def calculate(self, timePositions, iTimePositionDict):
''' calculate basic metrics '''
if not timePositions:
return self.result
# get max value, min value, max draw down
lastHigest = 0
maxDrawDownTimeStamp = 0
maxDrawDownPosition = 0
for (timeStamp, position) in timePositions:
if self.result[BasicMetric.MAX_TIME_VALUE][0] is None or self.result[BasicMetric.MAX_TIME_VALUE][1] < position:
self.result[BasicMetric.MAX_TIME_VALUE] = timeStamp, position
if self.result[BasicMetric.MIN_TIME_VALUE][0] is None or self.result[BasicMetric.MIN_TIME_VALUE][1] > position:
self.result[BasicMetric.MIN_TIME_VALUE] = timeStamp, position
if position > lastHigest:
lastHigest = position
maxDrawDownPosition = position
maxDrawDownTimeStamp = timeStamp
elif maxDrawDownPosition > position:
maxDrawDownPosition = position
maxDrawDownTimeStamp = timeStamp
self.result[BasicMetric.MAX_DRAW_DOWN] = (0, 0) if lastHigest == 0 else \
(maxDrawDownTimeStamp, 1 - (maxDrawDownPosition / lastHigest))
self.result[BasicMetric.START_TIME] = timePositions[0][0]
self.result[BasicMetric.END_TIME] = timePositions[-1][0]
self.result[BasicMetric.END_VALUE] = timePositions[-1][1]
self.result[BasicMetric.STDDEV] = stddev([timePosition[1] for timePosition in timePositions])
self.result[BasicMetric.SRATIO] = sharpeRatio([timePosition[1] for timePosition in timePositions])
self.result[BasicMetric.R_SQUARED] = rsquared([tp[1] for tp in timePositions], [iTimePositionDict.get(tp[0], tp[1]) for tp in timePositions])
return self.result
def calculate(self, timePositions):
''' calculate basic metrics '''
for (timeStamp, position) in timePositions:
if self.result[BasicMetric.MAX][0] is None or self.result[BasicMetric.MAX][1] < position:
self.result[BasicMetric.MAX] = timeStamp, position
if self.result[BasicMetric.MIN][0] is None or self.result[BasicMetric.MIN][1] > position:
self.result[BasicMetric.MIN] = timeStamp, position
self.result[BasicMetric.STDDEV] = stddev([timePosition[1] for timePosition in timePositions])
self.result[BasicMetric.SRATIO] = sharpeRatio([timePosition[1] for timePosition in timePositions])
return self.result
def calculate(self, timePositions):
''' calculate basic metrics '''
for (timeStamp, position) in timePositions:
if self.result[BasicMetric.MAX][0] is None or self.result[
BasicMetric.MAX][1] < position:
self.result[BasicMetric.MAX] = timeStamp, position
if self.result[BasicMetric.MIN][0] is None or self.result[
BasicMetric.MIN][1] > position:
self.result[BasicMetric.MIN] = timeStamp, position
self.result[BasicMetric.STDDEV] = stddev(
[timePosition[1] for timePosition in timePositions])
self.result[BasicMetric.SRATIO] = sharpeRatio(
[timePosition[1] for timePosition in timePositions])
return self.result
dam = DAMFactory.createDAM("sql", {'db': 'sqlite:////data/stock.sqlite'})
dateTicks = dam.readBatchTupleQuotes(symbols, 20031210, 20131210)
symbolTicks = {}
for timeStamp in sorted(dateTicks.iterkeys()):
for symbol, tick in dateTicks[timeStamp].iteritems():
if symbol not in symbolTicks:
symbolTicks[symbol] = []
symbolTicks[symbol].append(tick)
bads = []
goods = {} # symbol as key, std as value
for symbol, ticks in symbolTicks.iteritems():
avgClose = mean([tick.close for tick in ticks])
std = 100 * stddev([tick.close for tick in ticks]) / avgClose
print "std for %s is %f" % (symbol, std)
ticks = ticks[-30:]
avgVolumnDollar = mean([tick.volume * tick.close for tick in ticks])
if avgVolumnDollar > 1000000 and avgClose > 6 and avgClose < 100:
goods[symbol] = std
else:
bads.append(symbol)
print "=========bad %s==============" % len(bads)
print bads
sortedList = sorted(goods.iteritems(), key=lambda x: x[1])
print "=========good %s==============" % len(sortedList)
dateTicks = dam.readBatchTupleQuotes(symbols, 20031210, 20131210)
symbolTicks = {}
for timeStamp in sorted(dateTicks.iterkeys()):
for symbol, tick in dateTicks[timeStamp].iteritems():
if symbol not in symbolTicks:
symbolTicks[symbol] = []
symbolTicks[symbol].append(tick)
bads = []
goods = {} # symbol as key, std as value
for symbol, ticks in symbolTicks.iteritems():
avgClose = mean([tick.close for tick in ticks])
std = 100 * stddev([tick.close for tick in ticks])/avgClose
print "std for %s is %f" % (symbol, std)
ticks = ticks[-30:]
avgVolumnDollar = mean([tick.volume * tick.close for tick in ticks])
if avgVolumnDollar > 1000000 and avgClose > 6 and avgClose < 100:
goods[symbol] = std
else:
bads.append(symbol)
print "=========bad %s==============" % len(bads)
print bads
sortedList = sorted(goods.iteritems(), key=lambda x: x[1])
print "=========good %s==============" % len(sortedList)
