Best Stock Trading Strategy with 491% Return from Quant Fund Manager

• Ivan Scherman made a 491% return on the 2023 World Cup Futures Championship.
• He is a quantitative fund manager who uses hundreds of models to execute trades across multiple assets.
• It emphasizes evidence-based technical analysis and risk management to minimize human error.

Ivan Scherman is a certified contract market technician and hedge fund manager in Buenos Aires, Argentina.

Traded in spot, futures and options markets stocks, indices, commodities and currencies — basically anything accessible through the New York Stock Exchange, Chicago Mercantile Exchange, and other major U.S. exchanges.

And he’s good at what he does: According to competition records, Scherman finished first in the 2023 World Cup Futures Championship with a return of 491.4%.

The problem is that, although it is tempting to use such gains, he cannot do so as a human.

Scherman is a quantity fund Manager who can use hundreds of models to help execute operations. His job is to backtest models and then put them to work, monitoring their progress. The machine allows for true diversification by trading across multiple assets. S&P 500 right down to coffee and even soybeans.

His trading journey did not start as a quant or a trader. He became interested in the markets in 1996, while a law student at the University of Buenos Aires, after taking a course on trading and securitization. His interest led him to obtain a certificate in trading at the Argentine Institute of Financial Markets.

The more he learned, the more he slowly began doing small trades as a side hustle during his first few years as a trademark attorney.

“At first I thought I knew how to trade, but I didn’t. It was a bull market. I was trading in the Argentinian market,” Scherman said. “So no matter what I traded, I made a profit. When things started to get tough in the Argentinian market, I broke my bank.”

It was an experience that led him to adopt an approach that ignored human errors that often arise from emotional reactions.

Reveals your talent

It took him about four years to become a good trader, but that didn’t mean he was a good fund manager. When he left law to become a professional trader, his shortcomings were covered by his firm’s risk department, which prevented him from taking large losses on his trading. But because he had not learned to manage risk independently, he still bore losses on his personal account.

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He was taught how to apply a method when he read the book “Evidence-Based Technical Analysis” by David Aronson. scientific method designing or testing a trading hypothesis. One of these lessons was that an investor’s strategy should be tested in every market condition, including bullish, bearish, and sideways markets with high to low volatility. This is because a trader cannot predict the next market regime and must prove that his thesis can withstand the changes. The larger the historical sample, the better off the trader will be.

He learned additional lessons in risk management through more trial and error. The two most important points were that he did not put all his effort into a single trade and that he approached each venture as if it were a big loss. Instead, it should aim for small gains on different positions and include a stop-loss plan based on historical data for each thesis.

In his backtesting, he looks for repetitive behaviors to identify stop-loss points and measures their range to determine how they fail and the depth of losses they have historically produced. The stop loss will be set differently for each thesis. For example, if a particular pattern frequently falls by 2% before gaining, it will set a wider range early so that the trade does not stall. As a result, the stop loss will vary for each formation depending on its historical behavior.

Finally, it limits overall risk by keeping each position small, between 2% and 3% of its full portfolio.

“I need capital to offset this loss,” Scherman said. “I have some numbers in mind that are very important to me. For example, if you lose 10 percent, you will need 11 percent to make up for that loss. If you lose 20 percent, you need about 33 percent to make up for that loss. If you lose 50, you will have to win 100 percent to make up for that loss.”

When using models to backtest their theories, a trader can repeat this process by examining trading charts for each asset and model they trade to determine ranges within which a pattern may pull back. However, each stop loss should be tailored to the investor’s personal risk tolerance and capacity.

A simple trade from a quantity manager’s toolbook

Although much of what Scherman did cannot be replicated by a human, some clues and patterns can be derived from his models.

Essentially, a trader must learn to take a technical approach based on evidence and historical patterns rather than subjective opinions. There is no interpretation of the behavior of patterns in algorithmic trading. Instead, every rule, such as what an uptrend is, will be measured and understood by the computer. In other words, he stated that what can be understood by a calculation can also be understood by a human.

When it comes to a strategy that is in his models’ playbook, although it is not a model he uses, a simple strategy one can copy is to trade the S&P 500 using moving day averages.

Two conditions must be met here:

First, the day’s 200-day moving average is higher than the previous day.

Second, the S&P 500 index closed lower than previous days for three consecutive days. This decline, following the initial trend above, indicates a temporary pullback in a strong uptrend.

Scherman noted that given that the market moves in swings, it will close above the five-day moving average at some point.

“When closing above the five-day moving average, profits are made in 72% of the samples tested over 100 years.”

The chart below shows the gains that could hypothetically accumulate based on the purchase of one unit of SPX, based on specific trading patterns tested by Emerge Funds Investments through September 2024, over the number of trades in which this pattern has occurred since 1960.

Scherman noted that the closest way to replicate the index’s output is to use a large ETF that tracks it, such as the SPDR S&P 500 ETF (SPY). In a real trade, the win rate will remain the same, although there may be minor changes due to friction, including strike price differences.

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The chart below shows what the pattern might look like on the chart and its hypothetical entry and exit points. In the first option, the transaction is automatically executed before the market closes on the third day of the consecutive lowest close and sold at the close of the day when it is above the five-day moving average. The second option on the chart is the second way to enter the trade by buying at the open after the third consecutive lower close and exiting at the next open the day after the close above the five moving averages.

However, the model shows that the first option has better performance results.

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