def __init__(self):
config = GlobalConfig()
path = config.getConfig('SubConfigPath', 'StressTesting_win')
self.cp = configparser.ConfigParser()
self.cp.read(path, encoding='utf-8-sig')
td = TradingDay()
self.ltd = td.getLastTradingDay()
def __init__(self, product, enddate=None):
self.product = product
self.enddate = enddate
td = TradingDay()
ltd = td.getLastTradingDay()
if enddate is None:
self.enddate = ltd
def setBPIH(self):
BPI_table = self.getConfig('BPI_table', 'table')
td = TradingDay()
lastTradingDay = td.getLastTradingDay()
sqls = "select * from %s where Occur_Date = '%s'" % (BPI_table,
lastTradingDay)
return sqls
def __init__(self, productID, EndDate=None):
self.productID = productID
if EndDate is None:
td = TradingDay()
self.EndDate = td.getLastTradingDay()
else:
self.EndDate = EndDate
def __init__(self):
config = GlobalConfig()
path = config.getConfig('SubConfigPath', 'StressTesting_win')
self.cp = configparser.ConfigParser()
self.cp.read(path, encoding='utf-8-sig')
td = TradingDay()
self.ltd = td.getLastTradingDay()
print("---- %s has been initialized! ----" % self.__class__.__name__)
def __init__(self):
config = GlobalConfig()
path = config.getConfig('SubConfigPath', 'API_conf_win')
# path = config.getConfig('SubConfigPath', 'API_conf_linux')
# print(path)
self.cp = configparser.ConfigParser()
self.cp.read(path, encoding='utf-8-sig')
td = TradingDay()
self.ltd = td.getLastTradingDay()
def __init__(self):
config = GlobalConfig()
self.mailserver = config.getConfig('email', 'mailserver')
self.from_addr = config.getConfig('email', 'from_addr')
self.sender_password = config.getConfig('email', 'sender_password')
self.recipient = config.getConfig('email', 'recipient')
td = TradingDay()
self.date_of_subject = td.getLastTradingDay()
self.subject = config.getConfig('email', 'subject') + self.date_of_subject
self.attachmentFilePath = config.getConfig('email', 'attachmentFilePath')
def __init__(self, productID, EndDate=None):
config = GlobalConfig()
path = config.getConfig('SubConfigPath', 'StressTesting_win')
self.cp = configparser.ConfigParser()
self.cp.read(path, encoding='utf-8-sig')
td = TradingDay()
self.ltd = td.getLastTradingDay()
self.productID = productID
self.EndDate = EndDate
def portfolioVolatility(self):
td = TradingDay()
tradedays = td.getDuration(self.startdate, self.enddate)
df_pcntg = pd.DataFrame()
df_pcntg['TRADE_DT'] = tradedays
df_pcntg['portfolio'] = float(0)
# print(df_pcntg)
oc = OracleConnector()
oracle_connection = oc.getConn()
ms = MySQLConnector()
mysql_connection = ms.getConn()
for stock in self.stocklist:
if stock[-3:] == '.SH' or stock[-3:] == '.SZ':
sqls = """select TRADE_DT, S_DQ_PCTCHANGE "%s" from wind.AShareEODPrices where S_INFO_WINDCODE = '%s'and TRADE_DT >= '%s'and TRADE_DT <= '%s' order by TRADE_DT""" \
% (stock, stock, self.startdate, self.enddate)
df_stock = pd.read_sql(sql=sqls, con=oracle_connection)
# print(df_stock)
else:
sqls = """select L_DATE as TRADE_DT, round((EN_LAST_PRICE - EN_YESTERDAY_CLOSE_PRICE)/EN_YESTERDAY_CLOSE_PRICE, 4) as "%s"
from O32_THISSTOCKINFO where VC_REPORT_CODE = "%s" and L_DATE >= '%s' and L_DATE <= '%s' order by L_DATE""" \
% (stock, stock, self.startdate, self.enddate)
df_stock = pd.read_sql(sql=sqls, con=mysql_connection)
df_stock['TRADE_DT'] = df_stock['TRADE_DT'].astype(str)
df_stock[stock] = df_stock[stock] * 100
# print(df_stock)
weight = self.df_input.loc[self.df_input['VC_SCDM'] == stock,
'EN_SZZJZ'].values
# print(weight)
df_stock[stock] = df_stock[stock].astype(float) * weight / 100
# print(type(df_stock['TRADE_DT'][0]))
df_pcntg = pd.merge(df_pcntg, df_stock, how='outer', on='TRADE_DT')
df_pcntg = df_pcntg.fillna(0)
df_pcntg['portfolio'] += df_pcntg[stock]
# print(df_pcntg)
oc.closeConn()
ms.closeConn()
# print(list(df_pcntg['portfolio']))
# writer = pd.ExcelWriter("D:\PerformanceAnalysis/output.xlsx")
# print(df_pcntg)
# df_pcntg.to_excel(writer, 'Sheet2')
# writer.save()
vol = df_pcntg['portfolio'].std() * np.sqrt(20)
print("Stock Portfolio Volatility: %.4f" % vol)
return vol
def portfolioVolatility(self):
td = TradingDay()
tradedays = td.getDuration(self.startdate, self.enddate)
df_pcntg = pd.DataFrame()
df_pcntg['TRADE_DT'] = tradedays
df_pcntg['portfolio'] = float(0)
# print(df_pcntg)
oc = OracleConnector()
oracle_connection = oc.getConn()
ms = MySQLConnector()
mysql_connection = ms.getConn()
for pb in self.purebondlist:
sqls = """select TRADE_DT, b_anal_net_cnbd "%s" from wind.cbondanalysiscnbd where S_INFO_WINDCODE = '%s'and TRADE_DT >= '%s'and TRADE_DT <= '%s' order by TRADE_DT""" \
% (pb, pb, self.startdate, self.enddate)
sqls_minus1day = """select TRADE_DT, b_anal_net_cnbd "%s" from wind.cbondanalysiscnbd where S_INFO_WINDCODE = '%s'and TRADE_DT >= '%s'and TRADE_DT <= '%s' order by TRADE_DT""" \
% (pb, pb, self.startdate_minus1day, self.enddate_minus1day)
# print(sqls)
df_pb = pd.read_sql(sql=sqls, con=oracle_connection)
df_pb_minus1day = pd.read_sql(sql=sqls_minus1day,
con=oracle_connection)
# print(df_pb, df_pb_minus1day)
df_pb[pb] = (df_pb[pb] / df_pb_minus1day[pb] - 1) * 100
# print(df_pb)
weight = self.df_input.loc[self.df_input['VC_SCDM'] == pb,
'EN_SZZJZ'].values
# print(weight)
df_pb[pb] = df_pb[pb].astype(float) * weight / 100
# print(type(df_pb['TRADE_DT'][0]))
df_pcntg = pd.merge(df_pcntg, df_pb, how='outer', on='TRADE_DT')
# df_pcntg = df_pcntg.fillna(0)
df_pcntg['portfolio'] += df_pcntg[pb]
# print(df_pcntg)
oc.closeConn()
ms.closeConn()
# print(list(df_pcntg['portfolio']))
# writer = pd.ExcelWriter("D:\PerformanceAnalysis/output.xlsx")
# df_pcntg.to_excel(writer, 'Sheet2')
# writer.save()
vol = df_pcntg['portfolio'].std() * np.sqrt(20)
print("PB Portfolio Volatility: %.4f" % vol)
return round(vol, 4)
def __init__(self, productID, EndDate=None):
m = APIS()
sqls = m.setAPIS(productID, EndDate)
sqls_stock = m.stock(productID, EndDate)
sqls_purebond = m.pureBond(productID, EndDate)
sqls_convertablebond = m.convertableBond(productID, EndDate)
ms = MySQLConnector()
connection = ms.getConn()
self.df = pd.read_sql(sql=sqls, con=connection)
self.df_stock = pd.read_sql(sql=sqls_stock, con=connection)
self.df_purebond = pd.read_sql(sql=sqls_purebond, con=connection)
self.df_convertablebond = pd.read_sql(sql=sqls_convertablebond,
con=connection)
ms.closeConn()
# print(self.df)
td = TradingDay()
self.checker_naturaldays = td.getProductNaturalDayCounts(
productID, EndDate)
self.checker_tradedays = td.getProductTradingDayCounts(
productID, EndDate)
def portfolioVolatility(self):
td = TradingDay()
tradedays = td.getDuration(self.startdate, self.enddate)
df_pcntg = pd.DataFrame()
df_pcntg['TRADE_DT'] = tradedays
df_pcntg['portfolio'] = float(0)
# print(df_pcntg)
oc = OracleConnector()
oracle_connection = oc.getConn()
ms = MySQLConnector()
mysql_connection = ms.getConn()
for cb in self.cblist:
sqls = """select TRADE_DT, S_DQ_PCTCHANGE "%s" from wind.CBondEODPrices where S_INFO_WINDCODE = '%s'and TRADE_DT >= '%s'and TRADE_DT <= '%s' order by TRADE_DT""" \
% (cb, cb, self.startdate, self.enddate)
# print(sqls)
df_cb = pd.read_sql(sql=sqls, con=oracle_connection)
weight = self.df_input.loc[self.df_input['VC_SCDM'] == cb,
'EN_SZZJZ'].values
# print(weight)
df_cb[cb] = df_cb[cb].astype(float) * weight / 100
# print(type(df_cb['TRADE_DT'][0]))
df_pcntg = pd.merge(df_pcntg, df_cb, how='outer', on='TRADE_DT')
df_pcntg = df_pcntg.fillna(0)
df_pcntg['portfolio'] += df_pcntg[cb]
# print(df_pcntg)
oc.closeConn()
ms.closeConn()
# print(df_pcntg)
# print(list(df_pcntg['portfolio']))
# writer = pd.ExcelWriter("D:\PerformanceAnalysis/output.xlsx")
# df_pcntg.to_excel(writer, 'Sheet2')
# writer.save()
vol = df_pcntg['portfolio'].std() * np.sqrt(20)
print("CB Portfolio Volatility: %.4f" % vol)
return round(vol, 4)
from Common.GlobalConfig import GlobalConfig
from Common.TradingDay import TradingDay
from StressTesting.DailyGenerate_StressScenarioCal import DGSSC
# Get product list fromm config.ini
gc = GlobalConfig()
product_list_tmp = gc.getConfig('product', 'list')
product_list = product_list_tmp.split(", ")
print(product_list)
# Get last trade day
td = TradingDay()
date = td.getLastTradingDay()
# tradeday = "%s-%s-%s" % (lastTradingDay[:4], lastTradingDay[4:6], lastTradingDay[-2:])
# date = "20190211"
# Use product id and trade day to generate basic info and finish import to Basic Performance Info table
for p in product_list:
dgssc = DGSSC(p, date)
dgssc.Data_Insert()
def __init__(self):
td = TradingDay()
self.ltd = td.getLastTradingDay()
ss = StressScenario()
self.scenario = ss.ScenarioDataRetrieve('ScenarioList_Stock')
from Common.GlobalConfig import GlobalConfig
from Common.TradingDay import TradingDay
from PerformanceAnalysis.DailyGenerate_BasicPerformanceInfo import DGBPI
# ---------- Batch BPI data generate and import to MySQL ------------
# Get product list fromm config.ini
gc = GlobalConfig()
product_list_tmp = gc.getConfig('product', 'list')
product_list = product_list_tmp.split(", ")
print(product_list)
# Get tradedays since product founded
td = TradingDay()
for p in product_list:
tradedays = td.getProductDurationSinceFounded(p)
for tradeday in tradedays:
t = "%s-%s-%s" % (tradeday[:4], tradeday[4:6], tradeday[-2:])
dgbpi = DGBPI()
dgbpi.composeArray(p, t)
dgbpi.insertArray()
from Common.GlobalConfig import GlobalConfig
from Common.TradingDay import TradingDay
from PerformanceAnalysis.DailyGenerate_BasicPerformanceInfo import DGBPI
# ---------- Daily BPI data generate and import to MySQL ------------
# Get product list fromm config.ini
gc = GlobalConfig()
product_list_tmp = gc.getConfig('product', 'list')
product_list = product_list_tmp.split(", ")
print(product_list)
# Get last trade day
td = TradingDay()
lastTradingDay = td.getLastTradingDay()
tradeday = "%s-%s-%s" % (lastTradingDay[:4], lastTradingDay[4:6], lastTradingDay[-2:])
print(tradeday)
# Import by designated day, use only when amend history data
# td = "2018-10-15"
# Use product id and trade day to generate basic info and finish import to Basic Performance Info table
for p in product_list:
dgbpi = DGBPI()
dgbpi.composeArray(p, tradeday)
dgbpi.insertArray()