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Introduction
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T o obJectlve1y study the behavior of mechanical trading systems, various exper .
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imental materials and certain tools are needed. To study the behavior of a given entry or exit method, a simulation should be done using that method on a portion of a given market s past performance; that requires data. Clean, historical data for the market on which a method is being tested is the starting point. Once the data is available, software is needed to simulate a trading account. Such software should allow various kinds of trading orders to be posted and should emulate the behavior of trading a real account over the historical period of interest. Software of this kind is called a trading simulator. The model (whether an entry model, an exit model, or a complete system) may have a number of parameters that have to be adjusted to obtain the best results from the system and its elements, or a number of features to be tamed on or off. Here is where an optimizer plays its part, and a choice must be made among the several types of optimizers available. The simulations and optimizations will produce a plethora of results. The system may have taken hundreds or thousands of trades, each with its own profiVloss, maximum adverse excursion, and maximum favorable excursion. Also generated will be simulated equity curves, risk-to-reward ratios, profit factors, and other information provided by the trading simulator about the simulated trading account(s). A way to assess the significance of these results is needed. Is the apparent profitability of the trades a result of excessive optimization Could the system have been profitable due to chance alone, or might it really be a valid trading strategy If the system is valid, is it likely to hold up as well in the future, when actually being traded, as in
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the past Questions such as these require the basic machinery provided by inferential statistics. In the next several chapters, we will cover data, simulators, optimizers, and statistics. These items will be used throughout this book when examining entry and exit methods and when attempting to integrate entries and exits into complete trading systems.
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determination of what works, and what does not, cannot be made in the realm of commodities trading without quality data for use in tests and simulations. Several types of data may be needed by the trader interested in developing a profitable commodities trading system. At the very least, the trader will require historical pricing data for the commodities of interest. TYPES OF DATA Commodities pricing data is available for individual or continuous contracts. Individual contract data consists of quotations for individual commodities contracts. At any given time, there may be several contracts actively trading. Most speculators trade thefront-month contracts, those that are most liquid and closest to expiration, but are not yet past first notice date. As each contract nears expiration, or passes first notice date, the trader rolls over any open position into the next contract. Working with individual contracts, therefore, can add a great deal of complexity to simulations and tests. Not only must trades directly generated by the trading system be dealt with, but the system developer must also correctly handle rollovers and the selection of appropriate contracts. To make system testing easier and more practical, the continuous contract was invented. A continuous contract consists of appropriate individual contracts strung together, end to end, to form a single, continuous data series. Some data massaging usually takes place when putting together a continuous contract; the purpose is to close the gaps that occur at rollover, when one contract ends and another begins, Simple back-aajustment appears to be the most reasonable and popular gap-closing
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method (Schwager, 1992). Back-adjustment involves nothing more than the subtraction of constants, chosen to close the gaps, from all contracts in a series other than the most recent. Since the only operation performed on a contract s prices is the subtraction of a constant, all linear price relationships (e.g., price changes over time, volatility levels, and ranges) are preserved. Account simulations performed using back-adjusted continuous contracts yield results that need correction only for rollover costs. Once corrected for rollover, simulated trades will produce profits and losses identical to those derived from simulations performed using individual contracts. However, if trading decisions depend upon information involving absolute levels, percentages, or ratios of prices, then additional data series (beyond backadjusted continuous contracts) will be required before tests can be conducted. End-of-day pricing data, whether in the form of individual or continuous contracts, consists of a series of daily quotations. Each quotation, bar, or data point typically contains seven fields of information: date, open, high, low, close, volume, and open interest. Volume and open interest are normally unavailable until after the close of the following day; when testing trading methods, use only past values of these two variables or the outcome may be a fabulous, but essentially untradable, system! The open, high, low, and close (sometimes referred to as the settlement price) are available each day shortly after the market closes. Intraday pricing data consists either of a series of fixed-interval bars or of individual ticks. The data fields for fixed-interval bars are date, time, open, high, low, close, and tick volume. Tick volume differs from the volume reported for endof-day data series: For intraday data, it is the number of ticks that occur in the period making up the bar, regardless of the number of contracts involved in the transactions reflected in those ticks. Only date, time, and price information are reported for individual ticks: volume is not. Intraday tick data is easily converted into data with fixed-interval bars using readily available software. Conversion software is frequently provided by the data vendor at no extra cost to the consumer. In addition to commodities pricing data, other kinds of data may be of value. For instance, long-term historical data on sunspot activity, obtained from the Royal Observatory of Belgium, is used in the chapter on lunar and solar influences. Temperature and rainfall data have a bearing on agricultural markets. Various economic time series that cover every aspect of the economy, from inflation to housing starts, may improve the odds of trading commodities successfully. Do not forget to examine reports and measures that reflect sentiment, such as the Commitment of Traders (COT) releases, bullish and bearish consensus surveys, and put-call ratios. Nonquantitative forms of sentiment data, such as news headlines, may also be acquired and quantified for use in systematic tests. Nothing should be ignored. Mining unusual data often uncovers interesting and profitable discoveries. It is often the case that the more esoteric or arcane the data, and the more difficult it is to obtain, the greater its value!
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