def estimate(conn,t_index,p_date,l_mu,l_sigma):
#get the range from which will trigger the sigma event
table_name = "t_enriched_bloomberg_prices"
cur = conn.cursor()
querySql = "select one_day_change from {} where name='{}' and post_date <'{}' order by post_date desc limit 30 ".format(table_name,t_index,p_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,t_index,p_date)
cur.execute(querySql)
rows = cur.fetchall()
moving90 = []
for row in rows:
moving90.append(row[0])
querySql = "select current_value from {} where name='{}' and post_date <'{}' order by post_date desc limit 1 ".format(table_name,t_index,p_date)
cur.execute(querySql)
rows = cur.fetchall()
previous_value = 0.0
for row in rows:
previous_value = float(row[0])
m30 = sum(moving30)/len(moving30)
m90 = sum(moving90)/len(moving90)
std30 = calculator.calSD(moving30)
std90 = calculator.calSD(moving90)
s4Bottom = m30 - 4*std30
s4Upper = m30 + 4*std30
s3Bottom = m90 - 3*std90
s3Upper = m90 + 3*std90
bottom = s4Bottom
upper = s4Upper
if s4Bottom >= s3Bottom:
bottom = s3Bottom
if s3Upper <= s4Upper:
upper = s3Upper
#get the log range
bottom = math.log((bottom+previous_value)/previous_value)
upper = math.log((upper+previous_value)/previous_value)
n_dis = stats.norm(l_mu,l_sigma)
#compute the probility of negative sigama event(0412)
p_0412 = n_dis.cdf(bottom)
#compute the probility of positive sigama event(0411)
p_0411 = 1 - n_dis.cdf(upper)
"Insert into the prediction model"
# sql = "insert into t_ar_prediction (post_date,stock_index,zscore30,zscore90,change_percent,price,event_type,ord) values (?,?,?,?,?,?,?,?)"
# cur.execute(sql,(p_date,t_index,zscore30,zscore90,p_l,p_price,event_type,99))
sql = "insert into t_ar_garch_prediction(post_date,name,type,p_mu,p_sigma,p_0411,p_0412,ord) values (?,?,?,?,?,?,?,?)"
cur.execute(sql,(p_date,t_index,"Stock",l_mu,l_sigma,p_0411,p_0412,99))
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])
def warningCheck(surObj):
# 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'}
stockIndex = surObj["population"]
trendType = surObj["strength"]
date = surObj["shiftDate"]
try:
con = common.getDBConnection()
cur = con.cursor()
pClusster = trendType
sql = "select sub_sequence,last_price from t_daily_stockindex where stock_index=? and date<? order by date desc limit 1"
cur.execute(sql, (stockIndex, date))
row = cur.fetchone()
subSequence = row[0]
currentVal = row[1]
querySql = "select one_day_change from t_daily_stockindex where stock_index=? and sub_sequence>=? and sub_sequence<=?"
cur.execute(querySql, (stockIndex, subSequence - 29, subSequence))
rows = cur.fetchall()
moving30 = []
for row in rows:
moving30.append(row[0])
querySql = "select one_day_change from t_daily_stockindex where stock_index=? and sub_sequence>=? and sub_sequence<=?"
cur.execute(querySql, (stockIndex, subSequence - 89, subSequence))
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(stockIndex,
str(pClusster), m30, m90,
std30, std90, currentVal)
dailyRecord = {}
dailyRecord["date"] = date
dailyRecord["cBottom"] = cButtom
dailyRecord["cUpper"] = cUpper
dailyRecord["currentValue"] = currentVal
"Construct the warning message"
warningMessage = {}
date = surObj["date"]
derivedFrom = surObj["embersId"]
model = surObj["model"]
event = eventType
confidence = surObj["confidence"]
confidenceIsProbability = surObj["confidenceIsProbability"]
eventDate = surObj["shiftDate"]
population = surObj["population"]
location = surObj["location"]
warningMessage["date"] = date
warningMessage["derivedFrom"] = derivedFrom
warningMessage["model"] = model
warningMessage["eventType"] = event
warningMessage["confidence"] = confidence
warningMessage["confidenceIsProbability"] = confidenceIsProbability
warningMessage["eventDate"] = eventDate
warningMessage["population"] = population
warningMessage["location"] = location
embersId = hashlib.sha1(json.dumps(warningMessage)).hexdigest()
warningMessage["embersId"] = embersId
if eventType != "0000":
insert_warningmessage(warningMessage)
return warningMessage
else:
return None
except lite.Error, e:
print "Error: %s" % e.args[0]
def warningCheck(surObj):
# 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'}
stockIndex = surObj["population"]
trendType = surObj["strength"]
date = surObj["shiftDate"]
try:
con = common.getDBConnection()
cur = con.cursor()
pClusster = trendType
sql = "select sub_sequence,last_price from t_daily_stockindex where stock_index=? and date<? order by date desc limit 1"
cur.execute(sql,(stockIndex,date))
row = cur.fetchone()
subSequence = row[0]
currentVal = row[1]
querySql = "select one_day_change from t_daily_stockindex where stock_index=? and sub_sequence>=? and sub_sequence<=?"
cur.execute(querySql,(stockIndex,subSequence-29,subSequence))
rows = cur.fetchall()
moving30 = []
for row in rows:
moving30.append(row[0])
querySql = "select one_day_change from t_daily_stockindex where stock_index=? and sub_sequence>=? and sub_sequence<=?"
cur.execute(querySql,(stockIndex,subSequence-89,subSequence))
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(stockIndex,str(pClusster),m30,m90,std30,std90,currentVal)
dailyRecord = {}
dailyRecord["date"] = date
dailyRecord["cBottom"] = cButtom
dailyRecord["cUpper"] = cUpper
dailyRecord["currentValue"] = currentVal
"Construct the warning message"
warningMessage ={}
date = surObj["date"]
derivedFrom = surObj["embersId"]
model = surObj["model"]
event = eventType
confidence = surObj["confidence"]
confidenceIsProbability = surObj["confidenceIsProbability"]
eventDate= surObj["shiftDate"]
population = surObj["population"]
location = surObj["location"]
warningMessage["date"] = date
warningMessage["derivedFrom"] = derivedFrom
warningMessage["model"] = model
warningMessage["eventType"] = event
warningMessage["confidence"] = confidence
warningMessage["confidenceIsProbability"] = confidenceIsProbability
warningMessage["eventDate"] = eventDate
warningMessage["population"] = population
warningMessage["location"] = location
embersId = hashlib.sha1(json.dumps(warningMessage)).hexdigest()
warningMessage["embersId"] = embersId
if eventType != "0000":
insert_warningmessage(warningMessage)
return warningMessage
else:
return None
except lite.Error, e:
print "Error: %s" % e.args[0]
