EXPOSED: The Secret Behind Bitcoin’s Surge and Skyrocketing Asset Prices!

https://www.youtube.com/watch?v=Bf07LGWhFqc

 

What if I told you we’re on the brink of the biggest financial melt-up in history? Every major asset—from stocks to Bitcoin, gold, real estate—is going completely parabolic. But here’s the truth: What if this isn’t just a massive bubble? What if this is a gigantic wealth transfer that could leave you behind if you’re unprepared?

Have you noticed the signs? The continuous new highs, the bad economic news releases, and just a zero correlation between asset performance and what’s really going on?

Well, this is what’s happening right now. Asset prices, like I said—stocks, Bitcoin, gold, real estate, and everything else—are hitting record highs. This isn’t normal bubble behavior. In fact, in a typical bubble, one or two asset classes will go completely wild because of speculative mania, like the dot-com bubble in the early 2000s or the housing bubble of 2008.

But today, it’s everything. This is called a melt-up—a phenomenon where prices skyrocket seemingly without a reason. Unlike a bubble, there’s no widespread euphoria or fear of missing out. Instead, it’s driven by big money front-running inflation.

Take Bitcoin, for example. It’s smashing all-time highs, but there’s no ICO frenzy like in 2017. And let’s look at gold—it’s been quietly climbing to record levels for months now. Even real estate— the median house price in the US just hit $442,000. Everywhere you look, asset prices are climbing higher. And like I said, the reason is money printing.

Here’s a chart you need to see. So what we’re looking at here is the money supply chart, and you can see I’ve zoomed it in from 2020 all the way to 2024 (current date). When the chart goes up, that just means we’re printing more money into circulation.

Looking to the far left in 2020, you can see we printed an insane amount of money, which peaked out in January 2022. And you can probably guess what happened next: asset prices followed suit and started exploding to new all-time highs. Of course, this isn’t a coincidence—it’s cause and effect.

So, why does the money supply matter? Because it leads to inflation, like clockwork.

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, 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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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

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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).

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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.