Courses
1 courses available — from complete beginner to advanced systematic strategies.
Algorithmic Trading Fundamentals
Build the complete foundation for algorithmic trading. No programming experience or trading knowledge required. Whether you are a student, a manual trader with no coding background, or a developer with no trading experience, this course gives you everything you need: Python, statistics, data analysis, time series, trading fundamentals, and risk management, all taught through practical exercises.
- Python programming through hands-on trading exercises
- Core mathematics and statistics used in quant finance
- Financial data analysis with Pandas and NumPy
- Time series concepts: stationarity, resampling, OHLCV data
- Key trading metrics: returns, Sharpe ratio, drawdown
- Risk management and position sizing fundamentals
This course takes you from zero to able. Every topic is taught through real trading problems, so you learn the skill and see why it matters simultaneously. By the end you will be able to research, code, backtest, and evaluate a systematic trading strategy from scratch.
What you'll cover
- 1 Python FundamentalsLearn Python with real-world trading exercises so you learn algo trading and Python at the same time.
- 2 Mathematics FundamentalsLearn the core mathematics used in algorithmic trading.
- 3 Statistics FundamentalsLearn the core statistical concepts used every day in quantitative trading research.
- 4 Time Series FundamentalsUnderstand the structure, behaviour, and properties of financial time series before modelling or trading them.
- 5 Data Analysis FundamentalsLearn to load, manipulate, analyse, and export financial data using Pandas, the core tool of every quant researcher.
- 6 Trading FundamentalsUnderstand how markets actually work: how orders are placed, matched, and executed, and how positions are built and managed.
- 7 Risk FundamentalsLearn to measure, manage, and monitor the risk of a trading strategy from first principles.
Challenges
Basic Systematic Trading Strategies
Sometimes the best trading strategies are the most basic. Master the core systematic approaches: momentum, mean reversion, and breakout, that form the foundation of most professional quant desks.
- Define and implement rules-based systematic strategies
- Build momentum strategies using time-series and cross-sectional signals
- Construct mean reversion systems that profit from price extremes
- Develop breakout strategies with signal filters to reduce false entries
- Combine multiple strategies into a simple diversified portfolio
- Apply stop-losses, position limits, and drawdown controls
Momentum, mean reversion, and breakout strategies underpin the majority of systematic trading funds. They are simple enough to understand and implement rigorously, yet powerful enough to generate consistent edge when properly risk-managed. Every more advanced strategy you will ever build is a variation or combination of these three approaches.
What you'll cover
- 1 What Makes a Strategy Systematic?Define what it means to trade systematically: rules, signals, and removing emotion from every decision.
- 2 Momentum StrategiesBuild time-series and cross-sectional momentum strategies and understand the academic and practical evidence behind them.
- 3 Mean Reversion StrategiesIdentify over-extended moves and construct strategies that profit from price returning to equilibrium.
- 4 Breakout StrategiesTrade breakouts from consolidation ranges and learn how to filter false signals from genuine moves.
- 5 Risk & Position SizingAdd position limits, stop-losses, and drawdown controls so strategies survive adverse market conditions.
- 6 Putting It TogetherCombine multiple strategies into a simple portfolio, managing correlation and exposure across positions.
Challenges
Advanced Systematic Trading Strategies
Go beyond simple rules and build strategies with a genuine statistical edge. Covers machine learning, alternative data, portfolio construction, and the rigorous testing required to know if your strategy is real.
- Engineer features from price, volume, and alternative data
- Train and validate ML models on financial time series without data leakage
- Convert model predictions into actionable trade signals
- Build multi-signal portfolios using risk-parity and mean-variance techniques
- Stress-test strategies across market regimes and parameter perturbations
- Distinguish genuine statistical edge from overfit backtest results
Most systematic funds operating at scale today use machine learning to find edge that rule-based systems cannot. This course teaches you to build ML-driven strategies with the statistical rigour required to know whether your edge is real. Backtesting is easy. Knowing when to trust a backtest is the hard part.
What you'll cover
- 1 What Separates Advanced StrategiesUnderstand the leap from rule-based to model-driven strategies and the new failure modes that come with it.
- 2 Feature EngineeringTransform raw price, volume, and alternative data into informative features that machine learning models can learn from.
- 3 Model Selection & ValidationChoose the right model for financial prediction and use purged cross-validation to avoid data leakage on time-series data.
- 4 Building the Statistical EdgeTrain, tune, and evaluate a model until it demonstrates a robust, out-of-sample statistical edge.
- 5 From Predictions to SignalsConvert model output into actionable buy and sell signals with defined thresholds and confidence filters.
- 6 Portfolio ConstructionSize positions across multiple signals using risk-parity and mean-variance techniques to maximise risk-adjusted returns.
- 7 Robustness & Stress TestingTest your strategy across different market regimes and parameter perturbations to ensure it is not overfit.
Challenges
Market Making Strategies
Learn how professional market makers profit from the spread, manage inventory risk, and provide liquidity across crypto and equity markets. Build systematic market making strategies from quote placement to execution.
- Understand how market makers profit from the bid-ask spread
- Model and manage inventory risk as a market maker
- Determine optimal quote placement given volatility and spread
- Identify and defend against toxic order flow and informed traders
- Build low-latency execution logic for high-frequency environments
- Navigate order book microstructure and queue priority
Market making is how liquidity gets into markets. Professional market makers are on the other side of nearly every trade you execute. Understanding how they operate, how they price risk, and how they protect against adverse selection gives you a fundamentally different view of how markets work and how to trade within them.
What you'll cover
- 1 Market Making FundamentalsHow market makers operate, profit from the spread, and manage adverse selection risk.
- 2 Order Book DynamicsUnderstand order book microstructure, queue priority, and how prices move at the microscopic level.
- 3 Inventory ManagementModel and manage inventory risk: the core challenge of running a market making book.
- 4 Quote Placement & SizingDetermine optimal bid/ask placement and sizing given volatility, spread, and inventory constraints.
- 5 Adverse Selection & Anti-GamingIdentify toxic flow and design protections to prevent being picked off by informed traders.
- 6 Execution & LatencyBuild low-latency execution logic and understand the role of co-location and order routing.