Ultimate Algorithmic Trading System

loops from trade #1 to the last trade. The interior loop loops from the current trade
number to the last trade.
# start trade draw down analysis - utilizing the tradeTuple
tupleLen = len(tradeTuple)
tradeTuple = sorted(tradeTuple,key=itemgetter(0))
for x in range(0,tupleLen):
cumStartTradeEquity = 0
maxStartTradeDD = -99999999
maxCumEquity = 0
for y in range(x,tupleLen):
cumStartTradeEquity += tradeTuple[y][1]
maxCumEquity = max(maxCumEquity,cumStartTradeEquity)
maxStartTradeDD = max(maxStartTradeDD,maxCumEquity -
cumStartTradeEquity)
startTradeTuple += ((x,cumStartTradeEquity,
maxStartTradeDD),)
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GENETIC OPTIMIZATION, WALK FORWARD
The outer loop indexed by x controls the number of trade histories. The inner
loop indexed by y flows through the different trade histories and keeps track of
each history’s cumulative equity and maximum drawdown. At the beginning of each
trade history, these two values are zeroed out. Each trade’s P/L is stored as the
second element in the tuple tradeTuple. This value is extracted through each y
iteration and used to calculate the two performance metrics. Once an individual
history is created it is then stored in a list of tuples named startTradeTuple.
The first element in this tuple is x (the trade history number). The second element
cumStartTradeEquity stores the cumulative equity of the history and the third
element maxStartTradeDD stores the max drawdown of the history.
Once the trade histories are compiled, the bins that hold the different max
drawdowns must be constructed. The first step in the construction process is to
calculate the largest and smallest historic drawdown values.
minDD = 99999999
maxDD = 0
for y in range(0,len(startTradeTuple)):
print(startTradeTuple[y][0],' ',startTradeTuple[y][1],
' ',startTradeTuple[y][2])
if startTradeTuple[y][2] < minDD: minDD =
startTradeTuple[y][2]
if startTradeTuple[y][2] > maxDD: maxDD =
startTradeTuple[y][2]
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