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21

You could for example look at this research paper released by Deutsche Bank's Research group just yesterday which defines both high-frequency and ultra-high-frequency trading. In the paper it says Typically, a high frequency trader would not hold a position open for more than a few seconds. Empirical evidence reveals that the average U.S. stock is ...


17

The lead paper in the January 2011 Journal of Finance (Hendershott, Jones, and Menkveld) addresses algorithmic trading (AT). In short, they find that AT improves liquidity as measured by bid-offer spreads. Taking the econometrics as correct (it is in the Journal of Finance) the next question is if bid-offer spreads are a sufficient statistic for measuring ...


15

I'll take a stab at it, but this is a really broad question. A direct answer: Bayesian models often use "probability that the counter-party is informed." Indirect answers: I think your assumption is that the algorithm operates on each stock individually, and has no knowledge of what it's doing in any other stock. But, it is likely that the algorithm is ...


15

In fact you have three papers available to go further: The Avellaneda-Stoikov one, with proper model and an approximate solution The Bayraktar-Ludvkosli one, with a solution for the linear utility function The L-Guéant-Fernandez one, with a full solution for a generic utility function I prefer the last one ;{)}


14

A survey by FinAlternatives in 2009 concluded that "86% believe that the term “high-frequency trading” referred strictly to holding periods of only one day or less." (Aldridge 2009): There are two problems with this survey for our present discussion: (1) the meaning of the term has been clarified significantly since that survey and (2) it surveyed a wide ...


14

My definition is not pretty, but it's practical: If you trade based on 5- or 10-minute bars, I call that high-frequency trading. If you trade based on tick-by-tick data, including bids and offers, I call that ultra-high frequency trading. (Trading 1-minute bars is somewhere in between. Trading more slowly than 10-minute bars is "day trading".) I make this ...


11

I. Re: # of trades... According to WK Selph (former quant turned blogger) @ WK's High Frequency Trading How To: To give some idea of the data volumes, the Nasdaq TotalView ITCH feed, which is every event in every instrument traded on the Nasdaq, can have data rates of 20+ gigabytes/day with spikes of 3 megabytes/second or more. The ...


11

"quote spam", "book colouring", "quote stuffing", etc encompass any mechanism to modify the shape of the orderbook by a market participant who does not intend to really buy or sell shares thanks to these orders. It means that someone fills the bid side of the book with 10,000 shares at different levels of price and does not want to buy at all, or only 100 ...


10

Since Quant Cup 1's objective was an efficient price/time matching engine, the data structure of the winning implementation might partly be what you are looking for. Else the setup of LOBSTER is supposed to be quick.


9

This answer is my ongoing attempt to consolidate some recent commentary on this hot topic. A good place to start for anyone thinking about this question is the Economists's Buttonwood: Not So Fast, which mentions recent research by Biais and Woolley (2011) and Dichev, Huang, and Zhou (2011). Does Algorithmic Trading Improve Liquidity? This paper claims ...


9

Some cynical but functional definitions: It's what you can't model if you're not using tick by tick data It's what proper quant pricing theory doesn't know how to model yet It's information (order book behavior) that reflects momentary fluctuations in the supply/demand of a given contract, rather than its underlying value (eg an arbitrage free price) ...


9

I've not yet read it, but Lehalle's recent book is bound to be a goldmine of good micro-structure bits and pieces. Market Microstructure in Practice EDIT: I'm reading the book now, so far it's quite good.


9

Among matching rule, do not forget "auction calls", in most markets, you have one at the open and one at the close. To give you the main reasons to use one matching engine rather than another: Auction calls (i.e. fixings) are good to digest a lot of orders in a very short amount of time. It is why after a trading suspension, the trading starts with an ...


8

It seems that your question refers to the microstructure noise defined in papers about intraday volatility estimates. Originally, it comes from the bid-ask bounce, i.e. the fact that even if the volatility is zero, you have buyers and sellers at this price and consequently you observe prices at Bid or Ask prices, and not at mid-price. Because of that, if ...


8

The main issue measuring intraday volatility is called "signature plot": when you zoom in, the volatility measure (i.e. empirical quadratic variations) explode. Similarly you have the "Epps effect" for correlations: when you zoom in, the correlations collapse (it is at least a mechanical effect). For the volatility a lot of models can correct this: - first ...


7

The Eurodollar market is partially pro-rata. And there is a lot of HFT on it. Getting out of the book when conditions are not right is very much HFT.


7

Here's a blog post with a general overview of some possible implementations. http://www.quantcup.org/home/howtohft_howtobuildafastlimitorderbook


7

DSpace@MIT - High frequency trading system design and process management (non-printable) This thesis provides a detailed study composed of high frequency trading system design, system modeling and principles, and processes management for system development. Particular emphasis is given to backtesting and optimization, which are considered the most ...


7

The term has a different meaning to different people. to econometricians, microstructure noise is a disturbance that makes high frequency estimates of some parameters (e.g. realized volatility) very unstable. Generally this strand of the literature professes agnosticism as to the its origin; to market microstructure researchers, microstructure noise is a ...


7

There are rigorous econometric definitions, as has already been eluded to by others. For practical purposes, microstructure noise is a component of a price process that exhibits mean reversion on some (possibly time-varying) frequency. This reversion is particularly attractive to liquidity provisioners, who seek to profit from this noise component (along ...


7

Joel Hasbrouck (imho, a leading expert in market microstructure) has a paper on this: http://people.stern.nyu.edu/jhasbrou/Research/Working%20Papers/HS10-11-10.pdf From the abstract: Our conclusion is that increased low-latency activity improves traditional market quality measures such as short-term volatility, spreads, and displayed depth in the limit ...


7

Your question is very vague (e.g. what are you trying to measure, and what "tick data" do you have), but I'll give you some pointers: In general, when people consider how prices evolve, they will tend to think about things like volatility and correlation dynamics. So I would start by defining exactly what you want to measure. The irregularity of time ...


7

There are several. This list is from Giyenko et al (2008)---in their work they compare all these different measures--- and includes spread proxies and price impact proxies. As for spread proxies: "Effective Tick" (Holden 2007, Giyenko et al 2008) "Holden measure" (Holden 2007) "LOT Y-split" (Giyenko et al 2008) "Roll measure" (Roll 1984) "Gibbs measure" ...


7

This paper Dealing with the Inventory Risk. A solution to the market making problem, has a full bibliography and explains the intra day market making mechanism. The model is made of two components: a diffusion of the fair price (to model the market risk) a point process (with an intensity in $A \exp -k \delta$ (where $\delta$ is the distance to the fair ...


7

I recommend reading Cao, Hansch, and Wang (2004) "The Informational Content of an Open Limit Order Book". They present a simple model for an order-book price called the weighted price ($\mbox{WP}$): $$ \mbox{WP}^{n_1 - n_2} = \frac{\sum_{j=n_1}^{n_2} (Q_j^d P_j^d + Q_j^s P_j^s)}{(Q_j^d + Q_j^s)} $$ Where: $n$ is the order book level $Q_j$ is the size at ...


6

i am not a F# expert but when it comes to performance and thread safety try sorted list or hashset. sorted list if the data needs to be sorted (it gets sorted when added to the list) otherwise hashset, no sorting hence better performance. they are both generic. in addition i would think you need thread safety when reading/writing/updating your data in ...


6

HFT can be loosely defined as any strategy where your profitability is a function of latency.


6

The expression you have is fine. But more generally, for the intraday volatility, I don't think there "the correct definition". More like, whatever works in the given context. I found the following notes by Almgren pretty useful: http://cims.nyu.edu/~almgren/timeseries/notes7.pdf


6

Are you after the famous paper from Christie and Schultz? Christie,W., Schultz,P., 1994. Why do NASDAQ market makers avoid odd-eighth quotes? Journal of Finance 49,1813–18 40. From the abstract: On May 26 and 27, 1994 several national newspapers reported the findings of Christie and Schultz (1994) who cannot reject the hypothesis that market makers ...


6

The best paper is probably Relative Volume as a Doubly Stochastic Binomial Point Process - James Mcculloch. In this paper the volume is modelled via a Point Process, and theoretical laws are derived (with confident intervals, etc). And if you can wait few days (it will be available very soon), we put elements about this in Market Microstructure in Practice, ...



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