Easing Into EasyLanguage-DayTrade Edition [On SALE Now thru November]
Get Your Copy Now! On sale now for thru the end of November!
EZ-DT Pyramania is a strategy I introduced in the Day Trade Edition. The logic is rather simple – pyramid as the market moves through multiple levels during the trading day. – buy, buy, buy, dump or buy, dump, short, short, short, dump. The distance between the levels is constant. In the book, I showed an algorithm with a total of 6 levels with 7 edges.
Pyramania of @ES.D
Here the market opens @ 9:30 and the levels are instantly plotted and trades are executed as the market moves through the levels located above the open tick. Over the weekend, I had a reader ask how he could modify the code to plot the levels on the 24-hour @ES session. In the day session, I used the change in the date as the trigger for the calculation and plotting of the levels. Here is the day session version.
inputs:numSegments(6),numPlots(6);
arrays: segmentBounds[](0);
variables: j(0),loopCnt(0),segmentSize(0),avgRng(0); once Begin Array_SetMaxIndex(segmentBounds, numSegments); end;
if d <> d[1] Then // only works on the @ES.D or any .D session begin avgRng = average(range of data2,20); segmentSize = avgRng/numSegments; loopCnt = -1*numSegments/2; for j = 0 to numSegments begin segmentBounds[j] = openD(0) + loopCnt * segmentSize; loopCnt = loopCnt + 1; end; end;
//The following time constraint only works when all time stamps //are less than the end of day time stamp //This will not work when time = 1800 and endTime = 1700 if t < calcTime(sessionEndTime(0,1),-barInterval) Then begin if numPlots >= 1 then plot1(segmentBounds[0],"Level 0"); if numPlots >= 2 then plot2(segmentBounds[1],"Level 1"); if numPlots >= 3 then plot3(segmentBounds[2],"Level 2"); if numPlots >= 4 then plot4(segmentBounds[3],"Level 3"); if numPlots >= 5 then plot5(segmentBounds[4],"Level 4"); if numPlots >= 6 then plot6(segmentBounds[5],"Level 5"); if numPlots >= 7 then plot7(segmentBounds[6],"Level 6"); // plot8(segmentBounds[7],"Level 7"); // plot9(segmentBounds[8],"Level 8"); end;
Works great with @ES.D or any @**.D
I like this code because it exposes you to arrays, loops, and plotting multiple values. You can fix this by modifying and adding some code. I used the Trading Around Midnight blog post to get the code I needed to enable plotting around 0:00 hours. Here is the updated code:
once Begin Array_SetMaxIndex(segmentBounds, numSegments); end;
startTime = sessionStartTime(0,1); endTime = sessionEndTime(0,1); //let TS tell you when the market opens - remember the //first time stamp is the open time + bar interval if t = calcTime(sessionStartTime(0,1),barInterval) Then begin avgRng = average(range of data2,20); segmentSize = avgRng/numSegments; loopCnt = -1*numSegments/2; for j = 0 to numSegments begin segmentBounds[j] = open + loopCnt * segmentSize; loopCnt = loopCnt + 1; end; end;
// if startTime > endTime then you know you are dealing with // timees that more than likely bridges midnight // if time is greater then 1700 (end time) then you must // subtract an offset so it makes sense - endTimeOffset // play with the math and it will come to you if startTime > endTime then begin endTimeOffset = 0; if t >= startTime+barInterval and t<= 2359 then endTimeOffSet = 2400-endTime; end; if t-endTimeOffSet < endTime Then begin if numPlots >= 1 then plot1(segmentBounds[0],"Level 0"); if numPlots >= 2 then plot2(segmentBounds[1],"Level 1"); if numPlots >= 3 then plot3(segmentBounds[2],"Level 2"); if numPlots >= 4 then plot4(segmentBounds[3],"Level 3"); if numPlots >= 5 then plot5(segmentBounds[4],"Level 4"); if numPlots >= 6 then plot6(segmentBounds[5],"Level 5"); if numPlots >= 7 then plot7(segmentBounds[6],"Level 6"); // plot8(segmentBounds[7],"Level 7"); // plot9(segmentBounds[8],"Level 8"); end;
Modification to plot data around midnight
Here I let TS tell me with the market opens and then use some simple math to make sure I can plot with the time is greater than and less than the end of day time.
Plots from 18:05 through midnight until 17:00 the next day.
Email me if you have the book and want to companion code to the strategy – georgeppruitt@gmail.com
Have You Ever Wondered If You Just Reversed the Logic?
You have been there before. What you thought was a great trading idea turns out to be a big flop. We have all developed these types of algorithms. Then it hits you, just flip the logic and in turn the equity curve. Hold your horses! First off you have to make sure it’s not just the execution costs that is hammering the equity curve into oblivion. When testing a fresh trading idea, it is best to keep execution costs to zero. This way if your idea is a good one, but is simply backward, then you have a chance of creating something good out of something bad. I was playing around with a mean reversion day trading algorithm (on the @ES.D – day session of the mini S&P 500) that created the following equity curve. Remember to read the disclaimer concerning hypothetical performance before proceeding reading the rest of this blog. It is located under the DISCLAIMER – REAMDE! tab. By reading the rest of this blog post it implies that you understand the limitations of hypothetical back testing and simulated analysis.
The pandemic created a strong mean reversion environment. In the initial stage of this research, I did not set the executions costs – they defaulted to zero. My idea was to buy below the open after the market moved down from the high of the day a certain percentage of price. Since I was going to be buying as the market was moving down, I was willing to use a wide stop to see if I could hold on to the falling knife. Short entries were just the opposite -sell short above the open after the market rallied a certain percentage of price. I wanted to enter on a hiccup. Once the market moved down a certain range from the high of the day, I wanted to enter on a stop at the high of the prior bar. I figured if the price penetrated the high of the prior five-minute bar in a down move, then it would signal an eventual rotation in the market. Again, I was just throwing pasta against the wall to see what would stick. I even came up with a really neat name for the algorithm the Rubber Band system – stretch just far enough and the market is bound to slam back. Well, there wasn’t any pasta sticking. Or was there? If I flipped the equity curve 180 degrees, then I would have a darned good strategy. All it would take is to reverse the signals, sell short when I was buying and buy when I was selling short. Instead of a mean reversion scheme, this would turn into a momentum-based strategy.
if c < todaysOpen and todaysOpen-c = maxOpenMinusClose and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canBuy = True; if c > todaysOpen and c- todaysOpen = maxCloseMinusOpen and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canShort = True;
Guts of the complete failure.
Here I measure the maximum distance from the highest close above the open and the lowest close below the open. The distance between the two points is the range between the highest and lowest closing price of the current day. If the close is less than today’s open, and the range between the extremes of the highest close and lowest close of the trading day is greater than stretchPercent, then an order directive to buy the next bar at the current bar’s high is issued. The order is alive until it is filled, or the day expires. Selling short uses the same calculations but requires the close of the current bar to be above the open. The stretchPercent was set to 1 percent and the protective stop was set to a wide $2,000. As you can see from the equity curve, this plan did not work except for the time span of the pandemic. Could you optimize the strategy and make it a winning system. Definitely. But the 1 percent and $2000 stop seemed very logical to me. Since we are comparing the range of the data to a fixed price of the data, then we don’t need to worry about the continuous contract distortion. Maybe we would have to, if the market price was straddling zero. Anyways, here is a strategy using the same entry technique, but reversed, with some intelligent trade filtering. I figured a profit objective might be beneficial, because the stop was hit several times during the original test.
$2K was hit often!Using some trade filtering and stop loss and profit objective on the reversal of the original strategy.
If you like the following code, make sure you check out my books at Amazon.com. This type of code is used the Hi-Res and Day-Trading editions of the Easing_Into_Easylanguage series.
if c < todaysOpen and todaysOpen-c = maxOpenMinusClose and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canShort = True; if c > todaysOpen and c- todaysOpen = maxCloseMinusOpen and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canBuy = True;
if canTrade and t >= calcTime(dontTradeBefore,dontTradeBeforeOffset) and t < calcTime(dontTradeAfter,dontTradeAfterOffset) and t < sessionEndTime(0,1) Then begin if shortsToday = 0 and canShort = True Then sellshort next bar at l stop; if buysToday = 0 and canBuy = True Then buy next bar at h stop; end;
Trade filtering was obtained by limiting the duration during the trading day that a trade could take place. It’s usually wise to wait a few minutes after the open and a few minutes prior to the close to issue trade directives. Also, range compression of the prior day seems to help in many cases. Or at least not range expansion. I only allow one long entry or one short or both during the trading day – two entries only! Read the code and let me know if you have any questions. This is a good framework for other areas of research. Limiting entries using the mp variable is a neat technique that you can use elsewhere.
And as always let me know if you see any bugs in the code. Like Donnie Knuth says, “Beware of bugs in the above code; I have only proved it correct, not tried it!”
OptimizeDaysOfWeek = False; if optimizeNumber > 0 and optimizeNumber < 32 Then Begin currDOWStr = midStr(daysOfWeek,dayOfWeek(d),1); if inStr(dowArr[optimizeNumber],currDOWStr) <> 0 Then OptimizeDaysOfWeek = True; end;
if currentbar>=1 then if oscVal>oscVal[1] then plot1(mavDiff,"+AO") else plot2(mavDiff,"-AO")
Williams Awesome Oscillator Source Code
And here is what it looks like:
Williams Awesome Oscillator
The code reveals a value that oscillates around 0. First calculate the difference between the 5-day moving average of the daily midPoint (H+ L)/2 and the 34-day moving average of the midPoint. A positive value informs us that the market is in a bullish stance whereas a negative represents a bearish tone. Basically, the positive value is simply stating the shorter-term moving average is above the longer term and vice versa. The second step in the indicator calculation is to subtract the 5-day moving average of the differences from the current difference. If the second calculation is greater than the prior day’s calculation, then plot the original calculation value as green (AO+). If it is less (A0-), then paint the first calculation red. The color signifies the momentum between the current and the five-day smoothed value.
Here I am using the very handy countIf function. This function will tell you how many times a Boolean comparison is true out of the last N days. Her I use the function twice, but I could have replaced the second function call with mavDn = 30 – mavUp. So, I am counting the number of occurrences of when the mavDiff is positive and negative over the past 30-days. I also count the number of times the oscVal is greater than the prior oscVal. In other words, I am counting the number of green bars. I create a ratio between green bars and 10. If there are six green bars, then the ratio equals 60% This indicates that the ratio of red bars would be 40%. Based on these readings you can create trade entry directives.
if canShort and mavUp > numBarsAbove and mavDiff > minDiffAmt and oscRatio >= obRatio then sellShort next bar at open;
if canBuy and mavDn > numBarsBelow and mavDiff < -1*minDiffAmt and oscRatio <= osRatio Then buy next bar at open;
Trade Directives
If the number of readings out of the last 30 days is greater than numBarsAbove and mavDiff is of a certain magnitude and the oscillator ratio is greater than buyOSCRatio, then you can go short on the next open. Here we are looking for the market to converge. When these conditions are met then I think the market is overbought. You can see how I set up the long entries. As you can see from the chart it does a pretty good job. Optimizing the parameters on the crude oil futures yielded this equity curve.
Too few trades!
Not bad, but not statistically significant either. One way to generate more trades is to install some trade management such as protective stop and profit objective.
Using wide stop and profit objective.
Using a wide protective stop and large profit objective tripled the number of trades. Don’t know if it is any better, but total performance was not derived from just a couple of trades. When you are working with a strategy like this and overlay trade management you will often run into this situation.
Exiting while conditions to short are still turned on!
Here we either get stopped out or take a profit and immediately reenter the market. This occurs when the conditions are still met to short when we exit a trade. The fix for this is to determine when an exit has occurred and force the entry trigger to toggle off. But you have to figure out how to turn the trigger back on. I reset the triggers based on the number of days since the triggers were turned off – a simple fix for this post. If you want to play with this strategy, you will probably need a better trigger reset.
I am using the setStopLoss and setProfitTarget functionality via their own strategies – Stop Loss and Profit Target. These functions allow exit on the same as entry, which can be useful. Since we are executing on the open of the bar, the market could definitely move either in the direction of the stop or the profit. Since we are using wide values, the probability of both would be minimal. So how do you determine when you have exited a trade. You could look the current bar’s marketPosition and compare it with the prior bar’s value, but this doesn’t work 100% of the time. We could be flat at yesterday’s close, enter long on today’s open and get stopped out during the day and yesterday’s marketPosition would be flat and today’s marketPosition would be flat as well. It would be as if nothing occurred when in fact it did.
Take a look at this code and see if it makes sense to you.
if mp[1] = 1 and totalTrades > totTrades then canBuy = False;
if mp[1] = -1 and totalTrades > totTrades then canShort = False;
if mp[1] = 0 and totalTrades > totTrades then Begin if mavDiff[1] < 0 then canBuy = False; if mavDiff[1] > 0 then canShort = False; end;
totTrades = totalTrades;
Watch for a change in totalTrades.
If we were long yesterday and totalTrades (builtin keyword/function) increases above my own totTrades, then we know a trade was closed out – a long trade that is. A closed out short position is handled in the same manner. What about when yesterday’s position is flat and totalTrades increases. This means an entry and exit occurred on the current bar. You have to investigate whether the position was either long or short. I know I can only go long when mavDiff is less than zero and can only go short when mavDiff is greater than zero. So, all you need to do is investigate yesterday’s mavDiff to help you determine what position was entered and exited on the same day. After you determine if an exit occurred, you need to update totTrades with totalTrades. Once you determine an exit occurred you turn canBuy or canShort off. They can only be turned back on after N bars have transpired since they were turned off. I use my own barsSince function to help determine this.
if not(canBuy) Then if barsSince(canBuy=True,100,1,0) = 6 then canBuy = True; if not(canShort) Then if barsSince(canShort=True,100,1,0) = 6 then canShort = True;
if not(canBuy) Then if barsSince(canBuy=True,100,1,0) = numBarsTrigReset then canBuy = True; if not(canShort) Then if barsSince(canShort=True,100,1,0) = numBarsTrigReset then canShort = True;
if mp[1] = 1 and totalTrades > totTrades then canBuy = False;
if mp[1] = -1 and totalTrades > totTrades then canShort = False;
if mp[1] = 0 and totalTrades > totTrades then Begin if mavDiff[1] < 0 then canBuy = False; if mavDiff[1] > 0 then canShort = False; end;
if canShort and mavUp > numBarsAbove and mavDiff > minDiffAmt and oscRatio >= buyOSCRatio then sellShort next bar at open;
if canBuy and mavDn > numBarsBelow and mavDiff < -1*minDiffAmt and oscRatio <= shortOSRatio Then buy next bar at open;
The concept of Scope when it comes to a programming language can get very confusing very quickly. If you are new to programming and you think Scope is a mouthwash, no worries here. I will give a full explanation of Scope in terms of programming with the help of ChatGPT. Its response is italicized.
In Python, the scope of a variable refers to the region of the program where that variable can be accessed or referenced. There are two types of scopes in Python: global scope and local scope.
Here’s an example that demonstrates the concept of scope in Python:
# global variable x = 10
def my_func(): # local variable y = 5 print("x inside my_func:", x) # x is accessed from the global scope print("y inside my_func:", y) # y is accessed from the local scope
my_func()
print("x outside my_func:", x) # x is accessed from the global scope print("y outside my_func:") # y is not defined in the global scope, so this will raise a NameError
Example of Scope as demonstrated by Chat-GPT
In this example, we define a global variable x with a value of 10.
We then define a function my_func that has a local variable y with a value of 5. Inside the function, we print the values of x and y. The variable x is accessed from the global scope, which means it can be referenced from anywhere in the program.
The variable y, on the other hand, is only accessible within the function, which is the local scope. Outside the function, we print the value of x again, which is still accessible from the global scope. However, when we try to print the value of y, we get a NameError because y is not defined in the global scope.
Okay that was pretty formal. In simpler terms, a variable declared inside a function can only be seen inside that function, unless it is declared globally. Another thing about Scope, is that the variables have amnesia. Once you step out of the function the variable forgets what it was, and you can’t refer to its prior value upon return to the function.
Unless you are an EasyLanguage function. Scope is still constrained by an EasyLanguage function, because it is a separate piece of code held within the library of Analysis Techniques. Most of the time you can’t see what’s in the function unless you open it with the ELEditor. However, the variables that are defined inside the function do not suffer from amnesia. If you need to refer to a prior value of a locally declared variable, you can. This type of function is what EasyLanguage calls a Serial function. The only downside to this function is it slows processing down quite a bit.
Okay. To make a long story short I wanted to show the magic of EasyLanguage function that I have been working with on a project. This project includes some of Ehlers’ cycle analysis functions. The one I am going to discuss today is the HighRoof function – don’t worry I am not going to go into detail of what this function does. If you want to know just GOOGLE it or ask ChatGPT. I developed a strategy that used the function on the last 25 days of closing price data. I then turned around and fed the output of the first pass of the HighRoof function right back into the HighRoof function. Something similar to embedding functions.
doubleSmooth = average(average(c,20),20);
Sort of like a double smoothed moving average. After I did this, I started thinking does the function remember the data from its respective call? The first pass used closing price data, so its variables and their history should be in terms of price data. The second pass used the cyclical movements data that was output by the initial call to the HighRoof function. Everything turned out fine, the function remembered the correct data. Or seemed like it did. This is how you learn about any programming language – pull out your SandBox and do some testing. First off, here is my conversion of Ehlers’ HighRoof function in EasyLanguage.
This function requires just two inputs – the data (with a history) and a simple length or cut period. The first input is of type numericSeries and the second input is of type numericSimple. You will see the following line of code
This code prints out the last three historic values of the HighPass variable for each function call. I am calling the function twice for each bar of data in the Crude Oil futures continuous contract.
Starting at the top of the output you will see that on 1230206 the function was called twice with two different sets of data. As you can see the output of the first two lines is of a different magnitude. The first line is approximately an order or magnitude of 10 of the second line. If you go to lines 3 and 4 you will see the highPass[1] of lines 1 and 2 moves to highPass[2] and then onto highPass[3]. I think what happens internally is for every call on per bar basis, the variables for each function call are pushed into a queue in memory. The queue continues to grow for whatever length is necessary and then either maintained or truncated at some later time.
Why Is This So Cool?
In many languages the encapsulation of data with the function requires additional programming. The EasyLanguage function could be seen as an “object” like in object-oriented programming. You just don’t know you are doing it. EasyLanguage takes care of a lot of the behind-the-scenes data management. To do the same thing in Python you would need to create a class of Ehlers Roof that maintain historic data in class members and the calculations would be accomplished by a class method. In the case of calling the function twice, you would instantiate two classes from the template and each class would act independent of each other.
One last nugget of information. If you are going to be working with trigonometric functions such as Cosine, Sine or Tangent, make sure your arguments are in degrees not radians. In Python, you must use radians.
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[1] and low > low[1] and average(c,30) > average(c,30)[1] 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[1] means the high of yesterday and the HIGH[2] 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 [1] or [2] 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:
Buy array
SellShort array
Sell array
BuyToCover array
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 [100] part. Each array needs to be initialized // and we do this by placing a zero (0) in parentheses arrays: buyDates[100](0),sellDates[100](0), shortDates[100](0),buyToCoverDates[100](0);
// next you want the arrays that go together to have the same // index value - take a look at this
// note the buyDates[1] has a matching sellDates[1] // buyDates[2] has a matching sellDates[2] // -- and -- // shortDates[1] has a matching buyToCoverDates[1] // shortDates[2] has a matching buyToCoverDates[2]
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[1] < 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[1] < 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[1] < 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[1] = 0415;sellDates[1] = 0515; buyDates[2] = 0605;sellDates[2] = 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[1] < 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[1] < 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[1] < 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[1] < 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.
This pattern has been around for many years, and is still useful today in a day trading scheme. The pattern is quite simple: if today’s high exceeds yesterday’s high by a certain amount, then sell short as the market moves back through yesterday’s high. There are certain components of yesterday’s daily bar that are significant to day traders – the high, the low, the close and the day traders’ pivot. Yesterday’s high is considered a level of resistance and is often tested. Many times the market has just enough momentum to carry through this resistance level, but eventually peters out and then the bears will jump in and push the market down even more. The opposite is true when the bulls take over near the support level of yesterday’s low. Here is an example of Clear Out short and buy.
1st the high of yesterday is cleared out and then the low of yesterday.
How Do You Program this Simple Pattern?
The programming of this strategy is rather simple, if you are day trading. The key components are toggles that track the high and low of the day as the market penetrate the prior day’s high and low. Once the toggles are flipped on, then order directives can be placed. A max. trade stop loss can easily be installed via the SetStopLoss(500) function. You will also want to limit the number of entries, because in a congestive phase, this pattern could fire off multiple times. Once you intuitively program this, you will almost certainly run into an issue where a simple “trick” will bail you out. Remember the code does exactly what you tell it to do. Take a look at these trades.
When Back Testing TradeStation will convert stop orders to market orders.
On a Back Test, Stop Orders are Converted to Market Orders if Price Exceeds the Stop Level
In these example trades, the first trade is accurate as it buys yesterday’s low + one tick and then gets stopped out. Once a long is entered, the system logic requires the market to trade back below yesterday’s low before a long another entry is signaled at yesterday’s low. Here as you can see, the initial buy toggle is set to True and when a long position is entered the buy toggle is turned off. The market knee jerks back below yesterday’s low and stops out your long position. Since TradeStation’s paradigm is based on “next bar” execution, a long entry doesn’t occur as the wide bar crosses back up through yesterday’s low. This is a “bang-bang” situation as it happened very quickly. In a perfect world, you should have been quickly stopped out and re-entered back long at your price. However, the toggle isn’t turned back on until the low of the current bar falls a short distance below yesterday’s low. Since this toggle isn’t set before the market takes off, you don’t get your price. The toggle is eventually turned on and a buy stop order is issued and you can tell you get a ton of slippage. You actually buy the next bar’s open after the bar where the toggle was turned on. I dropped down to a one minute bar and still didn’t get the trade. A 10 second bar did generate the exit and re-entry at the correct levels, however. It did this because, the 10 second bar turned the toggle on in time for the stop order to be generated accurately.
Using a 10-second bar an accurate exit and entry were generated.
Okay – Can you rely on a 5 minute bar then?
Five minute bar data has been the staple of day trading systems for many years. However, if you want to test “bang-bang” algorithms you are probably better off dropping down to a N-seconds bar. However, this strategy as a whole is not “bang-bang” so with a little trick you can get more accurate entries and exits.
What’s the Trick?
In real-time trading, buy-stop orders below the market are rejected. So, the second and third trades that were presented would never have taken place. But, the backtest reflects the trades, and if you include execution costs, the performance might nudge you into not trading a possibly viable system. You can take advantage of the “next bar” paradigm by forcing the close of the current bar to be below a buy-stop price and above a sell short stop price. Does this trade look better? Again in a perfect world, you would have re-entered long on the wide bar that stopped us out. But I guarantee you a fast market condition was in effect. All a broker has to say to you when you complain about a fill is, “Sorry Dude! It was a fast market. Not held!” I can’t tell you how many times I requested a printout of fills over a few seconds from my brokers. It is like when a football coach tosses the RED FLAG. During the Pit Days you had a chance to get a fill cash adjustment because the broker was human and maybe he or she didn’t react quickly enough. But when electronic trade matching took over, an adjustment was highly unlikely. Heck, you sign off on this when you accept the terms of electronic trading. Fills are rarely made better.
The second and third trade don’t occur because you force the buy stop order to be valid.
How Does the Trick Affect Performance?
Here are the results over the past four months on different time frame resolutions.
10 Seconds Resolution.
10 seconds bar would be the most accurate if slippage is acceptable. And that is a big assumption on “bang-bang” days.
1 minute bar resolution.
The one minute bar is close but September is substantially off. Probably some “bang-bang” action.
5 minute bar resolution with “Trick”
This is close to the 10-second bar result. Fast market or “bang-bang” trades were reduced or eliminated with the “trick”.
5 minute bar resolution without “Trick.”
Surprisingly, the 5 minute bar without the “Trick” resembles the 10 seconds results. But we know this is not accurate as trades are fired off in a manner that goes against reality.
The two following table shows the impact of a $15 RT comm./slipp. per trade charge.
Without “Trick” and $15 RT
With “Trick” and $15 RT
Okay, Now That We Have That Figured Out How Do You Limit Trading After a Daily Max. Loss
Another concept I wanted to cover was limiting trades after a certain loss level was suffered on a daily basis. In the code, I only wanted to enter trades as long as the max. daily loss was less than or equal to $1,000 A fixed stop of $500 on a per trade basis was utilized as well. So, if you suffered two max. stop losses right off the bat ($1,000), you could still take one more trade. Now if you had a $500 winner and two $500 losses you could still take another trade.
Ouch! Two max losses, but still could take a third trade. Ouch again – stupid system.
Should I take that second trade? I just suffered three losses in a row. What to do? What to do? Damn straight you better that trade.
If you are going to trade a system, you better trade it systematically!
Now Onto the Code
//Illustrate trade stoppage after a certain loss has been //experienced and creating realistic stop orders.
if t = startTime then begin coBuy = False; coShort = False; beginOfDayProfit = netProfit; beginOfDayTotTrades = totalTrades; end;
canTrade = iff(t >=startTime and t < sess1EndTime,1,0);
if t >= startTime and h > highD(1) + clrOutBuyPer*(highD(1)-lowD(1)) then begin coShort = True; end;
if t >= startTime and l < lowD(1) - clrOutShortPer*(highD(1)-lowD(1)) then begin coBuy = True; end;
mp = marketPosition;
if canTrade = 1 and coShort and netProfit >= beginOfDayProfit - maxDailyLoss and c > highD(1) - minMove/priceScale then sellShort next bar at highD(1) - minMove/priceScale stop;
if mp = -1 then // toggle to turn off coShort - must wait for set up begin coShort = False; end;
if canTrade = 1 and coBuy and netProfit >= beginOfDayProfit - maxDailyLoss and c < lowD(1) + minMove/priceScale then buy next bar at lowD(1) + minMove/priceScale stop;
if mp = 1 then begin coBuy = False; end;
setStopLoss(500); setExitOnClose;
Strategy in its Entirety
You need to capture the NetProfit sometime during the day before trading commences. This block does just that.
if t = startTime then begin coBuy = False; coShort = False; beginOfDayProfit = netProfit; beginOfDayTotTrades = totalTrades; end;
Snippet that captures NetProfit at start of day
Now all you need to do is compare the current netProfit (EL keyword) to the beginOfDayProfit (user variable). If the current netProfit >= beginOfDayProfit – maxDailyLoss (notice I programmed greater than or equal to), then proceed with the next trade. The rest of the logic is pretty self explanatory, but to drive the point home, here is how I make sure a proper stop order is placed.
if canTrade = 1 and coShort and netProfit >= beginOfDayProfit - maxDailyLoss and c > highD(1) - minMove/priceScale then sellShort next bar at highD(1) - minMove/priceScale stop;
if mp = -1 then // toggle to turn off coShort - must wait for set up begin coShort = False; end;
Notice how I use the current bars Close - C and How I toggle coShort to False
If You Like This – Make Sure You Get My Hi-Res Edition of Easing Into EasyLanguage
This is a typical project I discuss in the second book in the Easing Into EasyLanguage Trilogy. I have held over the BLACK FRIDAY special, and it will stay in effect through December 31st. Hurry, and take advantage of the savings. If you see any mistakes, or just want to ask me a question, or have a comment, just shoot me an email.
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.
Seasonal Day Trader Settings
I opened the Optimization Report and saved as text. Excel had no problem opening it.
Optimization results output to Excel and cleaned up.
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;
setExitOnClose;
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;
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
Nice Trade. Around August 2014
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.
Same Trade but with rollovers
Still a very nice trade, but in reality you would have to endure six rollover trades and the associated execution costs.
Conclusion
Here is the mechanism of the rollover trade.
Roll out of old contract and roll into new contract
And now the performance results using $30 for round turn execution costs.
No-Rollovers
No Rollovers?
Now with rollovers
Many more trades with the 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.
You can choose type of trigger – (3) Dynamic or (4) Time based.
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[3]+19000000:8:0); firstMonthPrint = true; end; If(dayOfMonth(date[1]) < 25 and dayOfMonth(date) > 25 ) and firstMonthPrint = false then begin print(date[4]+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[9]+19000000:8:0,";"); firstMonthPrint = true; end; If(dayOfMonth(date[1]) < 25 and dayOfMonth(date) > 25 ) and firstMonthPrint = false then begin value1 = value1 + 1; print("rollArr[",value1:1:0,"]=",date[10]+19000000:8:0,";"); // print(date[4]+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.
Actual data I simulated rollovers with.
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 Open Interest Valley is the rollover date.
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.
Using an adjusted continuous contract you would not see these trades.
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.
Backtesting with [Trade Station,Python,AmiBroker, Excel]. Intended for informational and educational purposes only!
Get All Four Books in the Easing Into EasyLanguage Series - The Day Trade Edition is now Available!
Announcement – A Day Trade Edition will be added to my Easing Into EasyLanguage Series this year! This edition will be the fourth installment and will utilize concepts discussed in the Hi-Res and Advanced Topics editions. I will show how to develop and program algorithms that will enter after the open of the day and get out before the market closes. Hence, no overnight exposure. Most examples will be carried out on the mini Dow, Nasdaq, S&P500 and Russel. The programming skills that you will learn can be carried to any market that provides enough bang for the buck to day trade. Look for this edition later this year. But get geared up for it by reading the first three editions in the series now. Get your favorite QUANT the books they need!
The Cover of my latest book. The first in the series.
Hello to All! The Easing Into EasyLanguage Series is now complete with the publication of the Advanced Topics Edition. This series includes three educational editions. Start out with the Foundation Edition. It is designed for the new user of EasyLanguage or for those you would like to have a refresher course. There are 13 tutorials ranging from creating Strategies to PaintBars. Learn how to create your own functions or apply stops and profit objectives. Ever wanted to know how to find an inside day that is also a Narrow Range 7 (NR7?) Now you can, and the best part is you get over 4 HOURS OF VIDEO INSTRUCTION – one for each tutorial. All source code is available too, and if you have TradeStation, so are the workspaces. Plus you can always email George for any questions. george.p.pruitt@gmail.com.
Hi-Res Edition Cover
This book is for those that have read the Foundation Edition or have some experience working with EasyLanguage and the various functions that help make a trading decision. This book’s audience will be those programmers that want to take an idea, that requires an observation of intraday market movements to make a trading decision, and program it accurately. If you have programmed daily bar systems, and you want to drill down and add some components that require additional market information (like what came first – the high or the low), then you have come to the right place. If you want to buy and sell short in the same day and use trade management principles such as profit targets and stop losses then The Hi-Res Edition is the book you need. There are two paradigms that EasyLanguage covers: daily and intraday bar programming. It’s the same language, but the move from daily to intraday programming can be quite difficult. Learn all the essentials and shortcuts with this edition. 5 HOURS OF VIDEO INSTRUCTION in this Hi-Res edition – one for each tutorial. All source code is available too, and if you have TradeStation, so are the workspaces. Plus you can always email George for any questions. george.p.pruitt@gmail.com.
Advanced Topics Cover
Advanced Topics (AT) could cover a vast amount of ideas and concepts and be the length of “War and Peace” on steroids. Since this book is part of the series, I wanted to cover a handful of concepts that included the follow programming constructs. Arrays and their manipulation. Buffers (fixed length arrays) and the tools to maintain buffer elements with formulas for extraction and sorting. Finite State Machines using the switch-case construct and range based case values. Using original text graphic objects and retrieving and analyzing their properties to determine X and Y coordinate values of text location. Seasonality: The Ruggiero/Barna Universal Seasonal and the Sheldon Knight Seasonal methods. In AT, you will also find an introduction to EasyLanguage’s Project Concept and the steps to create one by adding/deleting component files. TradeStation now provides access to fundamental data such as Commitment of Traders – learn how to convert the Net Change indicator into a strategy utilizing the FundValue functionality. If you wanted to find out how to merge multiple time frames into a single indicator, you are in luck! Create a MTF indicator for yourself.
Get Day Trading Edition Today!
Day Trading (DT) – This is a surprise installment in my Easing into EasyLanguage Series, as I had only intended on three books. However, I think it will fit well with the other books. Daytrading is a very popular approach as overnight risk is eliminated. Don’t worry there is plenty of risk during the day too! However, it can be very difficult to accurately program a trading idea on higher resolution data such as five- or one-minute bars. Like my other books, there is no “Holy Grail” included. And if you are looking for a book that gets in and out of a trade in a few seconds, this is not the one for you. I discourage trading more than a handful of trades per day – this is best left up to the professionals. But, if you want to learn about volatility-based break outs, pyramiding, scaling out, zone-based trading, accurate trade accounting and having a peek at algorithms that once ruled the systematic daytrading industry, then this is the book for you. A beginner might have a little difficulty in following along with the tutorials. If you have read the first two books (Foundation and Hi-Res) in this series, you are good to go. Or if you have some experience working with EasyLanguage and minute data, you will be OK as well.
Pick up your copies today – e-Book or paperback format – at Amazon.com
