Category Archives: Must Know

Williams Awesome Oscillator Indicator and Strategy

Awesome Oscillator

This is a very simple yet telling analysis.  Here is the EasyLanguage:

[LegacyColorValue = true]; 


mavDiff= Average((h+l)/2,5)-Average((h+l)/2,34);
oscVal = mavDiff - average(mavDiff,5);

Plot3( 0, "ZeroLine" ) ;

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.

Awesome Indicator Strategy

mavDiff= Average((h+l)/2,5)-Average((h+l)/2,34);
oscVal = mavDiff - average(mavDiff,5);

mp = marketPosition;
mavUp = countIf(mavDiff > 0,30);
mavDn = countIf(mavDiff < 0,30);

value1 = countIf(oscVal > oscVal[1],10);

oscRatio = value1/10*100;
The beginning of an AO based Strategy

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
if mavDiff[1] < 0 then canBuy = False;
if mavDiff[1] > 0 then canShort = False;

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;

Complete strategy code:


mavDiff= Average((h+l)/2,5)-Average((h+l)/2,34);
oscVal = mavDiff - average(mavDiff,5);

mp = marketPosition;
mavUp = countIf(mavDiff > 0,30);
mavDn = countIf(mavDiff < 0,30);

value1 = countIf(oscVal > oscVal[1],10);

oscRatio = value1/10*100;

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
if mavDiff[1] < 0 then canBuy = False;
if mavDiff[1] > 0 then canShort = False;

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;

totTrades = totalTrades;
Inputs used to generate the equity curve.

The EasyLanguage Function: A Thing of Beauty

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.

I recently chatted with ChatGPT about Scope in terms of the Python programing language.

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


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.

//Ehlers HiRoof
Inputs: dataSeries(numericseries),cutPeriod(Numeric);

Vars: a1(0), b1(0), c1(0), c2(0), c3(0), Filt(0), Filt2(0),
alpha1(0),oneMinusAlpha1(0), highPass(0),myhp(0),degrees(0);
Vars: numTimesCalled(0);

//Highpass filter cyclic components whose periods are shorter than 48 bars

numTimesCalled = numTimesCalled + 1;

print(d," numTimesCalled ",numTimesCalled," highPass[1] ",highPass[1]," highPass[2] ",highPass[2]," highPass[3] ",highPass[3]);
degrees = .707*360 / CutPeriod;

alpha1 = (Cosine(degrees) + Sine(degrees) - 1) / Cosine(degrees);

oneMinusAlpha1 = 1-alpha1;

highPass = square(oneMinusAlpha1/2)*(dataSeries-2*dataSeries[1]+dataSeries[2]) +

Ehlers High Roof Function

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

 print(d," numTimesCalled ",numTimesCalled," highPass[1] ",highPass[1]," highPass[2] ",highPass[2]," highPass[3] ",highPass[3]);

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.

1230206.00 numTimesCalled  494.00 highPass[1]   -0.78 highPass[2]   -0.51 highPass[3]   -0.60
1230206.00 numTimesCalled 494.00 highPass[1] -0.05 highPass[2] -0.02 highPass[3] -0.06
1230207.00 numTimesCalled 495.00 highPass[1] -0.38 highPass[2] -0.78 highPass[3] -0.51
1230207.00 numTimesCalled 495.00 highPass[1] 0.04 highPass[2] -0.05 highPass[3] -0.02
1230208.00 numTimesCalled 496.00 highPass[1] 0.31 highPass[2] -0.38 highPass[3] -0.78
1230208.00 numTimesCalled 496.00 highPass[1] 0.16 highPass[2] 0.04 highPass[3] -0.05
1230209.00 numTimesCalled 497.00 highPass[1] 0.49 highPass[2] 0.31 highPass[3] -0.38
1230209.00 numTimesCalled 497.00 highPass[1] 0.15 highPass[2] 0.16 highPass[3] 0.04
1230210.00 numTimesCalled 498.00 highPass[1] 0.30 highPass[2] 0.49 highPass[3] 0.31
1230210.00 numTimesCalled 498.00 highPass[1] 0.07 highPass[2] 0.15 highPass[3] 0.16
1230213.00 numTimesCalled 499.00 highPass[1] 0.52 highPass[2] 0.30 highPass[3] 0.49
1230213.00 numTimesCalled 499.00 highPass[1] 0.08 highPass[2] 0.07 highPass[3] 0.15
1230214.00 numTimesCalled 500.00 highPass[1] 0.44 highPass[2] 0.52 highPass[3] 0.30
1230214.00 numTimesCalled 500.00 highPass[1] 0.04 highPass[2] 0.08 highPass[3] 0.07
Output of calling HighRoof twice per bar

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.

Here is my SandBox for Indicator

Value1 = EhlersHighRoof(close,25);
Value2 = EhlersHighRoof(value1,25);
Sandbox Playground for Ehlers Function


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.

Testing Seasonal Tendencies with an EasyLanguage Template

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:

  1. Buy array
  2. SellShort array
  3. Sell array
  4. 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),

// next you want the arrays that go together to have the same
// index value - take a look at this

buyDates[1] = 0415;sellDates[1] = 0515;
buyDates[2] = 0605;sellDates[2] = 0830;

shortDates[1] = 0915;buyToCoverDates[1] = 1130;
shortDates[2] = 0215;buyToCoverDates[2] = 0330;

// 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
if currentMMDD[1] < buyDates[n] and currentMMDD >= buyDates[n] Then
buyNow = True;

//Short Seasonal Entries toggle
shortNow = False;
for n = 1 to numshortDates
if currentMMDD[1] < shortDates[n] and currentMMDD >= shortDates[n] Then
shortNow = True;
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.

Here is the rest of the code:

inputs: dollarProfit(5000),dollarLoss(3000);
arrays: buyDates[100](0),sellDates[100](0),

vars: n(0),mp(0),currentMMDD(0),

vars: buyNow(False),shortNow(False),

// 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;

shortDates[1] = 0915;buyToCoverDates[1] = 1130;
shortDates[2] = 0215;buyToCoverDates[2] = 0330;
numshortDates = 2;
numbuyToCoverDates = 2;

mp = marketPosition;
currentMMDD = month(d of tomorrow)*100 + dayOfMonth(d of tomorrow);

//Long Seasonal Entries toggle
buyNow = False;
for n = 1 to numBuyDates
if currentMMDD[1] < buyDates[n] and currentMMDD >= buyDates[n] Then
buyNow = True;

//Short Seasonal Entries toggle
shortNow = False;
for n = 1 to numshortDates
if currentMMDD[1] < shortDates[n] and currentMMDD >= shortDates[n] Then
shortNow = True;

//Long Seasonal Exits toggle
sellNow = False;
if mp = 1 Then
for n = 1 to numSellDates
if currentMMDD[1] < sellDates[n] and currentMMDD >= sellDates[n] Then
sellNow = True;

//Short Seasonal Exits toggle
buyToCoverNow = False;
if mp = -1 Then
for n = 1 to numBuyToCoverDates
if currentMMDD[1] < buyToCoverDates[n] and currentMMDD >= buyToCoverDates[n] Then
buyToCoverNow = True;

// Long entry execution
if buyNow = True then
buy("Seas-Buy") next bar at open;
// Long exit execution
if mp = 1 then
if sellNow then
sell("Long Exit") next bar at open;

// Short entry execution
if shortNow then
sellShort("Seas-Short") next bar at open;
// short exit execution
if mp = -1 then
if buyToCoverNow then
buyToCover("short Exit") next bar at open;

Complete Seasonal Template EasyLanguage Code

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.

Helpful Code to Accurately Back Test Day Trading Systems with EasyLanguage

The Clear Out Pattern

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.

inputs: maxDailyLoss(1000),startTime(0935);
inputs: clrOutBuyPer(.10),clrOutShortPer(.10);

vars: coBuy(False),coShort(False),canTrade(0);
vars: beginOfDayProfit(0),beginOfDayTotTrades(0),mp(0);

if t = startTime then
coBuy = False;
coShort = False;
beginOfDayProfit = netProfit;
beginOfDayTotTrades = totalTrades;

canTrade = iff(t >=startTime and t < sess1EndTime,1,0);

if t >= startTime and h > highD(1) + clrOutBuyPer*(highD(1)-lowD(1)) then
coShort = True;

if t >= startTime and l < lowD(1) - clrOutShortPer*(highD(1)-lowD(1)) then
coBuy = True;

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
coShort = False;

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
coBuy = False;

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
coBuy = False;
coShort = False;
beginOfDayProfit = netProfit;
beginOfDayTotTrades = totalTrades;
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
coShort = False;
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.

Can Futures Traders Trust Continuous Contracts? [Part – 2]

Recap from Part -1

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.

  1. used my function to locate the First Notice Date in crude
  2. used the same function to print out exact EasyLanguage syntax
  3. chose to roll eight days before FND and had the function print out pure EasyLanguage
  4. the output created array assignments and loaded the calculated roll points in YYYMMDD format into the array
  5.  visually inspected non-adjusted continuous contracts that were spliced eight days before FND
  6. 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
condition1 = true;
arrCnt = arrCnt + 1;
if marketPosition = 1 then
sell("LongRollExit") this bar on close;
buy("LongRollEntry") next bar at open;
if marketPosition = -1 then
buyToCover("ShrtRollExit") this bar on close;
sellShort("ShrtRollEntry") next bar at open;

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.


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?

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.

Can Futures Traders Trust Continuous Contracts? [Part – 1]

 Well You Have To, Don’t You?

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
firstMonthPrint = true;
If(dayOfMonth(date[1]) < 25 and dayOfMonth(date) > 25 ) and firstMonthPrint = false then
firstMonthPrint = true;
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
value1 = value1 + 1;
firstMonthPrint = true;
If(dayOfMonth(date[1]) < 25 and dayOfMonth(date) > 25 ) and firstMonthPrint = false then
value1 = value1 + 1;
// print(date[4]+19000000:8:0);
firstMonthPrint = true;

// example of output

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.

Advanced Topics Edition of Easing Into EasyLanguage – NOW AVAILABLE!

Advanced Edition is now Available

Advanced Topics Cover

The last book in the Easing Into EasyLanguage Series has 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:

Data Aliasing with Minute Bars

Why It Is Important to Connect Variables with Correct Time Frame

I had a question about data aliasing from a reader of this blog.  Here is the debug code I used in the form of an Indicator.

//vars: myCloseData1(0),myCloseData2(0),
// myRSIData1(0),myRSIData2(0);

vars: myCloseData1(0),myCloseData2(0,data2),

myCloseData1 = close of data1;
myCloseData2 = close of data2;

myRSIData1 = rsi(close of data1,14);
myRSIData2 = rsi(close of data2,14);

Print(d," ",t," --------------- ");

print(" myCloseData1[0]: ",myCloseData1[0]," myCloseData2[0]: ",myCloseData2[0]);
print(" myCloseData1[1]: ",myCloseData1[1]," myCloseData2[1]: ",myCloseData2[1]);
print(" myCloseData1[2]: ",myCloseData1[2]," myCloseData2[2]: ",myCloseData2[2]);
print(" myCloseData1[3]: ",myCloseData1[3]," myCloseData2[3]: ",myCloseData2[3]);

print(" myRSIData1[0]: ",myRSIData1[0]," myRSIData2[0]: ",myRSIData2[0]);
print(" myRSIData1[1]: ",myRSIData1[1]," myRSIData2[1]: ",myRSIData2[1]);
print(" myRSIData1[2]: ",myRSIData1[2]," myRSIData1[2]: ",myRSIData2[2]);
print(" myRSIData1[3]: ",myRSIData1[3]," myRSIData1[3]: ",myRSIData2[3]);
Illustrating Difference Between Data Aliasing and Not Data Aliasing

If you have a higher resolution as Data 1 (5 minute in this case) than Data 2 (15 minute), then you must use Data Aliasing if you are going to use a variable to represent a price or a function output.  With out Data Aliasing all data references are in terms of Data 1.  When the 15 minute bar closes then the current [0] and one bar back[ 1] will be correct – going back further in time will reflect correct data.  During the 15 minute bar only the last value [0] will show the correct reading of the last completed 15 minute bar.  Once you tie the variable to its correct time frame variableName(0, Data2), you can then reference historic bars in the same fashion as if it were Data1.  This includes price references and function output values.

Check this chart out and see if it makes sense to you.  I dedicate a portion of a Tutorial on Data Aliasing in my latest book due out next week – Easing Into EasyLanguage – Advanced Topics.

Difference between using Data Aliasing and Not using Data Aliasing

A Tribute to Murray and His Inter-Market Research

Murray Ruggiero’s Inter-Market Research

Well it’s been a year, this month, that Murray passed away.  I was fortunate to work with him on many of his projects and learned quite a bit about inter-market convergence and divergence.  Honestly, I wasn’t that into it, but you couldn’t argue with his results.  A strategy that he developed in the 1990s that compared the Bond market with silver really did stand the test of time.  He monitored this relationship over the years and watched in wane.  Murray replaced silver with $UTY.

The PHLX Utility Sector Index (UTY) is a market capitalization-weighted index composed of geographically diverse public utility stocks.

He wrote an article for EasyLanguage Mastery by Jeff Swanson where he discussed this relationship and the development of inter-market strategies and through statistical analysis proved that these relationships added real value.

I am currently writing Advanced Topics, the final book in my Easing Into EasyLanguage trilogy, and have been working with Murray’s research.  I am fortunate to have a complete collection of his Futures Magazine articles from the mid 1990s to the mid 2000s.  There is a quite a bit of inter-market stuff in his articles.  I wanted, as a tribute and to proffer up some neat code, to show the performance and code of his Bond and $UTY inter-market algorithm.

Here is a version that he published a few years ago updated through June 30, 2022 – no commission/slippage.

Murray’s Bond and $UTY inter-market Strategy

Not a bad equity curve.  To be fair to Murray he did notice the connection between $UTY and the bonds was changing over the past couple of year.  And this simple stop and reverse system doesn’t  have a protective stop.   But it wouldn’t look much different with one, because the system looks at momentum of the primary  data and momentum of the secondary data and if they are in synch (either positively or negatively correlated – selected by the algo) an order is fired off.  If you simply just add a protective stop, and the momentum of the data are in synch, the strategy will just re-enter on the next bar.  However, the equity curve just made a new high  recently.  It has got on the wrong side of the Fed raising rates.  One could argue that this invisible hand has toppled the apple cart and this inter-market relationship has been rendered meaningless.

Murray had evolved his inter-market analysis to include state transitions.  He not only looked at the current momentum, but also at where the momentum had been.  He assigned the transitions of the momentum for the primary and secondary markets a value from one to four and he felt this state transition helped overcome some of the coupling/decoupling of the inter-market relationship.

However,  I wanted to test Murray’s simple strategy with a fixed $ stop and force the primary market to move from positive to negative or negative to positive territory while the secondary market is in the correct relationship.  Here is an updated equity curve.

George’s Adaptation and using a $4500 stop loss

This equity curve was developed  by using a $4500 stop loss.  Because I changed the order triggers, I reoptimized the length of the momentum calculations for the primary and secondary markets.  This curve is only better in the category of maximum draw down.  Shouldn’t we give Murray a chance and reoptimize his momentum length calculations too!  You bet.

Murray Length Optimizations

These metrics were sorted by Max Intraday Draw down.  The numbers did improve, but look at the Max Losing Trade value.  Murray’s later technology,  his State Systems, were a great improvement over this basic system.  Here is my optimization using a slightly different entry technique and a $4500 protective stop.

Standing on the Shoulders of a Giant

This system, using Murray’s overall research, achieved a better Max Draw Down and a much better Max Losing Trade.   Here is my code using the template that Murray provided in his articles in Futures Magazine and EasyLanguage Mastery.


// Code by Murray Ruggiero
// adapted by George Pruitt

Inputs: InterSet(2),LSB(0),Type(1),LenTr(4),LenInt(4),Relate(0);
Vars: MarkInd(0),InterInd(0);

If Type=0 Then
InterInd=Close of Data(InterSet)-CLose[LenInt] of Data(InterSet);

If Type=1 Then
InterInd=Close of Data(InterSet)-Average(CLose of Data(InterSet),LenInt);

if Relate=1 then
If InterInd > 0 and MarkInd CROSSES BELOW 0 and LSB>=0 then
Buy("GO--Long") Next Bar at open;
If InterInd < 0 and MarkInd CROSSES ABOVE 0 and LSB<=0 then
Sell Short("GO--Shrt") Next Bar at open;

if Relate=0 then begin
If InterInd<0 and MarkInd CROSSES BELOW 0 and LSB>=0 then
Buy Next Bar at open;
If InterInd>0 and MarkInd CROSSES ABOVE 0 and LSB<=0 then
Sell Short Next Bar at open;

Here the user can actually include more than two data streams on the chart.  The InterSet input allows the user to choose or optimize the secondary market data stream.  Momentum is defined by two types:

  • Type 0:  Intermarket or secondary momentum simply calculated by close of data(2) – close[LenInt] of date(2) and primary momentum calculated by close – close[LenTr]
  • Type 1:   Intermarket or secondary momentum  calculated by close of data(2) – average( close of data2, LenInt)  and primary momentum calculated by close – average(close, LenTr)

The user can also input what type of Relationship: 1 for positive correlation and 0 for negative correlation.  This template can be used to dig deeper into other market relationships.

George’s Modification

I simply forced the primary market to CROSS below/above 0 to initiate a new trade as long the secondary market was pointing in the right direction.

	If InterInd > 0 and MarkInd CROSSES BELOW 0 and LSB>=0 then 
Buy("GO--Long") Next Bar at open;
If InterInd < 0 and MarkInd CROSSES ABOVE 0 and LSB<=0 then
Sell Short("GO--Shrt") Next Bar at open;
Using the keyword CROSSES

This was a one STATE transition and also allowed a protective stop to be used without the strategy automatically re-entering the trade in the same direction.

Thank You Murray – we sure do miss you!

Murray loved to share his research and would want us to carry on with it.  I will write one or two blogs a year in tribute to Murray and his invaluable research.

Using Gradient to Shade Between Plots

Quickly Analyze Market Metrics with Gradient Based Shading

This is a simple indicator but it does involve some semi-advanced topics.  Just to let you know I am working on the third book in the Easing Into EasyLanguage series.  If you haven’t check out the first two, you might just want to head over to amazon and check those out.  This topic falls in the spectrum of the ideas that I will be covering in the Advanced Topics edition.  Also to let you know I just published the 2nd Edition of Trend Following Systems: A DIY Project – Batteries Included.  Check this out if you want to learn some Python and also see some pretty cool Trend Following algorithms – I include EasyLanguage too!

Shading Between Keltner Channels with RSI Intensity

The code that follows demonstrates how to shade between plots and adjust gradient in terms of the RSI reading.  I compiled this with MultiCharts, so I assume it will work there too – just let me know if it doesnt.  I found this code somewhere on the web when researching shading.  If I knew the original author I would definitely give full credit.   The code is rather simple, setting up the chart is just slightly more difficult.  The Keltner Channel was used to define the shading boundaries.  You could have just as easily used Bollinger Bands or anything that provided a range around the market.  Here’s the code.

inputs:  KeltnerLength( 90 ), KeltnerWid( 5 ), RSILength( 14 ), overbought( 70 ), oversold( 30 ); 
var: Avg( 0 ), Shift( 0 ), LowerBand( 0 ), UpperBand( 0 ), MyRSI( 0 ) ;

// Keltner

Avg = AverageFC( c, KeltnerLength ) ;
Shift = KeltnerWid * AvgTrueRange( Keltnerlength ) ;
UpperBand = Avg + Shift ;
LowerBand = Avg - Shift ;

Plot11( UpperBand, "UpperBand" ) ;
Plot12( LowerBand, "LowerBand" ) ;
Plot13( Avg, "MidLine" ) ;

// RSI

MyRSI = xaverage(RSI( c, RSILength ), 7) ;

var: projrsi(0);

// Get projected RSI in terms of the Upper and Lower Bands

projrsi = Avg + .01 * (UpperBand - LowerBand) * (MyRSI - 50) * 2.5;
if false then plot14( projrsi, "RSI" );

// Gradient background

var: barspacing( getappinfo( aibarspacing ) );
var: gradcolr(0);
// Remember how to use the IFF function?
gradcolr = iff( MyRSI > 50, GradientColor( projrsi, Avg, UpperBand, black, red),
GradientColor(projrsi, LowerBand, Avg, green, black) );

plot91( UpperBand, "ugrad", gradcolr, default, barspacing);
plot92( LowerBand, "lgrad");

// Show Bar - increase transparency of data to 100% so
// shading does not overlap the bar charts

plot4( c, "c");
plot5( h, "h");
plot6( l, "l");
Code to Shade with Gradient Based on a RSI Reading

That is a little bit of code that does a lot of work.  Here are the key lines and their explanations.

projrsi = Avg + .01 * (UpperBand – LowerBand) * (MyRSI – 50) * 2.5;

Remember the RSI outputs values between 0 and 100 – oscillates.  Assume RSI is in oversold territory at 24.

UpperBand = 16273 and LowerBand = 15023 and Avg = 15648

Let’s do the math:

  1. projrsi = 15468 + 0.01 * (16273 – 15023) * (24 – 50) * 2.5
  2. projrsi = 15468 + 0.01 * 1250  * – 26 * 2.5
  3. projrsi = 15468 + 12.5 * -65
  4. projrsi = 15468 – 165
  5. projrsi = 15308

Basically all this math is doing is keeping the RSI reading within the bounds of the Keltner Upper and Lower Channels.  You want a high RSI reading to be near the Upper Channel and a low RSI reading to be near the Lower Channel.   You can change up the formula to make more sense.

projrsi = Avg + (MyRSI – 50)/100 * (UpperBand – LowerBand) * 2.5

I have worked with computer graphics for many years and this is really a very neat formula.  The generic formula to constrain a value within a boundary is;

projrsi = LowerBand + (MyRSI / 100) * (UpperBand – LowerBand)

Here you take the LowerBand and add the percentage of the MyRSI/100 times the range.  This works too.  But the original formula scales or intensifies the RSI reading so you get much wider gradient spectrum.  The AVG is used as the center of gravity and the RSI is converted in terms of the middle 50 line.  A positive number, anything > 50, is then scaled higher in the range and a negative number, anything < 50 is scaled lower in the range.  In other words it makes a prettier and more informative picture.

The other important line in the code is

gradcolr = iff( MyRSI > 50, GradientColor( projrsi, Avg, UpperBand, black, red),
GradientColor(projrsi, LowerBand, Avg, green, black) );

This code uses the IFF function which basically replicates this

If MyRSI > 50 then

     gradColor = GradientColor(projrsi, Avg, UpperBand, black, red)


gradColor = GradientColor(projrsi,Avg,LowerBand,green,black);

GradientColor Function

GradientColor( dValue, dMin, dMax, nFromColor, nToColor )


Returns a specific color from a user defined gradient color range, such as Blue to White


  • dValue = value being passed-in that is within the specified Min/Max range of values
  • dMin = Starting value for the gradient range, where the nFromColor is displayed
  • dMax = Ending value for the gradient range, where the nToColor is displayed
  •  nFromColor = Starting color of the gradient
  • nToColor = Ending color of the gradient

Since the gradient shading will cover up your bars you will need to plot the bars as well.

Chart SetUp

The close should be POINT and the other inputs LINES.

Don’t Forget To Fade Out Your Data Chart

That’s it.  Like I stated earlier – I will be including things like this in the Advanced Topics edition.  I should have it wrapped sometime in July or August.