# Time Series Analysis & Algorithmic Trading in Python and R

Time Series Analysis & Algorithmic Trading 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

Requirements

• You should have an interest in quantitative finance and mathematics

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)?
• +++ TIME SERIES ANALYSIS +++

Section 6 - Time Series Fundamentals

• statistics basics (mean, variance and covariance)
• 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