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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