This is the third part of the Build Better Strategies series. In the previous part we’ve discussed the 10 most-exploited market inefficiencies and gave some examples of their trading strategies. In this part we’ll analyze the general process of developing a model-based trading system. As almost anything, you can do trading strategies in (at least) two different ways: There’s the ideal way, and there’s the real way. We begin with the ideal development process, broken down to 10 steps. Continue reading “Build Better Strategies! Part 3: The Development Process”
You’ve developed a new trading system. All tests produced impressive results. So you started it live. And are down by $2000 after 2 months. Or you have a strategy that worked for 2 years, but revently went into a seemingly endless drawdown. Situations are all too familiar to any algo trader. What now? Carry on in cold blood, or pull the brakes in panic?
Several reasons can cause a strategy to lose money right from the start. It can be already expired since the market inefficiency disappeared. Or the system is worthless and the test falsified by some bias that survived all reality checks. Or it’s a normal drawdown that you just have to sit out. In this article I propose an algorithm for deciding very early whether or not to abandon a system in such a situation. Continue reading “The Cold Blood Index”
For our financial hacking experiments (and for harvesting their financial fruits) we need some software machinery for research, testing, training, and live trading financial algorithms. There are many tools for algo trading, but no existing software platform today is really up to all those tasks. You have to put together your system from different software packages. Fortunately, two are normally sufficient. I’ll use Zorro and R for most articles on this blog, but will also occasionally look into other tools. Continue reading “Hacker’s Tools”