High frequency trading - page 11

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high frequency trading as i know occupies more than about 50% of swaps. few years ago it was about 60-70%. many companies lost huge money because of some problems in algorithm or smth else. so as this facts say, HFT is not a good thing. For simple trader, as I am, it's smth unreal.
 
Karaduman:
high frequency trading as i know occupies more than about 50% of swaps. few years ago it was about 60-70%. many companies lost huge money because of some problems in algorithm or smth else. so as this facts say, HFT is not a good thing. For simple trader, as I am, it's smth unreal.
What companies?
 
whisperer:
What companies?
«KNIGHT CAPITAL» - 2012, lost 450 million dollars
«GOLDMAN SACHS» - 2013, dozens of million $
NASDAQ - had problems with system, because of it during 3 hours trading was stopped
 I think there are more examples 
 
This paper examines every NASDAQ ITCH feed message for the S&P 500 stocks for 2012 and identifies clusters of extremely high and extremely low limit order cancellation activity. We find results consistent with the ideas that cancel clusters are the result of high frequency traders jockeying for queue position and reacting to information to establish a new price level. Furthermore, few trades seem to be executed during cancel clusters or even immediately after them. Low cancellation activity seems to be markedly different with many level changes all be caused by executions. Our results are consistent with high frequency trading firms behaving as agents who bring efficiency to the market without the need to have executions at intermediate prices. We also discuss the misconception that investors and low frequency trader are synonymous and its implications for policy given our results.
 
We develop a High Frequency (HF) trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders. By introducing a multi-factor mutually-exciting process we allow for feedback effects in market buy and sell orders and the shape of the limit order book (LOB). Our model accounts for arrival of market orders that influence activity, trigger one-sided and two-sided clustering of trades, and induce temporary changes in the shape of the LOB. We also model the impact that market orders have on the short-term drift of the midprice (short-term-alpha). We show that HF traders who do not include predictors of short-term-alpha in their strategies are driven out of the market because they are adversely selected by better informed traders and because they are not able to profit from directional strategies.
 

Renaissance Technologies has applied for a patent for an invention designed to beat high speed traders.Legendary Hedge Fund Wants to Use Atomic Clocks to Beat High-Speed Traders :http://www.bloomberg.com/news/articles/2016-07-07/jim-simons-has-a-killer-flash-boy-app-and-you-can-t-have-it    

 An application for a patent was published by the U.S. Patent and Trademark Office in February. The document, as reported by Bloomberg today, described an innovative way for executing synchronised trades in multiple exchanges, consisting of sophisticated algorithms, a host of computer servers and atomic clocks meticulouly calibrated to vibrations of irradiated cesium atoms to sync orders to within a few billionths of a second.http://www.financemagnates.com/institutional-forex/execution/renaissance-technologies-aims-hft-using-atomic-clock/

 
This paper provides a review of the literature on high-frequency trading and discusses various initiatives taken by regulatory authorities around the world to address its potential detrimental effects on market quality and investor welfare. Empirical evidence to date generally suggests that high-frequency trading has improved market quality during normal times. What is not clear is the role of high-frequency traders during episodic periods of market crash and extreme volatility. A fruitful area of future research may be a comparative analysis of the role of high-frequency traders and the efficacy of various regulatory initiatives across periods of varying market conditions.
 
We propose an optimization framework for market-making in a limit-order book, based on the theory of stochastic approximation. We consider a discrete-time variant of the Avellaneda-Stoikov model similar to its development in an article of Laruelle, Lehalle and Pagès in the context of optimal liquidation tactics. The idea is to take advantage of the iterative nature of the process of updating bid and ask quotes in order to make the algorithm optimize its strategy on a trial-and-error basis (i.e. on-line learning). An advantage of this approach is that the exploration of the system by the algorithm is performed in run-time, so explicit specifications of the price dynamics are not necessary, as is the case in the stochastic-control approach. As it will be discussed, the rationale of our method can be extended to a wider class of algorithmic-trading tactical problems other than market-making.
 
Contemporary financial markets have recently witnessed a sea change with the ‘algorithmic revolution’, as trading automats are used to smoothen the execution sequences and reduce market impact. Constantly monitored, they take an active part in the shaping of markets, and sometimes generate crises, when ‘they mess up’ or when ‘they do not want to work’, as traders say. Made of scripts (IT code), algorithms are designed to replicate trading patterns: to be accepted, they have to ‘behave’ according to different sets of texts (regulatory texts framing the marketplace). In this article, we draw on ethnographic fieldwork in order to describe algorithms and discuss the different facets of these widespread objects. We return a controversy articulating around the detachment of trading practices from traders, and its reattachment into a specific financial object. We then raise a few questions relating with the framing of practices: what does this shift in the organisation of trading activities generate on the daily routines taking place in trading rooms? Is it possible to make codes (algorithms) comply with codings (rulebooks and codes of conduct)? How does such ‘equipment’ matter to the marketplaces, and what exactly does it imply for the development of future regulation in the market space?
 
We investigate a class of market participants who follow strategies that anticipate local price trends. These anticipatory traders can correctly process information prior to the overall market and systematically act before other participants. They use manual and automated order entry methods and exhibit varying processing speeds, but most are not fast enough to be high frequency traders. In certain cases, other participants are shown to gain by detecting such trading and reacting to avoid adverse selection costs. To identify these traders, we devise methods to isolate local price paths using order book data from the WTI crude oil futures market.
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