Hello all,

– Screener and API extended with two new fields: Transaction Costs and Robustness
– 11 new Quantpedia Premium strategies
– 7 new related research papers
– 8 new backtests
– and finally, 6 new posts on our Quantpedia blog

We analyze a multi-asset calendar anomaly around U.S. federal elections that is consistent across assets and simple to trade. While our previous study documents a robust pre-election drift, the cross-asset pricing of its D+1 resolution remains unmapped beyond SPY. Using a diversified ETF basket spanning technology, emerging markets, real estate, high-yield credit, gold, oil, and foreign exchange, we show that election day itself is neutral to slightly positive — an equal-weight D0 long earns a modest 1.03% p.a. at a Sharpe of 0.19, which we document as the control arm of the experiment but do not trade — while the following session delivers a systematic sell-off across all seven legs as the political uncertainty premium collapses. An equal-weight short on D+1 yields 1.86% per annum with a Sharpe of 0.31 and a maximum drawdown of -4.16%, achieved over 13 trading days in 26 years. Intermediate Treasuries exhibit the inverse pattern, selling off in the four days before the vote and rallying from D0 to D+2, with a Sharpe of 0.48.

Meme stocks are commonly studied through social-media activity, but this approach limits both the historical scope of research and the signals available to practitioners. In A Century of Meme Stocks and the Price of Coordination, Chad Schmerling develops an alternative: a machine-learning model trained to identify the holdings of the Roundhill MEME ETF using market and firm-level data rather than textual inputs. By adapting the feature set to historical data availability, the paper constructs a measure of “memeness” extending back to 1926. For traders and portfolio managers, its principal contribution is a framework for identifying speculative activity and examining how its return implications vary with the investment horizon.

The Quantpedia Awards are back—and this time, we’re taking the winners’ announcement to the stage!

If you have been developing a systematic trading strategy, testing an investment idea, or turning your research into a paper, this is again your invitation to join the race…

In a previous article Building and Testing Trend-Following Strategies on One-Minute SPY Data, we investigated whether retail activity indicators derived from one-minute SPY data could be used to construct profitable trend-following strategies. The results suggested that we are able to construct strategies that are often able to achieve superior risk-adjusted performance. In this article, we examine an alternative hypothesis. Instead of assuming that changes in retail activity signal the continuation of an existing trend, we investigate whether persistent declines in order flow may create conditions for a subsequent market reversal. More specifically, we analyze situations in which selected trading activity indicators decrease for several consecutive trading days and evaluate whether such sequences are followed by above-average SPY returns.

Intraday trading strategies have gained increasing attention as advances in computing power and market data availability have made intraday strategy analysis more accessible. While many trading strategies are traditionally developed and evaluated using daily price data, shorter timeframes can provide additional opportunities to identify and exploit market trends within a single trading session. In this article, we investigate the performance of trend-following strategies based on selected technical indicators computed from one-minute price data for SPY ETF. The historical dataset, provided by Algoseek, serves as the basis for designing, backtesting, and comparing several intraday trading approaches.