About

Systematic Trading Researcher

I research systematic trading strategies, with a particular focus on trend following, time-series momentum, portfolio construction, market behavior, and trading-system implementation. My work combines financial reasoning, reproducible empirical research, statistical testing, and practical software development.

Profile photograph to be added

Location
CHICAGO, ILLINOIS
Focus
Systematic trading research, quantitative analysis, research engineering
GitHubpendingLinkedIn

Short biography

I am interested in understanding why systematic strategies work, how their performance changes through time, and how research results survive realistic implementation assumptions. My projects cover futures trend following, equity momentum, information diffusion, portfolio construction, transaction costs, strategy decay, and trading analytics. I aim to produce research that is transparent, reproducible, and honest about its limitations.

My work sits between three activities: framing an economic question precisely enough to test, building the data and code to test it honestly, and writing up what the result does and does not support.

How I approach research

  • Start from a mechanism, not from a pattern discovered by searching.
  • Write the specification before running the test, so choices are visible.
  • Build the simplest canonical baseline before any extension.
  • Treat costs, capacity, and rebalancing as economic assumptions, not settings.
  • Report uncertainty alongside every estimate.
  • Separate the empirical result from my interpretation of it.
  • State limitations in the same document as the conclusion, not after it.

Education

[EDUCATION DETAILS TO ADD] Institution, programme, and dates will be listed here once supplied. Nothing is claimed until it is.

What I am looking for

I am interested in quantitative research, systematic trading, financial data science, and research-engineering opportunities where careful empirical work and practical implementation matter.

Technical skills

Methods, code, and platforms

Only tools I actually use are listed. This section is deliberately easy to edit.

Research and statistics

  • Hypothesis development
  • Panel regressions
  • Newey–West inference
  • Clustered standard errors
  • Bootstrap confidence intervals
  • Structural-break analysis
  • Walk-forward and subperiod analysis
  • Drawdown and tail-risk analysis

Programming and data

  • Python
  • pandas
  • NumPy
  • SciPy
  • statsmodels
  • scikit-learn
  • SQL
  • Jupyter
  • Google Colab
  • Git and GitHub

Trading research

  • Time-series momentum
  • Trend following
  • Volatility scaling
  • Portfolio construction
  • Futures data
  • Equity cross-sectional research
  • Transaction costs
  • Slippage
  • Contract rolls
  • Performance attribution

Platforms and data workflows

  • RealTest
  • Norgate/MetaStock data
  • Broker APIs
  • Market-data pipelines
  • Dashboard development