UPDATE: Tracking the $50K Account 30 Day Progress

We have officially finished the first month of allowing a robot to manage my $50,000 account. Now, if you guys missed the first episode where I gave $50,000 to software, which I’m referencing as a robot, check out that video. You’ll understand exactly what I’m talking about in this video and exactly when this is coming out.

We mark the 30-day time period of how long the robot has been able to manage my funds. Let’s dive in and see how well we’ve been able to perform over the last 30 days, with only two hiccups, which I will get into in this video. Without further ado, let’s jump right to it.

Welcome back. If you’re new to the channel, my name is Matt Jimenez. And if you don’t know who I am, I am an entrepreneur who has worked with the greatest minds in finance over the last several years. I’m here to pour into you guys everything that they poured into me. In this episode, I want to go over the average monthly performance of the FED bot from Nurp. I want to track how well it’s performed from October 27th to the 1st of December, which is when I’m recording this video.

Nurp has serviced over 1,500 clients, and from the data they’ve collected from 1,500 people utilizing this software, they’ve gotten an average of 10 to 12% returns monthly. Now, when people hear this, they go crazy, and they’re like, “That’s impossible.” Keep in mind, guys, this is an average, not what everyone is doing month over month. There are some months that are much lower, and there are other months that are much higher. The mean of an average is finding the average; we take the highest earners, the lowest earners, and we find the middle. That’s what an average is, guys.

So a lot of people freak out; they DM me like, “What? That’s impossible. There’s no way you’re doing 10 to 12%.” Mind you, it’s an average of over 1,500 people utilizing the software, primarily the FED bot, which is the most popular one, and it’s the one I’m utilizing. So let’s go ahead and jump right into the screen share, where I’m going to pull up my FX book, which is a third-party provider where they track data. The FED bot is connected to my brokerage account, which is Fusion Markets Live, and it’s attached to my FX book, where they can record all the data. I’m going to show exactly how that looks for you guys.

Okay, here we are: the FED $50,000 account, Fusion Markets Live is the broker, 1:500 is my leverage. As you can see, since we started the account, which was active trading on October 27th till December 1st when I’m filming this, it’s about a month. In that time period, we did a whopping 2.78% gain for about 30 days of trading. The reason why I say “about” is because there was human error right over here where you see this drawdown. It’s not actually a drawdown. What happened was there was a change in passwords, and I guess, in doing that, I had to turn back on the software. This, again, is human error. This is why software is so much more effective in trading than humans because simple things like that don’t happen when it comes to software.

The reason for this mistake was merely me not being attentive and realizing that the auto trading was not active, which is a simple click of a button that I have to do, not the software. So that was about a 2-day pause in trading, and then again, we have this minor drawdown, which isn’t actually a drawdown. It’s unrealized. Down below is the equity growth, so the actual amount of capital that’s in the…

Visit UPDATE: Tracking the $50K Account 30 Day Progress to watch the fun video!

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.