Description
Artificial Intelligence for Trading is training in artificial intelligence to combat fraud and trading in financial markets, published by the specialized Udacity academy. This course is completely project-based and hands-on compared to other courses published by Udacity Academy, working through real-world and informative projects throughout. This important training was completely comprehensive and among the most important topics covered were various management, creation of effective decision-making and analysis factors, artificial intelligence algorithms for discovery, portfolio construction and management of existing elements. In it and… mentioned. During this training, he became familiar with the principles and basics of quantitative analysis.
Quantitative is a complex process that includes tasks such as data processing, creating and reviewing stock patterns, and portfolio management. In this training, he used the powerful Python programming language and used different algorithms to develop intelligent systems and verify different strategies from old and previous markets. Building multi-faceted models and optimizing them is one of the most important skills learned during this training.
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 modes of activity in each of them
- Risk factors and alpha
- Opinion mining using natural language processing
- Word processing and analysis of information and financial statements from different companies
- Deep learning
- Combine different signals and receive the 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
Course 1: Basic Quantitative Trading
Course Project: Trading with Momentum
Introduction
Stock prices
Market Mechanics
Data processing
Stock returns
Dynamic trading
Course 2: Advanced Quantitative Trading
Course project: Escape strategy
Quantitative 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
AND F
Portfolio Risk and Return
Portfolio Optimization
Course 4: Factor investing and Alpha research
Course project: Multifactor model
Factors Return Models
Risk factor models
Alpha factors
Advanced portfolio optimization with risk factor and alpha models
Course 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 of neural networks
Deep learning with PyTorch
Recurrent Neural Networks
Integrations and Word2Vec
RNN Sentiment Prediction
Course 7: Combining Multiple Signals
Course Project: Combining Signals for Improved Alpha
Preview
Decision trees
Model testing and evaluation
Random Forests
Feature Engineering
Overlapping labels
Importance of features
Course 8: Simulate Transactions with Historical Data
Course project: Backtesting
Introduction to backtesting
Optimization with transaction costs
Attribution
Artificial Intelligence for Trading Prerequisites
You should have some programming experience with Python and be familiar with statistics, linear algebra, and calculus.
Knowledge of Python programming:
- 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 knowledge of calculus and linear algebra:
- integrals and derivatives
- Linear combination, linear independence
- Matrix operations
- Eigenvectors, eigenvalues
Are you new to Python programming? Check out our free introductory data analysis course.
Need to refresh your knowledge of statistics and algebra? Discover our free statistics and linear algebra courses:
What software and versions do I need in this program?
To successfully complete this Nanodegree program, you will need to be able to download and run Python 3.7.
Pictures
Introduction to artificial intelligence for trading video
installation guide
After the clip, watch with your favorite reader.
english subtitles
Quality: 720p
Download link
File password(s): free download software
size
5.79 GB