Lecture 1 Introduction to ResearchA simple tutorial to help you get up to speed in the research environment.
Lecture 2 Introduction to PythonSome basic tools for working in the language.
Lecture 3 Introduction to NumPyHow to use NumPy for computing on data.
Lecture 4 Introduction to pandasAn introduction to using pandas to manage and analyze your data.
Lecture 5 Plotting DataA brief primer.
Lecture 6 MeansMeasures of centrality.
Lecture 7 VarianceMeasures of dispersion.
Lecture 8 Linear RegressionAn explanation of the technique and implementation in Python.
Lecture 9 Multiple Linear RegressionExpanding from one to many variables.
Lecture 10 Linear Correlation AnalysisA basic primer on correlation and how it relates to variance.
Lecture 11 Example: Long-Short Cross-Sectional MomentumAn example algorithm to go along with Linear Correlation Analysis.
Lecture 12 Random VariablesTheory and sample use cases.
Lecture 13 Statistical MomentsWays to think about distributions.
Lecture 14 Confidence IntervalsA primer in collaboration with Jeremiah Johnson at UNH.
Lecture 15 Hypothesis TestingHow to rigorously test your ideas with set confidence levels.
Lecture 16 Maximum Likelihood EstimationA basic intro developed in collaboration with Andrei Kirilenko at MIT Sloan.
Lecture 17 Spearman Rank CorrelationWhat to do when the relationship in your data is not necessarily linear.
Lecture 18 Beta HedgingHow to hedge your algorithm against risk factors.
Lecture 19 Example: Beta Hedging AlgorithmAn algorithm to go along with Beta Hedging.
Lecture 20 LeverageAn introduction to leverage in algorithmic trading and how it works.
Lecture 21 Introduction to Pairs TradingA complete workflow to building a basic pairs trading strategy on Quantopian.
Lecture 22 Example: Basic Pairs Trading AlgorithmA simple implementation of pairs trading.
Lecture 23 Example: Pairs Trading AlgorithmA more sophisticated pairs trading implementation.
Lecture 24 Position Concentration RiskWhy investing in few assets is very risky.
Lecture 25 Autocorrelation and AR ModelsAutocorrelation and how to model it to reduce tail risk.
Lecture 26 The Dangers of OverfittingHow overfitting can trick you into thinking your algorithm is good.
Lecture 27 Instability of EstimatesHow estimates can lie and ways to deal with that.
Lecture 28 Model MisspecificationViolation of assumptions can cause a model to falsely look good.
Lecture 29 Violations of Regression ModelsWhat happens when regression assumptions are violated.
Lecture 30 Regression Model InstabilityWhy your regression coefficients can change.
Lecture 31 Universe SelectionDefining a trading universe
Lecture 32 Integration, Cointegration, and StationarityHow non-stationarity can break traditional analyses.
Lecture 33 VaR and CVaRThe loss to which you are exposed.
Lecture 34 Arbitrage Pricing TheoryHow factor models can be used to predict returns.
Lecture 35 Fundamental Factor ModelsHow fundamental data can be used in factor models.
Lecture 36 Factor Risk ExposureEstimating exposure to risk factors using factor models.
Lecture 37 Long-Short EquityAn overview of the long-short equity strategy and how it can be used.
Lecture 38 Example: Long-Short Equity AlgorithmAn algorithm to go along with Long-Short Equity.
Lecture 39 Ranking Universes by FactorsHow to rank universes of assets and evaluate ranking systems.
Lecture 40 ARCH, GARCH, and GMMA primer on volatility forecasting models developed with Andrei Kirilenko.
Lecture 41 Kalman FiltersHow to use Kalman filters to get a good signal out of noisy data.
Lecture 42 Example: Kalman Filter Pairs TradeAn algorithm to go along with Kalman Filters.
Lecture 43 Example: Momentum AlgorithmAn algorithm to showcase an implementation of a momentum strategy.
Lecture 44 Case Study: Traditional Value FactorHow to build a long/short value factor.

The lectures 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 or other services by Quantopian.

In addition, the lectures neither constitute investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the lectures. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk – including loss of principal. You should consult with an investment professional before making any investment decisions.

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