Courses Market Microstructure and Algorithmic Trading
Course 8Coming soon
Market Microstructure and Algorithmic Trading
How orders become prices, how to execute large orders and make markets, and how to take a strategy from idea to paper trading.
- 5 parts
- 16 modules
- Before you start: Stochastic Calculus, Financial Econometrics and Machine Learning; one module each from Derivatives Modelling and Portfolio Optimization
Coming soon
Explore the syllabus below, and start with the courses it builds on.
How each module works
- 1The market problemWhy practitioners need the model
- 2Model and codeThe model, then its implementation
- 3Practice setQuick questions with instant feedback
- 4Case studyAn industry problem, solved in code
- 5Limits and risksWhen the model fails, and what to do
1Coming soon
Part 1
How markets work
1.1Market structureComing soon
- You will learn:
- Venues, Dark pools, Fragmentation, Order types, Auctions, Tick size, Fees.
- Case study:
- Follow one order through a venue
1.2The limit order bookComing soon
- You will learn:
- Book events and data, Rebuilding the book, Order-flow facts.
- Case study:
- Rebuild a book from message data
1.3Price formationComing soon
- You will learn:
- Roll, Kyle, Glosten–Milgrom, Adverse selection.
- Case study:
- Estimate the spread's components
1.4Price impact and liquidityComing soon
- You will learn:
- Square-root law, Propagator models, Liquidity measures.
- Case study:
- Measure impact from trades
2Coming soon
Part 2
Execution
2.1Transaction cost analysisComing soon
- You will learn:
- Implementation shortfall, VWAP and arrival benchmarks.
- Case study:
- A TCA report for a parent order
2.2Optimal executionComing soon
- You will learn:
- Almgren–Chriss and extensions, VWAP, TWAP, POV.
- Case study:
- Schedule a large sell order
3Coming soon
Part 3
Market making
3.1Market making modelsComing soon
- You will learn:
- Inventory risk, Avellaneda–Stoikov, Guéant–Lehalle–Fernandez-Tapia.
- Case study:
- A market maker's P&L by simulation
3.2Market making in practiceComing soon
- You will learn:
- Many assets, Options market making, Crypto, Risk limits.
- Case study:
- Quote a crypto pair with inventory limits
4Coming soon
Part 4
Strategies
4.1Statistical arbitrageComing soon
- You will learn:
- Pairs, Cointegration, OU mean reversion, Factor stat arb.
- Case study:
- A pairs-trading book
4.2Momentum, trend and carryComing soon
- You will learn:
- Time-series and cross-sectional momentum, Carry.
- Case study:
- Trend following across futures
4.3High-frequency signalsComing soon
- You will learn:
- Order-book imbalance, Hawkes processes, Queue position, Latency.
- Case study:
- Short-horizon prediction from imbalance
4.4Machine learning strategiesComing soon
- You will learn:
- Order-book features, Boosted trees, Deep models, RL for execution and quoting.
- Case study:
- ML signal versus a linear baseline
5Coming soon
Part 5
Research to production
5.1Backtesting done rightComing soon
- You will learn:
- Event-driven backtests, Slippage, Costs, Overfitting controls.
- Case study:
- Rebuild a published strategy honestly
5.2Risk and position sizingComing soon
- You will learn:
- Sizing, Kelly, Drawdown control, A portfolio of strategies.
- Case study:
- Size three strategies together
5.3Trading systemsComing soon
- You will learn:
- Architecture, Market data, Order management, Monitoring, Regulation basics.
- Case study:
- Design review of a small trading stack
5.4Capstone: idea to paper tradingComing soon
- You will learn:
- Signal, Backtest, Execution, TCA, Risk.
- Case study:
- Graded with a checklist