Hello all,
We hope you’re enjoying the middle of summer. Here’s a quick recap of the latest improvements and additions we’ve prepared for Quantpedia during the past month
– API users can now directly download the full research papers written by Quantpedia
– 10 new Quantpedia Premium strategies
– 2 new related research papers
– 7 new backtests
– and finally, 5 new posts on our Quantpedia blog
Ten new strategies have been added. Two new related research paper have been included into existing strategy reviews and five new short free blog posts have been published during last few weeks. Plus, seven trading strategies have been backtested in QuantConnect in the previous two weeks.
Trend following was one of the most persistent anomalies in finance for nearly two centuries, yet its performance deteriorated sharply after the 2008 financial crisis. An analysis of approximately 100 liquid futures contracts from 1995 to 2025 shows that this decline is highly selective. The decisive factor is not asset class, liquidity, market electronification, or strategy crowding, but volatility-normalized tick size. After 2008, trend-following profits collapsed almost entirely on small-tick contracts across all signal horizons, while remaining largely intact on large-tick contracts. This finding suggests that modern trend-following portfolios are fundamentally split into two distinct regimes governed by market microstructure rather than traditional asset classifications.
A profitable backtest is rarely the end of a research process. In professional quantitative research, the more important question often comes after the first positive result: is the strategy genuinely new, or is it simply another version of an already known factor, timing rule, or anomaly?
This is especially relevant when a researcher develops a new systematic strategy with a clean historical equity curve. The strategy may have acceptable risk-adjusted performance, stable drawdowns, and a logical trading rule, but those statistics alone do not prove that the idea is unique. A silver strategy, for example, may look different on the surface while still behaving like a known commodity timing model, a trend-following strategy, a volatility filter, or a broader macro factor exposure.
This article shows how Quantpedia API can be used as a benchmark dataset for validating new research. Instead of evaluating a new backtest in isolation, the strategy is compared against the Quantpedia universe of documented quantitative strategies. The workflow identifies nearest neighbours, assigns the strategy to a factor cluster, calculates a uniqueness score, and produces a research robustness report that can be used for deeper validation.
The goal is not to replace human research judgment. The goal is to create a structured robustness checker that helps researchers understand whether a new strategy is truly differentiated, redundant with known effects, or simply a variation of an existing Quantpedia strategy profile.
Recent interesting research from Cakici and Zaremba, highlights an often-overlooked aspect of machine learning for equity return prediction: the choice of prediction target. Rather than focusing on increasingly sophisticated model architectures or feature engineering, the authors show that how returns are represented during training has a much larger impact on predictive performance. In particular, models trained to predict stock ranks instead of raw return levels generate substantially stronger portfolio performance—roughly doubling both returns and Sharpe ratios in large-cap universes.
Financial markets are often viewed primarily through lens of equity index movements, as they attract most of the attention. However, commodities represent an important component of the global economy, and shocks in commodity markets can have a significant impact on broader financial assets.
From time to time, market stress originates outside equities. A recent example are the repeated US attacks on Iran, which increased uncertainty in energy markets and raised the risk of an oil supply shocks. A similar dynamic was observed in 2022 during the Russian invasion of Ukraine, when commodity prices moved sharply higher or during the US invasion of Iraq in 2002, when uncertainty in oil markets led to increased price volatility.
These events highlight the importance of analyzing portfolio behavior not only during equity bull and bear markets, but also during commodity-driven shocks.
We’ve already analysed tens of thousands of financial research papers and identified more than 700 attractive trading systems together with hundreds of related academic papers.
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