Quantum Yield is a financial technology company and fund platform delivering patent-pending AI forecasting engines, algorithmic trading strategies, and institutional-grade analytics across fixed income, mortgage finance, and structured products.
Quantum Yield is a Palo Alto–based financial technology company and fund platform specializing in AI-driven forecasting and algorithmic trading. Founded in 2025, we develop machine learning models that transform complex market and macroeconomic data into actionable intelligence for institutional investors, mortgage servicers, and structured finance participants.
Our Macrostructure engine is trained on decades of global economic data to model regime dynamics across growth, inflation, recession, and policy cycles — producing yield curve forecasts, mortgage servicing analytics, and structured finance automation across our four institutional platforms. On the Microstructure side, we combine proprietary real-time analytics, predictive modeling, and execution intelligence to power systematic trading strategies.
These two engines are not siloed — they form a unified forecasting-to-execution loop where macro regime intelligence defines directional bias and strategic positioning, while microstructure intelligence handles timing, execution, and precision. This integration, running on GPU infrastructure with sub-500ms latency, is what allows Quantum Yield to bridge the gap between ultra-fast HFT firms and longer-term quant hedge funds — and to scale across rates, futures, FX, equities, and digital assets with software leverage rather than organizational headcount. Our IP is protected by two provisional patents (64/000,855 and 64/065,987), and the platform operates through three legal entities: Quantum Yield Technologies Inc (S-Corp, Delaware), Quantum Yield GP LLC (Delaware), and Quantum Yield Fund LP (Delaware).
Years of macro feature data ingested — continuous history from 1970
Institutional platforms across yield curve, mortgage, and structured finance
LOB records processed per ticker per day through the microstructure training pipeline
Peak daily records across full ticker universe — CME L1/L2/L3 order book data
Quantum Yield is built around two interlocking AI engines. The Macrostructure layer models global economic regimes, yield curves, and mortgage portfolio dynamics — producing the directional bias and strategic positioning signals that drive portfolio construction. The Microstructure layer captures limit order book dynamics, order flow, and intraday liquidity conditions at millisecond resolution, enabling precision execution and short-horizon alpha generation. Together, they form a unified forecasting-to-execution loop.
Our macro engine is trained on decades of global historical economic data and learns regime dynamics across growth, inflation, recession, and policy cycles. It predicts the price behavior of financial products through yield curves, rates, and macro-linked instruments — applying scenario-aware forecasting rather than static extrapolation. This engine defines regime, direction, and strategic bias across all Quantum Yield platforms and fund strategies.
Our microstructure engine processes CME L1, L2, and L3 market data in real time, modeling the full breadth and depth of the limit order book to capture liquidity conditions, order flow dynamics, and execution signals at millisecond resolution. It predicts short-to-long horizon price movement and direction, models market impact and momentum, and is designed to operate in intraday and high-frequency trading environments — defining timing, execution, and precision for our proprietary fund strategies.
Our macrostructure intelligence is delivered through four institutional platforms spanning yield curve forecasting, mortgage servicing analytics, structured finance automation, and portfolio management.
AI-powered yield curve forecasting using hybrid LSTM + PCA models across daily, weekly, and monthly horizons. Covers all 11 standard U.S. Treasury tenors with real-time output, scenario analysis, and a strategy brewer for trading and hedging analytics.
Excel-based institutional analytics platform for structured finance security pricing, cash flow modeling, and portfolio management. Deployed with active institutional clients including AmWest Funding Corporation as a flagship securitizer reference deployment.
Quantum Yield's proprietary AI-driven forecasting platform for mortgage servicing behavior and portfolio performance. Built using advanced hybrid LSTM, PCA, and Bayesian Optimization methodologies, incorporating our patent-pending surrogate modeling and scenario-based forecasting technologies. Initially trained on approximately 1M loans as our flagship institutional deployment; designed to scale as a SaaS platform to 10,000+ mortgage servicers nationwide.
Prompt-driven AI for structured finance waterfall modeling. Reads deal documents and auto-converts structure terms into executable cash flow models — eliminating manual re-entry of sequential-pay, pro-rata, OC/IC triggers, and reserve accounts. Targets $300M–$500M+ in addressable deal flow across 500+ CLO managers, 1,000+ ABS issuers, and 3,000+ RMBS participants.
Quantum Yield's proprietary algorithmic trading engine is built on our Microstructure Intelligence layer — processing CME L1, L2, and L3 limit order book data in real time to predict short-to-long horizon price movement at millisecond resolution. The engine runs inside the QY GP LLC / Fund LP structure, executing fully automated strategies across ES futures with institutional-grade risk controls embedded at the execution layer. Risk metrics are tracked live at launchpad.quantumyield.ai.
Quantum Yield is organized as an integrated holding platform spanning a technology company, a general partner, and a limited partnership investment fund. This structure allows us to develop and license AI products through the technology entity while deploying proprietary and investor capital in tandem through the fund — aligning technology development, trading execution, and investor participation under one unified platform.
Our microstructure algo trading engine has been running live since February 2026. The following risk metrics are derived from daily PnL and reflect verified live system performance — not backtest results — on CME ES futures.
Quantum Yield sits at the intersection of three large and converging markets: AI-driven trading infrastructure, mortgage servicing analytics, and structured finance technology. We target a combined addressable opportunity exceeding $27B over our three-year horizon.
The global AI trading platform market was estimated at $13.45B in 2025, growing at 20% CAGR through 2030. Quantum Yield targets at least 8% market share by 2028, representing a ~$2.25B revenue opportunity.
10,000+ licensed mortgage servicers nationwide represent a scalable SaaS licensing target for QYSF. At $5K/month per servicer and 5% penetration, the ARR opportunity exceeds $30M — growing with the addressable servicer base.
500+ CLO managers, 1,000+ ABS issuers, and 3,000+ RMBS participants represent the direct addressable market for the QY Waterfall Engine — a $300M–$500M+ addressable technology opportunity with no scaled competitor today.
Traditional quant funds rely on large human research teams, static models, and strategy silos that separate macro, micro, and execution. Quantum Yield integrates all three into a unified AI-native system — macro intelligence defines regime and direction, microstructure intelligence defines timing and execution, and both layers share the same continuous in-sample / out-of-sample validation loop. This architecture scales across global markets, assets, horizons, and products with software leverage rather than organizational headcount.
30+ years across structured finance, securitization technology, and AI fintech. Senior roles at Merrill Lynch, Bank of America, and LaSalle Bank spanning CMBS/RMBS modeling, structured finance issuance, and analytics leadership. Founded Securitizer in 2016, building the institutional ⍺Waterfall platform for MBS, CMBS, ABS, and CLO. Founded Quantum Yield in 2025, holding two U.S. provisional patents for AI-native yield curve forecasting and macro-micro trading intelligence.
LinkedIn30+ years in institutional fixed income and capital markets across Bank of America, Legg Mason, Stifel, Baird, Fifth Third, and KeyBanc. Built high-grade credit trading desks from scratch at multiple institutions, scaling revenues to $38–45M annually and managing up to $800M in net risk. Generated $60M during the 2007–2009 crisis at BofA's #1 IG trading franchise. Joins Quantum Yield as CIO to bring institutional execution discipline to its AI-native trading platform.
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