I could be wrong, but I couldn’t find this functionality in EasyLanguage. I was testing a strategy that didn’t want to trade a specific week of the month. If it’s not built-in, then it must be created. I coded this functionality in Sheldon Knight’s Seasonality indicator but wanted to create a simplified universal version. I may have run into one little snag. Before discussing the snag, here is a Series function that returns my theoretical week of the month.
if month(d) <> month(d[1]) Then Begin weekCnt = 1; end;
if dayOfWeek(d)<dayOfWeek(d[1]) and month(d) = month(d[1]) Then begin weekCnt = weekCnt + 1; end; if weekCnt = 1 and dayofMonth(d) = 2 and dayOfWeek(D) = Sunday Then print("Saturday was first day of month"," ",d);
WeekOfMonth = weekCnt;
Series Function to Return Week Rank
This function is of type Series because it has to remember the prior output of the function call – weekCnt. This function is simple as it will not provide the week count accurately (at the very beginning of a test) until a new month is encountered in your data. It needs a ramp up (at least 30 days of daily or intraday data) to get to that new month. I could have used a while loop the first time the function was called and worked my way back in time to the beginning of the month and along the way calculate the week of the month. I decided against that, because if you are using tick data then that would be a bunch of loops and TradeStation can get caught in an infinite loop very easily.
This code is rather straightforward. If the today’s month is not equal to yesterday’s month, then weekCnt is set to one. Makes sense, right. The first day of the month should be the first week of the month. I then compare the day of week of today against the day of week of yesterday. If today’s day of week is less than the prior day’s day of week, then it must be a Sunday (futures markets open Sunday night to start the week) or Monday (Sunday = 0 and Monday = 1 and Friday = 5.) If this occurs and today’s month is the same as yesterday’s month, weekCnt is incremented. Why did I check for the month? What if the first trading day was a Sunday or Monday? Without the month comparison I would immediately increment weekCnt and that would be wrong. That is all there is to it? Or is it? Notice I put some debug code in the function code.
Is There a Snag?
April 2023 started the month on Saturday which is the last day of the week, but it falls in the first week of the month. The sun rises on Sunday and it falls in the second week of the month. If you think about my code, this is the first trading day of the month for futures:
month(April 2nd) = April or 4
month(March 31st) = March or 3
They do not equal so weekCnt is set to 1. The first trading day of the month is Sunday or DOW=0 and the prior trading day is Friday or DOW=5. WeekCnt is NOT incremented because month(D) doesn’t equal month(D[1]). Should WeekCnt be incremented? On a calendar basis it should, but on a trading calendar maybe not. If you are searching for the first occurrence of a certain day of week, then my code will work for you. On April 2nd, 2023, it is the second week of the month, but it is the first Sunday of April.
Thoughts??? Here are a couple of screenshots of interest.
Indicator in Histogram form representing the Week of MonthSome months are spread across SIX weeks.
This function is an example of where we let the data tell us what to do. I am sure there is calendar functions, in other languages, that can extract information from just the date. However, I like to extract from what is actually going to be traded.
Email me if you have any questions, concerns, criticism, or a better mouse trap.
How to reenter at a better price after a stop loss
A reader of this blog wanted to know how he could reenter a position at a better price if the first attempt turned out to be a loser. Here I am working with 5 minute bars, but the concepts work for daily bars too. The daily bar is quite a bit easier to program.
ES.D Day Trade using 30 minute Break Out
Here we are going to wait for the first 30 minutes to develop a channel at the highest high and the lowest low of the first 30 minutes. After the first 30 minutes and before 12:00 pm eastern, we will place a buy stop at the upper channel. The initial stop for this initial entry will be the lower channel. If we get stopped out, then we will reenter long at the midpoint between the upper and lower channel. The exit for this second entry will be the lower channel minus the width of the upper and lower channel.
Here are some pix. It kinda, sorta worked here – well the code worked perfectly. The initial thrust blew through the top channel and then immediately consolidated, distributed and crashed through the bottom channel. The bulls wanted another go at it, so they pushed it halfway to the midpoint and then immediately the bears came in and played tug of war. In the end the bulls won out.
Initial Break Out Failed. But 2nd Entry made a little.
Here the bulls had initial control and then the bears, and then the bulls and finally the bears tipped the canoe over.
We gave it the old college try didn’t we fellas.
This type of logic can be applied to any daytrade or swing trade algorithm. Here is the code for the strategy.
//working with 5 minute bars here //should work with any time frame
if t = calcTime(startTime,barInterval) Then begin periodHigh = -99999999; periodLow = 99999999; barCountToday = 1; totTrades = totalTrades; end;
if barCountToday <= numOfBarsHHLL Then Begin periodHigh = maxList(periodHigh,h); periodLow = minList(periodLow,l); end;
barCountToday = barCountToday + 1;
longEntriesToday = totalTrades - totTrades;
mp = marketPosition; if t <= stopTradeTime and barCountToday > numOfBarsHHLL Then Begin if longEntriesToday = 0 then buy("InitBreakOut") next bar at periodHigh + minMove/priceScale stop; if longEntriesToday = 1 Then buy("BetterBuy") next bar at (periodHigh+periodLow)/2 stop; end;
if mp = 1 then if longEntriesToday = 0 then sell("InitXit") next bar at periodLow stop; if longEntriesToday = 1 then sell("2ndXit") next bar at periodLow - (periodHigh - periodLow) stop;
SetExitOnClose;
The key concepts here are the time constraints and how I count bars to calculate the first 30 – minute channel. I have had problems with EasyLanguages’s entriesToday function, so I like how I did it here much better. On the first bar of the day, I set my variable totTrades to EasyLanguages built-in totalTrades (notice the difference in the spelling!) The keyword TotalTrades is immediately updated when a trade is closed out. So, I simply subtract my totTrades (the total number of trades at the beginning of the day) from totalTrades. If totalTrades is incremented (a trade is closed out) then the difference between the two variables is 1. I assign the difference of these two values to longEntriesToday. If longEntriesToday = 1, then I know I have entered long and have been stopped out. I then say, OK let’s get back long at a better price – the midpoint. I use the longEntriesToday variable again to determine which exit to use. With any luck the initial momentum that got us long initially will work its way back into the market for another shot.
It’s Easy to Create a Strategy Based Indicator
Once you develop a strategy, the indicator that plots entry and exit levels is very easy to derive. You have already done the math – just plot the values. Check this out.
//working with 5 minute bars here //should work with any time frame
if t = calcTime(startTime,barInterval) Then begin periodHigh = -99999999; periodLow = 99999999; barCountToday = 1; end;
if barCountToday <= numOfBarsHHLL Then Begin periodHigh = maxList(periodHigh,h); periodLow = minList(periodLow,l); end;
if barCountToday < numOfBarsHHLL Then begin noPlot(1); noPlot(2); noPlot(3); noPlot(4); End Else begin plot1(periodHigh,"top"); plot2((periodHigh+periodLow)/2,"mid"); plot3(periodLow,"bot"); plot4(periodLow - (periodHigh - periodLow),"2bot"); end;
barCountToday = barCountToday + 1;
So, that’s how you do it. You can use this code as a foundation for any system that wants to try and reenter at a better price. You can also use this code to develop your own time based break out strategy. In my new book, Easing Into EasyLanguage – the Daytrade EditionI will discuss topics very similar to this post.
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.
Only Trade the Best Segments of the Equity Curve – Cut Out Drawdown and Take Advantage of Run Ups! Really?
Equity curve feedback has been around for many years and seems highly logical, but one can’t get an industry-wide agreement on its benefit. The main problem is to know when to turn trading off and then back on as you track the equity curve. The most popular approach is to use a moving average of the equity curve to signal system participation. When the equity curve moves below 30, 60, or 90 period-moving average of equity, then just turn it off and wait until the curve crosses back above the average. This approach will be investigated in Part 2 of this series. Another approach is to stop trading once the curve enters a drawdown that exceeds a certain level and then start back up once the equity curve recovers. In this post, this method will be investigated.
Programmers Perspective
How do you go about programming this tool to start with. There are probably multiple ways of accomplishing this task, but the two I have most often observed were the two pass process and the inline simultaneous tracking of the synthetic and actual equity curves. The two pass process generates an unadulterated equity curve and stores the equity and trades either in memory or in a file. The second part of the process monitors the external equity curve along with the external trades synchronously and while trading is turned on, the trades are executed as they occur chronologically. When trading is turned off, the synthetic equity curve and trades are processed along the way. The second method is to create, which I have coined (maybe others too!), a synthetic equity curve and synthetic trades. I have done this in my TradingSimula_18 software by creating a SynthTrade Class. This class contains all the properties of every trade and in turn can use this information to create a synthetic equity curve. The synthetic equity curve and trades are untouched by the real time trading.
Start Simple
The creation of an equity curve monitor and processor is best started using a very simple system. One market algorithm that enters and exits on different dates, where pyramiding and scaling in or out are not allowed. The first algorithm that I tested was a mean reversion system where you buy after two consecutive down closes followed by an up close and then waiting one day. Since I tested the ES over the past 10 years you can assume the trend is up. I must admit that the day delay was a mistake on my behalf. I was experimenting with a four bar pattern and somehow forgot to look at the prior day’s action. Since this is an experiment it is OK!
if marketPosition <> 1 and (c[2] < c[3] and c[3] < c[4] and c[1] > = c[2]) then buy next bar at open;
//The exit is just as simple - //get out after four days (includeing entry bar) on the next bars open - no stops or profit objectives.
If barsSinceEntry > 2 then sell next bar at open;
Simple Strategy to test Synthetic Trading Engine
Here is the unadulterated equity curve using $0 for execution costs.
Non adjusted equity curve of our simple mean reversion system. Wait for a pull back and then a rally before entering.
The Retrace and Recover Method
In this initial experiment, trading is suspended once you reach a draw down of 10% from the peak of the equity curve and then resumes trading once a rally of 15% of the subsequent valley. Here is an intriguing graphic.
Green means ON. Red means OFF. The lower curve is the resultant curve.
I did this analysis by hand with Excel and it is best case scenario. Meaning that when trading is turned back on any current synthetic position is immediately executed in the real world. This experiment resulted in nearly the same drawdown but a large drop in overall equity curve growth – $75K.
Put the Synthetic Equity Curve Engine to the Test
Now that I had the confirmed results of the experiment, I used them as the benchmark against my TS-18 Synthetic Trade Engine. But before I installed the Equity Curve algorithm, I needed to make sure my synthetic trades lined up exactly with the real equity curve. The synthetic curve should align 100% with the real equity curve. If it doesn’t, then there is a problem. This is another reason to start with a simple trading strategy.
Take a look here where I print out the Synthetic Equity curve on a daily basis and compare it with the end result of the analysis.
Synth. matches Reality
Now let’s see if it worked.
Testing with Synth. Equity Curve Trading Turned ON!
The equity curves are very similar. However, there is a difference and this is caused by how one re-enters after trading is turned back on. In this version I tested waiting for a new trade signal which might take a few days. You could re-enter in three different ways:
Automatically enter synthetic position on the next bar’s open
Wait for a new trade signal
Enter immediately if you can get in at a better price
Using the 10% Ret. and 15% Rec. algorithm didn’t help at all. What if we test 10% and 10%.
10% Ret. and 10% Rec.
Now that performed better – more profit and less draw down. Now that I have the synthetic engine working on simple algorithms we can do all sorts of equity curve analysis. In the next installment in this series I will make sure the TS-18 Synthetic Engine can handle more complicated entry and exit algorithms. I have already tested a simple longer term trend following strategy on a medium sized portfolio and the synthetic engine worked fine. The retracement/recovery algorithm at 10%/15% did not work and I will go into the “whys” in my next post.
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.
Take a look at this video:
Backtesting with [Trade Station,Python,AmiBroker, Excel]. Intended for informational and educational purposes only!
Get All Five Books in the Easing Into EasyLanguage Series - The Trend Following Edition is now Available!
Announcement – A Trend Following edition has been added to my Easing into EasyLanguage Series! This edition will be the fifth and final installment and will utilize concepts discussed in the Foundation editions. I will pay respect to the legends of Trend Following by replicating the essence of their algorithms. Learn about the most prominent form of algorithmic trading. But get geared up for it by reading the first four editions in the series now. Get your favorite QUANT the books they need!
The Foundation Edition. The first in the series.
This series includes five editions that covers the full spectrum of the EasyLanguage programming language. Fully compliant with TradeStation and mostly compliant with MultiCharts. 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.
Hi-Res Edition Cover
This book is ideal for those who have completed the Foundation Edition or have some experience with EasyLanguage, especially if you’re ready to take your programming skills to the next level. The Hi-Res Edition is designed for programmers who want to build intraday trading systems, incorporating trade management techniques like profit targets and stop losses. This edition bridges the gap between daily and intraday bar programming, making it easier to handle challenges like tracking the sequence of high and low prices within the trading day. Plus, enjoy 5 hours of video instruction to guide you through each tutorial.
Advanced Topics Cover
The Advanced Topics Edition delves into essential programming concepts within EasyLanguage, offering a focused approach to complex topics. This book covers arrays and fixed-length buffers, including methods for element management, extraction, and sorting. Explore finite state machines using the switch-case construct, text graphic manipulation to retrieve precise X and Y coordinates, and gain insights into seasonality with the Ruggiero/Barna Universal Seasonal and Sheldon Knight Seasonal methods. Additionally, learn to build EasyLanguage projects, integrate fundamental data like Commitment of Traders, and create multi-timeframe indicators for comprehensive analysis.
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The Day Trading Edition complements the other books in the series, diving into the popular approach of day trading, where overnight risk is avoided (though daytime risk still applies!). Programming on high-resolution data, such as five- or one-minute bars, can be challenging, and this book provides guidance without claiming to be a “Holy Grail.” It’s not for ultra-high-frequency trading but rather for those interested in techniques like volatility-based breakouts, pyramiding, scaling out, and zone-based trading. Ideal for readers of the Foundation and Hi-Res editions or those with EasyLanguage experience, this book offers insights into algorithms that shaped the day trading industry.
Trend Following Cover.
For thirty-one years as the Director of Research at Futures Truth Magazine, I had the privilege of collaborating with renowned experts in technical analysis, including Fitschen, Stuckey, Ruggiero, Fox, and Waite. I gained invaluable insights as I watched their trend-following methods reach impressive peaks, face sharp declines, and ultimately rebound. From late 2014 to early 2020, I witnessed a dramatic downturn across the trend-following industry. Iconic systems like Aberration, CatScan, Andromeda, and Super Turtle—once thriving on robust trends of the 1990s through early 2010s—began to falter long before the pandemic. Since 2020 we have seen the familiar trends return. Get six hours of video instruction with this edition.
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