29
The specifics depend on if you're implementing for equities (order-based) or futures (level-based). I recommend https://web.archive.org/web/20110219163448/http://howtohft.wordpress.com/2011/02/15/how-to-build-a-fast-limit-order-book/ for a general overview of a good architecture for the former.
Building off of that, though, I have found that using array-...
24
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 ;{)}
24
My two favorites books on microstructure are:
Barry Johnson's Algorithmic Trading and DMA - very good on technological aspects and for an overview of needed implementations;
L and Laruelle's Market Microstructure in Practice - for common knowledge and understanding of market microstructure and its mechanisms.
Recently, three new books have been published:
...
14
This is a very difficult question.
First of all you should read Almgren's slides on the topic: Using a Simulator to Develop Execution Algorithms.
First you need to backtest your strategy against a "replayer". Ok it is not perfect, but it gives you information anyway. Provided you add some "sanity limitation" to this simulator (i.e. do not allow you ...
13
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.
11
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 ...
11
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 ...
11
A public order book gives traders information not only on the current price of a security, but also the volume and structure of the entire supply and demand schedule.
Such information can be used for arbitrage and market manipulation strategies in various ways:
Spoofing: Inserting a large limit order as an apparent buy or sell signal which is canceled any ...
11
I found this power point and this paper to be an excellent source on this topic.
Here is a quote from the paper:
A square-root singularity for small traded volumes is highly
non-trivial, and certainly not accounted for in Kyle’s classical model
of impact [11], which predicts a linear impact ∆ ∝ Q. A concave impact
function is often thought of as a ...
10
Classical book on market microstructure is: Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris. It's a bit outdated (2002) and missing few recent market developments like dark pools etc. but the way it currently is it's already highly recommended reading.
Personally I'm waiting for the next edition of the same book, and surely ...
10
I am not sure Dark Pools (DP) have been created to avoid "market manipulation". They have been created by firms because they found an advantage to create them (see Market Microstructure in Practice, L and Laruelle Eds.). The main reasons have been:
spare market fees, for DP created by brokers (like UBS MTF);
spare market impact, for block pools (like ITG/...
9
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 ...
9
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 we put elements about this in Market Microstructure in Practice, Chap 2.1.
Volume curves are analyzed, not only during the ...
9
If you want to learn more about price pressure, you should look after market impact of metaorders, which is a more adequate term.
Because of the microstructure (i.e. the mix of orderbboks dynamics, trading rules, participants behaviours and habits, etc), the more you buy or sell, the more you influence the price an unfavorable way (for your trades).
Just ...
8
I recently stumbled upon this question after needing to do this to do some real-time microstructure analysis and have taken a look at the various possible implementations. Here are some of the pros/cons of each implementation (I'll use C/C++ terminology):
Array:
You have an array of structs ordered from top of book (index 0) to worst (index N). You have to ...
8
The use of kernels to estimate volatility using intraday data is "nothing more" than combining:
intraday volatility estimation
kernel smoothing
Thus you have to take care about the "usual pits" of these two approaches.
Intraday volatility estimation. I hope you know the "signature plot" effect. Of course if you use the proper estimation method, it should ...
8
The best overview I have seen so far is this paper which lists 214 (!) factors (or anomalies if you like) on over one hundred (!) pages:
Harvey, Campbell R. and Liu, Yan and Zhu, Caroline, …and the Cross-Section of Expected Returns (February 3, 2015). Available at SSRN: https://ssrn.com/abstract=2249314 or http://dx.doi.org/10.2139/ssrn.2249314
Abstract: ...
8
The Queue Reactive Model (by Huang, L and Rosenbaum) is an improvement of what Cont and de Larrard (CL) did.
This model is capturing
the inflows and outflows in each queue given the current state of the orderbook (it is one of your remark)
but more importantly, once one queue depletes, the discovered quantity is not taken at random (like in the CL model)
...
8
You could also look at how each price level is made up.
For example how is the 18 lot on the bid price 0.0995 collated. Is it a 5 lots, 5 lots, 7 lots and 1 lot. You can do this on certain exchanges as they have an enhanced order book where you can see every insert, modification, delete and trade. This may require you to recreate the order book, which ...
7
Your first definition is wrong; I'm not sure where you got that from. Your second definition is correct: the ISO alerts the exchange that the submitting party has taken responsibility for RegNMS and requests a fill at only that venue's price; there is no routing away. Obviously, there is a huge red-tape burden to get permission to do this.
7
I think it's alive and well. I don't think there's a specific "decoupling" time, but if you look at e.g. Munnix et al. "Statistical causes for the Epps effect in microstructure noise", it seems that the biased correlation is about 60% of the real value for 1 min data and about 90% for 5 min data, so you could say that 5 min is pretty safe, but 1 min is ...
7
Very interesting question. I am not an expert on the subject, however, I was able to find a collection of papers on the subject that should get you started.
Here is a good and very informative paper that walks you through several tick by tick volatility estimators that seek to reduce the volatility imposed by market micro-structure:
Efficient estimation of ...
7
The most commonly-known approach to this is described in Inferring trade direction from intraday data (1991) by Lee and Ready. You will find that the non-trivial part has to do with classifying trades that are reported inside the spread. I believe you will find that the Lee-Ready algorithm will outperform the naive midpoint reference approach suggested by @...
7
They are a lot of open problems in market microstructure. To have an idea of the whole landscape, have a look at Market Microstructure in Practice, 2nd Edition, by L and Laruelle.
I would split them in
From the viewpoint of exchanges
Optimal fee schedules to "attract" liquidity (and hence efficient market makers), have a look at Optimal make-take ...
6
Stock market indices fall faster than they rise, in part, due to leveraged long investors. As individual stocks fall, investors must de-risk due to margin calls, and those margin calls may need to be met by selling other stocks. This causes correlations to increase as markets fall. This also causes indices to fall more quickly than they rise, since the ...
6
The round-trip latency from point A to a matching engine at point B can be thought of being comprised of two components:
$RTT_{total,A \rightarrow B} = RTT_{network\_transit,A \rightarrow B} + MPL_{matching\_engine,B}$
Where $RTT$ is the round-trip time and $MPL$ is the message processing latency (how long it takes to receive a message and produce an event)...
6
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 ...
6
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 ...
6
I can't help as much with public literature, but I did see a talk with a member of the FINRA data science team responsible for exactly this (event link below - perhaps you can track down the speaker).
I don't know the structure of your data, but the approach FINRA took was to develop trader-level attributes (not stock level) to create profiles for each ...
6
My understanding (devoid of any mathematical grounding) is as follows.
v = Turnover PER UNIT TIME
n = Shares you need to execute
therefore
n/v = Number of units of time required to execute your size at the normal turnover rate
Realized vol follows a SQRT(T) heuristic.
Given that we can now rewrite the transaction cost formula purely in terms of vol ...
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