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Finance & Accounting
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Quantitative Finance
Algorithmic Trading & Time Series Analysis in Python and R

Algorithmic Trading & Time Series Analysis in Python and R

Loại khoá học: Investing & Trading

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

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50.000 VND
90 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

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!

Bạn sẽ học được gì

Understand technical indicators (MA, EMA or RSI)

Understand random walk models

Understand autoregressive models

Understand moving average models

Understand heteroskedastic models and volatility modeling

Understand ARIMA and GARCH based trading strategies

Understand market-neutral strategies and how to reduce market risk

Understand cointegration and pairs trading (statistical arbitrage)

Understand machine learning approaches in finance

Yêu cầu

  • You should have an interest in quantitative finance and mathematics

Nội dung khoá học

43 sections

Introduction

2 lectures
Introduction
03:02
Why to use Python?
03:28

Environment Setup

3 lectures
Installing Python
01:53
Installing PyCharm
03:38
Installing R and RStudio
03:07

Stock Market Basics

6 lectures
Types of analyses
04:50
Stocks and shares
08:15
Commodities
05:51
Currencies and the FOREX
09:02
Short and long positions
06:41
Stock Markets Basics Quiz
4 questions

### USING TECHNICAL INDICATORS ###

1 lectures
Using technical indicators
00:08

Moving Average Indicator

7 lectures
What is the simple moving average (SMA) indicator?
07:16
Downloading data from Yahoo Finance
05:25
Support and resistance levels
03:06
Simple moving averages (SMA) implementation
10:16
Exponential weighting
02:43
Exponential moving average (EMA) implementation
02:57
Moving Averages Quiz
2 questions

Moving Average Crossover Strategy

5 lectures
Moving average crossover strategy I
02:35
Moving average crossover strategy II
05:52
Moving average crossover strategy III
04:20
Moving average crossover strategy IV
08:36
Moving average crossover strategy V
04:46

Relative Strength Indicator (RSI)

4 lectures
What is the relative strength indicator (RSI)?
05:27
Calculating the RSI values
10:48
Returns and logarithmic returns
05:25
RSI Quiz
2 questions

Relative Strength Indicator (RSI) Strategy

5 lectures
RSI trading strategy I
04:30
RSI trading strategy II
03:47
RSI trading strategy III
04:25
What is Sharpe ratio?
02:42
Calculating Sharpe ratio of a trading strategy
03:18

Backtrader Framework

6 lectures
What is backtrader?
04:28
Backtrader basics - handling data
04:17
Backtrader basics - using strategies
08:48
Backtrader basics - using indicators
08:48
Backtrader basics - results
05:35
Backtrader basics - broker info and commissions
03:12

Momentum & SMA Combined Trading Strategy

4 lectures
What strategy will we implement?
05:12
Average true range (ATR) indicator and position sizing
04:45
Average true range (ATR) indicator implementation
11:43
ATR Indicator Quiz
2 questions

Momentum & SMA Combined Trading Strategy Implementation

7 lectures
Momentum trading strategy implementation I
11:54
Momentum trading strategy implementation II
06:10
Momentum trading strategy implementation III
08:22
Momentum trading strategy implementation IV
05:53
Momentum trading strategy implementation V
12:59
Momentum trading strategy implementation VI
03:37
Momentum trading strategy implementation VII
11:11

### TIME SERIES ANALYSIS ###

1 lectures
Time series analysis
00:12

Time Series Analysis Fundamentals

8 lectures
What are mean, variance and correlation?
07:27
Downloading the data from Yahoo Finance
06:07
Calculating useful statistics
07:07
Stationarity
06:16
What is serial correlation (autocorrelation)?
11:05
Correlogram
04:21
Understanding the correlogram
06:17
Time Series Basics Quiz
5 questions

Random Walk Model

6 lectures
White noise introduction
08:56
White noise process example
03:47
What is random walk?
08:52
Random walk example
06:56
Modeling assets with random walk
06:01
Random Walk Quiz
3 questions

Autoregressive Model (AR)

5 lectures
Autoregressive model introduction
07:35
How to select the best model? (AIC and BIC)
07:47
Autoregressive model example
07:58
Modeling assets with autoregressive model
10:09
Autoregressive Model
2 questions

Moving Average Model (MA)

4 lectures
Moving average model introduction
06:25
Moving average model example
08:00
Modeling assets with moving average model
07:42
Moving Average Model Quiz
2 questions

Autoregressive Moving Average Model (ARMA)

6 lectures
Autoregressive moving average model introduction
03:41
What is the Ljung-Box test?
05:15
Autoregressive moving average model example
06:18
Autoregressive moving average model example II
10:06
Modeling assets with ARMA model
08:42
ARMA Model Quiz
2 questions

Autoregressive Integrated Moving Average Model (ARIMA)

4 lectures
ARIMA model introduction
05:06
ARIMA model example
04:28
Modeling assets with ARIMA model
05:08
ARIMA Model Quiz
1 question

Autoregressive Conditional Heteroskedastic Model (ARCH)

2 lectures
Heteroskedasticity in finance
06:12
ARCH model introduction
08:24

Generalized Autoregressive Heteroskedastic Model (GARCH)

4 lectures
GARCH model introduction
02:30
GARCH model example
06:51
Modeling assets with GARCH model
05:27
Heteroskedastic Model Quiz
3 questions

FOREX Trading Strategy Implementation

6 lectures
FOREX trading strategy implementation I
02:29
FOREX trading strategy implementation II
04:48
FOREX trading strategy implementation III
05:46
FOREX trading strategy implementation IV
06:23
FOREX trading strategy implementation V
03:54
FOREX trading strategy implementation VI
02:53

Stock Market Trading Strategy Implementation

2 lectures
Stock market trading strategy implementation I
01:28
Stock market trading strategy implementation II
03:09

### MARKET NEUTRAL TRADING STRATEGIES ###

3 lectures
Two types of risk and CAPM
04:49
Hedging the market risk
05:02
Market Neutral Strategies Quiz
3 questions

Mean Reversion

5 lectures
Ornstein-Uhlenbeck stochastic processes
05:23
Simulating Ornstein-Uhlenbeck processes
05:51
What is cointegration?
05:58
Testing cointegration
12:56
Mean Reversion Quiz
3 questions

Bollinger Bands

3 lectures
Bollinger bands introduction
05:32
Bollinger bands visualization
08:21
Bollinger Bands Quiz
2 questions

Bollinger Bands Trading Strategy Implementation

3 lectures
Bollinger bands trading strategy implementation I
04:02
Bollinger bands trading strategy implementation II
09:12
Bollinger bands trading strategy implementation III
02:56

Cross-Sectional Mean Reversion

1 lectures
What is the cross-sectional mean reversion strategy?
06:24

Cross-Sectional Mean Reversion Trading Strategy Implementation

2 lectures
Cross-sectional mean reversion implementation I
08:04
Cross-sectional mean reversion implementation II
05:04

### MACHINE LEARNING TRADING ALGORITHMS ###

1 lectures
Machine learning approaches
05:17

Logistic Regression

4 lectures
What is linear regression?
08:18
Optimization techniques
06:51
Logistic regression introduction
11:54
Maximum likelihood optimization
04:58

Logistic Regression Trading Strategy Implementation

2 lectures
Logistic regression strategy implementation I
11:15
Logistic regression strategy implementation II
07:18

Support Vector Machines (SVMs)

4 lectures
What are Support Vector Machines (SVMs)?
05:19
Linearly separable problems
14:10
Non-linearly separable problems
06:32
Kernel functions
09:49

Support Vector Classifier Trading Strategy Implementation

2 lectures
Support vector machines strategy implementation I
07:31
Support vector machines strategy implementation II
04:59

Machine Learning Algorithms and Indicators

3 lectures
SVM with SMA and RSI trading strategy I
05:09
SVM with SMA and RSI trading strategy II
06:12
SVM with SMA and RSI trading strategy III
01:44

### PYTHON PROGRAMMING CRASH COURSE ###

1 lectures
Python crash course introduction
02:06

Appendix #1 - Python Basics

21 lectures
First steps in Python
05:49
What are the basic data types?
04:45
Booleans
02:08
Strings
07:44
String slicing
06:47
Type casting
04:20
Operators
05:23
Conditional statements
04:41
How to use multiple conditions?
08:07
Logical operators
04:04
Loops - for loop
06:00
Loops - while loop
04:13
Exercise: calculating the average
00:08
Solution: calculating the average
00:06
What are nested loops?
02:55
Enumerate
03:51
Break and continue
05:32
Calculating Fibonacci-numbers
02:30
Exercise: Fibonacci-numbers
00:07
Solution: Fibonacci-numbers
00:20
Python Basics Quiz
5 questions

Appendix #2 - Functions

16 lectures
What are functions?
04:07
Defining functions
05:24
Positional arguments and keyword arguments
10:30
Returning values
02:26
Returning multiple values
03:14
Exercise: functions
00:09
Solution: functions
00:06
Yield operator
05:02
Local and global variables
02:12
What are the most relevant built-in functions?
04:26
What is recursion?
09:29
Exercise: recursion
00:10
Solution: recursion
00:14
Local vs global variables
04:16
The __main__ function
03:25
Functions Quiz
4 questions

Appendix #3 - Data Structures in Python

22 lectures
How to measure the running time of algorithms?
10:00
Data structures introduction
03:17
What are array data structures I
06:55
What are array data structures II
06:56
Lists in Python
05:43
Lists in Python - advanced operations
08:27
Lists in Python - list comprehension
05:56
(!!!) Python lists and arrays
00:22
Exercise: list comprehension
00:37
Solution: list comprehension
00:19
Measuring running time of lists
00:45
What are tuples?
03:58
Mutability and immutability
04:30
What are linked list data structures?
08:13
Doubly linked list implementation in Python
05:32
Hashing and O(1) running time complexity
08:03
Dictionaries in Python
09:41
Sets in Python
09:49
Exercise: constructing dictionaries
00:14
Solution: constructing dictionaries
00:09
Sorting
10:44
Data Structures Quiz
5 questions

Appendix #4 - Object Oriented Programming (OOP)

18 lectures
What is object oriented programming (OOP)?
02:18
Class and objects basics
03:00
Using the constructor
06:00
Class variables and instance variables
04:46
Exercise: constructing classes
00:11
Solution: constructing classes
00:08
Private variables and name mangling
04:31
What is inheritance in OOP?
03:49
The super keyword
04:24
Function (method) override
02:34
What is polymorphism?
04:25
Polymorphism and abstraction example
06:10
Exercise: abstraction
00:16
Solution: abstraction
00:12
Modules
06:00
The __str__ function
03:16
Comparing objects - overriding functions
08:00
Object Oriented Programming (OOP) Quiz
4 questions

Appendix #5 - NumPy

12 lectures
What is the key advantage of NumPy?
04:12
Creating and updating arrays
07:36
Dimension of arrays
09:12
Indexes and slicing
07:59
Types
04:43
Reshape
07:53
Exercise: reshape problem
00:12
Solution: reshape problem
00:05
Stacking and merging arrays
06:17
Filter
03:39
Running time comparison: arrays and lists
00:44
NumPy Quiz
4 questions

### R PROGRAMMING CRASH COURSE ###

1 lectures
R programming fundamentals
00:06

Appendix #6 - R Fundamentals

13 lectures
First steps
04:49
Vectors
06:22
Factors
03:16
Arrays
03:46
Matrixes
03:51
List
02:15
Data frames
03:37
Coercion
02:05
Packages
02:57
If - else
02:27
Loops - repeat, while and for
04:20
Custom functions
03:34
Custom operators
01:50

Course Materials (DOWNLOADS)

1 lectures
Course materials
00:02

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