def runVisualise():
fileName = 'data_/AMERICAN_MANAGEMENT_SYSTEMS_INC.csv'
#fileName = 'table.csv'
targetGroup = 89
#global data, headers
data, headers = para.readFile(fileName)
dates = data['Date']
close = data['Close']
groups = grouping.groupUp(data, data['Close'])
target = targetGroup
targetNext = target+const.ma
results = testalgos.compareAllGroupsBefore(groups, target)
results2 = testalgos.compareAllGroupsBefore(groups, targetNext)
#results2 = compareAllGroupsTo(groups, targetNext)
results.reverse()
results.sort(key=lambda x : x[2])
results2.sort(key=lambda x : x[2])
print('Target ' + str(target))
display.plotgroup(data, groups[target])
plt.show()
for v in results[0:10]:
print(v)
totalRank = 0
ranks = []
for v in results[0:10]:
rank = testalgos.getRank(results2, v[0]+const.ma)
totalRank += rank
ranks.append(rank)
print('Total Rank = ' + str(totalRank))
print(ranks)
display.plotAverageHighLow(groups, results[0:10], groups[targetNext][2])
return
for v in results[0:10]:
print()
print('Chosen group:')
print('Number ' + str(v[0]) + ', SimilarityScore = ' + str(v[2]))
print('Matching Group:')
print('Number ' + str(v[0]+const.ma) + ', Rank = ' + str(testalgos.getRank(results2, v[0]+const.ma)))
print('Plotting Target Next Group (blue)')
display.plotnormalizedWith(data, groups[targetNext], [similarity.byFirst])
print('Plotting Predicted Next Group (green)')
display.plotnormalizedWith(data, groups[v[0]+const.ma], [similarity.byFirst])
plt.show()
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)
def runDataSelectOn(fileName):
data, headers = para.readFile(fileName)
dates = data['Date']
if (len(dates) == 0):
#print('Empty File')
return
groups = grouping.groupUp(data, data['Close'])
matches = dataselect.findMatches(data, groups)
#print('Found ' + str(len(matches)) + 'matches')
if (len(matches) <= 0): return #print only when there is at least one match.
print(util.getNameOnly(fileName))
for group in matches:
display.printgroupattrs(group, dates)
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)
