PINstimation provides utilities for the estimation of probability of informed trading models: original PIN (PIN) in Easley and O’Hara (1992) and Easley et al. (1996); multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume- synchronized PIN (VPIN) in Easley et al. (2011, 2012). Various computation methods suggested in the literature are included. Data simulation tools and trade classification algorithms are among the supplementary utilities. The package enables fast and precise solutions for the sophisticated, error-prone and time-consuming estimation procedure of the probability of informed trading measures, and it is compact in the sense detailed estimation results can be achieved by solely the use of raw trade level data.
New Features in Version 0.1.1The functions
aggregate_trades()accept now, for their arguments
data, datasets of type
matrix. In the previous version, only dataframes are accepted; which did not allow users, for instance, to use
rollapply()of the package
zooin straightforward manner.
Introduction of the function
pin_bayes()that estimates the original pin model using a bayesian approach as described in Griffin et al.(2021).
A tutorial how to create sample datasets available here.
The easiest way to get PINstimation is the following:
To get a bugfix or to use a feature from the development version, you can install the development version of PINstimation from GitHub.
# install.packages("devtools") # library(devtools) devtools::install_github("monty-se/PINstimation")
Loading the package
If you are a frequent user of PINstimation, you might want to avoid
repetitively loading the package PINstimation whenever you open a new R
session. You can do that by adding PINstimation to
.R profile either manually, or using the function
To automatically load PINstimation, run
load_pinstimation_for_good(), and the following code will
be added to your .R profile.
After restart of the R session, PINstimation will be loaded
automatically, whenever a new R session is started. To remove the
automatic loading of PINstimation, just open the .R profile for editing
usethis::edit_r_profile(), find the code above, and delete
For a smooth introduction to, and useful tips on the main functionalities of the package, please refer to:
The package makes a series of original contributions to the literature:
An efficient, user-friendly, and comprehensive implementation of the standard models of probability of informed trading.
A first implementation of the estimation of the multilayer probability of informed trading (MPIN) as developed by Ersan (2016).
A comprehensive treatment of the estimation of the adjusted probability of informed trading as introduced by Duarte and Young (2009). This includes the implementation of the factorization of the AdjPIN likelihood function, various algorithms to generate initial parameter sets, and MLE method.
The introduction of the expectation-conditional maximization (ECM) algorithm as an alternative method to estimate the models of probability of informed trading. The contribution is both theoretical and computational. The theoretical contribution is included in the paper by Ghachem and Ersan (2022b). The implementation of the ECM algorithm allows the estimation of PIN, MPIN, as well as the adjusted PIN model.
Implementation of three layer-detection algorithms, namely of preexistent algorithm of Ersan (2016), as well as two newly developed algorithms, described in Ersan and Ghachem (2022a), and Ghachem and Ersan (2022b), respectively.
A first implementation of the estimation of the volume-synchronized probability of informed trading (VPIN) as introduced by Easley et al (2011, 2012).
One do-it-all function for trade classification in buyer-initiated or seller-initiated trades that implements the standard algorithms in the field, namely
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.