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Financial Trading in R

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс по выбору
Когда читается:
2-й курс, 2 модуль

Course Syllabus

Abstract

Курс проводится в формате смешанного обучения (blended learning). Онлайн-лекции читаются преподавателем ресурса DataCamp, профессиональным аналитиком Ильей Кипнисом (https://www.datacamp.com/courses/financial-trading-in-r). Проверку самостоятельной работы, проведение семинаров и экзамена осуществляет НИУ ВШЭ.
Learning Objectives

Learning Objectives

  • Обучение навыкам, необходимых для управляющих активами, в разработки торговых стратегий и их тестирования в пакете R.
  • Студенты приобретут опыт разработки собственных торговых стратегий, тестирования и измерения результативности на реальных исторических данных в пакете R.
Expected Learning Outcomes

Expected Learning Outcomes

  • Студенты должны знать классы индикаторов, параметры настройки индикаторов и правила согласования индикаторов.
  • Студенты должны уметь синтезировать системы индикаторов в торговые системы на основе неэффективностей ценообразования с учетом транзакционных издержек
  • Тестирование торговых стратегий на реальных данных с учетом транзакционных издержек должно обеспечивать устойчивый показатель альфы Дженсена
Course Contents

Course Contents

  • Trading strategies basics in R
    Students will learn the definition of trading, the philosophies of trading, and the pitfalls that exist in trading. This part covers both momentum and oscillation trading, along with some phrases to identify these types of philosophies. Students will learn about overfitting and how to avoid it, obtaining and plotting financial data, and using a well-known indicator in trading.
  • Trading signals and indicators
    Indicators are crucial for trading strategy. They are transformations of market data that allow a clearer understanding of its overall behavior, usually in exchange for lagging the market behavior. Here, students will be working with both trend types of indicators as well as oscillation indicators.
  • Executing a trade transaction
    Students will learn how to shape your trading transaction once you decide to execute on a signal. This theme will cover a basic primer on rules, and how to enter and exit positions. Students will also learn how to send inputs to order-sizing functions. By the end of this chapter, students will learn the gist of how rules function, and where you can continue learning about them.
  • Backtesting a trading strategy
    After a quantstrat strategy has been constructed, it's vital to know how to actually analyze the strategy's performance. This topic details just that. Students will learn how to read vital trade statistics, and view the performance of trading strategy over time. Students will also learn how to get a reward to risk ratio called the Sharpe ratio in two different ways.
Assessment Elements

Assessment Elements

  • non-blocking Самостоятельная работа
  • non-blocking Экзамен
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.3 * Самостоятельная работа + 0.7 * Экзамен
Bibliography

Bibliography

Recommended Core Bibliography

  • Harry Georgakopoulos. (2015). Quantitative Trading with R : Understanding Mathematical and Computational Tools From a Quant’s Perspective. Palgrave Macmillan.
  • Marcos Lopez de Prado. (2018). Advances in Financial Machine Learning. Wiley.

Recommended Additional Bibliography

  • Chris Conlan. (2016). Automated Trading with R : Quantitative Research and Platform Development. Apress.
  • D. Capocci. (2013). The Complete Guide to Hedge Funds and Hedge Fund Strategies. Palgrave Macmillan.
  • Gregoriou, G. N. (2015). Handbook of High Frequency Trading. Academic Press.
  • Irene Aldridge. (2013). High-Frequency Trading : A Practical Guide to Algorithmic Strategies and Trading Systems: Vol. 2nd edition. Wiley.
  • Rishi K. Narang. (2013). Inside the Black Box : A Simple Guide to Quantitative and High Frequency Trading: Vol. Second edition. Wiley.