Resources for developers using Python for scientific computing and quantitative analysis
Pandas is a great tool for time series analysis of financial market data. Because pandas DataFrames and Series work well with a date/time based index, they can be used effectively to analyze historical data. By financial market data, I mean data like historical price information on a publicly traded financial instrument. However, any sort of historical financial information can be analyzed.
This paper discusses the contrast between overnight and intraday stock returns. In the paper, we learn that overnight stock returns far outpace returns seen intraday during regular trading hours. In other words, stocks move the most when markets are not open, but when trading is taking place, the net returns seem to be close to zero. The paper claims this is a conspiracy where large hedge funds are manipulating the market. In this article, we will try to recreate the basic results from the article and look at one part of overnight returns that the article doesn’t discuss.
One of the milestones of the investment management application was to implement an end to end solution that starts by fetching company stock prices and builds a set of efficient and optimum portfolios using optimisation routines.
