def getPairQuote(jqETFID, jqFutureID, sdate, unitETFVolumns):
fMultiply= cfg.getFutureMultiply(jqFutureID)
mdata= cfg.getMergeData(jqETFID, jqFutureID, sdate)
mdata['bidp']= mdata.apply(lambda x:getMDPrice(x.b1_p, x.b2_p, x.b3_p, x.b4_p, x.b5_p, x.b1_v, x.b2_v, x.b3_v, x.b4_v, x.b5_v, unitETFVolumns), axis = 1)
mdata['askp']= mdata.apply(lambda x:getMDPrice(x.a1_p, x.a2_p, x.a3_p, x.a4_p, x.a5_p, x.a1_v, x.a2_v, x.a3_v, x.a4_v, x.a5_v, unitETFVolumns), axis = 1)
mdata= mdata[['bidp', 'askp', 'fb1_p', 'fa1_p']]
mdata['lesf']= mdata['askp']*unitETFVolumns- mdata['fb1_p']* fMultiply
mdata['self']= mdata['bidp']*unitETFVolumns- mdata['fa1_p']* fMultiply
return mdata
def getPairStatInfo(jqETFID, jqFutureID, listTradeDay, nIndex, nParamDay= 5):
listdata= []
for i in range(nParamDay):
mdata= cfg.getMergeData(jqETFID, jqFutureID, listTradeDay[nIndex- 1- i])
listdata.append(mdata[['current', 'fcurrent']])
mdata= pd.concat(listdata)
inid= mdata.mean()
fMultiply= cfg.getFutureMultiply(jqFutureID)
unitETFVolumns= np.round(inid['fcurrent']* fMultiply/ inid['current'], -2)
mdata['basis']= mdata['current']*unitETFVolumns- mdata['fcurrent']* fMultiply
basisMean= np.round( mdata['basis'].mean())
basisStd= np.round( mdata['basis'].std())
return [unitETFVolumns, basisMean, basisStd]
def simuDay(jqETFID, jqFutureID, listTradeDay, nIndex, tradeParam, iniPosition):
pdTrade= pd.DataFrame(columns=('time', 'symbol', 'type', 'volume', 'price'))
statInfo= getPairStatInfo(jqETFID, jqFutureID, listTradeDay, nIndex, tradeParam['nParamDay'])
pqdata= getPairQuote(jqETFID, jqFutureID, listTradeDay[nIndex], statInfo[0])
if not 'future' in iniPosition.index:
iniPosition.loc['future']= [0,0]
if not 'etf' in iniPosition.index:
iniPosition.loc['etf']= [0,0]
fposition= -iniPosition.loc['future', 'volume']
etfPosition= iniPosition.loc['etf', 'volume']
diffETF= statInfo[0]* fposition- etfPosition
if diffETF!= 0:
row= pqdata.iloc[0]
if diffETF> 0.1:
pdTrade= pdTrade.append({'time': row.name, 'symbol': jqETFID, 'type': 'etf', 'volume': diffETF, 'price': row['askp']}, ignore_index=True)
elif diffETF< -0.1:
pdTrade= pdTrade.append({'time': row.name, 'symbol': jqETFID, 'type': 'etf', 'volume': diffETF, 'price': row['bidp']}, ignore_index=True)
nNearTrade= 0
tradeSignal= 'hold'
for time, row in pqdata.iterrows():
#check if near trade
if nNearTrade> 0.1:
nNearTrade-= 1
elif tradeSignal!= 'hold':#check tradeSignal
nNearTrade= tradeParam['nTradeSilence']
if tradeSignal== 'buy':
pdTrade= pdTrade.append({'time': time, 'symbol': jqETFID, 'type': 'etf', 'volume': statInfo[0], 'price': row['askp']}, ignore_index=True)
pdTrade= pdTrade.append({'time': time, 'symbol': jqFutureID, 'type': 'future', 'volume': -1, 'price': row['fb1_p']}, ignore_index=True)
fposition+= 1
elif tradeSignal== 'sell':
pdTrade= pdTrade.append({'time': time, 'symbol': jqETFID, 'type': 'etf', 'volume': -statInfo[0], 'price': row['bidp']}, ignore_index=True)
pdTrade= pdTrade.append({'time': time, 'symbol': jqFutureID, 'type': 'future', 'volume': 1, 'price': row['fa1_p']}, ignore_index=True)
fposition-= 1
tradeSignal= 'hold'
else:#check trade
tradeSignal= checkTrade(row, statInfo, tradeParam['tradeThreshold'], tradeParam['positionThreshold'],
tradeParam['levelThreshold'], fposition)
pdPosition= pdTrade.groupby(['type']).agg({'volume':'sum', 'price':'count'})
if not 'future' in pdPosition.index:
pdPosition.loc['future']= [0,0]
if not 'etf' in pdPosition.index:
pdPosition.loc['etf']= [0,0]
pdPosition['volume']= pdPosition['volume'].add(iniPosition['volume'])
statInfo.append(pdPosition.loc['future', 'volume'])
row= pqdata.iloc[-1]
etfClose= (row.bidp+ row.askp) /2
futureClose= (row.fb1_p+ row.fa1_p) /2
pdPosition.loc['etf', 'price']= etfClose
pdPosition.loc['future', 'price']= futureClose
fMultiply= cfg.getFutureMultiply(jqFutureID)
nintraDayTrade= 0
PL= etfPosition* (etfClose- iniPosition.loc['etf', 'price'])+ iniPosition.loc['future', 'volume']* (futureClose- iniPosition.loc['future', 'price'])* fMultiply
if 'future' in pdTrade.type.values:
gdata= pdTrade.groupby(['type', 'volume']).agg({'price':'mean','time':'count'})
nintraDayTrade= gdata.loc[('future',slice(None)),'time'].max()
for index, row in gdata.iterrows():
if index[0]== 'etf':
PL+= index[1]* row.time* (etfClose- row.price)
elif index[0]== 'future':
PL+= index[1]* row.time* (futureClose- row.price)* fMultiply
statInfo.append(nintraDayTrade)
statInfo.append(PL)
return pdPosition, statInfo