https://github.com/monty-se/pinstimation

A comprehensive bundle of utilities for the estimation of probability of informed trading models: original 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). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.
https://github.com/monty-se/pinstimation

clustering-analysis expectation-maximisation-algorithm hierarchical-clustering information-asymmetry market-microstructure maximum-likelihood-estimation mixture-distributions poisson-distribution

Last synced: 8 months ago
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Host: GitHub
URL: https://github.com/monty-se/pinstimation
Owner: monty-se
License: gpl-3.0
Created: 2022-05-24T13:50:07.000Z (about 4 years ago)
Default Branch: master
Last Pushed: 2024-10-22T19:46:16.000Z (over 1 year ago)
Last Synced: 2025-10-14T11:12:27.819Z (9 months ago)
Topics: clustering-analysis, expectation-maximisation-algorithm, hierarchical-clustering, information-asymmetry, market-microstructure, maximum-likelihood-estimation, mixture-distributions, poisson-distribution
Language: R
Homepage: https://pinstimation.com/
Size: 5.27 MB
Stars: 40
Watchers: 2
Forks: 7
Open Issues: 1
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md

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README

          # PINstimation: Estimating Models of Probability of Informed Trading 

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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.

[![Like PINstimation? - Support our Work!](https://img.shields.io/badge/Like%20PINstimation%20-%20Support%20Our%20Work!-red?style=flat&logo=ko-fi&logoColor=white)](https://ko-fi.com/pinstimation)

## New features in Version 0.1.3

* We have updated the `initials_adjpin()` function, which generates initial parameter sets for the adjusted PIN model, to align with the algorithm outlined in Ersan and Ghachem (2024).

* The function `adjpin()` now includes the time spent on generating initial parameter sets in the total time displayed in the output.

  

*  The function `ivpin()` has been introduced, which implements an improved version of the Volume-Synchronized Probability of Informed Trading (VPIN), based on Lin and Ke (2017). The function uses maximum likelihood estimation to provide more stable VPIN estimates, particularly for small volume buckets or infrequent informed trades, and improves the predictability of flow toxicity.

## New features in Version 0.1.2

* 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 `isBuy` 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.

## Table of contents

* [Main functionalities](#main-functionalities)

* [Installation](#installation)

* [Examples](#examples)

  * [PIN model estimation](#example-1-estimate-the-pin-model)

  * [MPIN model estimation](#example-2-estimate-the-multilayer-pin-model)

  * [AdjPIN model estimation](#example-3-estimate-the-adjusted-pin-model)

  * [VPIN model estimation](#example-4-estimate-the-volume-adjusted-pin-model)

  * [Data classification](#example-5-estimate-the-adjpin-model-using-aggregated-high-frequency-data)

* [Resources](#resources)

* [Note to frequent users](#note-to-frequent-users)

* [Contributions](#contributions)

* [Alternative packages](#alternative-packages)

* [Getting help](#getting-help)

## Main functionalities

The functionalities that the package offers are summarized below:

* **PIN model**

  * estimate the PIN model using the functions `pin()`, `pin_yz()`, `pin_gwj()`, and `pin_ea()`.

  * compute initial parameter sets using the functions `initials_pin_yz()`, `initials_pin_gwj()`, and `initials_pin_ea()`.

  * generate simulation data following the PIN model using `generatedata_mpin(layers=1)`.

  * evaluate factorizations of the PIN likelihood functions using `fact_pin_eho()`, `fact_pin_lk()`, `fact_pin_e()`.

  * estimate the PIN model by **the Bayesian approach** (Gibbs Sampler) using `pin_bayes()` **(*)** .

* **MPIN model**

  * estimate the MPIN model using the functions `mpin_ml()` and `mpin_ecm()`.

  * compute initial parameter sets using `initials_mpin()`.

  * detect the number of layers in data using `detectlayers_e()`, `detectlayers_eg()`, and `detectlayers_ecm()`.

  * generate simulation data following the MPIN model using `generatedata_mpin()`.

  * evaluate the factorization of the MPIN likelihood function through `fact_mpin()`.

* **AdjPIN model**

  * estimate the AdjPIN model using the function `adjpin()`.

  * compute initial parameter sets using functions `initials_adjpin()`, `initials_adjpin_cl()`, and `initials_adjpin_rnd()`.

  * generate simulation data following the AdjPIN model using `generatedata_adjpin()`.

  * evaluate the factorization of the AdjPIN likelihood function through `fact_adjpin()`.

* **VPIN**

  * estimate the VPIN model using the function `vpin()`

* **Data classification**

  * Classify high-frequency data through `tick`, `quote`, `LR` and `EMO` algorithms using the function `aggregate_trades()`

## Installation

The easiest way to get PINstimation is the following:

```r

install.packages("PINstimation")

```

To get a bugfix or to use a feature from the development version, you

can install the development version of PINstimation from GitHub.

```r

# install.packages("devtools")

# library(devtools)

devtools::install_github("monty-se/PINstimation", build_vignettes = TRUE)

```

Loading the package

```r

library(PINstimation)

```

## Examples

### Example 1: Estimate the PIN model

We estimate the PIN model on preloaded dataset `dailytrades` using the initial parameter sets of Ersan & Alici (2016).

```r

estimate <- pin_ea(dailytrades)

```

```r

## [+] PIN Estimation started 

##   |[1] Likelihood function factorization: Ersan (2016)

##   |[2] Loading initial parameter sets   : 5 EA initial set(s) loaded

##   |[3] Estimating PIN model (1996)      : Using Maximum Likelihood Estimation

##   |+++++++++++++++++++++++++++++++++++++| 100% of PIN estimation completed

## [+] PIN Estimation completed

```

### Example 2: Estimate the Multilayer PIN model

We run the estimation of the MPIN model on preloaded dataset `dailytrades` using:

* the maximum-likelihood method.

```r

ml_estimate <- mpin_ml(dailytrades)

```

```r

## [+] MPIN estimation started

##   |[1] Detecting layers from data       : using Ersan and Ghachem (2022a)

##   |[=] Number of layers in the data     : 3 information layer(s) detected

##   |[2] Computing initial parameter sets : using algorithm of Ersan (2016)

##   |[3] Estimating the MPIN model        : Maximum-likelihood standard estimation

##   |+++++++++++++++++++++++++++++++++++++| 100% of mpin estimation completed

## [+] MPIN estimation completed

```

* the ECM algorithm.

```r

ecm_estimate <- mpin_ecm(dailytrades)

```

```r

## [+] MPIN estimation started

##   |[1] Computing the range of layers    : information layers from 1 to 8

##   |[2] Computing initial parameter sets : using algorithm of Ersan (2016)

##   |[=] Selecting initial parameter sets : max 100 initial sets per estimation

##   |[3] Estimating the MPIN model        : Expectation-Conditional Maximization algorithm

##   |+++++++++++++++++++++++++++++++++++++| 100% of estimation completed [8 layer(s)]

##   |[3] Selecting the optimal model      : using lowest Information Criterion (BIC)

## [+] MPIN estimation completed

```

Compare the aggregate parameters obtained from the ML, and ECM estimations.

```r

mpin_comparison <- rbind(ml_estimate@aggregates, ecm_estimate@aggregates)

rownames(mpin_comparison) <- c("ML", "ECM")

cat("Probabilities of ML, and ECM estimations of the MPIN model\n")

print(mpin_comparison)

```

Display the summary of the model estimates for all number of layers.

```r

summary <- getSummary(ecm_estimate)

show(summary)

```

```r

##          layers em.layers  MPIN Likelihood    AIC    BIC    AWE

## Model[1]      1         1 0.566  -3226.469 6462.9 6473.4 6508.9

## Model[2]      2         2 0.577   -800.379 1616.8 1633.5 1690.3

## Model[3]      3         3 0.574   -643.458 1308.9 1332.0 1410.0

## Model[4]      4         3 0.574   -643.458 1308.9 1332.0 1410.0

## Model[5]      5         3 0.574   -643.458 1308.9 1332.0 1410.0

## Model[6]      6         3 0.574   -643.458 1308.9 1332.0 1410.0

## Model[7]      7         4 0.575   -642.631 1313.3 1342.6 1441.9

## Model[8]      8         4 0.575   -642.631 1313.3 1342.6 1441.9

```

### Example 3: Estimate the Adjusted PIN model

We estimate the adjusted PIN model on preloaded dataset `dailytrades` using `20` initial parameter sets computed by the algorithm of Ersan and Ghachem (2022b).

```r

estimate_adjpin <- adjpin(dailytrades, initialsets = "GE")

show(estimate_adjpin)

```

```r

## [+] AdjPIN estimation started

##   |[1] Computing initial parameter sets : 20 GE initial sets generated

##   |[2] Estimating the AdjPIN model      : Maximum-likelihood Standard Estimation

##   |+++++++++++++++++++++++++++++++++++++| 100% of AdjPIN estimation completed

## [+] AdjPIN estimation completed

```

### Example 4: Estimate the Volume-adjusted PIN model

We run a VPIN estimation on preloaded dataset `hfdata` with `timebarsize` of `5` minutes (`300` seconds).

```r

estimate.vpin <- vpin(hfdata, timebarsize = 300)

show(estimate.vpin)

```

```r

## ----------------------------------

## VPIN estimation completed successfully.

## ----------------------------------

## Type object@vpin to access the VPIN vector.

## Type object@bucketdata to access data used to construct the VPIN vector.

## Type object@dailyvpin to access the daily VPIN vectors.

## 

## [+] VPIN descriptive statistics

## 

## |      | Min.  | 1st Qu. | Median | Mean  | 3rd Qu. | Max.  | NA's |

## |:-----|:-----:|:-------:|:------:|:-----:|:-------:|:-----:|:----:|

## |value | 0.101 |  0.185  | 0.238  | 0.244 |  0.29   | 0.636 |  49  |

## 

## 

## [+] VPIN parameters

## 

## | tbSize | buckets | samplength |   VBS    | #days |

## |:------:|:-------:|:----------:|:--------:|:-----:|

## |  300   |   50    |     50     | 36321.25 |  77   |

## 

## -------

## Running time: 3.753 seconds

```

### Example 5: Estimate the AdjPIN model using aggregated high-frequency data

We use the preloaded high-frequency dataset `hfdata`, prepare it for aggregation.

```r

data <- hfdata

data$volume <- NULL

```

We classify data using the LR algorithm with a time lag of `500` milliseconds (`0.5 s`), using the function `aggregate_data()`.

```r

daytrades <- aggregate_trades(data, algorithm = "LR", timelag = 500)

```

```r

## [+] Trade classification started

##   |[=] Classification algorithm         : LR algorithm

##   |[=] Number of trades in dataset      : 100 000 trades

##   |[=] Time lag of lagged variables     : 500 milliseconds

##   |[1] Computing lagged variables       : using parallel processing

##   |+++++++++++++++++++++++++++++++++++++| 100% of variables computed

##   |[=] Computed lagged variables        : in 7.68 seconds

##   |[2] Computing aggregated trades      : using lagged variables

## [+] Trade classification completed                

```

We use the obtained dataset to estimate the (adjusted) probability of informed trading via the standard Maximum-likelihood method.

```r

adjpin_ml <- adjpin(daytrades, method = "ML", initialsets = "GE")

```

```r

## [+] AdjPIN estimation started

##   |[1] Computing initial parameter sets : 20 GE initial sets generated

##   |[2] Estimating the AdjPIN model      : Maximum-likelihood Standard Estimation

##   |+++++++++++++++++++++++++++++++++++++| 100% of AdjPIN estimation completed

## [+] AdjPIN estimation completed

```

## Note to frequent users

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

`load_pinstimation_for_good()`.

To automatically load PINstimation, run `load_pinstimation_for_good()`,

and the following code will be added to your .R profile.

```r

if (interactive()) suppressMessages(require(PINstimation))

```

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 it.

## Resources

For a smooth introduction to, and useful tips on the main functionalities of the package, please refer to:

* The sections [Get Started](https://pinstimation.com/articles/PINstimation.html), and  [Online documentation](https://pinstimation.com/reference/index.html) on the package site.

* The package documentation in PDF format is available for download [here](https://pinstimation.com/documents/PINstimation_0.1.0.pdf).

* An overview of the scientific research underlying the package is available [here](https://pinstimation.com/research.html).

## Contributions

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 `Tick`, `Quote`, `LR`, and `EMO`.

## Alternative packages

To our knowledge, there are three preexisting R packages for the estimation of models of the probability of informed trading: [pinbasic](https://cran.r-project.org/package=pinbasic), [InfoTrad](https://cran.r-project.org/package=InfoTrad), and [FinAsym](https://cran.r-project.org/package=FinAsym).

## Getting help

If you encounter a clear bug, please file an issue with a minimal

reproducible example on [GitHub](https://github.com/monty-se/PINstimation/issues).

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