Udacity – Artificial Intelligence for Trading Nanodegree v1.0.0 2019-1 – Full Version
The description
Artificial Intelligence for Trading is a training in artificial intelligence to fight against fraud and trading on the financial markets, published by the specialized academy of Udacity. This course is completely project-based and hands-on compared to other courses published by Udacity Academy, working with real and instructive projects throughout. This important training course has been completely comprehensive and some of the most important topics covered in it include 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, he became familiar with the principles and bases of quantitative analysis.
Quantitative is a complex process that includes tasks such as data processing, creating and reviewing stock reasons, and portfolio management. In this training, he used the powerful Python programming language and used different algorithms to develop smart systems and check different strategies of old and previous markets. Building multi-faceted models and optimizing them is one of the most important skills acquired in this training.
What you will learn in artificial intelligence for trading
- Quantitative trade
- Different market mechanisms and creating trading signals based on them
- Design and development of business strategies
- Portfolio optimization
- Different financial markets and modes of doing business in each of them
- Risk factors and alpha
- Exploring Opinions Using Natural Language Processing
- Word processing and analysis of information and financial statements of different companies
- deep learning
- Combination of different signals and reception of final signals
- And …
Course specifications
Editor: audacity
Instructors: Cindy Lin, Arpan Chakraborty, Elizabeth Otto Hamel, Eddy Shyu, Brok Bucholtz, Parnian Barekatain, Juan Delgado, Luis Serrano, Cezanne Camacho and Mat Leonard
French language
Intermediate level
Number of lessons: 78
Duration: approx. 6 months
Course topics
Lesson 1: Basic Quantitative Trading
Course project: Trading with Momentum
Introduction
Stock prices
Market mechanics
Data processing
Stock returns
Dynamic trade
Lesson 2: Advanced Quantitative Trading
Course project: escape strategy
Quantitative workflow
Outliers and signal filtering
Regression
Time Series Modeling
Volatility
Pair trading and mean reversion
Lesson 3: Stocks, indices and ETFs
Course project: Smart Beta and portfolio optimization
Stocks, indices and funds
AND F
Portfolio risk and return
Portfolio optimization
Lesson 4: Factor investing and alpha research
Course project: multifactor model
Factors Models of returns
Risk Factor Models
Alpha factors
Advanced portfolio optimization with risk factor and alpha models
Lesson 5: Sentiment Analysis with Natural Language Processing
Course Project: Sentiment Analysis Using NLP
Introduction to Natural Language Processing
Word processor
Feature extraction
financial state
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
Embeds & Word2Vec
Sentiment Prediction RNN
Lesson 7: Combining multiple signals
Course project: Combining signals to improve alpha
Insight
Decision trees
Model testing and evaluation
Random Forests
Feature Engineering
Layered labels
Importance of Features
Lesson 8: Simulate transactions with historical data
Course project: Backtesting
Introduction to backtesting
Optimization with transaction costs
Award
Artificial Intelligence for Trading Prerequisites
You should have some programming experience with Python and be familiar with statistics, linear algebra, and differential calculus.
Python programming skills:
- Basic data structures
- Basic Numpy
Intermediate statistical knowledge:
- Average, median, mode
- Variance, standard deviation
- Random variables, independence
- Distributions, normal distribution
- T-test, p-value, statistical significance
Intermediate knowledge in calculus and linear algebra:
- Integrals and derivatives
- Linear combination, linear independence
- Matrix operations
- Eigenvectors, eigenvalues
New to Python programming? Check out our free introductory data analysis course.
Need to refresh your knowledge of statistics and algebra? Discover our free courses in statistics and linear algebra:
What software and versions will I need in this program?
To successfully complete this Nanodegree program, you must be able to download and run Python 3.7.
Pictures
Introductory video to artificial intelligence for trading
Installation guide
After ripping, watch with your favorite player.
english subtitle
Quality: 720p
Download link
File password(s): ngaur.com
Cut
5.79 GB