Not that there is anything wrong with the peasant trading method of marking up charts with targets and stop losses, but if that’s all there is to trading, I’d rather open a small bakery or work as an airline pilot. Or maybe make artisan cheese with milk from my own organic cows. Extracting money from markets is fun, exciting at times and generally satisfying for what it is, but the drudgery of hearing over and over about keeping your losers small, letting winners run, managing your risk with stops … ugh. You plan on doing this for how long again? No, for me there is just not enough there there to keep it stimulating. But once you start talking about algorithms and trading bots, now you’ve got my attention. Imagine a world where your little (or not so little) army of algorithmic robots are marching across the trading landscape and pillaging profits from semi-proficient chartists. Hmm, now that sounds like fun.
The linchpin of this trading fantasy is a good algorithm, of course. And getting your hands on one is not a simple task. You pretty much need to build it yourself. But this is the fun part you see. One should expect a little challenge and a high threshold for success. Skill acquisition involves getting re-acquainted with higher math and getting some solid programming proficiency under your belt. From my observations, the best languages to focus on include R, C#, Java and Python. I’ve personally chosen to focus on C# and R, and will attend to the others when time permits. R is quite nice as it is free and has a robust financial geek culture surrounding it. It’s true that this community has a few participants that have only a vague understanding of trading, but you’ll find that to be true at most prop firms anyway. And our friends in R world are quite smart in the programming department.
C# is a managed version of C++ (which is an object-oriented version of C) and the reason for choosing it is mostly because of the many C# neural network scripts out there. And because adaptive system solutions are deployable as Windows applications. Also, because it, like R, is based on a capital letter of the English alphabet.
For your higher math education, focus on statistical learning and its computer-science cousin known as machine learning. These two approaches share many things in common but they like to call things by different names. I don’t know, just to be different I suppose. You may find some interesting video lectures on everything from Bayesian inference to Support Vector Machines at videolectures.net, a treasure trove of highly educational videos.
Armed with programming language proficiency and a competent grasp of neural network architectures, you can go about creating our own prediction machine and wash your hands forever from the dirty task of pouring over charts and imagining patterns. This venture is fraught with danger and peril. Many have tried, few have succeeded. You can fool yourself just as easily with numbers as with chart patterns, maybe even more so. But just imagine for a moment pressing a button and watching your creation bring gold to your coffers while you sip on premium Oolong tea. If it doesn’t work, you can always make cheese. Organic of course.