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Moving Strategy on 3D Printing Companies

Simple moving average strategy on 3D printing Companies.

Due to the increase in venture capitalist and private eqiuty funding, firms are choosing to IPO later in the development of their business. As a result, investor in public equity markets are missing out on the chance to invest in promising companies that produce outsized returns relative to more mature companies. Most of the time, these younger firms are able to succeed disproportinately compared to their peers is that they are developing new technology or methods that either increase efficiency or create new markets.

The trading thesis is to invest in public companies that are developing promising technologies. To possibly multiply the return, trading algorithms will be used to buy and sell these promising equities based pricing inefficiencies detect in the market. However, the algoritms will only trade the equities identified as promising. This will effectively lower portfolio risk because only equities predicted to rise in price will be help. This fundamental analysis will be combined with a computational approach in order to try to avoid large losses and have outsized gains.

A promising technology with several notable public comapanies is 3D printing. This new technology has the promise to create an entire industry ranging from product development to organ creation. Companies like Stratasys, 3D Systems, Organovo, and Proto Labs are market leaders and have their equity floated on public markets.

1 response

Hey JM,
Cool! I like the idea of taking a portfolio, then applying a strategy to the each stock within. I noticed is that each day you are taking .4 and .15 of your current cash you have available. For example, on the first day you invest 40% of your cash in the first stock. On the 2nd day you will have 60% of your cash left, but again you take 40% of this amount. I made a fixed version below, using order_targets. I also was able to consolidate your code, hopefully you will find this much nicer. The working difference between the two algos is I'm taking 40% and 15% of the entire portfolio value (which is done automatically with order_target_percent) instead of 40% and 15% of the available cash. Unfortunately this doesn't perform as well as your original, but there are probably a bunch of ways you can tweak this to improve the results. You can see I have the weights at the top in the initialize function.

Gus

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