[Udacity] Artificial Intelligence AI for Trading v1.0.0
Artificial Intelligence For Trading
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.
- 6 months
- Study 5-10 hrs / week
CO – CREATED WITH
Become career-ready faster
INDUSTRY SIZE & DEMAND
In India, algo-trading is expected to cross 25% mark of the total equity cash market trade volume by 2020.
Quants in India get up to 4-5 openings on an average to apply in a week, which varies from domestic trading companies to large MNCs
RANKED #08 CNBC
Udacity ranked as the most disruptive learning company in the world for 2 years in a row by CNBC
Join a global community of quant traders and get a worldwide view of trading startegies with Worldquant
WHAT YOU LEARN
Study cutting edge Content
Prerequisites and Requirements
You should have some experience programming with Python, and be familiar with statistics, linear algebra, and calculus.Python programming knowledge
- Basic data structures
- Basic Numpy
Intermediate statistical knowledge
- Mean, median, mode
- Variance, standard deviation
- Random variables, independence
- Distributions, normal distribution
- T-test, p-value, statistical significance
Intermediate calculus and linear algebra knowledge
- Integrals and derivatives
- Linear combination, linear independence
- Matrix operations
- Eigenvectors, eigenvalues
New to Python programming? Check out our free Intro to Data Analysis course.
New to Python programming? Check out our free Programming Foundations with Pythoncourse.
Need to refresh your statistical and algebra knowledge? Check out our free statistics and linear algebra courses:
- Intro to Statistics free course
- Linear algebra refresher free course
TERM 1 : BASIC
TERM 2 : ADVANCED
AI Algorithms in Trading
Term fee includes
Best in-class content by industry leaders in the form of bite-size videos and quizzes.
Sentiment Analysis with Natural Language Processing
Learn the fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals.
Advanced Natural Language Processing with Deep Learning
Learn to apply deep learning in quantitative analysis and use recurrent neural networks and long short-term memory to generate trading signals.
Simulating Trades with Historical Data
Learn to refine trading signals by running rigorous back tests. Track your P&L while your algorithm buys and sells.
Combining Multiple Signals
Learn advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data.
Industry relevant projects + unlimited project reviews by our global reviewers
Sentiment Analysis using NLP
Deep Neural Network with News Data
Combine Signals for Enhanced Alpha
We guide and support you throughout your learning journey through these services.
Search-based Q&A forum
collaborate with fellow students & mentors
Project reviews & feedback
actionable feedback from expert project reviewers
Your Nanodegree journey
ENROLL IN TERM II
to enroll, write to [email protected], if you have completed Term 1
BRUSH UP ON PRE-REQUISITES
while you wait for classroom to open, brush up on pre-requisites
to know, write to [email protected], if you have completed Term 1In case you feel unsure about the program, we offer a full refund on cancelling within 7 days of classroom opening.
submit all projects within 3 months
COMPLETE TERM 2
finish requirements for graduation
You are eligible to take part in our career fest Propel.
Learn from top Industry Experts
Jonathan has previously held leadership roles such as Global Head of Equities at Millennium Management and Co-Head of Americas Equity Derivatives Trading at JPMorgan.
Cindy is a quantitative analyst with experience working for financial institutions such as Bank of America Merrill Lynch, Morgan Stanley, and Ping An Securities. She has an MS in Computational Finance from Carnegie Mellon University.
Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
Elizabeth Otto Hamel
Elizabeth received her PhD in Applied Physics from Stanford University, where she used optical and analytical techniques to study activity patterns of large ensembles of neurons. She formerly taught data science at The Data Incubator.
Eddy has worked at BlackRock, Thomson Reuters, and Morgan Stanley, and has an MS in Financial Engineering from HEC Lausanne. Eddy taught data analytics at UC Berkeley and contributed to Udacity’s Self-Driving Car program.
Brok has a background of over five years of software engineering experience from companies like Optimal Blue. Brok has built Udacity projects for the Self Driving Car, Deep Learning, and AI Nanodegree programs.
Size: 5.69 GB