When Mining for Data, Make Sure You Have Accurate Data
Take a look at these results with no execution costs.
Results of Data Mining on Natural Gas
These results were derived by applying a simple algorithm to the natural gas futures for the past 15 or so years. I wanted to see if there was a day of the week that produced the best results with the following entry and exit techniques:
Long entries only
Open range break out using a fraction of the N-day average range.
Buy in the direction of the moving average.
Yesterdays close must be above the X-day moving average of closing prices.
Yesterday must have a wider range than a fractional multiple of the average range.
A fixed $stop and $profit used to exit trades.
Other exits
Exit at low of prior day.
Exit at the close of today – so a day trade.
Here is a list of the parameters that can be optimized:
daysOfWeekToTrade(1) : 1 – Monday, 2 – Tuesday…
mavLen(20): moving average calculation length.
volLen(10): moving average calculation length for range.
volMult(0.25): average range multiplier to determine break out/
volComp(.5): yesterday’s range must be greater than this percentage.
stopLoss(500): stop loss in $
profitTarget(1000): profit objective in $
daysInTrade(0): 0 get out at end of day.
llLookBack(2): pattern derived stop value – use the lowest low of N-days or the stop loss in $, whichever is closer.
if dayOfWeek(d) = daysOfWeekToTrade Then Begin if close > average(c,mavLen) and range > average(range,volLen)*volComp then buy("DMDailyBuy") next bar at open of tomorrow + average(range,volLen)*volMult stop; end;
if daysInTrade = 0 then setExitOnClose; sell("LLxit") next bar at lowest(l,llLookBack) stop; if marketPosition = 1 then begin if barsSinceEntry = daysInTrade then sell("DaysXit") next bar at open; end;
Why Is This Algorithm Wrong? It’s Not It’s the Data!
The algorithm, speaking in terms of logic, is accurate. The data is wrong. You cannot test the exit technology of this algorithm with just four data points per day – open, high, low, and close. If this simple algorithm cannot be tested, then what can you test on a daily bar basis accurately?
Long or short entry, but not both.
If the algorithm can enter both long and short, you need to know what occurred first. Doesn’t matter if you enter via stop or limit orders. Using a stop order for both, then you need to know if the high occurred first and got you long, and then later the low and got you short. Or just the opposite. You can test, with the benefit of hindsight, if only one of the orders would have been elected. If only one is elected, then you can proceed. If both then no-go. You must be able to use future leak to determine if only one of the orders were fillable. TS-18 allows future leak BTW. I say that like it’s a good thing!
L or S position from either a market or stop, and a profit objective.
If a long position is entered above the open, via a stop, and then the market moves even higher, you can get out on a sell limit for a profit.
Same goes for the short side, but you need to know the magnitude of the low price in relation to the open.
L or S position from either a market or limit and a protective stop.
If short position is entered above the open, via a limit order, and the market moves even higher, you can exit the short position at a loss via a buy stop order.
Same goes for the long side, but you need to know the magnitude of the high in relation to the open.
Long pyramid on multiple higher stop or lower limit orders, but not both.
The market opens and then sells off. You can go long on a limit order below the open, then you go long another unit below the original limit price and so on…
The market opens and rallies. You can go long on a stop order above the open, then you can go long another unit above the original stop price and so on…
Short pyramid on multiple lower stop or higher limit orders.
Same as long entries but in opposite direction.
Can’t you look at the relationships of the open to low and open to high and close to high and close to low and determine which occurred first, the high or the low of the day. You can, but there is no guarantee. And you can’t determine the magnitude of intraday day swings. Assume the market opens and immediately moves down and gets you short via a stop order. And from looking at a daily chart, it looks like the market never turned around, so you would assume you had a profit going into the close. But in fact, after the market opened and moved down, it rallied back to the open enough to trigger a protective stop you had working.
The entry technique of this strategy is perfectly fine. It is only buying in the direction of a breakout. The problems arise when you apply a profit objective and a stop loss and a pattern-based stop and a market on close order. After buying did the market pull back to get you stopped out before moving up to get you out at profit objective? Did the market move up and then down to the prior lowest low of N days and then back up to get you long? Or just the opposite? In futures, the settlement price is calculated with a formula. You are always exiting at the settlement price with an MOC or setExitOnClose directive (daily bar basis.)
No Biggie – I Will Just Test with LIB (Look Inside Bar. That should fix it, right?
It definitely will increase accuracy, because you can see the intraday movements that make up the daily bar. So, your entries and exits should be executed more accurately. But you are still getting out at the settlement price which does not exist. Here are the results from using LIB with 5-minute bar resolution.
You saw the beauty now you see the beast.
We Know the Weakness, But What Can We Do?
Test on a 5-minute bar as Data1 and daily as Data2. This concept is what my Hi-Res Edition of Easing Into EasyLanguage is all about. Here the results of using a higher resolution data and exiting on the last tick of the trading day – a known quantity.
As accurate as you can get – well with 5 minute bars that is.
These results validate the LIB results, right? Not 100%, but very close. Perhaps this run makes more money because the settlement price on the particular days that the system entered a trade was perhaps lower than the last tick. In other words, when exiting at the end of the day, the last tick was more often higher than the settlement price.
In my next post, I will go over the details of developing an intraday system to replicate (as close as possible) a simple daily bar-based day trading system. Like the one we have here.
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
Optimization Ranges for Window Open and Open Duration
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.
Results of different opening and duration times.
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.
Allow TradeStation to Pyramid up to 2 positions with different signals.
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:
Didn’t have an intervening bar to update the 2nd entry price. The first entry price was used as the basis to calculate the stop loss!
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.
Fixed – just told TS to place the order at the close of the bar where we were filled!
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.)
Optimize across patterns and volatility and protective stops for 1st and 2nd entries.
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:
Williams Awesome Oscillator
The code reveals a value that oscillates around 0. First calculate the difference between the 5-day moving average of the daily midPoint (H+ L)/2 and the 34-day moving average of the midPoint. A positive value informs us that the market is in a bullish stance whereas a negative represents a bearish tone. Basically, the positive value is simply stating the shorter-term moving average is above the longer term and vice versa. The second step in the indicator calculation is to subtract the 5-day moving average of the differences from the current difference. If the second calculation is greater than the prior day’s calculation, then plot the original calculation value as green (AO+). If it is less (A0-), then paint the first calculation red. The color signifies the momentum between the current and the five-day smoothed value.
Here I am using the very handy countIf function. This function will tell you how many times a Boolean comparison is true out of the last N days. Her I use the function twice, but I could have replaced the second function call with mavDn = 30 – mavUp. So, I am counting the number of occurrences of when the mavDiff is positive and negative over the past 30-days. I also count the number of times the oscVal is greater than the prior oscVal. In other words, I am counting the number of green bars. I create a ratio between green bars and 10. If there are six green bars, then the ratio equals 60% This indicates that the ratio of red bars would be 40%. Based on these readings you can create trade entry directives.
if canShort and mavUp > numBarsAbove and mavDiff > minDiffAmt and oscRatio >= obRatio then sellShort next bar at open;
if canBuy and mavDn > numBarsBelow and mavDiff < -1*minDiffAmt and oscRatio <= osRatio Then buy next bar at open;
Trade Directives
If the number of readings out of the last 30 days is greater than numBarsAbove and mavDiff is of a certain magnitude and the oscillator ratio is greater than buyOSCRatio, then you can go short on the next open. Here we are looking for the market to converge. When these conditions are met then I think the market is overbought. You can see how I set up the long entries. As you can see from the chart it does a pretty good job. Optimizing the parameters on the crude oil futures yielded this equity curve.
Too few trades!
Not bad, but not statistically significant either. One way to generate more trades is to install some trade management such as protective stop and profit objective.
Using wide stop and profit objective.
Using a wide protective stop and large profit objective tripled the number of trades. Don’t know if it is any better, but total performance was not derived from just a couple of trades. When you are working with a strategy like this and overlay trade management you will often run into this situation.
Exiting while conditions to short are still turned on!
Here we either get stopped out or take a profit and immediately reenter the market. This occurs when the conditions are still met to short when we exit a trade. The fix for this is to determine when an exit has occurred and force the entry trigger to toggle off. But you have to figure out how to turn the trigger back on. I reset the triggers based on the number of days since the triggers were turned off – a simple fix for this post. If you want to play with this strategy, you will probably need a better trigger reset.
I am using the setStopLoss and setProfitTarget functionality via their own strategies – Stop Loss and Profit Target. These functions allow exit on the same as entry, which can be useful. Since we are executing on the open of the bar, the market could definitely move either in the direction of the stop or the profit. Since we are using wide values, the probability of both would be minimal. So how do you determine when you have exited a trade. You could look the current bar’s marketPosition and compare it with the prior bar’s value, but this doesn’t work 100% of the time. We could be flat at yesterday’s close, enter long on today’s open and get stopped out during the day and yesterday’s marketPosition would be flat and today’s marketPosition would be flat as well. It would be as if nothing occurred when in fact it did.
Take a look at this code and see if it makes sense to you.
if mp[1] = 1 and totalTrades > totTrades then canBuy = False;
if mp[1] = -1 and totalTrades > totTrades then canShort = False;
if mp[1] = 0 and totalTrades > totTrades then 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.
Indicator in Histogram form representing the Week of MonthSome months are spread across SIX weeks.
This function is an example of where we let the data tell us what to do. I am sure there is calendar functions, in other languages, that can extract information from just the date. However, I like to extract from what is actually going to be traded.
Email me if you have any questions, concerns, criticism, or a better mouse trap.
How to reenter at a better price after a stop loss
A reader of this blog wanted to know how he could reenter a position at a better price if the first attempt turned out to be a loser. Here I am working with 5 minute bars, but the concepts work for daily bars too. The daily bar is quite a bit easier to program.
ES.D Day Trade using 30 minute Break Out
Here we are going to wait for the first 30 minutes to develop a channel at the highest high and the lowest low of the first 30 minutes. After the first 30 minutes and before 12:00 pm eastern, we will place a buy stop at the upper channel. The initial stop for this initial entry will be the lower channel. If we get stopped out, then we will reenter long at the midpoint between the upper and lower channel. The exit for this second entry will be the lower channel minus the width of the upper and lower channel.
Here are some pix. It kinda, sorta worked here – well the code worked perfectly. The initial thrust blew through the top channel and then immediately consolidated, distributed and crashed through the bottom channel. The bulls wanted another go at it, so they pushed it halfway to the midpoint and then immediately the bears came in and played tug of war. In the end the bulls won out.
Initial Break Out Failed. But 2nd Entry made a little.
Here the bulls had initial control and then the bears, and then the bulls and finally the bears tipped the canoe over.
We gave it the old college try didn’t we fellas.
This type of logic can be applied to any daytrade or swing trade algorithm. Here is the code for the strategy.
//working with 5 minute bars here //should work with any time frame
if t = calcTime(startTime,barInterval) Then begin periodHigh = -99999999; periodLow = 99999999; barCountToday = 1; totTrades = totalTrades; end;
if barCountToday <= numOfBarsHHLL Then Begin periodHigh = maxList(periodHigh,h); periodLow = minList(periodLow,l); end;
barCountToday = barCountToday + 1;
longEntriesToday = totalTrades - totTrades;
mp = marketPosition; if t <= stopTradeTime and barCountToday > numOfBarsHHLL Then Begin if longEntriesToday = 0 then buy("InitBreakOut") next bar at periodHigh + minMove/priceScale stop; if longEntriesToday = 1 Then buy("BetterBuy") next bar at (periodHigh+periodLow)/2 stop; end;
if mp = 1 then if longEntriesToday = 0 then sell("InitXit") next bar at periodLow stop; if longEntriesToday = 1 then sell("2ndXit") next bar at periodLow - (periodHigh - periodLow) stop;
SetExitOnClose;
The key concepts here are the time constraints and how I count bars to calculate the first 30 – minute channel. I have had problems with EasyLanguages’s entriesToday function, so I like how I did it here much better. On the first bar of the day, I set my variable totTrades to EasyLanguages built-in totalTrades (notice the difference in the spelling!) The keyword TotalTrades is immediately updated when a trade is closed out. So, I simply subtract my totTrades (the total number of trades at the beginning of the day) from totalTrades. If totalTrades is incremented (a trade is closed out) then the difference between the two variables is 1. I assign the difference of these two values to longEntriesToday. If longEntriesToday = 1, then I know I have entered long and have been stopped out. I then say, OK let’s get back long at a better price – the midpoint. I use the longEntriesToday variable again to determine which exit to use. With any luck the initial momentum that got us long initially will work its way back into the market for another shot.
It’s Easy to Create a Strategy Based Indicator
Once you develop a strategy, the indicator that plots entry and exit levels is very easy to derive. You have already done the math – just plot the values. Check this out.
//working with 5 minute bars here //should work with any time frame
if t = calcTime(startTime,barInterval) Then begin periodHigh = -99999999; periodLow = 99999999; barCountToday = 1; end;
if barCountToday <= numOfBarsHHLL Then Begin periodHigh = maxList(periodHigh,h); periodLow = minList(periodLow,l); end;
if barCountToday < numOfBarsHHLL Then begin noPlot(1); noPlot(2); noPlot(3); noPlot(4); End Else begin plot1(periodHigh,"top"); plot2((periodHigh+periodLow)/2,"mid"); plot3(periodLow,"bot"); plot4(periodLow - (periodHigh - periodLow),"2bot"); end;
barCountToday = barCountToday + 1;
So, that’s how you do it. You can use this code as a foundation for any system that wants to try and reenter at a better price. You can also use this code to develop your own time based break out strategy. In my new book, Easing Into EasyLanguage – the Daytrade EditionI will discuss topics very similar to this post.
The concept of Scope when it comes to a programming language can get very confusing very quickly. If you are new to programming and you think Scope is a mouthwash, no worries here. I will give a full explanation of Scope in terms of programming with the help of ChatGPT. Its response is italicized.
In Python, the scope of a variable refers to the region of the program where that variable can be accessed or referenced. There are two types of scopes in Python: global scope and local scope.
Here’s an example that demonstrates the concept of scope in Python:
# global variable x = 10
def my_func(): # local variable y = 5 print("x inside my_func:", x) # x is accessed from the global scope print("y inside my_func:", y) # y is accessed from the local scope
my_func()
print("x outside my_func:", x) # x is accessed from the global scope print("y outside my_func:") # y is not defined in the global scope, so this will raise a NameError
Example of Scope as demonstrated by Chat-GPT
In this example, we define a global variable x with a value of 10.
We then define a function my_func that has a local variable y with a value of 5. Inside the function, we print the values of x and y. The variable x is accessed from the global scope, which means it can be referenced from anywhere in the program.
The variable y, on the other hand, is only accessible within the function, which is the local scope. Outside the function, we print the value of x again, which is still accessible from the global scope. However, when we try to print the value of y, we get a NameError because y is not defined in the global scope.
Okay that was pretty formal. In simpler terms, a variable declared inside a function can only be seen inside that function, unless it is declared globally. Another thing about Scope, is that the variables have amnesia. Once you step out of the function the variable forgets what it was, and you can’t refer to its prior value upon return to the function.
Unless you are an EasyLanguage function. Scope is still constrained by an EasyLanguage function, because it is a separate piece of code held within the library of Analysis Techniques. Most of the time you can’t see what’s in the function unless you open it with the ELEditor. However, the variables that are defined inside the function do not suffer from amnesia. If you need to refer to a prior value of a locally declared variable, you can. This type of function is what EasyLanguage calls a Serial function. The only downside to this function is it slows processing down quite a bit.
Okay. To make a long story short I wanted to show the magic of EasyLanguage function that I have been working with on a project. This project includes some of Ehlers’ cycle analysis functions. The one I am going to discuss today is the HighRoof function – don’t worry I am not going to go into detail of what this function does. If you want to know just GOOGLE it or ask ChatGPT. I developed a strategy that used the function on the last 25 days of closing price data. I then turned around and fed the output of the first pass of the HighRoof function right back into the HighRoof function. Something similar to embedding functions.
doubleSmooth = average(average(c,20),20);
Sort of like a double smoothed moving average. After I did this, I started thinking does the function remember the data from its respective call? The first pass used closing price data, so its variables and their history should be in terms of price data. The second pass used the cyclical movements data that was output by the initial call to the HighRoof function. Everything turned out fine, the function remembered the correct data. Or seemed like it did. This is how you learn about any programming language – pull out your SandBox and do some testing. First off, here is my conversion of Ehlers’ HighRoof function in EasyLanguage.
This function requires just two inputs – the data (with a history) and a simple length or cut period. The first input is of type numericSeries and the second input is of type numericSimple. You will see the following line of code
This code prints out the last three historic values of the HighPass variable for each function call. I am calling the function twice for each bar of data in the Crude Oil futures continuous contract.
Starting at the top of the output you will see that on 1230206 the function was called twice with two different sets of data. As you can see the output of the first two lines is of a different magnitude. The first line is approximately an order or magnitude of 10 of the second line. If you go to lines 3 and 4 you will see the highPass[1] of lines 1 and 2 moves to highPass[2] and then onto highPass[3]. I think what happens internally is for every call on per bar basis, the variables for each function call are pushed into a queue in memory. The queue continues to grow for whatever length is necessary and then either maintained or truncated at some later time.
Why Is This So Cool?
In many languages the encapsulation of data with the function requires additional programming. The EasyLanguage function could be seen as an “object” like in object-oriented programming. You just don’t know you are doing it. EasyLanguage takes care of a lot of the behind-the-scenes data management. To do the same thing in Python you would need to create a class of Ehlers Roof that maintain historic data in class members and the calculations would be accomplished by a class method. In the case of calling the function twice, you would instantiate two classes from the template and each class would act independent of each other.
One last nugget of information. If you are going to be working with trigonometric functions such as Cosine, Sine or Tangent, make sure your arguments are in degrees not radians. In Python, you must use radians.
Have you discovered a seasonal tendency but can’t figure out how to test it?
Are there certain times of the year when a commodity increases in price and then recedes? In many markets this is the case. Crude oil seems to go up during the summer months and then chills out in the fall. It will rise once again when winter hits. Early spring might show another price decline. Have you done your homework and recorded certain dates of the year to buy/sell and short/buyToCover. Have you tried to apply these dates to a historical back test and just couldn’t figure it out? In this post I am going to show how you can use arrays and loops to cycle through the days in your seasonal database (database might be too strong of a term here) and apply long and short entries at the appropriate times during the past twenty years of daily bar data.
Build the Quasi-Database with Arrays
If you are new to EasyLanguage you may not yet know what arrays are or you might just simply be scared of them. Now worries here! Most people bypass arrays because they don’t know how to declare them, and if they get passed that, how to manipulate them to squeeze out the data they need. You may not be aware of it, but if you have programmed in EasyLanguage the least bit, then you have already used arrays. Check this out:
if high > high[1] and low > low[1] and average(c,30) > average(c,30)[1] then buy next bar at open
In reality the keywords high and low are really arrays. They are lists that contain the entire history of the high and low prices of the data that is plotted on the chart. And just like with declared arrays, you index these keywords to get historic data. the HIGH[1] means the high of yesterday and the HIGH[2] means the high of the prior day. EasyLanguage handles the management of these array-like structures. In other words, you don’t need to keep track of the indexing – you know the [1] or [2] stuff. The declaration of an array is ultra-simple once you do it a few times. In our code we are going to use four arrays:
Buy array
SellShort array
Sell array
BuyToCover array
Each of these arrays will contain the month and day of month when a trade is to be entered or exited. Why not the year? We want to keep things simple and buy/short the same time every year to see if there is truly a seasonal tendency. The first thing we need to do is declare the four arrays and then fill them up.
// use the keyword arrays and : semicolon // next give each array a name and specify the // max number of elements that each array can hold // the [100] part. Each array needs to be initialized // and we do this by placing a zero (0) in parentheses arrays: buyDates[100](0),sellDates[100](0), shortDates[100](0),buyToCoverDates[100](0);
// next you want the arrays that go together to have the same // index value - take a look at this
// note the buyDates[1] has a matching sellDates[1] // buyDates[2] has a matching sellDates[2] // -- and -- // shortDates[1] has a matching buyToCoverDates[1] // shortDates[2] has a matching buyToCoverDates[2]
Our simple database has been declared, initialized and populated. This seasonal strategy will buy on:
April 15th and Exit on May 15th
June 5th and Exit on August 30th
It will sellShort on:
September 15th and Cover on November 30th
February 15th and Cover on March 30th
You could use this template and follow the pattern to add more dates to your database. Just make sure nothing overlaps.
Now, each chart has N dates of history plotted from the left to right. TradeStation starts out the test from the earliest date to the last date. It does this by looping one day at a time. The first thing we need to do is convert the bar date (TradeStation represents dates in a weird format – YYYMMDD – Jan 30, 2022 is represented by the number 1220130 – don’t ask why!!) to a format like the data that is stored in our arrays. Fortunately, we don’t have to deal with the year and EasyLanguage provides two functions to help us out.
Month(Date) = the month (1-12) of the current bar
DayOfMonth(Date) = the day of the month of the current bar
All we need to do is use these functions to convert the current bar’s date into terms of our database, and then test that converted date against our database. Take a look:
//convert the date into our own terms //say the date is December 12 //the month function returns 12 and the day of month returns 12 // 12*100 + 12 = 1212 --> MMDD - just waht we need //notice I look at the date of tomorrow because I want to take //action on the open of tomorrow.
currentMMDD = month(d of tomorrow)*100 + dayOfMonth(d of tomorrow);
//You might need to study this a little bit - but I am looping through each //array to determine if a trade needs to be entered. //Long Seasonal Entries toggle buyNow = False; for n = 1 to numBuyDates Begin if currentMMDD[1] < buyDates[n] and currentMMDD >= buyDates[n] Then Begin buyNow = True; end; end;
//Short Seasonal Entries toggle shortNow = False; for n = 1 to numshortDates Begin if currentMMDD[1] < shortDates[n] and currentMMDD >= shortDates[n] Then Begin shortNow = True; end; end;
Date conversion and looping thru Database
This code might look a little daunting, but it really isn’t. The first for-loop starts at 1 and goes through the number of buyDates. The index variable is the letter n. The loop starts at 1 and goes to 2 in increments of 1. Study the structure of the for-loop and let me know if you have any questions. What do you think this code is doing.
if currentMMDD[1] < buyDates[n] and currentMMDD >= buyDates[n] Then
As you know the 15th of any month may fall on a weekend. This code basically says, ” Okay if today is less than the buyDate and tomorrow is equal to or greater than buyDate, then tommorrow is either going to be the exact day of the month or the first day of the subsequent week (the day of month fell on a weekend.) If tomorrow is a trade date, then a conditional buyNow is set to True. Further down in the logic the trade directive is issued if buyNow is set to True.
Total of 4 loops – 2 for each long/short entry and 2 for each long/short exit.
// fill the arrays with dates - remember we are not pyramiding here // use mmdd format buyDates[1] = 0415;sellDates[1] = 0515; buyDates[2] = 0605;sellDates[2] = 0830; numBuyDates = 2; numSellDates = 2;
mp = marketPosition; currentMMDD = month(d of tomorrow)*100 + dayOfMonth(d of tomorrow);
//Long Seasonal Entries toggle buyNow = False; for n = 1 to numBuyDates Begin if currentMMDD[1] < buyDates[n] and currentMMDD >= buyDates[n] Then Begin buyNow = True; end; end;
//Short Seasonal Entries toggle shortNow = False; for n = 1 to numshortDates Begin if currentMMDD[1] < shortDates[n] and currentMMDD >= shortDates[n] Then Begin shortNow = True; end; end;
//Long Seasonal Exits toggle sellNow = False; if mp = 1 Then Begin for n = 1 to numSellDates Begin if currentMMDD[1] < sellDates[n] and currentMMDD >= sellDates[n] Then Begin sellNow = True; end; end; end;
//Short Seasonal Exits toggle buyToCoverNow = False; if mp = -1 Then Begin for n = 1 to numBuyToCoverDates Begin if currentMMDD[1] < buyToCoverDates[n] and currentMMDD >= buyToCoverDates[n] Then Begin buyToCoverNow = True; end; end; end;
// Long entry execution if buyNow = True then begin buy("Seas-Buy") next bar at open; end; // Long exit execution if mp = 1 then begin if sellNow then begin sell("Long Exit") next bar at open; end; end;
// Short entry execution if shortNow then begin sellShort("Seas-Short") next bar at open; end; // short exit execution if mp = -1 then begin if buyToCoverNow then begin buyToCover("short Exit") next bar at open; end; end;
Does it work? It does – please take my word for it. IYou can email me with any questions. However, TS 10 just crashed on me and is wanting to update, but I need to kill all the processes before it can do a successful update. Remember to always export your new code to an external location. I will post an example on Monday Jan 30th.
This pattern has been around for many years, and is still useful today in a day trading scheme. The pattern is quite simple: if today’s high exceeds yesterday’s high by a certain amount, then sell short as the market moves back through yesterday’s high. There are certain components of yesterday’s daily bar that are significant to day traders – the high, the low, the close and the day traders’ pivot. Yesterday’s high is considered a level of resistance and is often tested. Many times the market has just enough momentum to carry through this resistance level, but eventually peters out and then the bears will jump in and push the market down even more. The opposite is true when the bulls take over near the support level of yesterday’s low. Here is an example of Clear Out short and buy.
1st the high of yesterday is cleared out and then the low of yesterday.
How Do You Program this Simple Pattern?
The programming of this strategy is rather simple, if you are day trading. The key components are toggles that track the high and low of the day as the market penetrate the prior day’s high and low. Once the toggles are flipped on, then order directives can be placed. A max. trade stop loss can easily be installed via the SetStopLoss(500) function. You will also want to limit the number of entries, because in a congestive phase, this pattern could fire off multiple times. Once you intuitively program this, you will almost certainly run into an issue where a simple “trick” will bail you out. Remember the code does exactly what you tell it to do. Take a look at these trades.
When Back Testing TradeStation will convert stop orders to market orders.
On a Back Test, Stop Orders are Converted to Market Orders if Price Exceeds the Stop Level
In these example trades, the first trade is accurate as it buys yesterday’s low + one tick and then gets stopped out. Once a long is entered, the system logic requires the market to trade back below yesterday’s low before a long another entry is signaled at yesterday’s low. Here as you can see, the initial buy toggle is set to True and when a long position is entered the buy toggle is turned off. The market knee jerks back below yesterday’s low and stops out your long position. Since TradeStation’s paradigm is based on “next bar” execution, a long entry doesn’t occur as the wide bar crosses back up through yesterday’s low. This is a “bang-bang” situation as it happened very quickly. In a perfect world, you should have been quickly stopped out and re-entered back long at your price. However, the toggle isn’t turned back on until the low of the current bar falls a short distance below yesterday’s low. Since this toggle isn’t set before the market takes off, you don’t get your price. The toggle is eventually turned on and a buy stop order is issued and you can tell you get a ton of slippage. You actually buy the next bar’s open after the bar where the toggle was turned on. I dropped down to a one minute bar and still didn’t get the trade. A 10 second bar did generate the exit and re-entry at the correct levels, however. It did this because, the 10 second bar turned the toggle on in time for the stop order to be generated accurately.
Using a 10-second bar an accurate exit and entry were generated.
Okay – Can you rely on a 5 minute bar then?
Five minute bar data has been the staple of day trading systems for many years. However, if you want to test “bang-bang” algorithms you are probably better off dropping down to a N-seconds bar. However, this strategy as a whole is not “bang-bang” so with a little trick you can get more accurate entries and exits.
What’s the Trick?
In real-time trading, buy-stop orders below the market are rejected. So, the second and third trades that were presented would never have taken place. But, the backtest reflects the trades, and if you include execution costs, the performance might nudge you into not trading a possibly viable system. You can take advantage of the “next bar” paradigm by forcing the close of the current bar to be below a buy-stop price and above a sell short stop price. Does this trade look better? Again in a perfect world, you would have re-entered long on the wide bar that stopped us out. But I guarantee you a fast market condition was in effect. All a broker has to say to you when you complain about a fill is, “Sorry Dude! It was a fast market. Not held!” I can’t tell you how many times I requested a printout of fills over a few seconds from my brokers. It is like when a football coach tosses the RED FLAG. During the Pit Days you had a chance to get a fill cash adjustment because the broker was human and maybe he or she didn’t react quickly enough. But when electronic trade matching took over, an adjustment was highly unlikely. Heck, you sign off on this when you accept the terms of electronic trading. Fills are rarely made better.
The second and third trade don’t occur because you force the buy stop order to be valid.
How Does the Trick Affect Performance?
Here are the results over the past four months on different time frame resolutions.
10 Seconds Resolution.
10 seconds bar would be the most accurate if slippage is acceptable. And that is a big assumption on “bang-bang” days.
1 minute bar resolution.
The one minute bar is close but September is substantially off. Probably some “bang-bang” action.
5 minute bar resolution with “Trick”
This is close to the 10-second bar result. Fast market or “bang-bang” trades were reduced or eliminated with the “trick”.
5 minute bar resolution without “Trick.”
Surprisingly, the 5 minute bar without the “Trick” resembles the 10 seconds results. But we know this is not accurate as trades are fired off in a manner that goes against reality.
The two following table shows the impact of a $15 RT comm./slipp. per trade charge.
Without “Trick” and $15 RT
With “Trick” and $15 RT
Okay, Now That We Have That Figured Out How Do You Limit Trading After a Daily Max. Loss
Another concept I wanted to cover was limiting trades after a certain loss level was suffered on a daily basis. In the code, I only wanted to enter trades as long as the max. daily loss was less than or equal to $1,000 A fixed stop of $500 on a per trade basis was utilized as well. So, if you suffered two max. stop losses right off the bat ($1,000), you could still take one more trade. Now if you had a $500 winner and two $500 losses you could still take another trade.
Ouch! Two max losses, but still could take a third trade. Ouch again – stupid system.
Should I take that second trade? I just suffered three losses in a row. What to do? What to do? Damn straight you better that trade.
If you are going to trade a system, you better trade it systematically!
Now Onto the Code
//Illustrate trade stoppage after a certain loss has been //experienced and creating realistic stop orders.
if t = startTime then begin coBuy = False; coShort = False; beginOfDayProfit = netProfit; beginOfDayTotTrades = totalTrades; end;
canTrade = iff(t >=startTime and t < sess1EndTime,1,0);
if t >= startTime and h > highD(1) + clrOutBuyPer*(highD(1)-lowD(1)) then begin coShort = True; end;
if t >= startTime and l < lowD(1) - clrOutShortPer*(highD(1)-lowD(1)) then begin coBuy = True; end;
mp = marketPosition;
if canTrade = 1 and coShort and netProfit >= beginOfDayProfit - maxDailyLoss and c > highD(1) - minMove/priceScale then sellShort next bar at highD(1) - minMove/priceScale stop;
if mp = -1 then // toggle to turn off coShort - must wait for set up begin coShort = False; end;
if canTrade = 1 and coBuy and netProfit >= beginOfDayProfit - maxDailyLoss and c < lowD(1) + minMove/priceScale then buy next bar at lowD(1) + minMove/priceScale stop;
if mp = 1 then begin coBuy = False; end;
setStopLoss(500); setExitOnClose;
Strategy in its Entirety
You need to capture the NetProfit sometime during the day before trading commences. This block does just that.
if t = startTime then begin coBuy = False; coShort = False; beginOfDayProfit = netProfit; beginOfDayTotTrades = totalTrades; end;
Snippet that captures NetProfit at start of day
Now all you need to do is compare the current netProfit (EL keyword) to the beginOfDayProfit (user variable). If the current netProfit >= beginOfDayProfit – maxDailyLoss (notice I programmed greater than or equal to), then proceed with the next trade. The rest of the logic is pretty self explanatory, but to drive the point home, here is how I make sure a proper stop order is placed.
if canTrade = 1 and coShort and netProfit >= beginOfDayProfit - maxDailyLoss and c > highD(1) - minMove/priceScale then sellShort next bar at highD(1) - minMove/priceScale stop;
if mp = -1 then // toggle to turn off coShort - must wait for set up begin coShort = False; end;
Notice how I use the current bars Close - C and How I toggle coShort to False
If You Like This – Make Sure You Get My Hi-Res Edition of Easing Into EasyLanguage
This is a typical project I discuss in the second book in the Easing Into EasyLanguage Trilogy. I have held over the BLACK FRIDAY special, and it will stay in effect through December 31st. Hurry, and take advantage of the savings. If you see any mistakes, or just want to ask me a question, or have a comment, just shoot me an email.
Complete Strategy based on Sheldon Knight and William Brower Research
In my Easing Into EasyLanguage: Hi-Res Edition, I discuss the famous statistician and trader Sheldon Knight and his K-DATA Time Line. This time line enumerated each day of the year using the following nomenclature:
First Monday in December = 1stMonDec
Second Friday in April = 2ndFriApr
Third Wednesday in March = 3rdWedMar
This enumeration or encoding was used to determine if a certain week of the month and the day of week held any seasonality tendencies. If you trade index futures you are probably familiar with Triple Witching Days.
Four times a year, contracts for stock options, stock index options, and stock index futures all expire on the same day, resulting in much higher volumes and price volatility. While the stock market may seem foreign and complicated to many people, it is definitely not “witchy”, however, it does have what is known as “triple witching days.”
Triple witching, typically, occurs on the third Friday of the last month in the quarter. That means the third Friday in March, June, September, and December. In 2022, triple witching Friday are March 18, June 17, September 16, and December 16
Other days of certain months also carry significance. Some days, such as the first Friday of every month (employment situation), carry even more significance. In 1996, Bill Brower wrote an excellent article in Technical Analysis of Stocks and Commodities. The title of the article was The S&P 500 Seasonal Day Trade. In this article, Bill devised 8 very simple day trade patterns and then filtered them with the Day of Week in Month. Here are the eight patterns as he laid them out in the article.
Pattern 1: If tomorrow’s open minus 30 points is greater than today’s close, then buy at market.
Pattern 2: If tomorrow’s open plus 30 points is less than today’s close, then buy at market.
Pattern 3: If tomorrow’s open minus 30 points is greater than today’s close, then sell short at market.
Pattern 4: If tomorrow’s open plus 30 points is less than today’s close, then sell short at market.
Pattern 5: If tomorrow’s open plus 10 points is less than today’s low, then buy at market.
Pattern 6: If tomorrow’s open minus 20 points is greater than today’s high, then sell short at today’s close stop.
Pattern 7: If tomorrow’s open minus 40 points is greater than today’s close, then buy at today’s low limit.
Pattern 8: If tomorrow’s open plus 70 points is less than today’s close, then sell short at today’s high limit.
This article was written nearly 27 years ago when 30 points meant something in the S&P futures contract. The S&P was trading around the 600.00 level. Today the e-mini S&P 500 (big S&P replacement) is trading near 4000.00 and has been higher. So 30, 40 or 70 points doesn’t make sense. To bring the patterns up to date, I decided to use a percentage of ATR in place of a single point. If today’s range equals 112.00 handles or in terms of points 11200 and we use 5%, then the basis would equate to 11200 = 560 points or 5.6 handles. In the day of the article the range was around 6 handles or 600 points. So. I think using 1% or 5% of ATR could replace Bill’s point values. Bill’s syntax was a little different than the way I would have described the patterns. I would have used this language to describe Pattern1 – If tomorrow’s open is greater than today’s close plus 30 points, then buy at market – its easy to see we are looking for a gap open greater than 30 points here. Remember there is more than one way to program an idea. Let’s stick with Bills syntax.
10 points = 1 X (Mult) X ATR
20 points = 2 X (Mult) X ATR
30 points = 3 X (Mult) X ATR
40 points = 4 X (Mult) X ATR
50 points = 5 X (Mult) X ATR
70 points =7 X (Mult) X ATR
We can play around with the Mult to see if we can simulate similar levels back in 1996.
// atrMult will be a small percentage like 0.01 or 0.05 atrVal = avgTrueRange(atrLen) * atrMult;
//original patterns //use IFF function to either returne a 1 or a 0 //1 pattern is true or 0 it is false
The Day of Week In A Month is represented by a two digit number. The first digit is the week rank and the second number is day of the week. I thought this to be very clever, so I decided to program it. I approached it from a couple of different angles and I actually coded an encoding method that included the week rank, day of week, and month (1stWedJan) in my Hi-Res Edition. Bill’s version didn’t need to be as sophisticated and since I decided to use TradeStation’s optimization capabilities I didn’t need to create a data structure to store any data. Take a look at the code and see if it makes a little bit of sense.
newMonth = False; newMonth = dayOfMonth(d of tomorrow) < dayOfMonth(d of today); atrVal = avgTrueRange(atrLen) * atrMult; if newMonth then begin startTrading = True; monCnt = 0; tueCnt = 0; wedCnt = 0; thuCnt = 0; friCnt = 0; weekCnt = 1; end;
if not(newMonth) and dayOfWeek(d of tomorrow) < dayOfWeek(d of today) then weekCnt +=1;
dayOfWeekInMonth = weekCnt * 10 + dayOfWeek(d of tomorrow);
Simple formula to week rank and DOW
NewMonth is set to false on every bar. If tomorrow’s day of month is less than today’s day of month, then we know we have a new month and newMonth is set to true. If we have a new month, then several things take place: reinitialize the code that counts the number Mondays, Tuesdays, Wednesdays, Thursdays and Fridays to 0 (not used for this application but can be used later,) and set the week count weekCnt to 1. If its not a new month and the day of week of tomorrow is less than the day of the week today (Monday = 1 and Friday = 5, if tomorrow is less than today (1 < 5)) then we must have a new week on tomorrow’s bar. To encode the day of week in month as a two digit number is quite easy – just multiply the week rank (or count) by 10 and add the day of week (1-Monday, 2-Tuesday,…) So the third Wednesday would be equal to 3X10+3 or 33.
Use Optimization to Step Through 8 Patterns and 25 Day of Week in Month Enumerations
Stepping through the 8 patterns is a no brainer. However, stepping through the 25 possible DowInAMonth codes or enumerations is another story. Many times you can use an equation based on the iterative process of going from 1 to 25. I played around with this using the modulus function, but decided to use the Switch-Case construct instead. This is a perfect example of replacing math with computer code. Check this out.
switch(dowInMonthInc) begin case 1 to 5: value2 = mod(dowInMonthInc,6); value3 = 10; case 6 to 10: value2 = mod(dowInMonthInc-5,6); value3 = 20; case 11 to 15: value2 = mod(dowInMonthInc-10,6); value3 = 30; case 16 to 20: value2 = mod(dowInMonthInc-15,6); value3 = 40; case 21 to 25: value2 = mod(dowInMonthInc-20,6); value3 = 50; end;
Switch-Case to Step across 25 Enumerations
Here we are switching on the input (dowInMonthInc). Remember this value will go from 1 to 25 in increments of 1. What is really neat about EasyLanguage’s implementation of the Switch-Case is that it can handle ranges. If the dowInMonthInc turns out to be 4 it will fall within the first case block (case 1 to 5). Here we know that if this value is less than 6, then we are in the first week so I set the first number in the two digit dayOfWeekInMonth representation to 1. This is accomplished by setting value3 to 10. Now you need to extract the day of the week from the 1 to 25 loop. If the dowInMonthInc is less than 6, then all you need to do is use the modulus function and the value 6.
mod(1,6) = 1
mod(2,6) = 2
mod(3,6) = 3
This works great when the increment value is less than 6. Remember:
1 –> 11 (first Monday)
2 –> 12 (first Tuesday)
3 –> 13 (first Wednesday)
…
…
6 –> 21 (second Monday)
7 –> 22 (second Tuesday).
So, you have to get a little creative with your code. Assume the iterative value is 8. We need to get 8 to equal 23 (second Wednesday). This value falls into the second case, so Value3 = 20 the second week of the month. That is easy enough. Now we need to extract the day of week – remember this is just one solution, I guarantee there are are many.
mod(dowInMonthInc – 5, 6) – does it work?
value2 = mod(8-5,6) = 3 -> value3 = value1 + value2 -> value3 = 23. It worked. Do you see the pattern below.
case 6 to 10 – mod(dowInMonthInc – 5, 6)
case 11 to 15 – mod(dowInMonthInc – 10, 6)
case 16 to 20- mod(dowInMonthInc – 15, 6)
case 21 to25 – mod(dowInMonthInc – 20, 6)
Save Optimization Report as Text and Open with Excel
Here are the settings that I used to create the following report. If you do the math that is a total of 200 iterations.
Seasonal Day Trader Settings
I opened the Optimization Report and saved as text. Excel had no problem opening it.
Optimization results output to Excel and cleaned up.
I created the third column by translating the second column into our week of month and day of week vernacular. These results were applied to 20 years of ES.D (day session data.) The best result was Pattern #3 applied to the third Friday of the month (35.) Remember the 15th DowInMonthInc equals the third (3) Friday (5). The top patterns predominately occurred on a Thursday or Friday.
switch(dowInMonthInc) begin case 1 to 5: value2 = mod(dowInMonthInc,6); value3 = 10; case 6 to 10: value2 = mod(dowInMonthInc-5,6); value3 = 20; case 11 to 15: value2 = mod(dowInMonthInc-10,6); value3 = 30; case 16 to 20: value2 = mod(dowInMonthInc-15,6); value3 = 40; case 21 to 25: value2 = mod(dowInMonthInc-20,6); value3 = 50; end;
if value1 = dayOfWeekInMonth then begin if patternNum = 1 and patt1 = 1 then buy("Patt1") next bar at open; if patternNum = 2 and patt2 = 1 then buy("Patt2") next bar at open; if patternNum = 3 and patt3 = 1 then sellShort("Patt3") next bar at open; if patternNum = 4 and patt4 = 1 then sellShort("Patt4") next bar at open; if patternNum = 5 and patt5 = 1 then buy("Patt5") next bar at low limit; if patternNum = 6 and patt6 = 1 then sellShort("Patt6") next bar at close stop; if patternNum = 7 and patt7 = 1 then buy("Patt7") next bar at low limit; if patternNum = 8 and patt8 = 1 then sellShort("Patt8") next bar at high stop; end;
setExitOnClose;
The Full Monty of the ES-Seasonal-Day Trade
I think this could provide a means to much more in-depth analysis. I think the Patterns could be changed up. I would like to thank William (Bill) Brower for his excellent article, The S&P Seasonal Day Trade in Stocks and Commodities, August 1996 Issue, V.14:7 (333-337). The article is copyright by Technical Analysis Inc. For those not familiar with Stocks and Commodities check them out at https://store.traders.com/
Please email me with any questions or anything I just got plain wrong. George
Backtesting with [Trade Station,Python,AmiBroker, Excel]. Intended for informational and educational purposes only!
Get All Five Books in the Easing Into EasyLanguage Series - The Trend Following Edition is now Available!
Announcement – A Trend Following edition has been added to my Easing into EasyLanguage Series! This edition will be the fifth and final installment and will utilize concepts discussed in the Foundation editions. I will pay respect to the legends of Trend Following by replicating the essence of their algorithms. Learn about the most prominent form of algorithmic trading. But get geared up for it by reading the first four editions in the series now. Get your favorite QUANT the books they need!
The Foundation Edition. The first in the series.
This series includes five editions that covers the full spectrum of the EasyLanguage programming language. Fully compliant with TradeStation and mostly compliant with MultiCharts. Start out with the Foundation Edition. It is designed for the new user of EasyLanguage or for those you would like to have a refresher course. There are 13 tutorials ranging from creating Strategies to PaintBars. Learn how to create your own functions or apply stops and profit objectives. Ever wanted to know how to find an inside day that is also a Narrow Range 7 (NR7?) Now you can, and the best part is you get over 4 HOURS OF VIDEO INSTRUCTION – one for each tutorial.
Hi-Res Edition Cover
This book is ideal for those who have completed the Foundation Edition or have some experience with EasyLanguage, especially if you’re ready to take your programming skills to the next level. The Hi-Res Edition is designed for programmers who want to build intraday trading systems, incorporating trade management techniques like profit targets and stop losses. This edition bridges the gap between daily and intraday bar programming, making it easier to handle challenges like tracking the sequence of high and low prices within the trading day. Plus, enjoy 5 hours of video instruction to guide you through each tutorial.
Advanced Topics Cover
The Advanced Topics Edition delves into essential programming concepts within EasyLanguage, offering a focused approach to complex topics. This book covers arrays and fixed-length buffers, including methods for element management, extraction, and sorting. Explore finite state machines using the switch-case construct, text graphic manipulation to retrieve precise X and Y coordinates, and gain insights into seasonality with the Ruggiero/Barna Universal Seasonal and Sheldon Knight Seasonal methods. Additionally, learn to build EasyLanguage projects, integrate fundamental data like Commitment of Traders, and create multi-timeframe indicators for comprehensive analysis.
Get Day Trading Edition Today!
The Day Trading Edition complements the other books in the series, diving into the popular approach of day trading, where overnight risk is avoided (though daytime risk still applies!). Programming on high-resolution data, such as five- or one-minute bars, can be challenging, and this book provides guidance without claiming to be a “Holy Grail.” It’s not for ultra-high-frequency trading but rather for those interested in techniques like volatility-based breakouts, pyramiding, scaling out, and zone-based trading. Ideal for readers of the Foundation and Hi-Res editions or those with EasyLanguage experience, this book offers insights into algorithms that shaped the day trading industry.
Trend Following Cover.
For thirty-one years as the Director of Research at Futures Truth Magazine, I had the privilege of collaborating with renowned experts in technical analysis, including Fitschen, Stuckey, Ruggiero, Fox, and Waite. I gained invaluable insights as I watched their trend-following methods reach impressive peaks, face sharp declines, and ultimately rebound. From late 2014 to early 2020, I witnessed a dramatic downturn across the trend-following industry. Iconic systems like Aberration, CatScan, Andromeda, and Super Turtle—once thriving on robust trends of the 1990s through early 2010s—began to falter long before the pandemic. Since 2020 we have seen the familiar trends return. Get six hours of video instruction with this edition.
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