Algorithmic Trading & Time Series Analysis in Python and R

This course is about the fundamental basics of algorithmic trading. First of all you will learn about stocks, bonds and the fundamental basic of stock market and the FOREX. The main reason of this course is to get a better understanding of mathematical models concerning algorithmic trading and finance in the main.
We will use Python and R as programming languages during the lectures
IMPORTANT: only take this course, if you are interested in statistics and mathematics !!!
Section 1 – Introduction
•why to use Python as a programming language?
•installing Python and PyCharm
•installing R and RStudio
Section 2 – Stock Market Basics
•types of analyses
•stocks and shares
•commodities and the FOREX
•what are short and long positions?
+++ TECHNICAL ANALYSIS ++++
Section 3 – Moving Average (MA) Indicator
•simple moving average (SMA) indicators
•exponential moving average (EMA) indicators
•the moving average crossover trading strategy
Section 4 – Relative Strength Index (RSI)
•what is the relative strength index (RSI)?
•arithmetic returns and logarithmic returns
•combined moving average and RSI trading strategy
•Sharpe ratio
Section 5 – Stochastic Momentum Indicator
•what is stochastic momentum indicator?
•what is average true range (ATR)?
•portfolio optimization trading strategy
+++ TIME SERIES ANALYSIS +++
Section 6 – Time Series Fundamentals
•statistics basics (mean, variance and covariance)
•downloading data from Yahoo Finance
•stationarity
•autocorrelation (serial correlation) and correlogram
Section 7 – Random Walk Model
•white noise and Gaussian white noise
•modelling assets with random walk
Section 8 – Autoregressive (AR) Model
•what is the autoregressive model?
•how to select best model orders?
•Akaike information criterion
Section 9 – Moving Average (MA) Model
•moving average model
•modelling assets with moving average model
Section 10 – Autoregressive Moving Average Model (ARMA)
•what is the ARMA and ARIMA models?
•Ljung-Box test
•integrated part – I(0) and I(1) processes
Section 11 – Heteroskedastic Processes
•how to model volatility in finance
•autoregressive heteroskedastic (ARCH) models
•generalized autoregressive heteroskedastic (GARCH) models
Section 12 – ARIMA and GARCH Trading Strategy
•how to combine ARIMA and GARCH model
•modelling mean and variance
+++ MARKET-NEUTRAL TRADING STRATEGIES +++
Section 13 – Market-Neutral Strategies
•types of risks (specific and market risk)
•hedging the market risk (Black-Scholes model and pairs trading)
Section 14 – Mean Reversion
•Ornstein-Uhlenbeck stochastic processes
•what is cointegration?
•pairs trading strategy implementation
•Bollinger bands and cross-sectional mean reversion
+++ MACHINE LEARNING +++
Section 15 – Logistic Regression
•what is linear regression
•when to prefer logistic regression
•logistic regression trading strategy
Section 16 – Support Vector Machines (SVMs)
•what are support vector machines?
•support vector machine trading strategy
•parameter optimization
APPENDIX – R CRASH COURSE
•basics – variables, strings, loops and logical operators
•functions
APPENDIX – PYTHON CRASH COURSE
•basics – variables, strings, loops and logical operators
•functions
•data structures in Python (lists, arrays, tuples and dictionaries)
•object oriented programming (OOP)
•NumPy
Thanks for joining my course, let’s get started!