Description
Artificial Intelligence for Trading is an artificial intelligence training course for trading in financial markets published by Yudacity Academy. This training course, like other courses published in Yudacity Academy, is completely project-oriented and practical, and during its process, you will work with a series of real and informative projects. This training course is completely comprehensive and among the most important topics discussed in it are management of various assets, creation of transaction signals and analysis of their certainty, artificial intelligence algorithms for trading, portfolio creation and management of existing items. In it and … mentioned. During this course, you will get to know the basic principles of trading and quantitative analysis.
Quantitative trading is a complex process that consists of various tasks such as data processing, creating and checking trading signals and portfolio management. In this training course, you will use the powerful Python programming language and analyze old and previous market data with different algorithms to develop smart trading systems and check different strategies. Building multifaceted models and optimizing them is one of the most important skills learned in this training course.
What you will learn in the Artificial Intelligence for Trading course:
- 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 various companies
- Deep learning
- Combining different signals and receiving the final signals
- And …
Course details
Publisher: Udacity
teacher: Cindy LinArpan Chakraborty, Elizabeth Otto Hamel, Eddy Shyu, Brok Bucholtz, Parnian Barekatain, Juan Delgado, Luis Serrano, Cezanne Camacho and Mat Leonard
English language
Education level: Intermediate
Number of courses: 78
Duration of training: assuming 10 hours of work per week, about 6 months
Course headings
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
Introduction 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
Prerequisites of Artificial Intelligence for Trading course
You should have some programming experience 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:
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.
Course images
Artificial Intelligence for Trading course introduction video
Installation guide
After extract, watch with your favorite player.
English subtitle
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
5.79 GB
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