Udacity – Artificial Intelligence for Trading Nanodegree v1.0.0 2019-1
Udacity – Artificial Intelligence for Trading Nanodegree v1.0.0 2019-1 Downloadly IRSpace
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Artificial Intelligence for Trading is an artificial intelligence training course for combating fraud and trading in financial markets, published by Udacity’s specialized academy. This course is completely project-based and hands-on compared to other courses published by Udacity Academy, working with real-life and instructive projects throughout. This important training course has been completely comprehensive and among the most important topics raised in it, we can mention various managements, creation of effective factors on decision-making and analysis, artificial intelligence algorithms for discovery, portfolio construction and management of existing items. In it and … mentioned. During this training course, he got acquainted with the principles and basics of quantitative analysis.
Quantitative is a complex process that consists of tasks such as data processing, creating and reviewing stock reasons, and portfolio management. In this training course, he used the powerful Python programming language and used different algorithms to develop smart systems and check different strategies of the old and previous markets. Building multifaceted models and optimizing them is one of the most important skills learned in this training course.
What you will learn in Artificial Intelligence for Trading
- Quantitative trading
- Different market mechanics and creating trading signals based on them
- Design and development of trading strategies
- Portfolio optimization
- Different financial markets and methods of activity in each of them
- Risk factors and alpha
- Opinion mining using natural language processing
- Text processing and analysis of information and financial statements of different companies
- Deep learning
- Combining different signals and receiving the final signals
- And …
Course specifications
Publisher: Udacity
Instructors: Cindy Lin, Arpan Chakraborty, Elizabeth Otto Hamel, Eddy Shyu, Brok Bucholtz, Parnian Barekatain, Juan Delgado, Luis Serrano, Cezanne Camacho and Mat Leonard
Language: English
Level: Intermediate
Number of Lessons: 78
Duration: Approx. 6 Months
Course topics
Course 1: Basic Quantitative Trading
Course Project : Trading with Momentum
Introduction
Stock Prices
Market Mechanics
Data Processing
Stock Returns
Momentum Trading
Course 2: Advanced Quantitative Trading
Course Project: Breakout Strategy
Quant Workflow
Outliers and Filtering Signals
Regression
Time Series Modeling
Volatility
Pairs Trading and Mean Reversion
Course 3: Stocks, Indices, and ETFs
Course Project: Smart Beta and Portfolio Optimization
Stocks, Indices and Funds
ETFs
Portfolio Risk and Return
Portfolio Optimization
Course 4: Factor Investing and Alpha Research
Course Project: Multi-factor Model
Factors Models of Returns
Risk Factor Models
Alpha Factors
Advanced Portfolio Optimization with Risk and Alpha Factors Models
Course 5: Sentiment Analysis with Natural Language Processing
Course Project: Sentiment Analysis using NLP
Intro to Natural Language Processing
Text Processing
Feature Extraction
Financial Statements
Basic NLP Analysis
Course 6: Advanced Natural Language Processing with Deep Learning
Course Project: Sentiment Analysis with Neural Networks
Introduction to Neural Networks
Training Neural Networks
Deep Learning with PyTorch
Recurrent Neural Networks
Embeddings & Word2Vec
Sentiment Prediction RNN
Course 7: Combining Multiple Signals
Course Project: Combining Signals for Enhanced Alpha
Overview
Decision Trees
Model Testing and Evaluation
Random Forests
Feature Engineering
Overlapping Labels
Feature Importance
Course 8: Simulating Trades with Historical Data
Course Project: Backtesting
Intro to Backtesting
Optimization with Transaction Costs
Attribution
Artificial Intelligence for Trading Prerequisites
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.
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
What software and versions will I need in this program?
To successfully complete this Nanodegree program, you’ll need to be able to download and run Python 3.7.
Pictures
Artificial Intelligence for Trading introduction video
Installation guide
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English subtitle
Quality: 720p
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Size
5.79 GB
