Benchmarking backtest results against random strategies

//Randomly generate entries to match a strategy's simulation period, number of trades and trade duration
#define ASSETLOOP while(asset(loop("GBP/JPY", "GBP/USD", "USD/CAD")))
#define TradesLong AlgoVar[0]
#define TradesShort AlgoVar[1]
function getOpt(string param) {
    switch (Asset) {
        case "GBP/JPY":
        switch (param)
        {
            case "strategyTradesLong"  : return 114;
            case "strategyTradesShort" : return 0;
            case "strategyDuration"     : return 4;
        }
        case "GBP/USD":
        switch (param)
        {
            case "strategyTradesLong"  : return 128;
            case "strategyTradesShort" : return 0;
            case "strategyDuration"     : return 4;
        }
        case "USD/CAD":
        switch (param)
        {
            case "strategyTradesLong"  : return 0;
            case "strategyTradesShort" : return 113;
            case "strategyDuration"     : return 4;
        }
    }
}
function tradeRandom() {
    var StrategyTradesLong = getOpt("strategyTradesLong"); //Number of trades in the actual strategy
    var StrategyTradesShort = getOpt("strategyTradesShort");
    int StrategyDuration = getOpt("strategyDuration"); //Average trade length in the actual strategy strategy
    ExitTime = StrategyDuration;
    var NumOpen = NumOpenLong + NumOpenShort;
    if (random() > 0) { //prevents trades being taken on the same bars each run of the strategy
    if(NumOpen == 0 and TradesLong < StrategyTradesLong and random() > 0) { //may need to modify this line to get correct random trade frequency
            enterLong();
            TradesLong += 1;
            }
        if (NumOpen == 0 and TradesShort < StrategyTradesShort and random() < 0) { //may need to modify this line to get correct random trade frequency
            enterShort();
            TradesShort += 1;
            }
    }
}
function run() {
    set(LOGFILE);
    BarPeriod = 1440;
    StartDate = 2010; //Set start and end dates to match those from the acutal strategy's simulation
    EndDate = 2014;
    LookBack = 0;
    if (is(INITRUN)) TradesLong = 0;
    if (is(INITRUN)) TradesShort = 0;
    ASSETLOOP
    while(algo(loop("RAND")))
  {
    if(Algo == "RAND")
      tradeRandom();
   }
}

I ran this strategy 5,000 times and extracted the profit factor of each run using this Unix script that I ran with Cygwin.

#!/bin/bash
i=0
while [ $i -lt 5000 ]
do
    cd "C:/~/Zorro"
        ./Zorro -run random_trades
    cd "C:/~/Zorro/Log"
        sed -n 56p random_trades.txt  >> RandomTrades_GBPJPY.txt
        sed -n 57p random_trades.txt  >> RandomTrades_GBPUSD.txt
        sed -n 58p random_trades.txt  >> RandomTrades_USDCAD.txt
    i=`expr $i + 1`
done

This feels like a crude approach, but it let me move fast, which counts for a lot. In order to use this on a random strategy with different assets, the lines extracted from the performance report would need to be modified accordingly. Zorro can also be programmed to run a certain number of times using the NumTotalCycles parameter. The profit factor metric can then be calculated and stored in a histogram.

In the next post, I’ll explore some results obtained with this approach.