The course content is copied from ih2502mk. Will update the .ipynb file as I go through the lectures.
| Progress | Lecture # | Title | Description |
|---|---|---|---|
| Lecture 2 | Introduction to Python | Basic introduction to Python semantics and data structures | |
| Lecture 3 | Introduction to NumPy | Introduction to NumPy, a data computation library | |
| Lecture 4 | Introduction to pandas | Introduction to pandas, a library for managing and analyzing data | |
| Lecture 5 | Plotting Data | How to plot data with matplotlib | |
| Lecture 6 | Means | Understanding and calculating different types of means | |
| Lecture 7 | Variance | Understanding and calculating measures of dispersion | |
| Lecture 8 | Statistical Moments | Understanding skewness and kurtosis | |
| Lecture 9 | Linear Correlation Analysis | Understanding correlation and its relation to variance | |
| Lecture 10 | Instability of Estimates | How estimates can change with new data observations | |
| Lecture 11 | Random Variables | Understanding discrete and continuous random variables and probability distributions | |
| Lecture 12 | Linear Regression | Using linear regression to understand the relationship between two variables | |
| Lecture 13 | Maximum Likelihood Estimation | Basic introduction to maximum likelihood estimation, a method of estimating a probability distribution | |
| Lecture 14 | Regression Model Instability | Why regression coeffecients can change due to factors like regime change and multicollinearity | |
| Lecture 15 | Multiple Linear Regression | Multiple linear regression generalizes linear regression to multiple variables | |
| Lecture 16 | Violations of Regression Models | Different scenarios that can violate regression assumptions | |
| Lecture 17 | Model Misspecification | What can cause a bad model to look good | |
| Lecture 18 | Residual Analysis | How to analyze residuals to build healthier models | |
| Lecture 19 | Dangers of Overfitting | How overfitting can make a bad model seem attractive | |
| Lecture 20 | Hypothesis Testing | Statistical techniques for rejecting the null hypothesis | |
| Lecture 21 | Confidence Intervals | How to measure and interpret confidence intervals | |
| Lecture 22 | Spearman Rank Correlation | How to measure monotonic but non-linear relationships | |
| Lecture 23 | p-Hacking and Multiple Comparisons Bias | How to avoid getting tricked by false positives | |
| Lecture 24 | Leverage | Using borrowed money to amplify returns | |
| Lecture 25 | Position Concentration Risk | The riskiness of investing in a small number of assets | |
| Lecture 26 | Estimating Covariance Matrices | Using covariance matrices to model portfolio volatility | |
| Lecture 27 | Introduction to Volume, Slippage, and Liquidity | An overview of liquidity and how it can affect your trading strategies | |
| Lecture 28 | Market Impact Models | Understanding how your own trading activity moves the market price | |
| Lecture 29 | Universe Selection | Defining a trading universe | |
| Lecture 30 | The Capital Asset Pricing Model and Arbitrage Pricing Theory | Using CAPM and Arbitrage Pricing Theory to evaluate risk | |
| Lecture 31 | Beta Hedging | Hedging your algorithm's market risk | |
| Lecture 32 | Fundamental Factor Models | Using fundamental data in factor models | |
| Lecture 33 | Portfolio Analysis with pyfolio | Evaluating backtest performance using pyfolio | |
| Lecture 34 | Factor Risk Exposure | Understanding and measuring your algorithm's exposure to common risk factors | |
| Lecture 35 | Risk-Constrained Portfolio Optimization | Managing risk factor exposure | |
| Lecture 36 | Principal Component Analysis | Using PCA to understand the key drivers of portfolio returns | |
| Lecture 37 | Long-Short Equity | Introduction to market-neutral strategies | |
| Lecture 38 | Factor Analysis with Alphalens | Using Alphalens to evaluate alpha factors | |
| Lecture 39 | Why You Should Hedge Beta and Sector Exposures | How hedging common risk exposures can improve portfolio performance | |
| Lecture 40 | VaR and CVaR | Using Value at Risk to estimate potential loss | |
| Lecture 41 | Integration, Cointegration, and Stationarity | Introduction to stationarity and cointegration, which underpins pairs trading | |
| Lecture 42 | Introduction to Pairs Trading | A theoretical and practical introduction to pairs trading | |
| Lecture 43 | Autocorrelation and AR Models | Understanding how autocorrelation creates tail risk | |
| Lecture 44 | ARCH, GARCH, and GMM | Introduction to volatility forecasting models | |
| Lecture 45 | Kalman Filters | Using Kalman filters to extract signals from noisy data | |