Algorithmic Trading & Time Series Analysis in Python and R

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)?

portfolio optimization trading strategy

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

+++ MARKET-NEUTRAL TRADING STRATEGIES +++

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?

pairs trading strategy implementation

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!


https://rapidgator.net/file/9b8b2e34442edfbf6f91d8f3c6b70b99/Algorithmic_Trading_&_Time_Series_Analysis_in_Python_and_R.part1.rar.html
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