Software & Data Analytics
This stream is for students who want to build the tools the research runs on. Good research needs clean data, repeatable tests and code that another person can run without guessing what happened.
Example work
A few examples of what this stream could pick up inside SSI Labs.
01
Build a data check that flags missing candles, duplicated rows or impossible prices before a backtest runs.
02
Improve the backtester so fees, slippage, funding and fills are handled consistently.
03
Create charts or reports that compare strategy behaviour across regimes and cost assumptions.
Work
What this stream owns
- Ingest, clean and store market data in a form the group can trust.
- Maintain backtesting and simulation code used by research members.
- Build analytics views for PnL, drawdown, turnover, exposure and cost sensitivity.
- Add tests, logging and documentation so research code is reproducible.
- Turn rough notebooks into code someone else can read, run and review.
Preparation
What to build toward
- Python for data work, testing and research tooling.
- SQL or structured data handling for storing and querying market data.
- Software engineering basics: Git, tests, readable functions and documentation.
- Backtesting concepts: event order, fills, costs, slippage and point-in-time data.
Fit
Who should look here
- You like making messy data usable.
- You care whether code is repeatable, not just whether it runs once.
- You want a software path into quant work without pretending to be a trader on day one.
Reading
Further reading
A short list for understanding the work behind this stream.
- Zipline beginner tutorial
Event-driven backtesting with slippage, transaction costs, order delays and risk statistics.
- vectorbt portfolio documentation
Portfolio simulation, orders, trades, drawdowns and performance analysis in Python.
- Backtrader quickstart
A practical introduction to strategy objects, data feeds, indicators and a backtesting engine.