AR Alexander Roesler QR-focused MFE candidate

Alexander Roesler

Quantitative researcher building alpha, execution, and ML-driven research systems.

I am a Berkeley MFE candidate with an applied mathematics and statistics foundation, currently doing quantitative strategy research for KairosWealth on prediction and public markets, and joining Morgan Stanley Fixed Income Strats (Corporate Credit) as an intern in October 2026. My strongest fit is quantitative research, with adjacent interest in quant trading, quant development, and data-science roles that value rigorous experimentation.

Cross-sectional alpha research Execution and microstructure modeling ML and NLP signal development Research pipelines and validation
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Best fit: QR. Also interested in QT, QD, and data-science roles with strong modeling and implementation depth.

Now Quantitative strategy research contractor for KairosWealth via the Berkeley MFE industry project
Next Morgan Stanley Fixed Income Strats intern, Corporate Credit, Oct. 2026 to Jan. 2027
Signal Cross-sectional stat-arb, prediction-market microstructure, intraday execution, and walk-forward validation
Credentials Berkeley MFE GPA 3.93, UCLA Applied Mathematics, Actuarial Exam P, and Akuna Options 201

I care most about work that is statistically disciplined, implementation-aware, and easy to audit. The strongest environments for me are research teams that value careful validation, robust code, and concise communication.

alex_roesler@berkeley.edu Latest resume PDF Optional: view the creative portfolio version

About

Research-first, with a strong implementation bias.

My work sits at the intersection of quantitative research, software engineering, and model validation. I like turning noisy real-world problems into research pipelines that can survive scrutiny: realistic costs, no-lookahead discipline, reproducible experiments, and reporting that makes the result easy to inspect.

  • Strongest signal in alpha research, systematic trading ideas, and implementation-aware backtesting
  • Comfortable moving between statistics, optimization, machine learning, and production-style research code
  • Bias toward systems that are rigorous, legible, and useful to the next person reading them

Current Focus

What I am working on right now.

KairosWealth industry project

Since June 2026 I have been a quantitative strategy research contractor for KairosWealth, developing and backtesting systematic strategies across prediction and public markets. The work integrates order-book, price, volume, volatility, and alternative data, and evaluates every idea under increasingly realistic execution assumptions. The project is under NDA, so the description here stays at the level of process.

  • Lead-lag, relative-value, and market-microstructure analysis
  • Backtesting with realistic execution assumptions and out-of-sample discipline
  • Forward validation of the ideas that survive

MFE progress

  • Coursework: derivatives, empirical finance, stochastic calculus, fixed income markets, and financial data science
  • Term 1 industry project: NLP alpha signal research with Trexquant, Mar. to Jun. 2026
  • Current industry project: quantitative strategy research for KairosWealth, Jun. 2026 to present
  • Internship: Morgan Stanley Fixed Income Strats, Corporate Credit, Oct. 2026 to Jan. 2027
  • Expected graduation: March 2027

Projects

Projects that best signal QR, QT, and data-science readiness.

KairosWealth industry project 2026

Prediction-Market Strategy Research

Quantitative strategy research across prediction and public markets for KairosWealth: integrating order-book, price, volatility, and alternative data, and evaluating systematic strategies under increasingly realistic execution assumptions. Under NDA, so described at the level of process.

  • Lead-lag, relative-value, and microstructure analysis
  • Execution-aware backtesting and out-of-sample discipline
  • Forward validation of surviving ideas

Python, market microstructure, systematic research, backtesting

See on resume
Trexquant industry project 2026

NLP Alpha Signal Research

Built an end-to-end pipeline that transforms SEC EDGAR filings into tradable cross-sectional signals through embeddings, predictive modeling, and formulaic alpha construction.

  • NLP embeddings and text features
  • Walk-forward backtesting and signal decay
  • Regime robustness and turnover profiling

Python, statistical learning, NLP, cross-sectional backtesting

See on resume
Independent research 2025

Cross-Sectional Statistical Arbitrage

Market-neutral crypto stat-arb research on a 174-asset panel with PCA risk neutralization, signed-graph clustering, and strict out-of-sample portfolio construction.

  • 1.76 Sharpe, 29.2% annualized return net of 25 bps/side
  • Near-zero market exposure and ~27.5% daily turnover
  • No-lookahead daily OOS pipeline with liquidity filters

Python, pandas, scikit-learn, statsmodels, graph clustering

View repository
Independent research 2026

Alpha Factor Mining Framework

Built a US equities factor-research framework with point-in-time universe construction, embargo windows, transaction-cost modeling, and gated promotion criteria for factor selection.

  • Deflated Sharpe adjustment for multiple testing
  • Subperiod stability and sector concentration checks
  • Programmatic factor generation via constrained LLM prompts

Python, cross-sectional research, factor validation, tooling

View repository
Independent research 2026

LOB Engine C++

High-performance C++ limit order book engine for LOBSTER-style message data with dual backends, real-time microstructure analytics, and reproducible replay benchmarking.

  • Dual backends with deterministic parity tests
  • Real-time microstructure analytics and CSV export
  • Replay throughput benchmarked at 60.1M messages per second

C++, CMake, Python, market microstructure

View repository
Independent research 2025

Intraday Optimal Execution

Modeled temporary market impact from minute-level order book data, smoothed intraday liquidity with penalized B-splines, and solved for the cost-minimizing execution schedule.

  • Piecewise impact model with a concave power-law tail
  • GCV-tuned liquidity curve estimation
  • KKT-based execution schedule with Lagrange multiplier bisection

Python, optimization, microstructure, execution research

View repository

Resume

Condensed evidence for quick hiring review.

3.93 Berkeley MFE GPA
1.76 Crypto stat-arb Sharpe, 29.2% annualized
Exam P Passed Sep. 2025
Mar. 2027 Expected Berkeley MFE graduation
  • Incoming Morgan Stanley Fixed Income Strats intern (Corporate Credit), Oct. 2026 to Jan. 2027.
  • Current KairosWealth research contractor: building and falsifying systematic strategies across prediction and public markets.
  • Completed Trexquant industry project: end-to-end NLP alpha pipeline from SEC text to tradable cross-sectional predictors.
  • Independent QR work in stat-arb, factor mining, execution modeling, and order-book systems with implementation-aware validation.
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Professional and research experience

  • Morgan Stanley Fixed Income Strats (incoming): systematic pricing, adverse-selection modeling, and relative-value signals for corporate-bond markets.
  • KairosWealth quantitative strategy research contractor: order-book, price, volatility, and alternative data across prediction and public markets.
  • William Blair private wealth management internship building a Sharpe-ranked quant-fund screener and producing alpha/beta-based investment memos.
  • Research style centered on no-lookahead discipline, realistic costs, risk neutralization, and robustness under multiple testing.

Skills and Education

Tools, coursework, and training behind the signal.

Python NumPy pandas SciPy statsmodels scikit-learn PyTorch C++ R SQL Bash Docker Git Linux Jupyter Machine Learning NLP Monte Carlo Optimization Stochastic Calculus Pattern Recognition
2026-2027 UC Berkeley Haas

Master of Financial Engineering, GPA 3.93. Coursework in derivatives, empirical finance, stochastic calculus, fixed income markets, and financial data science, with industry projects for Trexquant and KairosWealth.

2021-2025 UCLA

B.S. Applied Mathematics, Minor in Statistics & Data Science, GPA 3.82, Dean’s List.

2022 Oxford Algorithmic Trading Programme

Vol-scaled momentum, debiased backtests, walk-forward out-of-sample research, CAPM/FF3 alpha, and risk stress testing.

Contact

Reach out directly.

Email alex_roesler@berkeley.edu GitHub github.com/Aroesler1 LinkedIn linkedin.com/in/alexroesler Resume Open the current PDF resume