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Investors see ANOTHER return from Masterworks (!!!!)
That’s 6 sales in 7 months. 29 all time. And the performance?
16.5%, 17.6%, and 17.8%, net annualized returns on sold works held longer than one year (See all 29 at Masterworks.com)
It’s not from stocks, private equity, or real estate… it’s from contemporary and post war art. Crazy, right?
With Masterworks, you don’t need to be a BILLIONAIRE to invest in multi-million dollar art anymore.
Historically, the segment overall has had attractive appreciation and low correlation to stocks.*
Masterworks targets works featuring legends like Banksy, Basquiat, and Picasso, identifying what they believe to have significant long-term appreciation potential, not just at the artist level but at the level of individual artworks.
As one of the largest players in the art market, with $1.3 billion invested over 500 artworks, they pass critical advantages through to their 70,000+ members to add art to their portfolios strategically.
Looking to diversify your investments in 2026?
*According to Masterworks data. Investing involves risk. Past performance is not indicative of future returns. See important Reg A disclosures at masterworks.com/cd.
② One strategy in this book returned 2.3× the S&P 500 on a risk-adjusted basis over 5 years.
Fully coded in Python. Yours to run today.
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AlgoEdge Research is a live research terminal for systematic traders — 33 strategies scored every trading day, a regime detector (HMM + Hurst + VIX), full Python notebooks for every model, a complete volatility suite, and options flow. Everything we research in this newsletter, running live, every day.
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Premium Members – Your Full Notebook Is Ready
The complete Google Colab notebook from today’s article (with live data, full Hidden Markov Model, interactive charts, statistics, and one-click CSV export) is waiting for you.
Preview of what you’ll get:
Free readers – you already got the full breakdown and visuals in the article. Paid members – you get the actual tool.
This notebook implements the full LazyPredict AutoML pipeline across four quantitative finance use cases:
Scenario 1: Basic Features + Logistic Regression
Scenario 2: Simple Momentum Strategy
Scenario 3: Extended Features + SVC
Key insight: Feature engineering matters more than model choice in financial ML.
