Trading
Soybean
Spreads
T
he price relationship betweentwo or more given commodity contracts is known as a spread. Spread trading is the purchase of one commodity contract and the simultaneous sale of another, related, futures contract. The price difference can change, and if it trends in the correct direc-tion, the change in the relationship of the prices will be profitable. There are two basic types of spreads: intercommodity and intracommodity spreads.An intercommodity spread is the purchase of a given commodity and the simultaneous sale of another related but different commod-ity. Examples of common intercommodity spreads are: the Treasury notes–Treasury bond spread, called the NOB spread; the corn– wheat spread; the T-bill–Eurodollar spread, called the TED spread, and the live cattle– feeder cattle spread. Trading intercommodity spreads involves speculation on the relation-ship between related but different markets. An intracommodity spread involves the purchase of one delivery month and the simultaneous sale of a different delivery month of the same commodity on the same exchange. A common example of an intracommodity or interdelivery spread is the July–November soybean spread.
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HEPROSANDCONSSince spread trading involves the simulta-neous purchase and sale of two (or more) commodity contracts, the trading of spreads involves a higher initial overhead cost in the form of commissions. Although most firms will give commission discounts for spread trading, the discounted commissions are
typi-HOLLIS BOGDANNFY
Here are the basics of trading a soybean commodity spread using a seasonal strategy.
System Week# Parameter Action Holding # of # Wins # Loss Total Avg Avg period trades P&L profit loss
SS-1 51 HC Sell 2 15 13 2 106 7/8 8 5/8 -2 SS-2 1 HC Sell 3 11 10 1 68 1/8 7 1/4 -3 SS-3 2 LC Sell 2 13 11 2 162 7/8 15 -1 SS-4 21 LC Sell 4 15 12 3 212 23 3/8 -23 SS-5 23 LC Sell 3 16 13 3 164 1/4 13 2/4 -3 SS-6 24 HC Sell 2 15 13 2 138 1/2 11 3/4 -1
cally still higher than the cost involved in a simple outright position. Spread trading, though generally accepted as less risky, can entail more risk because you have the opportunity for two positions to move against you, as the price of the long posi-tion contract can go down and the price of the short position contract can rise. As such, individuals should closely examine the fees and risks involved in spread posi-tions before trading.
Spread trading does have several dis-tinct advantages that outweigh the added costs associated with this type of trading: • The spreads have attractive margin requirements. The margin require-ment is the funds you deposit with your broker. For outright positions there is one level, and for spread trading the capital required is gener-ally half or less.
• There is less risk because the day-to-day changes in the price of the spread
When the immediate demand for soybeans is high, the July contract will increase in value relative to the November contract. Thus, if prices rise, then July soybeans will increase in value by a greater amount than November soybeans; if
prices decline, then July soybeans will decrease in value less than November soybeans.
When the immediate demand for soybeans is low, the July contract will decrease in value relative to the November contract. This means that if prices rise, then July soybeans is typically less than the day-to-day change in price of
an outright position.
• The price of the spread can demonstrate increased predictability due to the seasonal nature of the spread markets.
For these reasons, spread positions are considered hedged positions. The long position hedges the short position, be-cause if the long side of the spread is showing a loss, the short side of the spread should be showing a profit, or vice versa. This is the case most of the time. The hedged nature of spread trading can also be seen in the drastically lower margin requirements for spreads. In addition, because spreads are relationships, they tend to behave in a more rational manner compared to outright positions.
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HEOLDCROP–
NEWCROP SOYBEANSPREADThe most commonly followed soybean interdelivery spread is the July–November spread (Figure 1). This spread is a classic example of an old crop (July) to new crop (November) spread. Soybeans are typically
planted in March and harvested in November; as such, soybeans for July delivery are old crop, as they have been sitting in storage since last year’s crop, while the Novem-ber contract is this year’s crop. This spread is in essence a mea-sure on the immediate demand for soybeans, as opposed to the future demand for soybeans.
Soybeans are typically planted in
March and harvested in November;
as such, soybeans for July delivery
are old crop, as they have been
sitting in storage since last year’s
crop, while the November contract
is this year’s crop.
FIGURE 1: SPREAD MARKETS. The top chart is the July 1996 soybean contract and the middle chart is
the November 1996 soybean contract. The bottom chart is the spread chart, which is the difference between the closing prices of the two contracts. Notice that the spread chart has an uptrend, a downtrend and sideways trading patterns.
FIGURE 2: LOGIC FILTER RESULTS. Here are the results for each system. The specific rules are listed in the
sidebar titled “Rules for decision logic-based seasonal trading.”
TT CHARTBOOK (TECHNICAL TOOLS)
HYPOTHETICAL SYSTEM PERFORMANCE ON YEAR-BY-YEAR BASIS
Year # of # of # of Total profit/ trades winners losers loss ($)
1969 3 3 0 $187.50 1970 2 2 0 562.50 1971 3 3 0 325.00 1972 5 3 2 268.75 1973 2 2 0 4,175.00 1974 2 1 1 0 1975 5 4 1 2,750.00 1976 2 2 0 375.00 1977 3 3 0 10,725.00 1978 3 2 1 487.50 1979 2 1 1 175.00 1980 4 4 0 937.50 1981 2 2 0 2,525.00 1982 5 4 1 725.00 1983 5 5 0 2,212.50 1984 3 3 0 10,362.50 1985 1 1 0 287.50 1986 2 1 1 (12.50) 1987 2 2 0 237.50 1988 4 3 1 (612.50) 1989 2 1 1 2,525.00 1990 2 2 0 737.50 1991 4 2 2 (25.00) 1992 5 5 0 637.50 1993 3 3 0 400.00 1994 2 2 0 475.00 1995 2 2 0 287.50 1996 5 4 1 900.00
SUMMARY OF THE HYPOTHETICAL SYSTEM PERFORMANCE ON YEAR-BY-YEAR BASIS
No $50/contract commission round-turn commission # of years 28 28 # wins 24 21 # losses 4 7 % winning years 86% 75% Total profit $42,631.25 $34,131.25 Average year $1,522.54 $1,218.97
position in the July contract and a long position in the November contract. All profit and loss figures are quoted in cents, where one cent in the soybean market is equivalent to $50.00. No commissions or slippage were applied to the results. Figure 3 presents the hypothetical performance of the system on a year-by-year basis. Figure 4 summarizes the performance.
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REAKDOWNOFTWOPATTERNSIn the interest of brevity, I will detail only two of the six patterns, SS-3 and SS-4. I chose these two because one should have been completed by the time this article is published,
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UILDINGASYSTEMLike all agricultural markets to some extent, the soy-bean market is seasonal in nature; as such, I wanted to incorporate this seasonal-ity into my trading system. This system is a robust one for trading the July–Novem-ber soybean spread that incorporates the seasonal nature of both the soybean market and the soybean spread markets using event-based logic. Toward this end, the following controls are used for development and testing. The period covered is November 1 through July 20, 1969, through 1996. The spreads are quoted as the July delivery contract price minus the November delivery contract price, within the same calendar year. Long positions entails buying July and selling November; short positions indicate selling July and buying November. Therefore, the spread is always quoted as the position of the July contract.
This system is based on two parameter sets, the number of the calendar week and the close of the current week in relation to last week’s data. The first Friday (or last trading day of a calendar week) of the new year is always considered week 1, while the last Friday of the year (or last trading day of the last full week) is always considered week 52. Week numbers are assigned sequentially from these points.
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ECISIONLOGICFILTERINGThe basis of seasonal analysis is that the current trading time frame will act normally — meaning that this year will behave as the past years have, and hopefully the most profitable past years. In order to isolate the most profitable years from the dataset, I used an event that must be triggered in order to have a complete setup for the trade.
The decision logic being used for this example is a simple pattern: either a higher weekly close or a lower weekly close, indicated with an HC or an LC, respectively, in Figure 2 (see sidebars “Rules for decision logic based on seasonal trading” and “Performance breakdown of the decision logic seasonal trades”).
The week column denotes the week number in a standard 52-week year, as denoted in the controls for testing. The parameter is the decision logic to take the trade, with LC signifying a lower close on the appropriate week number as compared with the previous week, while HC signifies a close higher than the previous week’s close. The action column is the appropriate trade to be taken, with “sell” meaning a short will increase by a lesser amount than November soybeans; if prices decline, then July soybeans will decrease in value more than November soybeans.
FIGURE 3: PERFORMANCE. This table lists the results
year by year.
FIGURE 4: SUMMARY. The first column excludes
while the other could be viewed in real time.
SS-3 is a simple seasonal pat-tern combination. The rules for entry are simple. If the closing value of the July–November soy-bean spread (July contract minus November contract) for the sec-ond week of the year is less than the closing value of this spread on the first week of the year, then sell this spread (establish a short posi-tion in the July contract and a long position in the November contract). The tested holding period is for two complete calendar weeks, or until the end of the fourth week of the year. Figure 5 is a year-by-year listing of the trade results based on the seasonal approach. The rows in bold denote years in which trades would have been executed based on the decision logic listed sepa-rately in Figure 6. Figure 7 com-pares the seasonal trade perfor-mance to the system with the deci-sion filter.
Using the decision logic of a lower weekly close, coupled with the seasonal nature of this spread, gathers the most profitable trades and reduces the drawdowns asso-ciated with the seasonal phenom-ena. Using a simple filter or deci-sion criteria of a lower weekly close on the second week of the year retains 69% of the total profit associated with the seasonal phe-nomena with less than half the number of trades. The profit to potential loss ratio (“Average profit/maximum draw on profit-able trade”) is an attractive 1.22. The maximum drawdown on a profitable trade is an excellent in-dication of where to place a stop-loss for real-time trading of tested systems. This system calls for a stop-loss placed 10 cents above the closing value of the second week, or roughly $525.00 above
Here, the rules — including the time period for entry and the appropriate action for each spread trade — are detailed.
market; at this point, any lack of rain that has been forecast causes soybeans prices to climb, while actual precipitation generally leads to violent price drops.
The July–November soybean spread, however, acts in a RULES FOR DECISION LOGIC-BASED SEASONAL TRADING
Pattern: SS-1
Seasonal component: 51st week of the year through the 1st week of the following year
Current year’s dates: December 28, 1996, through January 4, 1997
Decision logic: Higher close on the 51st week (12/28) than the 50th week (12/21)
Action: If decision logic is true, sell July ‘97 soybeans and buy November ‘97 soybeans
Pattern: SS-2
Seasonal component: First week of the year through the 4th week of the year
Current year’s dates: January 4, 1997, through January 25, 1997
Decision logic: Higher close on the 1st week (01/04) than the 52nd week (12/31)
Action: If decision logic is true, sell July ’97 soybeans and buy November ’97 soybeans
Pattern: SS-3
Seasonal component: 2nd week of the year through the 4th week of the year
Current year’s dates: January 11, 1997, through January 25, 1997
Decision logic: Lower close on the 2nd week (01/11) than the 1st week (01/04)
Action: If decision logic is true, sell July ’97 soybeans and buy November ’97 soybeans
Pattern: SS-4
Seasonal component: 21st week of the year through the 25th week of the year
Current year’s dates: May 24, 1997, through June 21, 1997
Decision logic: Lower close on the 21st week (05/24) than the 20th week (05/17)
Action: If decision logic is true, sell July ’97 soybeans and buy November ’97 soybeans
Pattern: SS-5
Seasonal component: 22nd week of the year through the 25th week of the year
Current year’s dates: May 31, 1997, through June 21, 1997
Decision logic: Lower close on the 22nd week (05/31) than the 21st week (05/24)
Action: If decision logic is true, sell July ’97 soybeans and buy November ’97 soybeans
Pattern: SS-6
Seasonal component: 24th week of the year through the 26th week of the year
Current year’s dates: June 14, 1997, through June 28, 1997
Decision logic: Lower close on the 24th week (06/14) than the 23rd week (06/07)
Action: If decision logic is true, sell July ’97 soybeans and buy November ’97 soybeans
SS-1 SS-2 SS-3 SS-4 SS-5 SS-6 # of trades 15 11 13 15 16 15 # of profits 13 10 11 12 13 13 # of losses 2 1 2 3 3 2 % Profitable 87% 91% 85% 80% 81% 87% Total P&L $5,343.75 $3,406.25 $8,143.75 $10,600.00 $8,212.50 $6,925.00 Average P&L $356.25 $309.66 $626.44 $706.67 $513.28 $461.67 Average profit $429.33 $358.13 $755.68 $1,170.83 $672.12 $593.18 Average loss -$118.75 -$175.00 -$84.38 -$1,150.00 -$175.00 -$43.75 Average draw -$56.25 -$90.91 -$38.94 -$375.00 -$636.33 -$275.00 Average draw on a profitable trade -$4.81 $5.00 -$19.89 -$113.54 -$554.33 -$238.46 Maximum draw on a profitable trade -$275.00 -$87.50 -$512.50 -$687.50 -$4,900.00 -$1,675.00 Χ2 6.67 5.82 4.92 4.27 5.06 6.67 Stop level -5 3/4 -2 -10 2/4 -14 -98 1/4 -33 3/4
This table lists the results of each spread trading system.
PERFORMANCE BREAKDOWN OF THE DECISION LOGIC SEASONAL TRADES
the entry price.
By contrast, SS-4 is based on the seasonality of the July– November soybean spread during the latter part of May or, more precisely, the 21st week of the year. This is typically the height of the “weather-controlled” markets in the soybean
much more predictable manner during these chaotic times. For the last 28 years (1969 to 1996), the spread has narrowed for 20 of those years between the 21st week of the year and the 25th week of the year. Trading this seasonal bias alone, though, is precarious, as the July–November spread has had some violent rallies during this time frame; specifically, the drought of 1973 caused this spread to widen over $1.00 a bushel during our seasonal window. The years 1977, 1988 and 1989 all saw the spread gain over $0.20 a bushel during our seasonal window.
But by using the decision logic filter of a lower weekly close on the 21st week of the year, the only year with a rally over $0.20 a bushel remaining was 1988. Using the maxi-mum drawdown on a profitable trade as the guideline for setting a stop on this trade, one could place a stop-loss on this trade as close as $0.14 a bushel on the spread. Figure 8 illustrates the vast improvement upon the seasonal strategy that using decision logic adds.
A simple filter of a lower close almost doubles the gross profit of this system while reducing the number of trades by almost 50%. Though the reduced number of trades does degrade the significance of the results, the results are still significant by a chi-square† of 4.27 compared with 4.32 of the
straight seasonal. The average trade profit and loss number is increased by more than $500 because the chaotic activity of 1973 was filtered out. In addition, several zero to two-cent trades were removed, as well as five losing trades. Using the decision logic filter of a lower close turned a marginally attractive seasonal bias into a robust trading system with excellent risk-to-reward characteristics.
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NDINCONCLUSIONSpread trading requires more patience and a greater under-standing of the risks involved than outright position trading does, but the rewards far outweigh the added study. Spreads tend to trend more as well as behave in a more seasonal manner than the underlying futures markets. Those without trading patience, or who have a need for quick and frequent excitement, should probably not trade spreads, but those of you who wish to develop longer-term, accurate trading systems should pay closer attention to the spread markets.
By combining simple patterns with seasonal analysis of the spread markets, a decision logic filter can be used to gauge the seasonality of the spread, perhaps take advantage of the most profitable circumstances and avoid the marginal to low percentage profitable trades. Pattern recognition alone is a powerful tool in technical analysis, and combining it with the seasonal nature of the markets can lead to highly robust and historically attractive trading systems.
By trading spreads, one is able to trade more conserva-tively using sound money management because of the lower margin requirements. It is possible for a trader with a $5,000 account to risk 10% of his account while trading a longer-term system and having the stop-loss placed far enough away to avoid the market’s noise. Since spread data must be created by subtracting one price series from another, these markets have not been as mined for data as have the under-lying futures, so some of the market anomalies have not been as exploited.
Spread trading is an underexploited area of the futures
SEASONAL TRADES BASED SOLELY ON ENTRY ON THE 2ND WEEK OF YEAR AND EXIT ON THE 4TH WEEK OF THE YEAR
Seasonal Entry Exit P&L Drawdown Draw on trades price price in cents in cents profitable
trades in cents 1969 23 1/8 26 3/8 -3 1/4 -3 1/4 1970 15 3/8 11 7/8 3 2/4 2/4 2/4 1971 25 24 2/4 1/2 -1 1/8 -1 1/8 1972 17 5/8 20 2/4 -2 7/8 -5 1/4 1973 63 1/4 66 5/8 -3 3/8 -9 1974 15 2/4 16 - 2/4 - 2/4 1975 42 2/4 21 2/4 21 2/4 2/4 1976 -13 2/4 -13 - 2/4 -3 1977 46 3/4 36 10 3/4 -10 1/4 -10 1/4 1978 27 1/4 13 1/4 14 2 2 1979 42 1/4 41 2/4 3/4 -3 1/4 -3 1/4 1980 -23 2/4 -27 3 2/4 -1 1/4 -1 1/4 1981 18 -30 2/4 48 2/4 7 2/4 7 2/4 1982 -9 -13 1/4 4 1/4 -2 3/4 -2 3/4 1983 - 1/4 -13 2/4 13 1/4 2 3/4 2 3/4 1984 86 2/4 45 41 2/4 3 2/4 3 2/4 1985 3 2/4 5 3/4 -2 1/4 -5 1/4 1986 25 1/4 24 1/4 1 -16 2/4 -16 2/4 1987 15 1/4 15 1/4 -1 2/4 -1 2/4 1988 3/4 -5 5 3/4 -4 2/4 -4 2/4 1989 89 3/4 39 1/4 50 2/4 11 11 1990 -10 2/4 -9 2/4 -1 -3 2/4 1991 -9 2/4 -11 1 2/4 -5 3/4 -5 3/4 1992 -9 3/4 -13 1/4 3 2/4 - 3/4 - 3/4 1993 -3 3/4 -6 2/4 2 3/4 1/4 1/4 1994 59 1/4 43 1/4 16 2/4 2/4 1995 -14 1/4 -13 1/4 -1 -1 3/4 1996 47 1/4 40 2/4 6 3/4 -1 -1
FIGURE 6: This table shows the boldfaced trades from Figure 5. SS-3 TRADES
Lower weekly closes on the 2nd week, exit on the 4th week
SS-3 Entry Exit P&L Drawdown Draw on price price in cents in cents profitable trades
in cents 1971 25 24 2/4 2/4 -1 1/8 -1 1/8 1972 17 5/8 20 2/4 -2 7/8 -5 1/4 1974 15 2/4 16 - 2/4 - 2/4 1975 42 2/4 21 2/4 21 2/4 2/4 1977 46 3/4 36 10 3/4 -10 1/4 -10 1/4 1981 18 -30 2/4 48 2/4 7 2/4 7 2/4 1982 -9 -13 1/4 4 1/4 -2 3/4 -2 3/4 1983 - 1/4 -13 2/4 13 1/4 2 3/4 2 3/4 1988 3/4 -5 5 3/4 -4 2/4 -4 2/4 1989 89 3/4 39 1/4 50 2/4 11 11 1991 -9 2/4 -11 1 2/4 -5 3/4 -5 3/4 1992 -9 3/4 -13 1/4 3 2/4 - 3/4 - 3/4 1996 47 1/4 40 2/4 6 3/4 -1 -1
FIGURE 5: SEASONAL TRADES. This table lists every year’s performance, and
market, one that offers excellent risk-to-reward characteristics and a high degree of predictability. Spread markets are not subject to prices trading through levels that trigger stop-loss orders, only to see the prices return to the previous level. Spreads are an excellent tool for taking advantage of the seasonal and longer-term trends of the futures markets. The benefits of trading spreads far out-weigh the added work necessary for analy-sis and the higher costs associated with spread trading.
Scott Barrie is the head of research for Great Pacific Trading Co. He edits Great Pacific’s “Trend Watch” newsletter and “Seasonal Stratagems Report.” Mark Sanders, vice president of Great Pa-cific, contributed to this report.
SEASONAL PERFORMANCE AND SS-4 PERFORMANCE FOR THE SEASONAL WINDOW OF THE 21ST THROUGH 25TH WEEK
Seasonal Decision logic trades trades # of trades 28 15 # of profits 20 12 # of losses 8 3 % profitable 71% 80% Total P&L $5,331.25 $10,600.00 Average P&L $190.40 $706.67 Average profit $916.56 $1,170.83 Average loss -$1,625.00 -$1,150.00 Average draw -$657.81 -$375.00 Average draw on a profitable trade -$75.31 -$113.54 Maximum draw on a profitable trade -$687.50 -$687.50 Χ2 4.32 4.27
COMPARISON OF SEASONAL-BASED TRADES WITH AND WITHOUT THE DECISION LOGIC CRITERIA
Seasonal Decision logic trades trades # of trades 28 13 # of profits 20 11 # of losses 8 2 % profitable 71% 85% Total P&L $11,737.50 $8,143.75 Average P&L $419.20 $626.44 Average profit $623.75 $755.68 Average loss -$92.19 -$84.38 Average draw -$92.19 -$38.94 Average draw on a profitable trade -$50.31 -$19.89 Maximum draw on a profitable trade -$825.00 -$512.50 Χ2 4.32 4.92
FIGURE 7: SUMMARY. This table summarizes the
results from Figures 5 and 6.
FIGURE 8: SS-4 TRADES. The first column shows
what the results would be if the seasonal trade had been executed every year. The second column uses the SS-4 decision filter.
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ELATEDREADINGANDRESOURCEBarrie, Scott [1996]. “Pork bellies and the COT index,” Technical Analysis of STOCKS & COMMODITIES, Vol-ume 14: October.
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