Quant Finance Lectures (adapted Quantopian Lectures)¶

Learn quantitative finance with this comprehensive lecture series. Adapted from the Quantopian Lecture Series (GitHub source repo) and updated to use QuantRocket APIs and data.

Background¶

Quantopian was a crowd-sourced hedge fund that operated a research and backtesting platform where users could develop trading algorithms, a selection of which were licensed by Quantopian for inclusion in its hedge fund. To support its business objective, Quantopian developed several open-source Python libraries, including Zipline, one of QuantRocket's two backtesters. Quantopian also developed a comprehensive lecture series on quantitative finance, which it released under a Creative Commons license. The lectures were originally developed to use Python 2 and Quantopian data, but have here been updated to use Python 3 and QuantRocket data.

Following the closure of its crowd-sourced hedged fund, Quantopian shut down in 2020.

Lecture Series Overview¶

The lectures are divided into three sections:

Intro to Python: These lectures provide an introduction to Jupyter notebooks, Python, and several key data processing libraries in Python.
Topics in Statistics: Covering a variety of topics that are broadly applicable in the field of statistics, these lectures are not specific to finance but typically include example applications to finance.
Topics in Finance: Building on the statistics lectures, these lectures dive into topics more specific to finance and portfolio management.

For computational analysis, the lectures rely most heavily on Python's numerous scientific computing libraries. Some of the lectures on financial topics also rely on the Pipeline API, which is part of Zipline, a backtester which was created by Quantopian and is available in QuantRocket. For data, the lectures utilize randomly generated data as well as actual financial data from QuantRocket.

The primary focus of Quantopian's crowd-sourced hedge fund was to develop market-neutral, long-short equity strategies for institutional investors. Consequently, the lectures are tilted toward this particular style of investment.

Although the lectures use QuantRocket APIs to load data, they are not primarily QuantRocket tutorials. The purpose of the lectures is to provide a theoretical foundation in quantitative finance. Most of the computational methods employed in the lectures (excluding the Pipeline API) are readily available in any Python research environment. For practical guidance on how to backtest and deploy trading strategies in QuantRocket, other examples in the Code Library may be more suitable.

Data Requirements¶

Most lectures can be completed using free sample data from QuantRocket. A few lectures are designed to use fundamental data that requires a QuantRocket subscription with Sharadar data subscription. These lectures can still be completed without a QuantRocket subscription by simply reading along (without querying data) or by substituting free Sharadar sample data.

The data requirements for each lecture are noted in the table below.

Version¶

Due to the large variety of computational libraries used, the lectures are somewhat sensitive to version changes in the underlying libraries. The lectures work with QuantRocket version 2.10.0 or higher.

Contents¶

	Title	Description	Required Dataset(s)
Data Collection Tutorials
Data Collection 1	Free data for US Stocks	Collect free sample data for US Stocks.
Data Collection 2	Sharadar Price and Fundamental Data for US Stocks	Collect price and fundamental data for US Stocks from Sharadar. QuantRocket subscription and Sharadar subscription required.
Intro to Python
Lecture 1	Introduction to Notebooks	Introductory tutorial demonstrating how to use Jupyter Notebooks	Free data
Lecture 2	Introduction to Python	Basic introduction to Python semantics and data structures	-
Lecture 3	Introduction to NumPy	Introduction to NumPy, a data computation library	-
Lecture 4	Introduction to pandas	Introduction to pandas, a library for managing and analyzing data	Free data
Lecture 5	Plotting Data	How to plot data with matplotlib	Free data
Topics in Statistics
Lecture 6	Means	Understanding and calculating different types of means	Free data
Lecture 7	Variance	Understanding and calculating measures of dispersion	-
Lecture 8	Statistical Moments	Understanding skewness and kurtosis	Free data
Lecture 9	Linear Correlation Analysis	Understanding correlation and its relation to variance	Free data
Lecture 10	Instability of Estimates	How estimates can change with new data observations	Free data
Lecture 11	Random Variables	Understanding discrete and continuous random variables and probability distributions	Free data
Lecture 12	Linear Regression	Using linear regression to understand the relationship between two variables	Free data
Lecture 13	Maximum Likelihood Estimation	Basic introduction to maximum likelihood estimation, a method of estimating a probability distribution	Free data
Lecture 14	Regression Model Instability	Why regression coeffecients can change due to factors like regime change and multicollinearity	Free data
Lecture 15	Multiple Linear Regression	Multiple linear regression generalizes linear regression to multiple variables	Free data
Lecture 16	Violations of Regression Models	Different scenarios that can violate regression assumptions	Free data
Lecture 17	Model Misspecification	What can cause a bad model to look good	Free data Sharadar data
Lecture 18	Residual Analysis	How to analyze residuals to build healthier models	Free data
Lecture 19	Dangers of Overfitting	How overfitting can make a bad model seem attractive	Free data
Lecture 20	Hypothesis Testing	Statistical techniques for rejecting the null hypothesis	Free data
Lecture 21	Confidence Intervals	How to measure and interpret confidence intervals	-
Lecture 22	Spearman Rank Correlation	How to measure monotonic but non-linear relationships	Free data
Lecture 23	p-Hacking and Multiple Comparisons Bias	How to avoid getting tricked by false positives	-
Topics in Finance
Lecture 24	Leverage	Using borrowed money to amplify returns	-
Lecture 25	Position Concentration Risk	The riskiness of investing in a small number of assets	-
Lecture 26	Estimating Covariance Matrices	Using covariance matrices to model portfolio volatility	Free data
Lecture 27	Introduction to Volume, Slippage, and Liquidity	An overview of liquidity and how it can affect your trading strategies	Free data
Lecture 28	Market Impact Models	Understanding how your own trading activity moves the market price	Free data
Lecture 29	Universe Selection	Defining a trading universe	Free data
Lecture 30	The Capital Asset Pricing Model and Arbitrage Pricing Theory	Using CAPM and Arbitrage Pricing Theory to evaluate risk	Free data Sharadar data
Lecture 31	Beta Hedging	Hedging your algorithm's market risk	Free data
Lecture 32	Fundamental Factor Models	Using fundamental data in factor models	Sharadar data
Lecture 33	Portfolio Analysis with pyfolio	Evaluating backtest performance using pyfolio	-
Lecture 34	Factor Risk Exposure	Understanding and measuring your algorithm's exposure to common risk factors	Sharadar data
Lecture 35	Risk-Constrained Portfolio Optimization	Managing risk factor exposure	Sharadar data
Lecture 36	Principal Component Analysis	Using PCA to understand the key drivers of portfolio returns	Free data
Lecture 37	Long-Short Equity	Introduction to market-neutral strategies	-
Lecture 38	Factor Analysis with Alphalens	Using Alphalens to evaluate alpha factors	Free data
Lecture 39	Why You Should Hedge Beta and Sector Exposures	How hedging common risk exposures can improve portfolio performance	Free data
Lecture 40	VaR and CVaR	Using Value at Risk to estimate potential loss	Free data
Lecture 41	Integration, Cointegration, and Stationarity	Introduction to stationarity and cointegration, which underpins pairs trading	Free data
Lecture 42	Introduction to Pairs Trading	A theoretical and practical introduction to pairs trading	Free data
Lecture 43	Autocorrelation and AR Models	Understanding how autocorrelation creates tail risk	-
Lecture 44	ARCH, GARCH, and GMM	Introduction to volatility forecasting models	-
Lecture 45	Kalman Filters	Using Kalman filters to extract signals from noisy data	Free data

License¶

Licensed under the Creative Commons Attribution 4.0.