Dr. Wesley R. Gray at the Turnkey Analyst has generously released a new free tool to screen stocks for deep value according to Benjamin Graham’s time-tested formula. Please visit the site to read Dr. Gray’s post summarizing the criteria by which the screen functions and some additional words of wisdom from Graham. Oh, and lots of other great free tools there, too, including another quantitative value screener based on Dr. Gray’s criteria and an asset allocation tool. On the heels of the release of Troy Shu’s adaptivwealth asset allocation tool and all of the other free investing tools available, it’s never been better to be a do-it-yourself investor.
While it appears I rehash this topic several times, it’s important to continually learn and expose oneself to new perspectives when one is involved in trading system development. For quants, this typically involves reading new research that seems to be coming out all the time. For fundamental traders, it involves learning about new ways to value companies and examining the strengths and weaknesses of the classical models. My systems are based on technical analysis, but I take ideas from the quant side and the fundamental side wherever I can to test on my own systems. More often than not, I find that these ideas either directly improve my systems or give me ideas for how to develop new filters or techniques on my own.
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.
Over at Abnormal Returns is an interview that Tadas Viskanta had with Dr. Wesley Gray of Turnkey Analyst about his book, Quantitative Value. It makes a great case for systematic investing (i.e. investing based on rules, not intuition) and why removing emotion from the investing process is really the best way to success in the investment world. However, he goes a step farther by differentiating between investing based on logic and based on empirical data. The argument focuses on value investing, but I think the case presented is applicable to all styles of investment.