Andrew Campbell - Research Engineer at Ansatz Capital

Andrew Campbell

Research Engineer at Ansatz Capital

United States

Join Prog.AI to see contacts

Summary

🤩

Rockstar

🎓

Top School

Andrew Campbell is a research engineer with 11 years of experience applying quantitative research and data science to portfolio construction and factor analysis across hedge funds and startups. He has held roles from Quantitative Researcher at Citadel to Head of Data Science at OpenStore and contributes to well-known open-source finance projects like Quantopian's pyfolio and alphalens, where he implemented analytics and plotting tools for risk, sector exposure, and factor performance. Comfortable in Python data stacks (Pandas, NumPy, Seaborn), he bridges research and production by turning time-series and trade-level insights into robust tooling for slippage, round-trip trade analysis, and factor diagnostics. Based in the United States and grounded in an economics BA from Harvard, he pairs institutional quant rigor with a product-oriented mindset cultivated in both finance and consumer-tech settings. An Olympic-level athlete in his past, he brings discipline and a competitive edge to complex, team-driven problems.

11 years of coding experience

8 years of employment as a software developer

Bachelor of Arts (B.A.), Economics, Bachelor of Arts (B.A.), Economics at Harvard University

Github Skills (16)

data-visualizations10

finance10

pandas10

data-visualization10

data-visualisation10

risk-analysis10

seaborn10

statistical-models10

python10

modeling10

numpy10

data-analysis10

algorithmic-trading9

matplotlib9

performance-analysis9

Programming languages (4)

TypeScriptC++Jupyter NotebookPython

Github contributions (5)

quantopian/alphalens

Jun 2016 - Jul 2016

Performance analysis of predictive (alpha) stock factors

Role in this project:

Data Scientist

Contributions:203 commits, 2 pushes, 1 comment in 1 month

Contributions summary:Andrew's commits primarily focus on developing performance analysis tools for financial alpha factors. They implemented several plotting functions to visualize and analyze information coefficient, return distributions, turnover, and other performance metrics. The commits involve refactoring existing plotting and performance functions, demonstrating the user's ability to work with time series data and financial analysis techniques. The code uses libraries like Pandas, NumPy, and Seaborn, and involves creating and displaying key insights related to factor performance.

stock-marketalgorithmic-tradingpythonperformance-analysisnumpy

quantopian/pyfolio

Oct 2015 - Feb 2016

Portfolio and risk analytics in Python

Role in this project:

Back-end Developer / Data Scientist

Contributions:44 commits, 15 PRs, 73 pushes in 4 months

Contributions summary:Andrew implemented new features for portfolio and risk analytics. These features included mapping positions to sectors, calculating sector exposures, and creating plots to visualize these exposures. The user also introduced functionality for analyzing round trip trades, including calculating percentage returns and duration. Additionally, the user contributed to the development of a slippage sensitivity analysis and added support for applying slippage adjustments throughout the tear sheet plots.

riskanalyticspython

Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.

Request Free Trial