Easing Into EasyLanguage-DayTrade Edition [On SALE Now thru November]

EZ-DT Pyramania is a strategy I introduced in the Day Trade Edition. The logic is rather simple – pyramid as the market moves through multiple levels during the trading day. – buy, buy, buy, dump or buy, dump, short, short, short, dump. The distance between the levels is constant. In the book, I showed an algorithm with a total of 6 levels with 7 edges.

Here the market opens @ 9:30 and the levels are instantly plotted and trades are executed as the market moves through the levels located above the open tick. Over the weekend, I had a reader ask how he could modify the code to plot the levels on the 24-hour @ES session. In the day session, I used the change in the date as the trigger for the calculation and plotting of the levels. Here is the day session version.

inputs:numSegments(6),numPlots(6);

arrays: segmentBounds[](0);

variables: j(0),loopCnt(0),segmentSize(0),avgRng(0); once Begin Array_SetMaxIndex(segmentBounds, numSegments); end;

if d <> d[1] Then // only works on the @ES.D or any .D session begin avgRng = average(range of data2,20); segmentSize = avgRng/numSegments; loopCnt = -1*numSegments/2; for j = 0 to numSegments begin segmentBounds[j] = openD(0) + loopCnt * segmentSize; loopCnt = loopCnt + 1; end; end;

//The following time constraint only works when all time stamps //are less than the end of day time stamp //This will not work when time = 1800 and endTime = 1700 if t < calcTime(sessionEndTime(0,1),-barInterval) Then begin if numPlots >= 1 then plot1(segmentBounds[0],"Level 0"); if numPlots >= 2 then plot2(segmentBounds[1],"Level 1"); if numPlots >= 3 then plot3(segmentBounds[2],"Level 2"); if numPlots >= 4 then plot4(segmentBounds[3],"Level 3"); if numPlots >= 5 then plot5(segmentBounds[4],"Level 4"); if numPlots >= 6 then plot6(segmentBounds[5],"Level 5"); if numPlots >= 7 then plot7(segmentBounds[6],"Level 6"); // plot8(segmentBounds[7],"Level 7"); // plot9(segmentBounds[8],"Level 8"); end;

Works great with @ES.D or any @**.D

I like this code because it exposes you to arrays, loops, and plotting multiple values. You can fix this by modifying and adding some code. I used the Trading Around Midnight blog post to get the code I needed to enable plotting around 0:00 hours. Here is the updated code:

once Begin Array_SetMaxIndex(segmentBounds, numSegments); end;

startTime = sessionStartTime(0,1); endTime = sessionEndTime(0,1); //let TS tell you when the market opens - remember the //first time stamp is the open time + bar interval if t = calcTime(sessionStartTime(0,1),barInterval) Then begin avgRng = average(range of data2,20); segmentSize = avgRng/numSegments; loopCnt = -1*numSegments/2; for j = 0 to numSegments begin segmentBounds[j] = open + loopCnt * segmentSize; loopCnt = loopCnt + 1; end; end;

// if startTime > endTime then you know you are dealing with // timees that more than likely bridges midnight // if time is greater then 1700 (end time) then you must // subtract an offset so it makes sense - endTimeOffset // play with the math and it will come to you if startTime > endTime then begin endTimeOffset = 0; if t >= startTime+barInterval and t<= 2359 then endTimeOffSet = 2400-endTime; end; if t-endTimeOffSet < endTime Then begin if numPlots >= 1 then plot1(segmentBounds[0],"Level 0"); if numPlots >= 2 then plot2(segmentBounds[1],"Level 1"); if numPlots >= 3 then plot3(segmentBounds[2],"Level 2"); if numPlots >= 4 then plot4(segmentBounds[3],"Level 3"); if numPlots >= 5 then plot5(segmentBounds[4],"Level 4"); if numPlots >= 6 then plot6(segmentBounds[5],"Level 5"); if numPlots >= 7 then plot7(segmentBounds[6],"Level 6"); // plot8(segmentBounds[7],"Level 7"); // plot9(segmentBounds[8],"Level 8"); end;

Modification to plot data around midnight

Here I let TS tell me with the market opens and then use some simple math to make sure I can plot with the time is greater than and less than the end of day time.

Email me if you have the book and want to companion code to the strategy – georgeppruitt@gmail.com

Have You Ever Wondered If You Just Reversed the Logic?

You have been there before. What you thought was a great trading idea turns out to be a big flop. We have all developed these types of algorithms. Then it hits you, just flip the logic and in turn the equity curve. Hold your horses! First off you have to make sure it’s not just the execution costs that is hammering the equity curve into oblivion. When testing a fresh trading idea, it is best to keep execution costs to zero. This way if your idea is a good one, but is simply backward, then you have a chance of creating something good out of something bad. I was playing around with a mean reversion day trading algorithm (on the @ES.D – day session of the mini S&P 500) that created the following equity curve. Remember to read the disclaimer concerning hypothetical performance before proceeding reading the rest of this blog. It is located under the DISCLAIMER – REAMDE! tab. By reading the rest of this blog post it implies that you understand the limitations of hypothetical back testing and simulated analysis.

The pandemic created a strong mean reversion environment. In the initial stage of this research, I did not set the executions costs – they defaulted to zero. My idea was to buy below the open after the market moved down from the high of the day a certain percentage of price. Since I was going to be buying as the market was moving down, I was willing to use a wide stop to see if I could hold on to the falling knife. Short entries were just the opposite -sell short above the open after the market rallied a certain percentage of price. I wanted to enter on a hiccup. Once the market moved down a certain range from the high of the day, I wanted to enter on a stop at the high of the prior bar. I figured if the price penetrated the high of the prior five-minute bar in a down move, then it would signal an eventual rotation in the market. Again, I was just throwing pasta against the wall to see what would stick. I even came up with a really neat name for the algorithm the Rubber Band system – stretch just far enough and the market is bound to slam back. Well, there wasn’t any pasta sticking. Or was there? If I flipped the equity curve 180 degrees, then I would have a darned good strategy. All it would take is to reverse the signals, sell short when I was buying and buy when I was selling short. Instead of a mean reversion scheme, this would turn into a momentum-based strategy.

if c < todaysOpen and todaysOpen-c = maxOpenMinusClose and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canBuy = True; if c > todaysOpen and c- todaysOpen = maxCloseMinusOpen and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canShort = True;

Guts of the complete failure.

Here I measure the maximum distance from the highest close above the open and the lowest close below the open. The distance between the two points is the range between the highest and lowest closing price of the current day. If the close is less than today’s open, and the range between the extremes of the highest close and lowest close of the trading day is greater than stretchPercent, then an order directive to buy the next bar at the current bar’s high is issued. The order is alive until it is filled, or the day expires. Selling short uses the same calculations but requires the close of the current bar to be above the open. The stretchPercent was set to 1 percent and the protective stop was set to a wide $2,000. As you can see from the equity curve, this plan did not work except for the time span of the pandemic. Could you optimize the strategy and make it a winning system. Definitely. But the 1 percent and $2000 stop seemed very logical to me. Since we are comparing the range of the data to a fixed price of the data, then we don’t need to worry about the continuous contract distortion. Maybe we would have to, if the market price was straddling zero. Anyways, here is a strategy using the same entry technique, but reversed, with some intelligent trade filtering. I figured a profit objective might be beneficial, because the stop was hit several times during the original test.

If you like the following code, make sure you check out my books at Amazon.com. This type of code is used the Hi-Res and Day-Trading editions of the Easing_Into_Easylanguage series.

if c < todaysOpen and todaysOpen-c = maxOpenMinusClose and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canShort = True; if c > todaysOpen and c- todaysOpen = maxCloseMinusOpen and (maxCloseMinusOpen + maxOpenMinusClose)/c >= stretchPercent Then canBuy = True;

if canTrade and t >= calcTime(dontTradeBefore,dontTradeBeforeOffset) and t < calcTime(dontTradeAfter,dontTradeAfterOffset) and t < sessionEndTime(0,1) Then begin if shortsToday = 0 and canShort = True Then sellshort next bar at l stop; if buysToday = 0 and canBuy = True Then buy next bar at h stop; end;

Trade filtering was obtained by limiting the duration during the trading day that a trade could take place. It’s usually wise to wait a few minutes after the open and a few minutes prior to the close to issue trade directives. Also, range compression of the prior day seems to help in many cases. Or at least not range expansion. I only allow one long entry or one short or both during the trading day – two entries only! Read the code and let me know if you have any questions. This is a good framework for other areas of research. Limiting entries using the mp variable is a neat technique that you can use elsewhere.

And as always let me know if you see any bugs in the code. Like Donnie Knuth says, “Beware of bugs in the above code; I have only proved it correct, not tried it!”

Daily Bar Daytrade to 5 Minute Bars — Not So Easy!

Like in the previous post, I designed a simple optimization framework trying to determine the best day of the week to apply a long onlyvolatility based open range break out algorithm on natural gas. You might first ask why natural gas? Its a good market that has seasonal tendencies that can actually be day-traded – there is enough volatility and volume. Anyway, by simply slapping setExitOnClose at the end of your code turns your algorithm into one that exits every day at the settlement (fictional value that is derived via formula) price. You can always use LIB (look inside bar) to help determine the magnitude of intraday swings and there respective chronological order. But you are still getting out at the settlement price – which isn’t accurate. However, if you are trying to get a set of parameters that gets you into the ballpark, then this is a very acceptable approach. But you will always want to turn you daily bar strategy with LIB intro an intraday version for automation and accuracy purposes.

Day of Week Analysis On Intraday Data

When you optimize the day of week on daily bars, the first bar of the week is usually Monday. When testing with intraday bars on futures, the trading actually starts on Sunday evening. If your daily bar analysis reveals the best day of week is Wedneday, then you must inform TradeStation to take the trade from the opening on Tuesday night through the close on Wenesday. If you are executing a market order, then you just need to tell TradeStation to execute at Tuesday night’s open (1800 eastern standard time). The TRICK to allow trading throughout the 24 hour session (1800 to 1700) on just a particular day of the week is to capture the day of the week on the first bar of the trading session. Here is some bookkeeping you can take care of on the first bar of the trading session.

if t = sessionstartTime(0,1) + barInterval Then Begin

todaysOpen = o; openDOW = dayOfWeek(d); canBuy = False; if openDOW = daysOfWeekToTrade then canBuy = True; if canBuy and d[1] <> date of data2 then begin canBuy = False; print(d," turning off canBuy"); end;

if mp = 1 then curTradeDays = curTradeDays + 1; if mp = 0 then curTradeDays = 0; barsToday = 1; end;

Bookkeeping on the first bar of trading session

Here, I check to see if the first bar’s time stamp is equal to the sessionStarttime + barInterval. Remember TradeStation shows the close time of each bar as the time stamp. If adding a barInterval to the opening time results in a non-time, then use the calcTime function ( 1800 + 60 = 1860 – a non-time.) I first store the open of the session in my variable todaysOpen. Why not just use openD(0). Well openD(0) is great for sessions that don’t span midnight. If they span midnight then the openD returns the open of the12:00 am bar. Here is the output from August 9th with the open on August 8th at 1800. The two other values are midnight on August 8th and August 7th. So the openD, highD, lowD, and closeD functions involve the 24 hour clock and not the session.

On the first bar of the trading session I also capture the day of the week. The days of the week, using intraday data on a 24 hour session, range from Sunday to Thursday, instead of Monday thru Friday. CanBuy is turned on if the day of week equals the input provided by the user. When using daily bars and you optimize the day of the week, remember if you chose 2 or Tuesday you really mean Wednesday. If today is Tuesday, then buy next bar at… This will generate trades only on Wednesdays. When testing on intraday data you do not need to make a modification for the day of week. If the first bar of the trading session is Tuesday, then you will actually trade what is considered the Wednesday session that starts on Tuesday evening at 18:00.

What About Holidays?

Here is a quick trick to not trade on Holidays. The daily bar does not include preempted sessions in its database, whereas intraday data does. So, if at 18:00 the date of the prior bar does not match the date of the daily bar located in the Data2 slot, then you know that the prior day was preempted and you should not trade today. In other words, Data2 is missing. Remember, you only want to trade on days that are included in the daily bar database in your attempt to replicate the daily bar strategy.

date close moving average
1230703.00 data2 2.57382 2.70300
1230704.00 missing data2 2.57382 2.70300 <-- no trade
1230705.00 data2 2.57485 2.65100 <-- matches

How to count the daily bars since entry.

Here again you need to do some bookkeeping. If you are long on the first bar of the day, then you have been in the trade for at least a day. If you are long on two first bars of the trading session, then you have been long for two days. I use the variable, curTradeDays, to count the number of days we have been in the trade. If on the first bar of the trading session we are flat, then I reset curTradeDays to zero. Otherwise, I increment the variable and this only occurs once a day. You can optimize the daysInTrade input, but for this post we are just interested in getting out at the close on the day of entry.

Controlling one entry per day.

We only want one long entry per day in an attempt to replicate the daily bar system. There are several ways to do this, but comparing the current bar’s marketPosition to the prior bar’s value will inform you if a position has been transitioned from flat to long. If the prior bar’s position if flat and the current bar is now long, then we can turn our canBuy off or to false. If we get stopped out later and the market rallies again to our break out level, we will not execute at that level, because the trade directive will not be issued. Why not use the entriesToday function? Again midnight cause this function to reset. Say you go long at 22:00 and you do not want to enter another position until the tomorrow at 18:00. EntriesToday will forget you entered the long position at 22:00.

Cannot execute on the first bar of the day – does it matter?

Since this is an open range break out, we need to know what the open is prior to our break out calculation. Could the break out level be penetrated on the first 5 minute bar? Sure, but I bet it is rare. I tested this and it only occurred 4 or 5 times. In a back-test, if this occurs and the subsequent bar’s open is still above the break out level, TradeStation will convert the stop order to a market order and you will buy the open of the 2nd five minute bar. In real time trading, you must tell TradeStation to wait until the end of the first 5 minute bar before issuing the trade directive to replicate your back testing. If this is an issue, you can test with 5 minute bars, but execute on 1 minute bars. In other words, have your testing chart and an additional execution chart. Skipping the first 5 minute bar may be advantageous. Many times you will get a surge at the open and then the market falls back. By the time the first 5 minute bar concludes the market may be trading back below your break out level. You might bypass the bulge. But your still in the game. Here is how you can test the significance of this event.

if canBuy and h > todaysOpen + volAmt *volMult then Begin print(d," first bar penetration ",value67," ",value68," ",(c - (todaysOpen + volAmt *volMult))*bigPointValue ); value67 = value67 + 1; value68 = value68 + (c - (todaysOpen + volAmt *volMult))*bigPointValue; end;

Testing for penetration on first 5 minute bar of the day

By the end of the first bar of the day we know the open, high, low and close of the day thus far. We can test to see if the high would have penetrated the break out level and also calculate the profit or loss of the trade. In a back-test you will not miss this trade if the next bar’s open is still above the break out level. If the next bar’s open is below the break out level, then you may have missed a fake out break out. Again this is a rare event.

if t = sessionstartTime(0,1) + barInterval Then Begin todaysOpen = o; openDOW = dayOfWeek(d); canBuy = False; if openDOW = daysOfWeekToTrade then canBuy = True; if canBuy and d[1] <> date of data2 then begin canBuy = False; end;

if mp = 1 then curTradeDays =curTradeDays + 1; if mp = 0 then curTradeDays = 0; barsToday = 1;

volAmt = average(range of data2,volLen); movAvg = average(c,mavLen) of data2; if canBuy and h > todaysOpen + volAmt *volMult then Begin print(d," first bar penetration ",value67," ",value68," ",(c - (todaysOpen + volAmt *volMult))*bigPointValue ); value67 = value67 + 1; value68 = value68 + (c - (todaysOpen + volAmt *volMult))*bigPointValue; end; end;

if mp[1] = 0 and mp = 1 and canBuy then canBuy = False;

if canBuy and t <> sessionEndTime(0,1) Then Begin if barsToday > 1 and close of data2 > movAvg and range of data2 > volAmt * volComp then buy("DMIntraBuy") next bar at todaysOpen + volAmt *volMult stop; end; barsToday = barsToday + 1;

if daysInTrade = 0 then setExitOnClose;

sell("LLxit") next bar at lowest(l of data2,llLookBack) stop; if mp = 1 and daysInTrade > 0 then begin if curTradeDays = daysInTrade then sell("DaysXit") next bar at open; end;

Intrday system that replicates a daily bar day trader

We are using the setStopLoss and setProfitTarget functions in this code. But remember, if your continuous contract goes negative, then these functions will not work properly.

This type of programming is tricky, because you must use tricks to get EasyLanguage to do what you want it to do. You must experiment, debug, and program ideas to test the limitations of EasyLanguage to hone your craft.

A study between ice cream sales and crime rate demonstrated a high level of correlation. However, it would be illogical to assume that buying more ice cream leads to more crime. There are just too many other factors and variables involved to draw a conclusion. So, data mining with EasyLanguage may or may not lead to anything beneficial. One thing is you cannot hang your hat completely on this type of research. A reader of my books asked if there was evidence that pointed to the best time to enter and exit a day trade. Is it better to enter in the morning or in the afternoon or are there multiple trading windows throughout the day? I thought I would try to answer the question using TradeStation’s optimization capabilities.

Create a Search Space of Different Window Opening Times and Open Duration

My approach is just one of a few that can be used to help answer this question. To cut down on time and the size of this blog we will only look at day trading the @ES.D from the long side. The search space boundaries can be defined by when we open the trading window and how long we leave it open. These two variables will be defined by inputs so we can access the optimization engine. Here is how I did it with EasyLanguage code.

if t >= calcTime(openWindowTime,openWindowOffset) and t < calcTime(openWindowTime,openWindowOffset+windowDuration) Then Begin if entriesToday(d) = 0 and canBuy Then buy next bar at market; if entriesToday(d) = 0 and canShort Then sellshort next bar at market ; end;

if t = calcTime(openWindowTime,openWindowOffset+windowDuration) Then Begin if marketPosition = 1 then sell next bar at open; if marketPosition =-1 then buyToCover next bar at open; end; setExitOnClose;

Optimize when to open and how long to leave open

The openWindowTime input is the basis from where we open the trading window. We are working with the @ES.D with an open time of 9:30 AM eastern. The openWindowOffset will be incremented in minutes equivalent to the data resolution of the chart, five minutes. We will start by opening the window at 9:35 and leave it open for 60 minutes. The next iteration in the optimization loop will open the window at 9:40 and keep it open for 60 minutes as well. Here are the boundaries that I used to define our search space.

window opening times offset: 5 to 240 by 5 minutes

window opening duration: 60 to 240 by 5 minutes

A total of 1739 iterations will span our search space. The results state that waiting for twenty minutes before buying and then exiting 190 minutes later, worked best. But also entering 90 minutes after the open and exiting 4 hours later produced good results as well (no trade execution fee were utilized.) Initially I was going to limit entry to once per day, but then I thought it might be worthwhile to enter a fresh position, if the first one is stopped out, or pyramid if it hasn’t. I also thought, each entry should have its own protective stop amount. Would entering later require a small stop – isn’t most of the volatility, on average, expressed during the early part of the day.

Build a Strategy that Takes on a Secondary Trade as a New Position or One that is Pyramided.

This is not a simple strategy. It sounds simple and it requires just a few lines of code. But there is a trick in assigning each entry with its own exit. As you can see there is a potential for trade overlap. You can get long 20 minutes after the open and then add on 70 minutes (90 from the open) later. If the first position hasn’t been stopped out, then you will pyramid at the second trade entry. You have to tell TradeStation to allow this to happen.

System Rules

Enter long 20 minutes after open

Enter long 90 minutes after open

Exit 1st entry 190 minutes later or at a fixed $ stop loss

Exit 2nd entry 240 minutes later or at a fixed $ stop loss

Make sure you are out at the end of the day

Sounds pretty simple, but if you want to use different stop values for each entry, then the water gets very muddy.

AvgEntryPrice versus EntryPrice

Assume you enter long and then you add on another long position. If you examine EntryPrice you will discover that it reflects the initial entry price only. The built-in variable AvgEntryPrice will be updated with the average price between the two entries. If you want to key off of the second entry price, then you will need to do a little math.

Using this formula and simple algebra we can arrive at ep2 using this formula: ep2 = 2*ap – ep1. Since we already know ep1 and ap, ep2 is easy to get to. We will need this information and also the functionality of from entry. You tie entries and exits together with the keywords from entry. Here are the entry and exit trade directives.

if time = calcTime(openTime,entryTime1Offset) then buy("1st buy") next bar at open;

if time = calcTime(openTime,entryTime2Offset) then buy("2nd buy") next bar at open;

if time = calcTime(openTime,entryTime1Offset + exitTime1Offset) then sell("1st exit") from entry("1st buy") next bar at open;

if time = calcTime(openTime,entryTime2Offset + exitTime2Offset) then sell("2nd exit") from entry("2nd buy") next bar at open;

if mp = 1 Then Begin value1 = avgEntryPrice; if currentShares = 2 then value1 = avgEntryPrice*2 - entryPrice; sell("1st loss") from entry("1st buy") next bar at entryPrice - stopLoss1/bigPointValue stop; sell("2nd loss") from entry("2nd buy") next bar at value1 - stopLoss2/bigPointValue stop; end;

if mp = 1 and t = openTime + barInterval then sell("oops") next bar at open;

Entry and Exit Directives Code

The trade entry directives are rather simple, but you must use the calcTime function to arrive at the correct entry and exit times. Here we are using the benchmark, openTime and the offsets of entryTime1Offset and entryTime2Offset. This function adds (or subtracts if the offset is negative) the offset to the benchmark. This takes care of when the trading windows open, but you must add the entry1TimeOffset to exit1TimeOffset to calculate the duration the trading window is to remain open. This goes for the second entry window as well.

Now let’s look at the exit directives. Notice how I exit the 1st buy entry with the code from entry (“1st buy”). This ties the entry and exit directives together. This is pretty much straightforward as well. The tricky part arrives when we try to apply different money management stops to each entry. Exiting from the 1st buy requires us to simply subtract the $ in terms of points from entryPrice. We must use our new equation to derive the 2nd entry price when two contracts are concurrent. But what if we get stopped out of the first position prior to entering the second position? Should we continue using the formula. No. We need to fall back to entryPrice or avgEntryPrice: when only one contract or unit is in play, these two variables are equal. We initially assign the variable value1 to the avgEntryPrice and only use our formula when currentShares = 2. This code will work a majority of the time. But take a look at this trade:

This is an anomaly, but anomalies can add up. What happened is we added the second position and the market moved down very quickly – too quickly for the correct entry price to be updated. The stop out (2nd loss) was elected by using the 1st entry price, not the second. You can fix this with the following two solutions:

Increase data resolution and hope for an intervening bar

Force the second loss to occur on the subsequent trading bar after entry. This means you will not be stopped out on the bar of entry but will have to wait five minutes or whatever bar interval you are working with.

OK – Now How Do We Make this a Viable Trading System

If you refer back to the optimization results you will notice that the average trade (before execution costs) was around $31. Keep in mind we were trading every day. This is just the beginning of your research – did we find a technical advantage? No. We just found out that you can enter and exit at different times of the trading day, and you can expect a positive outcome. Are there better times to enter and exit? YES. You can’t trade this approach without adding some technical analysis – a reason to enter based on observable patterns. This process is called FILTERING. Maybe you should only enter after range compression. Or after the market closed up on the prior day, or if the market was an NR4 (narrow range 4.) I have added all these filters so you can iterate across them all using the optimization engine. Take a look:

filter1 = True; filter2 = True;

if filtNum1 = 1 then filter1 = close of data2 > close[1] of data2; if filtNum1 = 2 Then filter1 = close of data2 < close[1] of data2; if filtNum1 = 3 Then filter1 = close of data2 > open of data2; if filtNum1 = 4 Then filter1 = close of data2 < open of data2; if filtNum1 = 5 Then filter1 = close of data2 > (h data2 + l data2 + c data2)/3; if filtNum1 = 6 Then filter1 = close of data2 < (h data2 + l data2 + c data2)/3; if filtNum1 = 7 Then filter1 = openD(0) > close data2; if filtNum1 = 8 Then filter1 = openD(0) < close data2;

if filtNum2 = 1 Then filter2 = trueRange data2 < avgTrueRange(10) data2; if filtNum2 = 2 Then filter2 = trueRange data2 > avgTrueRange(10) data2; if filtNum2 = 3 Then filter2 = range data2 = lowest(range data2,4); if filtNum2 = 4 Then filter2 = range data2 = highest(range data2,4);

Filter1 and Filter2 - filter1 looks for a pattern and filter2 seeks range compression/expansion.

Let’s Search – And Away We Go

I will optimize across the different patterns and range analysis and different $ stops for each entry (1st and 2nd.)

Best Total Profit

Trade when today’s open is greater than yesterday’s close and don’t worry about the volatility. Use $550 for the first entry and $600 for the second.

Best W:L Ratio and Respectable Avg. Trade

This curve was created by waiting for yesterday’s close to be below the prior day’s and yesterday being an NR4 (narrow range 4). And using a $500 protective stop for both the 1st and 2nd entries.

Did We Find the Holy Grail? Gosh No!

This post served two purposes. One, how to set up a framework for data mining and two, create code that can handle things that aren’t apparently obvious – entryPrice versus avgEntryPrice!

if filter1 and filter2 Then begin if time = calcTime(openTime,entryTime1Offset) then buy("1st buy") next bar at open;

if time = calcTime(openTime,entryTime2Offset) then buy("2nd buy") next bar at open; end;

Incorporating Filter1 and Filter2 in the Entry Logic

OptimizeDaysOfWeek = False; if optimizeNumber > 0 and optimizeNumber < 32 Then Begin currDOWStr = midStr(daysOfWeek,dayOfWeek(d),1); if inStr(dowArr[optimizeNumber],currDOWStr) <> 0 Then OptimizeDaysOfWeek = True; end;

if currentbar>=1 then if oscVal>oscVal[1] then plot1(mavDiff,"+AO") else plot2(mavDiff,"-AO")

Williams Awesome Oscillator Source Code

And here is what it looks like:

The code reveals a value that oscillates around 0. First calculate the difference between the 5-day moving average of the daily midPoint (H+ L)/2 and the 34-day moving average of the midPoint. A positive value informs us that the market is in a bullish stance whereas a negative represents a bearish tone. Basically, the positive value is simply stating the shorter-term moving average is above the longer term and vice versa. The second step in the indicator calculation is to subtract the 5-day moving average of the differences from the current difference. If the second calculation is greater than the prior day’s calculation, then plot the original calculation value as green (AO+). If it is less (A0-), then paint the first calculation red. The color signifies the momentum between the current and the five-day smoothed value.

Here I am using the very handy countIf function. This function will tell you how many times a Boolean comparison is true out of the last N days. Her I use the function twice, but I could have replaced the second function call with mavDn = 30 – mavUp. So, I am counting the number of occurrences of when the mavDiff is positive and negative over the past 30-days. I also count the number of times the oscVal is greater than the prior oscVal. In other words, I am counting the number of green bars. I create a ratio between green bars and 10. If there are six green bars, then the ratio equals 60% This indicates that the ratio of red bars would be 40%. Based on these readings you can create trade entry directives.

if canShort and mavUp > numBarsAbove and mavDiff > minDiffAmt and oscRatio >= obRatio then sellShort next bar at open;

if canBuy and mavDn > numBarsBelow and mavDiff < -1*minDiffAmt and oscRatio <= osRatio Then buy next bar at open;

Trade Directives

If the number of readings out of the last 30 days is greater than numBarsAbove and mavDiff is of a certain magnitude and the oscillator ratio is greater than buyOSCRatio, then you can go short on the next open. Here we are looking for the market to converge. When these conditions are met then I think the market is overbought. You can see how I set up the long entries. As you can see from the chart it does a pretty good job. Optimizing the parameters on the crude oil futures yielded this equity curve.

Not bad, but not statistically significant either. One way to generate more trades is to install some trade management such as protective stop and profit objective.

Using a wide protective stop and large profit objective tripled the number of trades. Don’t know if it is any better, but total performance was not derived from just a couple of trades. When you are working with a strategy like this and overlay trade management you will often run into this situation.

Here we either get stopped out or take a profit and immediately reenter the market. This occurs when the conditions are still met to short when we exit a trade. The fix for this is to determine when an exit has occurred and force the entry trigger to toggle off. But you have to figure out how to turn the trigger back on. I reset the triggers based on the number of days since the triggers were turned off – a simple fix for this post. If you want to play with this strategy, you will probably need a better trigger reset.

I am using the setStopLoss and setProfitTarget functionality via their own strategies – Stop Loss and Profit Target. These functions allow exit on the same as entry, which can be useful. Since we are executing on the open of the bar, the market could definitely move either in the direction of the stop or the profit. Since we are using wide values, the probability of both would be minimal. So how do you determine when you have exited a trade. You could look the current bar’s marketPosition and compare it with the prior bar’s value, but this doesn’t work 100% of the time. We could be flat at yesterday’s close, enter long on today’s open and get stopped out during the day and yesterday’s marketPosition would be flat and today’s marketPosition would be flat as well. It would be as if nothing occurred when in fact it did.

Take a look at this code and see if it makes sense to you.

if mp[1] = 1 and totalTrades > totTrades then canBuy = False;

if mp[1] = -1 and totalTrades > totTrades then canShort = False;

if mp[1] = 0 and totalTrades > totTrades then Begin if mavDiff[1] < 0 then canBuy = False; if mavDiff[1] > 0 then canShort = False; end;

totTrades = totalTrades;

Watch for a change in totalTrades.

If we were long yesterday and totalTrades (builtin keyword/function) increases above my own totTrades, then we know a trade was closed out – a long trade that is. A closed out short position is handled in the same manner. What about when yesterday’s position is flat and totalTrades increases. This means an entry and exit occurred on the current bar. You have to investigate whether the position was either long or short. I know I can only go long when mavDiff is less than zero and can only go short when mavDiff is greater than zero. So, all you need to do is investigate yesterday’s mavDiff to help you determine what position was entered and exited on the same day. After you determine if an exit occurred, you need to update totTrades with totalTrades. Once you determine an exit occurred you turn canBuy or canShort off. They can only be turned back on after N bars have transpired since they were turned off. I use my own barsSince function to help determine this.

if not(canBuy) Then if barsSince(canBuy=True,100,1,0) = 6 then canBuy = True; if not(canShort) Then if barsSince(canShort=True,100,1,0) = 6 then canShort = True;

if not(canBuy) Then if barsSince(canBuy=True,100,1,0) = numBarsTrigReset then canBuy = True; if not(canShort) Then if barsSince(canShort=True,100,1,0) = numBarsTrigReset then canShort = True;

if mp[1] = 1 and totalTrades > totTrades then canBuy = False;

if mp[1] = -1 and totalTrades > totTrades then canShort = False;

if mp[1] = 0 and totalTrades > totTrades then Begin if mavDiff[1] < 0 then canBuy = False; if mavDiff[1] > 0 then canShort = False; end;

if canShort and mavUp > numBarsAbove and mavDiff > minDiffAmt and oscRatio >= buyOSCRatio then sellShort next bar at open;

if canBuy and mavDn > numBarsBelow and mavDiff < -1*minDiffAmt and oscRatio <= shortOSRatio Then buy next bar at open;

I could be wrong, but I couldn’t find this functionality in EasyLanguage. I was testing a strategy that didn’t want to trade a specific week of the month. If it’s not built-in, then it must be created. I coded this functionality in Sheldon Knight’s Seasonality indicator but wanted to create a simplified universal version. I may have run into one little snag. Before discussing the snag, here is a Series function that returns my theoretical week of the month.

if month(d) <> month(d[1]) Then Begin weekCnt = 1; end;

if dayOfWeek(d)<dayOfWeek(d[1]) and month(d) = month(d[1]) Then begin weekCnt = weekCnt + 1; end; if weekCnt = 1 and dayofMonth(d) = 2 and dayOfWeek(D) = Sunday Then print("Saturday was first day of month"," ",d);

WeekOfMonth = weekCnt;

Series Function to Return Week Rank

This function is of type Series because it has to remember the prior output of the function call – weekCnt. This function is simple as it will not provide the week count accurately (at the very beginning of a test) until a new month is encountered in your data. It needs a ramp up (at least 30 days of daily or intraday data) to get to that new month. I could have used a while loop the first time the function was called and worked my way back in time to the beginning of the month and along the way calculate the week of the month. I decided against that, because if you are using tick data then that would be a bunch of loops and TradeStation can get caught in an infinite loop very easily.

This code is rather straightforward. If the today’s month is not equal to yesterday’s month, then weekCnt is set to one. Makes sense, right. The first day of the month should be the first week of the month. I then compare the day of week of today against the day of week of yesterday. If today’s day of week is less than the prior day’s day of week, then it must be a Sunday (futures markets open Sunday night to start the week) or Monday (Sunday = 0 and Monday = 1 and Friday = 5.) If this occurs and today’s month is the same as yesterday’s month, weekCnt is incremented. Why did I check for the month? What if the first trading day was a Sunday or Monday? Without the month comparison I would immediately increment weekCnt and that would be wrong. That is all there is to it? Or is it? Notice I put some debug code in the function code.

Is There a Snag?

April 2023 started the month on Saturday which is the last day of the week, but it falls in the first week of the month. The sun rises on Sunday and it falls in the second week of the month. If you think about my code, this is the first trading day of the month for futures:

month(April 2nd) = April or 4

month(March 31st) = March or 3

They do not equal so weekCnt is set to 1. The first trading day of the month is Sunday or DOW=0 and the prior trading day is Friday or DOW=5. WeekCnt is NOT incremented because month(D) doesn’t equal month(D[1]). Should WeekCnt be incremented? On a calendar basis it should, but on a trading calendar maybe not. If you are searching for the first occurrence of a certain day of week, then my code will work for you. On April 2nd, 2023, it is the second week of the month, but it is the first Sunday of April.

Thoughts??? Here are a couple of screenshots of interest.

This function is an example of where we let the data tell us what to do. I am sure there is calendar functions, in other languages, that can extract information from just the date. However, I like to extract from what is actually going to be traded.

Email me if you have any questions, concerns, criticism, or a better mouse trap.

How to reenter at a better price after a stop loss

A reader of this blog wanted to know how he could reenter a position at a better price if the first attempt turned out to be a loser. Here I am working with 5 minute bars, but the concepts work for daily bars too. The daily bar is quite a bit easier to program.

ES.D Day Trade using 30 minute Break Out

Here we are going to wait for the first 30 minutes to develop a channel at the highest high and the lowest low of the first 30 minutes. After the first 30 minutes and before 12:00 pm eastern, we will place a buy stop at the upper channel. The initial stop for this initial entry will be the lower channel. If we get stopped out, then we will reenter long at the midpoint between the upper and lower channel. The exit for this second entry will be the lower channel minus the width of the upper and lower channel.

Here are some pix. It kinda, sorta worked here – well the code worked perfectly. The initial thrust blew through the top channel and then immediately consolidated, distributed and crashed through the bottom channel. The bulls wanted another go at it, so they pushed it halfway to the midpoint and then immediately the bears came in and played tug of war. In the end the bulls won out.

Here the bulls had initial control and then the bears, and then the bulls and finally the bears tipped the canoe over.

This type of logic can be applied to any daytrade or swing trade algorithm. Here is the code for the strategy.

//working with 5 minute bars here //should work with any time frame

if t = calcTime(startTime,barInterval) Then begin periodHigh = -99999999; periodLow = 99999999; barCountToday = 1; totTrades = totalTrades; end;

if barCountToday <= numOfBarsHHLL Then Begin periodHigh = maxList(periodHigh,h); periodLow = minList(periodLow,l); end;

barCountToday = barCountToday + 1;

longEntriesToday = totalTrades - totTrades;

mp = marketPosition; if t <= stopTradeTime and barCountToday > numOfBarsHHLL Then Begin if longEntriesToday = 0 then buy("InitBreakOut") next bar at periodHigh + minMove/priceScale stop; if longEntriesToday = 1 Then buy("BetterBuy") next bar at (periodHigh+periodLow)/2 stop; end;

if mp = 1 then if longEntriesToday = 0 then sell("InitXit") next bar at periodLow stop; if longEntriesToday = 1 then sell("2ndXit") next bar at periodLow - (periodHigh - periodLow) stop;

SetExitOnClose;

The key concepts here are the time constraints and how I count bars to calculate the first 30 – minute channel. I have had problems with EasyLanguages’s entriesToday function, so I like how I did it here much better. On the first bar of the day, I set my variable totTrades to EasyLanguages built-in totalTrades (notice the difference in the spelling!) The keyword TotalTrades is immediately updated when a trade is closed out. So, I simply subtract my totTrades (the total number of trades at the beginning of the day) from totalTrades. If totalTrades is incremented (a trade is closed out) then the difference between the two variables is 1. I assign the difference of these two values to longEntriesToday. If longEntriesToday = 1, then I know I have entered long and have been stopped out. I then say, OK let’s get back long at a better price – the midpoint. I use the longEntriesToday variable again to determine which exit to use. With any luck the initial momentum that got us long initially will work its way back into the market for another shot.

It’s Easy to Create a Strategy Based Indicator

Once you develop a strategy, the indicator that plots entry and exit levels is very easy to derive. You have already done the math – just plot the values. Check this out.

//working with 5 minute bars here //should work with any time frame

if t = calcTime(startTime,barInterval) Then begin periodHigh = -99999999; periodLow = 99999999; barCountToday = 1; end;

if barCountToday <= numOfBarsHHLL Then Begin periodHigh = maxList(periodHigh,h); periodLow = minList(periodLow,l); end;

if barCountToday < numOfBarsHHLL Then begin noPlot(1); noPlot(2); noPlot(3); noPlot(4); End Else begin plot1(periodHigh,"top"); plot2((periodHigh+periodLow)/2,"mid"); plot3(periodLow,"bot"); plot4(periodLow - (periodHigh - periodLow),"2bot"); end;

barCountToday = barCountToday + 1;

So, that’s how you do it. You can use this code as a foundation for any system that wants to try and reenter at a better price. You can also use this code to develop your own time based break out strategy. In my new book, Easing Into EasyLanguage – the Daytrade EditionI will discuss topics very similar to this post.

Have you discovered a seasonal tendency but can’t figure out how to test it?

Are there certain times of the year when a commodity increases in price and then recedes? In many markets this is the case. Crude oil seems to go up during the summer months and then chills out in the fall. It will rise once again when winter hits. Early spring might show another price decline. Have you done your homework and recorded certain dates of the year to buy/sell and short/buyToCover. Have you tried to apply these dates to a historical back test and just couldn’t figure it out? In this post I am going to show how you can use arrays and loops to cycle through the days in your seasonal database (database might be too strong of a term here) and apply long and short entries at the appropriate times during the past twenty years of daily bar data.

Build the Quasi-Database with Arrays

If you are new to EasyLanguage you may not yet know what arrays are or you might just simply be scared of them. Now worries here! Most people bypass arrays because they don’t know how to declare them, and if they get passed that, how to manipulate them to squeeze out the data they need. You may not be aware of it, but if you have programmed in EasyLanguage the least bit, then you have already used arrays. Check this out:

if high > high[1] and low > low[1] and average(c,30) > average(c,30)[1] then buy next bar at open

In reality the keywords high and low are really arrays. They are lists that contain the entire history of the high and low prices of the data that is plotted on the chart. And just like with declared arrays, you index these keywords to get historic data. the HIGH[1] means the high of yesterday and the HIGH[2] means the high of the prior day. EasyLanguage handles the management of these array-like structures. In other words, you don’t need to keep track of the indexing – you know the [1] or [2] stuff. The declaration of an array is ultra-simple once you do it a few times. In our code we are going to use four arrays:

Buy array

SellShort array

Sell array

BuyToCover array

Each of these arrays will contain the month and day of month when a trade is to be entered or exited. Why not the year? We want to keep things simple and buy/short the same time every year to see if there is truly a seasonal tendency. The first thing we need to do is declare the four arrays and then fill them up.

// use the keyword arrays and : semicolon // next give each array a name and specify the // max number of elements that each array can hold // the [100] part. Each array needs to be initialized // and we do this by placing a zero (0) in parentheses arrays: buyDates[100](0),sellDates[100](0), shortDates[100](0),buyToCoverDates[100](0);

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

// note the buyDates[1] has a matching sellDates[1] // buyDates[2] has a matching sellDates[2] // -- and -- // shortDates[1] has a matching buyToCoverDates[1] // shortDates[2] has a matching buyToCoverDates[2]

Our simple database has been declared, initialized and populated. This seasonal strategy will buy on:

April 15th and Exit on May 15th

June 5th and Exit on August 30th

It will sellShort on:

September 15th and Cover on November 30th

February 15th and Cover on March 30th

You could use this template and follow the pattern to add more dates to your database. Just make sure nothing overlaps.

Now, each chart has N dates of history plotted from the left to right. TradeStation starts out the test from the earliest date to the last date. It does this by looping one day at a time. The first thing we need to do is convert the bar date (TradeStation represents dates in a weird format – YYYMMDD – Jan 30, 2022 is represented by the number 1220130 – don’t ask why!!) to a format like the data that is stored in our arrays. Fortunately, we don’t have to deal with the year and EasyLanguage provides two functions to help us out.

Month(Date) = the month (1-12) of the current bar

DayOfMonth(Date) = the day of the month of the current bar

All we need to do is use these functions to convert the current bar’s date into terms of our database, and then test that converted date against our database. Take a look:

//convert the date into our own terms //say the date is December 12 //the month function returns 12 and the day of month returns 12 // 12*100 + 12 = 1212 --> MMDD - just waht we need //notice I look at the date of tomorrow because I want to take //action on the open of tomorrow.

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

//You might need to study this a little bit - but I am looping through each //array to determine if a trade needs to be entered. //Long Seasonal Entries toggle buyNow = False; for n = 1 to numBuyDates Begin if currentMMDD[1] < buyDates[n] and currentMMDD >= buyDates[n] Then Begin buyNow = True; end; end;

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

Date conversion and looping thru Database

This code might look a little daunting, but it really isn’t. The first for-loop starts at 1 and goes through the number of buyDates. The index variable is the letter n. The loop starts at 1 and goes to 2 in increments of 1. Study the structure of the for-loop and let me know if you have any questions. What do you think this code is doing.

if currentMMDD[1] < buyDates[n] and currentMMDD >= buyDates[n] Then

As you know the 15th of any month may fall on a weekend. This code basically says, ” Okay if today is less than the buyDate and tomorrow is equal to or greater than buyDate, then tommorrow is either going to be the exact day of the month or the first day of the subsequent week (the day of month fell on a weekend.) If tomorrow is a trade date, then a conditional buyNow is set to True. Further down in the logic the trade directive is issued if buyNow is set to True.

Total of 4 loops – 2 for each long/short entry and 2 for each long/short exit.

// fill the arrays with dates - remember we are not pyramiding here // use mmdd format buyDates[1] = 0415;sellDates[1] = 0515; buyDates[2] = 0605;sellDates[2] = 0830; numBuyDates = 2; numSellDates = 2;

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

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

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

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

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

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

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

Does it work? It does – please take my word for it. IYou can email me with any questions. However, TS 10 just crashed on me and is wanting to update, but I need to kill all the processes before it can do a successful update. Remember to always export your new code to an external location. I will post an example on Monday Jan 30th.

Complete Strategy based on Sheldon Knight and William Brower Research

In my Easing Into EasyLanguage: Hi-Res Edition, I discuss the famous statistician and trader Sheldon Knight and his K-DATA Time Line. This time line enumerated each day of the year using the following nomenclature:

First Monday in December = 1stMonDec

Second Friday in April = 2ndFriApr

Third Wednesday in March = 3rdWedMar

This enumeration or encoding was used to determine if a certain week of the month and the day of week held any seasonality tendencies. If you trade index futures you are probably familiar with Triple Witching Days.

Four times a year, contracts for stock options, stock index options, and stock index futures all expire on the same day, resulting in much higher volumes and price volatility. While the stock market may seem foreign and complicated to many people, it is definitely not “witchy”, however, it does have what is known as “triple witching days.”

Triple witching, typically, occurs on the third Friday of the last month in the quarter. That means the third Friday in March, June, September, and December. In 2022, triple witching Friday are March 18, June 17, September 16, and December 16

Other days of certain months also carry significance. Some days, such as the first Friday of every month (employment situation), carry even more significance. In 1996, Bill Brower wrote an excellent article in Technical Analysis of Stocks and Commodities. The title of the article was The S&P 500 Seasonal Day Trade. In this article, Bill devised 8 very simple day trade patterns and then filtered them with the Day of Week in Month. Here are the eight patterns as he laid them out in the article.

Pattern 1: If tomorrow’s open minus 30 points is greater than today’s close, then buy at market.

Pattern 2: If tomorrow’s open plus 30 points is less than today’s close, then buy at market.

Pattern 3: If tomorrow’s open minus 30 points is greater than today’s close, then sell short at market.

Pattern 4: If tomorrow’s open plus 30 points is less than today’s close, then sell short at market.

Pattern 5: If tomorrow’s open plus 10 points is less than today’s low, then buy at market.

Pattern 6: If tomorrow’s open minus 20 points is greater than today’s high, then sell short at today’s close stop.

Pattern 7: If tomorrow’s open minus 40 points is greater than today’s close, then buy at today’s low limit.

Pattern 8: If tomorrow’s open plus 70 points is less than today’s close, then sell short at today’s high limit.

This article was written nearly 27 years ago when 30 points meant something in the S&P futures contract. The S&P was trading around the 600.00 level. Today the e-mini S&P 500 (big S&P replacement) is trading near 4000.00 and has been higher. So 30, 40 or 70 points doesn’t make sense. To bring the patterns up to date, I decided to use a percentage of ATR in place of a single point. If today’s range equals 112.00 handles or in terms of points 11200 and we use 5%, then the basis would equate to 11200 = 560 points or 5.6 handles. In the day of the article the range was around 6 handles or 600 points. So. I think using 1% or 5% of ATR could replace Bill’s point values. Bill’s syntax was a little different than the way I would have described the patterns. I would have used this language to describe Pattern1 – If tomorrow’s open is greater than today’s close plus 30 points, then buy at market – its easy to see we are looking for a gap open greater than 30 points here. Remember there is more than one way to program an idea. Let’s stick with Bills syntax.

10 points = 1 X (Mult) X ATR

20 points = 2 X (Mult) X ATR

30 points = 3 X (Mult) X ATR

40 points = 4 X (Mult) X ATR

50 points = 5 X (Mult) X ATR

70 points =7 X (Mult) X ATR

We can play around with the Mult to see if we can simulate similar levels back in 1996.

// atrMult will be a small percentage like 0.01 or 0.05 atrVal = avgTrueRange(atrLen) * atrMult;

//original patterns //use IFF function to either returne a 1 or a 0 //1 pattern is true or 0 it is false

The Day of Week In A Month is represented by a two digit number. The first digit is the week rank and the second number is day of the week. I thought this to be very clever, so I decided to program it. I approached it from a couple of different angles and I actually coded an encoding method that included the week rank, day of week, and month (1stWedJan) in my Hi-Res Edition. Bill’s version didn’t need to be as sophisticated and since I decided to use TradeStation’s optimization capabilities I didn’t need to create a data structure to store any data. Take a look at the code and see if it makes a little bit of sense.

newMonth = False; newMonth = dayOfMonth(d of tomorrow) < dayOfMonth(d of today); atrVal = avgTrueRange(atrLen) * atrMult; if newMonth then begin startTrading = True; monCnt = 0; tueCnt = 0; wedCnt = 0; thuCnt = 0; friCnt = 0; weekCnt = 1; end;

if not(newMonth) and dayOfWeek(d of tomorrow) < dayOfWeek(d of today) then weekCnt +=1;

dayOfWeekInMonth = weekCnt * 10 + dayOfWeek(d of tomorrow);

Simple formula to week rank and DOW

NewMonth is set to false on every bar. If tomorrow’s day of month is less than today’s day of month, then we know we have a new month and newMonth is set to true. If we have a new month, then several things take place: reinitialize the code that counts the number Mondays, Tuesdays, Wednesdays, Thursdays and Fridays to 0 (not used for this application but can be used later,) and set the week count weekCnt to 1. If its not a new month and the day of week of tomorrow is less than the day of the week today (Monday = 1 and Friday = 5, if tomorrow is less than today (1 < 5)) then we must have a new week on tomorrow’s bar. To encode the day of week in month as a two digit number is quite easy – just multiply the week rank (or count) by 10 and add the day of week (1-Monday, 2-Tuesday,…) So the third Wednesday would be equal to 3X10+3 or 33.

Use Optimization to Step Through 8 Patterns and 25 Day of Week in Month Enumerations

Stepping through the 8 patterns is a no brainer. However, stepping through the 25 possible DowInAMonth codes or enumerations is another story. Many times you can use an equation based on the iterative process of going from 1 to 25. I played around with this using the modulus function, but decided to use the Switch-Case construct instead. This is a perfect example of replacing math with computer code. Check this out.

switch(dowInMonthInc) begin case 1 to 5: value2 = mod(dowInMonthInc,6); value3 = 10; case 6 to 10: value2 = mod(dowInMonthInc-5,6); value3 = 20; case 11 to 15: value2 = mod(dowInMonthInc-10,6); value3 = 30; case 16 to 20: value2 = mod(dowInMonthInc-15,6); value3 = 40; case 21 to 25: value2 = mod(dowInMonthInc-20,6); value3 = 50; end;

Switch-Case to Step across 25 Enumerations

Here we are switching on the input (dowInMonthInc). Remember this value will go from 1 to 25 in increments of 1. What is really neat about EasyLanguage’s implementation of the Switch-Case is that it can handle ranges. If the dowInMonthInc turns out to be 4 it will fall within the first case block (case 1 to 5). Here we know that if this value is less than 6, then we are in the first week so I set the first number in the two digit dayOfWeekInMonth representation to 1. This is accomplished by setting value3 to 10. Now you need to extract the day of the week from the 1 to 25 loop. If the dowInMonthInc is less than 6, then all you need to do is use the modulus function and the value 6.

mod(1,6) = 1

mod(2,6) = 2

mod(3,6) = 3

This works great when the increment value is less than 6. Remember:

1 –> 11 (first Monday)

2 –> 12 (first Tuesday)

3 –> 13 (first Wednesday)

…

…

6 –> 21 (second Monday)

7 –> 22 (second Tuesday).

So, you have to get a little creative with your code. Assume the iterative value is 8. We need to get 8 to equal 23 (second Wednesday). This value falls into the second case, so Value3 = 20 the second week of the month. That is easy enough. Now we need to extract the day of week – remember this is just one solution, I guarantee there are are many.

mod(dowInMonthInc – 5, 6) – does it work?

value2 = mod(8-5,6) = 3 -> value3 = value1 + value2 -> value3 = 23. It worked. Do you see the pattern below.

case 6 to 10 – mod(dowInMonthInc – 5, 6)

case 11 to 15 – mod(dowInMonthInc – 10, 6)

case 16 to 20- mod(dowInMonthInc – 15, 6)

case 21 to25 – mod(dowInMonthInc – 20, 6)

Save Optimization Report as Text and Open with Excel

Here are the settings that I used to create the following report. If you do the math that is a total of 200 iterations.

I opened the Optimization Report and saved as text. Excel had no problem opening it.

I created the third column by translating the second column into our week of month and day of week vernacular. These results were applied to 20 years of ES.D (day session data.) The best result was Pattern #3 applied to the third Friday of the month (35.) Remember the 15th DowInMonthInc equals the third (3) Friday (5). The top patterns predominately occurred on a Thursday or Friday.

switch(dowInMonthInc) begin case 1 to 5: value2 = mod(dowInMonthInc,6); value3 = 10; case 6 to 10: value2 = mod(dowInMonthInc-5,6); value3 = 20; case 11 to 15: value2 = mod(dowInMonthInc-10,6); value3 = 30; case 16 to 20: value2 = mod(dowInMonthInc-15,6); value3 = 40; case 21 to 25: value2 = mod(dowInMonthInc-20,6); value3 = 50; end;

if value1 = dayOfWeekInMonth then begin if patternNum = 1 and patt1 = 1 then buy("Patt1") next bar at open; if patternNum = 2 and patt2 = 1 then buy("Patt2") next bar at open; if patternNum = 3 and patt3 = 1 then sellShort("Patt3") next bar at open; if patternNum = 4 and patt4 = 1 then sellShort("Patt4") next bar at open; if patternNum = 5 and patt5 = 1 then buy("Patt5") next bar at low limit; if patternNum = 6 and patt6 = 1 then sellShort("Patt6") next bar at close stop; if patternNum = 7 and patt7 = 1 then buy("Patt7") next bar at low limit; if patternNum = 8 and patt8 = 1 then sellShort("Patt8") next bar at high stop; end;

setExitOnClose;

The Full Monty of the ES-Seasonal-Day Trade

I think this could provide a means to much more in-depth analysis. I think the Patterns could be changed up. I would like to thank William (Bill) Brower for his excellent article, The S&P Seasonal Day Trade in Stocks and Commodities, August 1996 Issue, V.14:7 (333-337). The article is copyright by Technical Analysis Inc. For those not familiar with Stocks and Commodities check them out at https://store.traders.com/

Please email me with any questions or anything I just got plain wrong. George

Backtesting with [Trade Station,Python,AmiBroker, Excel]. Intended for informational and educational purposes only!

Get All Four Books in the Easing Into EasyLanguage Series - The Day Trade Edition is now Available!

Announcement – A Day Trade Edition will be added to my Easing Into EasyLanguage Series this year! This edition will be the fourth installment and will utilize concepts discussed in the Hi-Res and Advanced Topics editions. I will show how to develop and program algorithms that will enter after the open of the day and get out before the market closes. Hence, no overnight exposure. Most examples will be carried out on the mini Dow, Nasdaq, S&P500 and Russel. The programming skills that you will learn can be carried to any market that provides enough bang for the buck to day trade. Look for this edition later this year. But get geared up for it by reading the first three editions in the series now. Get your favorite QUANT the books they need!

Hello to All! The Easing Into EasyLanguage Series is now complete with the publication of the Advanced Topics Edition. This series includes three educational editions. Start out with the Foundation Edition. It is designed for the new user of EasyLanguage or for those you would like to have a refresher course. There are 13 tutorials ranging from creating Strategies to PaintBars. Learn how to create your own functions or apply stops and profit objectives. Ever wanted to know how to find an inside day that is also a Narrow Range 7 (NR7?) Now you can, and the best part is you get over 4 HOURS OF VIDEO INSTRUCTION – one for each tutorial. All source code is available too, and if you have TradeStation, so are the workspaces. Plus you can always email George for any questions. george.p.pruitt@gmail.com.

This book is for those that have read the Foundation Edition or have some experience working with EasyLanguage and the various functions that help make a trading decision. This book’s audience will be those programmers that want to take an idea, that requires an observation of intraday market movements to make a trading decision, and program it accurately. If you have programmed daily bar systems, and you want to drill down and add some components that require additional market information (like what came first – the high or the low), then you have come to the right place. If you want to buy and sell short in the same day and use trade management principles such as profit targets and stop losses then The Hi-Res Edition is the book you need. There are two paradigms that EasyLanguage covers: daily and intraday bar programming. It’s the same language, but the move from daily to intraday programming can be quite difficult. Learn all the essentials and shortcuts with this edition. 5 HOURS OF VIDEO INSTRUCTION in this Hi-Res edition – one for each tutorial. All source code is available too, and if you have TradeStation, so are the workspaces. Plus you can always email George for any questions. george.p.pruitt@gmail.com.

Advanced Topics (AT) could cover a vast amount of ideas and concepts and be the length of “War and Peace” on steroids. Since this book is part of the series, I wanted to cover a handful of concepts that included the follow programming constructs. Arrays and their manipulation. Buffers (fixed length arrays) and the tools to maintain buffer elements with formulas for extraction and sorting. Finite State Machines using the switch-case construct and range based case values. Using original text graphic objects and retrieving and analyzing their properties to determine X and Y coordinate values of text location. Seasonality: The Ruggiero/Barna Universal Seasonal and the Sheldon Knight Seasonal methods. In AT, you will also find an introduction to EasyLanguage’s Project Concept and the steps to create one by adding/deleting component files. TradeStation now provides access to fundamental data such as Commitment of Traders – learn how to convert the Net Change indicator into a strategy utilizing the FundValue functionality. If you wanted to find out how to merge multiple time frames into a single indicator, you are in luck! Create a MTF indicator for yourself.

Day Trading (DT) – This is a surprise installment in my Easing into EasyLanguage Series, as I had only intended on three books. However, I think it will fit well with the other books. Daytrading is a very popular approach as overnight risk is eliminated. Don’t worry there is plenty of risk during the day too! However, it can be very difficult to accurately program a trading idea on higher resolution data such as five- or one-minute bars. Like my other books, there is no “Holy Grail” included. And if you are looking for a book that gets in and out of a trade in a few seconds, this is not the one for you. I discourage trading more than a handful of trades per day – this is best left up to the professionals. But, if you want to learn about volatility-based break outs, pyramiding, scaling out, zone-based trading, accurate trade accounting and having a peek at algorithms that once ruled the systematic daytrading industry, then this is the book for you. A beginner might have a little difficulty in following along with the tutorials. If you have read the first two books (Foundation and Hi-Res) in this series, you are good to go. Or if you have some experience working with EasyLanguage and minute data, you will be OK as well.

Pick up your copies today – e-Book or paperback format – at Amazon.com