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 for Finance covers the data stack used across the industry: pandas for time series, NumPy for vectorised computation, and the habits that keep research reproducible. Python for Algorithmic Trading moves to strategy code, broker interfaces and order handling. Building a Backtester with Backtrader teaches the event-driven design that makes simulated fills honest. Low-Latency C++ for Trading Systems covers 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
Short courses that fix the mathematics, statistics and code every later course assumes.
Practitioner courses in this track
Lab-based courses. You build, test and cost real strategies and systems on market data.
- Building a Backtester with Backtrader A working command of Backtrader, the open-source Python backtesting framework: feeds, strategies, indicators, realistic costs, position sizing and analyzers, and a clear view of what it cannot model.
- Python for Algorithmic Trading How research code becomes a trading system: an event-driven engine, a broker connection, order and position state, risk checks, logging, and a strategy that runs on a schedule without you watching it.
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. Four Foundation courses that fix the probability, statistics and Python every later course assumes, and a first look at how trading systems are built.
- Certificate in Algorithmic TradingSix courses and a capstone that take a strategy from idea to a costed, sized and validated system, with performance reported the way a trading desk would read it.
- Certificate in High-Frequency Trading and Market MicrostructureFive courses and a capstone on how orders meet: the limit order book, execution algorithms, venue routing, market making and the systems that run them.
- Certificate in Machine Learning for TradingFive courses and a capstone on applying machine learning to markets without fooling yourself: labelling, features, purged validation, overfitting tests and reinforcement learning.
- Professional Diploma in Algorithmic TradingThe top credential on the practitioner path. Eight courses and a 40-hour capstone covering everything between a trading idea and a book you could defend to a risk committee.
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 for Finance assumes basic programming in any language: variables, loops and functions. If you have never programmed, complete a general introduction to Python first.