Trading Algo Results After 8 Months

https://www.youtube.com/watch?v=sP1XUrXHeW8

It has officially been eight months since I’ve started using the quantitative trading algorithm from Nerve called the FED Bot. In this video, I want to go over its performance during this duration. So, without further ado, let’s see how we did.

Welcome! 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 share with you everything important to me. Today, I want to go over the performance that I’ve experienced in my portfolio utilizing quantitative trading strategies, primarily the FED Bot from Nurp. If you haven’t been documenting this journey with me, you can hit this link up above to see all the videos along the road of building this $50,000 account.

No more rambling – let’s see the data. Here is the Fed 50K account. I started this account back in late October but didn’t officially start trading until the 1st of November. Since I’ve started, we’ve done a whopping 32% so far, almost 33%. My daily change has been 11%, the monthly average has been 3.22%, and the max drawdown I’ve experienced is 8.40%.

Again, the account was started with $50,000, and right now the balance is sitting at $16,320. Essentially, I have profited $16,320 so far since starting this account. The best part about this is it’s been quite passive – probably as passive as you can get when it comes to making an active investment like quantitative trading.

To the far right, you see my graph – this is my equity curve and how well it has done. As you can see, it’s been extremely smooth. There have been some news releases that caused volatility in the markets, which in turn triggers the algorithm to become volatile as well. However, one thing I want to mention with the volatility is that typically, if it’s going against you, you’ll go into a drawdown. The drawdown, of course, would be unrealized, meaning it never really reflected in your overall balance.

But that’s not what I want to talk about. I want to focus on the actual drawdown. The yellow line represents equity growth, showing active and open positions. As you can see, every time it dips low, it has an equal or greater recovery to mimic its drawdown.

For instance, back on June 3rd, we had some crazy news finishing off the month previously. It trickled into the first week of June, resulting in a drawdown. This obviously led the account to rebound quite aggressively. As you can see, the actual growth in the account did not reflect the drawdown because none of the trades were closed negative; they were just reflected as negative using the margin. Once everything closed, it all closed out in profit, and the account actually grew quite a bit.

The same thing is happening right now. We’re currently in a drawdown, as you can see. The equity growth right here is down just a tad. This is what it looks like when you’re in a drawdown. I’m glad I’m shooting this video at this current moment because now you get to understand what I mean by it not reflecting in your account. As you can see, the equity line is clearly pulled back, but the balance in the account is not negative. This means the trades are open and active; they’re just running in a deficit, which is reflected in the equity curve.

The actual overall balance of $66,000 in the account is not affected until the trades are closed. If the software repeats itself, the trades will likely close in positive, and I might have a very good month here in July. But the purpose of this video is actually to go over the performance in June, so let’s scroll down just a tad.

One thing I want to highlight: I know you probably noticed I’m not in my studio, and that’s correct. I’m actually in Spain at the moment, having arrived over the weekend. Today is Friday. One thing I want to show you on this chart, which is the reason why I have it clicked on The Daily, is to demonstrate that while I’ve been in Spain, my software has been extremely active during this stay.

So, over the weekend, the markets are closed, and no trades are happening. But on Monday, when the markets are open, let’s look at how active this algorithm is while I enjoy the beautiful ambiance and views of Spain. Back on Monday, the account took 552 trades. Out of those, 492 were winning trades, and 60 were losing trades. This means we were 89% profitable on Monday.

Let’s see how we did on Tuesday. On Tuesday, we took a grand total of 697 trades. Of those, 591 were winning trades, which means only 106 were losing trades. We were 85% profitable on Tuesday. Mind you, I’m in Spain, not actively trading or looking for setups. This software has automated the whole experience of trading, allowing me to enjoy my time here while it handles the trades.

Now, let’s check out Wednesday. On Wednesday, we did a total of 781 trades, with 618 of them being winners. This means we won 79% of the trades and only lost 163 out of 781. On Thursday, the software became extremely active, doing almost 1,000 trades in a single day. On Thursday, we did 954 trades, 740 of which were winning trades, meaning we won 77% of the trades, and 224 were losers, resulting in a 23% loss.

Finally, let’s check Friday, which is today. Let me tell you what I’ve done so far on Friday. Today, while you visualize how well this does, I’ve been enjoying myself. In fact, doing this video is far more work than running this algorithm. I went out, jumped in the ocean, did a cold plunge, went to the sauna, worked out for an hour and a half, and did a bunch of administrative tasks for companies. While I’ve been doing all this by the pool, enjoying the views, the software took 9,912 trades today. Of those, 740 were winners, making our win ratio on Friday 81%. The losing trades totaled 172, meaning we lost 19% of the trades.

So, on Friday, while I’ve had this glorious day, my trading bot-managed my portfolio, made over 900 trades, and remained extremely profitable. In fact, here comes room service. Yes, you can come – just put it on the table. I’m shooting a video. Thank you!

As you can see, all the trading is still happening even while that’s going on. Now let’s scroll down just a tad and see how we’ve done for the month of June. For June, we did 4.41%, and that is one of the best performing months we’ve had so far on this account. Mind you, some people might say that 4% is low. I personally do not think 4% is low whatsoever. If you think about the compounded growth of 4% over time, it will be life-changing, quite frankly. Also, the algorithm is on the lowest risk setting possible. If you’re running an algorithm in your portfolio and don’t know how to change the risk, click this video up above. It’ll teach you exactly how to change the risk on any pair or the entire software and possibly beat my performance because mine is on the lowest setting.

Anyway, if you found any value or entertainment in this, please leave me a like and a comment. Click the description down below if you want to see if you can harness the power of quantitative trading in your portfolio. If you want to skip all the nonsense and speak directly to the team, go to the comments. There’s a pinned comment that will take you straight to the team, where you can talk to one of our members. They will tell you everything you need to know to make the most appropriate decision for your portfolio. Nonetheless, I’m going to go enjoy Spain, probably get some more sun, maybe take another dip in the ocean, or perhaps wrestle a shark. I don’t know. But either way, whatever I do, it’s still trading for me.

And like always, my friends, peace!


Please visit Trading Algo Results After 8 Months to watch the full video on YouTube!

author avatar
Matthew Jimenez
Matthew Jimenez | Algorithmic Trading Content by Nurp

Search Posts

Algorithmic Trading Accelerator

Schedule a meeting with us!

Matthew Jimenez

Matthew Jimenez | Algorithmic Trading Content by Nurp

Latest Posts

The programming languages most widely used for automated and algo trading are Python, C++, Java, C#, and increasingly Rust, with

The three most widely deployed forex automated trading strategies are trend-following systems on major currency pairs, mean-reversion systems on range-bound

The five best algo trading books to read are “Advances in Financial Machine Learning” by Marcos Lopez de Prado, “Algorithmic

Professional headshot of an Asian man in a black suit, white shirt, and light blue tie against a white background.

AI Quantitative
Researcher

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.

Portrait of a man with shoulder-length light brown hair and stubble, wearing a white shirt and black blazer against a gray background.
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.