One of the advantages of the Quantpedia Pro platform and its Portfolio Analysis toolkit is the ability to analyze not only multi-asset and multi-strategy portfolios but also individual equity curves. Users can upload virtually any return series or analyze assets already present in the database. The same analytical tools used for portfolio construction can therefore also be applied to single assets.

Given the current macro-driven environment, commodity markets—particularly crude oil—offer a relevant case study. The United States Oil Fund (USO) ETF serves as a practical proxy for oil price dynamics. By analyzing its equity curve through Quantpedia Pro, we can explore whether persistent patterns, behavioral effects, or structural inefficiencies exist and whether they can be transformed into systematic trading strategies.

What if a meaningful part of a usual trading strategy’s performance has nothing to do with your signal—but simply when you rebalance? A recent paper written by Carlo Zarattini & Alberto Pagani highlights a largely underestimated risk in systematic investing: rebalance timing luck (RTL). For practitioners running rotation or factor strategies, this is not noise—it’s a structural source of dispersion. Using a concentrated U.S. equity momentum strategy, the authors show that identical portfolios differing only by rebalance day can diverge by as much as ~350 bps in annual returns, compounding into dramatically different terminal wealth outcomes.

This study examines the profitability of mean-reversion trading strategies applied to binary outcome contracts on Polymarket, the world’s largest decentralized prediction market platform. We analyze three distinct contracts representing varying risk profiles: a quasi-risk-free instrument (No to “Will Jesus Christ return in 2025?”) and two high-yield speculative contracts (No to “Will China invade Taiwan in 2025?” and “Will the US confirm that aliens exist in 2025?”). Using high-frequency price data sampled at 10-minute intervals over approximately one year, we implement a parameterized mean-reversion framework across twelve strategy variants, testing robustness under varying liquidity constraints and transaction cost assumptions. Our findings reveal that while mean-reversion signals generate substantial alpha under passive limit-order execution (zero-spread scenario), strategy performance degrades significantly when more aggressive market orders are accounted for.

Many beginners enter the markets with the same silent assumption: if they study hard enough, find the right indicators, or discover the right strategy, they should eventually be able to generate high returns with manageable risk. The market appears full of examples that seem to confirm this belief. Screenshots of triple digit gains are everywhere. Backtests often look smooth. Social media makes it feel as if exceptional performance is common.

That perspective matters because most of the mistakes beginners make do not stem from a lack of ability or effort. They arise from starting with the wrong mental model and unrealistic expectations about how markets actually work.

Hello all,

What have we accomplished in the last month?

– Launch of Quantpedia’s API
– Invitation to Uncorrelated Puerto Rico conference
– Quantpedia Awards 2026 reminder
– 11 new Quantpedia Premium strategies
– 7 new related research papers
– 7 new backtests
– and finally, 7 new posts on our Quantpedia blog

The global investment environment is going through a period of meaningful structural change. The dominance of the U.S. dollar is increasingly being questioned, geopolitical tensions are rising, and macroeconomic uncertainty remains elevated. Together, these forces challenge the post-Global Financial Crisis environment in which U.S. equities consistently outperformed most international markets. As a result, investors may be approaching a turning point where relative returns between U.S. equities and international markets—especially Emerging Markets (EM)—begin to shift.

Rather than relying on static asset allocation approaches, we develop a dynamic allocation model that uses momentum and trend signals to generate practical timing signals between U.S. and EM equities. Emerging Markets are particularly interesting in this context because they tend to experience stronger regime shifts and larger performance cycles than developed international markets.