Have you discovered a seasonal tendency but can’t figure out how to test it?
Are there certain times of the year when a commodity increases in price and then recedes? In many markets this is the case. Crude oil seems to go up during the summer months and then chills out in the fall. It will rise once again when winter hits. Early spring might show another price decline. Have you done your homework and recorded certain dates of the year to buy/sell and short/buyToCover. Have you tried to apply these dates to a historical back test and just couldn’t figure it out? In this post I am going to show how you can use arrays and loops to cycle through the days in your seasonal database (database might be too strong of a term here) and apply long and short entries at the appropriate times during the past twenty years of daily bar data.
Build the Quasi-Database with Arrays
If you are new to EasyLanguage you may not yet know what arrays are or you might just simply be scared of them. Now worries here! Most people bypass arrays because they don’t know how to declare them, and if they get passed that, how to manipulate them to squeeze out the data they need. You may not be aware of it, but if you have programmed in EasyLanguage the least bit, then you have already used arrays. Check this out:
if high > high and low > low and average(c,30) > average(c,30) then buy next bar at open
In reality the keywords high and low are really arrays. They are lists that contain the entire history of the high and low prices of the data that is plotted on the chart. And just like with declared arrays, you index these keywords to get historic data. the HIGH means the high of yesterday and the HIGH means the high of the prior day. EasyLanguage handles the management of these array-like structures. In other words, you don’t need to keep track of the indexing – you know the  or  stuff. The declaration of an array is ultra-simple once you do it a few times. In our code we are going to use four arrays:
Each of these arrays will contain the month and day of month when a trade is to be entered or exited. Why not the year? We want to keep things simple and buy/short the same time every year to see if there is truly a seasonal tendency. The first thing we need to do is declare the four arrays and then fill them up.
// use the keyword arrays and : semicolon // next give each array a name and specify the // max number of elements that each array can hold // the  part. Each array needs to be initialized // and we do this by placing a zero (0) in parentheses arrays: buyDates(0),sellDates(0), shortDates(0),buyToCoverDates(0);
// next you want the arrays that go together to have the same // index value - take a look at this
// note the buyDates has a matching sellDates // buyDates has a matching sellDates // -- and -- // shortDates has a matching buyToCoverDates // shortDates has a matching buyToCoverDates
Our simple database has been declared, initialized and populated. This seasonal strategy will buy on:
April 15th and Exit on May 15th
June 5th and Exit on August 30th
It will sellShort on:
September 15th and Cover on November 30th
February 15th and Cover on March 30th
You could use this template and follow the pattern to add more dates to your database. Just make sure nothing overlaps.
Now, each chart has N dates of history plotted from the left to right. TradeStation starts out the test from the earliest date to the last date. It does this by looping one day at a time. The first thing we need to do is convert the bar date (TradeStation represents dates in a weird format – YYYMMDD – Jan 30, 2022 is represented by the number 1220130 – don’t ask why!!) to a format like the data that is stored in our arrays. Fortunately, we don’t have to deal with the year and EasyLanguage provides two functions to help us out.
Month(Date) = the month (1-12) of the current bar
DayOfMonth(Date) = the day of the month of the current bar
All we need to do is use these functions to convert the current bar’s date into terms of our database, and then test that converted date against our database. Take a look:
//convert the date into our own terms //say the date is December 12 //the month function returns 12 and the day of month returns 12 // 12*100 + 12 = 1212 --> MMDD - just waht we need //notice I look at the date of tomorrow because I want to take //action on the open of tomorrow.
currentMMDD = month(d of tomorrow)*100 + dayOfMonth(d of tomorrow);
//You might need to study this a little bit - but I am looping through each //array to determine if a trade needs to be entered. //Long Seasonal Entries toggle buyNow = False; for n = 1 to numBuyDates Begin if currentMMDD < buyDates[n] and currentMMDD >= buyDates[n] Then Begin buyNow = True; end; end;
//Short Seasonal Entries toggle shortNow = False; for n = 1 to numshortDates Begin if currentMMDD < shortDates[n] and currentMMDD >= shortDates[n] Then Begin shortNow = True; end; end;
Date conversion and looping thru Database
This code might look a little daunting, but it really isn’t. The first for-loop starts at 1 and goes through the number of buyDates. The index variable is the letter n. The loop starts at 1 and goes to 2 in increments of 1. Study the structure of the for-loop and let me know if you have any questions. What do you think this code is doing.
if currentMMDD < buyDates[n] and currentMMDD >= buyDates[n] Then
As you know the 15th of any month may fall on a weekend. This code basically says, ” Okay if today is less than the buyDate and tomorrow is equal to or greater than buyDate, then tommorrow is either going to be the exact day of the month or the first day of the subsequent week (the day of month fell on a weekend.) If tomorrow is a trade date, then a conditional buyNow is set to True. Further down in the logic the trade directive is issued if buyNow is set to True.
Total of 4 loops – 2 for each long/short entry and 2 for each long/short exit.
// fill the arrays with dates - remember we are not pyramiding here // use mmdd format buyDates = 0415;sellDates = 0515; buyDates = 0605;sellDates = 0830; numBuyDates = 2; numSellDates = 2;
mp = marketPosition; currentMMDD = month(d of tomorrow)*100 + dayOfMonth(d of tomorrow);
//Long Seasonal Entries toggle buyNow = False; for n = 1 to numBuyDates Begin if currentMMDD < buyDates[n] and currentMMDD >= buyDates[n] Then Begin buyNow = True; end; end;
//Short Seasonal Entries toggle shortNow = False; for n = 1 to numshortDates Begin if currentMMDD < shortDates[n] and currentMMDD >= shortDates[n] Then Begin shortNow = True; end; end;
//Long Seasonal Exits toggle sellNow = False; if mp = 1 Then Begin for n = 1 to numSellDates Begin if currentMMDD < sellDates[n] and currentMMDD >= sellDates[n] Then Begin sellNow = True; end; end; end;
//Short Seasonal Exits toggle buyToCoverNow = False; if mp = -1 Then Begin for n = 1 to numBuyToCoverDates Begin if currentMMDD < buyToCoverDates[n] and currentMMDD >= buyToCoverDates[n] Then Begin buyToCoverNow = True; end; end; end;
// Long entry execution if buyNow = True then begin buy("Seas-Buy") next bar at open; end; // Long exit execution if mp = 1 then begin if sellNow then begin sell("Long Exit") next bar at open; end; end;
// Short entry execution if shortNow then begin sellShort("Seas-Short") next bar at open; end; // short exit execution if mp = -1 then begin if buyToCoverNow then begin buyToCover("short Exit") next bar at open; end; end;
Does it work? It does – please take my word for it. IYou can email me with any questions. However, TS 10 just crashed on me and is wanting to update, but I need to kill all the processes before it can do a successful update. Remember to always export your new code to an external location. I will post an example on Monday Jan 30th.
Complete Strategy based on Sheldon Knight and William Brower Research
In my Easing Into EasyLanguage: Hi-Res Edition, I discuss the famous statistician and trader Sheldon Knight and his K-DATA Time Line. This time line enumerated each day of the year using the following nomenclature:
First Monday in December = 1stMonDec
Second Friday in April = 2ndFriApr
Third Wednesday in March = 3rdWedMar
This enumeration or encoding was used to determine if a certain week of the month and the day of week held any seasonality tendencies. If you trade index futures you are probably familiar with Triple Witching Days.
Four times a year, contracts for stock options, stock index options, and stock index futures all expire on the same day, resulting in much higher volumes and price volatility. While the stock market may seem foreign and complicated to many people, it is definitely not “witchy”, however, it does have what is known as “triple witching days.”
Triple witching, typically, occurs on the third Friday of the last month in the quarter. That means the third Friday in March, June, September, and December. In 2022, triple witching Friday are March 18, June 17, September 16, and December 16
Other days of certain months also carry significance. Some days, such as the first Friday of every month (employment situation), carry even more significance. In 1996, Bill Brower wrote an excellent article in Technical Analysis of Stocks and Commodities. The title of the article was The S&P 500 Seasonal Day Trade. In this article, Bill devised 8 very simple day trade patterns and then filtered them with the Day of Week in Month. Here are the eight patterns as he laid them out in the article.
Pattern 1: If tomorrow’s open minus 30 points is greater than today’s close, then buy at market.
Pattern 2: If tomorrow’s open plus 30 points is less than today’s close, then buy at market.
Pattern 3: If tomorrow’s open minus 30 points is greater than today’s close, then sell short at market.
Pattern 4: If tomorrow’s open plus 30 points is less than today’s close, then sell short at market.
Pattern 5: If tomorrow’s open plus 10 points is less than today’s low, then buy at market.
Pattern 6: If tomorrow’s open minus 20 points is greater than today’s high, then sell short at today’s close stop.
Pattern 7: If tomorrow’s open minus 40 points is greater than today’s close, then buy at today’s low limit.
Pattern 8: If tomorrow’s open plus 70 points is less than today’s close, then sell short at today’s high limit.
This article was written nearly 27 years ago when 30 points meant something in the S&P futures contract. The S&P was trading around the 600.00 level. Today the e-mini S&P 500 (big S&P replacement) is trading near 4000.00 and has been higher. So 30, 40 or 70 points doesn’t make sense. To bring the patterns up to date, I decided to use a percentage of ATR in place of a single point. If today’s range equals 112.00 handles or in terms of points 11200 and we use 5%, then the basis would equate to 11200 = 560 points or 5.6 handles. In the day of the article the range was around 6 handles or 600 points. So. I think using 1% or 5% of ATR could replace Bill’s point values. Bill’s syntax was a little different than the way I would have described the patterns. I would have used this language to describe Pattern1 – If tomorrow’s open is greater than today’s close plus 30 points, then buy at market – its easy to see we are looking for a gap open greater than 30 points here. Remember there is more than one way to program an idea. Let’s stick with Bills syntax.
10 points = 1 X (Mult) X ATR
20 points = 2 X (Mult) X ATR
30 points = 3 X (Mult) X ATR
40 points = 4 X (Mult) X ATR
50 points = 5 X (Mult) X ATR
70 points =7 X (Mult) X ATR
We can play around with the Mult to see if we can simulate similar levels back in 1996.
// atrMult will be a small percentage like 0.01 or 0.05 atrVal = avgTrueRange(atrLen) * atrMult;
//original patterns //use IFF function to either returne a 1 or a 0 //1 pattern is true or 0 it is false
The Day of Week In A Month is represented by a two digit number. The first digit is the week rank and the second number is day of the week. I thought this to be very clever, so I decided to program it. I approached it from a couple of different angles and I actually coded an encoding method that included the week rank, day of week, and month (1stWedJan) in my Hi-Res Edition. Bill’s version didn’t need to be as sophisticated and since I decided to use TradeStation’s optimization capabilities I didn’t need to create a data structure to store any data. Take a look at the code and see if it makes a little bit of sense.
newMonth = False; newMonth = dayOfMonth(d of tomorrow) < dayOfMonth(d of today); atrVal = avgTrueRange(atrLen) * atrMult; if newMonth then begin startTrading = True; monCnt = 0; tueCnt = 0; wedCnt = 0; thuCnt = 0; friCnt = 0; weekCnt = 1; end;
if not(newMonth) and dayOfWeek(d of tomorrow) < dayOfWeek(d of today) then weekCnt +=1;
dayOfWeekInMonth = weekCnt * 10 + dayOfWeek(d of tomorrow);
Simple formula to week rank and DOW
NewMonth is set to false on every bar. If tomorrow’s day of month is less than today’s day of month, then we know we have a new month and newMonth is set to true. If we have a new month, then several things take place: reinitialize the code that counts the number Mondays, Tuesdays, Wednesdays, Thursdays and Fridays to 0 (not used for this application but can be used later,) and set the week count weekCnt to 1. If its not a new month and the day of week of tomorrow is less than the day of the week today (Monday = 1 and Friday = 5, if tomorrow is less than today (1 < 5)) then we must have a new week on tomorrow’s bar. To encode the day of week in month as a two digit number is quite easy – just multiply the week rank (or count) by 10 and add the day of week (1-Monday, 2-Tuesday,…) So the third Wednesday would be equal to 3X10+3 or 33.
Use Optimization to Step Through 8 Patterns and 25 Day of Week in Month Enumerations
Stepping through the 8 patterns is a no brainer. However, stepping through the 25 possible DowInAMonth codes or enumerations is another story. Many times you can use an equation based on the iterative process of going from 1 to 25. I played around with this using the modulus function, but decided to use the Switch-Case construct instead. This is a perfect example of replacing math with computer code. Check this out.
switch(dowInMonthInc) begin case 1 to 5: value2 = mod(dowInMonthInc,6); value3 = 10; case 6 to 10: value2 = mod(dowInMonthInc-5,6); value3 = 20; case 11 to 15: value2 = mod(dowInMonthInc-10,6); value3 = 30; case 16 to 20: value2 = mod(dowInMonthInc-15,6); value3 = 40; case 21 to 25: value2 = mod(dowInMonthInc-20,6); value3 = 50; end;
Switch-Case to Step across 25 Enumerations
Here we are switching on the input (dowInMonthInc). Remember this value will go from 1 to 25 in increments of 1. What is really neat about EasyLanguage’s implementation of the Switch-Case is that it can handle ranges. If the dowInMonthInc turns out to be 4 it will fall within the first case block (case 1 to 5). Here we know that if this value is less than 6, then we are in the first week so I set the first number in the two digit dayOfWeekInMonth representation to 1. This is accomplished by setting value3 to 10. Now you need to extract the day of the week from the 1 to 25 loop. If the dowInMonthInc is less than 6, then all you need to do is use the modulus function and the value 6.
mod(1,6) = 1
mod(2,6) = 2
mod(3,6) = 3
This works great when the increment value is less than 6. Remember:
1 –> 11 (first Monday)
2 –> 12 (first Tuesday)
3 –> 13 (first Wednesday)
6 –> 21 (second Monday)
7 –> 22 (second Tuesday).
So, you have to get a little creative with your code. Assume the iterative value is 8. We need to get 8 to equal 23 (second Wednesday). This value falls into the second case, so Value3 = 20 the second week of the month. That is easy enough. Now we need to extract the day of week – remember this is just one solution, I guarantee there are are many.
mod(dowInMonthInc – 5, 6) – does it work?
value2 = mod(8-5,6) = 3 -> value3 = value1 + value2 -> value3 = 23. It worked. Do you see the pattern below.
case 6 to 10 – mod(dowInMonthInc – 5, 6)
case 11 to 15 – mod(dowInMonthInc – 10, 6)
case 16 to 20- mod(dowInMonthInc – 15, 6)
case 21 to25 – mod(dowInMonthInc – 20, 6)
Save Optimization Report as Text and Open with Excel
Here are the settings that I used to create the following report. If you do the math that is a total of 200 iterations.
I opened the Optimization Report and saved as text. Excel had no problem opening it.
I created the third column by translating the second column into our week of month and day of week vernacular. These results were applied to 20 years of ES.D (day session data.) The best result was Pattern #3 applied to the third Friday of the month (35.) Remember the 15th DowInMonthInc equals the third (3) Friday (5). The top patterns predominately occurred on a Thursday or Friday.
switch(dowInMonthInc) begin case 1 to 5: value2 = mod(dowInMonthInc,6); value3 = 10; case 6 to 10: value2 = mod(dowInMonthInc-5,6); value3 = 20; case 11 to 15: value2 = mod(dowInMonthInc-10,6); value3 = 30; case 16 to 20: value2 = mod(dowInMonthInc-15,6); value3 = 40; case 21 to 25: value2 = mod(dowInMonthInc-20,6); value3 = 50; end;
if value1 = dayOfWeekInMonth then begin if patternNum = 1 and patt1 = 1 then buy("Patt1") next bar at open; if patternNum = 2 and patt2 = 1 then buy("Patt2") next bar at open; if patternNum = 3 and patt3 = 1 then sellShort("Patt3") next bar at open; if patternNum = 4 and patt4 = 1 then sellShort("Patt4") next bar at open; if patternNum = 5 and patt5 = 1 then buy("Patt5") next bar at low limit; if patternNum = 6 and patt6 = 1 then sellShort("Patt6") next bar at close stop; if patternNum = 7 and patt7 = 1 then buy("Patt7") next bar at low limit; if patternNum = 8 and patt8 = 1 then sellShort("Patt8") next bar at high stop; end;
The Full Monty of the ES-Seasonal-Day Trade
I think this could provide a means to much more in-depth analysis. I think the Patterns could be changed up. I would like to thank William (Bill) Brower for his excellent article, The S&P Seasonal Day Trade in Stocks and Commodities, August 1996 Issue, V.14:7 (333-337). The article is copyright by Technical Analysis Inc. For those not familiar with Stocks and Commodities check them out at https://store.traders.com/
Please email me with any questions or anything I just got plain wrong. George
I had to wrap up Part -1 rather quickly and probably didn’t get my ideas across, completely. Here is what we did in Part – 1.
used my function to locate the First Notice Date in crude
used the same function to print out exact EasyLanguage syntax
chose to roll eight days before FND and had the function print out pure EasyLanguage
the output created array assignments and loaded the calculated roll points in YYYMMDD format into the array
visually inspected non-adjusted continuous contracts that were spliced eight days before FND
appended dates in the array to match roll points, as illustrated by the dip in open interest
Step 6 from above is very important, because you want to make sure you are out of a position on the correct rollover date. If you are not, then you will absorb the discount between the contracts into your profit/loss when you exit the trade.
Step 2 – Create the code that executes the rollover trades
Here is the code that handles the rollover trades.
// If in a position and date + 1900000 (convert TS date format to YYYYMMDD), // then exit long or short on the current bar's close and then re-enter // on the next bar's open
if d+19000000 = rollArr[arrCnt] then begin condition1 = true; arrCnt = arrCnt + 1; if marketPosition = 1 then begin sell("LongRollExit") this bar on close; buy("LongRollEntry") next bar at open; end; if marketPosition = -1 then begin buyToCover("ShrtRollExit") this bar on close; sellShort("ShrtRollEntry") next bar at open; end;
Code to rollover open position
This code gets us out of an open position during the transition from the old contract to the new contract. Remember our function created and loaded the rollArr for us with the appropriate dates. This simulation is the best we can do – in reality we would exit/enter at the same time in the two different contracts. Waiting until the open of the next bar introduces slippage. However, in the long run this slippage cost may wash out.
Step 3 – Create a trading system with entries and exits
The system will be a simple Donchian where you enter on the close when the bar’s high/low penetrates the highest/lowest low of the past 40 bars. If you are long, then you will exit on the close of the bar whose low is less than the lowest low of the past 20 bars. If short, get out on the close of the bar that is greater than the highest high of the past twenty bars. The first test will show the result of using an adjusted continuous contract rolling 8 days prior to FND
This test will use the exact same data to generate the signals, but execution will take place on a non-adjusted continuous contract with rollovers. Here data2 is the adjusted continuous contract and data1 is the non-adjusted.
Still a very nice trade, but in reality you would have to endure six rollover trades and the associated execution costs.
Here is the mechanism of the rollover trade.
And now the performance results using $30 for round turn execution costs.
Now with rollovers
The results are very close, if you take into consideration the additional execution costs. Since TradeStation is not built around the concept of rollovers, many of the trade metrics are not accurate. Metrics such as average trade, percent wins, average win/loss and max Trade Drawdown will not reflect the pure algorithm based entries and exits. These metrics take into consideration the entries and exits promulgated by the rollovers. The first trade graphic where the short was held for several months should be considered 1 entry and 1 exit. The rollovers should be executed in real time, but the performance metrics should ignore these intermediary trades.
I will test these rollovers with different algorithms, and see if we still get similar results, and will post them later. As you can see, testing on non-adjusted data with rollovers is no simple task. Email me if you would like to see some of the code I used in this post.
When I worked at Futures Truth, we tested everything with our Excalibur software. This software used individual contract data and loaded the entire history (well, the part we maintained) of each contract into memory and executed rollovers at a certain time of the month. Excalibur had its limitations as certain futures contracts had very short histories and rollover dates had to be predetermined – in other words, they were undynamic. Over the years, we fixed the short history problem by creating a dynamic continuous contract going back in time for the number of days required for a calculation. We also fixed the database with more appropriate rollover frequency and dates. So in the end, the software simulated what I had expected from trading real futures contracts. This software was originally written in Fortran and for the Macintosh. It also had limitations on portfolio analysis as it worked its way across the portfolio, one complete market at a time. Even with all these limitations, I truly thought that the returns more closely mirrored what a trader might see in real time. Today, there aren’t many, if any, simulation platforms that test on individual contracts. The main reasons for this are the complexity of the software, and the database management. However, if you are willing to do the work, you can get close to testing on individual contract data with EasyLanguage.
Step 1 – Get the rollover dates
This is critical as the dates will be used to roll out of one contract and into another. In this post, I will test a simple strategy on the crude futures. I picked crude because it rolls every month. Some data vendors use a specific date to roll contracts, such as Pinnacle data. In real time trading, I did this as well. We had a calendar for each month, and we would mark the rollover dates for all markets traded at the beginning of each month. Crude was rolled on the 11th or 12th of the prior month to expiration. So, if we were trading the September 2022 contract, we would roll on August 11th. A single order (rollover spread) was placed to sell (if long) the September contract and buy the October contract at the market simultaneously. Sometimes we would leg into the rollover by executing two separate orders – in hopes of getting better execution. I have never been able to find a historic database of when TradeStation performs its rollovers. When you use the default @CL symbol, you allow TradeStation to use a formula to determine the best time to perform a rollover. This was probably based on volume and open interest. TradeStation does allow you to pick several different rollover triggers when using their continuous data.
I am getting ahead of myself, because we can simply use the default @CL data to derive the rollover dates (almost.) Crude oil is one of those weird markets where LTD (last trade days) occurs before FND (first notice day.) Most markets will give you a notice before they back up a huge truck and dump a 1000 barrels of oil at your front door. With crude you have to be Johnny on the spot! Rollover is just a headache when trading futures, but it can be very expensive headache if you don’t get out in time. Some markets are cash settled so rollover isn’t that important, but others result in delivery of the commodity. Most clearing firms will help you unwind an expired contract for a small fee (well relatively small.) In the good old days your full service broker would give you heads up. They would call you and say, “George you have to get out of that Sept. crude pronto!” Some firms would automatically liquidate the offending contract on your behalf – which sounds nice but it could cost you. Over my 30 year history of trading futures I was caught a few times in the delivery process. You can determine these FND and LTD from the CME website. Here is the expiration description for crude futures.
Trading terminates 3 business day before the 25th calendar day of the month prior to the contract month. If the 25th calendar day is not a business day, trading terminates 4 business days before the 25th calendar day of the month prior to the contract month.
You can look this up on your favorite broker’s website or the handy calendars they send out at Christmas. Based on this description, the Sept. 2022 Crude contract would expire on August 20th and here’s why
August 25 is Tuesday
August 24 is Monday- DAY1
August 21 is Friday – DAY2
August 20 is Thursday – DAY3
This is the beauty of a well oiled machine or exchange. The FND will occur exactly as described. All you need to do is get all the calendars for the past ten years and find the 25th of the month and count back three business days. Or if the 25 falls on a weekend count back four business days. Boy that would be chore, would it not? Luckily, we can have the data and an EasyLanguage script do this for us. Take a look at this code and see if it makes any sense to you.
Case "@CL": If dayOfMonth(date) = 25 and firstMonthPrint = false then begin print(date+19000000:8:0); firstMonthPrint = true; end; If(dayOfMonth(date) < 25 and dayOfMonth(date) > 25 ) and firstMonthPrint = false then begin print(date+19000000:8:0); firstMonthPrint = true; end;
Code to printout all the FND of crude oil.
I have created a tool to print out the FND or LTD of any commodity futures by examining the date. In this example, I am using a Switch-Case to determine what logic is applied to the chart symbol. If the chart symbol is @CL, I look to see if the 25th of the month exists and if it does, I print the date 3 days prior out. If today’s day of month is greater than 25 and the prior day’s day of month is less than 25, I know the 25th occurred on a weekend and I must print out the date four bars prior. These dates are FN dates and cannot be used as is to simulate a rollover. You had best be out before the FND to prevent the delivery process. Pinnacle Date rolls the crude on the 11th day of the prior month for its crude continuous contracts. I aimed for this day of the month with my logic. If the FND normally fell on the 22nd of the month, then I should back up either 9 or 10 business days to get near the 11th of the month. Also I wanted to use the output directly in an EasyLanguage strategy so I modified my output to be exact EasyLanguage.
Case "@CL": If dayOfMonth(date) = 25 and firstMonthPrint = false then begin value1 = value1 + 1; print("rollArr[",value1:1:0,"]=",date+19000000:8:0,";"); firstMonthPrint = true; end; If(dayOfMonth(date) < 25 and dayOfMonth(date) > 25 ) and firstMonthPrint = false then begin value1 = value1 + 1; print("rollArr[",value1:1:0,"]=",date+19000000:8:0,";"); // print(date+19000000:8:0); firstMonthPrint = true; end;
Code to print our 9 or 10 bars prior to FND in actual EasyLanguage
Now. that I had the theoretical rollover dates for my analysis I had to make sure the data that I was going to use matched up exactly. As you saw before, you can pick the rollover date for your chart data. And you can also determine the discount to add or subtract to all prior data points based on the difference between the closing prices at the rollover point. I played around with the number of days prior to FND and selected non adjusted for the smoothing of prior data.
How did I determine 8 days Prior to First Notice Date? I plotted different data using a different number of days prior and determined 8 provided a sweet spot between the old and new contract data’s open interest. Can you see the rollover points in the following chart? Ignore the trades – these were a beta test.
The dates where the open interest creates a valley aligned very closely with the dates I printed out using my FND date finder function. To be safe, I compared the dates and fixed my array data to match the chart exactly. Here are two rollover trades – now these are correct.
This post turned out to be a little longer than I thought, so I will post the results of using an adjusted continuous contract with no rollovers, and the results using non-adjusted concatenated contracts with rollovers. The strategy will be a simple 40/20 bar Donchian entry/exit. You maybe surprised by the results – stay tuned.
The last book in the Easing Into EasyLanguage Serieshas finally been put to bed. Unlike the first two books in the series, where the major focus and objective was to introduce basic programming ideas to help get new EasyLanguages users up to speed, this edition introduces more Advanced topics and the code to develop and program them.
Buy this book to learn how to overcome the obstacles that may be holding you back from developing your ideal Analysis Technique. This book could be thousands of pages long because the number of topics could be infinite. The subjects covered in this edition provide a great cross-section of knowledge that can be used further down the road. The tutorials will cover subjects such as:
Arrays – single and multiple dimensions
Functions – creation and communicating via Passed by Value and Passed by Reference
Finite State Machine – implemented via the Switch-Case programming construct
String Manipulation – construction and deconstruction of strings using EasyLanguage functions
Hash Table and Hash Index – a data structure(s) that contains unique addresses of bins that can contain N records
Using Hash Tables – accessing and storing data related to unique Tokens
Token Generation – an individual instance of a type of symbol
Seasonality – in depth analysis of the Ruggiero/Barna and Sheldon Knight Universal Seasonal data
File Manipulation – creating, deleting and writing to external files
Using Projects – organizing Analysis Techniques by grouping support functions and code into a single entity
Text Graphic Objects – extracting text from a chart and storing the object information in arrays for later development into a strategy
Commitment of Traders Report – TradeStation only (not MultiChart compatible) code. Converting the COT indicator and using the FundValue functionality to develop a trading strategy
Multiple Time Frame based indicator – use five discrete time frames and pump the data into a single indicator – “traffic stop light” feel
Once you become a programmer, of any language, you must continually work on honing your craft. This book shows you how to use your knowledge as building blocks to complete some really cool and advanced topics.
Illustrating Difference Between Data Aliasing and Not Data Aliasing
If you have a higher resolution as Data 1 (5 minute in this case) than Data 2 (15 minute), then you must use Data Aliasing if you are going to use a variable to represent a price or a function output. With out Data Aliasing all data references are in terms of Data 1. When the 15 minute bar closes then the current  and one bar back[ 1] will be correct – going back further in time will reflect correct data. During the 15 minute bar only the last value  will show the correct reading of the last completed 15 minute bar. Once you tie the variable to its correct time frame variableName(0, Data2), you can then reference historic bars in the same fashion as if it were Data1. This includes price references and function output values.
Check this chart out and see if it makes sense to you. I dedicate a portion of a Tutorial on Data Aliasing in my latest book due out next week – Easing Into EasyLanguage – Advanced Topics.
Well it’s been a year, this month, that Murray passed away. I was fortunate to work with him on many of his projects and learned quite a bit about inter-market convergence and divergence. Honestly, I wasn’t that into it, but you couldn’t argue with his results. A strategy that he developed in the 1990s that compared the Bond market with silver really did stand the test of time. He monitored this relationship over the years and watched in wane. Murray replaced silver with $UTY.
The PHLX Utility Sector Index (UTY) is a market capitalization-weighted index composed of geographically diverse public utility stocks.
He wrote an article for EasyLanguage Mastery by Jeff Swanson where he discussed this relationship and the development of inter-market strategies and through statistical analysis proved that these relationships added real value.
I am currently writing Advanced Topics, the final book in my Easing Into EasyLanguage trilogy, and have been working with Murray’s research. I am fortunate to have a complete collection of his Futures Magazine articles from the mid 1990s to the mid 2000s. There is a quite a bit of inter-market stuff in his articles. I wanted, as a tribute and to proffer up some neat code, to show the performance and code of his Bond and $UTY inter-market algorithm.
Here is a version that he published a few years ago updated through June 30, 2022 – no commission/slippage.
Not a bad equity curve. To be fair to Murray he did notice the connection between $UTY and the bonds was changing over the past couple of year. And this simple stop and reverse system doesn’t have a protective stop. But it wouldn’t look much different with one, because the system looks at momentum of the primary data and momentum of the secondary data and if they are in synch (either positively or negatively correlated – selected by the algo) an order is fired off. If you simply just add a protective stop, and the momentum of the data are in synch, the strategy will just re-enter on the next bar. However, the equity curve just made a new high recently. It has got on the wrong side of the Fed raising rates. One could argue that this invisible hand has toppled the apple cart and this inter-market relationship has been rendered meaningless.
Murray had evolved his inter-market analysis to include state transitions. He not only looked at the current momentum, but also at where the momentum had been. He assigned the transitions of the momentum for the primary and secondary markets a value from one to four and he felt this state transition helped overcome some of the coupling/decoupling of the inter-market relationship.
However, I wanted to test Murray’s simple strategy with a fixed $ stop and force the primary market to move from positive to negative or negative to positive territory while the secondary market is in the correct relationship. Here is an updated equity curve.
This equity curve was developed by using a $4500 stop loss. Because I changed the order triggers, I reoptimized the length of the momentum calculations for the primary and secondary markets. This curve is only better in the category of maximum draw down. Shouldn’t we give Murray a chance and reoptimize his momentum length calculations too! You bet.
These metrics were sorted by Max Intraday Draw down. The numbers did improve, but look at the Max Losing Trade value. Murray’s later technology, his State Systems, were a great improvement over this basic system. Here is my optimization using a slightly different entry technique and a $4500 protective stop.
This system, using Murray’s overall research, achieved a better Max Draw Down and a much better Max Losing Trade. Here is my code using the template that Murray provided in his articles in Futures Magazine and EasyLanguage Mastery.
// Code by Murray Ruggiero // adapted by George Pruitt
If Type=0 Then Begin InterInd=Close of Data(InterSet)-CLose[LenInt] of Data(InterSet); MarkInd=CLose-CLose[LenTr]; end;
If Type=1 Then Begin InterInd=Close of Data(InterSet)-Average(CLose of Data(InterSet),LenInt); MarkInd=CLose-Average(CLose,LenTr); end;
if Relate=1 then begin If InterInd > 0 and MarkInd CROSSES BELOW 0 and LSB>=0 then Buy("GO--Long") Next Bar at open; If InterInd < 0 and MarkInd CROSSES ABOVE 0 and LSB<=0 then Sell Short("GO--Shrt") Next Bar at open;
end; if Relate=0 then begin If InterInd<0 and MarkInd CROSSES BELOW 0 and LSB>=0 then Buy Next Bar at open; If InterInd>0 and MarkInd CROSSES ABOVE 0 and LSB<=0 then Sell Short Next Bar at open; end;
Here the user can actually include more than two data streams on the chart. The InterSet input allows the user to choose or optimize the secondary market data stream. Momentum is defined by two types:
Type 0: Intermarket or secondary momentum simply calculated by close of data(2) – close[LenInt] of date(2) and primary momentum calculated by close – close[LenTr]
Type 1: Intermarket or secondary momentum calculated by close of data(2) – average( close of data2, LenInt) and primary momentum calculated by close – average(close, LenTr)
The user can also input what type of Relationship: 1 for positive correlation and 0 for negative correlation. This template can be used to dig deeper into other market relationships.
I simply forced the primary market to CROSS below/above 0 to initiate a new trade as long the secondary market was pointing in the right direction.
If InterInd > 0 and MarkInd CROSSES BELOW 0 and LSB>=0 then Buy("GO--Long") Next Bar at open; If InterInd < 0 and MarkInd CROSSES ABOVE 0 and LSB<=0 then Sell Short("GO--Shrt") Next Bar at open;
Using the keyword CROSSES
This was a one STATE transition and also allowed a protective stop to be used without the strategy automatically re-entering the trade in the same direction.
Thank You Murray – we sure do miss you!
Murray loved to share his research and would want us to carry on with it. I will write one or two blogs a year in tribute to Murray and his invaluable research.
Quickly Analyze Market Metrics with Gradient Based Shading
This is a simple indicator but it does involve some semi-advanced topics. Just to let you know I am working on the third book in the Easing Into EasyLanguage series. If you haven’t check out the first two, you might just want to head over to amazon and check those out. This topic falls in the spectrum of the ideas that I will be covering in the Advanced Topics edition. Also to let you know I just published the 2nd Edition of Trend Following Systems: A DIY Project – Batteries Included. Check this out if you want to learn some Python and also see some pretty cool Trend Following algorithms – I include EasyLanguage too!
The code that follows demonstrates how to shade between plots and adjust gradient in terms of the RSI reading. I compiled this with MultiCharts, so I assume it will work there too – just let me know if it doesnt. I found this code somewhere on the web when researching shading. If I knew the original author I would definitely give full credit. The code is rather simple, setting up the chart is just slightly more difficult. The Keltner Channel was used to define the shading boundaries. You could have just as easily used Bollinger Bands or anything that provided a range around the market. Here’s the code.
Basically all this math is doing is keeping the RSI reading within the bounds of the Keltner Upper and Lower Channels. You want a high RSI reading to be near the Upper Channel and a low RSI reading to be near the Lower Channel. You can change up the formula to make more sense.
Here you take the LowerBand and add the percentage of the MyRSI/100 times the range. This works too. But the original formula scales or intensifies the RSI reading so you get much wider gradient spectrum. The AVG is used as the center of gravity and the RSI is converted in terms of the middle 50 line. A positive number, anything > 50, is then scaled higher in the range and a negative number, anything < 50 is scaled lower in the range. In other words it makes a prettier and more informative picture.
Sometimes you just want to create a combined equity curve of several markets and for one reason or another you don’t want to use Maestro. This post will show you an indicator that you can insert into your TradeStation chart/strategies that will output monthly cumulative equity readings. After that I refresh my VBA skills a little bit by creating a VBA Macro/Script that will take the output and parse it into a table where the different months are the rows and the different market EOM equities, for those months, will be the columns. Then all the rows will be summed and then finally a chart will be produced. I will zip the Exel .xlsm and include it at the end of this post.
Part 1: Output Monthly Data to Print Log
You can determine the end of the month by comparing the current month of the date and the month of the prior date. If they are different, then you know you are sitting on the first trading day of the new month. You can then reach back and access the total equity as of yesterday. If you want you can also track the change in month equity. Here is the indicator code in EasyLanguage.
// Non plotting indicator that needs to be applied to // a chart that also has a strategy applied // one that produces a number of trades
if month(date) <> month(date) then begin monthlyEqu = totalEquity - priorMonthEqu; priorMonthEqu = totalEquity; print(getSymbolName,",",month(date):2:0,"-",year(date)+1900:4:0,",",monthlyEqu,",",totalEquity); end; totalEquity = i_ClosedEquity;
Indicator that exports the EOM equity values
The interesting part of this code is the print statement. You can use the month and year functions to extract the respective values from the date bar array. I use a formatted print to export the month and year without decimals. Remember 2021 in TradeStation is represented by 121 and all you have to do is add 1900 to get a more comfortable value. The month output is formatted with :2:0 and the year with :4:0. The first value in the format notation informs the computer to allow at least 2 or 4 values for the month and the year respectively. The second value following the second colon informs the computer that you do not want any decimals values (:0). Next insert the indicator into all the charts in your workspace. Here’s a snippet of the output on one market. I tested this on six markets and this data was the output generated. Some of the markets were interspersed and that is okay. You may have a few months of @EC and then a few months of @JY and then @EC again.
If you use this post’s EXCEL workbook with the macro you may need to tell EXCEL to forget any formatting that might already be inside the workbook. Open my workbook and beware that it will inform/warn you that a macro is located inside and that it could be dangerous. If you want to go ahead and enable the macro go ahead. On Sheet1 click in A1 and place the letter “G”. Then goto the Data Menu and then the Data Tools section of the ribbon and click Text to Columns. A dialog will open and ask you they type of file that best describes your data. Click the Delimited radio button and then next. You should see a dialog like this one.
Now copy all of the data from the Print-Log and paste it into Column A. If all goes right everything will be dumped in the appropriate rows and in a single column.
First select column A and go back to the Data Menu and the Data Tools on the Ribbon. Select Delimited and hit next, Choose comma because we used commas to separate our values. Click Next again. Eventually you will get to this dialog.
If you chose Date format for column 2 with MDY, then it will be imported into the appropriate column in a date format. This makes charting easier. If all goes well then you will have four columns of data – a column for Symbol, Date, Delta-EOM and EOM. Select column B and right click and select Format Cells.
Once you format the B column in the form of MM-YYYY you will have a date like 9-2007, 10-2007, 11-2007…
Running the VBA Macro/Script
On the Ribbon in EXCEL goto the Developer Tab. If you don’t see it then you will need to install it. Just GOOGLE it and follow their instructions. Once on the Develop Tab goto the Code category and click the Visual Basic Icon. Your VBA IDE will open and should like very similar to this.
If all goes according to plan after you hit the green Arrow for the Run command your spreadsheet should like similar to this.
The Date column will be needed to be reformatted again as Date with Custom MM-YYYY format. Now copy the table by highlighting the columns and rows including the headings and then goto to Insert Menu and select the Charts category.
This is what you should get.
This is the output of the SuperTurtle Trading system tested on the currency sector from 2007 to the present.
I have become very spoiled using Python as it does many things for you by simply calling a function. This code took my longer to develop than I thought it would, because I had to back to the really old school of programming (really wanted to see if I could do this from scratch) to get this done. You could of course eliminate some of my code by calling spread sheet functions from within EXCEL. I went ahead and did it the brute force way just to see if I could do it.
'read data from columns 'symbol, date, monthlyEquity, cumulativeEquity - format 'create arrays to hold each column of data 'use nested loops to do all the work 'brute force coding, no objects - just like we did in the 80s
Dim symbol(1250) As String Dim symbolHeadings(20) As String Dim myDate(1250) As Long Dim monthlyEquity(1250) As Double Dim cumulativeEquity(1250) As Double
'read the data from the cells dataCnt = 1 Do While Cells(dataCnt, 1) <> "" symbol(dataCnt) = Cells(dataCnt, 1) myDate(dataCnt) = Cells(dataCnt, 2) monthlyEquity(dataCnt) = Cells(dataCnt, 3) cumulativeEquity(dataCnt) = Cells(dataCnt, 4) dataCnt = dataCnt + 1 Loop dataCnt = dataCnt - 1
'get distinct symbolNames and use as headers symbolHeadings(1) = symbol(1) numSymbolheadings = 1 For i = 2 To dataCnt - 1 If symbol(i) <> symbol(i + 1) Then newSymbol = True For j = 1 To numSymbolheadings If symbol(i + 1) = symbolHeadings(j) Then newSymbol = False End If Next j If newSymbol = True Then numSymbolheadings = numSymbolheadings + 1 symbolHeadings(numSymbolheadings) = symbol(i + 1) End If End If Next i
'Remove duplicate months in date array 'have just one column of month end dates
Cells(2, 7) = myDate(1) dispRow = 2 numMonths = 1 i = 1 Do While i <= dataCnt foundDate = False For j = 1 To numMonths If myDate(i) = Cells(j + 1, 7) Then foundDate = True End If Next j If foundDate = False Then numMonths = numMonths + 1 Cells(numMonths + 1, 7) = myDate(i) End If i = i + 1 Loop 'put symbols across top of table 'put "date" and "cumulative" column headings in proper 'locations too
Cells(1, 7) = "Date" For i = 1 To numSymbolheadings Cells(1, 7 + i) = symbolHeadings(i) Next i
numSymbols = numSymbolheadings Cells(1, 7 + numSymbols + 1) = "Cumulative" 'now distribute the monthly returns in their proper 'slots in the table dispRow = 2 dispCol = 7 For i = 1 To numSymbols For j = 2 To numMonths + 1 For k = 1 To dataCnt foundDate = False If symbol(k) = symbolHeadings(i) And myDate(k) = Cells(j, 7) Then Cells(dispRow, dispCol + i) = cumulativeEquity(k) dispRow = dispRow + 1 Exit For End If 'for later use 'If j > 1 Then ' If symbol(k) = symbolHeadings(i) And myDate(k) < Cells(j, 7) And myDate(k) > Cells(j - 1, 7) Then ' dispRow = dispRow + 1 ' End If 'End If Next k Next j dispRow = 2 Next i 'now accumulate across table and then down For i = 1 To numMonths cumulative = 0 For j = 1 To numSymbols cumulative = cumulative + Cells(i + 1, j + 7) Next j Cells(i + 1, 7 + numSymbols + 1) = cumulative Next i End Sub
This a throwback to the 80s BASIC on many of the family computers of that era. If you are new to VBA you can access cell values by using the keyword Cells and the row and column that points to the data. The first thing you do is create a Module named combineEquityFromTS and in doing so it will create a Sub combineEquityFromTS() header and a End Sub footer. All of your code will be squeezed between these two statements. This code is ad hoc because I just sat down and started coding without much forethought.
Use DIM to Dimension an Arrays
I like to read the date from the cells into arrays so I can manipulate the data internally. Here I create five arrays and then loop through column one until there is no longer any data. The appropriate arrays are filled with their respective data from each row.
Once the data is in arrays we can start do parse it. The first thing I want is to get the symbolHeadings or markets. I know the first row has a symbol name so I go ahead and put that into the symbolHeadings array. I kept track of the number of rows in the data with the variable dataCnt. Here I use nested loops to work my way down the data and keep track of new symbols as I encounter them. If the symbol changes values, I then check my list of stored symbolHeadings and if the new symbol is not in the list I add it.
Since all the currencies will have the same month values I wanted to compress all the months into a single discrete month list. This isn’t really all that necessary since we could have just prefilled the worksheet with monthly date values going back to 2007. This algorithm is similar to the one that is used to extract the different symbols. Except this time, just for giggles, I used a Do While Loop.
As well as getting values from cells you can also put values into them. Here I run through the list of markets and put them into Cells(1, 7+i). When working with cells you need to make sure you get the offset correct. Here I wanted to put the market names in Row A and Columns: H, I, J, K, L, M.
Column H = 8
Column I = 9
Column J = 10
Column K =11
Column L = 12
Column M = 13
Cells are two dimensional arrays. However if you are going to use what is on the worksheet make sure you are referencing the correct data. Here I introduce dispRow and dispCol as the anchor points where the data I need to reference starts out.
Here I first parse through each symbol and extract the EOM value that matches the symbol name and month-year value. So if I am working on @EC and I need the EOM for 07-2010, I fist loop through the month date values and compare the symbol AND myDate (looped through with another for-loop) with the month date values. If they are the same then I dump the value in the array on to the spreadsheet. And yes I should have used this:
For j = dispRow to numMonths-1
Instead of –
For j = 2 to numMonths-1
Keeping arrays in alignment with Cells can be difficult. I have a hybrid approach here. I will clean this up later and stick just with arrays and only use Cells to extract and place data. The last thing you need to do is sum each row up and store that value in the Cumulative column.
This was another post where we relied on another application to help us achieve our objective. If you are new to EXCEL VBA (working with algorithms that generate trades) you can find out more in my Ultimate Algorithmic Trading System Toolbox book. Even if you don’t get the book this post should get you started on the right track.
SuperTrend is a trading strategy and indicator all built into one entity. There are a couple of versions floating around out there. MultiCharts and Sierra Chart both have slightly different flavors of this combo approach.
Ratcheting Trailing Stop Paradigm
This indic/strat falls into this category of algorithm. The indicator never moves away from your current position like a parabolic stop or chandelier exit. I used the code that was disclosed on Futures.io or formerly known as BigMikesTrading blog. This version differs from the original SuperTrend which used average true range. I like Big Mike’s version so it will discussed here.
Big Mike’s Math
The math for this indicator utilizes volatility in the terms of the distance the market has travelled over the past N days. This is determined by calculating the highest high of the last N days/bars and then subtracting the lowest low of last Ndays/bars. Let’s call this the highLowRange. The next calculation is an exponential moving average of the highLowRange. This value will define the market volatility. Exponential moving averages of the last strength days/bars highs and lows are then calculated and divided by two – giving a midpoint. The volatility measure (multiplied my mult) is then added to this midpoint to calculate an upper band. A lower band is formed by subtracting the volatility measure X mult from the midpoint.
Upper or Lower Channel?
If the closing price penetrates the upper channel and the close is also above the highest high of strength days/bars back (offset by one of course) then the trend will flip to UP. When the trend is UP, then the Lower Channel is plotted. Once the trend flips to DN, the upper channel will be plotted. If the trend is UP the lower channel will either rise with the market or stay put. The same goes for a DN trend – hence the ratcheting. Here is a graphic of the indicator on CL.
If you plan on using an customized indicator in a strategy it is always best to build the calculations inside a function. The function then can be used in either an indicator or a strategy.
Function Name: SuperTrend_BM
Function Type: Series – we will need to access prior variable values
if trend < 0 and trend > 0 then trendDN = True; if trend > 0 and trend < 0 then trendUP = True;
//ratcheting mechanism if trend > 0 then dn = maxList(dn,dn); if trend < 0 then up = minList(up,up);
// if trend dir. changes then assign // up and down appropriately if trendUP then up = xAvg + mult * xAvgRng; if trendDN then dn = xAvg - mult * xAvgRng;
if trend = 1 then ST = dn else ST = up;
STrend = trend;
SuperTrend_BM = ST;
SuperTrend ala Big Mike
The Inputs to the Function
The original SuperTrend did include the Strength input. This input is a Donchian like signal. Not only does the price need to close above/below the upper/lower channel but also the close must be above/below the appropriate Donchian Channels to flip the trend, Also notice we are using a numericRef as the type for STrend. This is done because we need the function to return two values: trend direction and the upper or lower channel value. The appropriate channel value is assigned to the function name and STrend contains the Trend Direction.
A Function Driver in the Form of an Indicator
A function is a sub-program and must be called to be utilized. Here is the indicator code that will plot the values of the function using: length(9), mult(1), strength(9).
// SuperTrend indicator // March 25 2010 // Big Mike https://www.bigmiketrading.com inputs: length(9), mult(1), strength(9);
vars: strend(0), st(0);
st = SuperTrend_BM(length, mult,strength,strend);
if strend = 1 then Plot1(st,"SuperTrendUP"); if strend = -1 then Plot2(st,"SuperTrendDN");
Function Drive in the form of an Indicator
This should be a fun indicator to play with in the development of a trend following approach. My version of Big Mike’s code is a little different as I wanted the variable names to be a little more descriptive.
Update Feb 28 2022
I forgot to mention that you will need to make sure your plot lines don’t automatically connect.
Can You Do This with Just One Plot1?
An astute reader brought it to my attention that we could get away with a single plot and he was right. The reason I initially used two plot was to enable the user to chose his/her own plot colors by using the Format dialog.
//if strend = 1 then Plot1(st,"SuperTrendUP"); //if strend = -1 then Plot2(st,"SuperTrendDN");
if strend = 1 then SetPlotColor(1,red); if strend = -1 then SetPlotColor(1,green);
Method to just use one Plot1
Backtesting with [Trade Station,Python,AmiBroker, Excel]. Intended for informational and educational purposes only!