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One of scientific discoveries was hidden for decades in a collection of handwritten notes.
The data was there all along, it just took the right lens to understand it.
This same idea is at the heart of Today’s Friday digest Acquisition from TradeSmith CEO Keith Kaplan.
Below, Keith draws a fascinating parallel between that remarkable moment and what’s happening in the stock market today. Investors now have more data than ever to analyze, but until recently, most of it was impossible to interpret in any meaningful way.
Now, thanks to artificial intelligence, this is starting to change.
Keith and his team have been using machine learning to uncover repeatable “signals” buried within massive data sets – patterns that have historically indicated high-probability trades, many with surprising consistency across very different market environments.
In today’s article, Keith explains how this system was put together, and how it turns a huge amount of information into a surprisingly simple and actionable strategy.
And if you missed it Keith Artificial intelligence signals trading event From Wednesday, you still can Watch the full replay – including live demos and trade examples – here.
I’ll let Keith take it from here.
I wish you a good evening,
Jeff Remsburg
Tycho Brahe’s mission in life was to be the first to explain how the planets actually move.
So, the obsessive 16th-century Danish astronomer spent more than two decades building the most accurate record of planetary motion the world had ever seen — and then guarded it so jealously that almost no one was allowed to see it.
In Brahe’s time, there were no telescopes. Every measurement he made was by eye, using instruments he designed and built himself on a small island off the coast of Denmark.
It was a data set unlike anything that had ever existed, page after page of handwritten numbers and precise planetary positions.
He couldn’t explain all that data on his own. So he brought in a brilliant young German mathematician named Johannes Kepler. But Brahe, who feared Kepler would make the discovery first, handed his student only enough data to be useful, and locked away the rest.
This arrangement lasted barely a year. In October 1601, Brahe died suddenly. Kepler inherited his notebooks and studied them intensively over the next four years.
What he found was evidence that everything astronomers had assumed since ancient Greece was wrong.
Kepler realized that the planets do not move in perfect circles. They moved in ellipses – slightly flattened ovals, with the sun setting to one side rather than the direct center.
Nearly a century later, Isaac Newton read Kepler’s laws of elliptical motion and came up with the force that explains them. Call it gravity.
One of the most important ideas in the history of science had been hiding in Brahe’s notebooks for decades. The data was always there. All I needed was someone who could make it readable.
I’m telling you this because the stock market has a Tycho Brahe problem too.
It generates more data in one trading day than Brahe recorded in his entire life. The problem is that for most of its existence, only a small portion of it was readable.
But today, thanks to artificial intelligence, it’s possible to find “signals” within that data — recurring patterns that indicate future movements in stocks, many with historical accuracy rates of 90% or better.
I know this because my team at TradeSmith and I built a new AI-powered trading tool that detected more than 200 of these signals across nearly 2,500 stocks.
As I showed the nearly 9,000 people who joined me Artificial intelligence signals trading event On Wednesday, in a six-year backtest, a typical portfolio of these signals trades generated a 12x return.
And in 2022 — the worst year for stocks in half a century — they posted an average backtested gain of 16.6% while the S&P 500 He falls Nearly 20%.
If you missed it, the replay is still available online. It’s packed with trading examples, strategy details, and on-screen demonstrations. Go here to watch it now.
Today, I want to share something I didn’t have time to cover on Wednesday — the amount of work and creativity that went into building our new system.
The answer is: more than I expected.
Can the weather in Paris move the stock market?
Our lead developer, Mike Carr, has been writing code for 40 years.
He spent 20 years in the US Air Force — coding nuclear missile trajectories, working on cryptography for the National Security Agency, and helping install an early version of the Internet at the Pentagon.
When he left the military, he continued to manage more than $200 million in client funds. He also became a certified market technician – a certification that only 4,500 people in the world have.
Two years ago, he joined TradeSmith to help us develop new analytics and strategies. He brought with him the kernel of an idea he had been working on for more than 20 years.
In 2003, Mike began conducting preliminary studies on signals. Every time he noticed a recurring pattern in the data that tended to precede the stock’s movement, he recorded it. He has traded this way for years, constantly testing what worked and what didn’t.
Then in 2016, he read a Bloomberg profile of Jim Simons’ famous hedge fund, Renaissance Technologies. One detail stuck with him: Simons had once found a tradable weather signal in Paris.
Mike realized that if he could find a signal in the Paris weather, the signals hiding in ordinary market data must be almost limitless. That was the moment he decided to stop looking for signals manually and start building a system that could catch them at scale – a system that ordinary investors could actually use.
Last year, we started feeding the roughly 150 signals it had collected into the AI system and pushed us to generate more signals like it. We’ve processed over a trillion database rows, and run each stock through 847 individual calculations. We tested every combination of price patterns, technical indicators, and calendar conditions we could find.
Then we ran into a problem we didn’t expect.
On any given day, the AI-powered signal generator was delivering a stream of 697 trade setups – all with historical accuracy rates of 75% or better. It was too much for any trader to handle, regardless of their level of experience.
So we spent the next six months solving a different problem: How do you take so many high-quality signals and deliver something that an investor can actually use?
The answer Mike came up with was Quality Score. It is a rating from 0 to 100 that takes into account each signal’s win rate and average returns and uses machine learning to see how effective it has been during similar market conditions in the past.
Pair that with a focused model portfolio, and Flood becomes an actionable shortlist.
Introducing our three-stock strategy
At any given moment, you hold three stocks in the S&P 500 — each stock selected by an algorithm based on its Quality Score and other key factors. When an exit signal sounds on one of the three, a new trade recommendation takes its place.
This is the whole strategy. Three jobs, always live. And each one was chosen not because a human liked a chart or a story… but because the algorithm chose the mathematically optimal trade.
We tested it again from January 1, 2020 through January 30, 2026 – a stretch that covered the Covid crash, the 2022 bear market, two years of historic inflation, rising interest rates, and two wars.
It was not a friendly period to stress test the trading system against him. But here’s what the main signals wallet produced:
- A 54% compound annual return – Compared to about 15% for the Standard & Poor’s 500 index over the same six years
- A Winning percentage: 73.4% Across hundreds of deals
- A Maximum withdrawal 18.1% — Less than the S&P 500 maximum drawdown of 25.4%
The maximum withdrawal limit from the model portfolio is worth stopping at.
Many trading systems can generate a high compound return in backtesting. Few can create an index that holds up better than a benchmark like the S&P 500 during its worst periods. This is the true test of whether the system is managing risk effectively or is just riding luck.
And that’s the goal of what we’ve spent the last 12 years (and in Mike’s case over 20 years) developing. Hedge funds have been doing this kind of work for decades — pattern recognition, machine learning, and disciplined rotation in and out of short-term trades. But until now, there has been nothing like this for ordinary investors.
I went into all the details during the launch event on Wednesday. So, if you haven’t already, make sure you do check it out While still online.
Keith Kaplan
CEO, Tradesmith
