Markets sure aren’t what they used to be.
In fact, trading is less profitable than it has ever been before according to experts like Professor Aswath Damodaran, Professor of Finance at New York University Stern School of Business.
While many wax nostalgic about simpler times when scuttlebutting and combing through annual reports were viable strategies to get an edge over the market, in reality, the days when a single retail investor could land on a golden ticket through spelunking are long gone.
“Information based trading has a shelf-life of 15 seconds,” says Damodaran, adding that the industry largely brought this self-inflicted creative destruction upon itself.
In their search for the magic bullet encased in pure alpha, institutional investors have unleashed game-changing forces such as high-frequency and algorithmic trading which plow through new information at a pace that would make even Fama and French blush. They have also made direct human influence over market moves increasingly rare.
According to current estimates, approximately 80% of investments are either quant-based or fully passive, with only one-fifth of trades actively plotted out by sentient lifeforms. Combine this with the index revolution that has swept over Main Street, and you have created a system that actively discourages individual participation.
For many, the underlying message is that the era of active retail investors is over. I’d agree, were it not for a stubborn subset of Reddit users that fail to comply. Instead, they are doing their utmost to make active “algotrading” more accessible to the masses.
Algorithmic trading: the last frontier for the democratization of finance
The stock market has never been more easily accessible.
While unthinkable only a few years ago, virtually everyone has the option to operate on a no-fee basis today. At the same time, the index revolution has driven costs down across the board and an increasing number of retail investors are dabbling in options and other derivatives.
As a result, individual investors have the widest array of investment choices in US history. However, algorithm-based investing is still largely the sole hunting grounds of the largest institutional investors and deep-pocketed hedge funds.
Amassing the infrastructure, data and Phds necessary to run portfolio-management systems sophisticated enough to host successful algos is an expensive game, and not many institutions, let alone individuals, are able to match the investments made by e.g. Blackrock
These daunting odds haven’t stopped thousands of independent coders from launching their own homebrew-algorithms.
As a matter of fact, all one needs to transform a Poor Man’s Hedge Fund into the next Renaissance Investments are a few courses on Udemy and the right Python library.
To infinity and beyond: the final frontier for democratization of investing
For those unfamiliar with Github, it is difficult to understand how much easier complex code-jobs have become over the past decade. When it comes to automated portfolio management, “the tools are out there for anyone who wants to learn,” explains Jose Portilla, head of Data Science at Pierian Data.
And where the traditional education system has been slow to respond to the swelling demand for tangible fintech skills, instructors like Portilla have stepped up and delivered via on-demand training and online ed-tech platforms. At the time of writing, searching for ‘python for finance’ results in more than 10,000 hits on Udemy alone, and core fintech skills such as python, excel and AWS are the three most sought-after skills on the platform.
Courses such as “Python for Financial Analysis and Algorithmic Trading” set students up with basic tools such as Python, Pandas, Numpy and SciKitLearn and have them running simple ARIMA models within the first hours of getting started. And with tutorials like these, the process of starting a one-woman quant fund has been literally condensed to a three hour code-along session.
Knowing how to connect a Jupyter notebook with Yahoo finance’s API doesn’t exactly a hedge fund make, and a certain dose of skepticism about how far redditors can go is certainly well justified. However, if the Gamestop
From homebrew algorithms to advanced financial alchemy
As of today, the subreddit on algotrading has 993,000 members with thousands more joining by the day. And while the group has nowhere near the clout of the now infamous r/WallStreetBets, it is quickly evolving into something much greater than the sum of its rag-tag parts.
The group’s banter zigzags between topics such as homegrown automated stock-screeners, back-testing libraries, drag-and-drop strategy editors and the thread is a safe space for sharing first-hand experiences on pioneering algo-facilitators such as Kryll and Mudrex where the next generation of diamond-handed day-traders are busy with earning their spurs.
Interest in trading options has skyrocketed in particular, with a growing cadre of redditors turning to pre-made tools and on-demand courses to tackle how to best take advantage of the copious APIs offered by Robinhood, Interactive Brokers
“The consumer side is doing some very creative things and the questions they come up with are much more sophisticated than mainstream media would have you believe,” Portilla explains while noting that there has been a clear shift in student interests over the past years.
Students that previously were content with more basic bootcamps are now looking for increasingly in-depth courses with tangible applications, including portfolio automation with the help of complex neural networks. On the other hand, more traditional topics such as fundamental analysis and deep valuation methods have become about as welcomed as an Instagram follow from mom. Given how raw of a deal our economy has handed to most millennials and Gen-Zs, the ‘get rich or go broke trying’ approach is understandable.
However, many will come to learn that dismissing the core concept of value is rarely the basis for a sound investment strategy.
Learning enough to become dangerous
After years of sitting at an apparently crooked table where the house always wins, an entire generation’s worth of retail investors is mentally primed for seeking instant financial gratification and inherently risky stratagems regardless of the cost.
Fueled by equal parts of desperation and deeply rooted distrust of mainstream institutions, the most recent cohorts are turning to their peers in virtually every market ranging from movie reviews on Rotten Tomatoes to seed-stage investments on Kickstarter.
So, while it’s no surprise how popular DIY-algotrading and its peripheral industry of courses, platforms and forums have become, most fintech-homebrewers are setting themselves up for disappointment.
“What most end up doing is learning just enough to make themselves dangerous,” says Damodaran.
Technology’s capacity to obfuscate and mislead grows in lockstep with its complexity, and hiding behind a wall of leased-out AI and borrowed code can give retail investors a false sense of comfort. More importantly, without a firm understanding of fundamental value, any strategy — automated or not — amounts to little more than running after arbitrary price-points.
Even if our redditors succeed in finding a magic bullet, it is unlikely to provide them with a long-lasting edge in today’s markets, let alone lasting profits.
In fact, the only parties that will benefit from the current algotrading gold rush with any reasonable certainty are middleware suppliers, platform-hosts, and data purveyors all of whom get paid regardless of whether their clients make bank or not.
And while all of the individual losses are sure to hurt, they have the benefit of making algotrading accessible to the masses.