def hand_turn_over(model):
# 能力分析 - 持股集中度、换手率
# fof_fund_stock_porfolio
if not del_data('fof_fund_stock_porfolio'):
raise Error("删除历史数据失败，任务结束")
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
btParms = {
'symbol': fundSymbols,
'startDate': '20020101',
'endDate': datetime.strftime(datetime.today(), '%Y%m%d')
}
self = fundTurnOver(btParms)
output = self.output
for i in output:
val = output[i]
for k, v in val.items():
dt = k # 时间
value = v # 值
symbo = i # 基金
id_ = uuid_util.gen_uuid()
ls = []
ls.append(id_)
ls.append(symbo)
ls.append(dt._short_repr.replace("-", ""))
ls.append(transformFloatIfAvaliable3(value))
ls.append("sys")
ls.append(datetime.now())
ls.append("sys")
ls.append(datetime.now())
sql = "INSERT INTO `fof`.`fof_fund_stock_porfolio`(`OBJECT_ID`, `J_WINDCODE`, `TRADE_DT`, `CHANGE_RATE`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ls))
def return_weight(model):
if not del_data('fof_fund_excess_return_weight'):
raise Error("删除历史数据失败，任务结束")
# fof_fund_excess_return_weight
# 一次性调取需要用到的公共数据
stockIndus, stockRet, indusRet, crIndus = publicData_stockAndIndustry()
# 批量生成基金经理任职区间信息
'''
首次存入数据库时需要全部运行，后续更新维护时只需更新在任基金经理信息即可.
选股能力仅针对于股票型与混合型基金
'''
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
output_all = []
for i in range(len(managerInfo)):
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
try:
test = stockPickingAbility_manager(btParms, stockIndus, stockRet,
indusRet, crIndus)
output = test.output
# output['compareReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\compareReturnIndustry.csv")
# output['excessReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnIndustry.csv")
# output['excessReturnQuarter'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnQuarter.csv")
# output['mainStcokReturn'].to_csv("c:\\users\\futanghang\\desktop\\mainStcokReturn.csv")
dda = output['excessReturnQuarter']
for k, v in dda.items():
# k time，v ：value
wCode = output['symbol'] # 基金Wind代码
managerName = output['manager'] # 基金经理名称
managerId = output['managerId'] # 基金经理id
id_ = uuid_util.gen_uuid() # objId
lst = []
lst.append(id_)
lst.append(wCode)
lst.append(managerId)
lst.append(managerName)
lst.append(k._short_repr.replace("-", ""))
lst.append(v)
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
ls = [transformFloatIfAvaliable4(i) for i in lst]
sql = "INSERT INTO `fof`.`fof_fund_excess_return_weight`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`, `MANAGER_NAME`, `TRADE_DT`, `EXCESS_RETURN`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ls))
except Exception as e:
pass
def risk_single(model):
# 风险因子业绩归因模块
# fof_single_attr_riskmodel
if not del_data('fof_single_attr_riskmodel'):
raise Error("删除历史数据失败，任务结束")
stockIndus, indusReturn, factorExposure, factorReturn = publicData_riskModel(
)
# 批量运行
'''
跨期归因结果需要存储多个周期值
单期归因结果仅存储成立以来的值即可
跨期归因和单期归因可以用两张表来存储数据
'''
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
cycleList = ['成立以来'] # 这个程序跟之前的程序有一点特殊，就是多期归因要存多个周期的结果，单期归因只存成立以来的结果
holdingTypeList = ['mainStockHolding', 'allStockHolding']
for holdingType in holdingTypeList:
for symbol in fundSymbols:
for cycle in cycleList:
btParms = {
'symbol': symbol,
'cycle': cycle,
'holdingType': holdingType
}
self = attribution_riskModel(btParms, stockIndus, indusReturn,
factorExposure, factorReturn)
output = self.output
tp = "1" if holdingType == 'mainStockHolding' else '2' # 持仓类型
param = output['singleAttr']
if len(param) == 0:
continue
for idx in param.index:
dt = idx # time
di = param.loc[idx].to_dict()
style = di['style'] # 风格因子归因
industry = di['industry'] # 行业因子归因
idiosyn = di['idiosyn'] # 特质因子归因
fundRet = di['fundRet'] # 基金当月收益率
lsd = []
id_ = uuid_util.gen_uuid()
lsd.append(id_)
lsd.append(output['symbol'])
lsd.append(dt._short_repr.replace("-", ""))
lsd.append(tp)
lsd.append(style)
lsd.append(industry)
lsd.append(idiosyn)
lsd.append(fundRet)
lsd.append("sys")
lsd.append(datetime.now())
lsd.append("sys")
lsd.append(datetime.now())
lsd = [transformFloatIfAvaliable4(l) for l in lsd]
sql = 'INSERT INTO `fof`.`fof_single_attr_riskmodel`(`OBJECT_ID`, `S_INFO_WINDCODE`, `TRADE_DT`, `DATA_TYPE`, `STYLE`, `INDUSTRY`, `IDIOSYN`, `FUND_RETURN`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)'
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(lsd))
def risk_multiple(model):
# 风险因子业绩归因模块
# fof_multi_attr_riskmodel
if not del_data('fof_multi_attr_riskmodel'):
raise Error("删除历史数据失败，任务结束")
stockIndus, indusReturn, factorExposure, factorReturn = publicData_riskModel(
)
# 批量运行
'''
跨期归因结果需要存储多个周期值
单期归因结果仅存储成立以来的值即可
跨期归因和单期归因可以用两张表来存储数据
'''
# dataframe 周期类型和数据库周期类型映射关系
cyleFormula = {'1': '1', '2': '2', '3': '3', '5': '4', '成立以来': '5'}
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
cycleList = ['1', '2', '3', '5', '成立以来']
holdingTypeList = ['mainStockHolding', 'allStockHolding']
for holdingType in holdingTypeList:
for symbol in fundSymbols:
for cycle in cycleList:
btParms = {
'symbol': symbol,
'cycle': cycle,
'holdingType': holdingType
}
self = attribution_riskModel(btParms, stockIndus, indusReturn,
factorExposure, factorReturn)
output = self.output
tp = "1" if holdingType == 'mainStockHolding' else '2' # 持仓类型
param = output['multiAttr']
if len(param) == 0:
continue
for idx in param.index:
di = param.loc[idx].to_dict()
indexName = idx # 因子名称
indexType = di['factorType'] # 因子分类
indexAttr = di['multiAttr'] # 因子贡献收益
cyleTp = cyleFormula[cycle] # 周期类型
lsd = []
id_ = uuid_util.gen_uuid()
lsd.append(id_)
lsd.append(output['symbol'])
lsd.append(tp)
lsd.append(cyleTp)
lsd.append(indexType)
lsd.append(indexName)
lsd.append(indexAttr)
lsd.append("sys")
lsd.append(datetime.now())
lsd.append("sys")
lsd.append(datetime.now())
lsd = [transformFloatIfAvaliable4(l) for l in lsd]
sql = 'INSERT INTO `fof`.`fof_multi_attr_riskmodel`(`OBJECT_ID`, `S_INFO_WINDCODE`, `DATA_TYPE`, `CYCLE_TYPE`, `FACTOR_TYPE`, `FACTOR_NAME`, `FACTOR_VALUE`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)'
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(lsd))
def industry_config_score(model):
# sql = "delete from fof_fund_industry_score "
# mysqlops.fetch_one(MysqlConf.DB.fof, sql)
if not del_data('fof_fund_industry_score'):
raise Error("删除历史数据失败，任务结束")
# 为提高运行速度，公共数据一次性提取，并作为参数传入要调用的类
stockIndus, indusNet = getPublicDataAllocation()
# 批量生成基金经理任职区间信息
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
'''
首次存入数据库时需要全部运行，后续更新维护时只需更新在任基金经理信息即可
'''
# 批量生成基金经理行业配置能力
for i in range(len(managerInfo)):
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
try:
test = industryAllocation_manager(btParms, stockIndus, indusNet)
output = test.output
scoreDF = output['alloScore'] # fof_fund_industry_score
for j, v in scoreDF.items():
sVal = v # 配置占股票市值比
dt_ = j # 时间
id_ = uuid_util.gen_uuid() # objId
wCode = output['symbol'] # 基金代码
lst = []
lst.append(id_)
lst.append(wCode)
lst.append(output['managerId'])
lst.append(output['manager'])
lst.append(dt_._short_repr.replace("-", ""))
lst.append(transformFloatIfAvaliable4(sVal))
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
sql = "INSERT INTO `fof`.`fof_fund_industry_score`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`,`MANAGER_NAME`,`TRADE_DT`, `RETURN_SCORE`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(lst))
except:
pass
def equ_timing(model):
if not del_data('fof_fund_timing'):
raise Error("删除历史数据失败，任务结束")
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
# 批量生成基金经理行业配置能力
for i in range(len(managerInfo)):
try:
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
test = equityTimingAbility_manager(btParms)
output = test.output
timing = output['hisTiming']
lst = timing.values.tolist()
for ls in lst:
ll = []
id_ = uuid_util.gen_uuid()
ll.append(id_)
ll.append(output['symbol'])
ll.append(output['managerId'])
ll.append(output['manager'])
ll.append(ls[0]) # start
ll.append(ls[1]) # end
ll.append(transformFloatIfAvaliable(ls[2])) # RISING
ll.append(transformFloatIfAvaliable(ls[3])) # HOLDINGS_BEGIN
ll.append(transformFloatIfAvaliable(ls[4])) # HOLDINGS_END
ll.append(transformFloatIfAvaliable(ls[5])) # TIMING_RESULT
ll.append(transformFloatIfAvaliable(ls[6])) # HOLDINGS_RATIO
ll.append(transformFloatIfAvaliable(output['winRatio']))
ll.append(transformFloatIfAvaliable4(output['avgTimingContr']))
ll.append("sys")
ll.append(datetime.now())
ll.append("sys")
ll.append(datetime.now())
sql = "INSERT INTO `fof`.`fof_fund_timing`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`, `MANAGER_NAME`, `START_DATE`, `END_DATE`, `RISING`, `HOLDINGS_BEGIN`, `HOLDINGS_END`, `TIMING_RESULT`, `HOLDINGS_RATIO`,`TIMING_WIN_RATIO`,`AVG_CONTRIBUTION`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
insert_one(MysqlConf.DB.fof, sql, tuple(ll))
except:
pass
def net_value(model):
if not del_data('fof_fundnav_style'):
raise Error("删除历史数据失败，任务结束")
# 基金净值风格划分 fof_fundnav_style
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
# fundSymbols=fundSymbols[0:20] #生产环境删除此句
output_all = []
i = 0
for symbol in fundSymbols:
i += 1
btParms = {'symbol': symbol, 'cycle': '成立以来'} # 离线入库时cycle设定为"成立以来"即可
test = fundStyle_nav(btParms)
output = test.output
tmp = output['regStat']
if tmp is None or len(tmp) == 0:
continue
for idx in tmp.index:
di = tmp.loc[idx].to_dict()
startDate = di['startDate']
endDate = di['endDate']
largeCoe = di['largeCoeff']
smallCoe = di['smallCoeff']
r2 = di['R2']
lst = []
id_ = uuid_util.gen_uuid() # objId
lst.append(id_)
lst.append(output['symbol'])
lst.append(startDate)
lst.append(endDate)
lst.append(largeCoe)
lst.append(smallCoe)
lst.append(r2)
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
ll = [transformFloatIfAvaliable4(j) for j in lst]
sql = "INSERT INTO `fof`.`fof_fundnav_style`(`OBJECT_ID`, `J_WINDCODE`, `START_DATE`, `END_DATE`, `LARGE_COEFFICIENTS`, `SMALL_COEFFICIENTS`,`R2`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s,%s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ll))
def holding_style_main(model):
if not del_data('fof_fund_tentop_stock_style'):
raise Error("删除历史数据失败，任务结束")
# 获取本模块公共数据
factorExposure = publicData_stockFactor()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
# fundSymbols = fundSymbols[0:10] # 生产环境删除此句
typeList = ['mainStockHolding', 'allStockHolding']
# 暴露度计算
# for holdingType in typeList:
for symbol in fundSymbols:
btParms = {'symbol': symbol, 'holdingType': 'mainStockHolding'}
test = fundFactorExposure(btParms, factorExposure)
output = test.output
tmp = output['hisExposure']
# if holdingType == 'mainStockHolding':
# 10 top
for time in tmp:
val = tmp[time]
for idx in val.index:
di = val.loc[idx].to_dict()
exp = di['portExposure']
expscore = di['portExposureScore']
ls = []
ls.append(uuid_util.gen_uuid())
ls.append(output['symbol'])
ls.append(time._short_repr.replace("-", ""))
ls.append(idx)
ls.append(transformFloatIfAvaliable3(exp))
ls.append(transformFloatIfAvaliable3(expscore))
ls.append("sys")
ls.append(datetime.now())
ls.append("sys")
ls.append(datetime.now())
sql = "INSERT INTO `fof`.`fof_fund_tentop_stock_style`(`OBJECT_ID`, `J_WINDCODE`, `TRADE_DT`, `INDICATOR_NAME`, `EXPOSE_VALUE`, `EXPOSE_SCORE`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ls))
def return_his(request):
if not del_data('fof_fund_main_stock_return_his'):
raise Error("删除历史数据失败，任务结束")
# fof_fund_main_stock_return_his
# logic_return_his()
# 一次性调取需要用到的公共数据
stockIndus, stockRet, indusRet, crIndus = publicData_stockAndIndustry()
# 批量生成基金经理任职区间信息
'''
首次存入数据库时需要全部运行，后续更新维护时只需更新在任基金经理信息即可.
选股能力仅针对于股票型与混合型基金
'''
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
for i in range(len(managerInfo)):
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
try:
test = stockPickingAbility_manager(btParms, stockIndus, stockRet,
indusRet, crIndus)
output = test.output
# output['compareReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\compareReturnIndustry.csv")
# output['excessReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnIndustry.csv")
# output['excessReturnQuarter'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnQuarter.csv")
# output['mainStcokReturn'].to_csv("c:\\users\\futanghang\\desktop\\mainStcokReturn.csv")
ddis = output['mainStcokReturn']
ddis = ddis.fillna(blank)
for idx in ddis.index:
# idx 股票代码，dict ：columns values
stockId = idx # dt
di = ddis.loc[idx].to_dict()
peizhiqishu = di['配置期数(季度)']
pingjunquanzhong = di['平均权重(%)']
indust = di['所属行业']
chiyoujidupingjunshouyi = di['持有季度平均收益(%)']
chiyoujidupingjunhangyechaoeshouyi = di['持有季度平均行业超额收益(%)']
stockName = di['stockName']
wCode = output['symbol'] # 基金Wind代码
managerName = output['manager'] # 基金经理名称
managerId = output['managerId'] # 基金经理id
id_ = uuid_util.gen_uuid() # objId
lst = []
lst.append(id_)
lst.append(wCode)
lst.append(managerId)
lst.append(managerName)
lst.append(stockId)
lst.append(stockName)
lst.append(indust)
lst.append(transformFloatIfAvaliable4(peizhiqishu))
lst.append(transformFloatIfAvaliable4(pingjunquanzhong))
lst.append(transformFloatIfAvaliable4(chiyoujidupingjunshouyi))
lst.append(
transformFloatIfAvaliable4(
chiyoujidupingjunhangyechaoeshouyi))
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
ls = [transformFloatIfAvaliable4(i) for i in lst]
sql = "INSERT INTO `fof`.`fof_fund_main_stock_return_his`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`, `MANAGER_NAME`, `G_WINDCODE`, `STOCK_NAME`,`INDUSTRY_NAME`, `HOLDING_PERIODS`, `AVG_WEIGHTS`, `AVG_RETURN`, `AVG_RETURN_QUARTER`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s,%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ls))
except Exception as e:
pass
def return_(model):
if not del_data('fof_fund_main_stock_return'):
raise Error("删除历史数据失败，任务结束")
# fof_fund_main_stock_return
# logic_return_()
# 一次性调取需要用到的公共数据
stockIndus, stockRet, indusRet, crIndus = publicData_stockAndIndustry()
# 批量生成基金经理任职区间信息
'''
首次存入数据库时需要全部运行，后续更新维护时只需更新在任基金经理信息即可.
选股能力仅针对于股票型与混合型基金
'''
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
output_all = []
for i in range(len(managerInfo)):
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
try:
test = stockPickingAbility_manager(btParms, stockIndus, stockRet,
indusRet, crIndus)
output = test.output
# output['compareReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\compareReturnIndustry.csv")
# output['excessReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnIndustry.csv")
# output['excessReturnQuarter'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnQuarter.csv")
# output['mainStcokReturn'].to_csv("c:\\users\\futanghang\\desktop\\mainStcokReturn.csv")
pdict = output['compareReturnIndustry']
for k in pdict:
df = pdict[k]
for index in df.index:
dt_ = index # dt
di = df.loc[index].to_dict()
xuangushouyilv = di['选股收益率']
ershiwufenweishu = di['25分位数']
wushifenweishu = di['50分位数']
qishiwufenweishuy = di['75分位数']
wCode = output['symbol'] # 基金Wind代码
managerName = output['manager'] # 基金经理名称
managerId = output['managerId'] # 基金经理id
id_ = uuid_util.gen_uuid() # objId
lst = []
lst.append(id_)
lst.append(wCode)
lst.append(managerId)
lst.append(managerName)
lst.append(dt_._short_repr.replace("-", ""))
lst.append(k)
lst.append(ershiwufenweishu)
lst.append(wushifenweishu)
lst.append(qishiwufenweishuy)
lst.append(xuangushouyilv)
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
ls = [transformFloatIfAvaliable4(i) for i in lst]
sql = "INSERT INTO `fof`.`fof_fund_main_stock_return`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`, `MANAGER_NAME`, `TRADE_DT`, `INDUSTRY_NAME`, `25_POINTS`, `50_POINTS`, `75_POINTS`, `INDUSTRY_RETURN`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ls))
except Exception as e:
pass
def return_total(model):
if not del_data('fof_fund_excess_return_total'):
raise Error("删除历史数据失败，任务结束")
# fof_fund_excess_return_total
# 一次性调取需要用到的公共数据
# logic_return_total()
# 一次性调取需要用到的公共数据
stockIndus, stockRet, indusRet, crIndus = publicData_stockAndIndustry()
# 批量生成基金经理任职区间信息
'''
首次存入数据库时需要全部运行，后续更新维护时只需更新在任基金经理信息即可.
选股能力仅针对于股票型与混合型基金
'''
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
output_all = []
for i in range(len(managerInfo)):
# if i<5: #生产环境下删除该句，输入前100个仅为调试使用
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
try:
test = stockPickingAbility_manager(btParms, stockIndus, stockRet,
indusRet, crIndus)
output = test.output
# output['compareReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\compareReturnIndustry.csv")
# output['excessReturnIndustry'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnIndustry.csv")
# output['excessReturnQuarter'].to_csv("c:\\users\\futanghang\\desktop\\excessReturnQuarter.csv")
# output['mainStcokReturn'].to_csv("c:\\users\\futanghang\\desktop\\mainStcokReturn.csv")
pdict = output['excessReturnIndustry']
for k, v in pdict.items():
# k 行业，v 值
indus = k
val = v
id_ = uuid_util.gen_uuid() # objId
wCode = output['symbol'] # 基金Wind代码
managerName = output['manager'] # 基金经理名称
managerId = output['managerId'] # 基金经理id
lst = []
lst.append(id_)
lst.append(wCode)
lst.append(managerId)
lst.append(managerName)
lst.append(indus)
lst.append(val)
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
ls = [transformFloatIfAvaliable4(i) for i in lst]
sql = "INSERT INTO `fof`.`fof_fund_excess_return_total`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`, `MANAGER_NAME`, `INDUSTRY_NAME`, `INDUSTRY_RETURN`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ls))
except:
output = {
'managerId': managerInfo.loc[i, 'managerId'],
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager'],
'excessReturnQuarter': [],
'excessReturnIndustry': [],
'compareReturnIndustry': {},
'mainStcokReturn': []
}
output_all.append(output)
def industry_config_avgscore(model):
if not del_data('fof_fund_industry_avgscore'):
raise Error("删除历史数据失败，任务结束")
# 为提高运行速度，公共数据一次性提取，并作为参数传入要调用的类
stockIndus, indusNet = getPublicDataAllocation()
# 批量生成基金经理任职区间信息
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
'''
首次存入数据库时需要全部运行，后续更新维护时只需更新在任基金经理信息即可
'''
# 批量生成基金经理行业配置能力
for i in range(len(managerInfo)):
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
try:
test = industryAllocation_manager(btParms, stockIndus, indusNet)
output = test.output
scoreDF = output['alloScore'] # fof_fund_stock_industry
distributeDF = output['alloDistribute'] # fof_fund_industry_score
evaluationDF = output[
'alloEvaluation'] # fof_fund_industry_avgscore
rdf = evaluationDF.T
for index in rdf.index:
res = rdf.loc[index]
indus = index # 中信行业名称
dt_ = '123' # 时间
score = res['平均配置得分'] # 配置收益得分
bili = res['平均配置比例']
zhanbi = res['配置次数占比']
# id_ = uuid_util.gen_uuid() # objId
wCode = output['symbol'] # 基金代码
lst = []
id_ = uuid_util.gen_uuid() # objId
lst.append(id_)
lst.append(wCode)
lst.append(output['managerId'])
lst.append(output['manager'])
# lst.append(dt_._short_repr.replace("-", ""))
lst.append(indus)
lst.append(transformFloatIfAvaliable4(bili))
lst.append(transformFloatIfAvaliable4(score))
lst.append(transformFloatIfAvaliable4(zhanbi))
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
sql = "INSERT INTO `fof`.`fof_fund_industry_avgscore`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`,`MANAGER_NAME`, `INDUSTRY_NAME`, `AVG_PERCENT`, `AVG_SCORE`, `AVG_TIMES`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES (%s, %s, %s, %s, %s, %s,%s, %s, %s, %s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(lst))
except:
pass
def industry_config_indust(model):
if del_data('fof_fund_stock_industry') == False:
raise Error("删除历史数据失败，任务结束")
# sql = "delete from fof_fund_stock_industry "
# mysqlops.fetch_one(MysqlConf.DB.fof, sql)
# 离线计算基金经理行业配置能力
# 为提高运行速度，公共数据一次性提取，并作为参数传入要调用的类
stockIndus, indusNet = getPublicDataAllocation()
# 批量生成基金经理任职区间信息
managerInfo = getData_fundManagerInfo_all()
fundSymbols = getData_fundSymbols(['股票型', '混合型'])
tmp = []
for row in managerInfo.index:
if managerInfo.loc[row, 'fund'] in fundSymbols:
tmp.append(managerInfo.loc[row].to_dict())
managerInfo = pd.DataFrame(tmp, columns=managerInfo.columns)
'''
首次存入数据库时需要全部运行，后续更新维护时只需更新在任基金经理信息即可
'''
# 批量生成基金经理行业配置能力
for i in range(len(managerInfo)):
btParms = {
'symbol': managerInfo.loc[i, 'fund'],
'manager': managerInfo.loc[i, 'manager']
}
try:
test = industryAllocation_manager(btParms, stockIndus, indusNet)
output = test.output
distributeDF = output['alloDistribute'] # fof_fund_stock_industry
# for i in stockIndus.index:
# print(i, "====", stockIndus[i])
# fof_fund_stock_industry
# sWindCode = i # 玩的股票代码
# indsName = stockIndus[i] # 行业名称?
wCode = output['symbol'] # 基金代码
for index in distributeDF.index:
res = distributeDF.loc[index]
for si in res.index:
indus = index # 中信行业名称
dt_ = si # 时间
profit = res[si] # 配置收益得分
id_ = uuid_util.gen_uuid() # objId
lst = []
lst.append(id_)
lst.append(wCode)
lst.append(output['managerId'])
lst.append(output['manager'])
lst.append(dt_._short_repr.replace("-", ""))
lst.append(indus)
lst.append(transformFloatIfAvaliable4(profit))
lst.append("sys")
lst.append(datetime.now())
lst.append("sys")
lst.append(datetime.now())
ls = [transformFloatIfAvaliable4(i) for i in lst]
sql = "INSERT INTO `fof`.`fof_fund_stock_industry`(`OBJECT_ID`, `J_WINDCODE`, `MANAGER_ID`,`MANAGER_NAME`,`TRADE_DT`, `INDUSTRY_NAME`, `STOCK_RATE`, `CREATE_USER_ID`, `CREATE_TIME`, `UPDATE_USER_ID`, `UPDATE_TIME`) VALUES ( %s, %s,%s, %s, %s, %s, %s, %s,%s, %s, %s)"
mysqlops.insert_one(MysqlConf.DB.fof, sql, tuple(ls))
except:
pass