Hi, I'm Dr. Andrew MacLean of Algorithm Science and one of my passions is finding trading strategies for stocks and ETFs. The strategies are found by running hundreds of thousands of backtests against daily, weekly or monthly OHLC data. They are unconventional in that the buy and sell strategies are allowed to be completely different, not symmetrical inverses like your normal run-of-the-mill strategies. I doubt you'll find anything like them outside of large financial institutions. I publish them here in the hope that others will find them interesting. You can also download SignalSolver from this site and do your own backtests.
I first used this kind of trading strategy in the financial crisis of 2008 after characterizing the movements of FAS and FAZ, the banking sector triple leveraged ETFs. I noticed that there was a certain percentage rise/fall from the daily open price that gave a good buy/sell signal. Since then I have spent a lot of time perfecting the technique for tracking them down and optimizing the parameters. I usually publish strategies which have given good reward-risk and consistent returns over time in preference to those which have just given stellar rewards at the expense of risk and consistency over time. And yes, I do trade algorithmically myself, mainly buy-the-dips, sell at fixed percentage swing algorithms on quality growth stocks. I use my techniques to find the optimum percentages.
I'm often asked if a strategy for stock X will work on stock Y. The answer is maybe, but there are nearly always better options. Every stock has different price movements, volatility and trending characteristics. When you characterize those movements into an algorithm it is tailored for that stock alone. The algorithm may work well for other stocks but you can almost certainly find better algorithms for them by crafting them individually. There are no one-size-fits-all trading strategies because every stock chart is different. And if they existed, wouldn't everybody be using them?
The strategies I publish have been profitable in the past but I make no claims as to whether they will continue to work in the future. So what use are they? Well, if you notice something has behaved consistently, you just may want to factor it into your decision making process.