method
(Schwager,
1992). Back-adjustment involves nothing more than the sub-
traction 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 con-
tracts. However, if trading decisions depend upon information involving absolute
levels, percentages, or ratios of prices, then additional data series (beyond back-
adjusted 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 end-
of-day data series: For intraday data, it is the number of ticks that occur in the peri-
od 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 soft-
ware 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 influ-
ences. Temperature and rainfall data have a bearing on agricultural markets.
Various economic time series that cover every aspect of the economy, from infla-
tion 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 head-
lines, 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!