def saveAllFactorsToInfluxdbByCodeAndDay(self, code, date):
code = str(code)
date = str(date)
database = INFLUXDBTICKFACTORDATABASE
measurement = str(code)
tag = {}
myfactor = factorBase()
mydata = pd.DataFrame()
data = pd.DataFrame()
factorList = TICKFACTORSNEEDTOUPDATE
for factor in factorList:
mymodule = importlib.import_module(factor['module'])
myclass = getattr(mymodule, factor['class'])
myinstance = myclass()
if data.shape[0] == 0:
tick = TickDataProcess()
#data=tick.getDataByDateFromLocalFile(code,date)
data = tick.getTickShotDataFromInfluxdbServer(code, date)
if data.shape[0] == 0:
#logger.warning(f'There is no tickShots of {code} in {date}')
return
highLimit = data.iloc[0]['highLimit']
preClose = data.iloc[0]['dailyPreClose']
if (highLimit / preClose - 1) < 0.06:
#logger.warning(f'The stock {code} is ST in {date}')
return
pass
factorData = myinstance.computerFactor(code, date, data)
if factorData.shape[0] > 0:
if mydata.shape[0] == 0:
mydata = factorData
else:
mydata = pd.merge(mydata,
factorData,
how='left',
left_index=True,
right_index=True)
#合并tick行情数据
mydata = pd.merge(mydata,
data[[
'code', 'date', 'tick', 'midPrice', 'realData',
'dailyPreClose', 'dailyOpen', 'B1', 'S1', 'BV1',
'SV1'
]],
how='left',
left_index=True,
right_index=True)
if mydata.shape[0] == 0:
return
mydata[
'increaseToday'] = mydata['midPrice'] / mydata['dailyPreClose'] - 1
mydata = mydata[mydata['tick'] < '145700000']
#删去涨跌停之后的数据
ceiling = mydata[(mydata['B1'] == 0) | (mydata['S1'] == 0)]
if ceiling.shape[0] > 0:
ceilingTime = ceiling['tick'].iloc[0]
mydata = mydata[mydata['tick'] < ceilingTime]
pass
if mydata.shape[0] == 0:
return
try:
logger.info(f'Recording factors to influxdb of {code} in {date}!')
InfluxdbUtility.saveDataFrameDataToInfluxdb(
mydata, database, measurement, tag)
except Exception as excp:
pass
#logger.error(f'{fileName} error! {excp}')
pass
def singleCode(self,
code: str,
startDate: str,
endDate: str,
parameters=[]):
days = list(TradedayDataProcess().getTradedays(startDate, endDate))
tickFactors = tickFactorsProcess()
tick = TickDataProcess()
daily = dailyFactorsProcess()
dailyKLine = KLineDataProcess('daily')
trade = []
for day in days:
#tickData=tick.getDataByDateFromLocalFile(code,day)
tickData = tick.getTickShotDataFromInfluxdbServer(code, day)
if tickData.shape[0] == 0:
continue
data = tickFactors.getTickFactorsOnlyByDateFromLocalFile(code, day)
data = pd.merge(data,
tickData,
how='left',
left_index=True,
right_index=True)
dailyData = daily.getSingleStockDailyFactors(code, day, day)
for col in dailyData.columns:
if col not in ['date', 'code']:
data[col] = dailyData[col].iloc[0]
dailyKLineData = dailyKLine.getDataByDate(code, day, day)
data['preClose'] = dailyKLineData['preClose'].iloc[0]
data['increaseToday'] = data['midPrice'] / data['preClose'] - 1
if np.isnan(data['weight300'].iloc[0]) == True:
continue
maxPosition = round(
data['weight300'].iloc[0] * 100000000 /
data['preClose'].iloc[0] / 100, -2)
features = [
'buyForce', 'sellForce', 'buySellForceChange', 'buySellSpread',
'differenceHighLow3m', 'midToVwap', 'midToVwap3m',
'midPrice3mIncrease', 'midPriceBV3m', 'midInPrevious3m',
'midStd60', 'increaseToday', 'closeStd20',
'buySellVolumeRatio2', 'buySellWeightedVolumeRatio2',
'buySellVolumeRatio5', 'buySellWeightedVolumeRatio5',
'buySellVolumeRatio10', 'buySellWeightedVolumeRatio10'
]
A = data[features]
A = self.dataSelect(A, 0.2)
A = A.values
maxWeight = np.array([
0.03218688, -0.0121024, -0.00970715, 0.48172206, 0.42610642,
0.10048948, -0.05574053, 0.08212702, -0.12357012, -0.00123216,
0.09529259, 0.00509518, 0.14970625, -0.00291313, 0.00402094,
-0.00452788, 0.00286216, 0.0020172, -0.00235546
])
minWeight = np.array([
-0.00385887, -0.01163938, 0.0043455, -0.01114819, -0.34286923,
0.08314041, 0.00154458, 0.12249813, -0.02194375, -0.00038749,
-0.02217015, 0.00610296, -0.09264385, -0.0020065, 0.00249547,
-0.00324293, 0.00501176, 0.00389697, -0.00294958
])
maxIntercept = 0.00079871
minIntercept = -0.00155935
mymax = A.dot(maxWeight) + maxIntercept
mymin = A.dot(minWeight) + minIntercept
data['maxPredict'] = mymax
data['minPredict'] = mymin
data['maxPredict'] = data['maxPredict'].ewm(span=2,
ignore_na=True,
adjust=True).mean()
data['minPredict'] = data['minPredict'].ewm(span=2,
ignore_na=True,
adjust=True).mean()
data['midPredict'] = (data['maxPredict'] + data['minPredict']) / 2
m = data[[
'midIncreaseMinNext5m', 'midIncreaseMaxNext5m', 'maxPredict',
'minPredict', 'midPredict'
]]
print(m.corr())
#long=data[(data['maxPredict']>0.01)]['midIncreaseMaxNext5m'].mean()-data['midIncreaseMaxNext5m'].mean()
#short=data[(data['minPredict']<-0.01)]['midIncreaseMinNext5m'].mean()-data['midIncreaseMinNext5m'].mean()
#print(long)
#print(short)
mycolumns = list(tickData.columns)
mycolumns.append('maxPredict')
mycolumns.append('minPredict')
data = data[mycolumns]
parameters = {
'maxPosition': maxPosition,
'longOpen': 0.015,
'shortOpen': -0.015,
'longClose': 0.01,
'shortClose': -0.01,
'transactionRatio': 0.2
}
#trade0=self.strategy(data,parameters)
#trade.append(trade0)
pass
if len(trade) == 0:
trade = pd.DataFrame()
else:
trade = pd.concat(trade)
trade['code'] = code
trade['fee'] = trade['price'] * 0.0001
selectBuy = trade['direction'] == 'buy'
selectSell = trade['direction'] == 'sell'
trade.loc[selectSell, 'fee'] = (trade['fee'] +
trade['price'] * 0.001)[selectSell]
trade.loc[selectBuy,
'cashChange'] = ((-trade['price'] - trade['fee']) *
trade['volume'])[selectBuy]
trade.loc[selectSell,
'cashChange'] = ((trade['price'] - trade['fee']) *
trade['volume'])[selectSell]
trade['amount'] = trade['price'] * trade['volume']
return trade
pass