Ultimate Algorithmic Trading System

The one trade that you took on Day 4 was a disaster. First off, you closed out a $12
loser ($112 − $100), and to add insult to injury your OTE is −$10 ($100 − $110).
You real cumulative losses stand at −$2 and your paper loss stands at −$10. All these
values are needed to calculate a daily equity stream and that is why we have: self.cls
TrdEquity, self.openTrdEquity, self.cumuClsEquity. The rest of the module
keeps track of the daily equity stream as well as the maximum equity peak, and
the subsequent equity valley. From these values the worst-case drawdown can be
derived.
def setEquityInfo(self,equityDate,equityItm,clsTrdEquity,
openTrdEquity):
self.equityDate.append(equityDate)
self.equityItm.append(equityItm)
self.cumuClsEquity += clsTrdEquity
tempEqu =self.cumuClsEquity+openTrdEquity
self.dailyEquityVal.append(tempEqu)
self.peakEquity = max(self.peakEquity,tempEqu)
maxEqu = self.peakEquity
self.minEquity = min(self.minEquity,tempEqu)
minEqu = self.minEquity
self.maxDD = max(self.maxDD,maxEqu-tempEqu)
maxDD = self.maxDD
maxDD = maxDD
The next class, systemMarketClass, is used to keep track of each system on each
individual market. I did this in case a future version of PSB might combine multiple
markets on multiple systems. You could have a bunch of these systemMarketClass
objects reflecting the performance on several system/markets; you then could
combine certain ones to produce a very efficient equity curve. For right now,
though, these class objects contain the following data:
def __init__(self):
self.systemName = ""
self.symbol = ""
self.tradesList =list()
self.equity = equityClass
self.avgWin = 0
self.avgLoss = 0
self.avgTrade = 0
self.profitLoss = 0
self.numTrades = 0
self.maxxDD = 0
self.perWins = 0
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USING PYTHON TO BACKTEST YOUR ALGORITHM
Each systemMarketClass contains the name of the system and the symbol. In
addition, it stores a list of trades, the equity stream, the average win, loss, and trade,
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