# Quantitative Finance Collector is a blog on Quantitative finance analysis, financial engineering methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management. Random thoughts on financial markets and personal staff are posted at the sub personal blog.

Mar
25

**pymex - Matlab in Python**: pymex embeds a Python interpreter in Matlab, allowing Matlab programmers write parts of their scripts in Python. Programmers are also able to use Python modules in Matlab.

**The Short Term Prediction of Analysts' Forecast Error**: a short term trading strategy based on predicting the error in analysts' earnings per share forecasts using publicly available information generates an annual gross abnormal return of 16.56%.

**R in Google Summer of Code 2012**: participating in a program receives US$5,000 for successful completion of a GSoC project using R language sponsored by Google.

**Comovement and Predictability Relationships Between Bonds and the Cross-Section of Stocks**: we find that bonds are robustly related to the cross-section of stock returns in both comovement and predictability patterns.

**10 Things the Public Need to Know About Quantitative Trading**: Infographic: 10 Things the Public Need to Know About Quantitative Trading.

Dec
7

My flight to Australia will be tomorrow, so this post is the one ahead of schedule.

**mlpy - Machine Learning Python**: mlpy is a free Python module for Machine Learning. It facilitates classification, regression, clustering and feature selection in Python.**Global Optimization Algorithms – Theory and Application**: a free ebook on global optimization, algorithms including Evolutionary Algorithms, Genetic Algorithms, Genetic Programming, Learning Classiﬁer Systems, Hill Climbing, Simulated Annealing... Not easy to understand but worth to save it.**Improved Moving Average Code**, and**Improved Moving Average?**: both posts are aiming to introduce a newly improved moving average trading strategy with detailed codes and examples.**How Expected Shortfall Can Simplify the Equally-Weighted Risk Contribution Portfolio**: compare the performance of portfolios under several construction strategies: minimum variance, equally weighted, and Expected Shortfall stable.
Nov
24

*Life is short, use Python*

I started to play with Python two weeks ago due to the limitation of R in terms of handling large data, then a friend of mine suggested me to try Python since I had to do data massage frequently, "Python is the best choice, trust me", he said. Although I was unwilling to learn another new software, I couldn't bear with the low efficiency of R (or of my work) for large data. You may realize my learning curve as: Excellent free CSV splitter --> MySQL+RMySQL package --> Several R packages including bigmemory and ff. But to be honest, none of them satisfies me either because of the limitation of the method (slow + malfunction) or of my own computer (short of memory).

I am shocked by python's extreme power and easy-to-use design after nearly two weeks, dealing with a 10GB CSV had never become so easy. More importantly, you can access R from Python almost seamlessly with the package RPY. To get started, I would like to recommend the following readings to all Python newbies like me: