Financial Engineering and Artificial Intelligence in Python

BY
Udemy

Mode

Online

Fees

₹ 549 2999

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Fees information
₹ 549  ₹2,999
certificate availability

Yes

certificate providing authority

Udemy

The syllabus

Welcome

  • Introduction and Outline
  • Where to get the code
  • Scope of the course
  • How to Practice
  • Warmup (Optional)

Financial Basics

  • 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)
  • Covariance and Correlation
  • Covariance and Correlation (Code)
  • Alpha and Beta
  • Alpha and Beta (Code)
  • Mixture of Gaussians
  • Mixture of Gaussians (Code)
  • Volatility Clustering
  • Price Simulation
  • Price Simulation (Code)
  • Financial Basics Section Summary
  • Suggestion Box

Time Series Analysis

  • 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
  • ACF (Autocorrelation Function)
  • PACF (Partial Autocorrelation Funtion)
  • ACF and PACF in Code (pt 1)
  • ACF and PACF in Code (pt 2)
  • Auto ARIMA and SARIMAX
  • Model Selection, AIC and BIC
  • ARIMA in Code (pt 2)
  • ARIMA in Code (pt 3)
  • ACF and PACF for Stock Returns
  • Forecasting
  • Time Series Analysis Section Conclusion

Portfolio Optimization and CAPM

  • 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)
  • Problems with Markowitz Portfolio Theory and Robust Estimation
  • Portfolio Optimization Section Conclusion

VIP: Algorithmic Trading

  • 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

VIP: The Basics of Reinforcement Learning

  • 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

VIP: Reinforcement Learning for Algorithmic Trading

  • 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

VIP: Statistical Factor Models and Unsupervised Machine Learning

  • Statistical Factor Models (Beginner)
  • Statistical Factor Models (Intermediate)
  • Statistical Factor Models (Advanced)
  • Statistical Factor Models (Code)

VIP: Regime Detection and Sequence Modeling with Hidden Markov Models

  • 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

Course Summary and Common Questions

  • Final Thoughts and Course Summary
  • Creating Your Personalized Trading Strategy
  • Applying This Course
  • Trading APIs and Deploying Your Strategy in the Real World
  • High Frequency Trading (HFT)
  • The Importance of Data
  • Why do I have to learn statistics to learn finance?
  • Get a Plug-and-Play Trading Bot Without Math
  • Slippage and Bid-Ask Spread

Extras

  • Colab Notebooks
  • VIP: Finance Enthusiasts, Beware of Marketers!

Setting Up Your Environment FAQ

  • Anaconda Environment Setup
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners FAQ

  • How to Code by Yourself (part 1)
  • How to Code by Yourself (part 2)
  • Proof that using Jupyter Notebook is the same as not using it

Effective Learning Strategies for Machine Learning FAQ

  • How to Succeed in this Course (Long Version)
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
  • Machine Learning and AI Prerequisite Roadmap (pt 1)
  • Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

  • What is the Appendix?
  • BONUS Lecture

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