My Experience: Udacity AI(Artificial Intelligence) for Trading Review 2023
LEARN TO MAKE FINANCIAL MODELS
AI FOR TRADING NANODEGREE
Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio
- Stay ahead of the competition
- Demand high paying jobs
- Build real-world projects
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Ever been on the trading floor of an investment bank?
You might see some talented traders doing calculations in their heads while some juggling with pen and paper to meticulously record their recent trades.
They call themselves artists, not traders.
But the traditional trading art form is disappearing slowly, being replaced by new tech-savvy experts. They don’t eat, sleep or take breaks. They just keep on analyzing the information fed to them to decode the best trading scenario at any given moment in time.
With the growing influence of Artificial Intelligence in trading, Udacity has come up with a nanodegree that teaches how to master AI algorithms for trading in the stock market and build a dashing career in trading.
In this Udacity Artificial Intelligence for Trading Review, I will take you through the entire structure and significance of this course if you are planning to make a career in trading. Keep reading!
How AI makes trading strategies?
Today AI system uses deep learning to train large neural networks that are capable of analyzing data from several sources, like social media, news, corporate filings, and market trends and develop trading strategies accordingly.
They use past performances and historical data to decipher the trading move at any moment, acting as an oracle for traders all around the world.
To date, Hedge fund companies relied on humans to built trading techniques, but no sooner many of them began using artificial intelligence for trading.
Before moving to the Udacity Artificial Intelligence for Trading Review, let’s see how many startups and hedge funds are making use of AI to make smarter decisions.
Back in 2010 algorithm trading accounted for 60% of total trading in the US. With the rise of AI, today traditional traders account for less than 10% of the total trading volume.
According to Preqin, approximately, 1360 hedge funds today use Artificial Intelligence algorithms in making the majority of their trading strategies.
Isn’t that interesting?
In Quantitative hedge firms, AI and Machine Learning have become an important tool in identifying trading signals. Some popular examples are
- Man AHL
- Bridgewater
- Citadel
- D.E. Shaw
Although these firms require a bit of human intervention, there is a model that doesn’t require any human intervention and are called “Pure AI Models”. Some popular examples that work on Pure AI include:
- Aidiyia Holdings,
- Cerebellum Capital,
- Taaffeite Capital Management
- Numerai
Apart from this, there exist lots of startups working to make use of AI for smarter trading.
No doubt, Hedge funds’ use of AI is like a catalyst to the trading industry particularly in investing, cost models and recruitment.
To increase your chances of getting hired by such leading firms, Udacity in partnership with WorldQuant, one of the leading asset management firms, has come up with a nanodegree program to give you an exposure to AI applications in trading and quantitative finance.
Udacity Artificial Intelligence for Trading Nanodegree Review
About Udacity's AI for Trading Course
In this online course, learners will be made to analyze data and build their own financial models for trading.
In addition, candidates will learn the basics of quantitative analysis, which includes data processing to portfolio management, along with the use of advanced machine learning techniques like natural language processing to random forest algorithms.
To craft this course, Udacity partnered with WorldQuant and other industry professionals having experience with firms like JPMorgan and Morgan Stanley.
“WorldQuant is committed to bringing opportunity to talent globally. This Nanodegree program is like a laser, it focuses exactly on what you need today to succeed,”
– Igor Tulchinsky, CEO of WorldQuant.
Further, you will get an opportunity to get in touch with quant traders and financial professionals with prior experience in hedge funds and fintech.
Students can get good support from study groups and detailed feedback from the reviewers for the projects.
Throughout this program, students receive world-class support through mentor-led study groups and detailed feedback from project reviewers. Industry experts from top financial institutions provide actionable insights and guidance during office hours, reinforcing the supportive community of students.
Is there any other course, you think, provides such wide exposure and training for AI applications in trading?
Also Read: Udacity Review – Will I get a job?
Cost and Duration of Artificial Intelligence for Trading Nanodegree
Cost: $399/Month (without discount)
Duration: 6 Months
You get 6 months‘ access to this course, where you need to spend at least 10hrs/week to complete the course on time.
In case you are unable to complete for the course within 6 months and require more time, you can opt for monthly prices later.
I suggest spending more time on the course, at least 15hrs/week so that you can utilize more time practicing.
The course will test your mettle and dedication for the program, but in the end, it will be worth it.
Frequency of Classes: The program is self-paced with project deadlines.
Tools Available: Lectures, student community, project reviews and forums
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Syllabus of AI for Trading Program
The course is well-structured to make you understand every concept in details. It consists of 8 different courses followed by a project after each course. Projects are a great source to learn the practical implications of your learnings. To complete the projects diligently to achieve mastery in quantitative finance.
Course 1 :
Quantitative analysis is the base of Quant Trading that includes mathematical computations that decide trading strategies.
The first course will teach you market mechanics and generating signals using stocks.
Project 1:
In momentum trading, traders buy and sell as per recent price trends.
In this project, you’ll be made to work and test on a momentum trading strategy. Learn to generate a trading signal from a moment indicator based on historical data of a given stock. Later you have to compute that signal to produce expected returns. In the end, you’ll have to perform a test to check whether there was any indication in the signal.
Course 2:
Getting to know about the workflow for signal generation as followed by a quant. Learn about Quant Workflows, Outliers etc.
Project 2:
Code and learn to evaluate a breakout signal. Run statistical tests and find the alpha. Also, learn to run various contexts of the model you have prepared, with or without outliers.
Course 3:
This course all about ETFs, Indices, Stocks .
Project 3:
Using smart beta methodology with optimization, you will be creating two portfolios. Calculate tracking errors to understand how well your portfolio performs. Also, calculate the portfolio’s turnover and find the accurate timing to rebalance.
Course 4 :
Using Advanced portfolio optimization, learn to make a portfolio and get to know about alpha and risk factors.
Project 4:
Use several techniques to try to analyze your alpha factors and how to select the best suitable for your portfolio. Work on risk models, leverage and various other constraints and work to optimization problems for the advanced portfolio.
Course 5 :
Use text processing to evaluate corporate filings and generate trading signals based on sentiment.
Project 5:
On the basis of sentiments, learn to invest in a company and the time to invest using NLP (Natural Language Processing)
Course 6 :
In this course you will be introduced to neural network and deep learning, sentiment prediction using RNN (Recurrent Neural Networks)
Project 6:
Using deep neural networks, try to analyze data from various sources. Construct LSTM networks.
Course 7:
Using advanced techniques learn like Decision Trees, Random Forests, Overlapping Labels etc. learn to select various factors.
Project 7:
Try to combine signals for enhanced alpha.
Course 8 :
In this last course, run backtests to refine a trading signal.
You’ll learn backtesting and Attribution.
Project 8:
Using Barra data, build a real backtester.
You can find the complex syllabus here.
What are the pre-requisites for the course?
Before heading to the course, you must understand the requirement of prior knowledge you should have. This being an advanced course requires a proper understanding of Python that includes basic data structure and basic NumPy.
So brush up your concepts on Python as it will be used heavily during the course.
Further, one must be known to statistics, linear algebra, and calculus.
Summing up, you should be good at following topics
- Python (basic data structure and basic NumPy)
- Linear Algebra (Linear combination, linear independence Matrix, Eigenvectors)
- Calculus (Derivatives, Integrals)
- Statistics (Mean, median, mode, variance, standard deviation, T-test, p-value, statistical significance)
If you want to revise your knowledge on statistics ad algebra, Udacity has a free course for you
Hope you are finding this Udacity Artificial Intelligence for Trading Review useful!
5 Benefits of joining this Artificial Intelligence for Trading Nanodegree from Udacity
#1. High demand for AI specialists
According to Indeed Hiring Labs, demand for AI specialists has doubled in recent years as most of the organizations today rely on Artificial Intelligence in making strategic decisions.
#2. Chance to learn from best finance professionals
As said earlier, Udacity has developed this online course in partnership with WorldQuant and professionals from firms like JPMorgan. Getting taught by such high profile instructors is always valuable.
#3. The opportunity of getting hired at World Quant
Nanodegree holders are eligible for the interview process for various positions at WorldQuant Virtual Research Center (VRC)
#4. Develop your own trading strategies
Once you know how to do it, you’ll keep on playing with it and come up with your self developed algorithms that can predict the market.
So you can be an Oracle now. Really?
#5. Expand your network
Get in touch with people at hedge funds, investment banks and fintech startups who can be your financial ‘gurus’
And now the downsides
According to Hackernoon, nobody has completely cracked trading using Machine Learning.
That’s right! It is very difficult to make your own trading strategies and requires a lot of dedication from your side.
The course could be cumbersome at certain moments as you will need to put more time and efforts on certain lessons.
Although Udacity claims 6 months time period, it might take longer if you do not spend more than 10 hours per week to the program.
The first few lectures might sound basic for an experienced trader as the course introduces you to tock, ETF and a few momentum-based strategies.
The first few lectures are designed considering students having no finance background, hence it might sound boring to some.
Term 2 gets a little serious and has real in-depth content.
Conclusion:
Though the first term might be a little elementary, the second term will provide you with in-depth content. I think, if you or anybody wants to launch themselves on quant trading career or finance, this is one of the best online course that teaches AI for Trading all across the globe.
The course seems well-structured in motivating and creating curiosity among individuals who dare to take risks and make a difference.
If you are passionate enough and have money in your pocket, why not give a try on the way to make millions?
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Josh Hutcheson
E-Learning Specialist in Online Programs & Courses
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