def warning_check(warningDomain, surObj, regeFlag=False, replayIO=None):
# surObj = {'embersId': 'f0c030a20e28a12134d9ad0e98fd0861fae7438b', 'confidence': 0.13429584033181682, 'strength': '4', 'derivedFrom': [u'5df18f77723885a12fa6943421c819c90c6a2a02', u'be031c4dcf3eb9bba2d86870683897dfc4ec4051', u'3c6571a4d89b17ed01f1345c80cf2802a8a02b7b'], 'shiftDate': '2011-08-08', 'shiftType': 'Trend', 'location': u'Colombia', 'date': '2012-10-03', 'model': 'Finance Stock Model', 'valueSpectrum': 'changePercent', 'confidenceIsProbability': True, 'population': 'COLCAP'}
stock_index = surObj["population"]
trend_type = surObj["strength"]
date = surObj["shiftDate"]
replayIO.write("Check whether the surrogate data trigger the warning.\n")
try:
pClusster = trend_type
table_name = "t_enriched_bloomberg_prices"
sql = "select currentValue from {} where name='{}' and postDate < '{}' order by postDate desc".format(table_name, stock_index, date)
current_val = 0.0
rs = warningDomain.select(sql, max_items=1)
for r in rs:
current_val = float(r['currentValue'])
replayIO.write("Retrive past 30 day's price daily change.\n")
querySql = "select oneDayChange from {} where name='{}' and postDate <'{}' order by postDate desc".format(table_name, stock_index, date)
rs = warningDomain.select(querySql, max_items=30)
moving30 = []
for r in rs:
moving30.append(float(r['oneDayChange']))
replayIO.write("\t %s\n" % json.dumps(moving30))
replayIO.write("Retrive past 90 day's price daily change.\n")
querySql = "select oneDayChange from {} where name='{}' and postDate <'{}' order by postDate desc".format(table_name, stock_index, date)
rs = warningDomain.select(querySql, max_items=90)
moving90 = []
for r in rs:
moving90.append(float(r['oneDayChange']))
replayIO.write("\t %s\n" % json.dumps(moving90))
m30 = sum(moving30) / len(moving30)
m90 = sum(moving90) / len(moving90)
std30 = calculator.calSD(moving30)
std90 = calculator.calSD(moving90)
eventType, cButtom, cUpper = \
dailySigmaTrends(stock_index, str(pClusster), m30, m90, std30, std90, current_val)
dailyRecord = {}
dailyRecord["date"] = date
dailyRecord["cBottom"] = cButtom
dailyRecord["cUpper"] = cUpper
dailyRecord["currentValue"] = current_val
"Construct the warning message"
warningMessage = {}
derivedFrom = {"derivedIds": [surObj["embersId"]]}
model = surObj["model"]
event = eventType
confidence = surObj["confidence"]
confidenceIsProbability = surObj["confidenceIsProbability"]
eventDate = surObj["shiftDate"]
population = surObj["population"]
location = surObj["location"]
comments = surObj["comments"]
comObj = json.loads(comments)
warningMessage["derivedFrom"] = derivedFrom
warningMessage["model"] = model
warningMessage["eventType"] = event
warningMessage["confidence"] = confidence
warningMessage["confidenceIsProbability"] = confidenceIsProbability
warningMessage["eventDate"] = eventDate
warningMessage["population"] = population
warningMessage["location"] = location
warningMessage["version"] = __version__
operateTime = datetime.utcnow().isoformat()
warningMessage["date"] = operateTime
comObj["trendVersion"] = CONFIG["trendRange"]["version"]
warningMessage["comments"] = json.dumps(comObj)
warningMessage["description"] = "Use Bayesian to predict stock sigma events"
embersId = hashlib.sha1(json.dumps(warningMessage)).hexdigest()
warningMessage["embersId"] = embersId
replayIO.write("Warning Message: \n\t%s\n" % json.dumps(warningMessage))
if eventType != "0000":
"push warningmessage to ZMQ"
with queue.open(WARNING_PORT, 'w', capture=True) as outq:
sleep(1)
outq.write(warningMessage)
replayIO.write("Publish Warningmessage to ZMQ!\n")
if not regeFlag:
insert_warningmessage(warningDomain, warningMessage)
if eventType != "0000":
return warningMessage
else:
return None
except lite.Error, e:
log.exception("Error: %s" % e.args[0])
def warning_check(conn,surObj,regeFlag=False):
# surObj = {'embersId': 'f0c030a20e28a12134d9ad0e98fd0861fae7438b', 'confidence': 0.13429584033181682, 'strength': '4', 'derivedFrom': [u'5df18f77723885a12fa6943421c819c90c6a2a02', u'be031c4dcf3eb9bba2d86870683897dfc4ec4051', u'3c6571a4d89b17ed01f1345c80cf2802a8a02b7b'], 'shiftDate': '2011-08-08', 'shiftType': 'Trend', 'location': u'Colombia', 'date': '2012-10-03', 'model': 'Finance Stock Model', 'valueSpectrum': 'changePercent', 'confidenceIsProbability': True, 'population': 'COLCAP'}
stock_index = surObj["population"]
trend_type = surObj["strength"]
date = surObj["shiftDate"]
cur = conn.cursor()
try:
pClusster = trend_type
table_name = "t_enriched_bloomberg_prices"
sql = "select current_value from {} where name='{}' and post_date < '{}' order by post_date desc limit 1".format(table_name,stock_index,date)
cur.execute(sql)
result = cur.fetchone()
current_val = float(result[0])
querySql = "select one_day_change from {} where name='{}' and post_date <'{}' order by post_date desc limit 30 ".format(table_name,stock_index,date)
cur.execute(querySql)
rows = cur.fetchall()
moving30 = []
for row in rows:
moving30.append(row[0])
querySql = "select one_day_change from {} where name='{}' and post_date <'{}' order by post_date desc limit 90 ".format(table_name,stock_index,date)
cur.execute(querySql)
rows = cur.fetchall()
moving90 = []
for row in rows:
moving90.append(row[0])
m30 = sum(moving30)/len(moving30)
m90 = sum(moving90)/len(moving90)
std30 = calculator.calSD(moving30)
std90 = calculator.calSD(moving90)
eventType,cButtom,cUpper = dailySigmaTrends(stock_index,str(pClusster),m30,m90,std30,std90,current_val)
dailyRecord = {}
dailyRecord["date"] = date
dailyRecord["cBottom"] = cButtom
dailyRecord["cUpper"] = cUpper
dailyRecord["currentValue"] = current_val
"Construct the warning message"
warningMessage ={}
derivedFrom = surObj["embersId"]
model = surObj["model"]
event = eventType
confidence = surObj["confidence"]
confidenceIsProbability = surObj["confidenceIsProbability"]
eventDate= surObj["shiftDate"]
population = surObj["population"]
location = surObj["location"]
comments = surObj["comments"]
comObj = json.loads(comments)
warningMessage["derivedFrom"] = derivedFrom
warningMessage["model"] = model
warningMessage["eventType"] = event
warningMessage["confidence"] = confidence
warningMessage["confidenceIsProbability"] = confidenceIsProbability
warningMessage["eventDate"] = eventDate
warningMessage["population"] = population
warningMessage["location"] = location
warningMessage["version"] = __version__
operateTime = datetime.now().isoformat()
warningMessage["dateProduced"] = operateTime
comObj["trendVersion"] = CONFIG["trendRange"]["version"]
warningMessage["comments"] = json.dumps(comObj)
warningMessage["description"] = "Use Bayesian to predict stock sigma events"
embersId = hashlib.sha1(json.dumps(warningMessage)).hexdigest()
warningMessage["embersId"] = embersId
if not regeFlag:
insert_warningmessage(conn,warningMessage)
if eventType != "0000":
return warningMessage
else:
return None
except lite.Error, e:
log.exception( "Error: %s" % e.args[0])
