Quantitative Finance Algorithmic Trading Ii Time Series
Technical Analysis (SMA and RSI), Time Series Analysis (ARIMA and GRACH), Machine Learning and Mean-Reversion Strategies
Last updated 2022-01-10 | 4.6
- Understand technical indicators (MA- EMA or RSI)
- Understand random walk models
- Understand autoregressive models
What you'll learn
* Requirements
* You should have an interest in quantitative finance and mathematicsDescription
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!
Who this course is for:
- Anyone who wants to learn the basics of algorithmic trading
Course content
43 sections • 236 lectures
Introduction Preview 03:02
Why to use Python? Preview 03:28
Installing PyCharm and Python on Windows Preview 00:20
Installing PyCharm and Python on Mac Preview 00:20
Installing R and RStudio Preview 03:07
Types of analyses Preview 04:50
Stocks and shares Preview 08:15
Commodities Preview 05:51
Currencies and the FOREX Preview 09:02
Short and long positions Preview 06:41
Stock Markets Basics Quiz
Using technical indicators Preview 00:08
What is the simple moving average (SMA) indicator? Preview 07:16
Downloading data from Yahoo Finance Preview 05:25
Support and resistance levels Preview 03:06
Simple moving averages (SMA) implementation Preview 10:16
Exponential weighting Preview 02:43
Exponential moving average (EMA) implementation Preview 02:57
Moving Averages Quiz
Moving average crossover strategy I Preview 02:35
Moving average crossover strategy II Preview 05:52
Moving average crossover strategy III Preview 04:20
Moving average crossover strategy IV Preview 08:36
Moving average crossover strategy V Preview 04:46
What is the relative strength indicator (RSI)? Preview 05:27
Calculating the RSI values Preview 10:48
Returns and logarithmic returns Preview 05:25
RSI Quiz
RSI trading strategy I Preview 04:30
RSI trading strategy II Preview 03:47
RSI trading strategy III Preview 04:25
What is Sharpe ratio? Preview 02:42
Calculating Sharpe ratio of a trading strategy Preview 03:18
What is backtrader? Preview 04:28
Backtrader basics - handling data Preview 04:17
Backtrader basics - using strategies Preview 08:48
Backtrader basics - using indicators Preview 08:48
Backtrader basics - results Preview 05:35
Backtrader basics - broker info and commissions Preview 03:12
What strategy will we implement? Preview 05:12
Average true range (ATR) indicator and position sizing Preview 04:45
Average true range (ATR) indicator implementation Preview 11:43
ATR Indicator Quiz
Momentum trading strategy implementation I Preview 11:54
Momentum trading strategy implementation II Preview 06:10
Momentum trading strategy implementation III Preview 08:22
Momentum trading strategy implementation IV Preview 05:53
Momentum trading strategy implementation V Preview 12:59
Momentum trading strategy implementation VI Preview 03:37
Momentum trading strategy implementation VII Preview 11:11
Time series analysis Preview 00:12
What are mean, variance and correlation? Preview 07:27
Downloading the data from Yahoo Finance Preview 06:07
Calculating useful statistics Preview 07:07
Stationarity Preview 06:16
What is serial correlation (autocorrelation)? Preview 11:05
Correlogram Preview 04:21
Understanding the correlogram Preview 06:17
Time Series Basics Quiz
White noise introduction Preview 08:56
White noise process example Preview 03:47
What is random walk? Preview 08:52
Random walk example Preview 06:56
Modeling assets with random walk Preview 06:01
Random Walk Quiz
Autoregressive model introduction Preview 07:35
How to select the best model? (AIC and BIC) Preview 07:47
Autoregressive model example Preview 07:58
Modeling assets with autoregressive model Preview 10:09
Autoregressive Model
Moving average model introduction Preview 06:25
Moving average model example Preview 08:00
Modeling assets with moving average model Preview 07:42
Moving Average Model Quiz
Autoregressive moving average model introduction Preview 03:41
What is the Ljung-Box test? Preview 05:15
Autoregressive moving average model example Preview 06:18
Autoregressive moving average model example II Preview 10:06
Modeling assets with ARMA model Preview 08:42
ARMA Model Quiz
ARIMA model introduction Preview 05:06
ARIMA model example Preview 04:28
Modeling assets with ARIMA model Preview 05:08
ARIMA Model Quiz
Heteroskedasticity in finance Preview 06:12
ARCH model introduction Preview 08:24
GARCH model introduction Preview 02:30
GARCH model example Preview 06:51
Modeling assets with GARCH model Preview 05:27
Heteroskedastic Model Quiz
FOREX trading strategy implementation I Preview 02:29
FOREX trading strategy implementation II Preview 04:48
FOREX trading strategy implementation III Preview 05:46
FOREX trading strategy implementation IV Preview 06:23
FOREX trading strategy implementation V Preview 03:54
FOREX trading strategy implementation VI Preview 02:53
Stock market trading strategy implementation I Preview 01:28
Stock market trading strategy implementation II Preview 03:09
Two types of risk and CAPM Preview 04:49
Hedging the market risk Preview 05:02
Market Neutral Strategies Quiz
Ornstein-Uhlenbeck stochastic processes Preview 05:23
Simulating Ornstein-Uhlenbeck processes Preview 05:51
What is cointegration? Preview 05:58
Testing cointegration Preview 12:56
Mean Reversion Quiz
Bollinger bands introduction Preview 05:32
Bollinger bands visualization Preview 08:21
Bollinger Bands Quiz
Bollinger bands trading strategy implementation I Preview 04:02
Bollinger bands trading strategy implementation II Preview 09:12
Bollinger bands trading strategy implementation III Preview 02:56
What is the cross-sectional mean reversion strategy? Preview 06:24
Cross-sectional mean reversion implementation I Preview 08:04
Cross-sectional mean reversion implementation II Preview 05:04
Machine learning approaches Preview 05:17
What is linear regression? Preview 08:18
Optimization techniques Preview 06:51
Logistic regression introduction Preview 11:54
Maximum likelihood optimization Preview 04:58
Logistic regression strategy implementation I Preview 11:15
Logistic regression strategy implementation II Preview 07:18
What are Support Vector Machines (SVMs)? Preview 05:19
Linearly separable problems Preview 14:10
Non-linearly separable problems Preview 06:32
Kernel functions Preview 09:49
Support vector machines strategy implementation I Preview 07:31
Support vector machines strategy implementation II Preview 04:59
SVM with SMA and RSI trading strategy I Preview 05:09
SVM with SMA and RSI trading strategy II Preview 06:12
SVM with SMA and RSI trading strategy III Preview 01:44
Python crash course introduction Preview 02:06
First steps in Python Preview 05:49
What are the basic data types? Preview 04:45
Booleans Preview 02:08
Strings Preview 07:44
String slicing Preview 06:47
Type casting Preview 04:20
Operators Preview 05:23
Conditional statements Preview 04:41
How to use multiple conditions? Preview 08:07
Logical operators Preview 04:04
Loops - for loop Preview 06:00
Loops - while loop Preview 04:13
Exercise: calculating the average Preview 00:08
Solution: calculating the average Preview 00:06
What are nested loops? Preview 02:55
Enumerate Preview 03:51
Break and continue Preview 05:32
Calculating Fibonacci-numbers Preview 02:30
Exercise: Fibonacci-numbers Preview 00:07
Solution: Fibonacci-numbers Preview 00:20
Python Basics Quiz
What are functions? Preview 04:07
Defining functions Preview 05:24
Positional arguments and keyword arguments Preview 10:30
Returning values Preview 02:26
Returning multiple values Preview 03:14
Exercise: functions Preview 00:09
Solution: functions Preview 00:06
Yield operator Preview 05:02
Local and global variables Preview 02:12
What are the most relevant built-in functions? Preview 04:26
What is recursion? Preview 09:29
Exercise: recursion Preview 00:10
Solution: recursion Preview 00:14
Local vs global variables Preview 04:16
The __main__ function Preview 03:25
Functions Quiz
How to measure the running time of algorithms? Preview 10:00
Data structures introduction Preview 03:17
What are array data structures I Preview 06:55
What are array data structures II Preview 06:56
Lists in Python Preview 05:43
Lists in Python - advanced operations Preview 08:27
Lists in Python - list comprehension Preview 05:56
(!!!) Python lists and arrays Preview 00:22
Exercise: list comprehension Preview 00:37
Solution: list comprehension Preview 00:19
Measuring running time of lists Preview 00:45
What are tuples? Preview 03:58
Mutability and immutability Preview 04:30
What are linked list data structures? Preview 08:13
Doubly linked list implementation in Python Preview 05:32
Hashing and O(1) running time complexity Preview 08:03
Dictionaries in Python Preview 09:41
Sets in Python Preview 09:49
Exercise: constructing dictionaries Preview 00:14
Solution: constructing dictionaries Preview 00:09
Sorting Preview 10:44
Data Structures Quiz
What is object oriented programming (OOP)? Preview 02:18
Class and objects basics Preview 03:00
Using the constructor Preview 06:00
Class variables and instance variables Preview 04:46
Exercise: constructing classes Preview 00:11
Solution: constructing classes Preview 00:08
Private variables and name mangling Preview 04:31
What is inheritance in OOP? Preview 03:49
The super keyword Preview 04:24
Function (method) override Preview 02:34
What is polymorphism? Preview 04:25
Polymorphism and abstraction example Preview 06:10
Exercise: abstraction Preview 00:16
Solution: abstraction Preview 00:12
Modules Preview 06:00
The __str__ function Preview 03:16
Comparing objects - overriding functions Preview 08:00
Object Oriented Programming (OOP) Quiz
What is the key advantage of NumPy? Preview 04:12
Creating and updating arrays Preview 07:36
Dimension of arrays Preview 09:12
Indexes and slicing Preview 07:59
Types Preview 04:43
Reshape Preview 07:53
Exercise: reshape problem Preview 00:12
Solution: reshape problem Preview 00:05
Stacking and merging arrays Preview 06:17
Filter Preview 03:39
Running time comparison: arrays and lists Preview 00:44
NumPy Quiz
R programming fundamentals Preview 00:06
First steps Preview 04:49
Vectors Preview 06:22
Factors Preview 03:16
Arrays Preview 03:46
Matrixes Preview 03:51
List Preview 02:15
Data frames Preview 03:37
Coercion Preview 02:05
Packages Preview 02:57
If - else Preview 02:27
Loops - repeat, while and for Preview 04:20
Custom functions Preview 03:34
Custom operators Preview 01:50
Course materials Preview 00:02
This course includes:
- 18.5 hours on-demand video
- Access on mobile and TV
- Certificate of completion
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