Courses
Beginner
Quantitative Trading Fundamentals
Build the complete foundation for quantitative trading. No programming experience or trading knowledge required. You will learn Python, mathematics, statistics, data analysis, time series, trading fundamentals, and risk management through practical exercises. A central theme of the course is mathematical modelling: markets cannot be represented with 100% real-world accuracy, so quants build simplified versions of market dynamics where models are approximating. You will learn what to predict, understand what a model can and cannot tell you, and use mathematics to turn trading ideas into testable predictions.
10 modules 307 exercises
What you'll learn
- Python programming
- Core mathematics and statistics with applications to quant trading.
- Financial data analysis with Pandas and NumPy
- Time Series concepts
- Key trading and risk management metrics
- How to do quantitative research to find your own edge and validate it.
What you'll do
- Conduct quantitative research.
- Develop a mathematical model to forecast.
- Perform statistical validation of your mathematical model.
- Develop a basic automated trading strategy based on your model.
- Develop a backtest of your strategy.
What you'll cover
- 1 Python FreeLearn Python with real-world trading exercises so you learn algo trading and Python at the same time.
- Mathematics PremiumBuild a deep mathematical foundation: covering core operators, the laws governing calculations, and the intuition to develop basic models.
- Statistics PremiumLearn practical statistics for analysing returns, measuring uncertainty, comparing strategies, and deciding whether observed performance is likely to be signal or noise.
- Probability PremiumLearn the probability concepts behind uncertainty, expected value, distributions, and risk. This module builds the bridge from deterministic maths to statistical reasoning.
- Data Analysis PremiumLearn to load, manipulate, analyse, and export financial data using Pandas, the core tool of every quant researcher.
- Time Series PremiumUnderstand the structure, behaviour, and properties of financial time series before modelling or trading them.
- Mathematical Modelling PremiumBuild mathematical models that turn market state into testable forecasts and trading signals.
- Quantitative Research PremiumLearn how to do empirical research to find statistical edges.
- Market Microstructure PremiumLearn the dynamics of markets to understand what causes price movements.
- Strategy PremiumLearn how to turn a statistical edge into a strategy that can execute that edge in the market.
Challenges
1
Quantitative Trading Fundamentals Final Challenge
Build an algorithmic trading strategy optimized for risk-adjusted returns. You are given a time series of prices for a real-world asset (which is anonymised). Use all the modelling skills that you've learnt in the course to build an algorithm with a strong annualized Sharpe ratio.
Annualized Sharpe >= 1.00
Beginner Coming Soon
Quantitative Trading Accelerator
A pratical course to learn how to build machine learning models and strategies for quant trading that focuses on hands-on application and results over academic rigour and theory.
4 modules 149 exercises
Coming Soon
What you'll learn
- Master pandas to work with financial data.
- How to build profitable machine learning models
- How to create quant trading strategies using machine learning models
- Learn different machine learning models from linear to non-linear models.
- Learn statistics that are important to quant trading.
- How to backtest a quant trading strategy from scratch.
- How to create portfolio-level quant trading strategies.
What you'll do
- Conduct quantitative research to find statistical patterns.
- Create machine learning models.
- Develop quant trading strategies that execute using machine learning models.
- Perform statistical validation of your models.
- Create your own backtesting framework for your strategies.
- Develop a portfolio-level quant trading strategy.
- Use methods to ensure your strategies are statistically validated
- Ensure you do not make the most common mistakes that can be very subtle
What you'll cover
- Data Engineering PremiumLearn how to create machine learning models for quantatative trading and how to avoid the common mistakes.
- Regression Models PremiumLearn how to create machine learning models for quantatative trading and how to avoid the common mistakes.
- Classification Models PremiumLearn how to create machine learning models for quantatative trading and how to avoid the common mistakes.
- Strategy PremiumLearn how to create machine learning models for quantatative trading and how to avoid the common mistakes.
Challenges
1
QTA Model Challenge
Develop a machine learning model that gives the best cumulative trade log returns for. You are given a time series of prices for a real-world asset.
Cumulative Sum of Log Returns >= 1.00
2
QTA Portfolio Challenge
Develop a portfolio-level strategy that can trade multiple assets. You are given a time series for 10 anonymised assets and must create a strategy to trade.
Score >= 5.00