Quantitative strategies on High Frequency Data - course taught in the winter semester


Detailed assessment rules 2019/2020

In-sample data and presentation and report templates

Presentation resulting from the template: example 1, example 2, example 3, example 4

Document resulting from the report template


Lectures

Lecture 1. organizational, Introduction to quantitative trading


lecture 1


Lecture 2. High-frequency data definition, characteristics and sources


lecture 2


Lecture 3. Evolution of HFT


lecture 3


Lecture 4. Review of the statistical and econometric foundations of trading strategies


lecture 4


Lecture 5. Mean-reverting, momentum strategies and pair trading


lecture 5


Lecture 6. Building an automated strategy - study of entries and exits


lecture 6


Lecture 7. Calculating position and pnl


lecture 7


Lecture 8. Evaluating performance of high-frequency strategies


lecture 8


Lecture 9. Fishing for ideas and strategy backtesting


lecture 9


Lecture 10. Statistical arbitrage strategies


lecture 10


Lecture 11. Event arbitrage strategies


Lecture 12. Exam consultations

Lab sections

Lab 0. Introduction to Rmarkdown


materials 0

Introduction to R Markdown - resulting file


R Markdown from RStudio

R Markdown - Quick tour

R Markdown - Cheat Sheet

R Markdown - Reference guide

Getting Started with R Markdown

All chunk options (for advanced users)

Wikipedia on Markdown

Writing your thesis with R Markdown



Lab 1. Dealing with time series data


materials 1


xts: eXtensible Time Series. Using the xts package

xts Cheat Sheet: Time Series in R


Lab 2. Frequency conversion, data aggregation


materials 2


Lab 3. Correlation, regression (rolling analyses)


materials 3


Lab 4. Rolling analyses - efficiency matters


materials 4


Lab 5. Cointegration, Granger causality (rolling analyses)


materials 5


Lab 6. Constructing a strategy setup using different entry/exit techniques


materials 6


Lab 7. Calculating positions and gross/net pnl


materials 7


Lab 8. Evaluating performance of the strategy


materials 8


Lab 9. Applying simple strategies


materials 9


Lab 10. Applying more advanced (pair trading) strategies


Lab 11. Applying more advanced (pair trading) strategies - filtering


Lab 12. C++ in R - efficiency matters


Lab 13. Students' presentations


Links

Finance in R

R in Finance - annual conference

The Whole Street - lots of quant finance blogs

MoneyScience - Financial Intelligence Network - financial blogs

The R Trader blog

QuantStrat TradeR blog

High Probability Trading blog

Geektrader A Systematic Trader's Blog

A physicist in Wall Street blog. For trading beginners

Quantum Financier blog. On engineering alpha and algorithmic trading

Math Trading Blog

Adaptive Trader. A Simple Trader

Timely Portfolio Blog

Au.Tra.Sy blog – Automated Trading System Systematic Trading research and development, with a flavour of Trend Following

CSS Analytics Blog - new concepts in quantitative research

rbresearch Blog - Quantitative research, trading strategy ideas, and backtesting for the FX and equity markets

Gekko Quant Blog – Quantitative Trading, Statistical Arbitrage, Machine Learning and Binary Options

Dekalog Blog - talks about statistics for developing a trading strategy

Keplerian Finance Blog - exploring the boundaries of quantitative finance

Butler's Math Blog - Applying Mathematics and Physical Science Principles to Everyday Life

Timely Portfolio Blog - for those who want to do the math

R

The Comprehensive R Archive Network

R Studio

R-bloggers - R news and tutorials

RCPP by Examples by Dirk Eddelbuettel

Financial Data Accessible from R

Why R is hard to learn?

ProgrammingR - Beginner to advanced resources for the R programming language

Advanced R by Hadley Wickham

Statistics, R, Graphics and Fun