Converting divergence into alpha
August started with a jolt: surprise splits on key central-bank boards, late-summer hurricane risks lifting global energy markets, and a burst of tariff headlines that kept forex desks on edge.
July rewarded flexibility; August demands speed. Preliminary figures show Gold Digger extending its lead at 66% YTD*, while Argos sits at 29% year-to-date*.
While most traders chase the next big win, the smartest money focuses on something far more valuable: consistency. Our research paper, authored by Senior Quant Strategist Marcin Borratynski, breaks down the mathematical foundations that separate professional portfolio construction from gambling.
Since Harry Markowitz revolutionized investing in 1952 with Modern Portfolio Theory, sophisticated investors have understood a simple truth: it’s not just what you own, but how your holdings move relative to each other. Ray Dalio later called the pursuit of uncorrelated return streams the “Holy Grail” of investing—and for good reason.
When strategies are properly uncorrelated, one approach’s bad day becomes another’s opportunity. The result? Dramatically lower volatility without sacrificing returns. Our research shows how combining 15+ uncorrelated strategies can reduce portfolio risk by up to 80% while maintaining strong performance.
This isn’t academic theory—it’s practical guidance for navigating regime-based markets where traditional cycle patterns no longer apply. With institutional flows reshaping crypto timing and volatility becoming more measured, understanding true diversification has never been more critical.
Download the full whitepaper here
For over a decade, crypto markets have pulsed to a familiar rhythm: bull runs in the year following a halving, consolidation in the second year, sharp drawdowns in the third year, and quiet accumulation in the fourth year. This four-year cycle, built largely around Bitcoin’s halving events, shaped everything from investor expectations to project roadmaps.
But this time feels different.
With institutional capital flowing in, spot ETFs thriving, altcoins expanding across chains, and Bitcoin’s scarcity narrative saturating, many are asking the same question: Is the cycle broken, or is it evolving?
Institutional flows blur the timing.
Allocations from pension funds, ETFs, and sovereign vehicles are now based on broader macro cycles, not just halving hype. This introduces capital at less predictable intervals, smoothing out once-violent peaks and troughs.
Retail is no longer driving price.
Retail used to spark the first wave of upside. Now, with Bitcoin at $100K+ and DeFi access still clunky, smaller investors often enter late or not at all. Less retail froth = fewer blow-off tops.
Altcoin dilution erodes the reflex rally.
In 2017 or 2021, a Bitcoin rally reliably meant ETH, SOL, and thousands of others would follow. Today, the sheer number of tokens and the capital fragmentation they cause mean even strong BTC rallies often leave alts behind.
Derivative structure is changing.
The rise of perpetuals, options, and structured crypto products has increased hedging activity, often dampening volatility. In prior cycles, leverage was mostly directional and reflexive. Now, it’s hedged and measured.
On-chain data shows no clean reset.
Wallet age, realized cap, and NUPL (Net Unrealized Profit/Loss) metrics show less “capitulation” and “euphoria” than previous cycles. The herd mentality that defined past transitions may be fading.
Old Cycle Thinking | Today’s Market Reality |
Wait for the halving, then buy | Opportunity windows are now macro- and event-driven |
Rotate from BTC to alts for 10x | Capital rotation is thinner, and narratives move faster |
Bear markets last 18+ months | Consolidation phases are shorter but more deceptive |
Everyone resets together | Divergence across chains, tokens, and use cases is widening |
The four-year cycle may not be dead, but it’s no longer the dominant force shaping crypto returns. What’s emerging instead is a regime-based market; one that moves in response to global liquidity, regulation, and technology adoption, not just Bitcoin’s supply curve.
For algorithmic strategies, this shift is an opportunity: fewer traders relying on predictable rhythms means greater inefficiency to capture. At Nurp, we’ve designed our strategies to rotate faster, adapt more nimbly, and lean less on outdated assumptions, because the new crypto market doesn’t care what happened four years ago.
We’ve launched a new way for traders to scale their capital, without jumping through hoops.**
Algo Funded Trader, or AFT as we’re calling it for short, is Nurp’s latest program that gives qualified users access to institutional-level buying power from day one. With AFT, your capital is paired with up to 5× equity credit issued by a third-party broker, allowing you to trade live markets with up to $1 million per account, with up to three accounts.***
There are no demo accounts, no profit splits, and no phase-based challenges. Just real capital, full flexibility, and total control over your funds.**
Your Deposit | Broker Credit | Total Trading Capital |
$30,000 (min) | $120,000 | $100,000 |
$50,000 | $200,000 | $250,000 |
$100,000 | $400,000 | $500,000 |
$200,000 (max) | $800,000 | $1,000,000 |
Use case: Many existing Nurp clients are now using AFT to deploy their favorite strategies, including Argos and All Weather, at a larger scale, without tying up more capital. The result: greater earning potential and more efficient capital use.**
If you’ve been looking for a faster path to larger returns, without institutional red tape, AFT may have been built for you.
Explore our offerings
Take control of your financial future with The Intelligent Trader – our flagship automated trading platform that has consistently delivered market-beating returns by using smart technology to trade across multiple markets 24/7, while built-in safety features minimize drawdown to help protect your investment.
Case Study: Algorithms That Deliver in Volatile Markets
See how Argos and Buterin outperformed during recent swings, with fully verified MyFXBook links for transparency. The study details entry logic, risk controls, and live P&L curves.
* Algorithmic trading software is not for everyone. Using algorithmic trading strategies may result in the complete loss of your trading account balance. For more information on our risk disclosures, please see our Disclosures page.
** Subject to the terms of the AFT Agreement. AFT Program is subject to fees. You may be subject to additional terms in connection with your relationship with the third party, and certain risks may arise from the use of the third-party brokerage services. For more information on third-party risk and our risk disclosures, please see our Disclosures page. Using algorithmic trading strategies may result in the complete loss of your trading account balance.
*** Use of AFT is subject to the terms of the AFT Agreement. Credit is issued by a third-party broker and not Nurp. Nurp is a third party to your relationship with the third-party broker. You may be subject to additional terms in connection with your relationship with the third party, and certain risks may arise from using the third-party brokerage services. Algorithmic trading software is not for everyone. Using algorithmic trading strategies may result in the complete loss of your trading account balance. Risk mitigation will not reduce all forms of risk. For more information on our risk disclosures, please see our Disclosures page.
Nurp is not an exchange, and Nurp does not pool client assets. Algorithmic trading software is not for everyone. Using algorithmic strategies for trading may result in the entire loss of the balance in your trading account. You should carefully consider whether you wish to subscribe to our services based on whether such trading is appropriate for your experience, financial objectives, and personal circumstances. For more information on our risk disclosures, please see our Disclosures page.
Explore our offerings
Take control of your financial future with The Intelligent Trader – our flagship automated trading platform that has consistently delivered market-beating returns by using smart technology to trade across multiple markets 24/7, while built-in safety features minimize drawdown to help protect your investment.
Case Study: Algorithms That Deliver in Volatile Markets
See how Argos and Buterin outperformed during recent swings, with fully verified MyFXBook links for transparency. The study details entry logic, risk controls, and live P&L curves.
→ Download our free case study and see all the important insights
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.
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
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).
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.
