CF

Data Structures and Algorithmic Trading: Machine Learning

2h 20m 32s
English
Paid

Data Structures and Algorithmic Trading: Machine Learning is a 22-lesson 2 hours 20 minutes self-paced course by Udemy. Data Structures and Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions over time.

Course facts

Lessons
22
Duration
2 hours 20 minutes
Level
All levels
Language
English
Updated
Instructor
Udemy
Price
Premium

Data Structures and Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions over time. They were developed so that traders do not need to constantly watch a stock and repeatedly send those slices out manually. Algorithmic trading is not an attempt to make a trading profit. It is simply a way to minimize the cost, market impact and risk in execution of an order, but if you can’t use this incredible tool, you might miss the right entry or exit spots that other traders will gladly take.

What if you could change that?

My complete Algorithmic Trading course will show you the exact techniques and strategies you need to succeed in the financial markets, master trading, build a forex robot and learn machine learning.

For less than a movie ticket, you will get over 4 hours of video lectures and the freedom to ask me any questions regarding the course as you go through it. :)

What Is In This Course?

Your Trading Will Never Be The Same.

Except if you’re already an excellent trader, know the importance of sorting, use array rotation, read technical analysis, finding shortest path in a graph and know AVL trees, you are going to lose more opportunities to code faster and more effectively.

In This Data Structures & Algorithmic Trading Training, You'll Learn:

  • Learn Algorithmic Thinking
  • Computational Problem Solving Using Algorithms
  • Algorithmic Design Process
  • Importance of Sorting
  • Array Rotation
  • Insertion Sort
  • Merge & Heap Sort
  • Greedy Algorithms
  • Topological Sorting
  • Breadth & Depth First Traversal
  • Hashing
  • Open Addressing
  • Finding Shortest Path in a graph
  • AVL trees
  • Computational Complexity and Machine Learning

Is This For You?

  • Do you want to do algorithmic trading with machine learning?
  • Are you afraid of not creating a good Forex robot and earn money through trading?
  • Do you think you will feel proud making successful trades with your Forex robots?

Then this course will definitely help you.

This course is essential to all Forex trader, stockbroker, entrepreneur, systematic traders, short term traders and anyone looking to learn algorithmic trading.

I will show you precisely what to do to solve these situations with simple and easy techniques that anyone can apply.

Why To Have Strong Algorithmic Trading Skills?

Let Me Show You Why To Have Strong Algorithmic Trading Skills:

1. You will succeed in the financial markets.

2. You will master trading.

3. You will build a forex robot.

4. You will learn machine learning.

Requirements:
  • No Prior Knowledge Or Work Is Necessary To Take This Course.
  • Pen And Paper For Precious Notes
Who this course is for:
  • All Forex Trader, Stockbroker, Entrepreneur, Systematic Traders, Short Term Traders
  • Anyone Looking To Learn Algorithmic Trading.
  • This Is Not For People Looking For A Quick Or Lazy Way Of Trading

What you'll learn:

  • Learn Algorithmic Thinking
  • Computational Problem Solving Using Algorithms
  • Algorithmic Design Process
  • Importance of Sorting
  • Array Rotation
  • Insertion Sort
  • Merge & Heap Sort
  • Greedy Algorithms
  • Topological Sorting
  • Breadth & Depth First Traversal
  • Hashing
  • Open Addressing
  • Finding Shortest Path in a graph
  • AVL trees
  • Computational Complexity and Machine Learning

Who teaches Data Structures and Algorithmic Trading: Machine Learning? Udemy

Udemy thumbnail

Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

What lessons are included in Data Structures and Algorithmic Trading: Machine Learning?

This is a demo lesson (10:00 remaining)

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#1: Introduction to Algorithmic Thinking
All Course Lessons (22)
#Lesson TitleDurationAccess
1
Introduction to Algorithmic Thinking Demo
05:21
2
Computational Problem Solving Using Algorithms
04:53
3
Algorithmic Design Process
14:10
4
Importance of Sorting
06:53
5
Array Rotation
04:03
6
Insertion Sort
13:26
7
Merge Sort
16:57
8
Sorting Types: Heaps
05:57
9
Heap Sort
12:46
10
Greedy Algorithms
04:42
11
Topological Sorting
04:28
12
Breadth First Traversal
05:31
13
Depth First Traversal
04:53
14
Introduction to Hashing
05:23
15
DIJKSTRA Algorithm with example
05:37
16
Open Addressing
04:29
17
Open Addressing explained with example
03:30
18
Finding Shortest Path In A Graph
02:43
19
AVL trees
02:24
20
AVL Trees Part 2
04:20
21
Computational Complexity and Machine Learning
02:55
22
Machine Learning
05:11
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Frequently asked questions

What prerequisites are needed for this course?
The course requires a basic understanding of programming concepts and some familiarity with data structures like arrays and trees. Prior experience with algorithms will be beneficial, especially in understanding complex topics like AVL trees and graph traversal methods.
What projects or practical exercises are included in the course?
The course includes practical exercises such as implementing algorithms like Insertion Sort, Merge Sort, and Heap Sort. Students will also work with graph algorithms like Dijkstra's Algorithm to find the shortest path, and apply greedy algorithms to solve computational problems.
Who is the target audience for this course?
This course is designed for individuals interested in algorithmic trading and data structures, particularly those who want to automate trading strategies or enhance their understanding of computational problem solving in financial contexts.
How does this course compare to other algorithm-focused courses?
While other courses may focus solely on algorithm design or data structures, this course integrates machine learning with algorithmic trading concepts, providing a unique perspective on how computational complexity affects trading strategies.
What specific tools or platforms are covered in the course?
The course focuses on algorithmic concepts and does not delve into specific software tools or trading platforms. Instead, it emphasizes the theoretical underpinnings of algorithmic trading and data structures such as AVL trees and hashing techniques.
What topics are not covered in this course?
The course does not cover live trading platforms, financial market analysis, or specific programming languages in depth. It centers on algorithmic principles and data structures applicable to trading strategies without focusing on market dynamics or language-specific implementations.
What is the time commitment required for this course?
The course consists of 22 lessons, all requiring a variable amount of time to understand and practice. Students should allocate sufficient time to work through complex topics such as graph traversal techniques and computational complexity discussions.