Introduction

AI is the new obsession everyone has these days. All the complex enumerations and technologies are combined with the aspect of AI. `One of the most exciting parts that capture people is its complexity towards almost each and everything. Speaking about the financial sectors, AI helps them to develop an in-depth analysis and detailed research. Hence, Udacity's AI for Trading Nanodegree program proves to be considered by many students.


Everything about AI Trading Nanodegree program
Udacity’s AI for trading Nano degree course is self-paced — despite the school, providing suggested course durations, classroom start dates, and the project due dates as a standard guideline for all students.

For this programming course, Udacity suggests it will take students four months studying for 10 hours per week to complete it. Start date is recommended but not mandatory for their Nanodegree programs — they have recommended start dates to guide students who start at the same time into various community channels.

Although Udacity Nanodegree courses are self-learning, as Udacity works on a subscription-based model, if you need to take a longer time to complete the class you sign up for, you may end up paying more in subscription fees than you initially thought.


Here's a quick brief of the primary features for Udacity's AI for Trading Nano Degree program:

In terms of Cost and Affordability, if you have ten hours per week for four months to learn CSS, HTML, Python, and JavaScript, $599 is affordable relative to other online coding schools. If price is your preference and you are only interested in learning these four languages, Udacity's Nanodegree program could be the one for you.

Now, if we're looking at Pacing, if you can learn CSS, HTML, Python, and JavaScript in four months for $599, a Udacity Nanodegree could be the correct choice. If you cannot graduate from Udacity within four months, it will cost you $399 per month until you graduate, making it probably more expensive than ever.

Considering Lifetime Access to materials, the material is not provided to you for more than a year after you enroll.

Although Udacity is Beginner-Friendly, and there are some prerequisites before you enroll in the program as

If you want a course with Project-Based practices, Udacity offers almost a similar project per program in their AI for Trading Nano Degree.

Additionally, Udacity provides Student Support and a Student Community.

Also, note that Udacity offers a two-day refund period with conditions if you're prone to buyer's remorse.



What is Udacity?
For the people who don't know what Udacity is, it is an online academy that happens to have some great connections. Apart from that, they mainly offer online technical courses which are free of cost. Whenever someone completes their respective practice, Udacity would provide them with a certificate of completion. Considering the present time, not just technical topics, but Udacity offers courses in other segments. Additionally, Udacity courses are proven to be pretty beneficial as well for many students. One of the popular courses offered by Udacity is the Nanodegree program.

When you sign up for Udacity's Nanodegree program, there are some perks and benefits that you come across. These extras help you grow personally and are suitable according to the students' requirements.



Part 1: Primary Quantitative Trading
Working with all the financial data and stock prices can be challenging, and you can fall into many traps. As compared to many other machine learning applications, you are trying to predict the future.

In theory, this Nanodegree is heavy because you're set up for failure if you don't know about the common pitfalls. And dwindling with your stock picking algorithms can get exorbitant.


Realizing market essentials
The place where you begin is at the starting point. Hence, you'll start by learning all about the stocks in general and market tactics.

It might be possible that many of you probably know everything about this already, but for the people learning it for the first time, it was undoubtedly considerate.


Proposing new alpha signals
In the next step, you are introduced to alpha signals, the building blocks of trading blueprints. But what are alpha signals? Alpha signals provide you with the costs of future stocks.

Most importantly, you will assimilate how to evaluate strategies such as stock returns and, later, execute one of the most available alpha signals.

In the initial artificial intelligence project for trading Nanodegree, your task is to create a trading strategy using momentum.

People who want to start building machine learning algorithms for trading stocks might become frustrated by the initial segments of the Nanodegree program.


Part 2: Complex Quantitative Trading
As soon as you complete the first part of the artificial intelligence for trading Nanodegree, you'll step into the shoes of a quant. Besides, people work with a trading approach without machine learning as well. Also, it is crucial to understand what they already do.


Quant workflow and best practices
There is too much at the end of the rope, so you need to be pretty cautious. The main goal is strong returns rather than hitting that one home run.

The lessons about outliers are also helpful because this is undoubtedly something that a lot of beginners overlook. Of course, when you begin working with machine learning algorithms, you can make them resistant to outliers in other ways.


Traditional analysis
Machine learning is excellent, but there are many purely statistical model tools you should master as well. The most general one is regression, but you also learn about data pre-processing since it is diplomatic.

In this section, you'll learn about some more advanced methods, and they even introduce neural networks at this point.

Volatility is another important topic when analyzing stocks. Here, high volatility means high risk but also high reward. These things are elementary for people who regularly work with financial data, so ensure you acquire the content well.

The primary aim of the second project is to create a breakout strategy. You'll base the design on what you learned in the previous lessons, and ultimately your goal is expediency.


Part 3: Stocks, Indices, and ETFs
You will see some other things that you can trade rather than just stocks. In this section, you learn about various other alternatives. Hence, these sessions also help you to understand how different associates handle their portfolios.

When you design portfolios, you want to score them and optimize them according to some metrics.

The project is to start enhancing your portfolio based on the new techniques you now know. Optimization is surprisingly similar to machine learning. The most significant difference is that you don't work with validation in the same way.

It's always fun to read about something you know significantly less about as it feels like something new.


Part 4: Factor Investing and Alpha Research
This section of the Nanodegree Trading shows some different aspects than other sections of the program.
Factors & Risk factors
The initial two lessons of these segments are now combined into one study known as factor models for returns. So, factor investing is another type of investment tactic where you're trying to find stocks with a low price compared to their essential value.

Next, you will learn how to model risk using factors. These lessons are also combined with a new course which is known as risk factor models.

Alpha factors

Alpha conveys your trading strategy's capability to defeat the stock market. It is the variance between the stock returns and the factors contributing to the returns.

Part 4 transferred its name to "Advanced Portfolio Optimization with Risk and Alpha Factors Models."

This section was the part of the AI for trading Nanodegree, and you realize how real investors work with managing risk, and it's definitely worth the interest.


Part 5: Sentiment Analysis with Natural

Language Processing
Part 5 is the place where you'll start looking at machine learning algorithms. This Nanodegree is primarily used to distinguish alpha factors that you can use to develop your projects.


Natural language processing
Natural language processing is an essential field of computer science where we want computers to understand language. Because of diverse innovations within deep learning, machine learning has wholly taken over NLP in recent years.

However, these lessons are about preparing for natural language processing using regex and other techniques to modify features from the respective text.


Extracting data from financial statements
This segment tells you more in detail about the feature extraction from data. Here, you will learn some valuable tools such as BeautifulSoup to get information from the internet and parse the HTML response for helpful information.

In the project, you will implement some fundamental inspections of the financial statements. Now, some people might call this AI, but I wouldn't. It's primarily developing comparability metrics that you can use as input later.

For the students, the following segment concentrates on natural language processing through deep learning.


Part 6: Advanced Natural Language

Rectifying with Deep Learning

Deep learning is something where most people work consistently. Now, part 6 begins with some background functionality with olfactory networks. If you've attended some other Udacity Nanodegrees before, you probably already know what this is.

Next, you will learn about perennial neural networks and word embeddings. Many people don't use RNN for text analysis anymore, but it works well for many more specific duties.

In this project, you have to develop an algorithm for sentiment analysis. It's a typical use-case where you want to know the underlying sentiment for a text.

Unfortunately, this is the only part of the Nanodegree that includes deep learning.


Part 7: Combining Multiple Signals
In this section, you learn more about some old-school machine learning algorithms, like decision trees, to combine the factors you studied before. Further, you also look at designing features for your algorithms and later explore the specs it detects.

In the project, you also apply a random forest algorithm for improving your knowledge in alpha trading.

It's good that they created a segment with machine learning where it's linked to trading. Otherwise, it would have been course quant analysis or something similar like that. Many people still think it's a bit weird to call this course AI for trading. The machine learning techniques are significantly related to statistical models and are nothing like most people when they hear AI.


Part 8: Replicating Trades with Historical Data
In this last section, all segments (apart from the NLP) come together in this previous section. Once you start replicating your trading strategies, everything becomes a lot more fun. Many beginners make mistakes in their simulation by adding information that wasn't available at the time, so pay close attention.

When you have good backtesting in place, you can quickly test and compare different trading strategies. It's a critical exercise that you need to master if you're serious about building AI algorithms for trading.


Conclusion
There are an equal amount of pros and cons in Udacity's AI for Trading Nanodegree program. This course is a heavy one and might require lots of practice if you are thinking about pursuing it. Further, it is a fantastic program for people who want to learn deeper about various aspects of AI.

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