Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.13 GB | Duration: 1h 6m
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
Screenshots
Homepage
https://www.udemy.com/course/algorithmic-trading-time-series-analysis-in-python-and-r-z/
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