# Tag Info

19

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 ...

16

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 ...

14

I would say in the context of trading in general (for HFT see my comment above) further developments of recurrent neural networks (RNN), e.g. so called historical consistent neural networks (HCNN) together with forecasting ensembles, are state of the art. I published an article on that which will be published this month by Springer Verlag (Zimmermann, ...

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 ...

14

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 ...

14

There are few things to consider. Trading moves the price, to minimize market impact and maximize return it is generally optimal to split an order in several child orders. See the Kyle model. Splitting optimally dependents on specific assumptions that you make. The simplest (and first) approach is that of Berstsimas and Lo (Optimal Control of Execution ...

11

All HFTs are event driven. In the most basic sense, they have some model that is a function of order book events. For every order book event the model calculates some micro price that is the HFTs perceived fair value. This is often a function of the current bid, ask, depth, last n trade prices, inventory, etc. Given the most up to date view of fair value, ...

11

There are many specialised products for HF tick data. In addition to KDB which you mentioned, there is OneTick, Vertica, Infobright, and some open-source ones like MonetDB etc. (see http://en.wikipedia.org/wiki/Column-oriented_DBMS). My experience is that Column Oriented Databases are overrated when it comes to tick data, because very often you request the ...

10

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 ...

10

HFT seems to be the big money making mystery machine these days. That's not correct. By its very nature, HFT can only produce a limited amount of revenue. The big money makers are still the large hedge funds that charge 2-and-20 on their \$10B worth of assets. There are not too many players there at the moment so markets are not completely ... 10 The best explanation/theory that I have heard about Knight's erratic trading was put forth by Nanex. I have pasted their summary of findings below. We believe Knight accidentally released the test software they used to verify that their new market making software functioned properly, into NYSE's live system. In the safety of Knight's test ... 9 IMO transaction data is a better approach, because you have both sides of the trade agreeing that the price is "right." The literature tends to decompose the transaction price$P$into a true/efficient price$P^e\$ plus micro-structure noise, which I think originates from Hasbrouck '93 in the Review of Financial Studies. So you end up with something like ...

8

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 ...

8

At higher frequencies the coastline is longer. Thus you can be more selective in your entries, or trade more. And by trading more you can get a higher statistical relevance for you system. When it will stop having an edge, you will be able to stop trading it before it eats into your previous profits. ie: if each day you make 0.5%, in 80 days you will have ...

8

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) ...

8

Intraday seasonality is a major factor in comparing volatility at different times of day. Most time series display significantly higher volatility in the morning EST than mid-day. For US exchange-traded products, volatility picks up again just before 4:00 PM EST. This is known as the u-shaped volatility pattern for exchange-traded products. A proper ...

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

The investor's holdings is a consequence of an investor's utility function interacting with the investor's perceived trading opportunity subject to constraints. (Indeed, the Kelly criterion is also utility maximizing.) We produced trades by re-balancing -- that is to say, we have new expectations of alpha or risk and the optimal portfolio net of these ...

7

There are typically two important metrics: Order to Accept. This measures the round-trip time it takes your application to send an order to the exchange and get an accept, cancel, or execute back. Think of it as the minimum amount of time required for you to ask the market to do something and know whether it's been done. This plays an important role when ...

7

I can think of an application in options pricing. I came across the following paper a long time ago but think it explains FT very eloquently as applied to pricing options under BS: http://maxmatsuda.com/Papers/2004/Matsuda%20Intro%20FT%20Pricing.pdf The fun starts on page 112 but it relies on the 1998 paper by Madan and Carr. What I like about the paper ...

6

You don't say what it is that you do with trade data that is made difficult by the bid-ask bounce. If it's for the purpose of establishing the price at which you can trade and it's at a frequency where the bid-ask bounce is a problem, then I think having realistic execution assumptions is the way to go. In particular this means that you should be mainly ...

6

My favorite culprit is quote stuffing, which can be used for a lot of things, including mapping the topology of the exchange servers themselves. The general idea is to look for bottlenecks which can then be lagged with more targeted quote-stuffing to create arb opportunities. Nanex's flash crash analysis covers this to some extent: ...

6

You won't know who made the trade, so you'll need to look at the quotes. Specifically, you should look to see if there are a lot of cancellations in the full order book. That will tell you if there's higher "churn" for a particular stock since HTFs often have low fill ratios (<1% for some shops). But you'll need to control for volatility since wild market ...

6

Market makers place quotes on both sides (ie, the bid and the ask). Depending on the market, the MM might even be contractually obligated to provide liquidity within some threshold. NYSE's designated market makers (who replaced the specialists a few years back) are an example. Even when there is no explicit requirement, the MM will quote both sides and ...

6

This answer summarizes some of my comments. HFT is certainly a very hot topic these days, but it's hard to point to any one reason. A large part of it is the mystery and the profits, but also part of it is the relative novelty. Note that there is no lack of papers about medium and low frequency strategies, it's just that they are not labeled as such. Medium ...

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

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 ...

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