But to understand how this mathematician saw profitable patterns to make billions, you will have to start with his Colombian story because I doubt he would have made billions if it wasn’t for his interesting background.

You see, before the mathematician saw profitable patterns on charts like these, he made a small investment with his Colombian friends. He urged them to start a business together, and they did. He owned 10% of the business with the money he had borrowed from his family.

But the problem was he wasn’t making enough as an instructor at MIT and later as an assistant professor at Harvard to pay back his debt. So he went to the Institute Of Defense Analyses in Princeton. This was a highly classified government operation designed to crack codes, and they paid very well.

This was great, but later, he left the institute and went to Stony Brook University to become the head of the math department. And while he was here, the business he started with Colombian friends got sold. And as a result, the mathematician was left with a good amount of profit.

Now, many people, when they stumble upon good capital, will invest in something safe. But the mathematician decided the trade currencies instead.

This was when he was in his late 30s, and he did pretty well. He didn’t believe in the efficient market theory, which says that it is pointless to search for undervalued stocks or to try to predict trends in the market through either fundamental or technical analysis. There are multiple reasons not to believe in this theory. Two of them are Warren Buffett consistently doing better than the markets in the long run, and market crashes where most things are trading below their fair price.

So the mathematician read newspapers to try to predict where the currencies were going. And even though he made quite a lot of money doing this, he believed it was pure luck because it was not based on mathematical models.

This is where things changed. After looking at the data, he realized that there was a pattern here.

So he hired mathematicians and started working on models. They would design an algorithm and then test it to see if it works. This was similar to the kinds of things he did at the Institute Of Defense analyses to crack the codes.

But he was not good at hiring the right people. The fundamental analyst he hired to work on the model made some money and then lost some money. So he decided to hire scientists because he knew better in that department. Later he said he didn’t like hiring people with an investment background, because he liked to think, that you can teach a physicist finance, but you can’t teach a finance person physics.

With more scientists working on creating the best way to make money from the market, their model kept getting better and better. What they were doing was gathering a lot of data and then finding patterns that were predictable and happened more often. We will see some of these working patterns in this video in a moment.

But what’s really interesting is that they didn’t just stop at analyzing the price data tick-by-tick but also looked at the weather conditions, annual reports, quarterly reports, historical data itself, volumes and almost everything that can move the price. Furthermore, they didn’t just stop at hiring mathematicians, they also hired astronomers and physicists. This was one of the most in-depth analyses done by scientists to make a profit from the financial markets.

Now, with all the experts working on the strategy, you might think their win rate would be very high, right? Well, this is where 90% of traders make a mistake and stay in the bottom 90%.

You see, the name of the mathematician was Jim Simons, and he used the strategies they created to form Renaissance Technologies, the asset manager that manages the Medallion Fund. While Warren Buffett compounded at around 19% annually, Jim Simons’ Medallion Fund generated a 66% gross annual return, or a 39% net annual return from 1988 to 2018. With this high compounding rate, they generated $100 billion in profit pretty quickly, resulting in Medallion Fund becoming the best fund of all time.

But even though the fund made a very high-profit percentage in a year on average, according to some sources, their win rate was only around 51%, or just above breakeven. That’s because when all those scientists were analyzing a large amount of data, they were not trying to find a pattern that had a very high win rate. Instead, they were trying to find patterns that have a very high probability of working consistently in the long run. If there was a pattern that only had a 1% edge, you could simply take a high number of trades to make a lot of money, which is exactly what they did.

On the other hand, 90% of traders won’t do that. Even if you give them a statistically proven strategy that has a very high probability of making money in the long run, they will not stick to a proven model and will chase the unrealistic high win-rate strategies.

After seeing how Medallion Fund generated high returns using predictable price patterns and data, new funds have emerged trying to do the same. Now, this can be a bit problematic because if you are a top fund that is creating strategies using market data that is publicly available, someone at some point can come up with a similar profitable strategy.

So their key to staying ahead of everyone was gathering a tremendous amount of data every single day, finding new approaches, and adapting to the new market as quickly as possible.

Out of these approaches, 3 strategies they used to make Billions over the years are known to the public on the surface level.

Number 1. Jim Simons said, in the old days, commodities and currencies prices had a tendency to trend. Not a continuous trend but a short-term trend. So if they decided to predict the direction, they would look at the average move of the last 20 or 10 days and make a prediction based on that trend direction. This is similar to many trend trading strategies we have tested 100 times on the Trading Rush channel.

Then there is strategy number 2. Since the price moves around its fair price in the long run, they tried to find charts where the price has moved away from the fair price in the short term. For example, if the price is moving like this, and this is the average line, when the price goes too far from the average price, they would trade towards the average price. This is also known as the Mean Reversion Strategy.

Then there is the third strategy. You see, in the financial markets, there are multiple exchanges and instruments to trade. So when they saw abnormalities in the prices, or in other words, if security was priced lower on one side and the same security was priced higher somewhere else, they would buy and sell the security at the same time, and profit from the difference in the prices. That’s a 100% guarantee of making a profit. But the third strategy will be difficult to execute in today’s market for retail traders as computers have taken over that department.

The first two strategies still work today, and you can find their data on the Trading Rush Channel.

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