adjpin() documentation example from 20 to 10 to reduce
example execution time.future::availableCores() with
parallelly::availableCores() to improve compatibility with
future package updates.ivpin(): This function implements
an improved version of the Volume-Synchronized Probability of Informed
Trading (VPIN) based on the work of Ke and Lin (2017). By employing a
maximum likelihood estimation, ivpin() enhances the
stability of VPIN estimates, especially in cases with small volume
buckets or infrequent informed trades. The function captures the
information embedded in volume time, generating more consistent and
reliable results. It is designed to improve the predictability of flow
toxicity in trading environments.
classify_trades() and
aggregate_trades() now accept negative
timelag values (treated as quote leads), with
updated documentation and examples on how lag/lead quotes are used in
the classification algorithms.
initials_adjpin(): Aligned the
function, which generates initial parameter sets for the adjusted PIN
model, with the algorithm outlined in Ersan and Ghachem (2024).
adjpin(): The function now includes
the time spent on generating initial parameter sets in the total time
displayed in the output. This enhancement provides a more comprehensive
view of the time taken for the entire process.
initials_adjpin_rnd(): Updated the
implementation for generating random initial parameter sets to align
with the algorithm described in Ersan and Ghachem (2024).
solve_eqx(): Enhanced the format
and performance of polynomial root calculations within the
conditional-maximization steps of the ECM algorithm.
initials_adjpin_cl(): Fixed an
issue with the calculation of the likelihood value according to the
algorithm of Cheng and Lai (2021).
detectlayers_eg(): Corrected the
return value to a single number when the number of information days is
equal to 1. Previously, it incorrectly returned a vector.
mpin_ecm(): Rectified an issue
where observations with zero probability in the E-step of the ECM
algorithm were assigned a fixed number of clusters (6).
Now, the function assigns a uniform probability of 1/cls,
where cls is the total number of clusters, to each
cluster.
future package. The updated code now uses lexical
scoping with the local() function to manage the variable
.lwbound between parallel function calls, preventing
unexpected results. Additionally, the maximum size of futures is no
longer set to +Inf upon package loading, leaving this
option adjustable by the user.Replaced the deprecated @docType package tag with
_PACKAGE to ensure proper generation of package
documentation.
Documentation now links output objects to their S4 class help
pages (e.g. \link{estimate.adjpin-class}), making it easier
to navigate to the corresponding class documentation from function
help.
Reduced computation in vignettes to improve build time.
We introduce a new function called classify_trades()
that enables users to classify high-frequency (HF) trades individually,
without aggregating them.
For each HF trade, the function assigns a variable that is set to
TRUE if the trade is buyer-initiated, or FALSE
if it is seller-initiated.
The aggregate_trades() function enables users to
aggregate high-frequency (HF) trades at different frequencies. In the
previous version, HF trades were automatically aggregated into daily
trade data. However, with the updated version, users can now specify the
desired frequency, such as every 15 minutes.
We identified and corrected an error in the
mpin_ecm() function. Previously, the function would
sometimes produce inconsistent results as the posterior distribution
allowed for the existence of information layers with a probability of
zero. We have now fixed this issue and the function produces correct
results.
We have made some updates to the mpin_ml() function
to better handle cases where the MPIN estimation fails for all initial
parameter sets. Specifically, we have fixed an error in the display of
the estimation results when such failure occurs. With these updates, the
function should now be able to handle such failures more robustly and
provide appropriate feedback.
We have simplified the ECM estimation functions, with a particular focus on the adjpin() function. We have improved the convergence condition of the iterative process used in the ECM estimation. Moreover, we rounded the values of the parameters at each iteration to a relevant number of decimals. This shall result in a faster convergence and prevent issues with decreasing likelihood values.
The functions pin(), pin_*(),
mpin_ml(), mpin_ecm(), adjpin(),
vpin(), and aggregate_trades() accept now, for
their arguments data, datasets of type matrix.
In the previous version, it only accepted dataframes, which did not
allow users, for instance, to use rollapply() of the
package zoo.
Introduction of the function pin_bayes() that
estimates the original pin model using a bayesian approach as described
in Griffin et al.(2021).
Fixed an error in the function initials_pin_ea() as
it used to produce some parameter sets with negative values for trade
intensity rates. The negative trade intensity rates are set to
zero.
Fixed two errors in the function vpin(): (1) A bug
in the calculation steps of vpin (2) The argument verbose
does not work properly.
Fixed an issue with resetting the plan for the future
(future::plan) used for parallel processing.
NEWS.md file to track changes to the
package.