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N o savvy trader would trade a system with a real account and risk real money
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without first observing its behavior on paper. A trading simulator is a software application or component that allows the user to simulate, using historical data, a trading account that is traded with a user-specified set of trading rules. The user s trading rules are written into a small program that automates a rigorous papertrading process on a substantial amount of historical data. In this way, the trading simulator allows the trader to gain insight into how the system might perform when traded in a real account. The r&on d &re of a trading simulator is that it makes it possible to efficiently back-test, or paper-trade, a system to determine whether the system works and, if so, how well.
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There are two major forms of trading simulators. One form is the integrated, easyto-use software application that provides some basic historical analysis and simulation along with data collection and charting. The other form is the specialized software component or class library that can be incorporated into user-written software to provide system testing and evaluation functionality. Software components and class libraries offer open architecture, advanced features, and high levels of performance, but require programming expertise and such additional elements as graphics, report generation, and data management to be useful. Integrated applications packages, although generally offering less powerful simulation and testing capabilities, are much more accessible to the novice.
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Regardless of whether an integrated or component-based simulator is employed, the trading logic of the user s system must be programmed into it using some computer language. The language used may be either a generic programming language, such as C+ + or FORTRAN, or a proprietary scripting language. Without the aid of a formal language, it would be impossible to express a system s trading rules with the precision required for an accurate simulation. The need for programming of some kind should not be looked upon as a necessary evil. Programming can actually benefit the trader by encouraging au explicit and disciplined expression of trading ideas. For an example of how trading logic is programmed into a simulator, consider TradeStation, a popular integrated product from Omega Research that contains an interpreter for a basic system writing language (called Easy Language) with bistorical simulation capabilities. Omega s Easy Language is a proprietary, tradingspecific language based on Pascal (a generic programming language). What does a simple trading system look like when programmed in Easy Language The following code implements a simple moving-average crossover system: ( Simple moving average crossover system in Easy Language) (length parameter ) Inputs: k (4) ; rf (close > Average~close, Led 1 And (Close [II c= Average ~ClOm , Len) [II ) Then (buys at open of next myc A , 1 contract At Market;
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If (Close (Close <= Average (Close, (Close. Len), And at open Of next bar L1, > *"verage ILen) [II, Then (sells 1 ContraCt At Market;
bar)
Sellc"B"J
This system goes long one contract at tomorrow s open when the close crosses above its moving average, and goes short one contract when the close crosses below the moving average. Each order is given a name or identifier: A for the buy: B for the sell. The length of the moving average (Len) may be set by the user or optimized by the software. Below is the same system programmed in Cf + using Scientific Consultant Services component-based C-Trader toolkit, which includes the C+ + Trading Simulator:
Except for syntax and naming conventions, the differences between the Cf + and Easy Language implementations are small. Most significant are the explicit references to the current bar (cb) and to a particular simulated trading account or simulator class instance (ts) in the C+ + implementation. In C+ +, it is possible to explicitly declare and reference any number of simulated accounts: this becomes important when working with portfolios and merasystems (systems that trade the accounts of other systems), and when developing models that incorporate an implicit walk-forward adaptation. SIMULATOR OUTPUT All good trading simulators generate output containing a wealth of information about the performance of the user s simulated account. Expect to obtain data on gross and net profit, number of winning and losing trades, worst-case drawdown, and related system characteristics, from even the most basic simulators. Better simulators provide figures for maximum run-up, average favorable and adverse excursion, inferential statistics, and more, not to mention highly detailed analyses of individual trades. An extraordinary simulator might also include in its output some measure of risk relative to reward, such as the annualized riskto-reward ratio (ARRR) or the Sharp Rario, an important and well-known measure used to compare the performances of different portfolios, systems, or funds (Sharpe, 1994). The output from a trading simulator is typically presented to the user in the form of one or more reports. Two basic kinds of reports are available from most trading simulators: the performance summary and the trade-by-trade, or detail, report. The information contained in these reports can help the trader evaluate a system s trading style and determine whether the system is worthy of real-money trading. Other kinds of reports may also be generated, and the information from the simulator may be formatted in a way that can easily be run into a spreadsheet for further analysis. Almost all the tables and charts that appear in this book were produced in this manner: The output from the simulator was written to a file that would be read by Excel, where the information was further processed and formatted for presentation.
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