Monthly Archives: December 2012

Related Concepts OF High-Frequency Trading

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3.3.1 Market Making The term market making refers to the strategy of quoting a simultaneous buy and sell limit order (quote) for a financial instrument in order to profit from the bid-ask spread. This can be either imposed by mandatory requirements set by market operators/regulators for entities covering that role (e.g. an official market maker such as the Designated Market Maker at the NYSE or Designated Sponsors at the Frankfurt Stock Exchange via the trading system XETRA), or voluntarily, i.e. without a determined obligation to quote. Several different terms are used to denote this kind of designated liquidity provision, e.g. market making with obligations, designated market making and registered market maker. Market makers frequently employ ―quote machines‖ which provide the respective electronic markets with their quotes. Quote machines are programs which generate, update and delete quotes according to a pre-set strategy. Due to the varying degree of sophistication among these programs, some of them employ techniques similar to HFTs, while others rely on the involvement of a human market maker. Since market making is a well known HFT strategy (Tradeworx 2010a), the following Figure 3 highlights the relationship between HFT and market making. Figure 3: Market making and HFT17 The figure shows the interferences denoted by numbers from one to three that span the activities of HFT in market-making18: (1) represents all other HFT strategies apart from market-making (for details see section 4.2), (2) represents HFT that applies market making strategies without acting as a designated liquidity provider and (3) represents HFT that applies market making and is registered as a designated liquidity provider, e.g. GETCO is a Designated Market Maker at NYSE (Bunge and Peterson 2010). 17 Areas without numbers refer to the part of market making and designated liquidity provision that is not undertaken by HFT. 18 As Figure 3 is not based on any numbers such as traded volume, the purpose is to illustrate the different possible combinations of market making and HFT and not to signify the proportions or dimensions of these combinations. Fragmentation makes HFT market making strategies more relevant as it enables market participants to quote on less active venues based on reference quotes/limits available, e.g., on the most liquid market for that instrument. Delineation of market making/quote machines to AT/HFT: quote machines originally supported market makers in fulfilling mandatory quotation obligations. Both mandatory and voluntary market making may apply HFT as a supporting technology. 3.3.2 Quantitative Portfolio Management (QPM)19 Quantitative portfolio managers use quantitative models to form investment portfolios. Chincarini and Kim define quantitative (equity) portfolio management in the following way: “The central, unifying element of quantitative equity portfolio management (QEPM) is the quantitative model that relates stock movements to other market data. Quantitative equity portfolio managers create such models to predict stock returns and volatility, and these predictions, in turn, form the basis for selecting stocks for the portfolio.” (Chincarini and Kim 2006)20 In contrast to HFTs, QPMs frequently hold positions for extended periods of time, whereas HFTs tend to liquidate their positions rapidly and usually end trading days without a significant position (―flat‖). Compared to AT and HFT, QPM has a higher degree of human intervention. QPMs use algorithms to generate trading decisions based on statistical calculations and data analysis techniques. While QPMs automate the process of portfolio selection and the generation of trading signals, a human portfolio manager will usually validate the results of his quantitative model before transferring it to a (human or automated) trader for execution. 19 Also known as Quantitative Investing. 20 An alternative definition of QPM is provided e.g. by Quoniam: “Quantitative portfolio management means the analysis and evaluation of situations relevant for the capital market using statistical methods.” Quoniam Asset Management GmbH (2010)

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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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Evolution of Electronic Trading

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Historical Background and Electronification of Securities Trading The electronification of securities trading commenced 40 years ago, when the National Association of Securities Dealers (NASD) started its computer-assisted market making system for automated quotation (AQ) in the U.S., forming what is nowadays known as NASDAQ (Black 1971a; Black 1971b). In Europe, the first computer-assisted equities exchanges launched their trading services in the 1980s, but not until the 1990s securities trading was organized in fully automated exchanges. The majority of market models of those fully automated equities exchanges are implemented as electronic central limit order books (CLOB), which store market participants‘ trading interests visible to and executable for all other connected traders. According to Pagano and Roell (1996) and Jain (2005), the transparency induced by the introduction of CLOBs reduces information asymmetry, enhances liquidity and supports efficient price determination. While prices were determined manually in floor trading, orders are matched automatically according to price-time priority in electronic trading systems.4 By applying uniform rules to all market participants, operational fairness and fair access to the respective trading venue shall be ensured (Harris 2003). Thereby, the electronification of securities markets and the electronic connectivity of market participants went hand in hand, leading to decentralized market access. Physical trading floors were not required any longer and have mostly been replaced by electronic trading systems. Investors can submit their orders electronically to a market‘s backend from remote locations. On the investors‘ side, human trading processes have been substituted by electronic systems, too. While systems generating automated quotes and stop-loss orders were the first technological artifacts that conquered the trading process, in recent years information technology (IT) has successively established and can nowadays be found on every stage of trading and post-trading processes. State-of-the-art technology has developed as a crucial competitive factor for market operators in recent decades and market participants themselves continued to further automate and optimize their trading processes along the entire value chain. 4 Although slight modifications exist, price-time priority has established as a de-facto standard in securities trading globally.

2.2 Drivers for Widespread Usage of Algorithmic/High-Frequency Trading The emergence of AT and HFT in the past went hand in hand with other market structural developments in European securities trading. In the following, multiple drivers for the rise of AT and HFT are identified, i.e. new market access models and fee structures, a significant reduction of latency and an increase in competition for and fragmentation of order flow5. In most markets, only registered members are granted direct access.6 Hence, those members are the only ones allowed to conduct trading directly, leading to their primary role as market access intermediaries for other investors. Market members performing that function are referred to as brokers7. In the past, those access intermediaries transformed their clients‘ general investment decisions into orders that were allocated to appropriate market venues. As the cost awareness of the buy side has increased over the years, brokers have begun to provide different market access models, i.e. direct market access (DMA) and sponsored access (SA). When an investor makes use of DMA, his orders are no longer touched by the broker, but rather forwarded directly to the markets through the broker‘s infrastructure. One key characteristic of DMA presents the fact that the respective broker can conduct pre-trade risk checks. Sponsored access (SA) represents a slightly different possibility for the buy side to access a marketplace. Here, an investment firm (that is not a member of the respective market) is enabled to route its orders to the market directly using a registered broker‘s member ID without using the latter‘s infrastructure (in contrast to DMA). Resulting from this setup, the sponsor can conduct pre-trade risk checks only if the option to conduct those checks is provided by the trading venue (filtered SA). In case of unfiltered (also referred to as naked) SA, the sponsor only receives a drop copy of each order to control his own risk exposure. A reduction in latency represents the main advantage of SA over DMA from a non-member firm‘s perspective and therefore is highly attractive for AT or HFT based trading strategies. Another driver for the success of AT and HFT is the new trading fee structures found in Europe. Market operators try to attract order flow that is generated automatically (i) by applying special discounts for algorithmic orders within their fee schedules. MTFs 5 Obviously, this list of drivers is not exhaustive. Other drivers that could be listed additionally include, e.g., the growing number of proprietary trading firms founded by former investment bank staffers and other mathematically/technically oriented traders. 6 Access is restricted to registered market members mainly due to post-trading issues, i.e. clearing and settlement. A pre-requisite for trading directly in a market is an approved relationship with the respective clearing house(s). 7 As brokers basically offer their services to other market participants they are also referred to as the sell side. Respective clients purchasing those services are referred to as the buy side (Harris 2003). implemented (ii) fee schedules with very aggressive levels to compete with incumbent exchanges. Furthermore, some MTFs like e.g. Chi-X, BATS or Turquoise started offering pricing schemes that are a novelty to European exchange fee schedules: (iii) asymmetric pricing (Jeffs 2009; Mehta 2008). With asymmetric pricing, market participants removing liquidity from the market (taker) are charged a higher fee while traders that submit liquidity to the market (maker) are charged a lower fee or are even provided with a rebate. Such an asymmetric fee structure is supposed to incentivize liquidity provision. Faced with the MTFs‘ aggressive pricing strategies, many European exchanges were urged to lower their fee levels as well, while others even adopted the asymmetric pricing regime. As will be further explained in section 4, market participants have specialized in making profits from those fee structures by applying trading algorithms. Although latency has always been of importance in securities trading, its role is more intensely stressed by market participants with AT/HFT on the rise. In traditional trading involving human interaction on trading floors, a trader could also profit from trading faster than others. Traders often benefited from their physical abilities, e.g. when they could run faster across the trading floor or shout louder than their counterparts and thus drew a market maker‘s or specialist‘s attention to their trading intentions. With algorithms negotiating on prices nowadays, those physical advantages are no longer needed. Nevertheless, in markets trading at high speed, the capability to receive data and submit orders at lowest latency is essential. When the market situation at the arrival of an order differs significantly from the market situation, which led to that particular trading decision, there is a risk that the order is no longer appropriate in terms of size and/or limit (Harris 2003; Brown and Holden 2005; Liu 2009). Hence, an order bears the risk of being executed at an improper price or not being executed at all. To minimize that risk, reducing the delay of data communication with the market‘s backend is of utmost importance to AT/HFT based strategies concerning market data receipt, order submissions and execution confirmations. In order to reduce latency8, automated traders make use of co-location or proximity services that are provided by a multitude of market operators.9 By co-locating their servers, market participants can place their trading machines directly adjacent to the market operator‘s infrastructure. Regulation in European securities trading has promoted the market penetration of AT/HFT as well: with the advent of MiFID (European Commission 2004), the European equity trading landscape became more complex. As intended by the regulator, competition among market venues has increased, and the available liquidity in a security is scattered among different market venues (Gomber et al. 2011b). This fragmentation of markets is a direct consequence of the harmonized level playing field for different types of trading venues intended by MiFID. In order to attract market share, new venues challenged the incumbent exchanges by lower trading fees and forced them to adapt their pricing schemes as well. These recently emerged MTFs steadily increased their market penetration. The lowered costs of trading (both explicit and implicit10) are beneficial for all market participants including issuers, as lower trading costs increase liquidity and thereby lower the cost of capital. However, Over the Counter (OTC)-trading11 represents a high and stable market share around 40% (see Figure 112). 0%20%40%60%80%100%OTCRMMTF Figure 1: Distribution of trading among regulated markets, MTFs and OTC, based on Thomson Reuters (2008, 2009, 2010) Market participants are urged to compare potential prices offered as well as different fee regimes across a multitude of market venues, which imposes increased search costs for the best available price. In addition, dark pools and OTC trading, which are exempted from pre-trade transparency, distort the clear picture of available prices. Against this background, algorithms support market participants to benefit from competition between markets and help to overcome negative effects from fragmentation of order flow

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, we have manufacturing and trading high quality since 2008

If you are interested in me our our company ,please feel free to contact me.

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Drivers for Widespread Usage of Algorithmic/High-Frequency Trading

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The emergence of AT and HFT in the past went hand in hand with other market structural developments in European securities trading. In the following, multiple drivers for the rise of AT and HFT are identified, i.e. new market access models and fee structures, a significant reduction of latency and an increase in competition for and fragmentation of order flow5. In most markets, only registered members are granted direct access.6 Hence, those members are the only ones allowed to conduct trading directly, leading to their primary role as market access intermediaries for other investors. Market members performing that function are referred to as brokers7. In the past, those access intermediaries transformed their clients‘ general investment decisions into orders that were allocated to appropriate market venues. As the cost awareness of the buy side has increased over the years, brokers have begun to provide different market access models, i.e. direct market access (DMA) and sponsored access (SA). When an investor makes use of DMA, his orders are no longer touched by the broker, but rather forwarded directly to the markets through the broker‘s infrastructure. One key characteristic of DMA presents the fact that the respective broker can conduct pre-trade risk checks. Sponsored access (SA) represents a slightly different possibility for the buy side to access a marketplace. Here, an investment firm (that is not a member of the respective market) is enabled to route its orders to the market directly using a registered broker‘s member ID without using the latter‘s infrastructure (in contrast to DMA). Resulting from this setup, the sponsor can conduct pre-trade risk checks only if the option to conduct those checks is provided by the trading venue (filtered SA). In case of unfiltered (also referred to as naked) SA, the sponsor only receives a drop copy of each order to control his own risk exposure. A reduction in latency represents the main advantage of SA over DMA from a non-member firm‘s perspective and therefore is highly attractive for AT or HFT based trading strategies. Another driver for the success of AT and HFT is the new trading fee structures found in Europe. Market operators try to attract order flow that is generated automatically (i) by applying special discounts for algorithmic orders within their fee schedules. MTFs 5 Obviously, this list of drivers is not exhaustive. Other drivers that could be listed additionally include, e.g., the growing number of proprietary trading firms founded by former investment bank staffers and other mathematically/technically oriented traders. 6 Access is restricted to registered market members mainly due to post-trading issues, i.e. clearing and settlement. A pre-requisite for trading directly in a market is an approved relationship with the respective clearing house(s). 7 As brokers basically offer their services to other market participants they are also referred to as the sell side. Respective clients purchasing those services are referred to as the buy side (Harris 2003).

implemented (ii) fee schedules with very aggressive levels to compete with incumbent exchanges. Furthermore, some MTFs like e.g. Chi-X, BATS or Turquoise started offering pricing schemes that are a novelty to European exchange fee schedules: (iii) asymmetric pricing (Jeffs 2009; Mehta 2008). With asymmetric pricing, market participants removing liquidity from the market (taker) are charged a higher fee while traders that submit liquidity to the market (maker) are charged a lower fee or are even provided with a rebate. Such an asymmetric fee structure is supposed to incentivize liquidity provision. Faced with the MTFs‘ aggressive pricing strategies, many European exchanges were urged to lower their fee levels as well, while others even adopted the asymmetric pricing regime. As will be further explained in section 4, market participants have specialized in making profits from those fee structures by applying trading algorithms. Although latency has always been of importance in securities trading, its role is more intensely stressed by market participants with AT/HFT on the rise. In traditional trading involving human interaction on trading floors, a trader could also profit from trading faster than others. Traders often benefited from their physical abilities, e.g. when they could run faster across the trading floor or shout louder than their counterparts and thus drew a market maker‘s or specialist‘s attention to their trading intentions. With algorithms negotiating on prices nowadays, those physical advantages are no longer needed. Nevertheless, in markets trading at high speed, the capability to receive data and submit orders at lowest latency is essential. When the market situation at the arrival of an order differs significantly from the market situation, which led to that particular trading decision, there is a risk that the order is no longer appropriate in terms of size and/or limit (Harris 2003; Brown and Holden 2005; Liu 2009). Hence, an order bears the risk of being executed at an improper price or not being executed at all. To minimize that risk, reducing the delay of data communication with the market‘s backend is of utmost importance to AT/HFT based strategies concerning market data receipt, order submissions and execution confirmations. In order to reduce latency8, automated traders make use of co-location or proximity services that are provided by a multitude of market operators.9 By co-locating their servers, market participants can place their trading machines directly adjacent to the market operator‘s infrastructure. 8 Actually, quantifying the economic value of low latency is hardly possible as measuring latency is difficult and the methodologies applied are inconsistent (Ende et al. 2011). 9 Proximity services refer to facility space that is made available by specialized network providers to market participants for the purpose of locating their network and computing hardware closer to the matching engines specifically in order to optimize the location with respect to multiple venues and to maximize flexibility. Co-location services are provided by a market operator and refer to a setup where a market participant‗s hardware is located directly next to a market‘s matching engine.
Regulation in European securities trading has promoted the market penetration of AT/HFT as well: with the advent of MiFID (European Commission 2004), the European equity trading landscape became more complex. As intended by the regulator, competition among market venues has increased, and the available liquidity in a security is scattered among different market venues (Gomber et al. 2011b). This fragmentation of markets is a direct consequence of the harmonized level playing field for different types of trading venues intended by MiFID. In order to attract market share, new venues challenged the incumbent exchanges by lower trading fees and forced them to adapt their pricing schemes as well. These recently emerged MTFs steadily increased their market penetration. The lowered costs of trading (both explicit and implicit10) are beneficial for all market participants including issuers, as lower trading costs increase liquidity and thereby lower the cost of capital. However, Over the Counter (OTC)-trading11 represents a high and stable market share around 40% (see Figure 112). 0%20%40%60%80%100%OTCRMMTF Figure 1: Distribution of trading among regulated markets, MTFs and OTC, based on Thomson Reuters (2008, 2009, 2010)
Market participants are urged to compare potential prices offered as well as different fee regimes across a multitude of market venues, which imposes increased search costs for the best available price. In addition, dark pools and OTC trading, which are exempted from pre-trade transparency, distort the clear picture of available prices. Against this background, algorithms support market participants to benefit from competition between markets and help to overcome negative effects from fragmentation of order flow.

This is Jill, manager of Zhejiang Kaixin Industrial & Trade Co.,Ltd
, we have manufacturing and trading high quality since 2008

If you are interested in me our our company ,please feel free to contact me.

Have a nice day!

our company website is :

www.gardenfurniture-china.com

China garden furniture China garden furniture

Historical Background and Electronification of Securities Trading

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The electronification of securities trading commenced 40 years ago, when the National Association of Securities Dealers (NASD) started its computer-assisted market making system for automated quotation (AQ) in the U.S., forming what is nowadays known as NASDAQ (Black 1971a; Black 1971b). In Europe, the first computer-assisted equities exchanges launched their trading services in the 1980s, but not until the 1990s securities trading was organized in fully automated exchanges. The majority of market models of those fully automated equities exchanges are implemented as electronic central limit order books (CLOB), which store market participants‘ trading interests visible to and executable for all other connected traders. According to Pagano and Roell (1996) and Jain (2005), the transparency induced by the introduction of CLOBs reduces information asymmetry, enhances liquidity and supports efficient price determination. While prices were determined manually in floor trading, orders are matched automatically according to price-time priority in electronic trading systems.4 By applying uniform rules to all market participants, operational fairness and fair access to the respective trading venue shall be ensured (Harris 2003). Thereby, the electronification of securities markets and the electronic connectivity of market participants went hand in hand, leading to decentralized market access. Physical trading floors were not required any longer and have mostly been replaced by electronic trading systems. Investors can submit their orders electronically to a market‘s backend from remote locations. On the investors‘ side, human trading processes have been substituted by electronic systems, too. While systems generating automated quotes and stop-loss orders were the first technological artifacts that conquered the trading process, in recent years information technology (IT) has successively established and can nowadays be found on every stage of trading and post-trading processes. State-of-the-art technology has developed as a crucial competitive factor for market operators in recent decades and market participants themselves continued to further automate and optimize their trading processes along the entire value chain.

This is Jill, manager of Zhejiang Kaixin Industrial & Trade Co.,Ltd
, we have manufacturing and trading high quality since 2008

If you are interested in me our our company ,please feel free to contact me.

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THE SCIENTIFIC APPROACH TO SYSTEM DEVELOPMEN

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This book is intended to accomplish a systematic and detailed analysis of theindividual components that make up a complete trading system. We are propos-ing nothing less than a scientific study of entries, exits, and other trading systemelements. The basic substance of the scientific approach as applied herein isas f0110ws:1. The object of study, in this case a trading system (or one or more of itselements), must be either directly or indirectly observable, preferablywithout dependence on subjective judgment, something easily achieved with proper testing and simulation software when working with com-plete mechanical trading systems.2. An orderly means for assessing the behavior of the object of study mustbe available, which, in the case of trading systems, is back-testing overlong periods of historical data, together with, if appropriate, the applica-tion of various models of statistical inference, the aim of the latter beingto provide a fix or reckoning of how likely a system is to hold up in thefuture and on different samples of data.3. A method for making the investigative task tractable by holding mostparameters and system components fixed while focusing upon the effectsof manipulating only one or two critical elements at a time.The structure of this book reflects the scientific approach in many ways.Trading systems are dissected into entry and exit models. Standardized methods forexploring these components independently are discussed and implemented, leadingto separate sections on entries and exits. Objective tests and simulations are run,and statistical analyses are performed. Results are presented in a consistent mannerthat permits direct comparison. This is “old hat” to any practicing scientist.Many traders might be surprised to discover that they, like practicing scien-tists, have a working knowledge of the scientific method, albeit in different guise!Books for traders often discuss “paper trading” or historical back-testing, or pre-sent results based on these techniques. However, this book is going to be moreconsistent and rigorous in its application of the scientific approach to the prob-lem of how to successfully trade the markets. For instance, few books in whichhistorical tests of trading systems appear offer statistical analyses to assess valid-ity and to estimate the likelihood of future profits. In contrast, this book includesa detailed tutorial on the application of inferential statistics to the evaluationof trading system performance.Similarly, few pundits test their entries and exits independently of oneanother. There are some neat tricks that allow specific system components to betested in isolation. One such trick is to have a set of standard entry and exit strate-gies that remain fixed as the particular entry, exit, or other element under study isvaried. For example, when studying entry models, a standardized exit strategy willbe repeatedly employed, without change, as a variety of entry models are testedand tweaked. Likewise, for the study of exits, a standardized entry technique willbe employed. The rather shocking entry technique involves the use of a randomnumber generator to generate random long and short entries into various markets!Most traders would panic at the idea of trading a system with entries based on thefall of the die; nevertheless, such entries are excellent in making a harsh test foran exit strategy. An exit strategy that can pull profits out of randomly enteredtrades is worth knowing about and can, amazingly, be readily achieved, at least for the S&P 500 (Katz and McCormick, March 1998, April 1998). The tests will bedone in a way that allows meaningful comparisons to be made between differententry and exit methods.To summarize, the core elements of the scientific approach are:1. The isolation of system elements2. The use of standardized tests that allow valid comparisons3. The statistical assessment of resultsTOOLS AND MATERIALS NEEDED FOR THE SCIENTIFICAPPROACHBefore applying the scientific approach to the study of the markets, a number ofthings must be considered. First, a universe of reliable market data on which toperform back-testing and statistical analyses must be available. Since this book isfocused on commodities trading, the market data used as the basis for our universeon an end-of-day time frame will be a subset of the diverse set of markets suppliedby Pinnacle Data Corporation: these include the agriculturals, metals, energyresources, bonds, currencies, and market indices. Intraday time-frame trading isnot addressed in this book, although it is one of our primary areas of interest thatmay be pursued in a subsequent volume. In addition to standard pricing data,explorations into the effects of various exogenous factors on the markets some-times require unusual data. For example, data on sunspot activity (solar radiationmay influence a number of markets, especially agricultural ones) was obtainedfrom the Royal Observatory of Belgium.Not only is a universe of data needed, but it is necessary to simulate one ormore trading accounts to perform back-testing. Such a task requires the use of atrading simulator, a software package that allows simulated trading accounts to becreated and manipulated on a computer. The C+ + Trading Simulator fromScientific Consultant Services is the one used most extensively in this bookbecause it was designed to handle portfolio simulations and is familiar to theauthors. Other programs, like Omega Research’s TradeStation or SystemWriterPlus, also offer basic trading simulation and system testing, as well as assortedcharting capabilities. To satisfy the broadest range of readership, we occasionallyemploy these products, and even Microsoft’s Excel spreadsheet, in our analyses.Another important consideration is the optimization of model parameters.When running tests, it is often necessary to adjust the parameters of some compo-nent (e.g., an entry model, an exit model, or some piece thereof) to discover thebest set of parameters and/or to see how the behavior of the model changes as itsparameters change. Several kinds of model parameter optimizations may be conducted. In manual optimization, the user of the simulator specifies a parameter thatis to be manipulated and the range through which that parameter is to be stepped;the user may wish to simultaneously manipulate two or more parameters in thismanner, generating output in the form of a table that shows how the parametersinteract to affect the outcome. Another method is brute force optimization, whichcomes in several varieties: The most common form is stepping every parameterthrough every possible value. If there are many parameters, each having many pos-sible values, running this kind of optimization may take years. Brute force opti-mization can, however, be a workable approach if the number of parameters, andvalues through which they must be stepped, is small. Other forms of brute forceoptimization are not as complete, or as likely to find the global optimum, but canbe run much more quickly. Finally, for heavy-duty optimization (and, if naivelyapplied, truly impressive curve-fitting) there are genetic algorithms. An appropri-ate genetic algorithm (GA) can quickly tind a good solution, if not a global opti-mum, even when large numbers of parameters are involved, each having largenumbers of values through which it must be stepped. A genetic optimizer is animportant tool in the arsenal of any trading system developer, but it must be usedcautiously, with an ever-present eye to the danger of curve-fitting. In the inves-tigations presented in this book, the statistical assessment techniques, out-of-sample tests, and such other aspects of the analyses as the focus on entire portfoliosprovide protection against the curve-fitting demon, regardless of the optimizationmethod employed

This is Jill, manager of Zhejiang Kaixin industrial & Trade Co.,Ltd
, we have manufacturing and trading high quality since 2008

If you are interested in me our our company ,please feel free to contact me.

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WHAT ARE GOOD ENTRIES AND EXIT

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Given a mechanical trading system that contains an entry model to generate entryorders and an exit model to generate exit orders (including those required formoney management), how are the entries and exits evaluated to determine whetherthey are good? In other words, what constitutes a good entry or exit?Notice we used the terms entry orders and exit orders, not entry or exit sig-nals. Why? Because “signals” are too ambiguous. Does a buy “signal” mean thatone should buy at the open of the next bar, or buy using a stop or limit order? Andif so, at what price? In response to a “signal” to exit a long position, does the exitoccur at the close, on a profit target, or perhaps on a money management stop?Each of these orders will have different consequences in terms of the resultsachieved. To determine whether an entry or exit method works, it must producemore than mere signals; it must, at smne point, issue highly specific entry and exitorders. A fully specified entry or exit order may easily be tested to determine itsquality or effectiveness.In a broad sense, a good entry order is one that causes the trader to enter themarket at a point where there is relatively low risk and a fairly high degree ofpotential reward. A trader’s Nirvana would be a system that generated entry ordersto buy or sell on a limit at the most extreme price of every turning point. Even if the exits were only merely acceptable, none of the trades would have more thanone or two ticks of adverse excursion (the largest unrealized loss to occur withina trade), and in every case, the market would be entered at the best obtainableprice. In an imperfect world, however, entries will never be that good, but they canbe such that, when accompanied by reasonable effective exits, adverse excursionis kept to acceptable levels and satisfying risk-reward ratios are obtained.What constitutes an elective exit? An effective exit must quickly extricate thetrader from the market when a trade has gone wrong. It is essential to preserve cap-ital from excessive erosion by losing trades; an exit must achieve this, however,without cutting too many potentially profitable trades short by converting them intosmall losses. A superior exit should be able to hold a trade for as long as it takes tocapture a significant chunk of any large move; i.e., it should be capable of riding asizable move to its conclusion. However, riding a sizable move to conclusion is nota critical issue if the exit strategy is combined with an entry formula that allows forreentry into sustained trends and other substantial market movements.In reality, it is almost impossible, and certainly unwise, to discuss entriesand exits independently. To back-test a trading system, both entries and exitsmust be present so that complete round-turns will occur. If the market is entered,but never exited, how can any completed trades to evaluate be obtained? An entrymethod and an exit method are required before a testable system can exist.However, it would be very useful to study a variety of entry strategies and makesome assessment regarding how each performs independent of the exits.Likewise, it would be advantageous to examine exits, testing different tech-niques, without having to deal with entries as well. In general, it is best to manip-ulate a minimum number of entities at a time, and measure the effects of thosemanipulations, while either ignoring or holding everything else constant. Is thisnot the very essence of the scientific, experimental method that has achieved somuch in other fields? But how can such isolation and control be achieved, allow-ing entries and exits to be separately, and scientifically, studied

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