A shiny interface to BatchGetSymbols

Hadley Wickham recently released an online version of Mastering Shiny. The book is great! If you haven’t read it, do it fast! On a side note, it is really amazing how much of good and curated content you can get for free in R. When I started programming back in 2007, the first step was buying a brand new – and sometimes expensive – book about the language. There were blogs and other sites, but most content was very basic and not curated, meaning that the posted code most of the time did not work.

Risk and return for B3

One of the subjects that I teach in my undergraduate finance class is the relationship between risk and expected returns. In short, the riskier the investment, more returns should be expected by the investor. It is not a difficult argument to make. All that you need to understand is to remember that people are not naive in financial markets. Whenever they make a big gamble, the rewards should also be large. Rational investors, on theory, would not invest in risky stocks that are likelly to yield low returns.

BatchGetSymbols is now parallel!

BatchGetSymbols is my most downloaded package by any count. Computation time, however, has always been an issue. While downloading data for 10 or less stocks is fine, doing it for a large ammount of tickers, say the SP500 composition, gets very boring. I’m glad to report that time is no longer an issue. Today I implemented a parallel option for BatchGetSymbols. If you have a high number of cores in your computer, you can seriously speep up the importation process. Importing SP500 compositition, over 500 stocks, is a breeze.

BatchGetSymbols 2.2

One of the main requests I get for package BatchGetSymbols is to add the choice of frequency of the financial dataset. Today I finally got some time to work on it. I just posted a new version of BatchGetSymbols in CRAN. The major change is that users can now set the time frequency of the financial data: dailly, weekly, monthly or yearly. Let’s check it out: library(BatchGetSymbols) ## Loading required package: rvest ## Loading required package: xml2 ## Loading required package: dplyr ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union ## library(purrr) ## ## Attaching package: 'purrr' ## The following object is masked from 'package:rvest': ## ## pluck library(ggplot2) my.

Investing for the Long Run

I often get asked about how to invest in the stock market. Not surprisingly, this has been a common topic in my classes. Brazil is experiencing a big change in its financial scenario. Historically, fixed income instruments paid a large premium over the stock market and that is no longer the case. Interest rates are low, without the pressure from inflation. This means a more sustainable scenario for low-interest rates in the future. Without the premium in the fixed income market, people turn to the stock market. We can separate investors according to their horizon.

Major update to BatchGetSymbols

I just released a long due update to package BatchGetSymbols. The files are under review in CRAN and you should get the update soon. Meanwhile, you can install the new version from Github: if (!require(devtools)) install.packages('devtools') devtools::install_github('msperlin/BatchGetSymbols') The main innovations are: Clever cache system: By default, every new download of data will be saved in a local file located in a directory chosen by user. Every new request of data is compared to the available local information. If data is missing, the function only downloads the piece of data that is missing.

Can we predict stock prices with Prophet?

Facebook recently released a API package allowing access to its forecasting model called prophet. According to the underling post: It's not your traditional ARIMA-style time series model. It's closer in spirit to a Bayesian-influenced generalized additive model, a regression of smooth terms. The model is resistant to the effects of outliers, and supports data collected over an irregular time scale (ingliding presence of missing data) without the need for interpolation. The underlying calculation engine is Stan; the R and Python packages simply provide a convenient interface. After reading it, I got really curious about the predictive performance of this method for stock prices.