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Mechanical trading systems - Weissman R.L.

Weissman R.L. Mechanical trading systems - Wiley publishing , 2005 . - 240 p.
ISBN 0-471-65435-3
Download (direct link): mechanicaltradingsystems2005.pdf
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Data Integrity: Expiration of Futures Contracts
The figures provided in Chapter 2 were either cash market charts, such as spot Interbank foreign exchange (Forex) or cash S&P 500 index, or they were futures contracts for a specific delivery month. This was fine for showcasing how specific technical indicators can be transformed into trading systems, but to generate 10 years of backtested results for a particular trading system on a portfolio, we need to address the issue of expiration of futures contracts.
Nearest Futures Charts The traditional method of dealing with expiration of futures contracts is known as linked nearest contract or nearest futures charting. The nearest futures chart is constructed by including the data history of the futures contract closest to expiration. Following the front month contract’s expiration, the chart begins displaying the price history of the new nearest futures contract.
The problem with these charts is that there are usually significant dif-
TABLE 3.1 Composition of backtested portfolio.
Asset Class Asset" Asset Symbol
Equity Indices CME E-Mini S&P 500b ES
Mid/Long-Term Rates CBOT Treasury notes TY
Short-Term Rates CME eurodollars ED
European Currencies IMM Swiss francc SF
Asian Currencies IMM Japanese yen JY
Energy Nymex crude oil CL
Metals Comex gold GC
Grains CBOT soybeans S
Meats CME lean hogs LH
Food & Fibers NYBOT cotton CT
aTo ensure uniformity, all assets shown are day session only.
fcCash S&P 500 Index x 50 was used to simulate CME E-mini S&P futures.
cDue the shift from D marks to euros during the backtested period, IMM Swiss francs were used for European currencies.
Data source: CQG, Inc.
Trend-Following Systems
ferences between the expiring contract’s final price and the initial price recorded for the new front month contract. This divergence between the two data sets could result in huge price gaps and, more important, for our purposes, false trading signals. For example, by comparing Figures 3.1 and 3.2, if the February lean hogs contract expired today, the nearest futures chart would rise by 332 points, probably triggering false trading signals in most intermediate-term trading systems.
Equalized Continuation Price Series Charts Most high-function-ality data providers enable their subscribers to overcome this problem of false trading signals on long-term nearest futures charts by providing equalized continuation or point-based back-adjusted data series charting. With an equalized continuation series chart, the problem of contract rollover is resolved by the trader choosing a specific number of days prior to expiration day as the trigger for rolling the data in the chart back to the older futures contract month’s data series.
Returning to the lean hogs contract rollover problem, if in March 2004 we were to backtest a particular trading system for lean hogs using a equalized continuation price series chart with a designated rollover date of January 19, 2004, as of that date our chart would begin to reflect February 2004
""[73 |20 [27 |03 [To [17 [24 [oT |C8 pfs |22 |29 |02 |i2~
FIGURE 3.1 February 2004 CME lean hogs futures.
©2004 CQG, Inc. All rights reserved worldwide.
- .
H- 5825
l = 5735 .
L = 5807/
A= + 102 •
y |13 |20 [27 p |ÏÔ [Ï7 [24 [01 P [Ts P |29 |02 |i?
FIGURE 3.2 April 2004 CME lean hogs futures. ©2004 CQG, Inc. All rights reserved worldwide.
data plus the 332-point differential between the February and April contracts. This is because on our designated rollover date the prices were:
February 2004 lean hogs = $5,475
April 2004 lean hogs = $5,807
Our continuous chart would add 332 to all February lean hogs data on and prior to the designated contract rollover date.1
Although equalized continuation charts are a tremendous improvement over nearest futures charts for data integrity in system backtesting, they are not without drawbacks. The first and most obvious problem is that the numbers displayed on these charts are derived through an artificial adjustment of prices, and so the price levels shown are worthless in terms of determining horizontal and trend-line support and resistance and retracement level.
Another problem with equalized continuation charting is that the process of deriving equivalent historical prices often leads to data within the series containing prices of zero or negative numbers. This prohibits our use of stop-loss levels based on a percentage of the contract’s value at time
Trend-Following Systems
of entry. Although we could always refer back to the actual historical prices at the time of entry to derive a percentage-based stop-loss level, there is no need to bother as there are a plethora of equally robust mechanisms for stop-loss placement that can be employed instead.
Point Value versus Percentage Changes in Data History A final issue applies not only to equalized continuation charts, but also to all of historical data. This is the problem of point value changes as opposed to percentage value changes. I will use equalized continuation charts to exemplify the issue. Equalized continuation charts merely adjust the price difference between today’s data and historical data, as illustrated by the lean hog example. In many instances, if the asset in question has experienced a longterm bull market trend, then the price differences between entry and exit will be dramatically different from the percentage differences.
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