How HRT and Jane Street Made $22.5B in One Quarter: The Models, the 50.1% Edge, and the SEBI Order

When the Strait of Hormuz closed in early 2026, three private trading firms captured more from the resulting market chaos than the trading divisions of JPMorgan and Goldman Sachs combined. This is a reconstruction of exactly how they did it, sourced to primary records.

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Why These Numbers Are Different From Every Prior Record

The Q1 2026 figures are not a gradual step up. They represent a structural break. Bloomberg first reported on May 11, 2026 that Hudson River Trading recorded $6.4 billion in trading revenue in Q1 — more than half the firm’s total haul across all of 2025 — and that HRT’s profit climbed approximately 175% to $4.2 billion, with EBITDA of $4.5 billion and $20 billion in net trading capital at quarter-end. The Financial Times confirmed the figures and reported that HRT’s net trading revenue in Q1 exceeded that of Bank of America or Wells Fargo in the same period. On the same day, Bloomberg reported Jane Street’s Q1 results: $16.1 billion in trading revenue — more than double its haul from the same period in 2025 — and net income more than doubled year-over-year to $10.3 billion. Reuters independently confirmed the same figures, though it described revenues as “up more than 40% from the same period last year” — a phrase that Bloomberg’s own data contradicts, as Q1 2025 net revenue implied by the H1 2025 figure of $17.3 billion (Bloomberg, September 2025) minus Q2 2025’s $10.1 billion leaves Q1 2025 at approximately $7.2 billion — making the year-on-year growth closer to 124%, consistent with “more than doubled.”

These are not directional bets that happened to work. The business model involves providing liquidity across hundreds of venues simultaneously, capturing spread while managing inventory risk using predictive models. Understanding precisely how that generates $22.5 billion in ninety days requires tracing the complete evidence chain: the macroeconomic transmission mechanism, the mathematical architecture of modern market making, the operational specifics documented in regulatory filings and interview transcripts, and the primary records from two court proceedings and one regulatory order that expose these strategies at execution level.

The Macro Catalyst: Mapping Cross-Asset Transmission

The Iran war did not create a single spike that quickly resolved. It created a sustained, multi-asset repricing cascade that persisted across weeks. The US war in Iran fuelled large swings in oil prices as most ships were blocked from passing through the Strait of Hormuz, with the fallout spreading to Treasuries and currencies as investors grappled with longer-term consequences to the global economy. The US Energy Information Administration’s Q1 2026 quarterly price review confirms the precise trajectory: Brent began the year at $61 per barrel, had risen to $72 per barrel by late February in response to escalating conflict risk, then surged sharply after military action on February 28 and the subsequent de facto closure of the Strait of Hormuz — finishing the quarter at $118 per barrel, the largest quarterly price increase on an inflation-adjusted basis in data going back to 1988. CNBC’s oil market timeline confirms the pre-war reference price: Brent was approximately $72 per barrel on February 27 — the last trading day before hostilities began, and peaked near $120 during March. CNN confirmed the pre-war baseline from a different angle, noting Brent was at approximately $73 per barrel before the war, and subsequently reached a wartime high of $126.41 during the late-April escalation.

The EIA report also documents the specific structural dislocation mechanism that created value for market makers: the Brent-WTI spread widened from approximately $4 per barrel at the start of Q1, peaked at $25 per barrel on March 31, and averaged $11 per barrel for the month — the highest Brent-WTI spread in over five years — reflecting elevated shipping costs and reduced oil flows near the Strait of Hormuz pressing on Brent specifically while US inventories and SPR releases cushioned WTI. This Brent-WTI dislocation, combined with steep backwardation in crude forward curves, created persistent cross-instrument mispricings that a unified, multi-asset market-making book could arbitrage continuously across the quarter.

For market makers, duration matters more than peak volatility. A spike that resolves in hours generates one wide spread on a large position. A regime that persists for weeks generates wide spreads on millions of transactions, repeated daily. Investors moved aggressively to hedge risk as concerns grew around AI disrupting software companies and uncertainty tied to the Iran conflict, with market anxiety intensifying in March after the outbreak of the US-Israeli war with Iran. This wave structure of demand meant institutional hedgers were repeatedly entering the market over weeks, not once.

The cross-asset correlation structure was also mechanically valuable. Analysts observed a direct inverse correlation between crude oil and S&P 500 futures intraday — each crude move triggering systematic equity selling. Firms whose predictive models had mapped this transmission function could quote on the receiving end of the transmission — equity index options, Treasury futures — with better-calibrated uncertainty than firms reacting independently to each asset class. A crude print that a well-trained model predicts will move equity futures a predictable amount removes that component from the adverse selection term. That is direct incremental P&L.

The Mathematical Foundation: Reservation Price, Optimal Spread, and the Alpha Layer

Two academic frameworks explain what these firms are actually optimizing. Both are publicly available in peer-reviewed form.

The foundational architecture is the Avellaneda and Stoikov (2008) model, published in Quantitative Finance, which solves for the optimal bid and ask quotes of a dealer managing inventory risk through a stochastic control problem. The model derives two outputs: a reservation price that adjusts the midprice based on inventory imbalance, and an optimal half-spread. The reservation price is defined as:

*r = s − q · γ · σ² · (T − t)**

Where s is the current midprice, q is current inventory, γ is risk aversion, σ² is variance, and (T − t) is remaining trading horizon. The optimal half-spread is:

*δ = (γ · σ² · (T − t)) / 2 + (1/γ) · ln(1 + γ/k)**

Where k captures the sensitivity of order arrival rates to spread width. Both components scale directly with σ². When crude and equity vol were simultaneously elevated in Q1 2026, the model-prescribed optimal spread widened in both markets. Dealers quoting at wider levels captured more revenue per transaction while still attracting flow from institutional hedgers who needed to transact regardless of spread width. Guéant, Lehalle, and Fernandez-Tapia (arXiv:1105.3115) extended this framework with closed-form solutions under inventory constraints, now the standard implementation reference for institutional market makers.

The Avellaneda-Stoikov baseline, however, assumes no directional view. This is precisely where HRT and Jane Street operate at a different level. Cartea and Wang’s “Market Making with Alpha Signals” (SSRN 2019, published in the International Journal of Theoretical and Applied Finance 2020) formally proves how a market maker possessing a momentum signal about short-term price direction can simultaneously minimize adverse selection costs, execute directional trades in anticipation of price changes, and manage inventory risk — with expected profits from the alpha signal increasing monotonically with risk tolerance, because the strategy employs more speculative market orders and performs more round-trip trades as tolerance rises. In operational terms: HRT and Jane Street are running the alpha-signal-augmented version of market making, not the passive baseline. The directional edge and the spread capture compound each other.

Hudson River Trading: Four Primary Sources on Architecture and Edge

Source 1: The Bloomberg Odd Lots Interview Transcript

On October 31, 2025, Bloomberg’s Odd Lots podcast published a 55-minute interview with Iain Dunning, HRT’s Head of AI Research, covering the firm’s use of AI to make short-term predictions about price that give its traders an edge. Dunning joined HRT from DeepMind, where his work included a paper on population-based reinforcement learning in multi-agent environments published in Science in 2019. His personal website confirms his current role: running HRT’s AI team, building “some of the most advanced models of financial markets in the world, using state-of-the-art techniques combined with massive compute and data”.

A PodMine transcript of the episode documents three specific disclosures: HRT’s AI models achieve approximately 50.1% accuracy in predicting short-term price movements — slightly better than random but sufficient for profitability at scale across millions of daily transactions; the firm consumes “tens of megawatts” of electricity for AI operations, described as more than most towns and cities; and since approximately 2014, HRT moved toward neural networks that consume all available market data rather than handcrafted features. The 50.1% accuracy figure is the most analytically important: at millions of trades daily, a 0.1 percentage point edge over random is a statistically enormous advantage that compounds directly with the Cartea-Wang alpha signal mechanism.

Source 2: SEC Rule 605 Data

In August 2025, Global Trading reported that among major wholesale market makers, HRT posted the lowest (best) share-weighted median execution quality (E/Q) ratio at 0.315, with Susquehanna (SIG) next at 0.335 — both clear of their competitive field — while Citadel Securities showed the most pronounced deterioration in the month, with its median E/Q worsening from 0.405 to 0.515. The E/Q measure, derived from SEC Rule 605 mandatory monthly disclosures, is the spread realized by market makers versus the NBBO midpoint divided by the prevailing NBBO spread — so a reading of 0 is a trade at midprice and 1 is a trade at the NBBO. HRT’s 0.315 versus SIG’s 0.335 represents a material advantage; both outperformed Citadel at 0.515 by a substantial margin. HRT’s own Rule 605 filings, disclosed monthly on its website, are the primary data source underlying these comparative analyses.

The execution quality advantage is the observable signature of superior short-term price prediction. A firm that predicts where prices will move in the next few seconds can offer better prices to retail orders with confidence the market will not immediately move against the filled position. The monthly Rule 605 E/Q differential is the empirically mandated, publicly disclosed record of that prediction advantage.

Source 3: The Bancara Credit Analysis

Bancara’s February 2026 analysis, drawing on S&P and Fitch credit rating research, reported that HRT held net capital of $2.5 billion at end-2024 and achieved net trading revenue of $3.7 billion in Q3 2025, representing 81% year-on-year growth and exceeding all prior quarterly results at that point — a figure itself now far surpassed by Q1 2026’s $6.4 billion. This establishes the trajectory clearly: Q3 2025’s then-record of $3.7 billion was already the highest in HRT’s documented history, and Q1 2026 exceeded it by 73%.

Source 4: CoreWeave’s Q1 2026 SEC Earnings Filing

CoreWeave’s Q1 2026 earnings press release, filed with the SEC in May 2026, lists Hudson River Trading explicitly as a “partner of choice for leading AI pioneers and enterprises”. This is primary documentary evidence of HRT’s active AI infrastructure investment — corroborating Dunning’s podcast disclosures about electricity consumption and the shift to GPU-intensive neural network training at scale.

Where HRT concentrates its architecture on a single integrated prediction system across 200-plus venues, Jane Street runs three structurally distinct revenue streams simultaneously — which is why the two firms’ Q1 results compound rather than merely add.

Jane Street: Three Revenue Streams, Three Primary Sources

Stream 1: Medium-Frequency Trading (Bloomberg/Reuters, May 2026)

Bloomberg’s people-familiar-with-the-matter sourcing explicitly identified medium-frequency trading strategies — machine-powered positions held for days or weeks — as the primary driver of Jane Street’s Q1 2026 performance. Reuters independently confirmed the same characterization: strategies “ranging from several minutes to days with the help of machines” drove the quarter. This is the key structural disclosure. Medium-frequency holding periods are better suited to geopolitical volatility regimes than pure HFT, because the signal half-life for macro-driven correlation shifts is measured in hours and days, not microseconds. The Iran-driven repricing of cross-asset correlations took hours to fully propagate. Firms holding positions for minutes to days captured the full repricing move. Firms that needed to flatten books every 30 seconds captured a single tick.

Stream 2: Private AI Portfolio

A CoreWeave 8-K exhibit filed with the SEC on April 15, 2026 contains the formal press release confirming that Jane Street committed approximately $6 billion to CoreWeave’s AI cloud platform and made a $1 billion equity investment in CoreWeave Class A common stock at $109.00 per share. The release quotes CoreWeave saying Jane Street “operates like a frontier lab, continually breaking new ground in deep learning and pushing the scale and complexity of their models.” This is the primary documentary source on both the financial terms and the strategic rationale.

Reuters confirmed Jane Street’s Q1 results were partly buoyed by its stakes in AI companies including Anthropic and CoreWeave — Jane Street had held a pre-existing CoreWeave position of approximately 19.99 million shares since August 2025, disclosed in a 13G SEC filing that made it the company’s fourth-largest shareholder at the time, and CoreWeave’s stock appreciated during Q1 2026. The separate $1 billion equity investment announced on April 15, 2026 — after Q1 ended — was an incremental addition to that prior position. Bloomberg reported that Jane Street’s Anthropic stake has appreciated with each successive funding round, with Bloomberg confirming on April 29, 2026 that Anthropic was weighing a fresh round at a valuation exceeding $900 billion — more than double its February 2026 valuation of $380 billion. Wikipedia documents that Jane Street also invested in Thinking Machines Lab’s $2 billion founding round in July 2025 at a $12 billion valuation alongside Andreessen Horowitz, Nvidia, AMD, and Cisco.

The mechanics of this revenue are documented in Jane Street’s SEC-filed financial statements. The 2024 annual financial statement of Jane Street Options, LLC (Form X-17A-5 filed February 27, 2025) shows the firm operates under SEC Rule 15c3–1(b)(1), the net capital exemption available exclusively to registered market makers, with a revolving credit facility from the parent entity of up to $6.5 billion outstanding at $1.463 billion as of year-end 2024. Private equity stake mark-ups pass directly through to parent entity equity without a corporate tax layer — a structure that makes the AI portfolio P&L highly capital-efficient relative to standalone fund structures.

Stream 3: India Options Franchise

The India strategy is the best-documented of the three, because litigation forced it into the public record from two directions simultaneously.

The India Anatomy: Two Trading Days From SEBI’s Own Files

SEBI’s interim order, numbered WTM/AN/MRD/MRD-SEC-3/31516/2025–26 and dated July 3, 2025, is a 105-page document describing trading surveillance across 18 derivative expiry days between January 2023 and March 2025. The document lists two strategies: an “Intra-day Index Manipulation” strategy observed on 15 days, and an “Extended Marking the Close” strategy observed on the remaining 3 days — the latter also appearing in NIFTY options in May 2025. The order’s background section states explicitly that SEBI initiated its preliminary examination based on April 2024 media reports about the Jane Street-Millennium lawsuit, which had inadvertently disclosed that Jane Street’s strategy involved India options.

January 17, 2024 — sourced directly from SEBI’s order as reported by Moneycontrol:

Between 9:15 AM and 11:46 AM, Jane Street purchased Bank Nifty constituent stocks and futures worth approximately ₹4,370 crore while simultaneously selling Bank Nifty options for approximately ₹32,115 crore. After noon, Jane Street sold Bank Nifty futures worth approximately ₹5,372 crore, creating a peak short position of approximately ₹46,620 crore in Bank Nifty index options, and the index closed near 46,064.45 — Jane Street made a profit of approximately ₹735 crore in the options segment and an intraday loss of approximately ₹61.6 crore in cash and futures, for a net gain of approximately ₹673.4 crore on that single expiry day.

July 10, 2024:

On July 10, 2024, Jane Street sold Bank Nifty futures worth approximately ₹2,800 crore and created a short position of approximately ₹44,154 crore in Bank Nifty options, with the resulting softening of the index closing generating a profit of approximately ₹225 crore.

SEBI’s analysis attributed the entire positive price impact in Bank Nifty during the morning trading patch on several examined days to Jane Street alone, with the rest of the market exerting net downward pressure simultaneously. SEBI’s order confirms that despite a caution letter from NSE issued on February 6, 2025, and Jane Street’s own commitments to the exchange, Jane Street continued to run very large cash-equivalent positions in index options as late as May 15, 2025. Continued operation after regulatory warning is the key element SEBI uses to support its characterization of the conduct as a “deliberately devised device” rather than coincidental hedging.

SEBI directed Jane Street to deposit ₹4,843.57 crore into an escrow account, representing alleged unlawful gains — the highest-ever impounding order issued by the regulator, according to SEBI Chairman Tuhin Kanta Pandey, who stated publicly that “market manipulation is not going to be tolerated”. Jane Street deposited the full amount and subsequently sought an extension from SEBI to respond to the interim order, confirming it was “engaging constructively” with the regulator while maintaining its characterization of the trades as standard index arbitrage. Business Standard reported in September 2025 that SEBI had expanded its probe beyond Bank Nifty to Sensex and other indices, with early findings suggesting wider alleged manipulation than covered in the July order.

The Manhattan Court Record: What the Trade Secret Case Reveals About Strategy Valuation

Jane Street filed its complaint against Millennium Management and former traders Douglas Schadewald and Daniel Spottiswood in the Southern District of New York in April 2024 under civil case number 24-cv-02783 before Judge Paul Engelmayer, with preliminary motions recorded under miscellaneous docket 1:24-mc-00175 on CourtListener. The strategy involved India options and had generated $1 billion in profits for Jane Street in 2023, a disclosure that emerged when lawyers for both sides inadvertently identified the market during a court hearing, as Bloomberg reported.

Jane Street alleged that its profits from the strategy fell approximately 50% in March 2024 after Millennium began using the same approach. Schadewald and Spottiswood disputed this, with defendants arguing that Jane Street’s India options team actually posted record results in the months after they departed, per previously sealed motions released in redacted form. The case was settled on mutually agreeable terms and dismissed by December 2024 according to a federal court filing, with terms undisclosed.

The case’s primary analytical value lies in the $1 billion single-strategy revenue figure it forced into the public record — and in the second-order consequence: SEBI’s own order confirms the investigation was triggered specifically by April 2024 Bloomberg and related media reports about the Millennium lawsuit disclosures. The lawsuit Jane Street filed to protect its strategy became the trigger for the regulatory scrutiny that cost approximately $564 million (₹4,843.57 crore) and India market access. Litigation in one jurisdiction created exposure in the jurisdiction where the strategy operated.

The Scale Arithmetic: What $22.5 Billion in One Quarter Implies

Hudson River Trading generated $6.4 billion in Q1 2026 revenue with approximately 1,000 employees, while Jane Street generated $16.1 billion with approximately 3,500 people — annualizing to approximately $18.4 million per head for HRT and approximately $25.6 million per head for Jane Street, compared to approximately $6 million per head at major investment bank front offices. The bank figure covers only revenue-generating staff. The HRT and Jane Street figures cover the entire firm including operations, compliance, and infrastructure.

In 2025, Jane Street, Hudson River, and Citadel Securities collectively generated more than $60 billion in trading revenues, per the Financial Times. The structural explanation for these margins is fixed-cost leverage: the marginal cost of additional revenue in this model is close to zero. Better models on the same infrastructure generate more without proportional headcount or capital additions. Banks cannot replicate this because their trading desks are embedded in institutions with fragmented legacy systems, risk committee overhead, and capital allocation tied to loan book requirements.

The Jane Street Options LLC annual financial statement (Form X-17A-5, 2024) shows the entity operating under SEC Rule 15c3–1(b)(1) — the net capital exemption available exclusively to registered market makers — with total assets of $16.4 billion and a revolving credit facility from the parent of up to $6.5 billion. This is the capital structure of a business that carries enormous notional exposure with a relatively compact equity base, justified by the speed and accuracy of its risk management.

Three Structural Risk Vectors That Do Not Appear in the P&L

Volatility normalization. Disruption Banking’s December 2025 analysis of HRT noted peer-leading profit margins of around 59% in Q3 2025, far outpacing the industry average. Q1 2026’s implied margin was substantially higher. Technology infrastructure costs of approximately $1 billion annually for HRT alone, calibrated to an elevated vol regime, compress significantly if geopolitical resolution returns markets to pre-2025 norms.

Regulatory extraterritoriality. Business Standard reported in June 2025 that SEBI had shared details of its investigation of Jane Street with the US SEC, establishing cross-jurisdictional precedent for review of expiry-day HFT mechanics. The India case demonstrated a compounding litigation risk: the lawsuit Jane Street initiated to protect its strategy became the trigger for regulatory scrutiny that cost approximately $564 million (₹4,843.57 crore) and market access, as confirmed by Business Standard’s July 14, 2025 deposit announcement. As these firms expand into Brazil, Taiwan, and other markets with growing derivatives ecosystems, the regulatory risk profile scales with each new geography — and the discovery footprint of any future litigation could again invite scrutiny.

AI homogenization and alpha decay. A 2026 arXiv paper by Chen and Meng, “AI-Driven Alpha Decay: Algorithmic Homogenization, Reflexive Signal Erosion, and the Paradox of Intelligent Markets,” validates empirically using SEC 13F holdings data across 99.5 million positions from 2013–2024 that simulated institutional portfolio convergence increased approximately 42% over the sample period as AI adoption increased. For market makers, the analog is model convergence: as more firms build similar neural network architectures on overlapping market data, the predictive edge degrades. The firms that currently dominate benefit from a first-mover advantage in compute infrastructure and training data accumulation — but that advantage erodes.

Conclusion: Prediction Is the Commodity, Scale Is the Moat

The alpha HRT and Jane Street are generating in Q1 2026 is not mysterious. It is the product of accurate short-term price prediction, applied at a scale that converts a sub-percentage-point statistical edge into tens of billions of annual revenue through compounding across millions of daily transactions. The EIA confirms the Iran war created the precise structural environment these models were built to exploit: the largest quarterly oil price increase on an inflation-adjusted basis in data going back to 1988, sustained for weeks, with cross-asset dislocations that required hours to reprice rather than milliseconds.

Bloomberg confirmed HRT’s Q1 2026 revenue was itself a record across any quarter in the firm’s documented history. Bloomberg separately confirmed Jane Street’s revenue more than doubled from Q1 2025 — a quarter that itself was not a low-base comparison. CoreWeave’s SEC filings confirm both firms are simultaneously investing the resulting capital into the next generation of training infrastructure. The feedback loop is self-reinforcing: trading revenue funds compute, compute improves models, better models improve trading revenue.

The Q1 2026 numbers are not a windfall. They are the compounding return on a decade of technology investment, harvested in the exact market regime those investments were designed to exploit.

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Navnoor Bawa is a quantitative researcher and financial journalist covering derivatives, market microstructure, and systematic trading strategies.

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