Lecture 1 Introduction to Research A simple tutorial to help you get up to speed in the research environment.
Lecture 2 Introduction to Python Some basic tools for working in the language.
Lecture 3 Introduction to NumPy How to use NumPy for computing on data.
Lecture 4 Introduction to pandas An introduction to using pandas to manage and analyze your data.
Lecture 5 Plotting Data A brief primer.
Lecture 6 Means Measures of centrality.
Lecture 7 Variance Measures of dispersion.
Lecture 8 Statistical Moments Ways to think about distributions.
Lecture 9 Linear Correlation Analysis A basic primer on correlation and how it relates to variance.
Lecture 10 Instability of Estimates How estimates can lie and ways to deal with that.
Lecture 11 Random Variables Theory and sample use cases.
Lecture 12 Linear Regression An explanation of the technique and implementation in Python.
Lecture 13 Maximum Likelihood Estimation A basic intro developed in collaboration with Andrei Kirilenko at MIT Sloan.
Lecture 14 Regression Model Instability Why your regression coefficients can change.
Lecture 15 Multiple Linear Regression Expanding from one to many variables.
Lecture 16 Violations of Regression Models What happens when regression assumptions are violated.
Lecture 17 Model Misspecification Violation of assumptions can cause a model to falsely look good.
Lecture 18 Residual Analysis Analysis of residuals leads to healthier models
Lecture 19 The Dangers of Overfitting How overfitting can trick you into thinking your algorithm is good.
Lecture 20 Hypothesis Testing How to rigorously test your ideas with set confidence levels.
Lecture 21 Confidence Intervals A primer in collaboration with Jeremiah Johnson at UNH.
Lecture 22 p-Hacking and Multiple Comparisons Bias Don't be tricked by false positives.
Lecture 23 Spearman Rank Correlation What to do when the relationship in your data is not necessarily linear.
Lecture 24 Leverage An introduction to leverage in algorithmic trading and how it works.
Lecture 25 Position Concentration Risk Why investing in few assets is very risky.
Lecture 26 Estimating Covariance Matrices Sample covariance matrices are unstable
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 Modeling market impact is an essential, and often overlooked, part of trading
Lecture 29 Universe Selection Defining a trading universe
Lecture 30 The Capital Asset Pricing Model and Arbitrage Pricing Theory An examination of the CAPM and Arbitrage Pricing Theory
Lecture 31 Beta Hedging How to hedge your algorithm against risk factors.
Lecture 32 Fundamental Factor Models How fundamental data can be used in factor models.
Lecture 33 Portfolio Analysis A walkthrough of how to fill the gaps in your portfolio's returns
Lecture 34 Factor Risk Exposure Estimating exposure to risk factors using factor models.
Lecture 35 Risk-Constrained Portfolio Optimization Investment strategies try to optimize returns given a risk budget. We’ll show you how to effectively monitor and manage your risk.
Lecture 36 Principal Component Analysis PCA is a common dimensionality reduction technique used in statistics and machine learning to analyze high-dimensional datasets
Lecture 37 Long-Short Equity An overview of the long-short equity strategy and how it can be used.
Lecture 38 Example: Long-Short Equity Algorithm An algorithm to go along with Long-Short Equity.
Lecture 39 Factor Analysis with Alphalens The statistics of determining whether a factor is suitable for a long-short equity algorithm
Lecture 40 Why You Should Hedge Beta and Sector Exposures (Part I) Here we examine the veracity of independent bets and their effect on the Sharpe ratio
Lecture 41 Why You Should Hedge Beta and Sector Exposures (Part II) We continue where we left off in part I, examining how small amounts of common factor risk can affect portfolios
Lecture 42 VaR and CVaR The loss to which you are exposed.
Lecture 43 Integration, Cointegration, and Stationarity How non-stationarity can break traditional analyses.
Lecture 44 Introduction to Pairs Trading A complete workflow to building a basic pairs trading strategy on Quantopian.
Lecture 45 Example: Basic Pairs Trading Algorithm A simple implementation of pairs trading.
Lecture 46 Example: Pairs Trading Algorithm A more sophisticated pairs trading implementation.
Lecture 47 Autocorrelation and AR Models Autocorrelation and how to model it to reduce tail risk.
Lecture 48 ARCH, GARCH, and GMM A primer on volatility forecasting models developed with Andrei Kirilenko.
Lecture 49 Kalman Filters How to use Kalman filters to get a good signal out of noisy data.
Lecture 50 Example: Kalman Filter Pairs Trade An algorithm to go along with Kalman Filters.
Lecture 51 Introduction to Futures An overview of the theory behind futures contracts
Lecture 52 Futures Trading Considerations Some particulars on trading futures contracts
Lecture 53 Mean Reversion on Futures Further exploration on mean reversion in futures markets
Lecture 54 Example: Pairs Trading on Futures A futures pairs trading algorithm
Lecture 55 Case Study: Traditional Value Factor How to build a long/short value factor.
Lecture 56 Case Study: Comparing ETFs A simple example of p-value testing on real data.

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