How To Get This Trading Algorithm For FREE!

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

 

In this video, I want to explain how I’m going to give away one of these softwares that cost thousands of dollars to one of the potential viewers completely for free. The only thing you need to do is be better than the software. Now let me explain.

Welcome! If you’re new to the channel, my name is Matt Jimenez. I’m an entrepreneur who has worked with some of the greatest minds in finance over the last several years. I’m here to pour into you guys everything that’s been poured into me.

Over the last year, I’ve been spending a lot of my time in the algorithmic trading space and have tested out a plethora of softwares. This is by far one of the best ones that I’ve ever experienced, and I want to be able to give it away to one of the viewers watching this. In this video, I’m going to explain exactly how you could potentially win one of these softwares.

But first, let’s go into the actual software and show you guys exactly what you have to do in order to acquire it.

Let’s take a look at the software.

So here is the software. The one that I’m speaking about is the Diio bot, issued by Nerp. This is my favorite software that I’ve used so far, and in fact, I think it’s only going to get better moving forward into the future, as a lot of the Quant department is putting a ton of effort into optimizing it because they see such great potential with how well it’s been performing.

Now, if you don’t know much about this one, it’s very interesting because it actually has a hedging mechanism. So, as the software takes positions, it’ll take counter positions to the initial ones to offset the difference.

What I mean by the difference is this: Let’s say it takes a long position, but the market keeps going down. It will then enter a bunch of short positions to maximize on the downside until the trade starts to turn around and move back to the upside. So, it’s playing both sides of a single asset, which is called hedging. Typically, people hedge to offset their potential downside, and that’s exactly what this software does.

Let’s take a closer look.

So, right here are all the open positions. This is the actual software. All these green dotted lines represent open positions, and this red line is a take-profit level. That means, once the market hits that line, the software will go ahead and close the position.

As you can see, it sold and closed out here, bought here, and bought here again. Now it’s in a large buy sequence, but it also found a short sequence in between, optimizing its performance and ultimately allowing me to reap more of the rewards from all of its activity.

I just bumped it up to a daily chart now. Look at this: all of these dotted lines are showing where a position actually closed out. So, it sold, sold, sold, sold. All these red arrows are sell positions in which the software closed out here. In the meantime, it was also doing buys—these green arrows show buys. It bought here, bought here, bought, bought, bought, and they all closed out pretty quickly.

Meanwhile, while it was in a large sell sequence, it’s now doing it again to the downside. As you can see, it’s entering into a buy sequence where it has a bunch of buy positions, but it also has entered a short position, along with a couple of other active ones at the moment.

Now, it does this across a multitude of different pairs that I have it attached to. Let’s look at all the active trades at the moment.

Here are all the active trades.

As you can see, for the Aussie New Zealand Dollar, it has sell positions, but it also has buy positions. This is the hedging part that I’m very interested in, and I love how well it’s been performing. I actually want to show you exactly my performance right after this.

So, these are all the positions at the moment, and this is what the software looks like. This is the interface, and this is exactly what you will experience if you were to win this exact software.

The team will go ahead and show you exactly how to attach the software to your brokerage account, so you’re not guessing about how this thing even works.

Please visit our YouTube channel to watch the full video!

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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.