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
Section 2 - Stock Market Basics
+++ 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
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
autocorrelation (serial correlation) and correlogram
Section 7 - Random Walk Model
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
Section 10 - Autoregressive Moving Average Model (ARMA)
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
+++ MARKET-NEUTRAL TRADING STRATEGIES +++
Section 13 - Market-Neutral Strategies
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)
APPENDIX - R CRASH COURSE
APPENDIX - PYTHON CRASH COURSE
basics - variables, strings, loops and logical operators
data structures in Python (lists, arrays, tuples and dictionaries)
object oriented programming (OOP)
Thanks for joining my course, let's get started!