obmodeling-package: obmodeling: Modeling, Analysis, and Graphics of Limit Order...

In jmazar/obmodeling: Modeling, Analysis, and Graphics of Limit Order Book Data

obmodeling: Modeling, Analysis, and Graphics of Limit Order Book Data

Description

The obmodeling package is designed to allow for modeling of order book features and dynamics based on L1 and L2 data. It includes analytical methods and graphics drawn from the microstructure literature and aims to be useful for both professional and academic researchers.

Details

The obmodeling package is designed to allow for modeling of order book features and dynamics based on L1 and L2 data. It includes analytical methods and graphiocs drawn from the microstructure literature and aims to be useful both for professional and academic researchers.

This is an R package which uses xts time series objects to manipulate and analyze:

• Depth of the market at a given time

• Volume

• Price movement through the day

• Weighted Midpoint (Cartea 2015)

• Market spread (de Jong 2009, 91–96)

• Measures of liquidity (Cartea 2015)

• Measures of volatility (Jong 2009, 92; Cartea 2015)

• PIN/VPIN (O. Easley D. 1996; L. de P. Easley D. 2012)

• Price pressure (Hendershot 2014; Cont 2011)

• Trade imbalance (Cont 2011)

Author(s)

Jeffrey Mazar and Brian G. Peterson

References

Cartea, Penalva, Jaimungal. 2015. Algorithmic and High-Frequency Trading. Cambridge.

Easley, López de Prado, D. 2012. “Flow Toxicity and Liquidity in a High-Frequency World.” Review of Financial Studies 25 (5): 1456–93.

Easley, O’Hara, D. 1996. “Liquidity, Information, and Infrequently Traded Stocks.” The Journal of Finance. 2 51 (4): 1405–36.

Hendershot, Menkveld. 2014. “Price Pressures.” Journal of Financial Econometrics 114: 405–23.

Jong, Rindi de. 2009. The Microstructure of Financial Markets. Cambridge.

Rama Cont, Sasha Stoikov, Arseniy Kukanov. 2011. “The Price Impact of Order Book Events.” ArXiv.

jmazar/obmodeling documentation built on March 27, 2022, 12:55 a.m.