Class description: introductory course on developing trading algorithms and algotrading industry as a whole. Class is held in financial lab with 9 Bloomberg terminals; students are learning to fast-prototype algorithms using R language and real data from markets. Focus is on practice, but good understanding of underlying theory is a must. Knowledge of Bloomberg platform and R language is an advantage.
Prerequisites: basics of financial markets, technical and fundamental analysis, financial mathematics, modern portfolio theory, statistics and probability theory.
Week 1. How to create, backtest and optimize the simplest trading strategy
Trading strategy representation in Bloomberg. Mnemonics BT, BTST, TECH. Entry/exit rules. Filters and signals. Money management rules. Indicator parameters. Optimization surface. Basic statistics of trading strategy backtest.
Week 2-3. Basics of algotrading
Algotrading as decision making and investment product. Basic elements of algorithmic strategy: alpha model, risk model, costs model, portfolio model, execution model. Types of trading algorithms: growth, value, order management, market making. The industry of algotrading: players, market parameters, products, users, solutions, data vendors and data types. Quantitative price regularities. Why (and does) algorithms have positive return? Technical trading rules and market efficiency controversy. Fundamental and technical analysis. Price impact of algotrading. The need for regulation.
Week 4-5. Quantitative parameters of financial time series
Brief intro to R Studio environment and R language. R-Bloomberg interface. Search for data and fields: SECF and FLDS mnemonics. Study of return distributions and autocorrelation functions. Stylized facts about returns and how it impacts returns of algorithms. Trading model in R. Trading hypothesis test and fast prototyping of trading algorithms in R.
Week 6. Midterm.
Week 7-8. System trading on fundamental information
FLDS and EQS mnemonics. Stock screens in R project. Types of fundamental strategies. Return attribution and profitability analysis of fundament strategies.
Week 9-10. Portfolio models for algorithmic trading. Portfolio representation in Bloomberg and R project. How portfolio of algorithms differs from portfolio of stocks. Money management for portfolio of algostrategies. Optimal F, portfolio optimization “a-la R.Vince”.
Week 11. Relative value strategies and statistical arbitrage.
Quantitative prerequisites of relative value strategies and and statarb. Cointegration. “Andrew Lo strategy” in R. Pairs trading in R and Bloomberg.
Week 12. Final exam.
- Rishi K. Narang. Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading
- Chan E. Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley Trading). // 2008. С. 1–204.
- Vince R. The Leverage Space Trading Model. // 2009. С. 224.
- С.В. Булашев. Статистика для трейдеров
- Sal L. Arnuk, Joseph C. Saluzzi. Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street are Destroying Investor Confidence and Your Portfolio
- David Aronson. Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals
- Emilio Tomasini, Urban Jaekle. Trading Systems: A New Approach to System Development and Portfolio Optimisation
Ernie Chan. Algorithmic Trading: Winning Strategies and Their Rationale