The S&P 500 Seasonal Day Trade

May 7, 2017 | Author: Allyson Harrell | Category: N/A
Share Embed Donate


Short Description

Download The S&P 500 Seasonal Day Trade...

Description

Stocks & Commodities V. 14:7 (333-337): The S&P 500 Seasonal Day Trade by William Brower, C.T.A. TRADING TECHNIQUES

The S&P 500 Seasonal Day Trade Is there a particular day of the week within a month that offers the most opportunity? Here’s a trading system based on the best days of the week for trading Standard & Poor’s 500 futures.

M

Mark James

uch research has been done on seasonal trading. Originally, the concept derived from the theory that there had to be certain times of the year when it is better to buy and other times when it is better to sell. If you take a commodity and run a simulation in which you pair up all possible entry days with all possible exit days in a given year, you should be able to locate the best days to trade. If you perform this task over several years of data, it is possible some patterns will appear to identify those days with the highest probability of success for both long and short trades. In 1994 I was present at a speech given by statistician Sheldon Knight that shed new light on this old trading strategy.

by William Brower, C.T.A. Copyright (c) Technical Analysis Inc.

Stocks & Commodities V. 14:7 (333-337): The S&P 500 Seasonal Day Trade by William Brower, C.T.A.

Knight runs a company using statistics in processing financial data and has more than 30 years’ experience in computerized analysis of stocks and futures. During the presentation in 1994, Knight reasoned that seasonal trading approaches were ineffective because commodity futures prices react to government reports and other events based on a monthly calendar, not on a yearly one. Many government reports are released not on the nth day of the calendar year, such as the fifth of every month, but on predetermined positions within a given month. For example, employment data comes out on the first Friday of every month, while money supply figures come out every Thursday. Likewise, the producer price index comes out on the second Wednesday of the month and export sales numbers are released on the fourth Thursday. Armed with this insight, Knight forged the concept of a seasonal trading system based on the day of week in a given month. He developed the K-data timeline method, which, according to him, was almost a successful position trading system, except for the sizable drawdowns. This was later improved upon to include the influence of the actual first notice days. Recalling Knight’s work in this area, I decided to use his concepts to develop trading systems.

DAY OF THE WEEK IN A MONTH DoWinMo listing 11 = 1st Monday 12 = 1st Tuesday 13 = 1st Wednesday

25 = 2nd Friday 31 = 3rd Monday 32 = 3rd Tuesday

44 = 4th Thursday 45 = 4th Friday 51 = 5th Monday

14 = 1st Thursday 15 = 1st Friday 21 = 2nd Monday

33 = 3rd Wednesday 34 = 3rd Thursday 35 = 3rd Friday

52 = 5th Tuesday 53 = 5th Wednesday 54 = 5th Thursday

22 = 2nd Tuesday 23 = 2nd Wednesday 24 = 2nd Thursday

41 = 4th Monday 42 = 4th Tuesday 43 = 4th Wednesday

55 = 5th Friday

FIGURE 1: Each DoWinMo is designated by a two-digit number, and all end in a number between 1 and 5, representing the day of the working week. The list includes numbers as high as 55, because the first 5 stands for the fifth occurrence. All months other than February have at least two days of the week that appear five times.

PATTERNS AND FILTERS DoWinMo numbers Pattern 1 Pattern 2 Pattern 3 Pattern 4

11, 15, 23 12, 15, 25, 42 14, 41 23

Pattern 5 Pattern 6 Pattern 7 Pattern 8

12, 15, 21, 33, 35, 42, >50 21, 32, 41, 43, >50 22, 33 14, 33, 34, 42

FIGURE 2: Here are the successful patterns and the DoWinMo numbers used as filters. Since there are very few trades for the DoWinMo numbers from 51 through 55, they were combined into one test filter designated by the >50.

A TRADING SYSTEM

I chose to build a system that day trades the Standard & Poor’s 500 futures contract. The trading system is based on two concepts. First, the entry rules use some very basic patterns, which I will explain later, and second, I use a unique filter to determine the day of the week in the IN-SAMPLE AND OUT-OF-SAMPLE TEST RESULTS Pattern 1 Pattern 2 Patttern 3 month for the actual day trades. This custom filter, In Out of In Out of In Out of which is called the Day of the Week in Month sample sample sample sample sample sample (DoWinMo), was created for TradeStation. Figure 1 11,700 24,800 44,190 57,025 9,175 16,325 denotes the listing of the day of the week in a month Net profit 67 73 130 139 58 51 by two-digit numbers. Each DoWinMo is designated Total trades % winners 48 56 62 63 52 61 by a two-digit number, and all end in a number Avg trade 175 340 340 410 158 320 between “1” and “5,” representing the day of the Largest win 2,500 4,300 10,225 5,000 6,600 3,700 working week. The list includes numbers as high as Largest loss 1,575 4,725 3,225 5,050 2,500 2,850 “55,” because the first “5” stands for the fifth occurPattern 5 Pattern 6 Patttern 7 rence. All months other than February have at least In Out of In Out of In Out of two days of the week that appear five times. For my sample sample sample sample sample sample entry rules, I created eight different entry patterns and 23,775 17,175 12,675 13,825 8,025 4,225 tested each pattern using the DoWinMo as a filter for Net profit trades 39 33 28 21 17 14 each of the possible 25 DoWinMo numbers. The eight Total % winners 67 79 79 62 77 79 patterns are as follows: Avg trade

610

520

453

658

472

302

Pattern 4 In Out of sample sample 14,950 25 60

3,975 24 50

598 5,450 1,925

166 1,850 2,425

Pattern 8 In Out of sample sample 3,450 11,125 10 23 60 70 345

484

Pattern 1 If tomorrow’s open minus 30 points is Largest win 4,025 3,625 2,250 6,850 1,900 1,825 1,200 3,675 Largest loss 1,300 2,500 825 2,100 525 1,500 1,125 1,025 greater than today’s close, then buy at market. Pattern 2 If tomorrow’s open plus 30 points is FIGURE 3: Here are the test results for each of the eight patterns. Of the eight, patterns 1, 3 and 4 are suspect because they have such large discrepancies in the average trade from in-sample to less than today’s close, then buy at market. Pattern 3 If tomorrow’s open minus 30 points is out-of-sample. Patterns 1 and 3 improve in the out-of-sample test in this category, but that does not justify keeping them in the system, and so patterns 1, 3 and 4 are tossed out. greater than today’s close, then sell at market. Pattern 4 If tomorrow’s open plus 30 points is less than today’s close, then sell at market. Pattern 5 If tomorrow’s open plus 10 points is less than Pattern 7 If tomorrow’s open minus 40 points is greater today’s low, then buy at today’s low stop. than today’s close, then buy at today’s low limit. Pattern 6 If tomorrow’s open minus 20 points is greater Pattern 8 If tomorrow’s open plus 70 points is less than today’s close, then sell at today’s high limit. than today’s high, then sell at today’s close stop. Copyright (c) Technical Analysis Inc.

Stocks & Commodities V. 14:7 (333-337): The S&P 500 Seasonal Day Trade by William Brower, C.T.A.

Open + 30 points

Open + 10 points

Today

Today

Tomorrow

If: tomorrow’s open + 30 points < today’s close Then: buy at today’s market

PATTERN 2 IN CHART FORM

Tomorrow

If: tomorrow’s open + 10 points < today’s low Then: buy at today’s low stop

PATTERN 5 IN CHART FORM

I created a continuous contract of the S&P 500 futures (using the back-adjusted, forward-roll method) from April 1982 to March 1996 using the database available from Genesis Financial Data Services. To avoid curve-fitting, I divided the data into two test periods. The in-sample test period was from April 1982 through December 1989 and the out-of-sample test period was from January 1990 through March 1996. I tested each pattern separately, filtering trades by the DoWinMo numbers for both the in-sample and the out-ofsample datasets. Test results that deviated significantly in winning percentage and/or average profit per trade (from the sample period to the out-of-sample period) disqualified the pattern on that DoWinMo day. I kept the patterns simple because the DoWinMo filter is powerful. Each pattern is a day trade using a simple market-onclose (MOC) exit. Further, each pattern uses the “open tomorrow” command. Since the trading signals for daily bars must be

Open - 20 points

Today

Tomorrow

If: tomorrow’s open - 20 points > today’s high Then: sell at today’s close stop

PATTERN 6 IN CHART FORM

Open - 40 points Open + 70 points

Today

Tomorrow

If: tomorrow’s open - 40 points > today’s close Then: buy at today’s low limit

PATTERN 7 IN CHART FORM

Today Tomorrow if: tomorrow’s open + 70 points < today’s close then: sell at today’s high limit

PATTERN 8 IN CHART FORM

FIGURE 4: FINAL PATTERNS IN CHART FORM. Here are the final patterns, shown in chart form. Note that patterns 1, 3 and 4 are dropped.

Copyright (c) Technical Analysis Inc.

Stocks & Commodities V. 14:7 (333-337): The S&P 500 Seasonal Day Trade by William Brower, C.T.A.

S&P 500 DoWinMo SEASONAL Out of sample

Combined

S&P 500 DoWinMo seasonal net profit and max intraday drawdown

160000

Net profit Total trades % winners Avg. trade Avg. winner

82,240 200 66 411 923

97,250 212 66 459 1,116

179,490 412 66 436 1,022

Avg. loser Largest win Largest loss Consec. win

582 10,225 3,225 8

819 6,850 5,050 15

704 10,225 5,050 15

3 5,175 3.08 100

4 9,050 2.65 100

4 9,050 2.82 100

Consec. loss Intraday drawdn. Profit factor Slippage & comm.

180000

FIGURE 5: Here are some of the system’s performance results. The high percentage of winning trades, high average trade and respectable profit factor are the system’s strong points.

provided the day before the trade is taken, all references to DoWinMo numbers identify the day before the trade. There are no restrictions on how many trades may take place in a day, but only one trade is allowed in the same direction at the same time. No stops of any sort are used. Figure 2 represents the successful patterns and the DoWinMo numbers are used as filters. Since there are very few trades for the DoWinMo numbers from 51 through 55, they were combined into one test filter designated by “>50.”

PATTERN-BY-PATTERN RESULTS Figure 3 lists the in-sample and the out-of-sample test results for each of the eight patterns. Of the eight, patterns 1, 3 and 4 are suspect because they have such large discrepancies in the average trade from in-sample to out-of-sample. Patterns 1 and 3 actually improve in the out-of-sample test in this category,

FIGURE 7: JUNE S&P FUTURES. The system enters (indicated by the arrow on the left-hand side of the bar) based on the pattern and exits at the close. Here are two trades during April.

140000 120000 100000 80000 60000 40000 20000 0

5/28/82 1/31/83 9/30/83 5/31/84 1/31/85 9/30/85 5/30/86 1/30/87 9/30/87 5/31/88 1/31/89 9/29/89 5/31/90 1/31/91 9/30/91 5/29/92 1/29/93 9/30/93 5/31/94 1/31/95 9/29/95

In sample

FIGURE 6: SYSTEM EQUITY CURVE. Here is a simulated track record for trading the system since 1982. The lower line is the maximum intraday drawdown.

but that does not justify keeping them in the system, and so patterns 1, 3 and 4 are tossed out of the system. Figure 4 displays the final patterns in a chart form. When testing a system, adhering to strict rules is important to avoid the Texas sharpshooter syndrome, well known to statisticians. Say a sharpshooter is blindfolded and allowed to fire 1,000 rounds at the side of a barn some 100 feet away. After doing so, he takes off the blindfold and approaches the barn, locating the area with the highest concentration of bullet holes. Then he draws bull’s-eye rings around that spot. To an observer who happens on the scene after the rounds are shot and the bull’s-eye rings are drawn, an erroneous conclusion may be drawn about the sharpshooter’s accuracy based on the spread of the bullet holes and the concentration at the bull’s-eye location. In reality, of course, the conclusion has no validity. This is an easy trap to fall into when testing a trading system,

FIGURE 8: JUNE S&P FUTURES. Here are six trades during May.

Copyright (c) Technical Analysis Inc.

Stocks & Commodities V. 14:7 (333-337): The S&P 500 Seasonal Day Trade by William Brower, C.T.A.

since all this historical testing is akin to drawing the bull’s-eye rings around the bullet holes after the fact. We need some means to improve the likelihood that our conclusion is not mere illusion. Requiring comparable results in both the in-sample and out-of-sample period is one way to do this. The best test, however, is to see the system work on real-time data.

THE LONG AND THE SHORT I have listed some of the system’s performance results in Figure 5 and an equity curve in Figure 6. The high percentage of winning trades (66%), high average trade ($436) and respectable profit factor (2.82) are the system’s strong points. The $9,050 maximum intraday drawdown (MIDD) is actually quite good in relation to the net profit. My rule of thumb is that a net profit to MIDD ratio above 10 is very good; our ratio is almost 20. The system had most of the profit on the long side ($138,440), which is not surprising, considering we threw out two sell patterns and one buy pattern. The short side made $41,050 and was 68% profitable with an average trade of $500. Interestingly, the largest MIDD occurred on the long side. The short side only reached $3,175. Figures 7 and 8 present some recent trades. The system’s biggest weakness might be that some would have trouble with the largest losing trade ($5,050). Most intraday traders like to keep the stops closer to $1,000, but money management stops only serve to degrade the performance of the system. A $1,000 stop cuts the combined net profits to less than $120,000 and increases the drawdown to $11,600. I ran the optimizer and found that larger stops, in general, improved performance. Money management stops

tend to work better with systems with a low percentage of winning trades.

IN SUMMARY Using the Day of Week in Month can be a powerful filter for pattern-based day-trading systems, and using a split dataset to generate in-sample and out-of-sample tests can help prevent the Texas sharpshooter syndrome. A major question remains as to how well this system will work in real-time trading. Of course, it is still possible this entire exercise is a sophisticated curve-fit. However, in the June 1996 S&P contract, and since completing system testing and development, I have had six out of six winning trades for a net profit of more than $17,000 on one lot. The concept seems to be working. William Brower, CTA is president of Inside Edge Systems, publisher of TS Express newsletter. He trains TradeStation S&C users, writes programs and tests systems.

Copyright (c) Technical Analysis Inc.

View more...

Comments

Copyright � 2017 SILO Inc.