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 p-Hacking and Multiple Comparisons BiasDon't be tricked by false positives.
Lecture 17 Maximum Likelihood EstimationA basic intro developed in collaboration with Andrei Kirilenko at MIT Sloan.
Lecture 18 Spearman Rank CorrelationWhat to do when the relationship in your data is not necessarily linear.
Lecture 19 Beta HedgingHow to hedge your algorithm against risk factors.
Lecture 20 Example: Beta Hedging AlgorithmAn algorithm to go along with Beta Hedging.
Lecture 21 LeverageAn introduction to leverage in algorithmic trading and how it works.
Lecture 22 Introduction to Pairs TradingA complete workflow to building a basic pairs trading strategy on Quantopian.
Lecture 23 Example: Basic Pairs Trading AlgorithmA simple implementation of pairs trading.
Lecture 24 Example: Pairs Trading AlgorithmA more sophisticated pairs trading implementation.
Lecture 25 Position Concentration RiskWhy investing in few assets is very risky.
Lecture 26 Autocorrelation and AR ModelsAutocorrelation and how to model it to reduce tail risk.
Lecture 27 The Dangers of OverfittingHow overfitting can trick you into thinking your algorithm is good.
Lecture 28 Instability of EstimatesHow estimates can lie and ways to deal with that.
Lecture 29 Model MisspecificationViolation of assumptions can cause a model to falsely look good.
Lecture 30 Violations of Regression ModelsWhat happens when regression assumptions are violated.
Lecture 31 Regression Model InstabilityWhy your regression coefficients can change.
Lecture 32 Universe SelectionDefining a trading universe
Lecture 33 Integration, Cointegration, and StationarityHow non-stationarity can break traditional analyses.
Lecture 34 VaR and CVaRThe loss to which you are exposed.
Lecture 35 Arbitrage Pricing TheoryHow factor models can be used to predict returns.
Lecture 36 Fundamental Factor ModelsHow fundamental data can be used in factor models.
Lecture 37 Factor Risk ExposureEstimating exposure to risk factors using factor models.
Lecture 38 Long-Short EquityAn overview of the long-short equity strategy and how it can be used.
Lecture 39 Example: Long-Short Equity AlgorithmAn algorithm to go along with Long-Short Equity.
Lecture 40 Ranking Universes by FactorsHow to rank universes of assets and evaluate ranking systems.
Lecture 41 Factor AnalysisThe statistics of determining whether a factor is suitable for a long-short equity algorithm
Lecture 42 ARCH, GARCH, and GMMA primer on volatility forecasting models developed with Andrei Kirilenko.
Lecture 43 Kalman FiltersHow to use Kalman filters to get a good signal out of noisy data.
Lecture 44 Example: Kalman Filter Pairs TradeAn algorithm to go along with Kalman Filters.
Lecture 45 Example: Momentum AlgorithmAn algorithm to showcase an implementation of a momentum strategy.
Lecture 46 Case Study: Traditional Value FactorHow to build a long/short value factor.
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