LOBSTER: high-frequency, easy-to-use and latest limit order book data for your research.

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Lobster Data Demo Codes | January 23, 2020

Great news!  Demo codes  on processing lobster data by using various programming languages are available in Internet.

  • LOBSTER parser in Google Go by Dan Cline from MIT, is available at here.    
  • LOBSTER demo code in Python by Arnaud Amsellem, an independent quant trader,  is available at here .

Many thanks to the contributors.

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Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book | February 14, 2019

Markus Bibinger from the University of Marburg, Christopher Neely from the Federal Reserve Bank of St. Louis and Lars Winkelmann from Free University Berlin published a paper using LOBSTER data. It is titled Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book and is forthcoming in the Journal of Econometrics.

Abstract: An extensive empirical literature documents a generally negative relation, named the “leverage effect,” between asset returns and changes of volatility. It is more challenging to establish such a return–volatility relationship for jumps in high-frequency data. We propose new nonparametric methods to assess and test for a discontinuous leverage effect — i.e. a covariation between contemporaneous jumps in prices and volatility. The methods are robust to market microstructure noise and build on a newly developed price-jump localization and estimation procedure. Our empirical investigation of six years of transaction data from 320 NASDAQ firms displays no unconditional negative covariation between price and volatility cojumps. We show, however, that there is a strong and significant discontinuous leverage effect if one conditions on the sign of price jumps and whether the price jumps are market-wide or idiosyncratic.

You can find the article here.

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Information of Liquidity Deeply in the Order Book | March 12, 2018

Although LOBSTER has the capability to generate the entire limit order book, it is currently restricted to a maximum of 200 quote level. As a result a frequently asked question is “Why can’t I generate the whole book?” Besides the data size consideration, the main rational behind this decision is that the liquidity deeply in the book is not likely to be informative.

First, algorithmic traders are not likely to react to deep liquidity, because:

  1. the market data feed of most of trading platform in the industry do not provide the full book information, and
  2. it is not optimal for algorithmic trading strategy to react to deep liquidity – Taking advantage of its speed, the algorithm gets enough time to react when the liquidity is showing up close to the market, say in 5 to 20 quote levels. A model taking an action when the liquidity is still hundreds of levels behind the market, disregarding the platform’s low-latency advantage, is clearly sub-optimal (originally, I use adjective “stupid” which is impolite but likely more proper).

Second, the lower frequency traders are incapable to rationally react to deep liquidity, since

  1. most of them do not have the data feed, and
  2. a human being can hardly analyse more than ten level quotes in a timely fashion.

Third, the deep liquidity in book is typically from uninformative sources:

  1. low-frequency traders who lack the capability to monitor the market in real time, and
  2. the market markers who are obligated to quote on both sides but are not willing to trade on either or both sides.

The the above screen shot from Ivo Zeba’s LOBSTER visualisation tool shows the liquidity distribution and price dynamics over a period.

  • Liquidity A was closed monitored and could be potentially informative in its first showing-up in the book. Since it was in low levels, LOBSTER outputted it “on time”.
  •  Liquidity B was in the mid-range in the book, LOBSTER outputted it. But it might not be really informative for price prediction at its first showing-up.
  • Like liquidity B, liquidity C was also very persistent and getting picked-off. It had not been outputted at the time of showing-up. However, LOBSTER did output it early enough for its price impact into the consideration for a meaningful model.

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