High-Frequency Trading Definitions and Related Concepts

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Why Algorithmic/High-Frequency Trading Need Clear Definition

In order to assess HFT concerning its relevance and impact on markets, first, a clear definition and delineation of the term HFT itself is required. This section aims at giving an overview of available academic and regulatory definitions and at excerpting a common notion that incorporates most of the existing perceptions. The derived definitions serve as the working definitions for the following sections. We follow the notion that HFT is a subset of AT as it is supported by e.g. Brogaard 2010. Therefore, AT will be treated first in subsection 3.2. Furthermore, subsection 3.3 lists related electronic trading concepts and technologies like market making, quantitative asset management and smart order routing in order to cover electronic trading concepts which share certain characteristics with AT/HFT

3.2 Delineating Algorithmic and High-Frequency Trading 3.2.1 Algorithmic Trading By now, the academic and general literature about AT is quite extensive. Thus, not surprisingly, the definitions of AT range from the very general ―Computerized trading controlled by algorithms‖ (Prix et al. 2007) to the rather specific: “In algorithmic trading (AT), computers directly interface with trading platforms, placing orders without immediate human intervention. The computers observe market data and possibly other information at very high frequency, and, based on a built-in algorithm, send back trading instructions, often within milliseconds. A variety of algorithms are used: for example, some look for arbitrage opportunities, including small discrepancies in the exchange rates between three currencies; some seek optimal execution of large orders at the minimum cost; and some seek to implement longer-term trading strategies in search of profits.” (Chaboud et al. 2009) Appendices II and III list academic and regulatory definitions on AT and HFT. The variety of formulations shows that there is no general agreement on a single definition. Rather than adding another definition to the list, we will try to extract the main characteristics of these definitions that are non-contradictive in academic literature
Throughout the literature, AT (and HFT as a subgroup) is viewed as a tool for professional traders that may observe market parameters or other information in real-time and automatically generates/carries out trading decisions without human intervention. It frequently applies DMA or SA technologies for order routing. Accordingly, we define AT as trading that reveals most but not necessarily all of the following characteristics in Table 1. As HFT is a subset of AT, these characteristics are also valid for HFT, which will be described in more detail in the next paragraph. Common Characteristics of AT and HFT 1) Pre-designed trading decisions 2) Used by professional traders 3) Observing market data in real-time 4) Automated order submission 5) Automated order management 6) Without human intervention 7) Use of direct market access Table 1: Common characteristics of AT and HFT However, there are characteristics specific to AT which are commonly not associated to HFT. Here, the focus is on the intelligent working of orders to minimize market impact relative to a pre-defined benchmark. In contrast to HFT, this (classical) part of AT may also relate to agent trading where customers hold securities over longer periods of time. Specific Characteristics of AT excluding HFT 1) Agent trading 2) Minimize market impact (for large orders) 3) Goal is to achieve a particular benchmark 4) Holding periods possibly days/week/months 5) Working an order through time and across markets Table 2: Specific characteristics of AT excluding HFT 3.2.2 High-frequency trading HFT is a newer phenomenon in the AT landscape and much less literature and definitions can be found. In the same manner as for AT, studying the definitions of HFT in academic literature was the basis for our working definition. Appendix III shows some typical definitions and descriptions for HFT in academic and regulatory documents. Authors typically13 specify that HFT strategies update their orders very quickly and have no over-night positions. The rapid submission of cancellations and deletions is necessary to realize small profits per trade. It is part of the business model to realize small profits in a large number of trades and hence, HFT focuses mainly on high liquid instruments. As a prerequisite, HFT needs to rely on high speed access to markets, i.e. low latencies or the usage of co-location/proximity services and individual data feeds. Table 3 shows basic features that are taken from the various definitions and are usually associated with HFT. Specific Characteristics of HFT 1) Very high number of orders 2) Rapid order cancellation 3) Proprietary trading14 4) Profit from buying and selling (as middleman) 5) No significant position at end of day (flat position) 6) Very short holding periods 7) Extracting very low margins per trade 8) Low latency requirement15 9) Use of co-location/proximity services and individual data feeds16 10) Focus on high liquid instruments Table 3: Specific characteristics of HFT Thus, similarly to AT we define HFT as trading that reveals most but not necessarily all of the above characteristics of Table 3 (obviously in combination with the characteristics listed in Table 1). Figure 2 sums up the characteristics of AT and HFT. In the lower left box, a list of typical properties is given that could be called ―classical‖ algorithmic trading that is specific for AT but is not associated with HFT. 13 Not necessarily all characteristics need to be fulfilled for every strategy that is regarded a HFT strategy. For example Tradeworx states that “some HFT strategies have no special speed requirements and do not even require collocation” (Tradeworx 2010a). 14 Proprietary traders utilize only their own capital for their trading activities (Harris 2003). 15 To trade at high frequencies, HFTs rely on sophisticated high-speed connections to the relevant marketplaces. 16 Co-location arrangements allow HFTs to place their trading engines close to the matching engines (servers) of a marketplace. This minimizes the time a signal needs to travel between the two engines (CFTC 2010). Individual data feeds can offer information faster than consolidated feeds, since it takes time to consolidate different feeds (SEC 2010a).

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