Academic & Industry Research

Selected Papers

My work explores the intersection of high-frequency market mechanics, Bayesian statistical models, and the upcoming shift to agentic financial systems.

Singularity Trading

Proprietary Research Strategy

We target persistent structural inefficiencies in prediction markets. Our approach combines cross-market relationship detection, probabilistic coherence enforcement, and Bayesian probability adjustment.

Structural Over Transient

We ignore short-lived patterns to focus on structural inefficiencies: fragmented liquidity, delayed arbitrage, and resolution-rule ambiguity that persist across regimes.

Adversarial Validation

Signals are treated as hypotheses and stress-tested. Event-driven backtesting reconstructs market sequences, exercising the same execution logic used in live trading.

Continuous Governance

Live monitoring feeds back into the research loop. Edge decay is detected early, and capital is dynamically reallocated based on regime-aware models.

Core Focus Areas

  • Market microstructure and liquidity dynamics
  • Cross-venue probability coherence and structural arbitrage via relationship detection
  • Resolution rule parsing & machine-readable contradiction detection
  • Hazard and time-decay modeling for information relevance
  • Reinforcement learning for adaptive sizing & execution under constraints
  • Synthetic market simulation & Monte Carlo stress testing

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MAY 2024

Bayesian Regime Detection in Semiconductor Markets

An analysis of HMM-LSTM hybrid models for identifying sudden shifts in volatility clusters within hardware-focused equity portfolios.

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Active Research Interest

MEV Impact on DeFi Portfolio Execution

Investigating how Maximal Extractable Value (MEV) inflates execution costs in decentralized exchanges, motivating a full-scale MSc thesis on MEV-aware reinforcement learning for portfolio management.

Sandwich Attack Frequency
33%

Of trades in high-gas regimes are sandwiched. Attackers strike when margins justify priority fees.

Cost Multiplier
2.9x

Execution costs jump from 27 bps to 77 bps during sandwich attacks.

Portfolio Drag
$250K

Annual loss on a $1M portfolio due to ignoring MEV regimes (2.5% of value).

High-Gas Risk
2x

Rebalancing costs double during high-gas periods vs low-gas baselines.

Planned Thesis Research

MEV-Aware Portfolio Management with RL

Can an RL policy that observes gas regimes and MEV conditions achieve lower expected shortfall while preserving Sharpe? My thesis builds an agent to answer this.

Methodology

  • AMM Simulation Environment

    Reproducible Uniswap v2 constant-product AMM with 2–4 assets, fee mechanics, and liquidity buckets.

  • MEV Shock Generator

    Rule-based engine injecting sandwich attack slippage and backrun patterns calibrated to real block data.

RL Agent Architecture

  • State Space

    Gas regimes, mempool proxies (tx count), pool depth, and realized volatility.

  • Reward Function

    r_t = PnL_t - λ * slippage - μ * tail_risk

  • Algorithms

    PPO (Proximal Policy Optimization) and DQN (Deep Q-Network) for discrete action ablation.