Editor’s Note: You know I believe artificial intelligence isn’t just the future – it’s rewriting the rules of every industry it touches. And now, it’s coming for the markets themselves.
In the essay below, TradeSmith‘s Keith Kaplan reveals how his team built a new “Super AI”: an algorithm capable of forecasting stock prices with up to 85% historical accuracy. Think of it as the financial world’s equivalent of the AI that just outperformed the world’s top weather models. And the results are jaw-dropping – annualized returns of 374% over the past five years, even through pandemics, crashes, and geopolitical chaos.
So, before you read Keith’s deep dive below, make sure to mark your calendar on October 15, 10:00 a.m. Eastern – for The Super AI Trading Event. You’ll see firsthand why this new technology could mark the beginning of a new era in trading… one where AI doesn’t just react to the market but predicts it.
Now, here’s Keith with the full story.
In early July 2024, Hurricane Beryl was tearing across the Caribbean with winds topping 165 miles per hour. By the time it made landfall, it had killed 36 people, left millions without power, and caused billions of dollars in damage.
For decades, forecasters have used giant supercomputers to track storms like this. They take readings from satellites, ships, and planes. Then they grind through complex physics equations about how air and water move. It can take more than an hour to produce a single 10-day forecast.
When Beryl formed, forecasts from a top European weather agency pointed to Mexico as the likely target for landfall.
But an experimental forecasting system called GraphCast, which can run on something as small as a laptop, disagreed. Days in advance, it predicted the storm would make landfall in Texas.
When Beryl struck Matagorda Bay, Texas, on July 8, it was GraphCast – not the world’s most advanced supercomputer – that had been right all along.
GraphCast was created by Google’s DeepMind AI lab. It’s trained on 40 years of weather data. And it can produce a 10-day forecast in 60 seconds.
Instead of solving complex equations, it spots hidden patterns in past weather data. This allows it to see further and faster than traditional physics-based models.
This was a turning point for meteorology. A laptop-scale AI beat the most powerful forecasting engines on Earth.
And if AI can decode the weather, what might it do with the other great chaotic system of modern life: the stock market?
How AI Is Learning to Predict Markets Like It Predicts Storms
My team of 74 researchers and developers at TradeSmith has been putting our $8 million annual budget to work to find out.
And after years of development and testing, we’ve created a new “Super AI.” Instead of learning from weather patterns, it learns from stock market data. And the results have blown us away.
It can project future prices of 2,334 stocks up to 21 trading days out – to the day and even the penny – with 85% backtested accuracy.
Today, I’ll show you how it works – and how you can use it to dramatically up your odds of success as an investor. First, it’s important to understand the common thread between predicting storm paths and stock market prices.
From Chaos Theory to AI Trading: The Butterfly Effect in Markets
In 1961, MIT meteorologist Edward Lorenz was tinkering with a rudimentary weather model on a Royal McBee computer.
It was the size of a fridge, spat out forecasts on long rolls of paper, and could take an hour just to process a handful of equations.
To save time, Lorenz tried a shortcut. He rounded one of his inputs from six decimal points to three. He let the computer run while he stepped out for coffee. When he came back, the forecast had transformed. A calm weather pattern had become a raging storm.
What Lorenz discovered is that tiny changes can snowball into huge effects. He called it the “butterfly effect” because a butterfly flapping its wings in Brazil might set off a tornado in Texas.
Weather systems can shift on a dime because they’re dynamic, not linear. Even the tiniest change, like a gust of wind or a seemingly minor increase in humidity, can cascade into a wildly different outcome.
Markets are the same.
Every trading day, markets absorb thousands of tiny shocks. A Fed comment, a surprise earnings miss, even a social-media post can shift sentiment.
These are financial “butterfly effects.” Tiny shifts that ripple through the system and hit the prices of thousands of stocks.
That’s why the world’s best hedge funds have spent decades building algorithms to capture them. Take Jim Simons’ Medallion Fund. It’s averaged 66% annual returns since 1988 by spotting financial butterfly effects hidden in vast stock market datasets.
At their core, forecasting storms and forecasting stock prices face the same problem. Both are dynamic systems where tiny changes create outsized consequences.
That’s why at TradeSmith, we built our own Super AI to read the market’s turbulence the way GraphCast reads the weather.
I don’t mind pulling back the curtain on it today. It’s nearly impossible to replicate. It takes decades of market data, thousands of hours of programming, and a dedicated research team to stitch it all together.
Which is why funds like Medallion keep their edge locked away.
The AI Model Forecasting Stock Prices With 85% Accuracy
It’s a kind of AI model called a TimeGPT.
It isn’t designed to write text or generate images. Instead, it forecasts what’s known as time-series data.
Think of stock prices like storm tracks: data points lined up in time, where each moment connects to the ones before, and hidden patterns influence what comes next.
GraphCast works the same way. It doesn’t solve physics equations from scratch. It learns from decades of weather data to see how storms grow and move.
And just as weather forecasters don’t want to miss the next hurricane, investors can’t afford to miss the next “Hurricane Nvidia,” “Hurricane Apple,” or “Hurricane Tesla.”
Our AI is built to see those storms before they hit.
You can see what I mean from the results from our backtests…
On July 27, 2023, our model predicted Opendoor (OPEN) would soon hit a price of $4.87.
The stock hit that price just 24 hours later. And my team booked a 9.4% gain on that pick.
That’s like growing your money 34 times in a year.
And you could have boosted that gain to 244% in just 24 hours with a special kind of trade.
Or take this past May, when our model predicted Tesla (TSLA) would hit $302.89 in 21 trading days.
It reached the price we forecast even faster than expected. We booked a 5.2% gain in just 24 hours. And you could have boosted it to 310% over the same time.
But as impressive as those gains are, we found you could have done even better with a five-stock portfolio strategy. You simply buy the best five trades every week – all with an unusually high 85% historical accuracy – and sell when they hit their projection.
Last year alone, you could have made a 602% gain with this five-stock strategy.
That’s more than 30x the return you’d have gotten holding the S&P 500 stocks for the year. And it’s more than 3x the return of Wall Street darling Nvidia (NVDA) over the same time.
And in a five-year study that included the pandemic, the 2022 crash, swings in interest rates, this year’s tariff tantrum, and two wars, this five-stock strategy returned an average annual gain 374%.
Even better, it’s simple to follow. With just a couple of minutes’ attention every week, it crushes the returns most investors are making.
That’s why I hope you’ll join me for my Super AI Trading Event. It kicks off Wednesday, Oct. 15, at 10 a.m. Eastern Time.
I’ll show you the technology behind our new Super AI and the gains it’s identified. I’ll also show why our five-stock strategy works – and how you can build your own AI portfolio to try to quadruple your money over the next 12 months.
And I’ll even pass along one of the top trades our system has identified as a thank you for joining.
