PINstimation 0.2.0

PINstimation 0.2.0

New Features

• 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).

Updates

• 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.

Bugfixes

• 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.

Dependency Management

• Future Package: Addressed two concerns related to the 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.

Documentation

• 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.

PINstimation v0.1.2

New Features

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• 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.

New Bugfixes

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• 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.

PINstimation v0.1.1

New Features

• 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, only dataframes
  are accepted; 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).

Bug Fixes

• 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 did not work properly.

• Fixed an issue with resetting the plan for the future (future::plan) used for parallel processing.

PINstimation v0.0.1-beta

What's Changed

• Added a new function pin_bayes() that implements the Bayesian approach of Griffin et al.(2021)
• Fixed small errors in the implementation of the function vpin()
• Fixed the code of initials_pin_ea() to avoid rare instances where the function produced negative trading rates
• Simplified the computation of the factorizations of the PIN likelihood functions
• Simplified the process of check and validation of the different function arguments

PINstimation v0.1.0

Initial release

Changes:
fixed future::plan reset