Can Weakening Morning Order Flow Predict SPY Reversals? 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. quantpedia.com/can-weakening… #intraday #extended #alternative #dataset #quant #algo #trading #strategy #reversal #equity #market #timing #orderflow #activity

Building and Testing Trend-Following Strategies on One-Minute SPY Data 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. quantpedia.com/building-and-… #intraday #extended #alternative #dataset #quant #algo #trading #strategy #trendfollowing #equity #market #timing #retail #activity

Do Airline Stocks Take Off Around U.S. Holidays? 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. quantpedia.com/do-airline-st… #holiday #seasonal #quant #trading #strategy #jets #uso #etf

From Barrier Crossings to Terminal Distributions: A Skellam-Based Options Pricing Framework for 0-DTE Markets The explosive growth of hyper-liquid 0-DTE markets has pushed traditional options pricing infrastructure to its breaking point, as continuous Black-Scholes calculus can collapse into an unusable point mass at expiration. Rather than patching a broken formula with hand-fitted tweaks, a new paper suggests dismantling legacy math by replacing continuous geometric Brownian motion with a discrete, order-book-driven structural layer. Instead of smoothing over intraday price action, this model captures the raw physical reality of high-frequency liquidity by deriving a closed-form framework where the implied volatility surface is built directly from actual market microstructure. quantpedia.com/from-barrier-… #0DTE #options #trading #pricing #quant #algo #strategies

Quantpedia in August 2026 Hello all, We hope you had a great end to the summer, whether August meant a final holiday or a return to the office. Here’s a quick recap of the latest developments we prepared for Quantpedia in the last days of summer… – new report in the Live Strategies section called Composite Analysis – 11 new Quantpedia Premium strategies – 5 new related research papers – 8 new backtests – and finally, 5 new posts on our Quantpedia blog quantpedia.com/quantpedia-in… #quant #algo #live #trading #strategies #backtesting

Do LLM "Crowds" Produce Investment Signals? An Empirical Test The integration of artificial intelligence into algorithmic trading has ignited a race to transform generative text into systematic alpha. A new paper written by Steven Edwards empirically investigates whether constructing a synthetic consensus using large language models can simulate information aggregation dynamics or if it merely acts as a sophisticated echo chamber. By utilizing an expansive framework to evaluate portfolio construction across distinct synthetic mandates, the study challenges whether generative agents can truly democratize the wisdom of crowds within highly efficient capital markets. quantpedia.com/do-llm-crowds… #trading #strategy #LLM #agents #quant #AI #signals

Why Average Strategy Performance Can Mislead Portfolio Research Average strategy performance is one of the most common shortcuts in portfolio research. It gives the researcher a clean benchmark, a single reference line, and a simple way to compare one strategy against a broader group of similar strategies. In many cases, this is useful. But it can also be misleading. The problem is that an average hides dispersion. Two peer groups can have the same average return, but the internal structure of those groups can be completely different. In one year, nearly all strategies may behave similarly and cluster around the median. In another year, the same average may hide a wide spread between winners and losers. For portfolio construction, this distinction matters. This case study shows how Quantpedia API can be used to go beyond the average peer group return and measure yearly performance dispersion across a group of trend-following strategies. The goal is not only to ask how the average strategy performed, but also how different the individual strategies were from each other. quantpedia.com/why-average-s… #benchmarking #trendfollowing #trading #strategies #peer #group

Boundaries of Time Series Momentum Time-series momentum stands as one of the most reliable and heavily backtested anomalies in quantitative finance, serving as a foundational alpha source for modern managed futures and trend-following strategies. However, a recent academic paper by Matti Suominen and Erik Hjalmarsson, titled “Boundaries of Time Series Momentum,” uncovers a structural vulnerability that every practitioner must account for. The authors demonstrate that while equity market trends persist reliably during normal business cycles, they systematically break down and aggressively reverse when market valuations reach historical extremes. This phenomenon establishes clear macro “boundaries” where chasing the trend shifts from a highly profitable strategy to a severe drawdown risk. quantpedia.com/boundaries-of… #momentum #trendfollowing #reversal #valuation #CAPE #bubble #managed #futures

Quantpedia API for Peer Group Strategy Analysis A single backtest can show that strategy was profitable, but it does not always show whether the strategy was competitive. This is especially true for systematic futures strategies. A trend-following strategy can have a positive Sharpe ratio, a long live-like performance history, and a reasonable drawdown, but those numbers are difficult to interpret without a relevant comparison group. A broad equity index is often not the right benchmark for this type of strategy. A monthly rebalanced multi-asset futures strategy has a different objective, different risk profile, and different return drivers than a long-only stock index. A more useful question is whether the strategy performs well compared with other systematic trend-following futures strategies. This case study shows how the Quantpedia API can be used to build a custom peer group benchmark for strategy evaluation. The workflow has two steps. First, Quantpedia strategy metadata is used to screen and define a comparable peer group. Second, the historical equity curves of the selected strategies are downloaded, converted into daily returns, and aggregated into an equal-weighted peer group benchmark. quantpedia.com/quantpedia-ap… #peers #managedfutures #cta #trendfollowing #api #quant #trading #strategy #benchmark

Sectoral Intramonth Momentum Cycle: Exploiting Turn-of-the-Month Patterns in Sector ETF Strategies We document a persistent intramonth momentum cycle in U.S. sector ETFs that yields meaningful risk-adjusted returns when properly sequenced. Using the nine original Select Sector SPDR ETFs and SPY as the market benchmark from December 1998 through June 2026, we show that trailing 252-day sector momentum generates a positive spread on the first trading day of the month—and then sharply reverses on days two and three. A third, independent leg of the cycle emerges in the window from ten to five trading days before month-end, consistent with the intramonth momentum cycle recently documented at the single-stock level by Nathan, Suominen and Tasa (2026). Stitching the three legs together into a single composite strategy delivers 5.99% annualized return at a 0.55 Sharpe ratio for the long-short variant, and 3.77% at 0.54 for the market-neutral variant—all while being invested fewer than half the trading days each month. Our contribution is twofold: we extend the calendar-anomaly literature from individual equities to sector-level portfolios, and we provide practitioners with a transparent, low-turnover framework that translates these academic patterns into actionable trade schedules. quantpedia.com/sectoral-intr… #momentum #reversal #turn-of-the-month #calendar #trading #strategy

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 quantpedia.com/quantpedia-in… #quant #trading #strategies #alternative #data #api

Is Trend Still Your Friend?: A Microstructural Account of the Demise of Short-Term Trend-Following 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. quantpedia.com/is-trend-stil… #trendfollowing #hft #microstructure

From Backtest to Benchmark: Validating New Strategies with Quantpedia API 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. quantpedia.com/from-backtest… #quantpedia #api #backtesting #benchmarking #clustering #peers #trading #strategies

Getting the Target Right in Return Prediction 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. quantpedia.com/getting-the-t… #machinelearning #ai #quant #factor #investing #stockpicking

Commodity Crisis Analysis - How Portfolios React to Commodity Shocks 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. quantpedia.com/commodity-cri… #commodity #crisis #crude #oil #scenario #analysis

Can AI Do Financial Research? Large language models are already capable of summarizing financial research, but are they ready to conduct it? In their latest paper, researchers from Google, Boston College, and Columbia introduce a framework where a large language model doesn’t just fetch data—it acts as an autonomous AI research agent capable of navigating the “hypothesis discovery loop.” By placing an LLM within a human-designed laboratory—complete with a symbolic language of 66 accounting primitives and a standardized backtesting pipeline—the authors tested whether AI can move beyond black-box predictions to generate economically legible and statistically robust signals. This isn’t just about throwing a transformer at a price series; it is a systematic attempt to automate the “propose–test–reflect” cycle that defines empirical finance. quantpedia.com/can-ai-do-fin… #ai #llm #agents #academic #quant #financial #research #chatgpt #claude

Quantpedia in June 2026 Hello all, What have we accomplished in the last month? – A new Live Strategies reporting section – Quantpedia Awards 2026 Winners Interview – 14 new Quantpedia Premium strategies – 3 new related research papers – 7 new backtests – and finally, 8 new posts on our Quantpedia blog quantpedia.com/quantpedia-in… #quant #trading #strategies #backtests #research

Quantpedia API as an On-Demand Factor Database Investors often face a simple but important problem. They receive a fund equity curve, a strategy track record, or a portfolio performance series, but they do not know what is actually inside. The manager may provide only a broad description, while the realized return stream may in practice be driven by a mix of momentum, tactical allocation, defensive overlays, cross-asset rotation, or other systematic effects. One way to approach this type of problem is to use a specialized Multi-Factor Analysis report available in Quantpedia Pro. However, this case study focuses on the second approach: building a custom workflow through the Quantpedia API and AI-assisted methodology design. Instead of treating Quantpedia only as a static library of strategy ideas, the workflow uses it as an on-demand database of factor-like return streams. The unknown curve becomes the object to explain, while the Quantpedia strategy universe becomes the set of candidate explanatory building blocks. In this test, the unknown equity curve was treated as a blind case. The “correct” answer was not used during the analysis. The task was therefore not to confirm a known decomposition, but to test whether an API-based workflow can identify which known systematic strategies best explain the behavior of a black-box curve. quantpedia.com/quantpedia-ap… #factor #analysis #trading #strategy #peers #quant #database #API #LLM #due #dilligence

Silicon vs. Satoshi: Tactical Asset Rotation Between NASDAQ-100 and Bitcoin In the modern retail attention economy, Bitcoin and the NASDAQ-100 are not merely separate assets; they are competing narratives. Both appeal to the same pool of speculative capital, the same appetite for asymmetric upside, and the same behavioral forces of FOMO, herding, and recency bias. When technology stocks dominate the imagination, capital clusters around QQQ and the artificial intelligence trade. When Bitcoin breaks out, the crowd’s attention pivots toward crypto’s promise of explosive upside. This paper tests whether that rotation in attention leaves a systematic footprint. Using Donchian breakout signals across QQQ and Bitcoin, with cash as a fallback during periods of consolidation, we examine whether investors can harvest momentum without remaining permanently exposed to either asset’s full drawdown profile. The results suggest that the answer is yes: retail attention does not move randomly. It rotates, it concentrates, and—when measured through price breakouts—it can be systematically exploited. quantpedia.com/silicon-vs-sa… #bitcoin #nasdaq #IBIT #QQQ #tech #stocks #technology #rotation #trading #strategy #quant

Guardrails Make the Researcher: What an AI Agent Got Right (And Wrong) Replicating Nine Equity Anomalies An autonomous research agent replicated nine published US-equity anomalies on clean, survivorship-free data. The question is not only what it found (out-of-sample decay is the rule, and on a faithful build none survive — the lone apparent survivor turned out to be a construction error the discipline caught) but whether you can trust an agent to find it, and the checks that decide the answer. quantpedia.com/guardrails-ma… #ai #agents #equity #factor #replications #quant #trading #strategies