Varsity Holdings – Quant Researcher – Strategy Development Intern

You will not be cleaning CSVs all day. We provide:

• Clean, institutional-grade historical & intraday data

• A production-grade C++ / Python backtesting engine

• Full research infra, logs, factor libraries, and analytics stack

• Clear research problems with real PnL impact

Your job is to turn ideas into strategies and features that make or save money.

* What You’ll Do

You will work directly with our quant team on systematic strategy R&D, including:

• Research new alpha ideas for intraday U.S. equities (long & short)

• Design quantifiable features / factors from price, volume, microstructure, regime, etc.

• Implement and test strategies in our backtesting framework

• Analyze large-scale backtest results:

  • PnL distributions

  • Drawdowns

  • Regime performance

  • Failure modes

• Improve existing strategies:

  • Entry logic

  • Exit logic

  • Filters

  • Risk logic

• Help build strategy variants and parameterized families instead of one-off ideas

• Write clean research notes: hypothesis → test → result → conclusion

If you perform well, your work can and will be deployed to live trading.

* What We Provide (You Don’t Have to Worry About This)

• * Clean, survivorship-bias-safe historical data

• * Intraday minute data, corporate actions, session logic handled

• * Fast backtesting engine

• * Research tooling, logs, analytics, dashboards

• * Existing factor & strategy libraries

• * Guidance on what actually works in small-cap / intraday trading

You focus on thinking, testing, and improving edge.

* What We’re Looking For

Must Have:

• Strong logical thinking and quantitative intuition

• Comfortable with Python (for research / analysis)

• Basic understanding of:

  • Backtesting

  • Overfitting

  • Data leakage

  • Regime dependency

• Able to think in if-this-then-that systematic rules

• Curious, skeptical, and not emotionally attached to ideas

Nice to Have:

• Experience with:

  • Trading strategies

  • Backtesting frameworks

  • Statistics / ML

  • Financial time series

• Experience with pandas / numpy / vectorized analysis

• Interest in market microstructure, intraday behavior, or small caps

We do NOT require:

• Prior profitable trading

• Finance degree

• Fancy ML background

We care about how you think, not your resume.

* What Kind of Problems You’ll Work On

Examples:

• Why do some parabolic shorts fail and squeeze while others fade cleanly?

• How to detect regime shifts in small caps?

• How to cluster trades into behavioral buckets?

• How to turn 10 similar strategies into one adaptive strategy?

• How to detect crowded vs fragile price structures?

• How to build context-aware filters instead of static rules?

* What You’ll Learn

• How real quant research is done in a trading firm

• How to avoid fake edges and backtest lies

• How to think in distributions, not examples

• How to design strategies as systems, not one-off setups

• How professional research pipelines are structured

• How ideas move from research → backtest → production

* Compensation & Upside

• Monthly stipend (depends on profile)

• High performers may get:

  • Extension to full-time

  • Performance-based bonus

  • Direct ownership over live strategies

* Culture Fit (Important)

• You must like being wrong and updating fast

• You must enjoy killing your own ideas

• You must prefer truth over ego

• You must be comfortable with:

  “90% of ideas don’t work. That’s normal.”