An Introduction to FinClub.AI

“These are uncertain times.”

This phrase always makes me smirk. I mean, sure, it’s a credible statement, but when was the last time things were certain? Everywhere you look, there’s a duopoly of the best of times and the worst of times. This couldn’t be truer than in today’s markets. We are 9+ years into the longest (arguably pseudo) bull market ever, and with recent corrections, soapbox traders are falling over themselves to either make the next bubble prediction or to contend with those that do.

The purpose of these articles is to try to help those who are relatively new to the trading community make sense of the diverse - and at times, predatory - investing ecosystem. They are also intended to shed light on general “best practices” we have observed during the development of FinClub.AI. This will not be a resource for the “pros” out there. Frankly, if you’re taking someone’s money in exchange for investing advice, you should already be well aware of the types of subjects I’m going to cover here.

As the title suggests, this particular article will focus on my own company. FinClub.AI is the culmination of several years’ worth of hundred-hour weeks from a small but highly skilled team of trading professionals, machine learning experts, and quantitative analysts (math nerds) like myself, not to mention enough pots of coffee to float an aircraft carrier. We utilize massive amounts of market data and artificial intelligence models to deliver the best possible setups on the U.S. equities markets. Our “picks” are rooted in risk mitigation, allowing only those with the highest probability of success and sufficient liquidity to prevent manipulation (a subject worthy of its own article) to pass. The information provided to our subscribers allows them to make better informed decisions, whether they trade a derivative security or the underlying equity, in a shorter amount of time… pretty simple really, at least from the outside looking in.

FinClub Image

What primarily differentiates us from our most similar competitors is the win rate our risk managed approach returns. Others will gloat and hide behind marketing materials what should be a concerning sub-50% result. Our model combs through tens of millions of data points on a daily basis, subjecting each symbol to countless rigorous criteria on its path to consistently delivering winning positions at a rate above 75%. We breathe easy with the thought of minimal drawdowns working in concert with the miracle of compounded returns. Looking back, it’s hard to tell the difference between the hard work and serendipity that brought us here. In any case, the takeaway is that the A.I. we’ve built is very, very good at what it does.

Artificial Intelligence trading solutions, much less quantitative algorithms (math programs), aren’t new. Pioneers in the field such as James Simons and Thomas Peterffy have yielded impressive results for decades outperforming the standard benchmarks; however, most of these companies have hidden their tech from the light of day, only to be used by internal hedge funds and prop shops. It’s their right to do so, but I can’t say it isn’t disappointing. It’s disappointing for the same reason I give when asked why we don’t do the same.

Actually, let me back up… Do we use our own product? Yes, of course we do. We use it because we like making money and we’re not idiots, but that wasn’t the question. The question was, “why would we let anyone else benefit from our software instead of JUST using it ourselves?” I hope you’re comfortable:

  1. This is not a zero-sum game.

    Say you’re playing basketball with one of your friends, and you’ve only got 18 points when he sinks a jumper to win with 21. In basketball, your friend takes home the win while you chalk up the L. That’s zero-sum. Now imagine those points were trading profits. Sure, you didn’t score the most points, but you’re going to start tomorrow with 18 more points than you had today. Similarly, your selling of an asset today for a profit doesn’t preclude someone else from selling the same asset for a profit tomorrow.

    In other words, allowing others to use our product to win at trading doesn’t lessen our ability to also win with the same program.

  2. Rising tides raise all ships.

    On a macro level, investor confidence – the same confidence which leads individuals to put money into the market – leads to growth and overall healthy market behavior. Unfortunately, there are still many sitting on the sidelines, reeling from the aftereffects of the GFC. What’s worse, there is no shortage of schemes designed to take advantage of those just entering. We believe this is effectively financial cannibalism, and providing the individual retail investor with reliable tools is our best way of improving conditions overall.

  3. It is morally wrong to arm the individual with a knife for a gunfight with institutional investors.

    I can’t think of a more direct way to say it. The big institutional investors (investment banks, large hedge funds, and so forth) have tools, resources, and capital at their disposal that you do not to put themselves at a near constant advantage. It is our hope that by providing FinClub.AI to retail investors, we can help tip the scale, and in doing so circumvent many of the rapacious practices we’re all familiar with. It is our founding principle to act in the best interests of the individual. Period.

I hope you’ve enjoyed reading this. It’s been my pleasure to shed a light on who we are and what we stand for.