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25

I'll just add that with Interactive Brokers you have to be aware of their cancel fees. Remember, Interactive Brokers owns Timber Hill, a very large and active market maker. They will discourage you from competing with Timber Hill through monetary disincentives, among other things. For example, if you send a directed order (i.e., you don't allow IB to SMART ...


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 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, ...


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 ...


14

Look at Genesis Trading. Most of the sales guys there are kinda like used car salesmen but they will work with you. Starting up with 50K should not be a problem for them. The offer full depth of book feeds if you are colocated with them. They do offer DMA and you can specify all routing instructions for your orders rather than getting stuck on IBs router. ...


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

DTN's IQFeed is really good, if a little expensive. I believe it starts at 80 dollars/month and then you add your exchange fees on top. To get access to the developer API you need to pay 300 dollars for a year's worth of access. Details: Real-Time, TRUE Tick-by-Tick Data on US and Canadian Equities (NYSE, NASDAQ, AMEX, Canadian Stock Exchanges) Delayed ...


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

This has nothing to do with IB in particular. The primary issue with retail data feeds is that they run over the Internet. That means dealing with a shared line and all of the latency spikes that comes with it. Institutional traders, even when they aren't co-located, build a private network pipe to their data vendor since that's the only way to prevent ...


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 ...


13

Holding period and trade frequency are two different things. If you have a high trade frequency, the name of the game is negotiating lower commissions. That being said, the TWS API gives you the same quality feed as you get using TWS itself. From Article on HFT Provided by Dirk Eddelbuettel in this question about HFT: High-frequency trading (HFT) is ...


13

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 ...


12

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

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

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 ...


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

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 ...


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 ...


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

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 ...


8

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 ...


8

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 ...


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

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

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 ...


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 ...


8

The flickered orders are postonly bid at 15.16. The exchange slides it back to 15.15 to avoid a locked market. Submitting firm sees the slideback and cancels. Then tries again. When the 15.16 offer is executed or cancelled out, the offer moves to 15.17 then the postonly bid at 15.16 goes through at the targeted price and gains good queue position.


8

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 ...



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