MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (1h 52m) | Size: 2 GB
Technical Analysis (SMA and RSI), Time Series Analysis (ARIMA and GRACH), Machine Learning and Mean-Reversion Strategies

What you’ll learn:
Understand technical indicators (MA, EMA or RSI)
Understand autoregressive models
Understand market-neutral strategies and how to reduce market risk
Understand machine learning approaches in finance
How to Perform a Multiple Time Frame Analysis
How to Trade Support and Resistance
How to Trade Fibonacci and Fibonacci Extension
How to Use Technical Overlays For Day Trading
Requirements
Passion and Enthusiasm for Learning
Strong desire of Getting Rich and Retiring Early
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)?
Section 6 – Autoregressive Moving Average Model (ARMA)
what is the ARMA and ARIMA models?
Ljung-Box test
integrated part – I(0) and I(1) processes
Section 7 – Heteroskedastic Processes
how to model volatility in finance
autoregressive heteroskedastic (ARCH) models
generalized autoregressive heteroskedastic (GARCH) models
Section 8 – ARIMA and GARCH Trading Strategy
how to combine ARIMA and GARCH model
modelling mean and variance
Section 9 – Market-Neutral Strategies
types of risks (specific and market risk)
hedging the market risk (Black-Scholes model and pairs trading)
Section 10 – Mean Reversion
Ornstein-Uhlenbeck stochastic processes
what is cointegration?
Bollinger bands and cross-sectional mean reversion
Who this course is for
If you want to Create a New Source of Passive Income, you’ve come to the right place!
If you want to find a Trading Strategy that Actually Works, you should not ignore this course!
If you are serious about Making Money Online by investing in the Stock Market, this course is for you!
Homepage

`https://www.udemy.com/course/algorithmic-trading-time-series-analysis-in-python-and-r/Passion%20and%20Enthusiasm%20for%20Learning`

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