def getCores(isRetry=True):
def getDummyCore(spotDict,objs):
dummyCore=core('dummy', spotDict)
objs.append(dummyCore)
return dummyCore
#####
def processCore(exch,spotDict,objs,n=None):
if SHARED_EXCH_DICT[exch]<1:
return getDummyCore(spotDict,objs)
else:
myCore = core(exch, spotDict, n=n)
objs.append(myCore)
return myCore
#####
ftx=cl.ftxCCXTInit()
ccyList = list(CR_QUOTE_CCY_DICT.keys())
spotDict = dict()
spotDict['USD'] = 1
try:
for ccy in ccyList:
spotDict[ccy]=cl.ftxGetMid(ftx,ccy+'/USD')
except:
print('[WARNING: FTX get prices failed! ....]')
return None, None, None, False
#####
objs = []
ftxCore=processCore('ftx',spotDict,objs)
bbCore=processCore('bb',spotDict,objs)
#####
bbtCores=[]
if SHARED_EXCH_DICT['bbt']==0:
bbtCores.append(getDummyCore(spotDict,objs))
else:
for n in range(SHARED_EXCH_DICT['bbt']):
bbtCores.append(processCore('bbt',spotDict,objs,n=n+1))
#####
kuCores=[]
if SHARED_EXCH_DICT['ku']==0:
kuCores.append(getDummyCore(spotDict,objs))
else:
for n in range(SHARED_EXCH_DICT['ku']):
kuCores.append(processCore('ku',spotDict,objs,n=n+1))
#####
kutCores=[]
if SHARED_EXCH_DICT['kut']==0:
kutCores.append(getDummyCore(spotDict,objs))
else:
for n in range(SHARED_EXCH_DICT['kut']):
kutCores.append(processCore('kut',spotDict,objs,n=n+1))
#####
isOk=True
try:
cl.parallelRun(objs)
except:
if not isRetry: return None, None, None, False
print('[WARNING: Parallel run failed! Rerunning in serial ....]')
isOk=True
for obj in objs:
try:
print('Running '+obj.name+' ....')
obj.run()
except:
pass
if not obj.isDone:
isOk=False
print('[WARNING: Corrupted results for ' + obj.name + '!]')
print()
coresDict=dict()
coresDict['ftx']=ftxCore
coresDict['bb']=bbCore
coresDict['bbt']=bbtCores
coresDict['ku']=kuCores
coresDict['kut']=kutCores
return coresDict, spotDict, objs, isOk
def ftxInit(self):
def cleanBorrows(ccy, df):
dummy = pd.DataFrame([[0, 0, 0, 0, 0, 0]], columns=['time', 'coin', 'size', 'rate', 'cost', 'proceeds'])
if len(df) == 0:
return dummy
df2 = df.copy()
df2 = df2[df2['coin'] == ccy]
if len(df2) == 0:
return dummy
df2 = df2.set_index('time').sort_index()
return df2
######
def makeFlows(ccy):
start_time = cl.getYest()
borrows = cleanBorrows(ccy, pd.DataFrame(self.api.private_get_spot_margin_borrow_history({'limit': 1000, 'start_time': start_time})['result']))
loans = cleanBorrows(ccy, pd.DataFrame(self.api.private_get_spot_margin_lending_history({'limit': 1000, 'start_time': start_time})['result']))
cl.dfSetFloat(borrows, ['cost','rate'])
cl.dfSetFloat(loans, 'proceeds')
prevBorrow = borrows.iloc[-1]['cost'] if borrows.index[-1] == self.payments.index[-1] else 0
prevLoan = loans.iloc[-1]['proceeds'] if loans.index[-1] == self.payments.index[-1] else 0
absBalUSD = abs(self.wallet.loc[ccy, 'total']*self.spotDict[ccy])
d=dict()
d['oneDayFlows'] = (loans['proceeds'].sum() - borrows['cost'].sum()) * self.spotDict[ccy]
d['oneDayFlowsAnnRet'] = d['oneDayFlows'] * 365 / absBalUSD
d['oneDayBorrowRate'] = borrows['rate'].mean() * 24 * 365
d['prevFlows']=(prevLoan - prevBorrow) * self.spotDict[ccy]
d['prevFlowsAnnRet']=d['prevFlows'] * 24 * 365 / absBalUSD
d['prevBorrowRate'] = borrows.iloc[-1]['rate'] * 24 * 365 if borrows.index[-1] == self.payments.index[-1] else 0
return d
######
self.api = cl.ftxCCXTInit()
self.wallet = cl.ftxGetWallet(self.api,self.validCcys)
ccys=self.validCcys.copy()
cl.appendUnique(ccys,'USDT')
for ccy in ccys:
self.spots.loc[ccy,'SpotDelta'] = self.wallet.loc[ccy,'total']
self.calcSpotDeltaUSD()
######
info = self.api.private_get_account()['result']
futs = pd.DataFrame(info['positions']).set_index('future')
cl.dfSetFloat(futs, 'size')
for ccy in self.validCcys:
ccy2 = ccy + '-PERP'
if ccy=='SHIB': ccy2='K'+ccy2 # Special fix for SHIB
if ccy2 in futs.index:
mult = -1 if futs.loc[ccy2, 'side']=='sell' else 1
if ccy == 'SHIB': mult*=1000 # Special fix for SHIB
self.futures.loc[ccy,'FutDelta']=futs.loc[ccy2,'size']*mult
self.calcFuturesDeltaUSD()
######
start_time=cl.getYest()
pmts = pd.DataFrame()
for ccy in self.validCcys:
pmts = pmts.append(pd.DataFrame(self.api.private_get_funding_payments({'future':ccy+'-PERP', 'start_time': start_time})['result']))
pmts = pmts.set_index('time').sort_index()
cl.dfSetFloat(pmts, 'payment')
self.payments = pmts
#####
self.oneDayIncome = -self.payments['payment'].sum()
self.prevIncome = -self.payments.loc[self.payments.index[-1]]['payment'].sum()
self.setAnnRets()
#####
flowsCcy=CR_FTX_FLOWS_CCYS.copy()
cl.appendUnique(flowsCcy,'USD')
cl.appendUnique(flowsCcy,'USDT')
for ccy in flowsCcy:
d=makeFlows(ccy)
self.oneDayFlows += d['oneDayFlows']
if d['oneDayFlows']==0 and d['prevFlows']==0 and not ccy in ['USD','USDT']:
self.flowsDict[ccy]=None
else:
z1 = '$' + str(round(d['oneDayFlows'])) + ' (' + str(round(d['oneDayFlowsAnnRet'] * 100)) + '% p.a.)'
z2 = '$' + str(round(d['prevFlows'])) + ' (' + str(round(d['prevFlowsAnnRet'] * 100)) + '% p.a.)'
self.flowsDict[ccy]=colored(('FTX 24h/prev ' + ccy + ' flows: ').rjust(37) + z1 + ' / ' + z2, 'blue')
if ccy in ['USD','USDT']: # Extra info for USD/USDT
zList = []
zList.append('na' if d['oneDayBorrowRate'] == 0 else str(round(d['oneDayBorrowRate'] * 100)) + '%')
zList.append('na' if d['prevBorrowRate'] == 0 else str(round(d['prevBorrowRate'] * 100)) + '%')
zList.append(str(round(cl.ftxGetEstBorrow(self.api, ccy) * 100)) + '%')
self.flowsDict[ccy+'2'] = ('FTX ' + ccy + ' 24h/prev/est: ').rjust(37) + '/'.join(zList) + ' p.a. ($' + str(round(self.wallet.loc[ccy, 'usdValue'] / 1000)) + 'K) '
#####
self.nav = self.wallet['usdValue'].sum()
#####
for ccy in self.validCcys:
oneDayFunding = 0
prevFunding = 0
estFunding = 0
if CR_CONFIGS_DICT['IS_CALC_PAYMENTS']:
df = pd.DataFrame(self.api.public_get_funding_rates({'future': ccy + '-PERP', 'start_time': start_time})['result']).set_index('time').sort_index()
cl.dfSetFloat(df, 'rate')
oneDayFunding = df['rate'].mean() * 24 * 365
prevFunding = df['rate'][-1] * 24 * 365
#####
if CR_CONFIGS_DICT['IS_CALC_ESTS']:
estFunding = cl.ftxGetEstFunding(self.api, ccy)
self.makeFundingStr(ccy, oneDayFunding, prevFunding, estFunding)
#####
self.makeIncomesStr()
#####
self.mf = float(info['marginFraction'])
self.mmReq = float(info['maintenanceMarginRequirement'])
self.freeCollateral = float(info['freeCollateral'])
totalPositionNotional = self.nav / self.mf
cushion = (self.mf - self.mmReq) * totalPositionNotional
delta = self.wallet.loc[self.validCcys, 'usdValue'].sum() + self.futures['FutDeltaUSD'].sum()
self.makeLiqStr(cushion=cushion,delta=delta)
#####
self.isDone=True
