Advanced Algorithmic Trading Workshop
Nick Firoozye
Это 12-часовой курс самостоятельного обучения (22 лекции), охватывающий весь цикл продвинутой алгоритмической торговли - стратегии, включающие как премию за риск, так и продвинутые стратегии, включая методологию исследований и разработок, а также подробные сведения, включая источники данных, базы данных, структуру классов от сигналов к стратегиям, к портфелям, к оптимизаторам и бэк-тестерам.
В данном курсе будут рассмотрены примеры стратегий по фьючерсам, по акциям, фиксированной прибыли и валютам.
После этого курса вы поймете весь процесс алгоритмической торговли, начиная от источников данных до создания торговых решений:
- Научитесь понимать источники стратегий премии за риск (risk premia strategies), например, доходность CTA и банковской стратегии количественного инвестирования.
- Соберете необходимые компоненты в систему количественных инвестиций из источников данных / API, баз данных, бэк-тестеров, оптимизаторов портфеля и инструментов управления рисками.
- Научитесь понимать более общие количественные стратегии, источники риска и основные торговые стратегии в каждом инвестиционном классе.
- Соберете подходящую структуру классов для алгоритмической торговли и оптимизации портфеля.
- Поймете место для методов и моделей машинного обучения, как их использовать и какие шаги предпринять, чтобы избежать переобучения.
- Узнаете, как проводить исследования в области алготрейдинга - шаги, которые необходимо предпринять для постоянного исследования и разработки новых стратегий и альфа-источников.
- Узнаете, как структурировать ваш код, как создать соответствующие классы, подклассы / мета-классы и т.д., чтобы уменьшить вероятность спагетти-кода и помочь вам организовать как базу кода, так и свои мысли о нем.
Доктор Ник Фирозью (Nick Firoozye) - ученый и статистик с более чем 20-летним опытом работы в финансовой индустрии, как в компаниях, занимающихся покупкой, так и продажей (buy and sell-side firms), в основном в сфере исследований. Он начал свою карьеру в Lehman Brothers, занимаясь моделированием MBS/ABS, возглавляя команды по стратегии портфеля и исследованиям EM Quant, а затем занимал различные руководящие должности в Goldman Sachs и DeutscheBank, а также в управляющих активами Sanford Bernstein и Citadel.
В настоящее время он является управляющим директором и главой Global Derivative Strategy, входящей в группу количественных стратегий, в Nomura, а также является почетным старшим преподавателем по компьютерным наукам в University College London (UCL), специализируясь на Robust Machine Learning в области финансов.
Доктор Ник Фирозью - соавтор книги "Управление неопределенностью, снижение риска - Борьба с неизвестным в оценке финансовых рисков и принятии решений" ("Managing Uncertainty, Mitigating Risk - Tackling the Unknown in Financial Risk Assessment and Decision Making") о роли неопределенности и неточной вероятности в финансах в свете многих недавних финансовых кризисов.
Сейчас Доктор Ник Фирозью пишет книгу об алгоритмических торговых стратегиях, основанных на его научной работе по той же теме в UCL, и на данном онлайн-курсе в Experfy.
Курс на английском.
Продолжительность: 22 лекции, 12 часов.
Course Description
12-hour self-paced course (22 lectures) covering the entire pipeline of advanced algorithmic trading - strategies including both risk premia and advanced strategies, including research and development methodology, and the gritty details including data sources, databases, class structure from signals to strategies to portfolios to optimizers to back-testers.
Numerous examples of strategies in futures will be gone through in detail, while strategies in equities, fixed income, and FX will be described and referenced.
After this course you will understand the entire process from data sources to trading decisions:
Dr. Nick Firoozye is a data scientist & statistician with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in research. He started his career in Lehman Brothers doing MBS/ABS modeling, heading teams in portfolio strategy and EM quant research, later taking a variety of senior roles at Goldman Sachs, and DeutscheBank, and at the asset managers, Sanford Bernstein, and Citadel. He is currently Managing Director and Head of Global Derivative Strategy, part of the Quantitative Strategy Group, at Nomura. He is currently an Honorary Senior Lecturer in Computer Science at University College London, focusing on Robust Machine Learning in finance. He recently co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty and imprecise probability in finance, in light of the many recent financial crises, and he is writing a book on Algorithmic Trading Strategies based on his recent Ph.D. course on the same topic offered at UCL and current online course at Experfy.
Curriculum
Module 1: Overview
Lecture 1 Introduction
Lecture 2 Risk Premia Investing (Time-Series)
Lecture 3
Module 2: Signals and Strategies
Lecture 4 All New Class Structure
Lecture 5 Momentum Factoids
Lecture 6 Trend following Practicalities
Lecture 7 Two Major Variants (Time-Series and Cross-Sectional Momentum)
Lecture 8 Rationale-Liquidity Provision and (Rational) Overraction
Lecture 9 Carry (Asset Expected Returns)
Module 3: Portfolio Strategies and Portfolio Optimization
Lecture 10 Value Measures (In Equity and In Self measures)
Lecture 11 Brownian Motion and ARMA Processes
Lecture 12 Models and Estimation
Lecture 13 Criteria and Model Selection
Module 4: Overfitting & Robustness
Lecture 14 Rule of Thumb vs More Exact Methods
We will discuss the prevalance of overfitting in both the Sciences and in Finance. Rules of thumb as a means of remaining parsimonious are altogther a reasonable approach to this area. We put these in context.
Lecture 15 Multiple Hypothesis Testing Methods
i) Adjusted SRs ii) Reality Test and similar bootstrap methods
Lecture 16 Backtesting
Lecture 17 Practices
Module 5: Implementation
Lecture 18 Python setups – Venvs & Packages
Lecture 19 Databases - SQL and No SQL
i) Creating Tradable Histories ii) Structuring the Project : Instruments, Signals, Strategies, Portfolios and Optimization methods ii) Signals, Transformations, Scalings. Signal Combination - Strategies iii) Portfolio Optimization iv) Performance Measures and Reporting v) Risk & Attribution
Lecture 20 Quant Analytics - Libraries
Lecture 21 Sources - Keeping up with new developments
Lecture 22 Conclusion
12-hour self-paced course (22 lectures) covering the entire pipeline of advanced algorithmic trading - strategies including both risk premia and advanced strategies, including research and development methodology, and the gritty details including data sources, databases, class structure from signals to strategies to portfolios to optimizers to back-testers.
Numerous examples of strategies in futures will be gone through in detail, while strategies in equities, fixed income, and FX will be described and referenced.
After this course you will understand the entire process from data sources to trading decisions:
- Understand sources of risk premia strategies, i.e., CTA and bank quant investment strategy returns.
- Assemble the necessary components from the data sources/APIs, to databases to back-testers to portfolio optimizers and risk management tools into a quant investment system.
- Understand more general quant strategies, the sources of risk, and basic trading strategies in each investment class.
- Assemble appropriate class structure for algorithmic trading optimization and portfolio construction.
- Understand the place for ML methods and models, how to use them and steps to take to avoid over-fitting.
- Know how to do research into algo trading - the steps to take to continually research and develop new strategies and alpha sources.
- Know how to structure your code, create appropriate classes, sub/meta-classes, etc, to reduce the chance of spaghetti-code, help you organize the both the code-base and your thoughts on it.
Dr. Nick Firoozye is a data scientist & statistician with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in research. He started his career in Lehman Brothers doing MBS/ABS modeling, heading teams in portfolio strategy and EM quant research, later taking a variety of senior roles at Goldman Sachs, and DeutscheBank, and at the asset managers, Sanford Bernstein, and Citadel. He is currently Managing Director and Head of Global Derivative Strategy, part of the Quantitative Strategy Group, at Nomura. He is currently an Honorary Senior Lecturer in Computer Science at University College London, focusing on Robust Machine Learning in finance. He recently co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty and imprecise probability in finance, in light of the many recent financial crises, and he is writing a book on Algorithmic Trading Strategies based on his recent Ph.D. course on the same topic offered at UCL and current online course at Experfy.
Curriculum
Module 1: Overview
Lecture 1 Introduction
Lecture 2 Risk Premia Investing (Time-Series)
Lecture 3
Module 2: Signals and Strategies
Lecture 4 All New Class Structure
Lecture 5 Momentum Factoids
Lecture 6 Trend following Practicalities
Lecture 7 Two Major Variants (Time-Series and Cross-Sectional Momentum)
Lecture 8 Rationale-Liquidity Provision and (Rational) Overraction
Lecture 9 Carry (Asset Expected Returns)
Module 3: Portfolio Strategies and Portfolio Optimization
Lecture 10 Value Measures (In Equity and In Self measures)
Lecture 11 Brownian Motion and ARMA Processes
Lecture 12 Models and Estimation
Lecture 13 Criteria and Model Selection
Module 4: Overfitting & Robustness
Lecture 14 Rule of Thumb vs More Exact Methods
We will discuss the prevalance of overfitting in both the Sciences and in Finance. Rules of thumb as a means of remaining parsimonious are altogther a reasonable approach to this area. We put these in context.
Lecture 15 Multiple Hypothesis Testing Methods
i) Adjusted SRs ii) Reality Test and similar bootstrap methods
Lecture 16 Backtesting
Lecture 17 Practices
Module 5: Implementation
Lecture 18 Python setups – Venvs & Packages
Lecture 19 Databases - SQL and No SQL
i) Creating Tradable Histories ii) Structuring the Project : Instruments, Signals, Strategies, Portfolios and Optimization methods ii) Signals, Transformations, Scalings. Signal Combination - Strategies iii) Portfolio Optimization iv) Performance Measures and Reporting v) Risk & Attribution
Lecture 20 Quant Analytics - Libraries
Lecture 21 Sources - Keeping up with new developments
Lecture 22 Conclusion