Category Archives: Free EasyLanguage

Implementing Turtle Algorithm into the Python Backtester

I include the Python Backtester in my latest book “The Ultimate Algorithmic Trading System Toolbox” book.  A good tutorial on how to use it would be to program the Turtle Algorithm in three different parts.   Here is part 1:

Entry Description: Buy on stop at highest high of last twenty days.  Short on lowest low of last twenty days.

Exit Description: Exit long on stop at lowest low of last ten days.  Exit short on highest high of past ten days.

Position Sizing:  Risk 2% of simulated 100K account on each trade.  Calculate market risk by utilizing the ten day ATR.  Size(shares or contracts) = $2,000/ATR in dollars.

Python code to  input into the backtester:

 

initCapital = 100000
riskPerTrade = .02
dollarRiskPerTrade = initCapital * riskPerTrade

        hh20 = highest(myHigh,20,i,1)
        ll20 = lowest(myLow,20,i,1)
        hh10 = highest(myHigh,10,i,1)
        ll10 = lowest(myLow,10,i,1)
        hh55 = highest(myHigh,55,i,1)
        ll55 = lowest(myLow,55,i,1)
        
        atrVal = sAverage(trueRanges,10,i,1)

 #Long Entry Logic
        if (mp==0 or mp==-1) and barsSinceEntry>1 and myHigh[i]>=hh20:
            profit = 0
            price = max(myOpen[i],hh20)
            numShares = max(1,int(dollarRiskPerTrade/(atrVal*myBPV)))
            tradeName = "Turt20Buy"

 #Short Logic
        if (mp==0 or mp==1) and barsSinceEntry>1 and myLow[i] <= ll20: 
            profit = 0 
            price = min(myOpen[i],ll20) 
            numShares = max(1,int(dollarRiskPerTrade/(atrVal*myBPV)))
            tradeName = "Turt20Shrt"

 #Long Exit Loss 
        if mp >= 1 and myLow[i] <= ll10 and barsSinceEntry > 1:
            price = min(myOpen[i],ll10)
            tradeName = "Long10-Liq"

 #Short Exit Loss
        if mp <= -1 and myHigh[i] >= hh10 and barsSinceEntry > 1:
            price = max(myOpen[i],hh10)
            tradeName = "Shrt10-Liq"
Turtle Part 1

 

This snippet only contains the necessary code to use in the Python Backtester – it is not in its entirety.

This algorithm utilizes a fixed fractional approach to position sizing.  Two percent or $2000 is allocated on each trade and perceived market risk is calculated by the ten-day average true range (ATR.)   So if we risk $2000 and market risk is $1000 then 2 contracts are traded.  In Part 2, I will introduce the N risk stop and the LAST TRADE LOSER Filter.

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Using Quandl Data

If you haven’t come across the great data resource Quandl.com I highly suggest in doing so.  A  portion of the data that I used in writing my latest book came from Quandl, specifically the wiki futures or CHRIS database.  Here is the link:

https://www.quandl.com/data/CHRIS

There is a ton of free futures data.  The different contracts are concatenated into large files.  However, the rollover discount is not taken into consideration so the data “as-is” is somewhat un – testable.  I am in the process of scrubbing the data as well as creating  a “Panama” adjustment to the contracts in the large files.  As soon as I complete this task I will provide the data on this website.  Purchasers of my latest book will find 10+ plus years of history of continuous futures data that I pieced together from several different sources, including Quandl.

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Further Clarification on Data Aliasing

I was speaking with Mike Chalek on the phone this weekend concerning Data Aliasing and he felt this post was a little confusing. After re-reading it I can see where he is coming from. Using the same example let me see if I can clarify: assume the trading day is Wednesday and you want to keep track of the slope of a 19-day weighted moving average of data2 (weekly bars) by using a variable. The following code will give an erroneous result:

wAvg = wAverage(c of data2,19);
mySlope = wAvg – wAvg[1];

If you interrogate mySlope intra-week then it will always be equal to zero. The wAvg is by default tied to data1 which in this case is daily bars. So the value of wAvg is carried over from one day to the next. It only changes when the average of the weekly bar changes and that only occurs on Friday.

There are two possible solutions:

Without the use of data aliasing – inLine function calls
mySlope = wAverage(c of data2,19) – wAverage(c[1] of data2,19) ;

With the use of data aliasing –
vars: wAvg(close of data2,0);

wAvg = wAverage(c of data2,19);
mySlop = wAvg – wAvg[1];

Either examples will work, but if you have several variables tied to a different data stream, then the code will be much cleaner looking using data aliasing – plus it cuts down on multiple function calls.

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Using Multiple Time Frames in a Strategy

I have been working on a project where the strategy combined daily and weekly bars.  Keeping track of the two time frames was, at one time, not that easy.  However, with TradeStation’s Data Aliasing it is no problem at all.  We all know that Data 1 is the highest resolution time frame and is the one used for trade execution.   Data 2 can be a different market or a different time from of the same market.  TradeStation allows for multiple data streams.  Take a look at the following output in table 1.  Wavg is a nine period moving average of weekly crude data.  Wavg[1] is the prior value of the moving average.  If you wanted to make a trading decision on a daily bar basis by looking at the slope of the Wavg you couldn’t.  The Wavg and Wavg[1] only changes at the beginning of the next week.  Most traders want to be able to make a trading decision intra-week by examining the current values of the Davg1, Davg2 and the slope of Wavg.  During the week the slope of Wavg is ZERO.

table 1
Date    Davg1 Davg2 Wavg Wavg[1]
1151019 46.94 46.38 46.17 46.17
1151020 47.01 46.54 46.17 46.17
1151021 47.00 46.69 46.17 46.17
1151022 46.95 46.74 46.17 46.17
1151023 46.93 46.70 46.54 46.17<< changed here
1151026 46.83 46.55 46.54 46.54
1151027 46.71 46.47 46.54 46.54
1151028 46.74 46.44 46.54 46.54
1151029 46.74 46.40 46.54 46.54
1151030 46.73 46.39 46.60 46.54
1151102 46.57 46.37 46.60 46.60
1151103 46.55 46.45 46.60 46.60
1151104 46.36 46.44 46.60 46.60

Now look at table 2.   The Wavg is not being updated on a daily  basis but on a weekly basis.  The current Wavg doesn’t become the prior Wavg on each daily bar.  Wavg[1] stays the same until a new weekly bar occurs.  You can now make a trading decision intra-week by examining the slope of the Wavg.  Each time frame update should only occur when a new bar of that same time frame is generated.  This feature is really cool and is easy to implement.  

table2
Date      Davg1 Davg2 Wavg Wavg[1]
1151019 46.94 46.38 46.17 45.75 < notice how the Wavg and Wavg[1] are always different
1151020 47.01 46.54 46.17 45.75
1151021 47.00 46.69 46.17 45.75
1151022 46.95 46.74 46.17 45.75
1151023 46.93 46.70 46.54 46.17
1151026 46.83 46.55 46.54 46.17
1151027 46.71 46.47 46.54 46.17
1151028 46.74 46.44 46.54 46.17
1151029 46.74 46.40 46.54 46.17
1151030 46.73 46.39 46.60 46.54
1151102 46.57 46.37 46.60 46.54
1151103 46.55 46.45 46.60 46.54
1151104 46.36 46.44 46.60 46.54

 

Here is the code that utilizes Data Aliasing. All I did was declare the weekly avg variable and tied it to data2.

vars: mavShortDaily(0),mavLongDaily(0);
vars: mavWeekly(0,data2);

mavShortDaily = average(c,19);
mavLongDaily = average(c,39);

mavWeekly = average(C of data2, 9);

If mavShortDaily > mavLongDaily and mavWeekly > mavWeekly[1] then buy this bar on close;
If mavShortDaily < mavLongDaily and mavWeekly < mavWeekly[1] then sellshort this bar on close;

print(date," ",mavShortDaily," ",mavLongDaily," ",mavWeekly," ",mavWeekly[1]);

Notice how the variable mavWeekly was tied to data2. When you delcare a variable that is tied to another data other than data1 you can put the data stream right in the variable delcaration : mavWeekly(0,data2).

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