a live strategy, traded daily on IB.

Research

The trading universe (SaaS stocks) was selected with the help of our client, and by considering the existing gross performance of SaaS-like indices (EMCLOUD, SKYY, PSJ) .

SaaS Indices

Starting from this point, the question becomes how to research, backtest, and trade this idea. Our client was already trading this micro sector by managing a quarterly rebalanced long portfolio by hand, with occasional protective shorting using QQQ. He was interested in looking at the feasibility of short-term trading a portfolio in this micro sector, trying to milk the volatility.

We wrote a python program for that, using our our version of Zipline (zipline-broker). This version of Zipline supports both backtesting and paper/live trading.

We call this micro-sector universe SaaSTech, and have run a zipline-broker backtest using the last few years pricing data. For more information see the next blog entry in this series.

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