Performance Snapshot
- Live Strategy Streams4+
- Research CadenceWeekly
- Coverage Horizon2022-2026
Coverage window: 2022-2026
Update cadence: Weekly research publication + revisions
THE QUANT LEDGER
Independent Quant Research Portfolio
Lead Story
Building Repeatable Edge With Transparent Models, Explicit Risk, and Ongoing Revision.
Every study in this portfolio includes methodology, assumptions, diagnostics, and invalidation criteria. Subscribers unlock full implementation notebooks and revision history.
Evidence Protocol
- Hypothesis-first research design with explicit failure criteria.
- Out-of-sample validation and rolling regime checks before promotion.
- Execution realism: fees, slippage, and liquidity constraints in every study.
All public claims are constrained to documented outputs and reproducible methodology context.
Featured Research
Liquidations cascades: Hawkes process modelization
Introduction and motivation When a user opens a perpetual position, is subject to a potential liquidation of the position in the event of having unrealized losses above the maintenance margin of the position. Adverse price movement triggers liquidations, which are executed as market orders on the opposite side of the b...
Research Chronicle
View full archiveLiquidations cascades: Hawkes process modelization
Introduction and motivation When a user opens a perpetual position, is subject to a potential liquidation of the position in the event of having unrealized losses above the maintenance margin of the position. Adverse price movement triggers liquidations, which are executed as market orders on the opposite side of the b...
Continue readingLiquidations cascades: Hawkes process modelization
Introduction and motivation Perpetual futures are subject to the liquidation of open positions. In such a scenario, adverse price movements trigger forced liquidations which are executed as market orders. These forced trades exert additional price pressure in the same direction as the initial move, increasing volatilit...
Continue readingRegime-Switching Volatility Models
Abstract Financial markets exhibit distinct regimes—calm, bullish periods versus high volatility, bearish crashes. Standard GARCH models often fail to adapt quickly enough to these shifts. This paper explores Hidden Markov Models (HMM) to classify market regimes and adjust leverage dynamically. HMM Formulation
Continue readingIntraday Momentum: Structural Alpha
Structural Inefficiencies Intraday momentum in crypto markets is often driven by liquidation cascades and forced buying. We identify structural alpha by analyzing the Funding Rate and Open Interest changes. Signal Construction
Continue readingSatellite Imagery as Fundamental Signal
Alternative Data in TradFi Using Computer Vision (YOLOv8) to count cars in retail parking lots gives us a proxy for quarterly revenue before earnings calls. Methodology
Continue readingOptimal Execution with RL
Reinforcement Learning for Execution Minimizing market impact is crucial for large orders. We train a PPO (Proximal Policy Optimization) agent to break up orders optimally. State Space
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Full research archive and post-publication revisions • Implementation notes, notebooks, and diagnostics • Methodology caveats, assumptions, and failure cases
Performance observations are presented with risk diagnostics and do not represent investment advice or guaranteed outcomes.