In this program, you’ll analyze real data and build financial models for trading. Whether you want to pursue a new job in finance, launch yourself on the path to a quant trading career, or master the latest AI applications in quantitative finance, this program offers you the opportunity to master valuable data and AI skills.
Includes:
01 - Welcome to the Nanodegree Program
02 - Get Help from Peers and Mentors
03 - Get Help with Your Account
04 - Stock Prices
05 - Market Mechanics
06 - Data Processing
07 - Stock Returns
08 - Momentum Trading
09 - Project 1 Trading with Momentum
01 - Quant Workflow
02 - Outliers and Filtering
03 - Regression
04 - Time Series Modeling
05 - Volatility
06 - Pairs Trading and Mean Reversion
07 - Project 2 Breakout Strategy
01 - Stocks, Indices, Funds
02 - ETFs
03 - Portfolio Risk and Return
04 - Portfolio Optimization
05 - Project 3 Smart Beta and Portfolio Optimization
01 - Factors
02 - Factor Models and Types of Factors
03 - Risk Factor Models
04 - Time Series and Cross Sectional Risk Models
05 - Risk Factor Models with PCA
06 - Alpha Factors
07 - Alpha Factor Research Methods
08 - Advanced Portfolio Optimization
09 - Project 4 Alpha Research and Factor Modeling
- Welcome To Term II
02 - Intro to Natural Language Processing
03 - Text Processing
04 - Feature Extraction
05 - Financial Statements
06 - Basic NLP Analysis
07 - Project 5 NLP on Financial Statements
01 - Introduction to Neural Networks
02 - Training Neural Networks
03 - Deep Learning with PyTorch
04 - Recurrent Neural Networks
05 - Embeddings Word2Vec
06 - Sentiment Prediction RNN
07 - Project 6 Sentiment Analysis with Neural Networks
01 - Overview
02 - Decision Trees
03 - Model Testing and Evaluation
04 - Random Forests
05 - Feature Engineering
06 - Overlapping Labels
07 - Feature Importance
08 - Project 7 Combining Signals for Enhanced Alpha
01 - Strengthen Your Online Presence Using LinkedIn
02 - Optimize Your GitHub Profile
01 - Intro to Backtesting
02 - Optimization with Transaction Costs
03 - Attribution
04 - Project 8 Backtesting
01 - Why Python Programming
02 - Data Types and Operators
03 - Control Flow
04 - Functions
05 - Scripting
01 - Introduction
02 - Vectors
03 - Linear Combination
04 - Linear Transformation and Matrices
01 - Jupyter Notebooks
02 - NumPy
03 - Pandas
01 - Descriptive Statistics - Part I
02 - Descriptive Statistics - Part II
03 - Admissions Case Study
04 - Probability
05 - Binomial Distribution
06 - Conditional Probability
07 - Bayes Rule
08 - Python Probability Practice
09 - Normal Distribution Theory
10 - Sampling distributions and the Central Limit Theorem
11 - Confidence Intervals
12 - Hypothesis Testing
13 - Case Study AB tests
01 - Linear Regression
02 - Naive Bayes
03 - Clustering
04 - Decision Trees
05 - Introduction to Kalman Filters
01 - Introduction to Neural Networks
01 - Intro to Computer Vision
01 - Intro to NLP

HOmepage:

https://www.udacity.com/course/ai-for-trading--nd880

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