Algorithmic Trading & Time Series Analysis in Python and R

  • Overview
  • Curriculum
  • Instructor
  • Review

About This Course

Technical Analysis (SMA and RSI), Time Series Analysis (ARIMA and GARCH), Machine Learning and Mean-Reversion Strategies

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!

  • Understand technical indicators (MA, EMA or RSI)

  • Understand random walk models

  • Understand autoregressive models

Course Curriculum

2 Lectures

1 Lectures

1 Lectures

2 Lectures

1 Lectures

2 Lectures

1 Lectures

1 Lectures

1 Lectures

1 Lectures

Instructor

Profile photo of Holczer Balazs
Holczer Balazs

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods...

Review
4.9 course rating
4K ratings
ui-avatar of Fernando Cedeno Poveda
Fernando C. P.
5.0
7 months ago

Excelente

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ui-avatar of Mayank Manka
Mayank M.
5.0
7 months ago

Professor is explaining concepts in depth and codes also written well understandable format step by step helping me to understand algo very well. therefore i liked the course material. Thanks for giving such wonderful experience of algo trading.

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ui-avatar of Stsee yeah
Stsee Y.
5.0
8 months ago

very quick and direct explaination. The coding excercises are also well explained and well connected to the lectures.

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ui-avatar of Manjinder
Manjinder
1.0
11 months ago

too many code issue. He should spend less time on explaining and more time on code functional correctness because there are too many people who would rely on the codes. Also, what a wrong data with momentum back testing. The inflated the returns by 300% of what actual would come had he corrected the code.

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ui-avatar of José Salvador Cortés García
José S. C. G.
4.0
11 months ago

Regular, va muy rapido para mí, debo dedicar mas tiempo.

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ui-avatar of Nelson Chávez Alvear
Nelson C. A.
4.5
1 year ago

Cursos de muy alto nivel... excelentes
Buenas explicaciones... muy claras

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ui-avatar of Benjamin A Dean
Benjamin A. D.
4.5
1 year ago

I've only gone through the introduction and the python install, but so far so good.

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ui-avatar of Wen Kuo Lin
Wen K. L.
4.5
1 year ago

This course is good for people who what to quickly gain the knowledge in time series analysis and algorithmic trading. It covers many aspects in technical indicators (SMA, RSI), time series models (ARIMA, GARCH), machine learning strategies, and statistical arbitrage. After the course, I have a better understanding about implementing various time series models, technical analysis, and machine learning approaches in finance. Even though, the trading strategies discussed in the course might need to be refined and tested to be useful, I really enjoyed taking this course.

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ui-avatar of Salvador Tallabs
Salvador T.
5.0
1 year ago

Muy bien explicado y fácil de entender

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ui-avatar of József Vass
József V.
5.0
1 year ago

Excellent coverage of trading concepts that will inspire you to get started.

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