3,048 Trades Later: A 6 Month Automated Trading Performance Update

https://www.youtube.com/watch?v=x0BFLWvgG_0

How Automated Trading Software Performed Over 6 Months

In the last 6 months, I’ve taken a total of 3,048 trades, all of which took zero effort from me. The reason is that I’ve been using algorithmic trading software to do all the decision-making for me. In this video, I want to go over the last 6 months of using this software and dive a bit into its performance over this period. Without further ado, let’s get straight to it.

What This Automated Trading Journey Has Looked Like So Far

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 share with you everything that they’ve shared with me. This video is going to be a $50,000 journey update, documenting this series to show you exactly the efficiencies of using software to trade in your portfolio rather than chaining yourself to a computer, trying to figure out what setup is good for you, what strategy to use, and how to execute trades effectively. I’ve completely automated that and put all of those efforts into the hands of an algorithmic trading software. I’ve been documenting this journey so you can see how well it performs and how easy it can be to implement something like this in your portfolio.

If you haven’t seen it from the very start, just go ahead and click this series up above, and you’ll be routed straight to when I initially started this account. You can see its overall growth.

How the Automated Trading Software Works

Now, before we dive into the data of its performance, I’d like to just prompt you on exactly what I’m using and how I’m using it. The software that I’m using is called the FED bot. It’s a software that you attach to your brokerage that executes trades on your behalf without you actually having to go on the charts and find a trade that suits you or your strategy. It executes trades, both entering and exiting, at a high volume. For someone to match the volume that this software can handle, you’d probably have to spend most of your life chained to your computer. Not only does this work effectively, but it has also bought back a bunch of my time without me having to learn how to trade, what to trade, and when to trade.

How Risk Settings Affect Automated Trading Performance

The cool thing about this software is that you can adjust the risk parameters to whatever suits you best. If you want to be more aggressive and take on more risk for a bigger reward, you can do that because you have full sovereignty over the software. The settings I’m using on this account are even below the recommended position sizing, making my account extremely conservative compared to a standard account or what others are doing in the community. If you join the community, you will see people achieving significant returns, but keep in mind that this isn’t indicative of future returns. Some have done upwards of 10%, and I’ve even seen 20% months using this software or a variety of other software to find these returns. The ability to adjust the risk is key.

Verified Automated Trading Performance After 6 Months

Now, let’s go straight to the performance. Here we have my FX book, a third-party verifier of the performance on this exact account, which I titled the FED 50k to keep it simple and easy to find. Since we started the account back in late October, or technically the beginning of November when it started having active trades, we’ve achieved a grand total of 23%. Our daily change is 0.1%, and our monthly average is 3.2%. Our biggest drawdown is 2.44%. For those who don’t understand drawdowns, it’s simply a fluctuation in your equity curve. The software doesn’t close out negative trades, so it doesn’t reflect in your overall account balance; it’s called an unrealized drawdown. This means you never actually took the loss. The software only closes out the winners, keeping the balance in your account high despite any apparent drawdown. On the flip side, unrealized gains are those that haven’t been closed, meaning they haven’t yet reflected in your account.

Account Growth, Drawdown, and Profit Overview

Now, let’s get back to the performance. The account started with $50,000, and there is currently $61,500 in it. Over here, you can see our equity curve, which is extremely smooth and largely absent of drawdowns. This looks like really steady growth. Scrolling down a bit, you can see the total profit is $11,544.76.

Key Automated Trading Stats: Win Rate, Loss Rate, and Profit Factor

Let’s get into some of the stats quickly. As mentioned at the beginning of this video, we’ve done a total of 3,048 trades. Here, you can see the number of trades taken and the win/loss statistics. We’ve won 2,513 trades out of 3,048, meaning we are 82% profitable. We’ve only lost 534 trades, representing 18%. This puts us at an 82% profitability rate.

Moving to the far right, an important metric is the profit factor, which is 2.31. This means we’re looking to gain 2.3 times what we are willing to lose, known as the risk-to-reward ratio. This ratio indicates we’re risking one to gain two, a favorable metric. Hovering over it, the profit factor shows how many times the gross profit (sum of all winning trades) exceeds the gross loss (sum of all losing trades). The higher the value, the better. Day traders often look for a 2:1 risk-to-reward ratio, and this software meets that standard.

April Performance and Why Automated Trading Can Be Uncorrelated

Scrolling to the bottom, let’s review April’s performance, as this video is dedicated to it. In April, we achieved 4.7%, which is excellent compared to many other investments. The best aspect of this software is its uncorrelated performance compared to other assets. If your portfolio is heavily invested in equities, it mirrors the economy’s performance. When the economy is doing well, your portfolio reflects that, but when it’s doing poorly, it also reflects that. What I like about this software is its uncorrelated nature, meaning it performs independently of the economy. The software plays both long and short positions, scaling into trades and offsetting drawdowns with profitable trades, continuously closing winners and slowly exiting losers. This approach has been extremely profitable, with no losing months or weeks, and I’ve already achieved over 20%.

Final Thoughts on This Automated Trading Software Update

I don’t know of another asset with better uncorrelated performance and passive nature. Nothing is completely passive, but this is the closest I’ve found. If you’re interested in the software I’m using, the link is down below. Click it to see if you can get your hands on software like this to enhance your portfolio performance. As always, my friends, peace.


Please visit 3,048 Trades Later.. 6 Month Update with Algo Trading! to watch the 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.