Models are meant to be useful
I’ve spent the last four years as a professional trader at two of Chicago’s best proprietary trading shops. Countless sleepless nights, both euphoric and traumatic memories, and thousands of hours of constantly staring at price feeds — to the extent that the back of my eyelids turned into my monitors — developed two essential features that the average 26 year old probably won’t have: a resume that doesn’t really truly impact anyone but myself and the ability to sleep intermittently.
I worked a lot. There were moments that spanned days when work was seeping into every minute of my life. Committing several virtual chess boards to memory with millions of dollars up for grabs kept me thinking about my opponents next moves. I spent months watching replays picking my opponents apart and analyzing exchange data in order to construct my best version of their reactions functions. I was curious throughout the entire day. Plus, we were operating around the clock. It was mentally rewarding and empowering to be in a situation where opportunities to get better, develop, and learn about one of my top interests was an everyday thing.
But this didn’t come without its costs and risks. It was a lot, and the day-to-day crush reminded me of the feedback you get from just about every ex-investment banker or trader. They lament over the idea that their work sucked the souls out of them. I never gave too much credence to the thought. The parlance just seemed like the industry standard of glossing over the topic of how hard and time consuming the work is. But over the years I’ve realized “getting the soul sucked out of me” is a diferrent way of expressing this notion: I am not nearly as useful to the stuff that really matters to me as I’d expect or like to be.
“Traders are smart enough to be greedy but not smart enough to be useful.”
-Chamath Palihapitiya, Founder of Social Capital
Prop trading is exhilarating work, but it is hardly useful to anyone outside the small world of prop trading.
Over time my experience as a trader navigating the uncertainties of financial markets developed a unique process of compiling, organizing, and interpreting all sorts of information about the world, which, without a technical skill set, relies on my intuition to be managed properly. This of course has its weaknesses, especially as the world continues to rapidly demand even more sophisticated and precise foresight using data as its fuel. This is why I’ve decided to improve my technical understanding of machine learning and randomness at General Assembly.
While I seek this education, having this naturally built intuition from professional discretionary and algorithmic trading experiences is also valuable.
Just about every landscape of our society is shifting; social norms are being nudged, there’s perpetual political tennis, central banks and governments are literally printing money and giving it to people, and technology isn’t slowing down. These conditions are ideal for those who adapt — something that trading (and just about any profession that demands performance) heavily depends on. On top of this, generational transitions are becoming centerpieces of a growing number of debatable topics, such as passive vs active investing, social issues, tech markets, and crypto. And I think it’s important that young professionals offer our input to Boomers in a way that’s conducive for positive change — which is an ideal I’ll do my best to uphold — so that we can move forward.
It’s equally important that as we move forward, we solve each days’ problems by properly and ethically using data as our compass.
This blog is my effort to contextualize and formalize “intuition” in a manner that is easily digestible and appeals to people of my demographic (Gen X, Millennials, Gen Z, and perhaps Boomers who would like to bend (or possibly break)).
I also feel like it’s appropriate to share that I‘m not a Bitcoin Maxi, or an ETH head, or someone who enjoys to participate in fights between tribes, but I do believe the future of finance resides on top of blockchain technology. In fact, as I write this, I can reflect on how glad I am to be spending most of my time learning how to build models and play with computers and data because they’re skills I look forward to using in digital economies.
