From papers to positions: what four AI models found in 468 futures-trading studies

Overview

Four frontier language models from four different developers, Opus 4.8, Codex 5.6 Sol, Grok 4.5, and GLM 5.2, were each given the same 468 academic trading-strategy summaries, distilled to their themes and rule-mapped onto liquid futures, and asked the same question: which of these effects survive as futures algorithms, and how should they be combined? The four reviews were mutually blind. Their outputs were checked for citation integrity (no fabricated references) and their load-bearing numbers were fact-checked against the source papers. The headline finding is a set of 10 futures themes on which the models independently converged, a layer of agreement between models sitting on top of the agreement between papers each model was asked to find. This post sets out those themes, the ideas each model saw alone, the handful of genuine disagreements, and a ranked shortlist of six candidate algorithms, with the crowding and in-sample caveats attached in plain sight.

How this was done

The barrier that stops most academic anomalies from becoming futures algorithms is not the maths. It is that the literature is written on cash equities or on idealised long/short portfolios, and says nothing about the roll, margin, and capacity that decide whether an effect is tradable on a handful of liquid contracts. The experiment here treats that translation problem directly, and uses four independent language models as the readers.

The corpus is 468 structured paper summaries covering equities, multi-asset, commodities, rates, FX, options, and crypto. Every reported Sharpe, return, and drawdown in the corpus lives only in free text, so each is an unverified in-sample claim by the originating paper. A deterministic pass distilled each paper to a compact record and rule-mapped it onto the most natural liquid futures class: equity-index (ES/NQ/RTY/YM), rates (ZN/ZB/ZF/GE), commodity (CL/GC/NG/HG/ZC), FX (6E/6J/6B/DX), volatility (VX), or crypto (BTC/ETH). The mapping was deliberately generous, so that every theme was considered as a futures candidate rather than only the papers already written on futures.

The four models each received a byte-identical bundle and prompt, and each returned the same four sections: unique insights, cross-confirmation across papers, novel algorithm designs, and a single best idea. Opus 4.8 ran as an agent; Codex 5.6 Sol, Grok 4.5, and GLM 5.2 ran through their own command-line tools. None of the four saw the prompt or output of any other. Afterwards, an automated check confirmed that every cited paper identifier resolved to a real record, and a separate adversarial agent fact-checked the 10 most load-bearing numbers against the source summaries.

Where four models independently agreed

The central result is not any single idea. It is that four models trained on different data, by different organisations, with different biases, read the same evidence separately and produced almost the same short list. Table 1 sets it out.

The most striking feature of that list is where the emphasis fell. On trend (M1), the panel did not dwell on the signal, which is the corpus’s most-cited and most-crowded effect ([1]). It dwelt on the machinery around it: closed-loop volatility targeting rather than open-loop inverse-vol sizing, where optimal leverage sits near $\mu/\sigma^2$ ([18]; [19]); a changepoint gate that cuts risk at turning points, where trend systems bleed ([2]); and a refusal to hold the naive full-short equity leg in bear regimes, where the optimal exposure is near zero rather than $-1$ ([3]). The models also agreed that stacking similar indicators is fake diversification: moving averages, breakouts, and trend filters encode nearly the same weighted sum of past returns ([22]), and adjacent lookbacks are redundant ([23]). Real diversification comes from economically different markets and separated timescales.

On volatility (M2), all four re-derived the same structural point. The VIX-futures basis has little power to forecast spot VIX, but it does forecast VIX-futures returns ([4]). What trades is the roll of the contract itself, so a short-VX position is a carry trade, gated by the term structure and sized inversely to implied volatility ([5]). Every model attached the same warning: the trade is crowded and catastrophically left-tailed.

The commodity theme (M3) is the one place the futures structure gives a real rather than a borrowed edge. Basis-momentum and the front-of-curve spread ([6]; [7]) cannot be expressed in cash or ETF form, because roll yield and curve shape are the product. The session and flow effects (M4, M5, M8) are similar: overnight returns dominate the trading day across roughly two dozen markets ([9]; [8]), and ES and NQ trade almost around the clock, so a futures book can hold the paid session and stand flat through the unpaid one. That is an effect futures capture better than the cash market that produced it.

What each model saw that the others missed

Consensus is only half the value of a panel. The other half is what each reader caught alone.

Opus 4.8 produced the sharpest synthesis, “Convex Carry”: pair the short-VX carry with a fast trend overlay sized so its crisis payoff covers the 95th-percentile loss of the volatility sleeve. Run separately they are two crowded, dangerous trades. Run together, the trend leg is a self-funding tail hedge, and SPAN margin offsets make the book capital-efficient.

Codex 5.6 Sol gave the cleanest decomposition: a futures curve carries two distinct alphas, slow carry continuation and fast front-spread reversal, that should be traded as separate sleeves rather than blended. That is the “Curve-Speed Barbell”, with a weekly first-versus-second calendar-spread reversal bolted onto the monthly carry sleeve ([24]).

Grok 4.5 was the most operationally ruthless. Its hardest check: naive market-making on CME futures nets roughly zero after adverse selection across 13 contract roots ([25]), which kills any earn-the-spread idea before it starts. It also noted that listwise learning-to-rank beats independent per-asset scoring for cross-asset momentum ([26]).

GLM 5.2 supplied the best micro-insights: statistical-arbitrage residuals revert about 2.4x faster in rank space than in name space ([27]), which motivates a rank-space index-futures pairs book; and 52-week-high proximity beats 12-1 momentum in small-cap and non-US panels, so the edge lives in RTY and NQ while ES is the beta backdrop ([28]).

Where they disagreed

The disagreements are narrow, and they mark the frontier of what the evidence does not settle. The clearest split is what to build first. Grok and GLM both chose the regime-gated trend core, with commodity basis-momentum second and VX carry a small third. Opus and Codex preferred a single specialist sleeve. That looks like a difference of risk appetite rather than of fact: the trend core is the higher-capacity, more durable choice, while the specialist sleeves offer more edge per dollar at lower capacity. On the equity short, all four rejected the naive full-short, but the fixes differed, from a small negative band to a hard zero to a positive deep-loser tilt during recoveries. Four models independently rejecting the same construction, while proposing different replacements, is itself a strong signal that the naive short is the real mistake.

A futures book, assembled from four reviews

No single model laid it out in one place, but the four reviews assemble into one multi-sleeve book, in which every sleeve is backed by multiple papers and at least three of the four models:

• Core. Regime/changepoint-gated, volatility-targeted cross-asset trend, with long lookbacks, concave exposure, and an equity-short cap in crises.
• Sleeve two. Commodity basis-momentum and front-curve carry, optionally with the fast weekly calendar-spread reversal barbell.
• Sleeve three. Term-structure-gated short-VX carry with a hard kill-switch, ideally in the Convex Carry form where a trend overlay covers its tail.
• Overlays. The overnight/EU-open harvester, gamma-conditioned intraday ES/NQ, turn-of-month flow timing, the FOMC strip, and the FX carry-to-PPP switch, each on a small risk budget.
• Risk bus. Closed-loop volatility targeting plus a breadth and correlation crash filter ([29]; [30]).
• Governance. Backtest-overfitting probability, deflated Sharpe ratios, and walk-forward consistency, applied to every sleeve ([21]; [31]).

Six candidate algorithms, ranked

Ranked by paper-level cross-confirmation and model-level agreement, penalised for crowding and fragility.

1. Regime-gated cross-asset trend core. All four models; the highest-capacity, least glamorous winner. The edge is the risk machinery, not clever entries ([2]; [32]). The CTA space is crowded, so survival rests on diversification, restraint at changepoints, and tight cost control.
2. Convex Carry (Opus). Short-VX carry tail-hedged by a trend overlay, sized jointly, gated hard on contango ([4]; [5]). Short volatility is negatively skewed and crowded, so validation must run through 2018, 2020, and 2022.
3. Commodity Curve-Speed Barbell (Codex). Slow front-curve carry plus a fast weekly front-spread reversal as separate sleeves ([6]; [24]), executed as calendar spreads. Capacity is limited in NG, HG, and ZC.
4. Rank-space index-futures stat-arb (GLM). A mean-reversion book that ranks by normalised price to re-identify the residual, exploiting the faster rank-space reversion ([27]). Index-pair spreads carry factor mismatch and need dynamic re-estimation.
5. Overnight harvester with an FOMC overlay. Hold ES/NQ only in the paid overnight window ([8]), lever the pre-FOMC drift ([12]), and short front-VX for the post-FOMC crush ([13]). Gap risk and timestamp sensitivity are real.
6. Gamma-conditioned intraday ES/NQ. Highest edge per trade, lowest capacity, most fragile ([10]; [11]). The growth of zero-days-to-expiry options has changed dealer positioning since the sample periods, and a published replication already finds the effect weaker ([33]).

The panel was unanimous on what not to build first: unfiltered short volatility, naive futures market-making ([25]), an equity factor-zoo mapped onto ES ([20]), high-frequency crypto without a punishing cost model ([34]), and calendar seasonality as a core rather than an overlay.

Fact-checking the headline numbers

An independent agent checked the 10 most load-bearing numbers against the source papers. Nine of the 10 were traceable, word for word, to figures the papers state themselves: the turn-of-month concentration ([16]), the overnight window’s return and Sharpe ([8]), the VIX-basis result ([4]), the roughly 18% basis-momentum spread ([6]), the 2.4x rank-space ratio ([27]), the near-zero net from market-making ([25]), the 36.6% crisis return of an equity-capped trend variant ([32]), the near-zero optimal bear-regime exposure ([3]), and the roughly 49-basis-point pre-FOMC drift ([12]). No mis-citations were found.

One claim was overstated. The line that “76-78% of crisis alpha” comes from beta-timing ([35]) mixes two things: those figures describe the beta-timing sleeve’s share of the strategy’s total risk and return, while the crisis property is shown separately through downside correlations. That claim is corrected to the weaker, accurate form.

The broader verdict is the point of the whole exercise. These numbers are faithful summaries of what each paper claims, but every one is a single-paper, in-sample, often gross-of-cost result from a short or idiosyncratic window. They are hypotheses to re-test, not settled facts.

The caveats that matter

Three constraints bind everything above. First, the edge the panel kept pointing at lives in construction, not signals: the raw anomalies are known and crowded, and what remains is in how exposure is shaped, sized, and gated. That is the part hardest to arbitrage away and easiest to get wrong. Second, the corpus is roughly 60% equity and cross-sectional, which inflates the apparent case for equity-index momentum; the strongest recommendations are the futures-native effects, where the structure gives a real edge, rather than single-name factors that die when compressed into one index contract. Third, four language models are not four independent researchers. They share training data and priors, so their agreement is correlated to an unknown degree, and a theme absent from all four might be genuinely weak or merely underrepresented in their shared priors. Independent agreement is evidence, even when the agents are imperfectly independent.

What to do next

Four steps follow from this. Reproduce before trusting: rebuild each sleeve on point-in-time, roll-adjusted futures data with realistic costs, and put it through the governance battery the panel itself demanded, treating every cited number as a hypothesis. Test the architecture as a whole, not just the parts, since the strongest claim here is that jointly sizing oppositely-signed-in-stress premia and combining decorrelated sleeves under one volatility controller beats running any sleeve alone; the 2018, 2020, and 2022 stress episodes are the discriminating experiments. Spend the first research budget on the futures-native effects, basis-momentum, VX carry, and the overnight session, before competing with the entire CTA industry on equity-index trend. And widen the panel: the protocol is cheap, so extending the corpus and re-running it as new models arrive turns a one-off exercise into a standing instrument for tracking where the evidence, and the crowd, are moving.

References

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2. Kieran Wood, Stephen Roberts & Stefan Zohren (2021). Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection. https://arxiv.org/abs/2105.13727

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35. Roberto Croce, Avishek Hazrachoudhury, Sladja Carton & Suren Karapetyan (2026). Trend-following crisis alpha: Does it come from beta timing or market selection?.