def processIndividual(q):
while not exitFlag:
queueLock.acquire()
if not workQueue.empty():
data = q.get()
individualId = data[0]
date = data[1]
startTime = data[2]
endTime = data[3]
mtmObject = MTM()
rewardMatrixObject = RewardMatrix()
qMatrixObject = QMatrix()
dbObject = DBUtils()
dbObject.dbConnect()
print('Calculating mtm for all trades of this individual')
mtmObject.calculateMTM(individualId, gv.aggregationUnit, date,
startTime, date, endTime, dbObject)
print('Calculating reward matrix for these individuals')
rewardMatrix = rewardMatrixObject.computeRM(
individualId, date, startTime, date, endTime, dbObject)
print('Calculating q matrix for these individuals')
qMatrixObject.calculateQMatrix(rewardMatrix, individualId,
dbObject)
queueLock.release()
#dbObject.dbClose()
else:
queueLock.release()
print(
'Since the individual has asset, adding the trade to new tradesheet'
)
dbObject.insertNewTrade(
tradeId, individualId,
tradeType, entryDate,
entryTime, entryPrice,
entryQty, exitDate, exitTime,
exitPrice)
dbObject.updateIndividualAsset(
gv.dummyIndividualId,
usedAsset)
dbObject.updateIndividualAsset(
individualId, usedAsset)
print('Calculating mtm for all the trades taken in last hour')
mtmObject.calculateMTM(gv.aggregationUnit, date, startTime,
date, endTime, dbObject)
print('Fetching individuals which took trades in last hour')
resultIndividuals = dbObject.getIndividuals(
date, startTime, date, endTime)
for individualId, dummy in resultIndividuals:
print('Calculating reward matrix for these individuals')
rewardMatrix = rewardMatrixObject.computeRM(
individualId, date, startTime, date, endTime, dbObject)
print('Calculating q matrix for these individuals')
qMatrixObject.calculateQMatrix(rewardMatrix, individualId,
dbObject)
print('Reallocating asset for these individuals')
reallocationObject.reallocate(date, startTime, date, endTime,
dbObject)
if endTime < dayEndTime: