Lectures

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 Linear Regression An explanation of the technique and implementation in Python.
Lecture 9 Multiple Linear Regression Expanding from one to many variables.
Lecture 10 Linear Correlation Analysis A basic primer on correlation and how it relates to variance.
Lecture 12 Random Variables Theory and sample use cases.
Lecture 13 Statistical Moments Ways to think about distributions.
Lecture 14 Confidence Intervals A primer in collaboration with Jeremiah Johnson at UNH.
Lecture 15 Hypothesis Testing How to rigorously test your ideas with set confidence levels.
Lecture 16 p-Hacking and Multiple Comparisons Bias Don't be tricked by false positives.
Lecture 17 Maximum Likelihood Estimation A basic intro developed in collaboration with Andrei Kirilenko at MIT Sloan.
Lecture 18 Spearman Rank Correlation What to do when the relationship in your data is not necessarily linear.
Lecture 19 Beta Hedging How to hedge your algorithm against risk factors.
Lecture 21 Leverage An introduction to leverage in algorithmic trading and how it works.
Lecture 22 Introduction to Pairs Trading A complete workflow to building a basic pairs trading strategy on Quantopian.
Lecture 23 Example: Basic Pairs Trading Algorithm A simple implementation of pairs trading.
Lecture 24 Example: Pairs Trading Algorithm A more sophisticated pairs trading implementation.
Lecture 25 Position Concentration Risk Why investing in few assets is very risky.
Lecture 26 Autocorrelation and AR Models Autocorrelation and how to model it to reduce tail risk.
Lecture 27 The Dangers of Overfitting How overfitting can trick you into thinking your algorithm is good.
Lecture 28 Instability of Estimates How estimates can lie and ways to deal with that.
Lecture 29 Model Misspecification Violation of assumptions can cause a model to falsely look good.
Lecture 30 Violations of Regression Models What happens when regression assumptions are violated.
Lecture 31 Regression Model Instability Why your regression coefficients can change.
Lecture 32 Universe Selection Defining a trading universe
Lecture 33 Integration, Cointegration, and Stationarity How non-stationarity can break traditional analyses.
Lecture 34 VaR and CVaR The loss to which you are exposed.
Lecture 35 Arbitrage Pricing Theory How factor models can be used to predict returns.
Lecture 36 Fundamental Factor Models How fundamental data can be used in factor models.
Lecture 37 Factor Risk Exposure Estimating exposure to risk factors using factor models.
Lecture 38 Long-Short Equity An overview of the long-short equity strategy and how it can be used.
Lecture 39 Example: Long-Short Equity Algorithm An algorithm to go along with Long-Short Equity.
Lecture 41 Factor Analysis The statistics of determining whether a factor is suitable for a long-short equity algorithm
Lecture 42 ARCH, GARCH, and GMM A primer on volatility forecasting models developed with Andrei Kirilenko.
Lecture 43 Kalman Filters How to use Kalman filters to get a good signal out of noisy data.
Lecture 44 Example: Kalman Filter Pairs Trade An algorithm to go along with Kalman Filters.
Lecture 45 Introduction to Futures An overview of the theory behind futures contracts
Lecture 46 Futures Trading Considerations Some particulars on trading futures contracts
Lecture 47 Introduction to Volume, Slippage, and Liquidity An overview of liquidity and how it can affect your trading strategies
Lecture 48 Mean Reversion on Futures Further exploration on mean reversion in futures markets
Lecture 49 Example: Pairs Trading on Futures A futures pairs trading algorithm
Lecture 51 Case Study: Traditional Value Factor How to build a long/short value factor.

The lectures on this website are provided for informational purposes only and do not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor do they constitute an offer to provide investment advisory services by Quantopian.

In addition, the lectures offer no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

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