Volume Synchronized Probability of Informed Trading

The Volume Synchronized Probability of Informed Trading, commonly known as VPIN, is a mathematical model used in financial markets for multiple purposes.

Basic Info

• Version - Written in Python 2.7.1
• Keywords
  • VPIN
  • Random control
  • Market micro-structure

Prerequisite

• Numpy as np
• Pandas as pd
• Matplotlib as plt

Usage

Dataset

• Data source: wind
• Sampling time range: Jan 2015-Oct 2018

Correspondence

Please do not hesitate to submit an issue or contact via email.

Output

• Correlation between VPIN gap versus tick price
• Market volume and sample population

Landscape

Introduction

For high frequency trading, market maker need information to make a profit in an informed trading, because reverse selection may cause losses in transactions. The VPIN method intends to measure the probability of market informed transaction; as a predictive sign of the market liquidity risk.

Methodology

Overview

For transactions in the sampling period, the entile volume is divided into 50 baskets. VBS (i.e. Volume of Basket) is defined as total transaction volume in each separate basket.

Filling Procedure

• Fill of basket starts when transaction starts
• When volume of transaction exceeds the upper bound, calculate r, which indicates the rest of transaction amount
• Loop of aforementioned process generate a series of baskets.

Implications

From a visualized perspective, time series remained stationary. Meanwhile, CSI-300 normally fluctuate dramatically after enlargement of VPIN index.

Review of code

VPIN.ipynb

Please refer to the entire code project via this document, including sample outputs.

Sample Output