Ultimate Algorithmic Trading System

Once the frequency of each drawdown is stored in each bin the total number of
drawdown occurrences must be calculated.
freqSum = sum(bins)
Python has a cool list method sum. This method sums up all of the values in the
list in one fell swoop. This is the beauty of an object-oriented language. Once the
total is calculated, the cumulative probabilities is just a loop away.
binProb = list()
for y in range(0,numBins):
if y == 0:
binProb.append(bins[y]/freqSum)
else:
binProb.append(bins[y]/freqSum + binProb[y-1])
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The probability of bin #1 is simply the number of drawdowns in that bin divided
by the total number of max drawdowns. The subsequent bin’s probabilities are
the probability of the current bin plus the prior bin’s probability. This process
accumulates the frequencies of the drawdowns. Here is a printout of a probability
distribution of an algorithm trading different markets.
GENETIC OPTIMIZATION, WALK FORWARD
Probability of DD < 1547 is 0.060
Probability of DD < 3094 is 0.080
Probability of DD < 4643 is 0.080
Probability of DD < 6192 is 0.280
Probability of DD < 7743 is 0.320
Probability of DD < 9294 is 0.400
Probability of DD < 10847 is 0.400
Probability of DD < 12400 is 0.420
Probability of DD < 13955 is 0.640
Probability of DD < 15510 is 0.660
Probability of DD < 17067 is 0.680
Probability of DD < 18624 is 0.820
Probability of DD < 20183 is 0.880
Probability of DD < 21742 is 0.880
Probability of DD < 23303 is 0.900
Probability of DD < 24864 is 0.920
Probability of DD < 26427 is 0.940
Probability of DD < 27990 is 0.960
Probability of DD < 29555 is 0.980
Probability of DD < 31120 is 1.000
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