Turtle (Last Trade Was A Loser (LTL)) Filter – Part 2 in Series

How many of you believe if the last trade was a winner, the probability of the next trade being a loser is higher? The Turtles believed this and in this post I introduce the concept of filtering trades based on the prior trade’s success.

Here is a list of trades without the filter:


20090506    Turt20Buy  1  91.75000       0.00       0.00
20090622   Long10-Liq  1 103.14000   11290.00   11290.00
20090702   Turt20Shrt  1 102.26000       0.00       0.00
20090721   Shrt10-Liq  1 100.80000    1360.00   12650.00
20090803    Turt20Buy  1 104.61000       0.00       0.00
20090817   Long10-Liq  1 101.99000   -2720.00    9930.00
20090824    Turt20Buy  1 107.71000       0.00       0.00
20090902   Long10-Liq  1 100.97000   -6840.00    3090.00
20090902   Turt20Shrt  1 100.10000       0.00       0.00
20090917   Shrt10-Liq  1 105.87000   -5870.00   -2780.00
20090924   Turt20Shrt  1 100.02000       0.00       0.00
20091008   Shrt10-Liq  1 104.72000   -4800.00   -7580.00
20091012    Turt20Buy  1 106.13000       0.00       0.00
20091030   Long10-Liq  1 109.43000    3200.00   -4380.00
20091113   Turt20Shrt  1 108.95000       0.00       0.00
20091223   Shrt10-Liq  1 106.03000    2820.00   -1560.00

And here are the trades with filter engaged:


20090506    Turt20Buy  1  91.75000       0.00       0.00
20090622   Long10-Liq  1 103.14000   11290.00   11290.00
20090824    Turt20Buy  1 107.71000       0.00       0.00
20090902   Long10-Liq  1 100.97000   -6840.00    4450.00
20090902   Turt20Shrt  1 100.10000       0.00       0.00
20090917   Shrt10-Liq  1 105.87000   -5870.00   -1420.00
20090924   Turt20Shrt  1 100.02000       0.00       0.00
20091008   Shrt10-Liq  1 104.72000   -4800.00   -6220.00
20091012    Turt20Buy  1 106.13000       0.00       0.00
20091030   Long10-Liq  1 109.43000    3200.00   -3020.00
20100126   Turt20Shrt  1 104.09000       0.00       0.00
20100218   Shrt10-Liq  1 108.12000   -4130.00   -7150.00
20100219    Turt20Buy  1 109.37000       0.00       0.00
20100322   Long10-Liq  1 108.96000    -510.00   -7660.00

Check for yourself – you will notice that a trade after a winner is skipped. Trades are not picked back up until a loser is reported. Trading like this is quite easy but backtesting is quite a bit more difficult. I talk about this in the book where you have to switch between actual trading and simulated trading. The beauty of the Python BackTester is that it is somewhat easy to incorporate this into the testing logic. All you have to do is determine if the prior real or simulated trade is a loser. If it isn’t then you still must keep track of all trades, but don’t book the trades that follow the winner. I have created a testing module that does just that. Here is a snippet of the code:

# 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))) if mp >= 1:
            profit,trades,curShares=exitPos(price,myDate[i],"RevLongLiq",curShares,lastTradeLoser)
                if lastTradeLoser < 1 : listOfTrades.append(trades) todaysCTE = profit if profit > 0 :
                    if lastTradeLoser < 0 : lastTradeLoser = 0 if lastTradeLoser > 0 : lastTradeLoser +=1
                else:
                    lastTradeLoser = -1
                mp = 0
            mp -= 1
            tradeName = "Turt20Shrt"
            marketPosition[i] = mp
            entryPrice.append(price)
            entryQuant.append(numShares)
            curShares = curShares + numShares
            trades = tradeInfo('sell',myDate[i],tradeName,entryPrice[-1],numShares,1)
            barsSinceEntry = 1
            if lastTradeLoser < 1:
               listOfTrades.append(trades) <strong># book the trade if lastTradeLoser < 1 else don't</strong>
               totProfit += profit         <strong># book the profit from the trade</strong>
Turtle Part 1

I will include this in an update to the Python backtester for registered users of this site.

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.

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.