def refreshBDMPrice(self, event):
out = blpapiwrapper.simpleReferenceDataRequest(self.dic,
'PX_MID')['PX_MID']
self.bdm.lock.acquire()
self.bdm.df['BGN_MID'] = out.astype(float)
self.bdm.lock.release()
pub.sendMessage('BGN_PRICE_UPDATE', message=MessageContainer('empty'))
def refresh_bond_universe():
"""Function to refresh bond universe. Function is called by:
FlowTradingGUI > MainForm.onUpdateBondUniverse()
"""
bonds = pandas.ExcelFile(DEFPATH + "bonduniverse.xls").parse("list", index_col=0, has_index_names=True)
targetBonds = (
bonds.loc[pandas.isnull(bonds["SECURITY_NAME"]), "REGS"] + " Corp"
).to_dict() # this works better than coupon
targetFields = list(set(bonds.columns) - set(["REGS", "144A", "TRADITION", "BGC", "GARBAN", "TULLETT", "GFI"]))
bonds.loc[targetBonds.keys(), targetFields] = blpapiwrapper.simpleReferenceDataRequest(targetBonds, targetFields)
print bonds.loc[targetBonds.keys(), targetFields]
bonds.to_excel(DEFPATH + "bonduniverse.xls", "list")
print "The file bonduniverse.xls has been updated."
def updateNewTradesByISIN(self):
#THERE SHOULD NOT BE MORE THAN ONE RECORD PER BOOK AND ISIN - THE KEY IS BOOK-ISIN
self.th.positionsByISINBook = self.th.positionsByISINBook[self.th.positionsByISINBook['SOD_Pos']!=0].copy()
self.new_trades = self.th.df[self.th.df['Date']==todayDateSTR].copy()
self.new_trades['TradePnL'] = 0.0
if self.bdmReady:
self.new_trades = self.new_trades.join(self.bdm.df['MID'], on='Bond')
riskFreeIsins = []
for issuer in self.riskFreeIssuers:
riskFreeIsins = riskFreeIsins + list(self.new_trades.loc[self.new_trades['Issuer']==issuer,'ISIN'])
if len(riskFreeIsins)>0:
riskFreePrices = blpapiwrapper.simpleReferenceDataRequest(dict(zip(riskFreeIsins, map(lambda x:x + '@BGN Corp', riskFreeIsins))), 'PX_MID')
for (i,row) in riskFreePrices.iterrows():
self.new_trades.loc[self.new_trades['ISIN']==i,'MID'] = float(row['PX_MID'])#this works because bond name == isin for UST and bunds but it's not very clean
self.positionDeltas = self.new_trades.groupby(['Book','ISIN'])[['Qty','MK']]
reclist = []
nkeylist = []
for (k,grp) in self.positionDeltas:
key = k[0] + '-' + k[1]
if key in self.th.positionsByISINBook.index:
self.th.positionsByISINBook.at[key,'Qty'] = self.th.positionsByISINBook.at[key,'SOD_Pos'] + grp['Qty'].sum()
self.th.positionsByISINBook.at[key,'MK'] = grp['MK'].sum()
else:
lr = self.new_trades.loc[self.new_trades['ISIN']==k[1]].iloc[-1]#take the last trade -> ONLY FOR STATIC DATA
bond = lr['Bond']
pf = self.th.positions.at[bond,'PRINCIPAL_FACTOR']
r = self.th.positions.at[bond,'RISK_MID']
lc = self.cntrymap.at[lr['Country'],'LongCountry']
series = 'REGS' if k[1]==bonds.loc[bond,'REGS'] else '144A'
rec = [bond,k[0],lr['CCY'],k[1],lr['Issuer'], lr['Country'],lc,0,series, grp['Qty'].sum(), grp['MK'].sum(), lr['Price'], pandas.np.nan, pf, r]
reclist.append(rec)
nkeylist.append(key)
if reclist != []:
reclistdf = pandas.DataFrame(data=reclist, columns=['Bond','Book','CCY','ISIN','Issuer','Country','LongCountry','SOD_Pos', 'Series','Qty', 'MK', 'EODPrice', 'PriceY', 'PRINCIPAL_FACTOR','RISK_MID'], index=nkeylist)
self.th.positionsByISINBook = self.th.positionsByISINBook.append(reclistdf, verify_integrity=True)
for (k,grp) in self.positionDeltas:#calculate trade pnl
key = k[0] + '-' + k[1]
idx = (self.new_trades['ISIN']==k[1]) & (self.new_trades['Book']==k[0])
self.new_trades.loc[idx,'TradePnL'] = self.new_trades.loc[idx,'Qty']*(self.new_trades.loc[idx,'MID']-self.new_trades.loc[idx,'Price'])/100.
try:
self.th.positionsByISINBook.at[key,'TradePnL'] = self.th.positionsByISINBook.at[key,'PRINCIPAL_FACTOR'] * self.new_trades.loc[idx,'TradePnL'].sum()
except:
print 'error finding a price for ' + key
pass
def treeRebuild(self):
self.traded_bonds = self.th.df[self.th.df['Date']==todayDateSTR]['Bond'].drop_duplicates().dropna().copy()
new_bonds = list(set(self.traded_bonds) - set(self.displayPositions.index))
self.th.positions['EODPrice'] = self.EODPrices
self.th.positions['EODPrice'].fillna(0.0,inplace=True)
for bond in new_bonds:
self.th.positions.loc[bond,'EODPrice'] = self.th.df[self.th.df['Bond']==bond].iloc[-1]['Price'] # we take the last traded price
self.EODPrices = self.th.positions['EODPrice'].copy()
if self.bdmReady:
self.th.positions['PRINCIPAL_FACTOR'] = self.bdm.df['PRINCIPAL_FACTOR']
self.th.positions['RISK_MID'] = self.bdm.df['RISK_MID']
#Following 2 lines get rid of some runtime warning - possibly a bug - http://stackoverflow.com/questions/30519487/pandas-error-invalid-value-encountered
self.th.positions['PRINCIPAL_FACTOR'].fillna(1.0,inplace=True) # in doubt principal is 1
self.th.positions['RISK_MID'].fillna(0.0,inplace=True) # in doubt risk is 0
else:
self.th.positions['PRINCIPAL_FACTOR'] = 1.0
self.th.positions['RISK_MID'] = 0.0
self.th.positionsByISINBook['PRINCIPAL_FACTOR'] = 1.0
self.th.positionsByISINBook['RISK_MID'] = 0.0
###
if len(new_bonds) > 0:
self.th.positions.loc[new_bonds,'PRINCIPAL_FACTOR'] = self.bdm.df['PRINCIPAL_FACTOR']
self.th.positions.loc[new_bonds,'RISK_MID'] = self.bdm.df['RISK_MID']
spbonds = list(set(SPECIALBONDS) & set(new_bonds))
if len(spbonds)>0:
dc = dict(zip(spbonds,map(lambda x:bonds.loc[x,'REGS']+ ' Corp',spbonds)))
output = blpapiwrapper.simpleReferenceDataRequest(dc,['WORKOUT_OAS_MID_MOD_DUR'])
self.th.positions.loc[spbonds,'RISK_MID'] = output['WORKOUT_OAS_MID_MOD_DUR'].astype(float)
self.th.positions['USDQty'] = self.th.positions.apply(lambda row:row['Qty']/ccy.loc[row['CCY'],'2016'],axis=1)
self.th.positions['EODValue'] = self.th.positions['EODPrice']*self.th.positions['USDQty']/100.*(self.th.positions['PRINCIPAL_FACTOR'])
self.th.positions['Risk'] = -self.th.positions['USDQty']*self.th.positions['RISK_MID']/10000
for issuer in self.riskFreeIssuers:
self.th.positions.loc[self.th.positions['Issuer']==issuer,'Risk'] = 0.0 # UST HAVE NO CREDIT RISK
self.displayPositions = self.th.positions.loc[list(self.displayPositions.index)+new_bonds]#SOD risk + new trades
self.displayPositions = self.displayPositions.join(self.cntrymap['LongCountry'],on='Country')
self.displayGroup = self.displayPositions.groupby(['Region','LongCountry','Issuer','Bond']).sum()
self.th.positionsByISINBook['PriceT'] = self.th.positionsByISINBook['PriceT'].astype(float)#NEEDED OTHERWISE DEFAULTS TO int64
for (i,row) in self.th.positionsByISINBook.iterrows():
try:
self.th.positionsByISINBook.at[i,'PriceT'] = self.bdm.df.at[row['Bond'],'MID']
except:
self.th.positionsByISINBook.at[i,'PriceT'] = pandas.np.nan # for UST and unrecognized bonds
riskFreeIsins = []
for issuer in self.riskFreeIssuers:
riskFreeIsins = riskFreeIsins + list(self.th.positionsByISINBook.loc[self.th.positionsByISINBook['Issuer']==issuer,'ISIN'])
if len(riskFreeIsins) > 0:
riskFreePrices = blpapiwrapper.simpleReferenceDataRequest(dict(zip(riskFreeIsins, map(lambda x:x + '@BGN Corp', riskFreeIsins))), 'PX_MID')
for (i,row) in riskFreePrices.iterrows():
self.th.positionsByISINBook.loc[self.th.positionsByISINBook['ISIN']==i,'PriceT'] = float(row['PX_MID'])
self.th.positionsByISINBook.drop(['PRINCIPAL_FACTOR','RISK_MID','EODPrice'], axis=1, inplace=True)
self.th.positionsByISINBook = self.th.positionsByISINBook.join(self.EODPrices, on='Bond')
self.th.positionsByISINBook = self.th.positionsByISINBook.join(self.bdm.df['PRINCIPAL_FACTOR'], on='Bond')
self.th.positionsByISINBook = self.th.positionsByISINBook.join(self.bdm.df['RISK_MID'], on='Bond')
for issuer in self.riskFreeIssuers:
self.th.positionsByISINBook.loc[self.th.positionsByISINBook['Issuer']==issuer,'RISK_MID'] = 0.0 # UST HAVE NO CREDIT RISK
self.th.positionsByISINBook.loc[self.th.positionsByISINBook['Issuer']==issuer,'PRINCIPAL_FACTOR'] = 1.0
self.th.positionsByISINBook[['PRINCIPAL_FACTOR','RISK_MID']] = self.th.positionsByISINBook[['PRINCIPAL_FACTOR','RISK_MID']].astype(float)
self.th.positionsByISINBook['SODPnL'] = self.th.positionsByISINBook['SOD_Pos'] * self.th.positionsByISINBook['PRINCIPAL_FACTOR'] * (self.th.positionsByISINBook['PriceT'] - self.th.positionsByISINBook['PriceY'])/100.
self.updateNewTradesByISIN() # at that point prices and principal factors are ready already if self.bdmReady
self.th.positionsByISINBook['SODPnL'].fillna(0.0, inplace = True)
self.th.positionsByISINBook['TradePnL'].fillna(0.0, inplace = True)
self.th.positionsByISINBook['MK'].fillna(0.0, inplace = True)
self.th.positionsByISINBook['TotalPnL'] = self.th.positionsByISINBook['SODPnL']/self.th.positionsByISINBook.apply(lambda row:ccy.loc[row['CCY'],'2016'],axis=1) + self.th.positionsByISINBook['TradePnL']/self.th.positionsByISINBook.apply(lambda row:ccy.loc[row['CCY'],'2016'],axis=1)
self.th.positionsByISINBook['USDQty'] = self.th.positionsByISINBook.apply(lambda row:row['Qty']/ccy.loc[row['CCY'],'2016'],axis=1)
self.th.positionsByISINBook['EODValue'] = self.th.positionsByISINBook['EODPrice']*self.th.positionsByISINBook['USDQty']/100.*(self.th.positionsByISINBook['PRINCIPAL_FACTOR'])
self.th.positionsByISINBook['Risk'] = -self.th.positionsByISINBook['USDQty']*self.th.positionsByISINBook['RISK_MID']/10000
self.displayPositionsBook = self.th.positionsByISINBook
self.displayGroupBook = self.th.positionsByISINBook.groupby(['Book','LongCountry','Issuer','Bond','Series']).sum()
pub.sendMessage('REDRAW_RISK_TREE', message=MessageContainer('empty'))
def fillHistoricalPricesAndRating(self):
"""Fill historical prices and ratings. Function is called when the pricer menu first launches.
"""
time_start = time.time()
self.buildPriceHistory()
savepath = TEMPPATH + 'bondhistoryrating.csv'
#If bondhistoryratingUAT.csv doesn't exist, download data and write file.
cols = [
'SNP', 'MDY', 'FTC', 'P1D', 'P1W', 'P1M', 'Y1D', 'Y1W', 'Y1M',
'ACCRUED', 'D2CPN', 'SAVG', 'ISP1D', 'ISP1W', 'ISP1M', 'RISK_MID',
'PRINCIPAL_FACTOR', 'SIZE'
]
if not (os.path.exists(savepath)) or datetime.datetime.fromtimestamp(
os.path.getmtime(
savepath)).date() < datetime.datetime.today().date():
isins = self.df['ISIN'] + BBGHand + ' Corp'
isins = list(isins.astype(str))
##
flds = [
'RTG_SP', 'RTG_MOODY', 'RTG_FITCH', 'INT_ACC',
'DAYS_TO_NEXT_COUPON', 'YRS_TO_SHORTEST_AVG_LIFE', 'RISK_MID',
'PRINCIPAL_FACTOR', 'AMT_OUTSTANDING'
]
out = blpapiwrapper.simpleReferenceDataRequest(
pandas.Series((self.df['ISIN'] + ' Corp').values,
index=self.df.index).to_dict(), flds)[flds]
#loop
for f in flds:
self.df[bbgToBdmDic[f]] = out[f]
self.df['RISK_MID'].fillna(0, inplace=True)
##
self.df.drop([
'P1D', 'P1W', 'P1M', 'Y1D', 'Y1W', 'Y1M', 'ISP1D', 'ISP1W',
'ISP1M'
],
axis=1,
inplace=True)
dbPriceHistory = pandas.read_csv(PHPATH + 'dbPriceHistory.csv',
index_col=0)
dbYieldHistory = pandas.read_csv(PHPATH + 'dbYieldHistory.csv',
index_col=0)
dbSpreadHistory = pandas.read_csv(PHPATH + 'dbSpreadHistory.csv',
index_col=0)
hdt = []
if self.dtYesterday.strftime('%Y%m%d') in dbPriceHistory.columns:
hdt.append(self.dtYesterday.strftime('%Y%m%d'))
else:
self.df['P1D'] = pandas.np.nan
self.df['Y1D'] = pandas.np.nan
self.df['ISP1D'] = pandas.np.nan
if self.dtLastWeek.strftime('%Y%m%d') in dbPriceHistory.columns:
hdt.append(self.dtLastWeek.strftime('%Y%m%d'))
else:
self.df['P1W'] = pandas.np.nan
self.df['Y1W'] = pandas.np.nan
self.df['ISP1W'] = pandas.np.nan
if self.dtLastMonth.strftime('%Y%m%d') in dbPriceHistory.columns:
hdt.append(self.dtLastMonth.strftime('%Y%m%d'))
else:
self.df['P1M'] = pandas.np.nan
self.df['Y1M'] = pandas.np.nan
self.df['ISP1M'] = pandas.np.nan
ohdt = [
self.dtYesterday.strftime('%Y%m%d'),
self.dtLastWeek.strftime('%Y%m%d'),
self.dtLastMonth.strftime('%Y%m%d')
]
self.df = self.df.join(dbPriceHistory[hdt], on='ISIN')
self.df.rename(columns={
ohdt[0]: 'P1D',
ohdt[1]: 'P1W',
ohdt[2]: 'P1M'
},
inplace=True)
self.df = self.df.join(dbYieldHistory[hdt], on='ISIN')
self.df.rename(columns={
ohdt[0]: 'Y1D',
ohdt[1]: 'Y1W',
ohdt[2]: 'Y1M'
},
inplace=True)
self.df = self.df.join(dbSpreadHistory[hdt], on='ISIN')
self.df.rename(columns={
ohdt[0]: 'ISP1D',
ohdt[1]: 'ISP1W',
ohdt[2]: 'ISP1M'
},
inplace=True)
self.df[cols].to_csv(savepath)
self.df['ACCRUED'] = self.df['ACCRUED'].apply(
lambda x: '{:,.2f}'.format(float(x)))
self.df['D2CPN'].fillna(-1, inplace=True)
self.df['D2CPN'] = self.df['D2CPN'].astype(int)
self.df[['RISK_MID', 'PRINCIPAL_FACTOR', 'SIZE'
]] = self.df[['RISK_MID', 'PRINCIPAL_FACTOR',
'SIZE']].astype(float)
self.df[['SNP', 'MDY',
'FTC']] = self.df[['SNP', 'MDY', 'FTC'
]].fillna('NA') # ,'ACCRUED','D2CPN'
self.df[['SNP', 'MDY', 'FTC',
'ACCRUED']] = self.df[['SNP', 'MDY', 'FTC',
'ACCRUED']].astype(str)
#Otherwise, load and read from file.
else:
print 'Found existing file from today'
df = pandas.read_csv(savepath, index_col=0)
self.df[cols] = df[cols]
self.df[[
'RISK_MID', 'PRINCIPAL_FACTOR', 'SIZE', 'SAVG', 'ISP1D',
'ISP1W', 'ISP1M'
]] = self.df[[
'RISK_MID', 'PRINCIPAL_FACTOR', 'SIZE', 'SAVG', 'ISP1D',
'ISP1W', 'ISP1M'
]].astype(float)
self.df[['SNP', 'MDY', 'FTC']] = self.df[['SNP', 'MDY',
'FTC']].astype(str)
self.df['ACCRUED'].fillna(
-1, inplace=True
) #HACK SO NEXT LINE DOESN'T BLOW UP - WE DON'T WANT TO PUT 0 THERE!
self.df['ACCRUED'] = self.df['ACCRUED'].astype(float)
self.df['ACCRUED'] = self.df['ACCRUED'].apply(
lambda x: '{:,.2f}'.format(float(x)))
self.df['D2CPN'].fillna(
-1, inplace=True
) #HACK SO NEXT LINE DOESN'T BLOW UP - WE DON'T WANT TO PUT 0 THERE!
self.df['D2CPN'] = self.df['D2CPN'].astype(int)
print 'History fetched in: ' + str(
int(time.time() - time_start)) + ' seconds.'
def fillHistoricalPricesAndRating(self):
"""Fill historical prices and ratings. Function is called when the pricer menu first launches.
"""
time_start = time.time()
savepath = TEMPPATH+'bondhistoryrating.csv'
#If bondhistoryratingUAT.csv doesn't exist, download data and write file.
if not (os.path.exists(savepath)) or datetime.datetime.fromtimestamp(
os.path.getmtime(savepath)).date() < datetime.datetime.today().date():
isins = self.df['ISIN'] + BBGHand + ' Corp'
isins = list(isins.astype(str))
#rtgaccBLP = blpapiwrapper.BLPTS(isins,
# ['RTG_SP', 'RTG_MOODY', 'RTG_FITCH', 'INT_ACC', 'DAYS_TO_NEXT_COUPON','YRS_TO_SHORTEST_AVG_LIFE','RISK_MID','PRINCIPAL_FACTOR','AMT_OUTSTANDING'])
#rtgaccStream = StreamWatcher(self,'RTGACC')
#rtgaccBLP.register(rtgaccStream)
#rtgaccBLP.get()
#rtgaccBLP.closeSession()
##
flds = ['RTG_SP', 'RTG_MOODY', 'RTG_FITCH', 'INT_ACC', 'DAYS_TO_NEXT_COUPON','YRS_TO_SHORTEST_AVG_LIFE','RISK_MID','PRINCIPAL_FACTOR','AMT_OUTSTANDING']
out = blpapiwrapper.simpleReferenceDataRequest(pandas.Series((self.df['ISIN'] + ' Corp').values, index=self.df.index).to_dict(),flds)[flds]
#loop
for f in flds:
self.df[bbgToBdmDic[f]] = out[f]
self.df['RISK_MID'].fillna(0, inplace=True)
##
priceHistory = blpapiwrapper.BLPTS(isins, ['PX_LAST', 'YLD_YTM_MID'], startDate=self.dtLastMonth,endDate=self.dtToday)
priceHistoryStream = StreamWatcherHistory(self)
priceHistory.register(priceHistoryStream)
priceHistory.get()
priceHistory.closeSession()
#Based on today's shortest to average life, calculate the SAVG for yesterday, last week, and last month
self.df['SAVG'] = self.df['SAVG'].astype(float)
self.df['SAVG1D'] = self.df['SAVG']+(self.dtToday - self.dtYesterday).days/365.0
self.df['SAVG1W'] = self.df['SAVG']+(self.dtToday - self.dtLastWeek).days/365.0
self.df['SAVG1M'] = self.df['SAVG']+(self.dtToday - self.dtLastMonth).days/365.0
#Create DataFrames for Swap Rates of different currencies
US1D = SwapHistory('USD',self.dtYesterday)
US1W = SwapHistory('USD',self.dtLastWeek)
US1M = SwapHistory('USD',self.dtLastMonth)
CHF1D = SwapHistory('CHF',self.dtYesterday)
CHF1W = SwapHistory('CHF',self.dtLastWeek)
CHF1M = SwapHistory('CHF',self.dtLastMonth)
EUR1D = SwapHistory('EUR',self.dtYesterday)
EUR1W = SwapHistory('EUR',self.dtLastWeek)
EUR1M = SwapHistory('EUR',self.dtLastMonth)
CNY1D = SwapHistory('CNY',self.dtYesterday)
CNY1W = SwapHistory('CNY',self.dtLastWeek)
CNY1M = SwapHistory('CNY',self.dtLastMonth)
#Compute Historical Risk Free Rate for each bonds.
currencyList1D = {'USD':US1D,'CHF':CHF1D,'EUR':EUR1D,'CNY':CNY1D}
self.populateRiskFreeRates(currencyList1D,'INTSWAP1D','SAVG1D')
currencyList1W = {'USD':US1W,'CHF':CHF1W,'EUR':EUR1W,'CNY':CNY1W}
self.populateRiskFreeRates(currencyList1W,'INTSWAP1W','SAVG1W')
currencyList1M = {'USD':US1M,'CHF':CHF1M,'EUR':EUR1M,'CNY':CNY1M}
self.populateRiskFreeRates(currencyList1M,'INTSWAP1M','SAVG1M')
# get ISpread over past dates
self.df['ISP1D'] = (self.df['Y1D']-self.df['INTSWAP1D'])*100
self.df['ISP1W'] = (self.df['Y1W']-self.df['INTSWAP1W'])*100
self.df['ISP1M'] = (self.df['Y1M']-self.df['INTSWAP1M'])*100
self.df[['SNP', 'MDY', 'FTC', 'P1D', 'P1W', 'P1M', 'Y1D', 'Y1W', 'Y1M', 'ACCRUED', 'D2CPN','SAVG','ISP1D','ISP1W','ISP1M','RISK_MID','PRINCIPAL_FACTOR','SIZE']].to_csv(savepath)
self.df['ACCRUED'] = self.df['ACCRUED'].apply(lambda x: '{:,.2f}'.format(float(x)))
self.df['D2CPN'].fillna(-1, inplace=True)
self.df['D2CPN'] = self.df['D2CPN'].astype(int)
self.df[['RISK_MID','PRINCIPAL_FACTOR','SIZE']] = self.df[['RISK_MID','PRINCIPAL_FACTOR','SIZE']].astype(float)
self.df[['SNP', 'MDY', 'FTC']] = self.df[['SNP', 'MDY', 'FTC']].fillna('NA') # ,'ACCRUED','D2CPN'
self.df[['SNP', 'MDY', 'FTC', 'ACCRUED']] = self.df[['SNP', 'MDY', 'FTC', 'ACCRUED']].astype(str)
#Otherwise, load and read from file.
else:
print 'Found existing file from today'
df = pandas.read_csv(savepath, index_col=0)
self.df[['SNP', 'MDY', 'FTC', 'P1D', 'P1W', 'P1M', 'Y1D', 'Y1W', 'Y1M', 'ACCRUED', 'D2CPN','SAVG','ISP1D','ISP1W','ISP1M']] = df[['SNP', 'MDY', 'FTC', 'P1D', 'P1W', 'P1M', 'Y1D', 'Y1W', 'Y1M', 'ACCRUED', 'D2CPN','SAVG','ISP1D','ISP1W','ISP1M']]
self.df[['RISK_MID','PRINCIPAL_FACTOR','SIZE']] = df[['RISK_MID','PRINCIPAL_FACTOR','SIZE']].astype(float)
self.df[['SNP', 'MDY', 'FTC']] = self.df[['SNP', 'MDY', 'FTC']].astype(str)
self.df['ACCRUED'].fillna(-1,inplace=True)#HACK SO NEXT LINE DOESN'T BLOW UP - WE DON'T WANT TO PUT 0 THERE!
self.df['ACCRUED'] = self.df['ACCRUED'].astype(float)
self.df['ACCRUED'] = self.df['ACCRUED'].apply(lambda x: '{:,.2f}'.format(float(x)))
self.df['D2CPN'].fillna(-1, inplace=True)#HACK SO NEXT LINE DOESN'T BLOW UP - WE DON'T WANT TO PUT 0 THERE!
self.df['D2CPN'] = self.df['D2CPN'].astype(int)
self.df[['SAVG','ISP1D','ISP1W','ISP1M']] = self.df[['SAVG','ISP1D','ISP1W','ISP1M']].astype(float)
#pxhist = self.df[['P1D', 'P1W', 'P1M', 'Y1D', 'Y1W', 'Y1M']]
#print pxhist[pxhist.isnull().any(axis=1)]
print 'History fetched in: ' + str(int(time.time() - time_start)) + ' seconds.'