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5

I am not aware on any rules preventing a too high number of entries at a limit price. Nevertheless you usually have controls for each trader id. A trader cannot have too many orders in the book or send them at a too high frequency. [EDIT] Moreover, on most trading platforms you cannot have orders too far away from the mid (or a reference price like the ...


4

You are usually given an option to either - Request a re-transmission of the messages you missed (through a different channel). Request a snapshot of the current book from a dedicated server. Both are likely TCP based.


4

My Answer You should set your limit order to: $s (v+1)^{-0.0314192 \sqrt{t}}$ where $s$ is the current price, $t$ is the time in years you're willing to wait, and $v$ is the annual volatility as a percentage. If you want to be $p$ percent sure (instead of 0.98), set your limit order to: $s (v+1)^{-\sqrt{\pi } \sqrt{t} \text{erf}^{-1}(1-p)}$ Of course, ...


3

A lot of people are working on this and at this time a lot of money and investment is chasing this. As a result market microstructure is changing a lot. Liquidity is becoming much less visible. You have stocks that might trade a 100 million shares a day but the sizes you will see in the book at any time in a lit exchange like Nasdaq might not be more than ...


3

One way to do this is a simple Monte-Carlo simulation. There are formulae you can use to get the likelihood of a stock being below a price if you know the stock's volatility and time frame - see for example this question. For an unknown time frame, the Monte-Carlo method is (IMO) simpler than the mathematics. You would simply run a number of simulations ...


2

To slice up an order you can use several execution strategies. TWAP which will execute small slices of your order over a time period VWAP which will spread your order over time and try to minimize slippage against the vwap benchmark for a given instrument POV which will split your order up into smaller chunks and attempt to keep your order filled as a ...


2

Most of the big players offer a suite of execution algorithms for big orders, as seen in this listing from Credit Suisse. Very generally speaking, the algorithms will have a pedigree going back to volume weighted average pricing schedules, or perhaps to the famous paper by Almgren and Chriss. They have various modifications, including use of "unusual" ...


2

I am not familiar with F#. I have implemented this many times in C++. I would go with fixed length arrays. Fr me performance is paramount. In C++, one is better off handling holes than allocating memory on the heap and add the complexity of a cache miss.


2

Have a look to this paper, the methodology is well defined: Simulating and analyzing order book data: The queue-reactive model, by Huang, L and Rosenbaum. You need first to define properly the events of which you want to estimate the intensity. I would suggest insert cancel trade. Then for each available tick of price $p$ (not each limit): normalize ...


2

A "flickering" order is one which is repeatedly submitted and cancelled (whether it's at the top of book or not). The answer from @chollida mentions that "the goal typically is to either slow down competitors quotes by flooding the gateway interface with noise" but I don't think that's necessarily true. Rather, I think many flickering quotes are caused by ...


1

This reference price is also sometimes called intrinsic price. One of the simplest ways to improve it in regards to the mid-price (assuming you have the depth data) is the following: define a parameter: the size of a hypothetical market order. Let's say it's about the typical sum of first 3-10 order book levels of the instrument; execute a Buy order with ...


1

This paper is a microstructure paper (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2185452) that you may be looking at. We will assume the complex dynamics originated by the interaction between sell- and buy- side (limit and market orders) can be modelled in terms of a continuous stochastic process Mt, the microprice, which we assume to be a ...



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