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    • Financial Engineering and Artificial Intelligence in Python

    Financial Engineering and Artificial Intelligence in Python

    BY
    Udemy

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    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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