def testAlgo(algo, target):
# global data
# testAlgoOnData(algo, target, data['Close'])
#
#def testAlgoOnData(algo, target, dataList):
dates = data['Date']
groups = grouping.groupUp(data, data['Close'])
targetNext = target+const.ma
if targetNext >= len(groups):
return None
similarity._normalizeFuns = [similarity.byMean]
similarity._measureFun = algo
results = compareAllGroupsBefore(groups, target)
results2 = compareAllGroupsBefore(groups, targetNext)
results.reverse()
results.sort(key=lambda x : x[2])
results2.sort(key=lambda x : x[2])
### Uses Average Data: useOnlyAverageData = True
#tradePolicy = tradingmeasure.dontSell
#tradePolicy = tradingmeasure.sellOrKeep
#tradePolicy = tradingmeasure.riskAverseSellOrKeep
#tradePolicy = tradingmeasure.largestReturn
### Doesn't use Average Data: useOnlyAverageData = False
tradePolicy = tradingmeasure.confidenceFilter(0.2, tradingmeasure.sellOrKeep)
useOnlyAverageData = False
totalRank = 0
lpScore = 0
nResults = 10
for v in results[0:nResults]:
rank = getRank(results2, v[0]+const.ma)
totalRank += rank
lpScore += similarity.computeWith(groups[v[0]+const.ma], groups[targetNext], [similarity.byFirst], similarity.lpNorms(2))
dataLists = getDataLists(groups, results[0:nResults], const.ma)
if usingOnlyAverageData:
dataLists = tradingmeasure.averageData(dataLists)
money = tradingmeasure.computeWithFunOn(dataLists, groups[targetNext][2], tradePolicy)
#print(money)
totalRank *= 100 # normalize totalRank for equal weightage.
totalRank /= len(results2) # normalize totalRank for equal weightage.
return (lpScore/nResults, totalRank/nResults, money)
def decideStrategy(knownData, groupSize, predictSize, chart = None):
global tradePolicy, tradingPreprocess, similarityMeasure
dates = knownData['Date']
fullData = knownData['Close']
points = getDataselectPoints(knownData)
if len(fullData)-1 not in points:
return dontTrade()
groups = groupByLast(knownData, fullData, groupSize)
if (len(groups) < 20): return dontTrade()
target = len(groups) - 1
"""
matches = dataselect.findMatches(knownData, groups, 2)
printGroups(matches)
#printGroup(groups[target])
if groups[target] not in matches:
return dontTrade()
"""
similarity._normalizeFuns = [similarity.byMean]
similarity._measureFun = similarityMeasure
results = compareAllGroupsBefore(groups, groupSize, target)
results.reverse()
results.sort(key=lambda x : x[1])
nResults = 10
#print(','.join(map(lambda x : str(x[0]), results[0:nResults])))
dataLists = getDataLists(fullData, groups, results[0:nResults], predictSize)
strategy = createStrategyFromPolicy(dataLists)
if chart != None:
avgData = tradingmeasure.averageData(dataLists)
chart.writeChart(avgData)
return strategy
def testAlgoWeighted(algo, target, weightDataFun, data):
dates = data['Date']
groupsWeighted = grouping.groupUp(data, weightDataFun(data))
groupsClose = grouping.groupUp(data, data['Close'])
targetNext = target+const.ma
if targetNext >= len(groupsWeighted):
return None
similarity._normalizeFuns = [similarity.byMean]
similarity._measureFun = algo
results = testalgos.compareAllGroupsBefore(groupsWeighted, target)
results2 = testalgos.compareAllGroupsBefore(groupsClose, targetNext)
results.reverse()
results.sort(key=lambda x : x[2])
results2.sort(key=lambda x : x[2])
tradePolicy = tradingmeasure.sellOrKeep
usingOnlyAverageData = True
totalRank = 0
lpScore = 0
nResults = 10
for v in results[0:nResults]:
rank = testalgos.getRank(results2, v[0]+const.ma)
totalRank += rank
lpScore += similarity.computeWith(groupsClose[v[0]+const.ma], groupsClose[targetNext], [similarity.byFirst], similarity.lpNorms(2))
dataLists = testalgos.getDataLists(groupsClose, results[0:nResults], const.ma)
if usingOnlyAverageData:
dataLists = tradingmeasure.averageData(dataLists)
money = tradingmeasure.computeWithFunOn(dataLists, groupsClose[targetNext][2], tradePolicy)
#totalRank *= 100 # normalize totalRank for equal weightage.
#totalRank /= len(results2) # normalize totalRank for equal weightage.
return (lpScore/nResults, totalRank/nResults, money)