ml-finance-python

README.md

(6780B)

 # Chapter 04: Alpha Factor Research & Evaluation
 
 Alpha factors aim to predict the price movements of assets in the investment universe based on the available market, fundamental, or alternative data. A factor may combine one or several input variables, but assumes a single value for each asset every time the strategy evaluates the factor. 
 
 Trade decisions typically rely on relative values across assets. Trading strategies are often based on signals emitted by multiple factors, and we will see that machine learning (ML) models are particularly well suited to integrate the various signals efficiently to make more accurate predictions.
 
 This chapter provides a framework for understanding how factors work and how to measure their performance, for example using the information coefficient (IC). It demonstrates how to engineer alpha factors from data using Python libraries offline and on the Quantopian platform. It also introduces the `zipline` library to backtest factors and the `alphalens` library to evaluate their predictive power. More specifically, this chapter covers:
 
 - How to characterize, justify and measure key types of alpha factors
 - How to create alpha factors using financial feature engineering
 - How to use `zipline` offline to test individual alpha factors
 - How to use `zipline` on Quantopian to combine alpha factors and identify more sophisticated signals
 - How the information coefficient (IC) measures an alpha factor's predictive performance
 - How to use `alphalens` to evaluate predictive performance and turnover
 
 ## Engineering Alpha Factor
 
 Alpha factors are transformations of market, fundamental, and alternative data that contain predictive signals. They are designed to capture risks that drive asset returns. One set of factors describes fundamental, economy-wide variables such as growth, inflation, volatility, productivity, and demographic risk. Another set consists of tradeable investment styles such as the market portfolio, value-growth investing, and momentum investing.
 
 There are also factors that explain price movements based on the economics or institutional setting of financial markets, or investor behavior, including known biases of this behavior. The economic theory behind factors can be rational, where the factors have high returns over the long run to compensate for their low returns during bad times, or behavioral, where factor risk premiums result from the possibly biased, or not entirely rational behavior of agents that is not arbitraged away.
 
 ### Important Factor Categories
 
 In an idealized world, categories of risk factors should be independent of each other (orthogonal), yield positive risk premia, and form a complete set that spans all dimensions of risk and explains the systematic risks for assets in a given class. In practice, these requirements will hold only approximately.
 
 - [Dissecting Anomalies](http://schwert.ssb.rochester.edu/f532/ff_JF08.pdf) by Eugene Fama and Ken French (2008)
 - [Explaining Stock Returns: A Literature Review](https://www.ifa.com/pdfs/explainingstockreturns.pdf) by James L. Davis (2001)
 - [Market Efficiency, Long-Term Returns, and Behavioral Finance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=15108) by Eugene Fama (1997)
 - [The Efficient Market Hypothesis and It's Critics](https://pubs.aeaweb.org/doi/pdf/10.1257/089533003321164958) by Burton Malkiel (2003)
 - [The New Palgrave Dictionary of Economics](https://www.palgrave.com/us/book/9780333786765) (2008) by Steven Durlauf and Lawrence Blume, 2nd ed.
 - [Anomalies and Market Efficiency](https://www.nber.org/papers/w9277.pdf) by G. William Schwert25 (Ch. 15 in Handbook of the- "Economics of Finance", by Constantinides, Harris, and Stulz, 2003)
 - [Investor Psychology and Asset Pricing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=265132), by David Hirshleifer (2001)
 
 ### How to transform Data into Factors
 
 - The notebook [feature_engineering.ipynb](00_data/feature_engineering.ipynb) in the [data](00_data) directory illustrates how to engineer basic factors.
 
 #### References
 
 - [The Barra Equity Risk Model Handbook](https://www.alacra.com/alacra/help/barra_handbook_GEM.pdf)
 - [Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk](https://www.amazon.com/Active-Portfolio-Management-Quantitative-Controlling/dp/0070248826) by Richard Grinold and Ronald Kahn, 1999
 - [Modern Investment Management: An Equilibrium Approach](https://www.amazon.com/Modern-Investment-Management-Equilibrium-Approach/dp/0471124109) by Bob Litterman, 2003
 - [Quantitative Equity Portfolio Management: Modern Techniques and Applications](https://www.crcpress.com/Quantitative-Equity-Portfolio-Management-Modern-Techniques-and-Applications/Qian-Hua-Sorensen/p/book/9781584885580) by Edward Qian, Ronald Hua, and Eric Sorensen
 - [Spearman Rank Correlation](https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php)
 
 
 ## Seeking Signals - How to use `zipline`
 
 The open source [zipline](http://www.zipline.io/index.html) library is an event-driven backtesting system maintained and used in production by the crowd-sourced quantitative investment fund [Quantopian](https://www.quantopian.com/) to facilitate algorithm-development and live-trading. It automates the algorithm's reaction to trade events and provides it with current and historical point-in-time data that avoids look-ahead bias.
 
 - `zipline` installation: see [docs](http://www.zipline.io/index.html) and the introduction to `zipline` in [Chapter 2](../02_market_and_fundamental_data/02_data_providers/04_zipline) for more detail.
 
 ## Separating signal and noise – how to use alphalens
 
 This section introduces the [alphalens](http://quantopian.github.io/alphalens/) library for the performance analysis of predictive (alpha) factors.
 
 - `alphalens` installation see [docs](http://quantopian.github.io/alphalens/) for detail
 
 Alphalens depends on:
 
 -  [matplotlib](https://github.com/matplotlib/matplotlib)
 -  [numpy](https://github.com/numpy/numpy)
 -  [pandas](https://github.com/pydata/pandas)
 -  [scipy](https://github.com/scipy/scipy)
 -  [seaborn](https://github.com/mwaskom/seaborn)
 -  [statsmodels](https://github.com/statsmodels/statsmodels)
 
 ## Alternative Algorithmic Trading Libraries
 
 - [QuantConnect](https://www.quantconnect.com/)
 - [Alpha Trading Labs](https://www.alphalabshft.com/)
 - [WorldQuant](https://www.worldquantvrc.com/en/cms/wqc/home/)
 - Python Algorithmic Trading Library [PyAlgoTrade](http://gbeced.github.io/pyalgotrade/)
 - [pybacktest](https://github.com/ematvey/pybacktest)
 - [Trading with Python](http://www.tradingwithpython.com/)
 - [Interactive Brokers](https://www.interactivebrokers.com/en/index.php?f=5041)