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Using a Dictionary to Create a Trading System

Dictionary Recap

Last month’s post on using the elcollections dictionary was a little thin so I wanted to elaborate on it and also develop a trading system around the best patterns that are stored in the dictionary.  The concept of the dictionary exists in most programming languages and almost all the time uses the (key)–>value model.  Just like a regular dictionary a word or a key has a unique definition or value.  In the last post, we stored the cumulative 3-day rate of return in keys that looked like “+ + – –” or “+ – – +“.  We will build off this and create a trading system that finds the best pattern historically based on average return.  Since its rather difficult to store serial data in a Dictionary I chose to use Wilder’s smoothing average function.

Ideally, I Would Have Liked to Use a Nested Dictionary

Initially, I played around with the idea of the pattern key pointing to another dictionary that contained not only the cumulative return but also the frequency that each pattern hit up.  A dictionary is designed to have  unique key–> to one value paradigm.  Remember the keys are strings.  I wanted to have unique key–> to multiple values. And you can do this but it’s rather complicated.  If someone wants to do this and share, that would be great.  AndroidMarvin has written an excellent manual on OOEL and it can be found on the TradeStation forums.  

Ended Up Using A Dictionary With 2*Keys Plus an Array

So I didn’t want to take the time to figure out the nested dictionary approach or a vector of dictionaries – it gets deep quick.  So following the dictionary paradigm I came up with the idea that words have synonyms and those definitions are related to the original word.  So in addition to having keys like “+ + – -” or “- – + -” I added keys like “0”, “1” or “15”.  For every  + or – key string there exists a parallel key like “0” or “15”.  Here is what it looks like:

–  –  –  –  = “0”
– – – + = “1”
– – + – = “2”

You can probably see the pattern here.  Every “+” represents a 1 and every “0” represent 0 in a binary-based numbering system.  In the + or – key I store the last value of Wilders average and in the numeric string equivalent, I store the frequency of the pattern.

Converting String Keys to Numbers [Back and Forth]

To use this pattern mapping I had to be able to convert the “++–” to a number and then to a string.  I used the numeric string representation as a dictionary key and the number as an index into an array that store the pattern frequency.  Here is the method I used for this conversion.  Remember a method is just a function local to the analysis technique it is written.

//Lets convert the string to unique number
method int convertPatternString2Num(string pattString)
Vars: int pattLen, int idx, int pattNumber;
begin
pattLen = strLen(pattString);
pattNumber = 0;
For idx = pattLen-1 downto 0
Begin
If MidStr(pattString,pattLen-idx,1) = "+" then pattNumber = pattNumber + power(2,idx);
end;
Return (pattNumber);
end;
String Pattern to Number

This is a simple method that parses the string from left to right and if there is a “+” it is raised to the power(2,idx) where idx is the location of “+” in the string.  So “+  +  –  –  ” turns out to be 8 + 4 + 0 + 0 or 12.

Once I retrieve the number I used it to index into my array and increment the frequency count by one.  And then store the frequency count in the correct slot in the dictionary.

patternNumber = convertPatternString2Num(patternString); 
//Keep track of pattern hits
patternCountArray[patternNumber] = patternCountArray[patternNumber] + 1;
//Convert pattern number to a string do use as a Dictionary Key
patternStringNum = numToStr(patternNumber,2);
//Populate the pattern number string key with the number of hits
patternDict[patternStringNum] = patternCountArray[patternNumber] astype double;
Store Value In Array and Dictionary

Calculating Wilder’s Average Return and Storing in Dictionary

Once I have stored an instance of each pattern [16] and the frequency of each pattern[16] I calculate the average return of each pattern and store that in the dictionary as well.

//Calculate the percentage change after the displaced pattern hits
Value1 = (c - c[2])/c[2]*100;
//Populate the dictionary with 4 ("++--") day pattern and the percent change
if patternDict.Contains(patternString) then
Begin
patternDict[patternString] = (patternDict[patternString] astype double *
(patternDict[patternStringNum] astype double - 1.00) + Value1) / patternDict[patternStringNum] astype double;
end
Else
begin
patternDict[patternString] = value1;
// print("Initiating: ",patternDict[patternString] astype double);
end;
(pAvg * (N-1) + return) / N

When you extract a value from a collection you must us an identifier to expresses its data type or you will get an error message : patternDict[patternString] holds a double value {a real number}  as well as patternDict[patternStringNum] – so I have to use the keyword asType.  Once I do my calculation I ram the new value right back into the dictionary in the exact same slot.  If the pattern string is not in the dictionary (first time), then the Else statement inserts the initial three-day rate of return.

Sort Through All of The Patterns and Find the Best!

The values in a dictionary are stored in alphabetic order and the string patterns are arranged in the first 16 keys.  So I loop through those first sixteen keys and extract the highest return value as the “best pattern.”

//  get the best pattern that produces the best average 3 bar return
vars: hiPattRet(0),bestPattString("");
If patternDict.Count > 29 then
Begin
index = patternDict.Keys;
values = patternDict.Values;
hiPattRet = 0;
For iCnt = 0 to 15
Begin
If values[iCnt] astype double > hiPattRet then
Begin
hiPattRet = values[iCnt] astype double ;
bestPattString = index[iCnt] astype string;
end;
end;
// print(Date," BestPattString ",bestPattString," ",hiPattRet:8:4," CurrPattString ",currPattString);
end;
Extract Best Pattern From All History

If Today’s Pattern Matches the Best Then Take the Trade

// if the current pattern matches the best pattern then bar next bar at open
If currPattString = BestPattString then buy next bar at open;
// cover in three days
If barsSinceEntry > 2 then sell next bar at open;
Does Today Match the Best Pattern?

If today matches the best pattern then buy and cover after the second day.

Conclusion

I didn’t know if this code was worth proffering up but I decided to posit it because it contained a plethora of programming concepts: dictionary, method, string manipulation, and array.  I am sure there is a much better way to write this code but at least this gets the point across.

Contents of Dictionary at End of Run

++++    0.06
+++- -0.08
++-+ 0.12
++-- -0.18
+-++ 0.08
+-+- 0.40
+--+ -0.46
+--- 0.34
-+++ 0.20
-++- 0.10
-+-+ 0.23
-+-- 0.31
--++ 0.02
--+- 0.07
---+ 0.22
---- 0.46
0.00 103.00
1.00 128.00
10.00 167.00
11.00 182.00
12.00 146.00
13.00 168.00
14.00 163.00
15.00 212.00
2.00 157.00
3.00 133.00
4.00 143.00
5.00 181.00
6.00 151.00
7.00 163.00
8.00 128.00
9.00 161.00
Contents of Dictionary

Example of Trades

Pattern Dictionary System

 

Code in Universum

//Dictionary based trading sytem
//Store pattern return
//Store pattern frequency
// by George Pruitt
Using elsystem.collections;

vars: string keystring("");
vars: dictionary patternDict(NULL),vector index(null), vector values(null);
array: patternCountArray[100](0);

input: patternTests(8);

var: patternTest(""),tempString(""),patternString(""),patternStringNum("");
var: patternNumber(0);
var: iCnt(0),jCnt(0);
//Lets convert the string to unique number
method int convertPatternString2Num(string pattString)
Vars: int pattLen, int idx, int pattNumber;
begin
pattLen = strLen(pattString);
pattNumber = 0;
For idx = pattLen-1 downto 0
Begin
If MidStr(pattString,pattLen-idx,1) = "+" then pattNumber = pattNumber + power(2,idx);
end;
Return (pattNumber);
end;


once begin
clearprintlog;
patternDict = new dictionary;
index = new vector;
values = new vector;
end;

//Convert 4 day pattern displaced by 2 days
patternString = "";
for iCnt = 5 downto 2
begin
if(close[iCnt]> close[iCnt+1]) then
begin
patternString = patternString + "+";
end
else
begin
patternString = patternString + "-";
end;
end;

//What is the current 4 day pattern
vars: currPattString("");
currPattString = "";

for iCnt = 3 downto 0
begin
if(close[iCnt]> close[iCnt+1]) then
begin
currPattString = currPattString + "+";
end
else
begin
currPattString = currPattString + "-";
end;
end;

//Get displaced pattern number
patternNumber = convertPatternString2Num(patternString);
//Keep track of pattern hits
patternCountArray[patternNumber] = patternCountArray[patternNumber] + 1;
//Convert pattern number to a string do use as a Dictionary Key
patternStringNum = numToStr(patternNumber,2);
//Populate the pattern number string key with the number of hits
patternDict[patternStringNum] = patternCountArray[patternNumber] astype double;
//Calculate the percentage change after the displaced pattern hits
Value1 = (c - c[2])/c[2]*100;
//Populate the dictionary with 4 ("++--") day pattern and the percent change
if patternDict.Contains(patternString) then
Begin
patternDict[patternString] = (patternDict[patternString] astype double *
(patternDict[patternStringNum] astype double - 1.00) + Value1) / patternDict[patternStringNum] astype double;
end
Else
begin
patternDict[patternString] = value1;
// print("Initiating: ",patternDict[patternString] astype double);
end;
// get the best pattern that produces the best average 3 bar return
vars: hiPattRet(0),bestPattString("");
If patternDict.Count > 29 then
Begin
index = patternDict.Keys;
values = patternDict.Values;
hiPattRet = 0;
For iCnt = 0 to 15
Begin
If values[iCnt] astype double > hiPattRet then
Begin
hiPattRet = values[iCnt] astype double ;
bestPattString = index[iCnt] astype string;
end;
end;
// print(Date," BestPattString ",bestPattString," ",hiPattRet:8:4," CurrPattString ",currPattString);
end;
// if the current pattern matches the best pattern then bar next bar at open
If currPattString = BestPattString then buy next bar at open;
// cover in three days
If barsSinceEntry > 2 then sell next bar at open;
Pattern Dictionary Part II

 

 

 

Multi-Time Frame – Using Built-in Indicators and Multi Data Charts

A reader of this blog wanted to be able to use different time frames and some built-in indicators and output the information in a similar fashion as I did in the original MTF post.  There are numerous ways to program this but the two easiest are to use data structures such as arrays or vectors or use TradeStation’s own multi data inputs.  The more complicated of the two would be to use arrays and stay compliant with Multicharts.  Or in that same vein use vectors and not stay compliant with Multicharts.  I chose, for this post, the down and dirty yet compliant method.  [NOTE HERE! When I started this post I didn’t realize it was going to take the turn I ended up with.  Read thoroughly before playing around with the code to see that it is what you are really, really looking for.]  I created a multi data chart with five-time frames: 5,10,15,30 and 60 minutes.  I then hid data2 thru data5.  I created an MTF indicator that plots the relationship of the five time frames applied to the ADX indicator with length 14.  If the ADX > 20 then the plot will be green else it will be red.  If all plots align, then the composite plot will reflect the alignment color.

Using the MTF indicator with ADX
{EasyLanguage MultiTime Frame Indicator)
written by George Pruitt - copyright 2019 by George Pruitt
}


Inputs:adxLen(14),adxTrendVall(20);

vars: adxData1(0),adxData2(0),adxData3(0),adxData4(0),adxData5(0);


If barNumber > 1 then
Begin

adxData1 = adx(adxLen) of data1;
adxData2 = adx(adxLen) of data2;
adxData3 = adx(adxLen) of data3;
adxData4 = adx(adxLen) of data4;
adxData5 = adx(adxLen) of data5;

Condition10 = adxData1 > adxTrendVall;
Condition11 = adxData2 > adxTrendVall;
Condition12 = adxData3 > adxTrendVall;
Condition13 = adxData4 > adxTrendVall;
Condition14 = adxData5 > adxTrendVall;

If condition10 then setPlotColor(1,Green) else SetPlotColor(1,Red);
If condition11 then setPlotColor(2,Green) else SetPlotColor(2,Red);
If condition12 then setPlotColor(3,Green) else SetPlotColor(3,Red);
If condition13 then setPlotColor(4,Green) else SetPlotColor(4,Red);
If condition14 then setPlotColor(5,Green) else SetPlotColor(5,Red);

condition6 = condition10 and condition11 and condition12 and condition13 and condition14;
Condition7 = not(condition10) and not(condition11) and not(condition12) and not(condition13) and not(condition14);

If condition6 then setPlotColor(7,Green);
If condition7 then setPlotColor(7,Red);

If condition6 or condition7 then plot7(7,"trend");

Plot6(5,"line");
Plot1(4,"t1");
Plot2(3,"t2");
Plot3(2,"t3");
Plot4(1,"t4");
Plot5(0,"t5");

end;
MTF with 5 data streams and ADX

This code is very similar to the original MTF indicator, but here I simply pass a pointer to the different time frames to the ADX function.  Since the ADX function only requires a length input I had assumed I could use the following format to get the result for each individual time frame:

adxData1 = adx(14) of data1;

adxData2 = adx(14) of data2;

This assumption worked out.

But are we really getting what we really, really want?  I might be putting too much thought into this but of the five-time frame indicator dots, only the 5-minute will change on a 5-minute basis.  The 10-min dot will stay the same for two 5-min bars.  The dots will reflect the closing of the PRIOR time frame and the current 5-min bar is ignored in the calculation.  This may be what you want, I will leave that up to you.  Here is an illustration of the delay in the different time frames.

So when you look at each dot color remember to say to yourself – this is the result of the prior respective time frame’s closing price.  You can say to yourself, “Okay this is the ADX of the current 5-minute bar and this is the ADX of the prior 10-minute close and this is the ADX of the prior 15 minutes close and so on and so on.   We all know that the last 5 minutes will change all of the time frames closing tick, but it may or may not change the price extremes of those larger time frames.   I will show you how to do this in the next post.   If you want to see the impact of the last 5- minutes, then you must build your bars internally and dynamically.

 

Calculating Position Size with Optimal F

I had a reader of the blog ask how to use Optimal F.  That was really a great question.  A few posts back I provided the OptimalFGeo function but didn’t demonstrate on how to use it for allocation purposes.  In this post, I will do just that.

I Have Optimal F – Now What?

From Ralph Vince’s book, “Portfolio Management Formulas”, he states: “Once the highest f is found, it can readily be turned into a dollar amount by dividing the biggest loss by the negative optimal f.  For example, if our biggest loss is $100 and our optimal f is 0.25, then -$100/ 0.25 = $400.  In other words, we should bet 1 unit for every $400 we have in our stake.”

Convert Optimal F to dollars and then to number of shares

In my example strategy, I start out with an initial capital of $50,000 and allow reinvestment of profit or loss.  The protective stop is set as 3 X ATR(10).  A fixed $2000 profit objective is also utilized.  The conversion form Optimal F to position size is illustrated by the following lines of code:

//keep track of biggest loss
biggestLoss = minList(positionProfit(1),biggestLoss);
//calculate the Optimal F with last 10 trades.
OptF = OptimalFGeo(10);
//reinvest profit or loss
risk$ = initCapital$ + netProfit;
//convert Optimal F to $$$
if OptF <> 0 then numShares = risk$ / (biggestLoss / (-1*OptF));
Code snippet - Optimal F to Position Size
  1. Keep track of biggest loss
  2. Calculate optimal F with OptimalFGeo function – minimum 10 trades
  3. Calculate Risk$ by adding InitCapital to current NetProfit (Easylanguage keyword)
  4. Calculate position size by dividing Risk$  by the quotient of biggest loss and (-1) Optimal F

I applied the Optimal F position sizing to a simple mean reversion algorithm where you buy on a break out in the direction of the 50-day moving average after a lower low occurs.

Code listing:

vars: numShares(0),initCapital$(50000),biggestLoss(0),OptF(0),risk$(0);


//keep track of biggest loss
biggestLoss = minList(positionProfit(1),biggestLoss);
//calculate the Optimal F with last 10 trades.
OptF = OptimalFGeo(10);
//reinvest profit or loss
risk$ = initCapital$ + netProfit;
//convert Optimal F to $$$
if OptF <> 0 then numShares = risk$ / (biggestLoss / (-1*OptF));
numShares = maxList(1,numShares);
//if Optf <> 0 then print(d," ",t," ",risk$ / (biggestLoss / (-1*OptF))," ",biggestLoss," ",optF);

if c > average(c,50) and low < low[1] then Buy numShares shares next bar at open + .25* range stop;

setStopPosition;
setProfitTarget(2000);

setStopLoss(3*avgTrueRange(10)*bigPointValue);
Strategy Using Optimal F

I have included the results below.  At one time during the testing the number of contracts jumped up to 23.  That is 23 mini Nasdaq futures ($20 * 7,300) * 23.  That’s a lot of leverage and risk.  Optimal doesn’t  always mean the best risk mitigation.  Please let me know if you find any errors in the code or in the logic.

 

Here is the ELD that incorporates the Strategy and the Function.USINGOPTIMALF

 

A Bar Scoring System – inspired by Keith Fitschen

In Keith’s wonderful book, “Building Reliable Trading Sytems”, he reveals several algorithms that classify an instruments’ movement potential.  In the part of the book that is titled Scoring by a Bar Type Criterion, he describes eight different two-day patterns that involve 3 different criteriaEight different Bar-Types

He looks at the relationship between today’s open and today’s close, today’s close and yesterday’s close, and today’s close in terms of the day’s range.  Bar-Types 1 to 4 all have the close of today >= close of yesterday.  Bar-Types 5 to 8 have close of today < close of yesterday.

I wanted to program this into my TradeStation and do some research to see if the concept is valid.  In his book, Keith tested a lot of different stocks and commodities.  In this post, I just test the ES, US, and Beans.  This form of research can be used to enhance an existing entry technique.

Here is how I defined the eight different bar types:

array : barTypeArray[8](false); 

midRange = (h + l)/2;

barTypeArray[0] = c >= c[1] and c > o and c >= midRange;
barTypeArray[1] = c >= c[1] and c > o and c < midRange;
barTypeArray[2] = c >= c[1] and c < o and c >= midRange;
barTypeArray[3] = c >= c[1] and c < o and c < midRange;
barTypeArray[4] = c < c[1] and c > o and c >= midRange;
barTypeArray[5] = c < c[1] and c > o and c < midRange;
barTypeArray[6] = c < c[1] and c < o and c >= midRange;
barTypeArray[7] = c < c[1] and c < o and c <= midRange;
Defining Eight Different Bar Types

I used a brute force approach by creating an 8-element array of boolean values.  Remember EasyLanguage uses a 0 index.  If the two -day pattern matches one of the eight criteria I assign the element a true value.  If it doesn’t match then a false value is assigned.  I use an input value to tell the computer which pattern I am looking for.  If I choose Bar-Type[0] and there is a true value in that array element then I take a trade.   By providing this input I can optimize over all the different Bar-Types.

Input : 
BarTypeNumber(0), // which bar type
buyOrSell(1), //1 to buy 2 to sell
numDaysToHold(2); //how many days to hold position




For cnt = 0 to 7 //remember to start at 0
Begin
If barTypeArray[cnt] = true then whichBarType = cnt;
end;

If whichBarType = BarTypeNumber then
begin
if buyOrSell = 1 then buy this bar on close;
if buyOrSell = 2 then sellshort this bar on close;
end;
Loop Thru Array to find Bar Type

Here are some results of looping through all eight Bar-Types, Buy and Sell, and holding from 1 to 5 days.

ES – ten – year results – remember these are hypothetical results with no commission or slippage.

Here’s what the equity curve looks like.   Wild swings lately!!

Beans:

Bonds

Keith was right – look at the Bar Category that bubbled to the top every time – the most counter-trend pattern.  My Bar-Type Number 7  is the same as Keith’s 8.  Here is the code in its entirety.

{Bar Scoring by Keith Fitschen
from his book "Building Reliable Trading Systems" 2013 Wiley}

Input : BarTypeNumber(0),
buyOrSell(1),
numDaysToHold(2);

vars: midRange(0);
array : barTypeArray[8](false);

midRange = (h + l)/2;

barTypeArray[0] = c >= c[1] and c > o and c >= midRange;
barTypeArray[1] = c >= c[1] and c > o and c < midRange;
barTypeArray[2] = c >= c[1] and c < o and c >= midRange;
barTypeArray[3] = c >= c[1] and c < o and c < midRange;
barTypeArray[4] = c < c[1] and c > o and c >= midRange;
barTypeArray[5] = c < c[1] and c > o and c < midRange;
barTypeArray[6] = c < c[1] and c < o and c >= midRange;
barTypeArray[7] = c < c[1] and c < o and c <= midRange;

vars: whichBarType(0),cnt(0);

For cnt = 0 to 7
Begin
If barTypeArray[cnt] = true then whichBarType = cnt;
end;

If whichBarType = BarTypeNumber then
begin
if buyOrSell = 1 then buy this bar on close;
if buyOrSell = 2 then sellshort this bar on close;
end;

If barsSinceEntry = numDaysToHold then
begin
If marketPosition = 1 then sell this bar on close;
If marketPosition =-1 then buytocover this bar on close;
end;
Bar Scoring Example

Keith’s book is very well researched and written.  Pick one up if you can find one under $500.  I am not kidding.  Check out Amazon.

 

 

What’s Our Vector Victor – Tiptoeing in the EL Collections

Tired of Manipulating Arrays – Try a Vector and a Queue

Vectors:

An array like structure but are dynamic and have a plethora of tools at your disposal.  Arrays are cool and can be multi-dimensional and can be easily manipulated.  But they require a lot of forethought as to how much size to reserve for their implementation.  Now don’t think this is going to be an advanced EasyLanguage tutorial, because it’s really not.  Most of us TRS-80, Ti-99/4A, Vic-20 and Commodore 64 trained programmers of the early ’80s have not welcomed objects with open arms and that is really a mistake.  In this sense we are like cavemen – we have all of the rudimentary tools at our disposal and can create some really cool stuff and we can really understand what we are doing.  With time and effort, we can get to the same place as object-oriented programmers.  We just don’t like the concept of using other’s tools as much as we like using ours.  So if you aren’t classically trained in programming you may have an advantage when tieing into the objects of a programming language.  This little tutorial is a very brief glimpse into a whole different world of programming.  The beauty is you can combine “old school” programming with objects – even if you don’t understand how the objects are truly constructed.    I want to introduce the concept of the Vector and the Queue-  truly cool Swiss Army knives.  First the vector.  Let’s just jump into some of the code – it really is simple.

Object Instantiation – a long word for declaring variable:
Using elsystem.collections;

Vars: Vector opVector(NULL),
Vector hiVector(Null),
Vector loVector(Null),
Vector clVector(Null),
Vector barVector(Null),
Queue timeStampQue(Null);
Once
Begin
barVector = new Vector;
opVector = new Vector;
hiVector = new Vector;
loVector = new Vector;
clVector = new Vector;
timeStampQue = new Queue;
end;
Instantiating and Declaring Vectors and Queue

You have to tell EasyLanguage you want to use some of the tools in the elsystem.collections.  You do this by simply tell it you are Using elsystem.collections.  The word collections is a catch-all for a bunch of different types of data structures.  Remember data structures are just programming constructs used to hold data – like an array.  All the variables that you declare in EasyLanguage are arrays – you just aren’t really aware of it.   When you index into them to get prior values then you become slightly aware of it.  In this portion of code, I create five vectors and one queue and assign them the Null or an empty value.  I just finished a programming gig where I had to build dynamically sized bars from the base data.  Kind of like creating 15, 30, 60-minute bars from a 5-minute bar chart or stream.   I did this using arrays because I wanted to be able to index into them to go back in time and I didn’t how far I wanted to go back.  So I declared some arrays with large dimensions to be safe.  This really takes a bite out of your resources which costs space and time.  I had played with Vector like objects in Python, so I thought I would post about them here and show how cool they are.  Remember this is a rudimentary program and could be streamlined and cleaned up.  Each vector will store their respective time, open, high, low and close values of the combined bar.  In a later post, I would like to do this with a Dictionary.  So the opVector will hold the open price, the hiVector will hold the high price and so on.

Build a Queue – why the extra ue?

I want to build 15-minute bars from 5-minute bars so I need to know when to sample the data to properly collect the corresponding data.  If I start at 9:30 then I want to sample the data at 9:45 and look back three bars to get the open and the highest high and the lowest low.  The close will simply be the close of the 9:45 bar.  I want to do this at 9:45, 10:00. 10:15 and so on.  I could manipulate the time and use the modulus function to see if the minutes are multiples of 15 and I tried this but it didn’t work too well.  So I thought since I was already in the collections why not build a list or a queue with all the timestamps I would need.  This is how I did it.

vars: hrs(0),mins(0),barMult(3),combBarTimeInterval(0),totBarsInHour(0),startTime(930),endTime(1615),cnt(0);

Once
Begin
mins = fracPortion(t/100);
combBarTimeInterval = barInterval*barMult;
While value1 < endTime
Begin
cnt = cnt + 1;
Value1 = calcTime(startTime,cnt*combBarTimeInterval);
// print("Inside queue : ",Value1," ",cnt*combBarTimeInterval);
timeStampQue.Enqueue(Value1);
end;
end;
Populating A Queue With Time Stamps

I simply use the CalcTime function to add 15-minute intervals to the start time and then I add them to the queue:  timeStampQue.Enqueue(Value1);  You access the methods or tools to a class by using the dot (” . “) notation.  Once I instantiated or created the timeStampQue I gained access to all the tools that belong to that object.  The Enqueue method simply appends the list the value that you pass it.  I would have preferred the method to be labeled simply add.  How did I figure out the right method name you ask?  I accessed the Dictionary from the View menu in the TDE.  Here is a picture to help:

Dictionary:

I use the keyword Once to just execute the code one time.  You could have said if BarNumber = 1, but why not use the tools at your disposal,   I figured out the combBarTimeInterval by using the 5-minute bar multiplier (3).  I then looped from startTime to endTime in 15-minute intervals and stored the timeStamps in the queue.  So every time stamp I need is in the timeStampQue.  All I need now is to compare the time of the 5-minute bar to the time stamps inside the queue.  This is where using object really come in handy.

Queue Methods:

Old school would have looped through all of the elements in the list and compared them to the value I was seeking and if found it would return true.  In the object world, I can simply ask the object itself to see if the value is in it:

condition1 = timeStampQue.Contains(t);

Cool!  If condition1 is true then I know I am sitting on the 5-minute bar that shares the same timestamp as a 15-minute bar.  If the time stamps are the same then I can start building the large timeframe from the lower timeframe.  You add elements to a vector by using the insert method.  I simply looked it up in the dictionary.   I had to specify where to insert the value in the vector.  I simply inserted each value into the [0] location.  Remember we are inserting so everything else in the vector is moved down.

Vector Methods:

 

If condition1 then
Begin
barVector.insert(0,t);
opVector.insert(0,open[2]);
hiVector.insert(0,highest(h[0],3));
loVector.insert(0,lowest(l[0],3));
clVector.insert(0,close[0]);
end;
Inserting Values at Vector Location 0

I only need to keep track of the last 10 15-minute bars, so once the vector count exceeded 10, I simply popped off the value at the back end – pop_back().  I figured this out by looking at Martin Whittaker’s awesome website – www.markplex.com. 

 

If opVector.Count > 10 then 
begin
barVector.pop_back();
opVector.pop_back();
hiVector.pop_back();
loVector.pop_back();
clVector.pop_back();
end;
Popping the Back-End

To check my work I printed the 15-minute bars on each 5-minute bar to make sure the bars were being built properly.  These data structures expect an object to be inserted, added, popped so when you print out one of their values you have to tell the print statement what the object should be translated as.  Here the keyword asType comes into play.  Take a look at my code, and you will see what I mean.  I hope this gets you excited about objects because the collections class can save you a ton of time and is really cool.  Use it and you can brag that you are an OOP programmer at your next cocktail party.

Code Listing:
Using elsystem.collections;

Vars: Vector opVector(NULL),
Vector hiVector(Null),
Vector loVector(Null),
Vector clVector(Null),
Vector barVector(Null),
Queue timeStampQue(Null);
Once
Begin
barVector = new Vector;
opVector = new Vector;
hiVector = new Vector;
loVector = new Vector;
clVector = new Vector;
timeStampQue = new Queue;
end;

vars: hrs(0),mins(0),barMult(3),combBarTimeInterval(0),totBarsInHour(0),startTime(930),endTime(1615),cnt(0);

Once
Begin
mins = fracPortion(t/100);
combBarTimeInterval = barInterval*barMult;
While value1 < endTime
Begin
cnt = cnt + 1;
Value1 = calcTime(startTime,cnt*combBarTimeInterval);
// print("Inside queue : ",Value1," ",cnt*combBarTimeInterval);
timeStampQue.Enqueue(Value1);
end;
end;



condition1 = timeStampQue.Contains(t);

Print(d," ",t," ",condition1);

If condition1 then
Begin
barVector.insert(0,t);
opVector.insert(0,open[2]);
hiVector.insert(0,highest(h[0],3));
loVector.insert(0,lowest(l[0],3));
clVector.insert(0,close[0]);
end;

If opVector.Count > 10 then
begin
barVector.pop_back();
opVector.pop_back();
hiVector.pop_back();
loVector.pop_back();
clVector.pop_back();
end;

vars:vectCnt(0);
print(d," ",t);
If opVector.Count > 9 then
Begin
For vectCnt = 0 to 9
begin
print(vectCnt," ",barVector.at(vectCnt) astype int," ",opVector.at(vectCnt) astype double," ",hiVector.at(vectCnt) astype double," ",loVector.at(vectCnt) astype double," ",clVector.at(vectCnt) astype double);
end;
end;
Program in its Entirety

 

George’s EasyLanguage BarsSince Function – How Many Bars Since?

BarsSince Function in EasyLanguage

Have you ever wondered how many bars have transpired since a certain condition was met?  Some platforms provide this capability:

If ExitFlag and (c crosses above average within 3 bars) then

TradeStation provides the MRO (Most Recent Occurrence) function that provides a very similar capability.  The only problem with this function is that it returns a -1 if the criteria are not met within the user provided lookback window.  If you say:

myBarsSinceCond = MRO(c crosses average(c,200),20,1) < 3

And c hasn’t crossed the 200-day moving average within the past twenty days the condition is still set to true because the function returns a -1.

I have created a function named BarsSince and you can set the false value to any value you wish.  In the aforementioned example, you would want the function to return a large number so the function would provide the correct solution.  Here’s how I did it:

inputs: 
Test( truefalseseries ),
Length( numericsimple ),
Instance( numericsimple ) , { 0 < Instance <= Length}
FalseReturnValue(numericsimple); {Return value if not found in length window}

value1 = RecentOcc( Test, Length, Instance, 1 ) ;
If value1 = -1 then
BarsSince = FalseReturnValue
Else
BarsSince = value1;
BarsSince Function Source Code

And here’s a strategy that uses the function:

inputs: profTarg$(2000),protStop$(1000),
rsiOBVal(60),rsiOSVal(40),slowAvgLen(100),
fastAvgLen(9),rsiLen(14),barsSinceMax(3);

Value1 = BarsSince(rsi(c,rsiLen) crosses above rsiOSVal,rsiLen,1,999);
Value2 = BarsSince(rsi(c,rsiLen) crosses below rsiOBVal,rsiLen,1,999);

If c > average(c, slowAvgLen) and c < average(c,fastAvgLen) and Value1 <barsSinceMax then buy next bar at open;

If c < average(c, slowAvgLen) and c > average(c,fastAvgLen) and Value2 <barsSinceMax then sellshort next bar at open;

setStopLoss(protStop$);
setProfitTarget(profTarg$)
Strategy Utilizing BarsSince Function

The function requires four arguments:

  1. The condition that is being tested [e.g.  rsi > crosses above 30]
  2. The lookback window [rsiLen – 14 bars in this case]
  3. Which occurrence [1 – most recent; 2- next most recent; etc…]
  4. False return value [999 in this case; if condition is not met in time]

A Simple Mean Reversion Using the Function:

Here are the results of this simple system utilizing the function.

Optimization Results:

I came up with this curve through a Genetic Optimization:

The BarsSince function adds flexibility or fuzziness when you want to test a condition but want to allow it to have a day (bar) or two tolerance.  In a more in-depth analysis, the best results very rarely occurred on the day the RSI crossed a boundary.   Email me with questions of course.

 

 

An ES Day Trading Model Explained – Part 2

This is a continuation post or Part 2 of the development of the ES day trading system with EasyLanguage.

If you can understand this model you can basically program any of your day trading ideas.

Inputs Again:

First I want to revisit our list of inputs and make a couple of changes before proceeding.

inputs: volCalcLen(10),orboBuyPer(.2),orboSellPer(.2); 
inputs: volStopPer(.7),Stop$(500);
inputs: volThreshPer(.3),ProfThresh$(250);
inputs: volTrailPer(.2),Trail$(200);
inputs: endTradingTime(1500);
Modification to our inputs

If we want to optimize these values then we can’t use the keyword bigPointValue in the input variable default value.  So I removed them – also I added an input endTradingTime(1500).  I wanted to cut off our trading at a given time – no use entering a trade five minutes prior to the closing.

Disengage the Vol or $Dollar Trade Management:

I may have muddied the waters a little with having volatility and $ values simultaneously.  You can use either for the initial protective stop, profit threshold, and trailing stop amount.  You can disengage them by using large values.  If you want to ignore all the $ inputs just add a couple of 00 to each of the values:

Stop$(50000), ProfThres$(25000),Trail$(50000)

If you want to ignore the volatility trade management stops just put a large number in front of the decimal.

volStopPer(9.7), volThreshPer(9.3), volTrailPer(9.2)

If you make either set large then the algorithm will use the values closest to the current market price.

Computations:

Let’s now take a look at the computations that are done on the first bar of the day:

If d <> d[1] then
Begin
rangeSum = 0.0; // range calculation for entry
For iCnt = 1 to volCalcLen
Begin
rangeSum = rangeSum + (highD(iCnt) - lowD(iCnt));
end;
vol = rangeSum/volCalcLen;
buyPoint = openD(0) + vol*orboBuyPer;
sellPoint = openD(0) - vol*orboSellPer;

longStopAmt = vol * volStopPer;
longStopAmt = minList(longStopAmt,Stop$/bigPointValue);

shortStopAmt = vol *volStopPer;
shortStopAmt = minList(shortStopAmt,Stop$/bigPointValue);

longThreshAmt = vol * volThreshPer;
longThreshAmt = minList(longThreshAmt,ProfThresh$/bigPointValue);

shortThreshAmt = vol * volThreshPer;
shortThreshAmt = minList(shortThreshAmt,ProfThresh$/bigPointValue);

longTrailAmt = vol * volTrailPer;
longTrailAmt = minList(longTrailAmt,Trail$/bigPointValue);

shortTrailAmt = vol * volTrailPer;
shortTrailAmt = minList(shortTrailAmt,Trail$/bigPointValue);

longTrailLevel = 0;
shortTrailLevel = 999999;
buysToday = 0;
shortsToday = 0;
end;
Once a day computations

I determine it is the first bar of the day by comparing the current 5-minute bar’s date stamp to the prior 5-minute bar’s date stamp.  If they are not the same then you have the first bar of the day.  The first thing I do is calculate the volatility of the current market by using a for-loop to accumulate the day ranges for the past volCalcLen days.  I start the iterative process using the iCnt index and going from 1 back in time to volCalcLen.  I use iCnt to index into the function calls HighD and LowD.  Indexing is not really the right word here – that is more appropriate when working with arrays.  HighD and LowD are functions and we are passing the values 1 to volCalcLen into the functions and summing their output.  When you do this you will get a warning “A series function should not be called more than once with a given set of parameters.”  Sounds scary but it seems to work just fine.  If you don’t do this then you have to include a daily bar on the chart.  I like to keep things as simple as possible.   Once I sum up the daily ranges I then divide by volCalcLen to get the average range over the few days.  All of the vol based variables will use this value.

Entries:

Entry is based off a move away from the opening in terms of volatility.  If we use 0.2 (twenty percent) as orboBuyPer then the algorithm will buy on a stop 20% of the average range above the open tick.  Sell short is just the opposite.   We further calculate the longStopAmt as a function of vol and a pure $ amount.  I am using the minList function to determine the smaller of the two values  .This is how I disengage either the vol value or the $ value.  The other variables are also calculated just once a day: shortStopAmt, longThreshAmt, shortThreshAmt, longTrailAmt, shortTrailAmt. You could calculate every bar but that would be inefficient. I am also resetting four values at the beginning of the day:  longTrailLevel, shortTrailLevel, buysToday and shortsToday.

 

The Mighty MP:

mp = marketPosition;

If mp = 1 and mp[1] <> 1 then buysToday = buysToday + 1;
If mp = -1 and mp[1] <> -1 then shortsToday = shortsToday + 1;
MarketPosition monitoring and determining Buys/Shorts Today

I like using a variable to store each bar’s marketPosition.  In this case I am using MP.  By aliasing the marketPosition function call to a variable allows us to do this:

if mp = 1 and mp[1] <> 1 then buysToday = buysToday + 1;

This little line does a bunch of stuff.  If the current bar’s position is 1 and the prior bars position is not one then we know we have just entered a long position.  So every time this happens throughout the day the buysToday is incremented.  ShortsToday works just the same.  Pitfall warning:  If you strategy enters and exits on the same bar then this functionality will not work!  Neither will the call to the marketPosition function.  It will look like nothing happened.  If you need to keep track of the number of trades make sure you can only enter or exit on different bars.  If you stuff is so tight then drop down to a 1 minute or tick bar.

Controlling the Nmber of Buys/Shorts for the Day:

if time < endTradingTime and buysToday < 1 then Buy("ORBo-B") next bar at buyPoint stop;
if time < endTradingTime and shortsToday < 1 then Sellshort("ORBo-S") next bar at sellPoint stop;


If marketposition = 1 then
Begin
longExitPoint = entryPrice - longStopAmt;
sell("L-Exit") next bar at longExitPoint stop;
end;

If marketposition = -1 then
Begin
shortExitPoint = entryPrice + shortStopAmt;
buyToCover("S-Exit") next bar at shortExitPoint stop;
end;

If marketPosition = 1 and maxContractProfit/bigPointValue >= longThreshAmt then
Begin
longTrailLevel = maxList(highest(h,barsSinceEntry) - longTrailAmt,longTrailLevel);
sell("TrailSell") next bar at longTrailLevel stop;
end;

If marketPosition = -1 and maxContractProfit/bigPointValue >= shortThreshAmt then
Begin
shortTrailLevel = minList(lowest(l,barsSinceEntry) + shortTrailAmt,ShortTraillevel);
buyToCover("TrailCover") next bar at shortTrailLevel stop;
end;


SetExitOnClose;
Controlled trade directives

Notice how I am controlling the trade directives using the if statements.  I only want to enter a long position when the time is right and I haven’t already entered a long position for the day.  If you don’t control the trade directives, then these orders are placed on every bar, in our case every 5-minutes.  If you have pyramiding turned off then once you are long the buy directive is ignored.  This is an important concept – let’s say you just want to buy and short only one time per day trade session.  If you don’t control this directive, then it will fire off an order every five minutes.    You don’t want this -at least I hope you don’t.

So controlling the time and number of entries is paramount.  If you don’t control the time of entry then the day can arrive at the last bar of the day and fire off an order for the opening of the next day.  A big no, no !

Put To Work:

Here are the inputs I used to generate the trades in the graphic that follows.

Not Doing Exactly What You Want:

Here is what most day traders are looking for.   I made a comment on the chart – make sure you read it – it is another pitfall.

The trailing stop had to wait for the bar to complete to determine if the profit reached the threshold.  A little slippage here.  You can overcome this if you use the BuiltIn Percent Trailing Strategy or by using the SetPercentTrailing function call.

However, you lose the ability to really customize your algorithms by using the builtin functionalility.  You could drop down to a one minute bar and probably get out nearer your trailing stop amount.

Download the ELD:

Here you go!

GEODAYTRADERV1.01

An ES Day Trading Model Explained – Part 1

Open Range BreakOut with Trade Management

How difficult is it to program a day trading system using an open range break out, protective stop and a trailing stop?  Let’s see.  I started working on this and it got a little more detailed than I wanted so I have now split it up into two posts.  Part 2 will follow very soon.

What inputs do we need?  How about the number of days used in the volatility calculation?  What percentage of the volatility from the open do you want to buy or sell?  Should we have a different value for buys and sells?  Do we want to use a volatility based protective stop or a fixed dollar?  How about a trailing stop?  Should we wait to get to a certain profit level before engaging the trailing stop?  Should it also be based on volatility or fixed dollar amt?  How much should we trail – again an amount of volatility or fixed dollar?

Proposed inputs:

inputs: volCalcLen(10), orboBuyPer(.2), orboSellPer(.2), volStopPer(.7), $Stop(500), volProfThreshPer(.5), $ProfThresh(250),volTrailPer(.2),$Trail(200);

That should do it for the inputs – we can change later if necessary.

Possible pitfalls:

This is where I will save you some time.  If we use an open range break out entry we must limit the number of entries or TradeStation will continue to execute as long as the price is above our buy level.  You might ask, “That’s what we want -right?”  What if we use a tight stop and we get stopped out of our first position.  Do you want to buy again later in the day?  What if we use a trailing stop and we get out of the market above the buy level.  What will TradeStation do?  It will follow your exact instructions and buy again if you don’t control the number of allowed entries.  Do you want to carry the buy and sell stops overnight for execution on the open of the next day – probably not!  So we not only need to control the number of entries buy we also need to control the time period we can enter a trade.

Calculations:

We need to determine the volatility and a good way do this is calculating the average range over the past N days.   There are two ways to do this: 1) incorporate a daily chart as data2 and use a built-in function for the calculation or 2) use a for-loop and use the built-in functions HighD and LowD and just use one data feed.   Both have their drawbacks.  The first is you need to have a multi-data chart and the second you get a warning that you shouldn’t put a series function call inside the body of a for-loop.   I have done it both ways and I prefer to deal with the warning – so far it has worked out nicely – so let’s go with a single data chart.

Building the code:

Inputs:

Since we are combining a volatility and fixed $ amount in our trade management, you will need to set either the vol or dollar amounts to a high value to disable them.  You can use both but I am taking the smaller of the respective values.

inputs: volCalcLen(10),orboBuyPer(.2),orboSellPer(.2); 
inputs: volStopPer(.7),$Stop(500/bigPointValue);
inputs: volThreshPer(.5),$ProfThresh(250/bigPointValue);
inputs: volTrailPer(.2),$Trail(200/bigPointValue);
Inputs We Will Need - Can Changer Later

Variables:

vars:vol(0),buyPoint(0),sellPoint(0),
longStopAmt(0),shortStopAmt(0),longExitPoint(0),shortExitPoint(0),
longThreshAmt(0),shortThreshAmt(0),
longTrailAmt(0),shortTrailAmt(0),
longTrailLevel(0),shortTrailLevel(0),
hiSinceLong(0),loSinceShort(0),mp(0),
rangeSum(0),iCnt(0),
buysToday(0),shortsToday(0)
Variables That We Might Need

Once A Day Calculations:

Since we will be working with five-minute bars we don’t want to do daily calculations on each bar.  If we do it will slow down the process.  So let’s do these calculation on the first bar of the day only.

If d <> d[1] then
Begin
rangeSum = 0.0; // range calculation for entry
For iCnt = 1 to volCalcLen
Begin
rangeSum = rangeSum + (highD(iCnt) - lowD(iCnt));
end;
vol = rangeSum/volCalcLen;
buyPoint = openD(0) + vol*orboBuyPer;
sellPoint = openD(0) - vol*orboSellPer;

longStopAmt = vol * volStopPer;
longStopAmt = minList(longStopAmt,Stop$);

shortStopAmt = vol *volStopPer;
shortStopAmt = minList(shortStopAmt,Stop$);

longThreshAmt = vol * volThreshPer;
longThreshAmt = minList(longThreshAmt,ProfThresh$);

shortThreshAmt = vol * volThreshPer;
shortThreshAmt = minList(shortThreshAmt,ProfThresh$);

longTrailAmt = vol * volTrailPer;
longTrailAmt = minList(longTrailAmt,Trail$);

shortTrailAmt = vol * volTrailPer;
shortTrailAmt = minList(shortTrailAmt,Trail$);

longTrailLevel = 0;
shortTrailLevel = 999999;
buysToday = 0;
shortsToday = 0;
end;
Do These Just Once A Day

 

For All of You Who Don’t Want To Wait – Beta Version Is Available Below:

In my next post, I will dissect the following code for a better understanding.  Sorry I just ran out of time.

inputs: volCalcLen(10),orboBuyPer(.2),orboSellPer(.2); 
inputs: volStopPer(.7),Stop$(500/bigPointValue);
inputs: volThreshPer(.3),ProfThresh$(250/bigPointValue);
inputs: volTrailPer(.2),Trail$(200/bigPointValue);


vars:vol(0),buyPoint(0),sellPoint(0),
longStopAmt(0),shortStopAmt(0),longExitPoint(0),shortExitPoint(0),
longThreshAmt(0),shortThreshAmt(0),
longTrailAmt(0),shortTrailAmt(0),
longTrailLevel(0),shortTrailLevel(0),
hiSinceLong(0),loSinceShort(0),mp(0),
rangeSum(0),iCnt(0),
buysToday(0),shortsToday(0);

If d <> d[1] then
Begin
rangeSum = 0.0; // range calculation for entry
For iCnt = 1 to volCalcLen
Begin
rangeSum = rangeSum + (highD(iCnt) - lowD(iCnt));
end;
vol = rangeSum/volCalcLen;
buyPoint = openD(0) + vol*orboBuyPer;
sellPoint = openD(0) - vol*orboSellPer;

longStopAmt = vol * volStopPer;
longStopAmt = minList(longStopAmt,Stop$);

shortStopAmt = vol *volStopPer;
shortStopAmt = minList(shortStopAmt,Stop$);

longThreshAmt = vol * volThreshPer;
longThreshAmt = minList(longThreshAmt,ProfThresh$);

shortThreshAmt = vol * volThreshPer;
shortThreshAmt = minList(shortThreshAmt,ProfThresh$);

longTrailAmt = vol * volTrailPer;
longTrailAmt = minList(longTrailAmt,Trail$);

shortTrailAmt = vol * volTrailPer;
shortTrailAmt = minList(shortTrailAmt,Trail$);

longTrailLevel = 0;
shortTrailLevel = 999999;
buysToday = 0;
shortsToday = 0;
end;

mp = marketPosition;

If mp = 1 and mp[1] <> 1 then buysToday = buysToday + 1;
If mp = -1 and mp[1] <> -1 then shortsToday = shortsToday + 1;

if time < sessionendTime(0,1) and buysToday < 1 then Buy("ORBo-B") next bar at buyPoint stop;
if time < sessionendTime(0,1) and shortsToday < 1 then Sellshort("ORBo-S") next bar at sellPoint stop;


If marketposition = 1 then
Begin
longExitPoint = entryPrice - longStopAmt;
sell("L-Exit") next bar at longExitPoint stop;
end;

If marketposition = -1 then
Begin
shortExitPoint = entryPrice + shortStopAmt;
buyToCover("S-Exit") next bar at shortExitPoint stop;
end;

If marketPosition = 1 and maxContractProfit/bigPointValue >= longThreshAmt then
Begin
longTrailLevel = maxList(highest(h,barsSinceEntry) - longTrailAmt,longTrailLevel);
sell("TrailSell") next bar at longTrailLevel stop;
end;

If marketPosition = -1 and maxContractProfit/bigPointValue >= shortThreshAmt then
Begin
shortTrailLevel = minList(lowest(l,barsSinceEntry) + shortTrailAmt,ShortTraillevel);
buyToCover("TrailCover") next bar at shortTrailLevel stop;
end;


SetExitOnClose;
Beta Version - I will clean up later and post it

 

EasyLanguage Code for Optimal F (Multi-Charts and VBA too!)

Optimal F in EasyLanguage for TradeStation and MultiCharts

Here is the code for the Optimal F calculation.  For a really good explanation of Optimal f, I refer you to Ralph Vince’s Book Portfolio Management FORMULAS.  We had programmed this years ago for our Excalibur software and I was surprised the EasyLanguage code was not really all that accessible on the internet.  Finding the Optimal f is found through an iterative process or in programmers terms a loop.  The code is really quite simple and I put it into a Function.  I decided to create this function because I wanted to demonstrate the ideas from my last post on how a function can store variable and array data.  Plus this code should be readily available somewhere out there.

//OptimalFGeo by George Pruitt
//My interpretation Sept. 2018
//www.georgepruitt.com
//georgeppruitt@gmail.com

input: minNumTrades(numericSimple);
vars: totalTradesCount(0),tradeCnt(0);
array: tradesArray[500](0);

vars: iCnt(00),jCnt(00),grandTot(0),highI(0);
vars: optF(0.0),gMean(0.0),fVal(0.0),HPR(0.0),TWR(0.0),hiTWR(0.0);
vars: biggestLoser(0.0),gat(0.0);

totalTradesCount = totalTrades;
If totalTradesCount > totalTradesCount[1] then
begin
tradeCnt = tradeCnt + 1;
tradesArray[tradeCnt] = positionProfit(1);
end;

// Taken from my Fortran library - GPP and Vince Book PMF

optF = 0.0;
gMean = 1.00;
gat = 0.00;
//Only calculate if new trade
IF(tradeCnt>minNumTrades and totalTradesCount > totalTradesCount[1]) then
Begin
biggestLoser = 0;
grandTot = 0;
For iCnt = 1 to tradeCnt //get the biggest loser
begin
grandTot = grandTot + tradesArray[iCnt];
IF(tradesArray[iCnt]<biggestLoser) then biggestLoser = tradesArray[iCnt];
end;
// print(grandTot," ",biggestLoser);
IF({grandTot > 0 and} biggestLoser <0) then
begin
// print("Inside TWR Calculations");
highI = 0;
hiTWR = 0.0;
for iCnt = 1 to 100
begin
fVal = .01 * iCnt;
TWR = 1.0;
for jCnt = 1 to tradeCnt // calculate the Terminal Wealth Relative
begin
HPR = 1. + (fVal * (-1*tradesArray[jCnt]) / biggestLoser);
TWR = TWR * HPR;
// print(fVal," ",iCnt," " ,jCnt," Trades ",tradesArray[jCnt]," HPR ",HPR:6:4," TWR : ",TWR:6:4," hiTWR",hiTWR:6:4," bl ",biggestLoser);
end;
// print(iCnt," ",TWR," ",hiTWR);
IF(TWR>hiTWR) THEN
begin
hiTWR = TWR;
optF = fVal; // assign optF to fVal in case its the correct one
end
else
break; //highest f found - stop looping
end;
If (TWR <= hiTWR or optF >= 0.999999) then
begin
TWR = hiTWR;
OptimalFGeo = optF; //assign optF to the name of the function
end;
gmean = power(TWR,(1.0 / tradeCnt));

if(optF<>0) then GAT = (gMean - 1.0) * (biggestLoser / -(optF));
print(d," gmean ",gmean:6:4," ",GAT:6:4); // I calculate the GMEAN and GeoAvgTrade
end;
end;
Optimal F Calculation by Ralph Vince code by George Pruitt

VBA version of Optimal F

For those of you who have a list of trades and want to see how this works in Excel here is the VBA code:

Sub OptimalF()

Dim tradesArray(1000) As Double
i = 0
biggestLoser = 0#
Do While (Cells(3 + i, 1) <> "")
tradesArray(i) = Cells(3 + i, 1)
If tradesArray(i) < bigLoser Then biggestLoser = tradesArray(i)
i = i + 1
Loop
tradeCnt = i - 1
highI = 0
hiTWR = 0#
rc = 3
For fVal = 0.01 To 1 Step 0.01
TWR = 1#
For jCnt = 0 To tradeCnt
HPR = 1# + (fVal * (-1 * tradesArray(jCnt)) / biggestLoser)
TWR = TWR * HPR
Cells(rc, 5) = jCnt
Cells(rc, 6) = tradesArray(jCnt)
Cells(rc, 7) = HPR
Cells(rc, 8) = TWR
rc = rc + 1
Next jCnt
Cells(rc, 9) = fVal
Cells(rc, 10) = TWR
rc = rc + 1

If (TWR > hiTWR) Then
hiTWR = TWR
optF = fVal
Else
Exit For
End If

Next fVal
If (TWR <= hiTWR Or optF >= 0.999999) Then
TWR = hiTWR
OptimalFGeo = optF
End If
Cells(rc, 8) = "Opt f"
Cells(rc, 9) = optF
rc = rc + 1
gMean = TWR ^ (1# / (tradeCnt + 1))
If (optF <> 0) Then GAT = (gMean - 1#) * (biggestLoser / -(optF))
Cells(rc, 8) = "Geo Mean"
Cells(rc, 9) = gMean
rc = rc + 1
Cells(rc, 8) = "Geo Avg Trade"
Cells(rc, 9) = GAT

End Sub
VBA code for Optimal F

I will attach the eld and .xlsm file a little later.

Click Here for OptimalF Spreadsheet.

 

 

Function Variable Data Survives Between Calls

Function Variable Data Survives from One Call to the Next – A Pretty Nifty Tool in EasyLanguage!

Creating a function that can store data and then have that data survive on successive function calls without having to pass information back and forth is really a cool and powerful tool in EasyLanguage.  In most programming languages, the variables defined in a function are local to that particular bit of code and once program execution exits the function, then the data is destroyed.  There are two exceptions (in other languages) that come to mind – if the variable is passed back and forth via their addresses, then the data can be maintained or if the variable is global in scope to the function and the calling program.  EasyLanguage prevents you from having to do this and this can definitely save on headaches.  I wrote a function that defines an array that will hold a list of trades.  Once the number of trades reaches a certain level, I then calculate a moving average of the last 10 trades.  The average is then passed back to the calling strategy.  Here is the simple code to the function.

 

{Function Name:   StoreTradesFunc by George Pruitt}
{Function to Calculate the average trade for past N trades.
----------------------------------------------------------
Function remembers the current trade count in tradeCnt.
It also remembers the values in the array tradesArray.
It does this between function calls.
Values - simple and array - undoubtedly are global to the function}

input: avgTradeCalcLen(numericSimple);
vars: totalTradesCount(0),tradeCnt(0);
array: tradesArray[500](0);

totalTradesCount = totalTrades;
If totalTradesCount > totalTradesCount[1] then
begin
tradeCnt = tradeCnt + 1;
tradesArray[tradeCnt] = positionProfit(1);
// print("Storing data ",tradesArray[tradeCnt]," ",tradeCnt);
end;

If totalTrades > avgTradeCalcLen then
begin
Value2 = 0;
For value1 = totalTrades downTo totalTrades - avgTradeCalcLen
begin
Value2 = value2 + tradesArray[value1];
end;
print("Sum of last 10 Trades: ",value2);
StoreTradesFunc = value2/avgTradeCalcLen;
end;
Store A List of Trades in a Function

I call this function on every bar (daily would be best but you could do it on intra-day basis) and it polls the function/keyword totalTrades to see if a new trade has occurred.  If one has, then the variable tradeCnt is incremented and the trade result is inserted into the tradesArray array by using the tradeCnt as the array index.  When you come back into the function from the next bar of data tradeCnt and tradesArray are still there for you and most importantly still intactIn other words there values are held static until you change them and remembered.  This really comes in handy when you want to store all the trades in an array and then do some type of calculation on the trades and then have that information readily available for use in the strategy.  My example just provides the average trade for the past ten trades.  But you could do some really exotic things like Optimal F.  The thing to remember is once you define a variable or an array in a function and start dumping stuff in them, the stuff will be remembered throughout the life of the simulation.  The function data and variables are encapsulated to just the function scope – meaning I can’t access tradesArray outside of the function.  One last note – notice how I was able to determine a new trade had occurred.  I assigned the result of totalTrades to my own variable totalTradesCount and then compared the value to the prior bar’s value.  If the values were different than I knew a new trade had just completed.