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Financial Engineering and Artificial Intelligence in Python
Module Content
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Welcome
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Introduction and Outline
Where to get the code
Scope of the course
How to Practice
Warmup (Optional)
Financial Basics
31 Topics
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Financial Basics Section Introduction
Getting Financial Data
Getting Financial Data (Code)
Understanding Financial Data
Understanding Financial Data (Code)
Dealing with Missing Data
Dealing with Missing Data (Code)
Returns
Adjusted Close, Stock Splits, and Dividends
Adjusted Close (Code)
Back to Returns (Code)
QQ-Plots
QQ-Plots (Code)
The t-Distribution
The t-Distribution (Code)
Skewness and Kurtosis
Confidence Intervals
Confidence Intervals (Code)
Statistical Testing
Statistical Testing (Code)
Time Series Analysis
31 Topics
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Time Series Analysis Section Introduction
Efficient Market Hypothesis
Random Walk Hypothesis
The Naive Forecast
Simple Moving Average (Theory)
Simple Moving Average (Code)
Exponentially-Weighted Moving Average (Theory)
Exponentially-Weighted Moving Average (Code)
Simple Exponential Smoothing for Forecasting (Theory)
Simple Exponential Smoothing for Forecasting (Code)
Holt’s Linear Trend Model (Theory)
Holt’s Linear Trend Model (Code)
Holt-Winters (Theory)
Holt-Winters (Code)
Autoregressive Models – AR(p)
Moving Average Models – MA(q)
ARIMA
ARIMA in Code (pt 1)
Stationarity
Stationarity Code
Portfolio Optimization and CAPM
22 Topics
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Portfolio Optimization Section Introduction
The S&P500
What is Risk?
Why Diversify?
Describing a Portfolio (pt 1)
Describing a Portfolio (pt 2)
Visualizing Random Portfolios and Monte Carlo Simulation (pt 1)
Visualizing Random Portfolios and Monte Carlo Simulation (pt 2)
Maximum and Minimum Portfolio Return
Maximum and Minimum Portfolio Return in Code
Mean-Variance Optimization
The Efficient Frontier
Mean-Variance Optimization And The Efficient Frontier in Code
Global Minimum Variance (GMV) Portfolio
Global Minimum Variance (GMV) Portfolio in Code
Sharpe Ratio
Maximum Sharpe Ratio in Code
Portfolio with a Risk-Free Asset and Tangency Portfolio
Risk-Free Asset and Tangency Portfolio in Code
Capital Asset Pricing Model (CAPM)
Algorithmic Trading
9 Topics
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Algorithmic Trading Section Introduction
Trend-Following Strategy
Trend-Following Strategy in Code (pt 1)
Trend-Following Strategy in Code (pt 2)
Machine Learning-Based Trading Strategy
Machine Learning-Based Trading Strategy in Code
Classification-Based Trading Strategy in Code
Using a Random Forest Classifier for Machine Learning-Based Trading
Algorithmic Trading Section Summary
The Basics of Reinforcement Learning
12 Topics
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Reinforcement Learning Section Introduction
Elements of a Reinforcement Learning Problem
States, Actions, Rewards, Policies
Markov Decision Processes (MDPs)
The Return
Value Functions and the Bellman Equation
What does it mean to “learn”?
Solving the Bellman Equation with Reinforcement Learning (pt 1)
Solving the Bellman Equation with Reinforcement Learning (pt 2)
Epsilon-Greedy
Q-Learning
How to Learn Reinforcement Learning
Reinforcement Learning for Algorithmic Trading
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Trend-Following Strategy with Reinforcement Learning API
Trend-Following Strategy Revisited (Code)
Q-Learning in an Algorithmic Trading Context
Representing States
Q-Learning for Algorithmic Trading in Code
Statistical Factor Models and Unsupervised Machine Learning
4 Topics
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Statistical Factor Models (Beginner)
Statistical Factor Models (Intermediate)
Statistical Factor Models (Advanced)
Statistical Factor Models (Code)
Regime Detection and Sequence Modeling with Hidden Markov Models
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Why Sequence Models? (pt 1)
Why Sequence Models? (pt 2)
HMM Parameters
HMM Tasks and the Viterbi Algorithm
HMM for Modeling Volatility Clustering in Code
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