It has been brought to my attention by a very astute reader of the book that the ordering of the buy/sell/longliq/shortliq directives creates a potential error by giving preference to entries.  The code in the book and my examples thus far test for a true condition first in the entry logic and then in the exit logic.  Let’s say your long exit stop in the Euros is set at 11020, but your reversal is set at 11000.  The python back tester will skip the exit at 11020 and reverse at 11000.  This only happens if both stops are hit on the same day.  If this happens you will incur a 20 point additional loss.  You can prevent this by using logic similar to:

if ((mp == 0 or (mp == -1 and stb < stopb)) and myHigh[D0] >= stb) :

Notice bow I compare the price level of stb and stopb [stb – reversal and stopb – liquidation].  I added this to the long entry logic – the code is only executed if the entry is less than the exit (closer to the current market).  I am in the process of restructuring the flow so that all orders will be examined on a bar by bar basis and the ones that should take place (chronologically speaking) will do so and be reflected in the performance metrics.  This can cause multiple trades on a single bar.  This is what happens in real trading and should be reflected in the PSB.  This is where the lack of a GOTO creates a small headache in Python.  The ESB already takes this into consieration.  I will post when I finalize the code.

I have just wrapped up the latest version of the Python System Back-tester (PSB).

I have added some more portfolio performance metrics and you will see these in the performance reports.  The most useful addition is the concept of the .POR file when you run a system.  Instead of having to select your data files each time you run a system, you can build a .POR file with a list of files/markets you want to batch run.

Here is an example of a Portfolio file:

TY.CSV

CU.CSV

SB.CSV

S2.CSV

QG.CSV

QM.CSV

C2.CSV

Just make sure you put the .POR file inside the same folder that contains your testing data.  I have included a TestPortfolio.por file in version 2.0.  I would treat the different versions as completely separate applications.  Your existing .py algorithm files will need to be slightly modified to work with version 2.0.

This line of code needs to be modified from this:

systemMarket.setSysMarkInfo(sysName,myComName,listOfTrades,equity)

to this:

systemMarket.setSysMarkInfo(sysName,myComName,listOfTrades,equity,initCapital)

This line is near the bottom of the overall loop.  I added the initCapital variable so you could do position sizing and the performance metrics would reflect this initial value.

And set initCapital to a pertinent value.  I put it right below the sysName variable:

sysName = ‘BollingerBandSys’ #System Name here

initCapital = 100000 #starting account balance

Also I corrected a small bug in the main loop.  You should change this:

for i in range(len(myDate) – numBarsToGoBack,len(myDate)):

to

for i in range(len(myDate) – (numBarsToGoBack-rampUp),len(myDate)):

In another post I will show how the portfolio performance metrics have changed.  I hope you like the new version.  I will be adding a library of trading systems utilizing this new version in a few days.

If you want to download Version 2.0 – just go the the following link

PSBVersion2.0

All the unnecessary lines of the Python System Back-Testing Module have been hidden.  Only the lines that you need to develop the next great algorithm are included.  Reads sort  of like English.  This snippet introduces you to the use of functions and lists – two major components of the Python language.  If you buy my latest book – “The Ultimate Algorithmic Trading System Toolbox” then simply email me or sign up through the contact form and you will get version 2.0 for free!

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>

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