CEO’s MOM Invests $60,000 Into Trading Algorithm

https://www.youtube.com/watch?v=0Gfe2h9dAmk

What if your mom could be a professional trader? Or better yet, what if your mom could perform as a professional trader without even knowing how to trade well? In this video, I want to go over something a bit comical. This is the CEO of Nurp. He set up an account for his mom back in April of last year to trade for her, and this is the data.

Welcome back! If you’re new to the channel, my name is Matt Jimenez. I’m an entrepreneur who has worked with the greatest minds in finance over the last several years, and I’m here to pour into you guys everything they pour into me. In this video, I want to go over Jeff Sekinger’s mom’s account. If you don’t know who Jeff Sekinger is, he’s the owner and CEO of Nurp, and his mom actually has a trading account with the software running since April last year. Mind you, she’s actually not a trader. She doesn’t have any idea how to trade or execute trades. All she knows how to do is let the software trade for her. It’s quite interesting to see the results because this really illustrates how you can give this software to someone who doesn’t know anything about trading whatsoever, and they’re able to achieve this type of performance. Enough rambling, let’s look at the data.

Okay, so here is Judy’s Big Fed account. Judy is Jeff’s mom. Imagine how much you need to trust your product in order to give it to your own mother. That right there screams trust. Now, let’s look at how well she’s actually done. Since April 17th, 2023, she’s achieved 134%. Today’s date is May 21st, 2024, so it hasn’t even been two years with the software, and she’s already achieved over 100% returns. Her daily change is 21%, and her monthly average is 6.59%. Again, guys, when we’re speaking of averages, don’t be so focused on them. An average, if you don’t understand, let me educate you, is the number you get when you take the highest, the lowest, and everything in between, add them together, and then divide that number by how many inputs you have. That’s how you find an average. An average is an average; it’s not an ultimate, it’s not an absolute, it’s not an infinite. The reason I emphasize this is because when we say 10% on average or 6% on average, people comment and message me directly, saying, “Well, this month you didn’t do that number, this month you didn’t do that percent you said.” Correct, it’s an average. Not every month is going to be that; it’s an average.

Now, back to the charts. Her largest drawdown was 20.54%. That’s quite hefty in comparison to the settings that I have on my Fed account. My Fed account has only seen just shy of a 3% drawdown, but mind you, I’ve only seen a 20% gain. With more risk, oftentimes comes greater reward. Clearly, I’m not taking enough risk, which is why my overall reward is far less than 134%. She started this account with $60,000 and she’s already withdrawn $44,500. Watching this, or if you know someone, I bet you Jeff’s mom is a better trader than your friend. You heard me right. Jeff’s mom is calling out all traders to a challenge. Can you outperform her software manually trading? If so, message us. We’d love to see who is the better trader over an extended period of time.

This week, she’s achieved 9%, so almost 1% for the week. This month, she’s achieved 1.68%. Today is the 21st of May, so maybe she might get to 2% this month. But this year, she’s already done almost 20%, sitting at 18.56%, which is putting her at a grand total of $973 for this year in profit. I just had a mini panic attack because I didn’t know I was recording; I thought I forgot to hit record.

Now, this account has done a total of 6,185 trades, which is quite a lot in comparison to all the other software I’ve reviewed, and also quite a lot in comparison to my own personal experience. Mine has only done a little over 3,400 trades; we’re probably at 3,500 by the time you watch this. Her profitability is 76%, which means she loses 24% of the trades that she takes. Out of the 6,185 trades, she’s actually won 4,692 trades. That’s crazy! She doesn’t know how to trade, but she’s won more than half. She’s won 76% of trades. That’s insane. Her average loss is $31 and her average win is $19.

Now, her profit factor, and if you guys don’t know what this is, this is something I always like to look at because this measures how much you’re willing to risk in order to gain. In this case, she’s sitting at right about two. This is 1.96, which means she’s willing to risk one to gain 1.96. Of course, not her, but the software, because she’s not actually trading.

Now, let’s go down to the fun part, where we can track all of her months on a nice clean bar chart showing the representation of the percentage she achieved each month. Let’s go back to 2023. In 2023, when she started the account back in April, she achieved 10.59%. In May, she achieved 6.27%. In June, she achieved 12.49%. In July, this looks like her best month yet, sitting at 17.45%. In August, 3.91%. In September, 4.49%. In October, 5.47%. In November, 3.88%. In December, 7.17%.

Moving over to 2024, so far she’s already achieved 25% in January. In February, almost 2%, sitting at 1.73%. In March, she achieved 2.98%. In April, she achieved 8.57%. In the current month of May, she’s already sitting at 1.68%, and we still have about a week left in the month.

All that data is extremely impressive. I’m not even kidding. I think that they would actually be down to see if there’s a manual trader who can outperform her performance, and she doesn’t even know how to trade. So if you know somebody, send this video to them. This is a friendly invite to a competition of a manual trader versus software trader. We’ll make it interesting. The winner can have an extremely good incentive, but first, we’ve got to find someone who’s down for it. If you’re interested in how you could trade just like Jeff’s mom, or even better, hit the link in the description or in the pinned comments, and the team will see if it’s a good fit for you. If not, like always, my friends, safe trading.

Peace.


Please visit CEO’s MOM Invests $60,000 Into Trading Algorithm to watch the full video on YouTube!

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Matthew Jimenez
Matthew Jimenez | Algorithmic Trading Content by Nurp

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Bingham Zhou

Bingham Zhou, CFA, has over 15 years of experience as a quantitative researcher. His expertise spans systematic equity strategies, CTA trend-following, and interest rate proprietary trading in both U.S. and Asian markets. He holds advanced degrees from MIT, Carnegie Mellon, and Yale.

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Quant–Investment Strategist
Greg doscher

Greg Doscher was a CFO for many years who built out many quantitative strategies and investment tools to manage and enhance risk adjusted returns in the company’s pension plan. Prior to joining Nurp, he consolidated his skills in coding and discretionary trading to develop a comprehensive and fully automated algorithmic trading system deployed across 200+ futures markets and cryptocurrencies that encompassed all of the trading strategies he had honed over the last 22 years in finance

Quant–Investment Strategist
Marcin Borratynski

Marcin was Head of Quant IT at the USD 4bn+ CERN Pension Fund, where he spent nearly a decade building quantitative asset allocation systems and implementing algorithmic investment strategies for a multi-asset institutional portfolio.Before joining Nurp Marcin was also Senior Quant Strategist at Evooq, a Swiss-based fund managing four strategies across equities, gold, and equity derivatives.Marcin holds a degree in Computer Science an MBA from the University of Geneva and the Certificate in Quantitative Finance (CQF).

Product Manager

Abhayjit Anand

Abhay has worked with Nurp since 2022. As a Product Strategist, he focuses on building, refining, and commercializing algorithmic trading strategies. He brings seven years of experience in financial trading – combining macro research, technical analysis, quantitative strategy development, and market psychology. Alongside his work at Nurp, Abhay also serves as an Investment Analyst at Orca Capital. Before entering financial markets professionally, he spent eight years at IBM, including three years in the AI & data division as a Delivery Lead managing complex implementation projects.