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 PlusBuild a deep mathematical foundation: covering core operators, the laws governing calculations, and the intuition to develop basic models.
- Statistics PlusLearn practical statistics for analysing returns, measuring uncertainty, comparing strategies, and deciding whether observed performance is likely to be signal or noise.
- Probability PlusLearn the probability concepts behind uncertainty, expected value, distributions, and risk. This module builds the bridge from deterministic maths to statistical reasoning.
- Data Analysis PlusLearn to load, manipulate, analyse, and export financial data using Pandas, the core tool of every quant researcher.
- Time Series PlusUnderstand the structure, behaviour, and properties of financial time series before modelling or trading them.
- Mathematical Modelling PlusBuild mathematical models that turn market state into testable forecasts and trading signals.
- Quantitative Research PlusLearn how to do empirical research to find statistical edges.
- Market Microstructure PlusLearn the dynamics of markets to understand what causes price movements.
- Strategy PlusLearn 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 PlusLearn how to create machine learning models for quantatative trading and how to avoid the common mistakes.
- Regression Models PlusLearn how to create machine learning models for quantatative trading and how to avoid the common mistakes.
- Classification Models PlusLearn how to create machine learning models for quantatative trading and how to avoid the common mistakes.
- Strategy PlusLearn 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
Intermediate Coming Soon
Machine Learning Trading Strategies
Learn machine learning to make future predictions and how to use those predictions to make trading strategies.
Prerequisites: Quantitative Trading Fundamentals
4 modules 0 exercises
Coming Soon
What you'll learn
- Machine Learning fundamentals
- Teach a machine learning model to learn patterns from market data
- Create a statistical edge with machine learning
What you'll do
- Train several machine learning to find several patterns in the market data
- Develop best practises to avoid data leakage and overfitting.
- Create a strategy around a ML model's prediction.
What you'll cover
- Machine Learning Fundamentals PlusLearn the fundamental concepts of machine learning so you can understand how it works under the hood when you train models.
- Regression PlusLearn how to predict real-valued numbers - such as prices, price deltas, returns and log returns
- Clasification PlusLearn how to predict labels and what the labels should be
- Testing PlusLearn how to test a machine learning without any data leakage or overfitting
Challenges
1
Regression Strategy: Maximise Returns
Using anonymous real market data, build a regression-based trading strategy. Your goal is to generate the highest possible total return.
Strategy must be net profitable. Ranked against other submissions by total return.
2
Classification Strategy: Maximise Sharpe
Using the same anonymous real market data, build a classification-based trading strategy. Your goal is the highest risk-adjusted return.
Strategy must maximise risk-adjusted returns. Ranked against other submissions by Sharpe ratio.
Advanced Coming Soon
Advanced Machine Learning Trading Strategies
Build production-grade ML trading strategies with richer features, stronger validation, model ensembles, regime awareness, and portfolio construction techniques designed to survive live markets.
Prerequisites: Machine Learning Trading Strategies
5 modules 0 exercises
Coming Soon
What you'll learn
- Engineer robust features from price, volume, volatility, order flow, and cross-asset relationships
- Use walk-forward validation, purged cross-validation, and embargo periods to reduce leakage
- Combine models with ensembles, stacking, and confidence-weighted signals
- Adapt models across market regimes using clustering and regime classifiers
- Turn predictions into position sizing, risk limits, and portfolio allocations
- Monitor live model decay, drift, and execution-aware performance
What you'll do
- Research a model-driven strategy with realistic validation and trading constraints.
- Compare single-model signals with ensemble and regime-aware approaches.
- Build a full ML trading pipeline from feature generation to portfolio sizing.
- Stress-test the strategy for overfitting, instability, and changing market conditions.
What you'll cover
- neural-network-fundamentals PlusLearn the core fundametnals of neural networks so you understand the math and principles of them
- Non-linear Regression PlusLearn how to use neural networks for non-linear regression
- Non-linear Classification PlusLearn how to use neural networks for non-linear classification
- Sequence Modelling PlusLean how to apply sequence modelling to trading.
- Deep Learning PlusLean how to apply deep learning to quant trading strategies and the common problems with applying it to financial markets.
Challenges
1
Ensemble Strategy: Maximise Sharpe
Build an ensemble-based trading strategy and compete on risk-adjusted performance across hidden evaluation periods.
Strategy must be net profitable and maximise Sharpe across out-of-sample evaluation windows.
2
Regime-Aware Strategy
Detect changing market regimes and adapt your model, signal thresholds, or position sizing accordingly.
Strategy must outperform a static baseline while controlling drawdown across multiple regimes.
Advanced Coming Soon
Market Making Strategies
Learn how professional market makers profit from the spread, manage inventory risk, and provide liquidity across markets. Build systematic market making strategies from quote placement to execution.
Prerequisites: Advanced Machine Learning Trading Strategies
5 modules 0 exercises
Coming Soon
What you'll learn
- Understand how market makers profit from the bid-ask spread
- Understand how market makers skew their spread
- Model and manage inventory risk as a market maker
- Identify and defend against toxic order flow and informed traders
- Build low-latency execution logic for high-frequency environments
- Understand order book microstructure
What you'll do
- Develop a basic market making algorithm
- Learn how to quote
- How to manage inventory risk
What you'll cover
- Spread PlusHow market makers make markets using the spread.
- Bias PlusHow market makers bias their prices to minimize adverse selection.
- Order Book Dynamics PlusUnderstand order book microstructure, queue priority, and how prices move at the microscopic level.
- Inventory Management PlusModel and manage inventory risk: the core challenge of running a market making book.
- Execution & Latency PlusBuild low-latency execution logic and understand the role of co-location and order routing.