Udemy - Algorithmic Trading & Time Series Analysis in Python and R

Technical Analysis (SMA and RSI), Time Series Analysis (ARIMA and GRACH), Machine Learning

What you'll learn
Understand technical indicators (MA, EMA or RSI)
Understand moving average models
Understand heteroskedastic models and volatility modeling Understand ARIMA and GARCH based trading strategies
Understand cointegration and pairs trading (statistical arbitrage)
Understand machine learning approaches in finance

Description
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