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Learn quantitative trading by building

Work through structured lessons, write code, complete challenges, and develop the technical foundations needed for serious quantitative research.

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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

£2,000 lifetime access

Or access every course with All Access for £200/month.

or Subscribe for £200/mo
What you'll learn
  • Python and financial data analysis with Pandas and NumPy.
  • Mathematics, probability, and statistics for quantitative trading.
  • Financial time series, including returns, stationarity, autocorrelation, and volatility.
  • Forecasting, classification, feature engineering, and model validation.
  • Disciplined quantitative research and how to avoid misleading results.
  • Market microstructure, strategy design, position sizing, and risk management.
What you'll do
  • Prepare and analyse financial data.
  • Build forecasting and classification models.
  • Test trading hypotheses and validate signals out of sample.
  • Turn model forecasts into trade signals and position sizes.
  • Account for execution costs, slippage, and market impact.
  • Build and evaluate a risk-managed strategy backtest.

What you'll cover

  1. 1
    Python Free
    Learn Python with real-world trading exercises so you learn algo trading and Python at the same time.
  2. Mathematics Premium
    Build a deep mathematical foundation: covering core operators, the laws governing calculations, and the intuition to develop basic models.
  3. Statistics Premium
    Learn practical statistics for analysing returns, measuring uncertainty, comparing strategies, and deciding whether observed performance is likely to be signal or noise.
  4. Probability Premium
    Learn the probability concepts behind uncertainty, expected value, distributions, and risk. This module builds the bridge from deterministic maths to statistical reasoning.
  5. Data Analysis Premium
    Learn to load, manipulate, analyse, and export financial data using Pandas, the core tool of every quant researcher.
  6. Time Series Premium
    Understand the structure, behaviour, and properties of financial time series before modelling or trading them.
  7. Mathematical Modelling Premium
    Build mathematical models that turn market state into testable forecasts and trading signals.
  8. Quantitative Research Premium
    Learn how to do empirical research to find statistical edges.
  9. Market Microstructure Premium
    Learn the dynamics of markets to understand what causes price movements.
  10. Strategy Premium
    Learn 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

Quantitative Trading Accelerator

A practical course on building machine learning models and quantitative trading strategies, with an emphasis on hands-on application and results rather than academic rigour and theory.

4 modules 29 exercises

£2,000 lifetime access

Or access every course with All Access for £200/month.

or Subscribe for £200/mo
What you'll learn
  • The quantitative research process.
  • How to identify and evaluate statistical anomalies.
  • Machine learning techniques, from linear to non-linear models.
  • Statistics essential for quantitative trading.
  • Principles of reliable strategy backtesting.
  • Production strategy architecture and Hyperliquid deployment.
  • How to recognize and avoid common quantitative research mistakes.
What you'll do
  • Research financial data to uncover statistical patterns.
  • Build and evaluate machine learning models.
  • Develop trading strategies driven by model predictions.
  • Validate strategies using statistical tests.
  • Build a backtesting framework from scratch.
  • Deploy and run a trading strategy on Hyperliquid.

What you'll cover

  1. Data Engineering Premium
    Learn how to source market data and prepare it for quantitative research.
  2. First Quant Trading Strategy Premium
    Build your first quant trading strategy that you can deploy on Hyperliquid.
  3. Second Quant Trading Strategy Premium
    Learn how to create a strategy that executes based on your model's predictions.
  4. Third Quant Trading Strategy Premium
    Turn a backtested strategy into production code and deploy it safely to Hyperliquid.