- Introduction and Outline
- Where to get the code
- Scope of the course
- How to Practice
- Warmup (Optional)
Online
₹ 2,999
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
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Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 2,999
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Welcome
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