Calendar effect

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.

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.

Holidays put people in motion. In the days surrounding major U.S. holidays, airports become busier as travelers visit their families or take advantage of extended weekends. Financial markets themselves are known to display a holiday-related seasonality. In our previous research on the Pre-Holiday Effect in Commodities, we identified a short-term price drift in crude oil and gasoline before major U.S. holidays. Increased travel and the associated expectation of higher fuel consumption offered one possible explanation. This naturally raises another question: if holiday travel leaves a seasonal footprint in energy markets, can it also be detected in the stocks of the airlines transporting those travelers? To investigate this possibility, we analyze the performance of the U.S. Global Jets ETF (JETS) around major U.S. holidays. We first examine its daily returns from ten trading days before to ten trading days after each holiday and use the resulting return profile to identify the strongest seasonal windows. We then formulate two directional JETS strategies and a JETS–USO strategy.

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.

Cryptocurrency markets have matured into a distinct asset class characterized by extreme volatility, deep liquidity pools, and worldwide retail participation. Traditional equity and commodity markets exhibit a well-documented pre-holiday effect, where returns on trading days immediately preceding public holidays tend to outperform other days. Given that Bitcoin is often described as the archetypal absolute risk asset, it is natural to hypothesize that any calendar-driven anomalies observed in equities should manifest—or even amplify—in crypto markets.

However, unlike equity markets, where institutional investors and marketing calendars drive collective behavior, crypto markets are more dispersed, retail-dominated, and influenced by nontraditional information flows. This article investigates whether the classic pre-holiday effect applies to Bitcoin and assesses the extent to which it can be amplified by an attention-grabbing momentum filter based on local price highs.