This is What Happened After 95 Days of Algorithmic Trading

https://www.youtube.com/watch?v=35DdsGnt9bA

We just wrapped up the month of February, which means we’ve had a grand total of 95 days utilizing algorithmic trading software to manage my portfolio for myself. And in this video, I just want to go over some of the results that I’ve experienced over the last 95 days. Now, mind you, when I say 95, I’m excluding weekends because the market does not trade on weekends. So, I’m just counting for the actual trading days. Without further ado, let’s get straight to it.

Today’s day is March 2nd, at the time that I’m filming this. I want to go over February’s gains. For those of you who may not know, I’ve been tracking $50,000 in a software that trades for you, and it’s called the FED bot. The software is sold by a company called Nurp, which is the name of this channel, of course.

Now, I want to go over some of the results that we have experienced in the month of February specifically. Before we get into this, mind you guys, I want you to know that I excluded weekends, as I said in my intro. And secondly, I want to advise you that I am using the most conservative settings when it comes to this software. And that’s the beauty of it when you have this software and you own the license. You can do whatever you want. You could take more risk, you could take less risk, you could do literally whatever you want, however you feel. But what I did was leave it at the lowest possible settings to manage my $50,000.

Now, without further ado, let me stop rambling and get straight to the results. Okay, so here we have my FX book, which is a third-party vendor that will track all of your data on a specific trading account attached to a brokerage. So, as you can see at the top, this is the Fed 50,000 bot.

In the middle, where you see this chart, that is my Equity curve. And as you could see, it is probably the most smooth Equity curve that I’ve ever seen, actually. And another thing that may stand out is the drawdowns. They’re pretty mild, almost to the point of being non-existent. So, overall, the account is up 15.64%. The daily change is 11%, the monthly average is 3.28%, so every single month I am on average getting 3.28%. And the max drawdown that I’ve had so far was 2.44%. Now, mind you guys, a lot of people seem to not understand drawdowns entirely. And if you want to understand them, hit this link above, and that is a video that I solely addressed what drawdowns actually are because I’ve had so many people reach out to me saying, ‘Hey, I don’t understand drawdowns and what does it mean when you’re down x amount.’ That video addresses it. But for the sake of this video, I will go over it extremely briefly.

Now, a drawdown is simply unrealized, and what I mean by unrealized is not reflected in the account. And I’m speaking on the behalf of this exact chart that we’re looking at. Yes, we may see a 2.4% drawdown, but that never actually reflected into the actual balance of the account. Reason being is because the trades were not closed all in negative, meaning we did not close out the month and or week negative. Just how there’s unrealized drawdowns, there’s also unrealized profits. So, that being said, do not freak out when you see the 2.4%. A lot of people get very weary about things, ‘Well, oh my God, 2.4%, that’s crazy.’ Well, I actually think it’s the opposite, crazy. I think it’s crazy good. I’ve seen things that are far worse than 2.4%, and in fact, like I said, we’ve never closed a week nor a month negative. That drawdown is unrealized and never ever reflected in my account balance.

Okay, enough of drawdowns, back to the results. The account balance is sitting at $57,812.072. We have a total profit of $7,820.72, which is absolutely amazing because technically the account made $7,000. And I’m not exaggerating when I say I did absolutely nothing other than checking up on the software. I made zero decisions, I did absolutely nothing. So, essentially, this account has generated almost $88,000 with probably as close to passive as you could possibly get. All right, so now let’s check the monthly changes. January we saw a 3.84% gain, and for the month of February, we saw a 2.41% gain. Today, like I said, is March 2nd, and we are already up 0.16%…

Please visit This is What Happened After 95 Days of Algorithmic Trading 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.