This Trading Algorithm Cuts Risk By 99%, While Boosting Profit Potential
Growing up, I had a friend who was, and remains to this day, one of the most gifted mathematical minds I'd ever met.
We'll call him Leonard for the purposes of anonymity.
Leonard wasn't just good at math; he was a natural at it. The school's curriculum didn't matter, as he was always seemingly years ahead, an advantage he could thank his mathematician father for — both for the genes and the discipline it took to keep his mind honed.
Instead of hanging out and getting in trouble with the more mediocre brains in the neighborhood, Leonard spent his time studying and doing math problems under the strict supervision of his dad.
It was because of this discipline and commitment to executing his father's wishes that my own father implored me to “be more like Leonard.”
But this just wasn't within my capabilities. I lacked the mathematical talent Leonard was born with, but, perhaps more importantly, I wanted to spend time with my friends blowing things up, setting them on fire, or just roaming the neighborhood as a pack of feral bipeds.
Sometime around the start of high school, Leonard's commitment to math took on a more directed nature.
His father, who I had always stood in awe of as a mysterious genius, had found a personal goal for himself.
The Fine Line Between Genius and Insanity
He was going to become fabulously wealthy by developing an infallible stock-picking engine.
Like I said, I was no genius, but this idea immediately made me wonder if Leonard's dad had finally gone off the deep end.
Predicting which stock was going to ascend using pure math seemed like modern-day alchemy to me, even though back then I had only the most basic understanding of what stocks were and how the markets operated.
And I wasn't alone. When word spread of this endeavor, I heard plenty of the other adults in our immediate social circle mocking the idea, calling Leonard's dad a fool, or, worse yet, a man who had come to the brink of insanity and then, confidently, marched right on off it.
By this time, Leonard's math skills had evolved into an equally impressive talent for programming, and so his father, the practical man he was, put his son to work coding his stock-picking algorithm with the same dedication that he had spent honing his math skills in his younger days.
If we saw Leonard rarely prior to this, we now saw him hardly at all.
Every day after school he would come home, take his position in front of the computer screen, and bang away at the keys until dinnertime — after which he would do his homework and then go back to coding until he could code no more.
This went on for years.
No Keg Stands for Leonard
The project, as far as I knew, took Leonard through high school, and he continued to work on it remotely through his first years of college at Dartmouth.
To test this thing, Leonard's dad had him run the algorithm against live trading on the Nasdaq. The electronic brain they had built even had access to a bank account, but instead of risking real money, they always used simulated cash while it was still in development.
But one day, Leonard accidentally gave the machine access to the bank account during a live testing cycle.
They promptly lost $5,000 in just a few minutes of high-frequency trading.
This was the last time I heard Leonard ever talk about his dad's trading engine and, at least I hope, the last time he spent any of his precious time trying to make it work.
The point of the story, of course, is to illustrate that predictive trading algorithms are a myth and, at least until Google builds a supercomputer that actually takes into account the thought patterns of everyone on Earth, very much like the fake science of alchemy.
But what about all those automated trading systems used by the big investment banks, you might ask...
Well, that's a different animal altogether.
The modern trading algorithms employed by powerful institutions are purely reactive in nature, taking into account certain known triggers that, when present, set off equally proven responses at speeds no human trader could approach.
The resulting gains are generally tiny, but, when stacked up thousands or tens of thousands at a time, do produce results.
It's not magic or even mysterious. It's basic math combined with the speed of electrons.
But a few years back, this whole experience that Leonard was forced to endure did get me thinking... Was there a way to at least narrow down the list of possibilities using mathematical deduction?
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Thinking Outside the Box
Never mind finding the next big winner using an electronic crystal ball. What if we simply excluded all of the bad candidates and cut the list of prospects down to the point where making a choice would be less like picking a lottery number and more like making a more common, more rational decision... say, which house or which car to buy?
I spent years trying to figure out the filters I would use, and using a basic stock screener (like the kind you find on any online trading platform), I started to apply my filters to a list of real companies.
Early on in the game, I decided to focus on stocks that exhibited the biggest daily fluctuations.
These weren't the huge blue-chip companies that traded millions of shares per day. These were the small, more volatile stocks that traded thinly and responded to catalysts like press releases on a much more noticeable basis.
I chose these small stocks for one simple reason: If individual events in a company's history made a bigger impact on share price, then I could profit more by responding to these events.
I didn't have the power to make a million trades a day the way the automated algorithms did, after all, so I had to make it count with every trade I executed.
In the end, I came up with a filtration system consisting of just five steps... but the results were astounding.
I was able to cut the list of potential target companies down from over 30,000 — which was the number of small companies trading on the public markets — to just a few dozen.
The Golden Rule of Multiple-Choice Tests: Use the Process of Elimination
More importantly, however, my filters minimized risk based on basic, known criteria like profit margin and cash reserves, while maximizing the impact of individual events in the news.
I had, in effect, created a stock-picking algorithm, but I'd done it backwards. I wasn't picking stocks; I was eliminating those that were less likely to produce the effects I wanted.
Cutting the list from 30,000 to just a few dozen effectively cut the chance of an undesired outcome down by close to 99.9%.
Of course, that left me with the task of vetting that remaining 0.1% manually.
There was simply no escaping that part.
But as Leonard, his father, and everyone else who saw their story unfold know all too well, there simply are no shortcuts in life.
To date, my filtration process, combined with that bit of manual evaluation at the end, has yielded results that, sad to say, made Leonard’s algorithm look like nothing more than a gigantic number salad.
And I continue to use it, refining it from time to time, to this very day.
Want to see it in action for yourself?
I'd be glad to show you. Just click here and check out the presentation.
Fortune favors the bold,
Coming to us from an already impressive career as an independent trader and private investor, Alex's specialty is in the often misunderstood but highly profitable development-stage microcap sector. Focusing on young, aggressive, innovative biotech and technology firms from the U.S. and Canada, Alex has built a track record most Wall Street hedge funders would envy. Alex contributes his thoughts and insights regularly to Wealth Daily. To learn more about Alex, click here.
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