Specialist track, course code PY
Programming for Quantitative Finance
Python for trading research and backtesting, C++ for systems where microseconds matter. Code you can run, review and put into production.
Trading research is software, and Python for trading is where most of it is written. A strategy exists only as code that reads data, makes decisions and records what happened, and the quality of that code decides whether a backtest can be trusted. Look-ahead bias, survivorship bias and silent data errors are programming errors before they are statistical ones.
What does the programming track teach?
Python and SQL for Finance covers the data stack used across the industry: pandas for time series, NumPy for vectorized computation, the queries and point-in-time tables that feed them, and the habits that keep research reproducible. Python for Algorithmic Trading, with Backtrader, moves to strategy code, broker interfaces and order handling, and the event-driven design that makes simulated fills honest. C++ for Quantitative Finance and Low-Latency Systems teaches the language as pricing libraries and trading systems use it, then memory layout, lock-free queues and measurement, the techniques used where a few microseconds decide who trades.
How are the programming courses assessed?
The Foundation course by quizzes and a graded notebook. The practitioner and researcher courses by code review: you submit a working repository against a written specification, and faculty check correctness, tests and the handling of market data edge cases such as gaps, halts and corporate actions.
Foundation courses in this track
The mathematics, statistics, data and market knowledge every later course assumes.
Practitioner courses in this track
Lab-based courses. You build, test and cost real strategies and systems on market data.
Researcher courses in this track
Proof-level courses. Stochastic models, estimation theory and the literature behind each method.
Credentials that use this track
- Foundation Certificate in Computational FinanceThe entry quantitative finance certificate, and the opening stage of both the Professional Diploma and the Fellowship. Six courses that settle the mathematics, statistics, data and market knowledge every later course assumes, closed by a capstone.
- Certificate in Algorithmic TradingSeven courses and a capstone that take a strategy from idea to a costed, sized and validated system, traded on paper and reported the way a trading desk would read it.
- Certificate in High-Frequency Trading and Market MicrostructureFour courses and a capstone on how orders meet: the limit order book, execution algorithms and their costs, venue routing, market making and the C++ systems that run them.
- Certificate in Statistical ArbitrageSeven courses and a capstone on market-neutral trading: time series foundations, pairs and baskets, cointegration, regimes, costs and the validation that keeps spread strategies honest.
- Professional Diploma in Algorithmic TradingThe top credential on the practitioner path. Twenty-one courses and an 80-hour capstone covering everything between the mathematics and a book you could defend to a risk committee.
- Fellowship in Computational FinanceThe top credential on the researcher path. Twenty-one courses and a dissertation supervised in writing and examined by two faculty members.
Questions applicants ask
Python or C++ for quantitative finance?
Python for research, data work and most strategy development; C++ where latency is the product, as in market making and high-frequency execution. Most desks use both. The track teaches Python first and C++ at Researcher level.
Do I need to know how to program before starting?
Python and SQL for Finance assumes basic programming in any language: variables, loops and functions. If you have never programmed, complete a general introduction to Python first.