03 - QUANTITATIVE FINANCE

Data-Driven
Investing

The Quantitative Finance department focuses on data-driven investing and algorithmic trading strategies. Members develop systematic investment models using Python, QuantConnect, Alpaca, and statistical research workflows - combining finance, mathematics, and computer science to analyze markets and develop automated strategies.

Quantitative Finance
AREAS OF WORK

Algorithmic Trading Strategies

Design, backtest, and refine systematic trading strategies using historical and live market data. Evaluate performance with rigorous statistical frameworks.

Factor-Based Investing

Construct multi-factor models that capture systematic return drivers across equities. Research factors including momentum, value, quality, and low-volatility.

Statistical Arbitrage

Identify and exploit transient price dislocations using cointegration analysis, pairs trading, and cross-sectional momentum.

Market Data Analysis

Process and analyze large financial datasets, price histories, and portfolio data to surface actionable signals.

Machine Learning in Finance

Apply supervised and unsupervised learning techniques to financial prediction problems, regime detection, and portfolio optimization.

Live Trading

Deploy models that trade on paper. If they are performant, live deploy them to make trades with real capital.

TOOLS & TECH STACK
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PythonPrimary development language - pandas, numpy, scipy, sklearn
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QuantConnect SponsorshipThe quant team is specifically sponsored by QuantConnect for strategy research and backtesting
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Broker & ExecutionAlpaca for paper trading, brokerage connectivity, and live strategy workflows
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Statistical ModelingTime-series analysis, regression, Bayesian methods
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Backtesting FrameworksQuantConnect and internal research workflows
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Version Control & NotebooksGit, Jupyter, collaborative research workflows
IN PRACTICE
Quant team at work
SKILLS MEMBERS DEVELOP
Statistical modeling
Algorithmic strategy development
Data analysis and visualization
Quantitative portfolio construction
Risk management and performance attribution
Python and scientific computing
CAREER OUTCOMES
Quantitative TradingCitadel, Two Sigma, DE Shaw, Jane Street
Hedge FundsSystematic and discretionary strategies
FintechAlgorithmic infrastructure and product
Data ScienceApplied ML across finance and tech
Risk ManagementMarket risk and quant analytics at banks
ResearchAcademic and institutional finance research

Interested in Quant?

Python experience helpful but not required. We train from the ground up.

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