Ultimate Algorithmic Trading System

A loop is used to flow through each trade history and keep track of the maximum
and minimum values stored in the third element of the startTradeTuple (max
drawdown of each history).
Twenty bins will be used in this example to store the individual drawdown values.
The bin size is calculated by dividing the range of drawdowns by 20. The size of
the bin is stored in the variable binInc (bin increment). Each bin is constructed by
storing each bin’s boundary values in a list of tuples named binTuple. Each binTuple
contains three elements: binNumber, binBottom, and binTop. The boundary values
are calculated by starting at the smallest max drawdown of the histories (bottom of the
first bin) and adding the bin size to get the top of the first bin. The next bin’s bottom
boundary becomes the prior bin’s top and its top is calculated by adding the binInc.
This process is repeated until all 20 bin tuples are filled up with the correct values.
numBins = 20
binTuple = list()
binInc = (maxDD - minDD)/20.0
binBot = minDD
for y in range(0,numBins):
binTop = binBot + binInc
binTuple += ((y,binBot,binTop),)
print(binTuple[y][1],' ',binTuple[y][2])
binBot = binTop + y
Once the bins are constructed all the different histories’ max drawdowns are
distributed into them. This is accomplished by comparing the max drawdown with
each bin’s top and bottom boundaries. If the drawdown value falls between the two
boundaries, it is then placed into that bin. A blank list of bins is initially created.
bins = list()
bins[:] = []
for x in range(0,numBins):
bins.append(0)
The different histories are looped through and are placed into the different bins.
Well, not really, just the frequencies of the drawdowns are stored.
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GENETIC OPTIMIZATION, WALK FORWARD
for x in range(0,len(startTradeTuple)):
for y in range(0,numBins):
tempDD = startTradeTuple[x][2]
tempBot = binTuple[y][1]
tempTop = binTuple[y][2]
if (tempDD >= binTuple[y][1] and tempDD <
binTuple[y][2]):
bins[y] += 1
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