In the first installment of this two-part series, we discussed the intermittent shocks affecting the price of real commodities. We looked at hogs as an example of dramatic price swings and then built a simple model to trade these price shocks using live cattle futures. The model was based upon a three-month moving average of monthly closing price momentum, expressed as a percentage.

Building this model is easy with an Excel spreadsheet and clean historical closing data. After the model is built, data are updated at the end of each month. This is an excellent tool in the part-time trader’s arsenal. It produces long-term signals and is always in the market. For example, if the cattle model signals a sell order in March 2010, the trader can enter a position and hold short until the next buy. Alternatively, other short-term strategies can be oriented in the direction with the long-term signal, providing a certain level of comfort due to duplication of signals from different methods.

As a quick refresher, we know that price is dependent upon supply and demand and external events occur without warning, sending prices soaring or crashing. Some events are significant, causing a severe imbalance in price, which can take longer for supply or demand factors to respond accordingly. It is these shocks, and the responses to them, that we seek to trade.

Analysis reveals that different markets measure and react to shocks in different ways. The basic model for cattle produced 10 completed trades over a 15-year period with 100% accuracy. It did suffer drawdowns in some trades, but each was completed with a profit, averaging in excess of $7,000, after slippage and commissions. Adding a second “trading” contract increased the average net profit to over $10,000.

BEYOND CATTLE

Now we’ll turn to other commodities and build slightly more complex models. The last article depicted major turning points in hogs since 1994 using high and low prices and produced 423.48 points of swing.

For our new model, we start with the same simple three-month moving average of momentum, our cattle model, but we observe, as with most commodities, that prices tend to explode on the upside more than they crash on the downside. This may result from a speculator short squeeze, a demand panic or a true depletion of supply. Generally, a combination of these generates the extreme upward price increases we’ve seen.

So, we set bands at different levels if needed and assume the sell level may be greater than the buy trigger. Levels of 11.4% and -8.4% are good points for selling and buying, respectively. The model generates nine completed trades for a gross profit of 80.80 points, six wins averaging 14.5 points each and three losses averaging 2.1 points. This may not be the best we can achieve.

Consequently, let us construct a second statistic: one-year price momentum. Compute a percentage momentum statistic of this month’s price close vs. the same month the prior year. For example, in June 2008, hogs closed at 71.77¢. In June 2009, hogs closed at 58.67¢, a drop of 13.10¢, or -18.25%. When we analyze this statistic, we see it is highly dependent upon any prior shock in price, but still generates 135.80 points over five profitable trades. We might still do better.

To soften this shock, we generate a modified statistic. First, compute a three-month moving average of price for the prior year. Next, generate a momentum statistic for this month divided by the average generated 11 months ago — that is, the average of prices 11, 12 and 13 months ago. We call this the “hog model.”

We will set our bands at 49.5% sell side and -34.0% and -49.5% on the buy side. This time, they will be penetration bands. Instead of waiting for a retracement after momentum exceeds the trigger level, simply execute the trade any time the statistic trades beyond the key number.

For example, consider the set: -7.4%, 36.2%, 38.1%, 50.3%, 54.9%, 33.3%, 61.4%, 62% and 21.3%. In those months, assuming we are long, we will sell on the close of the 50.3% month, not wait for the 33.3% retracement. In addition, because the statistic fell below the 49.5% level and then climbed above it again, add another sell position at 61.4%. As a result, our five profitable trades now yield 254.87 points of profit. And, on the buy side, we will go long at a penetration of -34%, and again if in a subsequent month price plunges and the statistic exceeds -49.5%, a phenomenon we don’t expect to see except in unusual circumstances.

As with cattle, add a second contract on the initial reversal trade with a profit target of 17.50¢. If the signal repeats, add a contract, but do not add the profit target contract unless the prior extra contract has already satisfied its target. Doing this raises the gross profit to 361.43 (see “Two contracts are better than one” ).

The model produces nine profitable trades with an average gain of 40.15 points. Assuming cost and slippage of a whopping $500 per trade, that is still over $140,000 profit for the model — roughly $9,300 per year. Even though the drawdowns can be difficult, such as from July 1998 to the low in November 1998 (about $16,500 on the two contracts), the model does successfully catch the peaks and valleys. As of July 24, 2009, the model remains short one contract from 79.07¢ with a closing price of 59.05¢, for an unrealized gain of an additional 20 points.