I didn’t say a very good Free System! This code is really cool so I thought I would share with you. Take a look at this rather cool picture.

Thanks to a reader of this blog (AG), I got this idea and programmed a very simple day trading system that incorporated a volatility trailing stop. I wanted to make sure that I had it programmed correctly and always wanted to draw a box on the chart – thanks to (TJ) from MC forums for getting me going on the graphic aspect of the project.

Since I have run out of time for today – need to get a haircut. I will have to wait till tomorrow to explain the code. But real quickly the system.

Buy x% above first y bar high and then set up a trailing stop z% of y bar average range – move to break-even when profits exceed $w. Opposite goes for the short side. One long and one short only allowed during the day and exit all at the close.

What the heck here is the code for the Strategy.

inputs: startTradeTime(930),startTradeBars(6),endTradeTime(1530),
breakOutVolPer(0.5),trailVolPer(.25),breakEven$(500);
vars: longsToday(0),shortsToday(0),
longStop(0),shortStop(0),
longTrail(0),shortTrail(0),
trailVolAmt(0),
barCount(0),highToday(0),lowToday(0),
volAmt(0),mp(0);
if t = startTradeTime + barinterval then
begin
longsToday = 0;
shortsToday = 0;
longStop = 0;
shortStop = 0;
longTrail = 0;
shortTrail = 99999999;
barCount = 0;
highToday = 0;
lowToday = 999999999;
end;
highToday = maxList(h,highToday);
lowToday = minList(l,lowToday);
mp = marketPosition;
barCount +=1;
if barCount >= startTradeBars then
begin
volAmt = average(range,startTradeBars);
if barCount = startTradeBars then
begin
longStop = highToday + breakOutVolPer * volAmt;
shortStop = lowToday - breakOutVolPer * volAmt;
end;
if t < endTradeTime then
begin
if longsToday = 0 then buy("volOrboL") next bar at longStop stop;
if shortsToday = 0 then sellShort("volOrboS") next bar shortStop stop;
end;
trailVolAmt = volAmt * trailVolPer;
if mp = 1 then
begin
longsToday +=1;
if c > entryPrice + breakEven$/bigPointValue then
longTrail = maxList(entryPrice,longTrail);
longTrail = maxList(c - trailVolAmt,longTrail);
sell("L-TrlX") next bar at longTrail stop;
end;
if mp = -1 then
begin
shortsToday +=1;
if c < entryPrice - breakEven$/bigPointValue then
shortTrail = minList(entryPrice,shortTrail);
shortTrail = minList(c + trailVolAmt,shortTrail);
buyToCover("S-TrlX") next bar at shortTrail stop;
end;
end;
setExitOnClose;

I will comment in a later post!

And the code for the Strategy Tracking Indicator.

inputs: startTradeTime(930),startTradeBars(6),endTradeTime(1530),
breakOutVolPer(0.5),trailVolPer(.25),breakEven$(500);
vars: longsToday(0),shortsToday(0),
longStop(0),shortStop(0),
longTrail(0),shortTrail(0),
trailVolAmt(0),
barCount(0),highToday(0),lowToday(0),
volAmt(0),mp(0);
if t = startTradeTime + barinterval then
begin
longsToday = 0;
shortsToday = 0;
longStop = 0;
shortStop = 0;
longTrail = 0;
shortTrail = 99999999;
barCount = 0;
highToday = 0;
lowToday = 999999999;
mp = 0;
end;
highToday = maxList(h,highToday);
lowToday = minList(l,lowToday);
barCount +=1;
vars: iCnt(0),mEntryPrice(0),myColor(0);
if barCount >= startTradeBars then
begin
volAmt = average(range,startTradeBars);
if barCount = startTradeBars then
begin
longStop = highToday + breakOutVolPer * volAmt;
shortStop = lowToday - breakOutVolPer * volAmt;
for iCnt = 0 to startTradeBars-1
begin
plot1[iCnt](longStop,"BuyBO",default,default,default);
plot2[iCnt](shortStop,"ShrtBo",default,default,default);
end;
end;
if t < endTradeTime then
begin
if longsToday = 0 and h >= longStop then
begin
mp = 1;
mEntryPrice = maxList(o,longStop);
longsToday += 1;
end;
if shortsToday = 0 and l <= shortStop then
begin
mp = -1;
mEntryPrice = minList(o,shortStop);
shortsToday +=1;
end;
plot3(longStop,"BuyBOXTND",default,default,default);
plot4(shortStop,"ShrtBOXTND",default,default,default);
end;
trailVolAmt = volAmt * trailVolPer;
if mp = 1 then
begin
if c > mEntryPrice + breakEven$/bigPointValue then
longTrail = maxList(mEntryPrice,longTrail);
longTrail = maxList(c - trailVolAmt,longTrail);
plot5(longTrail,"LongTrail",default,default,default);
end;
if mp = -1 then
begin
if c < mEntryPrice - breakEven$/bigPointValue then
shortTrail = minList(mEntryPrice,shortTrail);
shortTrail = minList(c + trailVolAmt,shortTrail);
plot6(shortTrail,"ShortTrail",default,default,default);
end;
end;

Cool code for the indicator

Very Important To Set Indicator Defaults Like This

For the BO Box use these settings – its the first 4 plots:

The box is created by drawing thick semi-transparent lines from the BuyBo and BuyBOXTND down to ShrtBo and ShrtBOXTND. So the Buy components of the 4 first plots should be Bar High and the Shrt components should be Bar Low. I didn’t specify this the first time I posted. Thanks to one of my readers for point this out!

Also I used different colors for the BuyBo/ShrtBo and the BuyBOXTND/ShrtBOXTND. Here is that setting:

The darker colored line on the last bar of the break out is caused by the overlap of the two sets of plots.

Since this is part 1 we are just going to go over a very simple system: SAR (stop and reverse) at highest/lowest high/low for past 20 days.

A 2D Array in EasyLanguage is Immutable

Meaning that once you create an array all of the data types must be the same. In a Python list you can have integers, strings, objects whatever. In C and its derivatives you also have a a data structure (a thing that stores related data) know as a Structure or Struct. We can mimic a structure in EL by using a 2 dimensional array. An array is just a list of values that can be referenced by an index.

array[1] = 3.14

array[2] = 42

array[3] = 2.71828

A 2 day array is similar but it looks like a table

array[1,1], array[1,2], array[1,3]

array[2,1], array[2,2], array[2,3]

The first number in the pair is the row and the second is the column. So a 2D array can be very large table with many rows and columns. The column can also be referred to as a field in the table. To help use a table you can actually give your fields names. Here is a table structure that I created to store trade information.

trdEntryPrice (0) – column zero – yes we can have a 0 col. and row

trdEntryDate(1)

trdExitPrice (2)

trdExitDate(3)

trdID(4)

trdPos(5)

trdProfit(6)

trdCumuProfit(7)

So when I refer to tradeStruct[0, trdEntryPrice] I am referring to the first column in the first row.

This how you define a 2D array and its associate fields.

In EasyLanguage You are Poised at the Close of a Yesterday’s Bar

This paradigm allows you to sneak a peek at tomorrow’s open tick but that is it. You can’t really cheat, but it also limits your creativity and makes things more difficult to program when all you want is an accurate backtest. I will go into detail, if I haven’t already in an earlier post, the difference of sitting on Yesterday’s close verus sitting on Today’s close with retroactive trading powers. Since we are only storing trade information when can use hindsight to gather the information we need.

Buy tomorrow at highest(h,20) stop;

SellShort tomorrow at lowest(l,20) stop;

These are the order directives that we will be using to execute our strategy. We can also run a Shadow System, with the benefit of hindsight, to see where we entered long/short and at what prices. I call it a Shadow because its all the trades reflected back one bar. All we need to do is offset the highest and lowest calculations by 1 and compare the values to today’s highs and lows to determine trade entry. We must also test the open if a gap occurred and we would have been filled at the open. Now this code gets a bit hairy, but stick with it.

Shadow System

stb = highest(h,20);
sts = lowest(l,20);
stb1 = highest(h[1],20);
sts1 = lowest(l[1],20);
buy("Sys-L") 1 contract next bar at stb stop;
sellShort("Sys-S") 1 contract next bar at sts stop;
mp = marketPosition*currentContracts;
totTrds = totalTrades;
if mPos <> 1 then
begin
if h >= stb1 then
begin
if mPos < 0 then // close existing short position
begin
mEntryPrice = tradeStruct[numTrades,trdEntryPrice];
mExitPrice = maxList(o,stb1);
tradeStruct[numTrades,trdExitPrice] = mExitPrice;
tradeStruct[numTrades,trdExitDate] = date;
mProfit = (mEntryPrice - mExitPrice) * bigPointValue - mCommSlipp;
cumuProfit += mProfit;
tradeStruct[numTrades,trdCumuProfit] = cumuProfit;
tradeStruct[numTrades,trdProfit] = mProfit;
print(d+19000000:8:0," shrtExit ",mEntryPrice:4:5," ",mExitPrice:4:5," ",mProfit:6:0," ",cumuProfit:7:0);
print("-------------------------------------------------------------------------");
end;
numTrades +=1;
mEntryPrice = maxList(o,stb1);
tradeStruct[numTrades,trdID] = 1;
tradeStruct[numTrades,trdPOS] = 1;
tradeStruct[numTrades,trdEntryPrice] = mEntryPrice;
tradeStruct[numTrades,trdEntryDate] = date;
mPos = 1;
print(d+19000000:8:0," longEntry ",mEntryPrice:4:5);
end;
end;
if mPos <>-1 then
begin
if l <= sts1 then
begin
if mPos > 0 then // close existing long position
begin
mEntryPrice = tradeStruct[numTrades,trdEntryPrice];
mExitPrice = minList(o,sts1);
tradeStruct[numTrades,trdExitPrice] = mExitPrice;
tradeStruct[numTrades,trdExitDate] = date;
mProfit = (mExitPrice - mEntryPrice ) * bigPointValue - mCommSlipp;
cumuProfit += mProfit;
tradeStruct[numTrades,trdCumuProfit] = cumuProfit;
tradeStruct[numTrades,trdProfit] = mProfit;
print(d+19000000:8:0," longExit ",mEntryPrice:4:5," ",mExitPrice:4:5," ",mProfit:6:0," ",cumuProfit:7:0);
print("---------------------------------------------------------------------");
end;
numTrades +=1;
mEntryPrice =minList(o,sts1);
tradeStruct[numTrades,trdID] = 2;
tradeStruct[numTrades,trdPOS] =-1;
tradeStruct[numTrades,trdEntryPrice] = mEntryPrice;
tradeStruct[numTrades,trdEntryDate] = date;
mPos = -1;
print(d+19000000:8:0," ShortEntry ",mEntryPrice:4:5);
end;
end;

Shadow System - Generic forany SAR System

Notice I have stb and stb1. The only difference between the two calculations is one is displaced a day. I use the stb and sts in the EL trade directives. I use stb1 and sts1 in the Shadow System code. I guarantee this snippet of code is in every backtesting platform out there.

All the variables that start with the letter m, such as mEntryPrice, mExitPrice deal with the Shadow System. Theyare not derived from TradeStation’s back testing engine only our logic. Lets look at the first part of just one side of the Shadow System:

if mPos <> 1 then
begin
if h >= stb1 then
begin
if mPos < 0 then // close existing short position
begin
mEntryPrice = tradeStruct[numTrades,trdEntryPrice];
mExitPrice = maxList(o,stb1);
tradeStruct[numTrades,trdExitPrice] = mExitPrice;
tradeStruct[numTrades,trdExitDate] = date;
mProfit = (mEntryPrice - mExitPrice) * bigPointValue - mCommSlipp;
cumuProfit += mProfit;
tradeStruct[numTrades,trdCumuProfit] = cumuProfit;
tradeStruct[numTrades,trdProfit] = mProfit;
print(d+19000000:8:0," shrtExit ",mEntryPrice:4:5," ",mExitPrice:4:5," ",mProfit:6:0," ",cumuProfit:7:0);
print("-------------------------------------------------------------------------");
end;

mPos and mEntryPrice and mExitPrice belong to the Shadow System

if mPos <> 1 then the Shadow Systems [SS] is not long. So we test today’s high against stb1 and if its greater then we know a long position was put on. But what if mPos = -1 [short], then we need to calculate the exit and the trade profit and the cumulative trade profit. If mPos = -1 then we know a short position is on and we can access its particulars from the tradeStruct 2D array. mEntryPrice = tradeStruct[numTrades,trdEntryPrice]. We can gather the other necessary information from the tradeStruct [remember this is just a table with fields spelled out for us.] Once we get the information we need we then need to stuff our calculations back into the Structure or table so we can regurgitate later. We stuff date in to the following fields trdExitPrice, trdExitDate, trdProfit and trdCumuProfit in the table.

Formatted Print: mEntryPrice:4:5

Notice in the code how I follow the print out of variables with :8:0 or :4:5? I am telling TradeStation to use either 0 or 5 decimal places. The date doesn’t need decimals but prices do. So I format that so that they will line up really pretty like.

Now that I take care of liquidating an existing position all I need to do is increment the number of trades and stuff the new trade information into the Structure.

The same goes for the short entry and long exit side of things. Just review the code. I print out the trades as we go along through the history of crude. All the while stuffing the table.

If LastBarOnChart -> Regurgitate

On the last bar of the chart we know exactly how many trades have been executed because we were keeping track of them in the Shadow System. So it is very easy to loop from 0 to numTrades.

if lastBarOnChart then
begin
print("Trade History");
for arrIndx = 1 to numTrades
begin
value20 = tradeStruct[arrIndx,trdEntryDate];
value21 = tradeStruct[arrIndx,trdEntryPrice];
value22 = tradeStruct[arrIndx,trdExitDate];
value23 = tradeStruct[arrIndx,trdExitPrice];
value24 = tradeStruct[arrIndx,trdID];
value25 = tradeStruct[arrIndx,trdProfit];
value26 = tradeStruct[arrIndx,trdCumuProfit];
print("---------------------------------------------------------------------");
if value24 = 1 then
begin
string1 = buyStr;
string2 = sellStr;
end;
if value24 = 2 then
begin
string1 = shortStr;
string2 = coverStr;
end;
print(value20+19000000:8:0,string1,value21:4:5," ",value22+19000000:8:0,string2,
value23:4:5," ",value25:6:0," ",value26:7:0);
end;
end;

Add 19000000 to Dates for easy Translation

Since all trade information is stored in the Structure or Table then pulling the information out using our Field Descriptors is very easy. Notice I used EL built-in valueXX to store table information. I did this to make the print statements a lot shorter. I could have just used tradeStruct[arrIndx, trdEntry] or whatever was needed to provide the right information, but the lines would be hard to read. To translate EL date to a normal looking data just add 19,000,000 [without commas].

If you format your PrintLog to a monospaced font your out put should look like this.

Why Would We Want to Save Trade Information?

The answer to this question will be answered in Part 2. Email me with any other questions…..

Why Can’t I Just Test with Daily Bars and Use Look-Inside Bar?

Good question. You can’t because it doesn’t work accurately all of the time. I just default to using 5 minute or less bars whenever I need to. A large portion of short term, including day trade, systems need to know the intra day market movements to know which orders were filled accurately. It would be great if you could just flip a switch and convert a daily bar system to an intraday system and Look Inside Bar(LIB) is theoretically that switch. Here I will prove that switch doesn’t always work.

Daily Bar System

Buy next bar at open of the day plus 20% of the 5 day average range

SellShort next at open of the day minus 20% of the 5 day average range

If long take a profit at one 5 day average range above entryPrice

If short take a profit at one 5 day average range below entryPrice

If long get out at a loss at 1/2 a 5 day average range below entryPrice

If short get out at a loss at 1/2 a 5 day average range above entry price

Allow only 1 long and 1 short entry per day

Get out at the end of the day

Simple Code for the System

value1 = .2 * average(Range,5);
value2 = value1 * 5;
Buy next bar at open of next bar + value1 stop;
sellShort next bar at open of next bar - value1 stop;
setProfitTarget(value2*bigPointValue);
setStopLoss(value2/2*bigPointValue);
setExitOnClose;

Simplified Daily Bar DayTrade System using ES.D Daily

Looks great with just the one hiccup: Bot @ 3846.75 and the Shorted @ 3834.75 and then took nearly 30 handles of profit.

Now let’s see what really happened.

Intraday Code to Control Entry Time and Number of Longs and Shorts

Not an accurate representation so let’s take this really simple system and apply it to intraday data. Approaching this from a logical perspective with limited knowledge about TradeStation you might come up with this seemingly valid solution. Working on the long side first.

//First Attempt
if d <> d[1] then value1 = .2 * average(Range of data2,5);
value2 = value1 * 5;
if t > sess1startTime then buy next bar at opend(0) + value1 stop;
setProfitTarget(value2*bigPointValue);
setStopLoss(value2/2*bigPointValue);
setExitOnClose;

First Simple Attempt

This looks very similar to the daily bar system. I cheated a little by using

if d <> d[1] then value1 = .2 * average(Range of data2,5);

Here I am only calculating the average once a day instead of on each 5 minute bar. Makes things quicker. Also I used

if t > sess1StartTime then buy next bar at openD(0) + value1 stop;

I did that because if you did this:

buy next bar at open of next bar + value1 stop;

You would get this:

That should do it for the long side, right?

So now we have to monitor when we can place a trade and monitor the number of long and short entries.

How does this look!

So here is the code. You will notice the added complexity. The important things to know is how to control when an entry is allowed and how to count the number of long and short entries. I use the built-in keyword/function totalTrades to keep track of entries/exits and marketPosition to keep track of the type of entry.

Take a look at the code and you can see how the daily bar system is somewhat embedded in the code. But remember you have to take into account that you are stepping through every 5 minute bar and things change from one bar to the next.

vars: buysToday(0),shortsToday(0),curTotTrades(0),mp(0),tradeZoneTime(False);
if d <> d[1] then
begin
curTotTrades = totalTrades;
value1 = .2 * average(Range of data2,5);
value2 = value1 * 5;
buysToday = 0;
shortsToday = 0;
tradeZoneTime = False;
end;
mp = marketPosition;
if totalTrades > curTotTrades then
begin
if mp <> mp[1] then
begin
if mp[1] = 1 then buysToday = buysToday + 1;
if mp[1] = -1 then shortsToday = shortsToday + 1;
end;
if mp[1] = -1 then print(d," ",t," ",mp," ",mp[1]," ",shortsToday);
curTotTrades = totalTrades;
end;
if t > sess1StartTime and t < sess1EndTime then tradeZoneTime = True;
if tradeZoneTime and buysToday = 0 and mp <> 1 then
buy next bar at opend(0) + value1 stop;
if tradeZoneTime and shortsToday = 0 and mp <> -1 then
sellShort next bar at opend(0) - value1 stop;
setProfitTarget(value2*bigPointValue);
setStopLoss(value2/2*bigPointValue);
setExitOnClose;

Proper Code to Replicate the Daily Bar System with Accuracy

Here’s a few trade examples to prove our code works.

Okay the code worked but did the system?

Conclusion

If you need to know what occurred first – a high or a low in a move then you must use intraday data. If you want to have multiple entries then of course your only alternative is intraday data. This little bit of code can get you started converting your daily bar systems to intraday data and can be a framework to develop your own day trading/or swing systems.

Can I Prototype A Short Term System with Daily Data?

You can of course use Daily Bars for fast system prototyping. When the daily bar system was tested with LIB turned on, it came close to the same results as the more accurately programmed intraday system. So you can prototype to determine if a system has a chance. Our core concept buyt a break out, short a break out, take profits and losses and have no overnight exposure sounds good theoretically. And if you only allow 2 entries in opposite directions on a daily bar you can determine if there is something there.

A Dr. Jekyll and Mr. Hyde Scenario

While playing around with this I did some prototyping of a daily bar system and created this equity curve. I mistakenly did not allow any losses – only took profits and re-entered long.

Venalicius Cave! Don’t take a loser you and will reap the benefits. The chart says so – so its got to be true – I know right?

The same chart from a different perspective.

Moral of the Story – always look at your detailed Equity Curve. This curve is very close to a simple buy and hold strategy. Maybe a little better.

In this post I simply wanted to convert the intraday ratcheting stop mechanism that I previously posted into a daily bar mechanism. Well that got me thinking of how many different values could be used as the amount to ratchet. I came up with three:

I have had requests for the EasyLanguage in an ELD – so here it is – just click on the link and unZip.

So this was going to be a great start to a post, because I was going to incorporate one of my favorite programming constructs : Switch-Case. After doing the program I thought wouldn’t it be really cool to be able to optimize over each scheme the ratchet and trail multiplier as well as the values that might go into each scheme.

In scheme one I wanted to optimize the N days for the ATR calculation. In scheme two I wanted to optimize the $ amount and the scheme three the percentage of a 20 day standard deviation. I could do a stepwise optimization and run three independent optimizations – one for each scheme. Why not just do one global optimization you might ask? You could but it would be a waste of computer time and then you would have to sift through the results. Huh? Why? Here is a typical optimization loop:

Scheme

Ratchet Mult

Trigger Mult

Parameter 1

1 : ATR

1

1

ATR (2)

2 : $ Amt

1

1

ATR (2)

3 : % of Dev. Amt

1

1

ATR (2)

1 : ATR

2

1

ATR (2)

2 : $ Amt

2

1

ATR (2)

Notice when we switch schemes the Parameter 1 doesn’t make sense. When we switch to $ Amt we want to use a $ Value as Parameter 1 and not ATR. So we could do a bunch of optimizations across non sensical values, but that wouldn’t really make a lot of sense. Why not do a conditional optimization? In other words, optimize only across a certain parameter range based on which scheme is currently being used. I knew there wasn’t an overlay available to use using standard EasyLanguage but I thought maybe OOP, and there is an optimization API that is quite powerful. The only problem is that it was very complicated and I don’t know if I could get it to work exactly the way I wanted.

EasyLanguage is almost a full blown programming language. So should I not be able to distill this conditional optimization down to something that I could do with such a powerful programming language? And the answer is yes and its not that complicated. Well at least for me it wasn’t but for beginners probably. But to become a successful programmer you have to step outside your comfort zones, so I am going to not only explain the Switch/Case construct (I have done this in earlier posts) but incorporate some array stuff.

When performing conditional optimization there are really just a few things you have to predefine:

Scheme Based Optimization Parameters

Exact Same Number of Iterations for each Scheme [starting point and increment value]

Complete Search Space

Total Number of Iterations

Staying inside the bounds of your Search Space

Here are the optimization range per scheme:

Scheme #1 – optimize number of days in ATR calculation – starting at 10 days and incrementing by 2 days

Scheme #2 – optimize $ amounts – starting at $250 and incrementing by $100

Scheme #3 – optimize percent of 20 Bar standard deviation – starting at 0,25 and incrementing by 0.25

I also wanted to optimize the ratchet and target multiplier. Here is the base code for the daily bar ratcheting system with three different schemes. Entries are based on penetration of 20 bar highest/lowest close.

inputs:
ratchetMult(2),trailMult(2),
volBase(True),volCalcLen(20),
dollarBase(False),dollarAmt(250),
devBase(False),devAmt(0.25);
vars:longMult(0),shortMult(0);
vars:ratchetAmt(0),trailAmt(0);
vars:stb(0),sts(0),mp(0);
vars:lep(0),sep(0);
if volBase then
begin
ratchetAmt = avgTrueRange(volCalcLen) * ratchetMult;
trailAmt = avgTrueRange(volCalcLen) * trailMult;
end;
if dollarBase then
begin
ratchetAmt =dollarAmt/bigPointValue * ratchetMult;
trailAmt = dollarAmt/bigPointValue * trailMult;
end;
if devBase then
begin
ratchetAmt = stddev(c,20) * devAmt * ratchetMult;
trailAmt = stddev(c,20) * devAmt * trailMult;
end;
if c crosses over highest(c[1],20) then buy next bar at open;
if c crosses under lowest(c[1],20) then sellshort next bar at open;
mp = marketPosition;
if mp <> 0 and mp[1] <> mp then
begin
longMult = 0;
shortMult = 0;
end;
If mp = 1 then lep = entryPrice;
If mp =-1 then sep = entryPrice;
// Okay initially you want a X point stop and then pull the stop up
// or down once price exceeds a multiple of Y points
// longMult keeps track of the number of Y point multiples of profit
// always key off of lep(LONG ENTRY POINT)
// notice how I used + 1 to determine profit
// and - 1 to determine stop level
If mp = 1 then
Begin
If h >= lep + (longMult + 1) * ratchetAmt then longMult = longMult + 1;
Sell("LongTrail") next bar at (lep + (longMult - 1) * trailAmt) stop;
end;
If mp = -1 then
Begin
If l <= sep - (shortMult + 1) * ratchetAmt then shortMult = shortMult + 1;
buyToCover("ShortTrail") next bar (sep - (shortMult - 1) * trailAmt) stop;
end;

Daily Bar Ratchet System

This code is fairly simple. The intriguing inputs are:

volBase[True of False] and volCalcLen [numeric Value]

dollarBase [True of False] and dollarAmt [numeric Value]

devBase [True of False] and devAmt [numeric Value]

If volBase is true then you use the parameters that go along with that scheme. The same goes for the other schemes. So when you run this you would turn one scheme on at a time and set the parameters accordingly. if I wanted to use dollarBase(True) then I would set the dollarAmt to a $ value. The ratcheting mechanism is the same as it was in the prior post so I refer you back to that one for further explanation.

So this was a pretty straightforward strategy. Let us plan out our optimization search space based on the different ranges for each scheme. Since each scheme uses a different calculation we can’t simply optimize across all of the different ranges – one is days, and the other two are dollars and percentages.

Enumerate

We know how to make TradeStation loop based on the range of a value. If you want to optimize from $250 to $1000 in steps of $250, you know this involves [$1000 – $250] / $250 + 1 or 3 + 1 or 4 interations. Four loops will cover this entire search space. Let’s examine the search space for each scheme:

ATR Scheme: start at 10 bars and end at 40 by steps of 2 or [40-10]/2 + 1 = 16

$ Amount Scheme: start at $250 and since we have to have 16 iterations [remember # of iterations have to be the same for each scheme] what can we do to use this information? Well if we start $250 and step by $100 we cover the search space $250, $350, $450, $550…$1,750. $250 + 15 x 250. 15 because $250 is iteration 1.

Percentage StdDev Scheme: start at 0.25 and end at 0.25 + 15 x 0.25 = 4

So we enumerate 16 iterations to a different value. The easiest way to do this is to create a map. I know this seems to be getting hairy but it really isn’t. The map will be defined as an array with 16 elements. The array will be filled with the search space based on which scheme is currently being tested. Take a look at this code where I show how to define an array of 16 elements and introduce my Switch/Case construct.

array: optVals[16](0);
switch(switchMode)
begin
case 1:
startPoint = 10; // vol based
increment = 2;
case 2:
startPoint = 250/bigPointValue; // $ based
increment = 100/bigPointValue;
case 3:
startPoint = 0.25; //standard dev
increment = 0.25*minMove/priceScale;
default:
startPoint = 1;
increment = 1;
end;
vars: cnt(0),loopCnt(0);
once
begin
for cnt = 1 to 16
begin
optVals[cnt] = startPoint + (cnt-1) * increment;
end;
end

Set Up Complete Search Space for all Three Schemes

This code creates a 16 element array, optVals, and assigns 0 to each element. SwitchMode goes from 1 to 3.

if switchMode is 1: ATR scheme [case: 1] the startPoint is set to 10 and increment is set to 2

if switchMode is 2: $ Amt scheme [case: 2] the startPoint is set to $250 and increment is set to $100

if switchMode is 3: Percentage of StdDev [case: 3] the startPoint is set to 0.25 and the increment is set to 0.25

Once these two values are set the following 15 values can be spawned by the these two. A for loop is great for populating our search space. Notice I wrap this code with ONCE – remember ONCE is only executed at the very beginning of each iteration or run.

once begin for cnt = 1 to 16 begin optVals[cnt] = startPoint + (cnt-1) * increment; end; end

Based on startPoint and increment the entire search space is filled out. Now all you have to do is extract this information stored in the array based on the iteration number.

Switch(switchMode)
Begin
Case 1:
ratchetAmt = avgTrueRange(optVals[optLoops]) * ratchetMult;
trailAmt = avgTrueRange(optVals[optLoops]) * trailMult;
Case 2:
ratchetAmt =optVals[optLoops] * ratchetMult;
trailAmt = optVals[optLoops] * trailMult;
Case 3:
ratchetAmt =stddev(c,20) * optVals[optLoops] * ratchetMult;
trailAmt = stddev(c,20) * optVals[optLoops] * trailMult;
Default:
ratchetAmt = avgTrueRange(optVals[optLoops]) * ratchetMult;
trailAmt = avgTrueRange(optVals[optLoops]) * trailMult;
end;
if c crosses over highest(c[1],20) then buy next bar at open;
if c crosses under lowest(c[1],20) then sellshort next bar at open;
mp = marketPosition;
if mp <> 0 and mp[1] <> mp then
begin
longMult = 0;
shortMult = 0;
end;
If mp = 1 then lep = entryPrice;
If mp =-1 then sep = entryPrice;
// Okay initially you want a X point stop and then pull the stop up
// or down once price exceeds a multiple of Y points
// longMult keeps track of the number of Y point multiples of profit
// always key off of lep(LONG ENTRY POINT)
// notice how I used + 1 to determine profit
// and - 1 to determine stop level
If mp = 1 then
Begin
If h >= lep + (longMult + 1) * ratchetAmt then longMult = longMult + 1;
Sell("LongTrail") next bar at (lep + (longMult - 1) * trailAmt) stop;
end;
If mp = -1 then
Begin
If l <= sep - (shortMult + 1) * ratchetAmt then shortMult = shortMult + 1;
buyToCover("ShortTrail") next bar (sep - (shortMult - 1) * trailAmt) stop;
end;

Extract Search Space Values and Rest of Code

Switch(switchMode) Begin Case 1: ratchetAmt = avgTrueRange(optVals[optLoops])ratchetMult; trailAmt = avgTrueRange(optVals[optLoops]) trailMult; Case 2: ratchetAmt =optVals[optLoops] * ratchetMult; trailAmt = optVals[optLoops] * trailMult; Case 3: ratchetAmt =stddev(c,20)optVals[optLoops] ratchetMult; trailAmt = stddev(c,20) * optVals[optLoops] * trailMult;

Notice how the optVals are indexed by optLoops. So the only variable that is optimized is the optLoops and it spans 1 through 16. This is the power of enumerations – each number represents a different thing and this is how you can control which variables are optimized in terms of another optimized variable. Here is my optimization specifications:

And here are the results:

The best combination was scheme 1 [N-day ATR Calculation] using a 2 Mult Ratchet and 1 Mult Trail Trigger. The best N-day was optVals[2] for this scheme. What in the world is this value? Well you will need to back engineer a little bit here. The starting point for this scheme was 10 and the increment was 2 so if optVals[1] =10 then optVals[2] = 12 or ATR(12). You can also print out a map of the search spaces.

vars: cnt(0),loopCnt(0);
once
begin
loopCnt = loopCnt + 1;
// print(switchMode," : ",d," ",startPoint);
// print(" ",loopCnt:2:0," --------------------");
for cnt = 1 to 16
begin
optVals[cnt] = startPoint + (cnt-1) * increment;
// print(cnt," ",optVals[cnt]," ",cnt-1);
end;
end;

This was a elaborate post so please email me with questions. I wanted to demonstrate that we can accomplish very sophisticated things with just the pure and raw EasyLanguage which is a programming language itself.

A reader of this blog wanted a conversion from my Ratchet Trailing Stop indicator into a Strategy. You will notice a very close similarity with the indicator code as the code for this strategy. This is a simple N-Bar [Hi/Lo] break out with inputs for the RatchetAmt and TrailAmt. Remember RatchetAmt is how far the market must move in your favor before the stop is pulldown the TrailAmt. So if the RatchetAmt is 12 and the TrailAmt is 6, the market would need to move 12 handles in your favor and the Trail Stop would move to break even. If it moves another 12 handles then the stop would be moved up/down by 6 handles. Let me know if you have any questions – this system is similar to the one I just posted.

inputs: ratchetAmt(6),trailAmt(6);
vars:longMult(0),shortMult(0),myBarCount(0);
vars:stb(0),sts(0),buysToday(0),shortsToday(0),mp(0);
vars:lep(0),sep(0);
If d <> d[1] then
Begin
longMult = 0;
shortMult = 0;
myBarCount = 0;
mp = 0;
lep = 0;
sep = 0;
buysToday = 0;
shortsToday = 0;
end;
myBarCount = myBarCount + 1;
If myBarCount = 6 then // six 5 min bars = 30 minutes
Begin
stb = highD(0); //get the high of the day
sts = lowD(0); //get low of the day
end;
If myBarCount >=6 and t < calcTime(sess1Endtime,-3*barInterval) then
Begin
if buysToday = 0 then buy("NBar-Range-B") next bar stb stop;
if shortsToday = 0 then sellShort("NBar-Range-S") next bar sts stop;
end;
mp = marketPosition;
If mp = 1 then
begin
lep = entryPrice;
buysToday = 1;
end;
If mp =-1 then
begin
sep = entryPrice;
shortsToday = 1;
end;
// Okay initially you want a X point stop and then pull the stop up
// or down once price exceeds a multiple of Y points
// longMult keeps track of the number of Y point multipes of profit
// always key off of lep(LONG ENTRY POINT)
// notice how I used + 1 to determine profit
// and - 1 to determine stop level
If mp = 1 then
Begin
If h >= lep + (longMult + 1) * ratchetAmt then longMult = longMult + 1;
Sell("LongTrail") next bar at (lep + (longMult - 1) * trailAmt) stop;
end;
If mp = -1 then
Begin
If l <= sep - (shortMult + 1) * ratchetAmt then shortMult = shortMult + 1;
buyToCover("ShortTrail") next bar (sep - (shortMult - 1) * trailAmt) stop;
end;
setExitOnClose;

I Used my Ratchet Indicator for the Basis of this Strategy

What is Better: 30, 60, or 120 Minute Break-Out on ES.D

Here is a simple tutorial you can use as a foundation to build a potentially profitable day trading system. Here we wait N minutes after the open and then buy the high of the day or short the low of the day and apply a protective stop and profit objective. The time increment can be optimized to see what time frame is best to use. You can also optimize the stop loss and profit objective – this system gets out at the end of the day. This system can be applied to any .D data stream in TradeStation or Multicharts.

Logic Description

get open time

get close time

get N time increment

15 – first 15 minute of day

30 – first 30 minute of day

60 – first hour of day

get High and Low of day

place stop orders at high and low of day – no entries late in day

calculate buy and short entries – only allow one each*

apply stop loss

apply profit objective

get out at end of day if not exits have occurred

Optimization Results [From 15 to 120 by 5 minutes] on @ES.D 5 Minute Chart – Over Last Two Years

Simple Orbo EasyLanguage

I threw this together rather quickly in a response to a reader’s question. Let me know if you see a bug or two. Remember once you gather your stops you must allow the order to be issued on every subsequent bar of the trading day. The trading day is defined to be the time between timeIncrement and endTradeMinB4Close. Notice how I used the EL function calcTime to calculate time using either a +positive or -negative input. I want to sample the high/low of the day at timeIncrement and want to trade up until endTradeMinB4Close time. I use the HighD and LowD functions to extract the high and low of the day up to that point. Since I am using a tight stop relative to today’s volatility you will see more than 1 buy or 1 short occurring. This happens when entry/exit occurs on the same bar and MP is not updated accordingly. Somewhere hidden in this tome of a blog you will see a solution for this. If you don’t want to search I will repost it tomorrow.

//Optimizing Time to determine a simple break out
//Only works on .D data streams
Inputs: timeIncrement(15),endTradeMinB4Close(-15),stopLoss$(500),profTarg$(1000);
vars: firstBarTime(0),lastBarTime(0),buyStop(0),shortStop(0),
calcStopTime(0),quitTradeTime(0),buysToday(0),shortsToday(0),mp(0);
firstBarTime = sessionStartTime(0,1);
lastBarTime = sessionEndTime(0,1);
calcStopTime = calcTime(firstBarTime,timeIncrement);
quitTradeTime = calcTime(lastBarTime,endTradeMinB4Close);
If time = calcStopTime then
begin
buyStop = HighD(0);
shortStop = LowD(0);
buysToday = 0;
shortsToday = 0;
End;
if time >= calcStopTime and time < quitTradeTime then
begin
if buysToday = 0 then Buy next bar at buyStop stop;
if shortsToday = 0 then Sell short next bar at shortStop stop;
end;
mp = marketPosition;
If mp = 1 then buysToday = 1;
If mp = -1 then shortsToday = 1;
SetStopLoss(stopLoss$);
setProfitTarget(profTarg$);
setExitOnClose;

I was recently testing the idea of a short term VBO strategy on the ES utilizing very tight stops. I wanted to see if using a tight ATR stop in concert with the entry day’s low (for buys) would cut down on losses after a break out. In other words, if the break out doesn’t go as anticipated get out and wait for the next signal. With the benefit of hindsight in writing this post, I certainly felt like my exit mechanism was what was going to make or break this system. In turns out that all pre conceived notions should be thrown out when volatility enters the picture.

System Description

If 14 ADX < 20 get ready to trade

Buy 1 ATR above the midPoint of the past 4 closing prices

Place an initial stop at 1 ATR and a Profit Objective of 1 ATR

Trail the stop up to the prior day’s low if it is greater than entryPrice – 1 ATR initially, and then trail if a higher low is established

Wait 3 bars to Re-Enter after going flat – Reversals allowed

That’s it. Basically wait for a trendless period and buy on the bulge and then get it out if it doesn’t materialize. I knew I could improve the system by optimizing the parameters but I felt I was in the ball park. My hypothesis was that the system would fail because of the tight stops. I felt the ADX trigger was OK and the BO level would get in on a short burst. Just from past experience I knew that using the prior day’s price extremes as a stop usually doesn’t fair that well.

Without commission the initial test was a loser: -$1K and -$20K draw down over the past ten years. I thought I would test my hypothesis by optimizing a majority of the parameters:

ADX Len

ADX Trigger Value

ATR Len

ATR BO multiplier

ATR Multiplier for Trade Risk

ATR Multiplier for Profit Objective

Number of bars to trail the stop – used lowest lows for longs

Results

As you can probably figure, I had to use the Genetic Optimizer to get the job done. Over a billion different permutations. In the end here is what the computer pushed out using the best set of parameters.

Optimization Report – The Best of the Best

ADX – Does it Really Matter?

Take a look at the chart – the ADX is mostly in Trigger territory – does it really matter?

A Chart is Worth a 1000 Words

What does this chart tell us?

Was the parameter selection biased by the heightened level of volatility? The system has performed on the parameter set very well over the past two or three years. But should you use this parameter set going into the future – volatility will eventually settle down.

Now using my experience in trading I would have selected a different parameter set. Here are my biased results going into the initial programming. I would use a wider stop for sure, but I would have used the generic ADX values.

I would have used 14 ADX Len with a 20 trigger and risk 1 to make 3 and use a wider trailing stop. With trend neutral break out algorithms, it seems you have to be in the game all of the time. The ADX was supposed to capture zones that predicated break out moves, but the ADX didn’t help out at all. Wider stops helped but it was the ADX values that really changed the complexion of the system. Also the number of bars to wait after going flat had a large impact as well. During low volatility you can be somewhat picky with trades but when volatility increases you gots to be in the game. – no ADX filtering and no delay in re-Entry. Surprise, surprise!

Alogorithm Code

Here is the code – some neat stuff here if you are just learning EL. Notice how I anchor some of the indicator based variables by indexing them by barsSinceEntry. Drop me a note if you see something wrong or want a little further explanation.

Inputs: adxLen(14),adxTrig(25),atrLen(10),atrBOMult(1),atrRiskMult(1),atrProfMult(2),midPtNumBar(3),posMovTrailNumBars(2),reEntryDelay(3);
vars: mp(0),trailLongStop(0),trailShortStop(0),BSE(999),entryBar(0),tradeRisk(0),tradeProf(0);
vars: BBO(0),SBO(0),ATR(0),totTrades(0);
mp = marketPosition;
totTrades = totalTrades;
BSE = barsSinceExit(1);
If totTrades <> totTrades[1] then BSE = 0;
If totalTrades = 0 then BSE = 99;
ATR = avgTrueRange(atrLen);
SBO = midPoint(c,midPtNumBar) - ATR * atrBOMult;
BBO = midPoint(c,midPtNumBar) + ATR * atrBOMult;
tradeRisk = ATR * atrRiskMult;
tradeProf = ATR * atrProfMult;
If mp <> 1 and adx(adxLen) < adxTrig and BSE > reEntryDelay and open of next bar < BBO then buy next bar at BBO stop;
If mp <>-1 and adx(adxLen) < adxTrig AND BSE > reEntryDelay AND open of next bar > SBO then sellshort next bar at SBO stop;
If mp = 1 and mp[1] <> 1 then
Begin
trailLongStop = entryPrice - tradeRisk;
end;
If mp = -1 and mp[1] <> -1 then
Begin
trailShortStop = entryPrice + tradeRisk;
end;
if mp = 1 then sell("L-init-loss") next bar at entryPrice - tradeRisk[barsSinceEntry] stop;
if mp = -1 then buyToCover("S-init-loss") next bar at entryPrice + tradeRisk[barsSinceEntry] stop;
if mp = 1 then
begin
sell("L-ATR-prof") next bar at entryPrice + tradeProf[barsSinceEntry] limit;
trailLongStop = maxList(trailLongStop,lowest(l,posMovTrailNumBars));
sell("L-TL-Stop") next bar at trailLongStop stop;
end;
if mp =-1 then
begin
buyToCover("S-ATR-prof") next bar at entryPrice -tradeProf[barsSinceEntry] limit;
trailShortStop = minList(trailShortStop,highest(h,posMovTrailNumBars));
// print(d, " Short and trailStop is : ",trailShortStop);
buyToCover("S-TL-Stop") next bar at trailShortStop stop;
end;

I have seen a plethora of posts on the Turtle trading strategies where the rules and code are provided. The codes range from a mere Donchian breakout to a fairly close representation. Without dynamic portfolio feedback its rather impossible to program the portfolio constraints as explained by Curtis Faith in his well received “Way Of The Turtle.” But the other components can be programmed rather closely to Curtis’ descriptions. I wanted to provide this code in a concise manner to illustrate some of EasyLanguage’s esoteric constructs and some neat shortcuts. First of all let’s take a look at how the system has performed on Crude for the past 15 years.

If a market trends, the Turtle will catch it. Look how the market rallied in 2007 and immediately snapped back in 2008, and look at how the Turtle caught the moves – impressive. But see how the system stops working in congestion. It did take a small portion of the 2014 move down and has done a great job of catching the pandemic collapse and bounce. In my last post, I programmed the LTL (Last Trader Loser) function to determine the success/failure of the Turtle System 1 entry. I modified it slightly for this post and used it in concert with Turtle System 2 Entry and the 1/2N AddOn pyramid trade to get as close as possible to the core of the Turtle Entry/Exit logic.

Can Your Program This – sure you CAN!

I will provide the ELD so you can review at your leisure, but here are the important pieces of the code that you might not be able to derive without a lot of programming experience.

If mp[1] <> mp and mp <> 0 then
begin
if mp = 1 then
begin
origEntry = entryPrice;
origEntryName = "Sys1Long";
If ltl = False and h >= lep1[1] then origEntryName = "Sys2Long";
end;
if mp =-1 then
begin
origEntry = entryPrice;
origEntryName = "Sys1Short";
If ltl = False and l <= sep1[1] then origEntryName = "Sys2Short";
end;
end;

Keeping Track Of Last Entry Signal Price and Name

This code determines if the current market position is not flat and is different than the prior bar’s market position. If this is the case then a new trade has been executed. This information is needed so that you know which exit to apply without having to forcibly tie them together using EasyLanguage’s from Entry keywords. Here I just need to know the name of the entry. The entryPrice is the entryPrice. Here I know if the LTL is false, and the entryPrice is equal to or greater/less than (based on current market position) than System 2 entry levels, then I know that System 2 got us into the trade.

If mp = 1 and origEntryName = "Sys1Long" then Sell currentShares shares next bar at lxp stop;
If mp =-1 and origEntryName = "Sys1Short" then buyToCover currentShares shares next bar at sxp stop;
//55 bar component - no contingency here
If mp = 0 and ltl = False then buy("55BBO") next bar at lep1 stop;
If mp = 1 and origEntryName = "Sys2Long" then sell("55BBO-Lx") currentShares shares next bar at lxp1 stop;
If mp = 0 and ltl = False then sellShort("55SBO") next bar at sep1 stop;
If mp =-1 and origEntryName = "Sys2Short" then buyToCover("55SBO-Sx") currentShares shares next bar at sxp1 stop;

Entries and Exits

The key to this logic is the keywords currentShares shares. This code tells TradeStation to liquidate all the current shares or contracts at the stop levels. You could use currentContractscontracts if you are more comfortable with futures vernacular.

AddOn Pyramiding Signal Logic

Before you can pyramid you must turn it on in the Strategy Properties.

If mp = 1 and currentShares < 4 then buy("AddonBuy") next bar at entryPrice + (currentShares * .5*NValue) stop;
If mp =-1 and currentShares < 4 then sellShort("AddonShort") next bar at entryPrice - (currentShares * .5*NValue) stop;

This logic adds positions on from the original entryPrice in increments of 1/2N. The description for this logic is a little fuzzy. Is the N value the ATR reading when the first contract was put on or is it dynamically recalculated? I erred on the side of caution and used the N when the first contract was put on. So to calculate the AddOn long entries you simply take the original entryPrice and add the currentShares * .5N. So if currentShares is 1, then the next pyramid level would be entryPrice + 1* .5N. If currentShares is 2 ,then entryPrice + 2* .5N and so on an so forth. The 2N stop trails from the latest entryPrice. So if you put on 4 contracts (specified in Curtis’ book), then the trailing exit would be 2N from where you added the 4th contract. Here is the code for that.

Liquidate All Contracts at Last Entry – 2N

vars: lastEntryPrice(0);
If cs <= 1 then lastEntryPrice = entryPrice;
If cs > 1 and cs > cs[1] and mp = 1 then lastEntryPrice = entryPrice + ((currentShares-1) * .5*NValue);
If cs > 1 and cs > cs[1] and mp =-1 then lastEntryPrice = entryPrice - ((currentShares-1) * .5*NValue);
//If mp = -1 then print(d," ",lastEntryPrice," ",NValue);
If mp = 1 then sell("2NLongLoss") currentShares shares next bar at lastEntryPrice-2*NValue stop;
If mp =-1 then buyToCover("2NShrtLoss") currentShares shares next bar at lastEntryPrice+2*NValue Stop;

Calculate Last EntryPrice and Go From There

I introduce a new variable here: cs. CS stands for currentShares and I keep track of it from bar to bar. If currentShares or cs is less than or equal to1 I know that the last entryPrice was the original entryPrice. Things get a little more complicated when you start adding positions – initially I couldn’t remember if EasyLanguage’s entryPrice contained the last entryPrice or the original – turns out it is the original – good to know. So, if currentShares is greater than one and the current bar’s currentShares is greater than the prior bar’s currentShares, then I know I added on another contract and therefore must update lastEntryPrice. LastEntryPrice is calculated by taking the original entryPrice and adding (currentShares-1) * .5N. Now this is the theoretical entryPrice, because I don’t take into consideration slippage on entry. You could make this adjustment. So, once I know the lastEntryPrice I can determine 2N from that price.

Getting Out At 2N Trailing Stop

If mp = 1 then sell("2NLongLoss") currentShares shares next bar at lastEntryPrice-2*NValue stop;
If mp =-1 then buyToCover("2NShrtLoss") currentShares shares next bar at lastEntryPrice+2*NValue Stop;

Get Out At LastEntryPrice +/-2N

That’s all of the nifty code. Below is the function and ELD for my implementation of the Turtle dual entry system. You will see some rather sophisticated code when it comes to System 1 Entry and this is because of these scenarios:

What if you are theoretically short and are theoretically stopped out for a true loser and you can enter on the same bar into a long trade.

What if you are theoretically short and the reversal point would result in a losing trade. You wouldn’t record the loser in time to enter the long position at the reversal point.

What if you are really short and the reversal point would results in a true loser, then you would want to allow the reversal at that point

There are probably some other scenarios, but I think I covered all bases. Just let me know if that’s not the case. What I did to validate the entries was I programmed a 20/10 day breakout/failure with a 2N stop and then went through the list and deleted the trades that followed a non 2N loss (10 bar exit for a loss or a win.) Then I made sure those trades were not in the output of the complete system. There was quite a bit of trial and error. If you see a mistake, like I said, just let me know.

Remember I published the results of different permutations of this strategy incorporating dynamic portfolio feedback at my other site www.trendfollowingsystems.com. These results reflect the a fairly close portfolio that Curtis suggests in his book.

Last Trade Was a Loser Filter – To Use or Not To Use

Premise

A major component of the Turtle algorithm was to skip the subsequent 20-day break out if the prior was a winner. I guess Dennis believed the success/failure of a trade had an impact on the outcome of the subsequent trade. I have written on how you can implement this in EasyLanguage in prior posts, but I have been getting some questions on implementing FSM in trading and thought this post could kill two birds with one stone: 1) provide a template that can be adapted to any LTL mechanism and 2) provide the code/structure of setting up a FSM using EasyLanguage’s Switch/Case structure.

Turtle Specific LTL Logic

The Turtle LTL logic states that a trade is a loser if a 2N loss occurs after entry. N is basically an exponential-like moving average of TrueRange. So if the market moves 2N against a long or short position and stops you out, you have a losing trade. What makes the Turtle algorithm a little more difficult is that you can also exit on a new 10-day low/high depending on your position. The 10-day trailing exit does not signify a loss. Well at least in this post it doesn’t. I have code that says any loss is a loss, but for this explanation let’s just stick to a 2N loss to determine a trade’s failure.

How To Monitor Trades When Skipping Some Of Them

This is another added layer of complexity. You have to do your own trade accounting behind the scenes to determine if a losing trade occurs. Because if you have a winning trade you skip the next trade and if you skip it how do you know if it would have been a winner or a loser. You have to run a theoretical system in parallel with the actual system code.

Okay let’s start out assuming the last trade was a winner. So we turn real trading off. As the bars go by we look for a 20-Day high or low penetration. Assume a new 20-Day high is put in and a long position is established at the prior 20-Day high. At this point you calculate a 2N amount and subtract if from the theoretical entry price to obtain the theoretical exit price. So you have a theoMP (marketPosition) and a theoEX (exit price.) This task seems pretty simple, so you mov on and start looking for a day that either puts in a new 10-Day low or crosses below your theoEX price. If a new 10-Day low is put in then you continue on looking for a new entry and a subsequent 2N loss. If a 2N loss occurs, then you turn trading back on and continue monitoring the trades – turning trading off and then back on when necessary. In the following code I use these variables:

state – 0: looking for an entry or 1: looking for an exit

lep – long entry price

sep– short entry price

seekLong – I am seeking a long position

seekShort – I am seeking a short position

theoMP – theoretical market position

theoEX – theoretical exit price

lxp – long exit price

sxp – short exit price

Let’s jump into the Switch/Case structure when state = 0:

Switch(state)
Begin
Case 0:
lep = highest(h[1],20) + minMove/priceScale;
sep = lowest(l[1],20) - minMove/priceScale;
If seekLong and h >= lep then
begin
theoMP = 1;
theoEX = maxList(lep,o) - 2 * atr;
// print(d," entered long >> exit at ",theoEX," ",atr);
end;
If seekShort and l <= sep then
begin
theoMP = -1;
theoEX = minList(sep,o) + 2 * atr;
end;
If theoMP <> 0 then
begin
state = 1;
cantExitToday = True;
end;

State 0 (Finite State Set Up)

The Switch/Case is a must have structure in any programming language. What really blows my mind is that Python doesn’t have it. They claim its redundant to an if-then structure and it is but its so much easier to read and implement. Basically you use the Switch statement and a variable name and based on the value of the variable it will flow to whatever case the variable equates to. Here we are looking at state 0. In the CASE: 0 structure the computer calculates the lep and sep values – long and short entry levels. If you are flat then you are seeking a long or a short position. If the high or low of the bar penetrates it respective trigger levels then theoMP is set to 1 for long or -1 for short. TheoEX is then calculated based on the atr value on the day of entry. If theoMP is set to either a 1 or -1, then we know a trade has just been triggered. The Finite State Machine then switches gears to State 1. Since State = 1 the next Case statement is immediately evaluated. I don’t want to exit on the same bar as I entered (wide bars can enter and exit during volatile times) I use a variable cantExitToday. This variable delays the Case 1: evaluation by one bar.

State = 1 code:

Case 1:
If not(cantExitToday) then
begin
lxp = maxList(theoEX,lowest(l[1],10)-minMove/priceScale);
sxp = minList(theoEX,highest(h[1],10)+minMove/priceScale);
If theoMP = 1 and l <= lxp then
begin
theoMP = 0;
seekLong = False;
if lxp <= theoEX then
ltl = True
Else
ltl = False;
end;
If theoMP =-1 and h >= sxp then
begin
theoMP = 0;
seekShort = False;
if sxp >= theoEX then
ltl = True
else
ltl = False;
end;
If theoMP = 0 then state = 0;
end;
cantExitToday = False;
end;

State = 1 (Switching Gears)

Once we have a theoretical position, then we only examine the code in the Case 1: module. On the subsequent bar after entry, the lxp and sxp (long exit and short exit prices) are calculated. Notice these values use maxList or minList to determine whichever is closer to the current market action – the 2N stop or the lowest/highest low/high for the past 10-days. Lxp and sxp are assigned whichever is closer. Each bar’s high or low is compared to these values. If theoMP = 1 then the low is compared to lxp. If the low crosses below lxp, then things are set into motion. The theoMP is immediately set to 0 and seekLong is turned to False. If lxp <= a 2N loss then ltl (last trade loser) is set to true. If not, then ltl is set to False. If theoMP = 0 then we assume a flat position and switch the FSM back to State 0 and start looking for a new trade. The ltl variable is then used in the code to allow a real trade to occur.

Strategy Incorporates Our FSM Output

vars:N(0),mp(0),NLossAmt(0);
If barNumber = 1 then n = avgTrueRange(20);
if barNumber > 1 then n = (n*19 + trueRange)/20;
If useLTLFilter then
Begin
if ltl then buy next bar at highest(h,20) + minMove/priceScale stop;
if ltl then sellShort next bar at lowest(l,20) -minMove/priceScale stop;
end
Else
Begin
buy next bar at highest(h,20) + minMove/priceScale stop;
sellShort next bar at lowest(l,20) -minMove/priceScale stop;
end;
mp = marketPosition;
If mp <> 0 and mp[1] <> mp then NLossAmt = 2 * n;
If mp = 1 then
Begin
Sell("LL10-LX") next bar at lowest(l,10) - minMove/priceScale stop;
Sell("2NLS-LX") next bar at entryPrice - NLossAmt stop;
end;
If mp =-1 then
Begin
buyToCover("HH10-SX") next bar at highest(h,10) + minMove/priceScale stop;
buyToCover("2NLS-SX") next bar at entryPrice + NLossAmt stop;
end;

Strategy Code Using ltl filter

This code basically replicates what we did in the FSM, but places real orders based on the fact that the Last Trade Was A Loser (ltl.)

Does It Work – Only Trade After a 2N-Loss

Without Filter on the last 10-years in Crude Oil

With Filter on the last 10-years in Crude Oil

I have programmed this into my TradingSimula-18 software and will show a portfolio performance with this filter a little later at www.trendfollowingsystems.com.

I had to do some fancy footwork with some of the code due to the fact you can exit and then re-enter on the same bar. In the next post on this blog I will so you those machinations . With this template you should be able to recreate any last trade was a loser mechanism and see if it can help out with your own trading algorithms. Shoot me an email with any questions.

This system has been around for several years. Its based on the belief that fund managers start pouring money into the market near the end of the month and this creates momentum that lasts for just a few days. The original system states to enter the market on the close of the last bar of the day if the its above a certain moving average value. In the Jaekle and Tomasini book, the authors describe such a trading system. Its quite simple, enter on the close of the month if its greater than X-Day moving average and exit either 4 days later or if during the trade the closing price drops below the X-Day moving average.

EasyLanguage or Multi-Charts Version

Determining the end of the month should be quite easy -right? Well if you want to use EasyLanguage on TradeStation and I think on Multi-Charts you can’t sneak a peek at the next bar’s open to determine if the current bar is the last bar of the month. You can try, but you will receive an error message that you can’t mix this bar on close with next bar. In other words you can’t take action on today’s close if tomorrow’s bar is the first day of the month. This is designed, I think, to prevent from future leak or cheating. In TradeStation the shift from backtesting to trading is designed to be a no brainer, but this does provide some obstacles when you only want to do a backtest.

LDOM function – last day of month for past 15 years or so

So I had to create a LastDayOfMonth function. At first I thought if the day of the month is the 31st then it is definitely the last bar of the month. And this is the case no matter what. And if its the 30th then its the last day of the month too if the month is April, June, Sept, and November. But what happens if the last day of the month falls on a weekend. Then if its the 28th and its a Friday and the month is blah, blah, blah. What about February? To save time here is the code:

Inputs: movAvgPeriods(50);
vars: endOfMonth(false),theDayOfWeek(0),theMonth(0),theDayOfMonth(0),isLeapYear(False);
endOfMonth = false;
theDayOfWeek = dayOfWeek(date);
theMonth = month(date);
theDayOfMonth = dayOfMonth(date);
isLeapYear = mod(year(d),4) = 0;
// 29th of the month and a Friday
if theDayOfMonth = 29 and theDayOfWeek = 5 then
endOfMonth = True;
// 30th of the month and a Friday
if theDayOfMonth = 30 and theDayOfWeek = 5 then
endOfMonth = True;
// 31st of the month
if theDayOfMonth = 31 then
endOfMonth = True;
// 30th of the month and April, June, Sept, or Nov
if theDayOfMonth = 30 and (theMonth=4 or theMonth=6 or theMonth=9 or theMonth=11) then
endOfMonth = True;
// 28th of the month and February and not leap year
if theDayOfMonth = 28 and theMonth = 2 and not(isLeapYear) then
endOfMonth = True;
// 29th of the month and February and a leap year or 28th, 27th and a Friday
if theMonth = 2 and isLeapYear then
Begin
If theDayOfMonth = 29 or ((theDayOfMonth = 28 or theDayOfMonth = 27) and theDayOfWeek = 5) then
endOfMonth = True;
end;
// 28th of the month and Friday and April, June, Sept, or Nov
if theDayOfMonth = 28 and (theMonth = 4 or theMonth = 6 or
theMonth = 9 or theMonth =11) and theDayOfWeek = 5 then
endOfMonth = True;
// 27th, 28th of Feb and Friday
if theMonth = 2 and theDayOfWeek = 5 and theDayOfMonth = 27 then
endOfMonth = True;
// 26th of Feb and Friday and not LeapYear
if theMonth = 2 and theDayOfWeek = 5 and theDayOfMonth = 26 and not(isLeapYear) then
endOfMonth = True;
// Memorial day adjustment
If theMonth = 5 and theDayOfWeek = 5 and theDayOfMonth = 28 then
endOfMonth = True;
//Easter 2013 adjustment
If theMonth = 3 and year(d) = 113 and theDayOfMonth = 28 then
endOfMonth = True;
//Easter 2018 adjustment
If theMonth = 3 and year(d) = 118 and theDayOfMonth = 29 then
endOfMonth = True;
if endOfMonth and c > average(c,movAvgPeriods) then
Buy("BuyDay") this bar on close;
If C <average(c,movAvgPeriods) then
Sell("MovAvgExit") this bar on close;
If BarsSinceEntry=4 then
Sell("4days") this bar on close;

Last Day Of Month Function and Strategy

All the code is generic except for the hard code for days that are a consequence of Good Friday.

All this code because I couldn’t sneak a peek at the date of tomorrow. Here are the results of trading the ES futures sans execution costs for the past 15 years.

What if it did the easy way and executed the open of the first bar of the month.

If c > average(c,50) and month(d) <> month(d of tomorrow) then
buy next bar at open;
If barsSinceEntry >=3 then
sell next bar at open;
If marketPosition = 1 and c < average(c,50) then
sell next bar at open;

Buy First Day Of Month

The results aren’t as good but it sure was easier to program.

TradingSimula-18 Version

Since you can use daily bars we can test this with my TradingSimula-18 Python platform. And we will execute on the close of the month. Here is the snippet of code that you have to concern yourself with. Here I am using Sublime Text and utilizing their text collapsing tool to hide non-user code:

This was easy to program in TS-18 because I do allow Future Leak – in other words I will let you sneak a peek at tomorrow’s values and make a decision today. Now many people might say this is a huge boo-boo, but with great power comes great responsibility. If you go in with eyes wide open, then you will only use the data to make things easier or even doable, but without cheating. Because you are only going to cheat yourself. Its in your best interest do follow the rules. Here is the line that let’s you leak into the future.

If isNewMonth(myDate[curBar+1])

The curBar is today and curBar+1 is tomorrow. So I am saying if tomorrow is the first day of the month then buy today’s close. Here you are leaking into the future but not taking advantage of it. We all know if today is the last day of the month, but try explaining that to a computer. You saw the EasyLanguage code. So things are made easier with future leak, but not taking advantage of .

Here is a quick video of running the TS-18 Module of 4 different markets.

I have just recently opened my Digital Store. I am going to provide reasonably priced LEARN AS YOU GO products. Right now I have an immediate download of the Camarilla Suite for $10 and the EL-DayTrader Framework 1.3 for $39. The code in the Framework has been extracted from work over the years with EasyLanguage. I incorporated one of Jeff Swanson’s Better Break-Out strategies in the Framework to demonstrate how to go beyond the pre-programmed concepts that are built-in. You will learn DAY OF WEEK manipulation and how to convert patterns into a string of plusses or minuses. You will also learn how to optimize time constraints. Of course I will tech support via email (usually with 24 hours.) Visit George’s Digital Store from the main menu.