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Boundaries of Time Series Momentum

August 28, 2026

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.

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Quantpedia API for Peer Group Strategy Analysis

August 26, 2026

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.

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Sectoral Intramonth Momentum Cycle: Exploiting Turn-of-the-Month Patterns in Sector ETF Strategies

August 17, 2026

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.

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Is Trend Still Your Friend?: A Microstructural Account of the Demise of Short-Term Trend-Following

July 29, 2026

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.

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Silicon vs. Satoshi: Tactical Asset Rotation Between NASDAQ-100 and Bitcoin

July 2, 2026

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.

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Testing an AI-Assisted Research Workflow for Multi-Asset Pullback Strategy Discovery

June 19, 2026

This study investigates short-term price reversals—temporary retracements following adverse daily returns—and develops a systematic trading framework to capture this effect across multiple asset classes. Using daily data from six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities over the period 2006–2025, the strategy applies a long-term trend filter based on a 200-day moving average combined with a multi-day pullback trigger. Trades are executed dynamically with volatility-adjusted position sizing and equal-weighted allocation across active signals. Parameter sweeps, sensitivity analyses, and sub-period tests are conducted to evaluate the robustness of the approach, including variations in moving average length, number of consecutive down days, holding periods, and alternative momentum indicators such as short-term RSI. The study also explores the practical integration of AI tools— ChatGPT and Claude—to assist in research, analysis, and visualization, assessing their effectiveness in generating coherent quantitative insights.

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