Mike from Quantstart posted a great, comprehensive look at the process and necessary steps in creating an algorithmic trading strategy. It’s a long article, but it is a great survey of all of the tools you will need to get the job done (you can find many of the tools mentioned in the article, and even more on the Algorithmic Trading and Autotrading Universe page). Just to summarize the main points, Mike points out that in order to maintain discipline and successfully trade (both on a discretionary and non-discretionary basis), one needs to know one’s own personal preferences. If you create a strategy or use someone else’s, and it doesn’t match your own preferences, you will be tempted to intervene and override the trading rules set forth by the strategy, which will result in failure.
This isn’t related to automated trading systems, but as it overlaps with algorithmic trading, I thought I would mention that Troy Shu has released a beta web app called adaptivwealth [sic] to help retail investors with their asset allocation decisions. Asset allocation is essentially how you divide your investments between different asset classes, such as stocks, bonds, and alternatives (commodities, forex, precious metals, etc.). As Mr. Shu points out, several financial services startups are attempting to address the question of optimal asset allocation (see The Algorithmic Trading and Autotrading Universe under the Model Selection section for a list), but they each suffer from various weaknesses, so he’s attempting to address the problem with his own solution: the adaptive asset allocation tool, which essentially adapts its asset allocation decisions to various market conditions, whereas the other services in this industry tend to pick a fixed portfolio and stick with it, bull or bear market.
Daniel Fernandez at mechanicalforex.com has put up a good analysis of the qualities that profitable systems have in common when thinking about automated trading system development In this specific context, he explores what makes it more likely to have a system that performs well with a set of historical data (“in sample”) with subsequent data (either historical data that’s beyond the in sample data set, or using actual data going forward, aka “out of sample”). He finds that a sufficient number of trades, high linear regression coefficients, and high system quality numbers (see here and here for some discussion about SQN) all contribute to success when using one’s automated trading system in the market going forward. However, those characteristics are just a starting point, and he closes with this caveat:
News site Forex Magnates brings to light another entrant in the social trading arena called ForexGlobes. I signed up to see if this site has anything new to offer in the increasingly-competitive social trading world. Unfortunately, it’s still in beta, so some critical components appeared to be missing (or at least, I could not find them): it doesn’t appear that signal providers will be compensated for their signals, so sharing trades is purely for bragging rights at this stage. In addition, while ForexGlobes claims to be partnered with several forex brokers, I could not for the life of me figure out how to link my live account. In addition, it has a strong resemblance to Tradeo in look and feel, so the jury is still out as to how ForexGlobes intends to differentiate itself. In any case, here’s the bottom line from Forex Magnates:
MIT will run an experiment to see if social media investing improves performance. It’s doing so by giving $100 each to 10,000 Asians to trade forex on eToro by following high-performing traders and copying their trades.
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