code pdate

client.py

-import pdb
-import collections
 import time
-
+import collections
 from datetime import datetime, timedelta
 from factor import factor_growth, historical_value, factor_per_share_indicators, factor_cash_flow, factor_contrarian, factor_earning
 from factor.ttm_fundamental import *
@@ -11,10 +9,8 @@ from vision.file_unit.income import Income
 from vision.file_unit.valuation import Valuation
 from vision.file_unit.industry import Industry
 from vision.file_unit.indicator import Indicator
-
 from factor.utillities.trade_date import TradeDate
 from ultron.cluster.invoke.cache_data import cache_data
-from ultron.utilities.short_uuid import unique_machine, decode
 
 
 def get_trade_date(trade_date, n):
@@ -48,7 +44,6 @@ def get_basic_growth_data(trade_date):
     :param trade_date: 交易日
     :return:
     """
-
     trade_date_pre_year = get_trade_date(trade_date, 1)
     trade_date_pre_year_2 = get_trade_date(trade_date, 2)
     trade_date_pre_year_3 = get_trade_date(trade_date, 3)
@@ -262,7 +257,6 @@ def get_basic_history_value_data(trade_date):
     ttm_factor_sets_5 = ttm_factor_sets_5.drop(columns={"trade_date"})
 
     # ttm_factor_sets_sum = ttm_factor_sets_sum.rename(columns={"net_profit": "net_profit_5"})
-
     ttm_factor_sets = pd.merge(ttm_factor_sets, ttm_factor_sets_3, on='symbol')
     ttm_factor_sets = pd.merge(ttm_factor_sets, ttm_factor_sets_5, on='symbol')
     # ttm_factor_sets = pd.merge(ttm_factor_sets, ttm_factor_sets_sum, on='symbol')
@@ -375,12 +369,12 @@ def get_basic_cash_flow(trade_date):
                                      Income.total_operating_cost, Income.net_profit,
                                      Income.np_parent_company_owners]
                    }
-    ttm_factor_sets = get_ttm_fundamental([], ttm_factors, trade_date).reset_index()
-    ttm_factor_sets = ttm_factor_sets[-ttm_factor_sets.duplicated()]
+    ttm_cash_flow_sets = get_ttm_fundamental([], ttm_factors, trade_date).reset_index()
+    ttm_cash_flow_sets = ttm_cash_flow_sets[-ttm_cash_flow_sets.duplicated()]
     # 合并
-    ttm_factor_sets = pd.merge(ttm_factor_sets, valuation_sets, on="symbol")
+    ttm_cash_flow_sets = pd.merge(ttm_cash_flow_sets, valuation_sets, on="symbol")
 
-    return tp_cash_flow, ttm_factor_sets
+    return tp_cash_flow, ttm_cash_flow_sets
 
 
 def get_basic_constrain(trade_date):
@@ -408,10 +402,10 @@ def get_basic_constrain(trade_date):
                                    Income.administration_expense
                                    ]}
 
-    ttm_factors_sets = get_ttm_fundamental([], ttm_factors, trade_date).reset_index()
-    ttm_factors_sets = ttm_factors_sets[-ttm_factors_sets.duplicated()]
+    ttm_constrain_sets = get_ttm_fundamental([], ttm_factors, trade_date).reset_index()
+    ttm_constrain_sets = ttm_constrain_sets[-ttm_constrain_sets.duplicated()]
 
-    return balance_sets, ttm_factors_sets
+    return balance_sets, ttm_constrain_sets
 
 
 def get_basic_earning(trade_date):
@@ -572,9 +566,9 @@ if __name__ == '__main__':
         print('per_share_cal_time:{}'.format(time3 - time2))
 
         # cash flow
-        tp_cash_flow, ttm_factor_sets = get_basic_cash_flow(date_index)
+        tp_cash_flow, ttm_cash_flow_sets = get_basic_cash_flow(date_index)
         cache_data.set_cache(session4 + "1", date_index, tp_cash_flow.to_json(orient='records'))
-        cache_data.set_cache(session4 + "2", date_index, ttm_factor_sets.to_json(orient='records'))
+        cache_data.set_cache(session4 + "2", date_index, ttm_cash_flow_sets.to_json(orient='records'))
         factor_cash_flow.factor_calculate(date_index=date_index, session=session4)
         time4 = time.time()
         print('cash_flow_cal_time:{}'.format(time4 - time3))

factor/factor_cash_flow.py

@@ -45,7 +45,8 @@ class FactorCashFlow(FactorBase):
         super(FactorCashFlow, self)._create_tables(create_sql, drop_sql)
 
     # 经营活动净现金流（TTM）/负债（TTM）
-    def nocf_to_t_liability_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def nocf_to_t_liability_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'total_liability']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptCFToLiabilityTTM'] = np.where(
@@ -56,7 +57,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动净现金流（TTM）/带息负债（TTM）
-    def nocf_to_interest_bear_debt_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def nocf_to_interest_bear_debt_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'total_liability', 'interest_bearing_liability']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptCFToIBDTTM'] = np.where(
@@ -67,7 +69,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动净现金流（TTM）/净负债（TTM）
-    def nocf_to_net_debt_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def nocf_to_net_debt_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'net_liability']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptCFToNetDebtTTM'] = np.where(CalcTools.is_zero(cash_flow.net_liability.values), 0,
@@ -77,7 +80,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 销售商品和提供劳务收到的现金（TTM）/营业收入（TTM）
-    def sale_service_cash_to_or_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def sale_service_cash_to_or_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['goods_sale_and_service_render_cash', 'operating_revenue']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['SaleServCashToOptReTTM'] = np.where(
@@ -88,7 +92,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动产生的现金流量净额（TTM）/营业收入（TTM）
-    def cash_rate_of_sales_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def cash_rate_of_sales_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'operating_revenue']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptCFToRevTTM'] = np.where(
@@ -99,7 +104,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动产生的现金流量净额（TTM）/(营业总收入（TTM）-营业总成本（TTM）)
-    def nocf_to_operating_ni_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def nocf_to_operating_ni_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'total_operating_revenue', 'total_operating_cost']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptCFToNetIncomeTTM'] = np.where(
@@ -111,7 +117,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动产生的现金流量净额（TTM）/流动负债（TTM）
-    def oper_cash_in_to_current_liability_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def oper_cash_in_to_current_liability_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'total_current_liability']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptCFToCurrLiabilityTTM'] = np.where(
@@ -122,7 +129,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 期末现金及现金等价物余额（TTM）/流动负债（TTM）
-    def cash_to_current_liability_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def cash_to_current_liability_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['cash_and_equivalents_at_end', 'total_current_assets']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['CashRatioTTM'] = np.where(CalcTools.is_zero(cash_flow.total_current_assets.values),
@@ -133,7 +141,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动产生的现金流量净额（TTM）/（长期借款（TTM）+ 短期借款（TTM）+ 总市值 - 期末现金及现金等价物（TTM）
-    def cfo_to_ev_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def cfo_to_ev_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'longterm_loan', 'shortterm_loan', 'market_cap',
                         'cash_and_equivalents_at_end']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
@@ -147,7 +156,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # （经营活动产生的金流量净额（TTM） - 净利润（TTM）） /总资产（TTM）
-    def acca_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def acca_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'net_profit', 'total_assets']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptOnReToAssetTTM'] = np.where(CalcTools.is_zero(cash_flow.total_assets.values), 0,
@@ -158,7 +168,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动产生的现金流量净额（TTM）/归属于母公司所有者的净利润（TTM）
-    def net_profit_cash_cover_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def net_profit_cash_cover_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'np_parent_company_owners']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['NetProCashCoverTTM'] = np.where(
@@ -169,7 +180,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动产生的现金流量净额（TTM）/总资产（TTM）
-    def oper_cash_in_to_asset_ttm(self, ttm_cash_flow, factor_cash_flow):
+    @staticmethod
+    def oper_cash_in_to_asset_ttm(ttm_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'total_assets']
         cash_flow = ttm_cash_flow.loc[:, columns_list]
         cash_flow['OptToAssertTTM'] = np.where(CalcTools.is_zero(cash_flow.total_assets.values),
@@ -180,7 +192,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 销售商品和提供劳务收到的现金（Latest）/营业收入（Latest）
-    def sales_service_cash_to_or_latest(self, tp_cash_flow, factor_cash_flow):
+    @staticmethod
+    def sales_service_cash_to_or_latest(tp_cash_flow, factor_cash_flow):
         columns_list = ['goods_sale_and_service_render_cash', 'operating_revenue']
         cash_flow = tp_cash_flow.loc[:, columns_list]
         cash_flow['SalesServCashToOR'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
@@ -191,7 +204,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经验活动产生的现金流量净额 / 营业收入
-    def cash_rate_of_sales_latest(self, tp_cash_flow, factor_cash_flow):
+    @staticmethod
+    def cash_rate_of_sales_latest(tp_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'operating_revenue']
         cash_flow = tp_cash_flow.loc[:, columns_list]
         cash_flow['CashOfSales'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
@@ -202,7 +216,8 @@ class FactorCashFlow(FactorBase):
         return factor_cash_flow
 
     # 经营活动产生的现金流量净额（Latest）/(营业总收入（Latest）-营业总成本（Latest）)
-    def nocf_to_operating_ni_latest(self, tp_cash_flow, factor_cash_flow):
+    @staticmethod
+    def nocf_to_operating_ni_latest(tp_cash_flow, factor_cash_flow):
         columns_list = ['net_operate_cash_flow', 'total_operating_revenue',
                         'total_operating_cost']
         cash_flow = tp_cash_flow.loc[:, columns_list]
@@ -223,7 +238,6 @@ def calculate(trade_date, cash_flow_dic, cash_flow):  # 计算对应因子
 
     factor_cash_flow = pd.DataFrame()
     factor_cash_flow['symbol'] = tp_cash_flow.index
-    # tp_cash_flow.set_index('symbol', inplace=True)
 
     # 非TTM计算
     factor_cash_flow = cash_flow.nocf_to_operating_ni_latest(tp_cash_flow, factor_cash_flow)
@@ -248,14 +262,6 @@ def calculate(trade_date, cash_flow_dic, cash_flow):  # 计算对应因子
     cash_flow._storage_data(factor_cash_flow, trade_date)
 
 
-def do_update(self, start_date, end_date, count):
-    # 读取本地交易日
-    trade_date_sets = self._trade_date.trade_date_sets_ago(start_date, end_date, count)
-    for trade_date in trade_date_sets:
-        self.calculate(trade_date)
-    print('----->')
-
-
 @app.task()
 def factor_calculate(**kwargs):
     print("cash_flow_kwargs: {}".format(kwargs))

factor/factor_contrarian.py

@@ -45,7 +45,8 @@ class FactorContrarian(FactorBase):
         super(FactorContrarian, self)._create_tables(create_sql, drop_sql)
 
     # 销售成本率=营业成本(TTM)/营业收入(TTM)
-    def sales_cost_ratio_ttm(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def sales_cost_ratio_ttm(tp_contrarian, factor_contrarian):
         columns_list = ['operating_cost', 'operating_revenue']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['SalesCostTTM'] = np.where(
@@ -57,7 +58,8 @@ class FactorContrarian(FactorBase):
         return factor_contrarian
 
     # 销售税金率=营业税金及附加(TTM)/营业收入(TTM)
-    def tax_ratio_ttm(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def tax_ratio_ttm(tp_contrarian, factor_contrarian):
         columns_list = ['operating_tax_surcharges', 'operating_revenue']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['TaxRTTM'] = np.where(
@@ -69,7 +71,8 @@ class FactorContrarian(FactorBase):
         return factor_contrarian
 
     # 财务费用与营业总收入之比=财务费用(TTM)/营业总收入(TTM)
-    def financial_expense_rate_ttm(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def financial_expense_rate_ttm(tp_contrarian, factor_contrarian):
         columns_list = ['financial_expense', 'total_operating_cost']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['FinExpTTM'] = np.where(
@@ -81,7 +84,8 @@ class FactorContrarian(FactorBase):
         return factor_contrarian
 
     # 营业费用与营业总收入之比=销售费用(TTM)/营业总收入(TTM)
-    def operating_expense_rate_ttm(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def operating_expense_rate_ttm(tp_contrarian, factor_contrarian):
         columns_list = ['sale_expense', 'total_operating_cost', 'total_operating_revenue']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['OperExpTTM'] = np.where(
@@ -93,7 +97,8 @@ class FactorContrarian(FactorBase):
         return factor_contrarian
 
     # 管理费用与营业总收入之比=管理费用/营业总收入
-    def admini_expense_rate_ttm(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def admini_expense_rate_ttm(tp_contrarian, factor_contrarian):
         columns_list = ['administration_expense', 'total_operating_revenue']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['AdminExpTTM'] = np.where(
@@ -105,7 +110,8 @@ class FactorContrarian(FactorBase):
         return factor_contrarian
 
     # 销售期间费用率 = (财务费用 + 销售费用 + 管理费用) / (营业收入)
-    def period_costs_rate_ttm(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def period_costs_rate_ttm(tp_contrarian, factor_contrarian):
         columns_list = ['financial_expense', 'sale_expense', 'administration_expense', 'operating_revenue']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['PeridCostTTM'] = np.where(
@@ -118,7 +124,8 @@ class FactorContrarian(FactorBase):
         return factor_contrarian
 
     # 负债合计/有形资产(流动资产+固定资产)
-    def debt_tangible_equity_ratio_latest(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def debt_tangible_equity_ratio_latest(tp_contrarian, factor_contrarian):
         columns_list = ['total_liability', 'total_current_liability', 'fixed_assets']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['DTE'] = np.where(
@@ -130,7 +137,8 @@ class FactorContrarian(FactorBase):
         return factor_contrarian
 
     # 债务总资产比=负债合计/资产合计
-    def debts_asset_ratio_latest(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def debts_asset_ratio_latest(tp_contrarian, factor_contrarian):
         columns_list = ['total_liability', 'total_assets']
         contrarian = tp_contrarian.loc[:, columns_list]
         contrarian['DA'] = np.where(
@@ -142,7 +150,8 @@ class FactorContrarian(FactorBase):
 
     # InteBearDebtToTotalCapital = 有息负债/总资本   总资本=固定资产+净运营资本  净运营资本=流动资产-流动负债
     # InteBearDebtToTotalCapital = 有息负债/(固定资产 + 流动资产 - 流动负债)
-    def inte_bear_debt_to_total_capital_latest(self, tp_contrarian, factor_contrarian):
+    @staticmethod
+    def inte_bear_debt_to_total_capital_latest(tp_contrarian, factor_contrarian):
         columns_list = ['interest_bearing_liability', 'fixed_assets', 'total_current_assets',
                         'total_current_liability']
         contrarian = tp_contrarian.loc[:, columns_list]

factor/factor_earning.py

@@ -76,7 +76,8 @@ class FactorEarning(FactorBase):
                     )ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name)
         super(FactorEarning, self)._create_tables(create_sql, drop_sql)
 
-    def net_profit_ratio(self, ttm_earning, factor_earning):
+    @staticmethod
+    def net_profit_ratio(ttm_earning, factor_earning):
         """
         销售净利率（Net profit ratio）
         销售净利率=净利润/营业收入
@@ -93,7 +94,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def operating_profit_ratio(self, ttm_earning, factor_earning):
+    @staticmethod
+    def operating_profit_ratio(ttm_earning, factor_earning):
         """
         营业净利率
         营业净利率=营业利润/营业收入
@@ -110,7 +112,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def np_to_tor(self, ttm_earning, factor_earning):
+    @staticmethod
+    def np_to_tor(ttm_earning, factor_earning):
         """
         净利润与营业总收入之比
         净利润与营业总收入之比=净利润/营业总收入
@@ -127,7 +130,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def operating_profit_to_tor(self, ttm_earning, factor_earning):
+    @staticmethod
+    def operating_profit_to_tor(ttm_earning, factor_earning):
         """
         营业利润与营业总收入之比
         营业利润与营业总收入之比=营业利润/营业总收入
@@ -144,7 +148,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def gross_income_ratio(self, ttm_earning, factor_earning):
+    @staticmethod
+    def gross_income_ratio(ttm_earning, factor_earning):
         """
         销售毛利率
         销售毛利率=（营业收入-营业成本）/营业收入
@@ -162,7 +167,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def ebit_to_tor(self, ttm_earning, factor_earning):
+    @staticmethod
+    def ebit_to_tor(ttm_earning, factor_earning):
         """
         息税前利润与营业总收入之比
         息税前利润与营业总收入之比=（利润总额+利息支出-利息收入)/营业总收入
@@ -183,7 +189,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def roa(self, ttm_earning, factor_earning):
+    @staticmethod
+    def roa(ttm_earning, factor_earning):
         """
         资产回报率
         资产回报率=净利润/总资产
@@ -200,11 +207,12 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def roa5(self, ttm_earning_5y, factor_earning):
+    @staticmethod
+    def roa5(ttm_earning_5y, factor_earning):
         """
         5年权益回报率
         5年权益回报率=净利润/总资产
-        :param ttm_earning:
+        :param ttm_earning_5y:
         :param factor_earning:
         :return:
         """
@@ -217,7 +225,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def roe(self, ttm_earning, factor_earning):
+    @staticmethod
+    def roe(ttm_earning, factor_earning):
         """
         权益回报率
         权益回报率=净利润/股东权益
@@ -234,10 +243,11 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def roe5(self, ttm_earning_5y, factor_earning):
+    @staticmethod
+    def roe5(ttm_earning_5y, factor_earning):
         """
         5年权益回报率
-        :param ttm_earning:
+        :param ttm_earning_5y:
         :param factor_earning:
         :return:
         """
@@ -250,11 +260,12 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def roe_diluted(self, tp_earning, factor_earning):
+    @staticmethod
+    def roe_diluted(tp_earning, factor_earning):
         """
         净资产收益率（摊薄）
         净资产收益率（摊薄）=归属于母公司的净利润/期末归属于母公司的所有者权益
-        :param ttm_earning:
+        :param tp_earning:
         :param factor_earning:
         :return:
         """
@@ -269,7 +280,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def roe_avg(self, ttm_earning, factor_earning):
+    @staticmethod
+    def roe_avg(ttm_earning, factor_earning):
         """
         资产收益率（平均）
         资产收益率（平均）=归属于母公司的净利润*2/(期末归属于母公司的所有者权益
@@ -289,7 +301,6 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-
     def roe_weighted(self, ttm_earning, factor_earning):
         """
         :param ttm_earning:
@@ -298,7 +309,8 @@ class FactorEarning(FactorBase):
         """
         pass
 
-    def roe_cut(self, tp_earning, factor_earning):
+    @staticmethod
+    def roe_cut(tp_earning, factor_earning):
         """
         :param tp_earning:
         :param factor_earning:
@@ -338,7 +350,8 @@ class FactorEarning(FactorBase):
         """
         pass
 
-    def roa_ebit_ttm(self, ttm_earning, factor_earning):
+    @staticmethod
+    def roa_ebit_ttm(ttm_earning, factor_earning):
         """
         总资产报酬率
         ROAEBIT = EBIT*2/(期初总资产+期末总资产）
@@ -360,7 +373,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def operating_ni_to_tp_ttm(self, ttm_earning, factor_earning):
+    @staticmethod
+    def operating_ni_to_tp_ttm(ttm_earning, factor_earning):
         """
         经营活动净收益/利润总额
         （注，对于非金融企业 经营活动净收益=营业总收入-营业总成本；
@@ -382,7 +396,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def operating_ni_to_tp_latest(self, tp_earning, factor_earning):
+    @staticmethod
+    def operating_ni_to_tp_latest(tp_earning, factor_earning):
         """
         经营活动净收益/利润总额
         （注，对于非金融企业 经营活动净收益=营业总收入-营业总成本；
@@ -404,7 +419,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def invest_r_associates_to_tp_ttm(self, ttm_earning, factor_earning):
+    @staticmethod
+    def invest_r_associates_to_tp_ttm(ttm_earning, factor_earning):
         """
         对联营和营公司投资收益/利润总额
         :param ttm_earning:
@@ -421,7 +437,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def invest_r_associates_to_tp_latest(self, tp_earning, factor_earning):
+    @staticmethod
+    def invest_r_associates_to_tp_latest(tp_earning, factor_earning):
         """
         对联营和营公司投资收益/利润总额
         :param tp_earning:
@@ -438,7 +455,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def npcut_to_np(self, tp_earning, factor_earning):
+    @staticmethod
+    def npcut_to_np(tp_earning, factor_earning):
         """
         扣除非经常损益后的净利润/净利润
         :param tp_earning:
@@ -455,7 +473,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def interest_cover_ttm(self, ttm_earning, factor_earning):
+    @staticmethod
+    def interest_cover_ttm(ttm_earning, factor_earning):
         """
         利息保障倍数
         InterestCover=(TP + INT_EXP - INT_COME)/(INT_EXP - INT_COME)
@@ -467,21 +486,18 @@ class FactorEarning(FactorBase):
         columns_list = ['total_profit', 'financial_expense', 'interest_income']
         earning = ttm_earning.loc[:, columns_list]
         earning['interest_cover_ttm'] = np.where(
-            CalcTools.is_zero(earning.financial_expense.values-
-                              earning.interest_income.values), 0,
-            (earning.total_profit.values +
-             earning.financial_expense.values -
-             earning.interest_income.values)
-            / (earning.financial_expense.values -
-               earning.interest_income.values)
-                )
+            CalcTools.is_zero(earning.financial_expense.values - earning.interest_income.values), 0,
+            (earning.total_profit.values + earning.financial_expense.values - earning.interest_income.values) /
+            (earning.financial_expense.values - earning.interest_income.values))
         earning = earning.drop(columns_list, axis=1)
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def degm(self, ttm_earning, ttm_earning_p1y, factor_earning):
+    @staticmethod
+    def degm(ttm_earning, ttm_earning_p1y, factor_earning):
         """
         毛利率增长，与去年同期相比
+        :param ttm_earning_p1y:
         :param ttm_earning:
         :param factor_earning:
         :return:
@@ -506,7 +522,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def net_non_oi_to_tp_ttm(self, ttm_earning, factor_earning):
+    @staticmethod
+    def net_non_oi_to_tp_ttm(ttm_earning, factor_earning):
         """
         营业外收支净额/利润总额
         :param ttm_earning:
@@ -525,7 +542,8 @@ class FactorEarning(FactorBase):
         factor_earning = pd.merge(factor_earning, earning, on="symbol")
         return factor_earning
 
-    def net_non_oi_to_tp_latest(self, tp_earning, factor_earning):
+    @staticmethod
+    def net_non_oi_to_tp_latest(tp_earning, factor_earning):
         """
         营业外收支净额/利润总额
         :param tp_earning:

factor/factor_growth.py

@@ -60,27 +60,31 @@ class Growth(FactorBase):
                     )ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name)
         super(Growth, self)._create_tables(create_sql, drop_sql)
 
-    def historical_net_asset_grow_rate(self, tp_historical_growth):
+    @staticmethod
+    def historical_net_asset_grow_rate(tp_historical_growth):
         """
         净资产增长率
         :param tp_historical_growth:
-        :param factor_historical_growth:
         :return:
         """
         columns_lists = ['symbol', 'total_owner_equities', 'total_owner_equities_pre_year']
         historical_growth = tp_historical_growth.loc[:, columns_lists]
+
         if len(historical_growth) <= 0:
             return
+
         fun = lambda x: ((x[0] / x[1]) - 1.0 if x[1] and x[1] != 0 and x[0] is not None and x[1] is not None else None)
 
         historical_growth['NetAsset1YChg'] = historical_growth[['total_owner_equities',
                                                                 'total_owner_equities_pre_year']].apply(fun, axis=1)
 
-        historical_growth = historical_growth.drop(columns=['total_owner_equities', 'total_owner_equities_pre_year'], axis=1)
+        historical_growth = historical_growth.drop(columns=['total_owner_equities',
+                                                            'total_owner_equities_pre_year'], axis=1)
         # factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return historical_growth
 
-    def historical_total_asset_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_total_asset_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         总资产增长率
         :param tp_historical_growth:
@@ -99,7 +103,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_operating_revenue_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_operating_revenue_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         营业收入增长率
         :param tp_historical_growth:
@@ -118,7 +123,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_operating_profit_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_operating_profit_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         营业利润增长率
         :param tp_historical_growth:
@@ -137,7 +143,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_total_profit_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_total_profit_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         利润总额增长率
         :param tp_historical_growth:
@@ -156,7 +163,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_net_profit_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_net_profit_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         净利润增长率
         :param tp_historical_growth:
@@ -175,7 +183,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_np_parent_company_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_np_parent_company_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         归属母公司股东的净利润增长率
         :param tp_historical_growth:
@@ -195,7 +204,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_net_profit_grow_rate_3y(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_net_profit_grow_rate_3y(tp_historical_growth, factor_historical_growth):
         """
         净利润3年复合增长率
         :param tp_historical_growth:
@@ -214,7 +224,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_net_profit_grow_rate_5y(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_net_profit_grow_rate_5y(tp_historical_growth, factor_historical_growth):
         """
         净利润5年复合增长率
         :param tp_historical_growth:
@@ -233,7 +244,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_operating_revenue_grow_rate_3y(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_operating_revenue_grow_rate_3y(tp_historical_growth, factor_historical_growth):
         """
         营业收入3年复合增长率
         :param tp_historical_growth:
@@ -254,7 +266,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_operating_revenue_grow_rate_5y(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_operating_revenue_grow_rate_5y(tp_historical_growth, factor_historical_growth):
         """
         营业收入5年复合增长率
         :param tp_historical_growth:
@@ -275,7 +288,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_net_cash_flow_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_net_cash_flow_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         缺数据
         净现金流量增长率
@@ -295,7 +309,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_np_parent_company_cut_yoy(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_np_parent_company_cut_yoy(tp_historical_growth, factor_historical_growth):
         """
         缺失数据
         归属母公司股东的净利润（扣除）同比增长
@@ -315,27 +330,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_egro_(self, tp_historical_growth, factor_historical_growth):
-        """
-        5年收益增长率
-        :param tp_historical_growth:
-        :param factor_historical_growth:
-        :return:
-        """
-        if len(tp_historical_growth) < 3:
-            return
-        columns_lists = ['symbol', 'net_profit', 'net_profit_pre_year_1', 'net_profit_pre_year_2',
-                         'net_profit_pre_year_3', 'net_profit_pre_year_4']
-        regr = linear_model.LinearRegression()
-        # 读取五年的时间和净利润
-        historical_growth = tp_historical_growth.loc[:, columns_lists]
-
-        regr.fit(tp_historical_growth['x'].values.reshape(-1, 1), tp_historical_growth['y'].values)
-
-        weight = regr.coef_
-        mean = tp_historical_growth['y'].mean()
-
-    def historical_egro(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_egro(tp_historical_growth, factor_historical_growth):
         columns_lists = ['symbol', 'net_profit', 'net_profit_pre_year_1', 'net_profit_pre_year_2',
                          'net_profit_pre_year_3', 'net_profit_pre_year_4']
         regr = linear_model.LinearRegression()
@@ -368,11 +364,9 @@ class Growth(FactorBase):
         #                                             range(0, 5)) else None)
 
         historical_growth['coefficient'] = historical_growth.apply(fun2, axis=1)
-
         historical_growth['mean'] = historical_growth[['net_profit', 'net_profit_pre_year_1', 'net_profit_pre_year_2', 'net_profit_pre_year_3', 'net_profit_pre_year_4']].fillna('nan').mean(axis=1)
 
         fun1 = lambda x: x[0] / abs(x[1]) if x[1] != 0 and x[1] is not None and x[0] is not None else None
-
         historical_growth['NetPft5YAvgChgTTM'] = historical_growth[['coefficient', 'mean']].apply(fun1, axis=1)
 
         # historical_growth = historical_growth.drop(
@@ -384,7 +378,8 @@ class Growth(FactorBase):
 
         return factor_historical_growth
 
-    def historical_sue(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_sue(tp_historical_growth, factor_historical_growth):
         """
         未预期盈余
         :param tp_historical_growth:
@@ -396,8 +391,10 @@ class Growth(FactorBase):
         historical_growth = tp_historical_growth.loc[:, columns_lists]
         if len(historical_growth) <= 0:
             return
-        historical_growth['mean'] = historical_growth[['net_profit', 'net_profit_pre_year_1', 'net_profit_pre_year_2', 'net_profit_pre_year_3', 'net_profit_pre_year_4']].fillna(0.0).mean(axis=1)
-        historical_growth['std'] = historical_growth[['net_profit', 'net_profit_pre_year_1', 'net_profit_pre_year_2', 'net_profit_pre_year_3', 'net_profit_pre_year_4']].fillna(0.0).std(axis=1)
+        historical_growth['mean'] = historical_growth[['net_profit', 'net_profit_pre_year_1', 'net_profit_pre_year_2',
+                                                       'net_profit_pre_year_3', 'net_profit_pre_year_4']].fillna(0.0).mean(axis=1)
+        historical_growth['std'] = historical_growth[['net_profit', 'net_profit_pre_year_1', 'net_profit_pre_year_2',
+                                                      'net_profit_pre_year_3', 'net_profit_pre_year_4']].fillna(0.0).std(axis=1)
 
         fun = lambda x: (x[0] - x[1]) / x[2] if x[2] !=0 and x[1] is not None and x[0] is not None and x[2] is not None else None
 
@@ -409,7 +406,8 @@ class Growth(FactorBase):
 
         return factor_historical_growth
 
-    def historical_suoi(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_suoi(tp_historical_growth, factor_historical_growth):
         """
         未预期毛利
         :param tp_historical_growth:
@@ -438,21 +436,15 @@ class Growth(FactorBase):
         historical_growth['std'] = historical_growth[['gi_2', 'gi_3', 'gi_4', 'gi_5']].std(axis=1)
 
         fun = lambda x: ((x[0] - x[1]) / x[2] if x[2] != 0 and x[1] is not None and x[0] is not None and x[2] is not None else None)
-
-        # historical_growth['StdUxpGrPft1YTTM'] = (historical_growth['gi_1'] - historical_growth['mean']) / historical_growth['std']
         historical_growth['StdUxpGrPft1YTTM'] = historical_growth[['gi_1', 'mean', 'std']].apply(fun, axis=1)
 
-        # historical_growth = historical_growth.drop(columns=['operating_revenue', 'operating_revenue_pre_year_1', 'operating_revenue_pre_year_2',
-        #                                                     'operating_revenue_pre_year_3', 'operating_revenue_pre_year_4',
-        #                                                     'operating_cost', 'operating_cost_pre_year_1', 'operating_cost_pre_year_2',
-        #                                                     'operating_cost_pre_year_3', 'operating_cost_pre_year_4', 'std', 'mean',
-        #                                                     'gi_1', 'gi_2', 'gi_3', 'gi_4', 'gi_5'], axis=1)
         historical_growth = historical_growth[['symbol', 'StdUxpGrPft1YTTM']]
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
 
         return factor_historical_growth
 
-    def historical_financing_cash_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_financing_cash_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         筹资活动产生的现金流量净额增长率
         :param tp_historical_growth:
@@ -472,7 +464,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_oper_cash_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_oper_cash_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         经营活动产生的现金流量净额
         :param tp_historical_growth:
@@ -492,7 +485,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_invest_cash_grow_rate(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_invest_cash_grow_rate(tp_historical_growth, factor_historical_growth):
         """
         投资活动产生的现金流量净额
         :param tp_historical_growth:
@@ -512,7 +506,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def historical_sgro(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def historical_sgro(tp_historical_growth, factor_historical_growth):
         """
         五年营业收入增长率
         :param tp_historical_growth:
@@ -539,19 +534,18 @@ class Growth(FactorBase):
                 return False
 
         def fun2(x):
-            aa = x[['operating_revenue', 'operating_revenue_pre_year_1', 'operating_revenue_pre_year_2', 'operating_revenue_pre_year_3', 'operating_revenue_pre_year_4']].fillna('nan').values.reshape(-1, 1)
+            aa = x[['operating_revenue', 'operating_revenue_pre_year_1', 'operating_revenue_pre_year_2',
+                    'operating_revenue_pre_year_3', 'operating_revenue_pre_year_4']].fillna('nan').values.reshape(-1, 1)
             if has_non(aa):
                 return None
             else:
                 regr.fit(aa, range(0, 5))
                 return regr.coef_[-1]
 
-        # fun = lambda x: (regr.coef_[-1] if regr.fit(x[['operating_revenue', 'operating_revenue_pre_year_1',
-        #                                                'operating_revenue_pre_year_2', 'operating_revenue_pre_year_3',
-        #                                                'operating_revenue_pre_year_4']].values.reshape(-1, 1),
-        #                                             range(0, 5)) else None)
         historical_growth['coefficient'] = historical_growth.apply(fun2, axis=1)
-        historical_growth['mean'] = historical_growth[['operating_revenue', 'operating_revenue_pre_year_1', 'operating_revenue_pre_year_2', 'operating_revenue_pre_year_3', 'operating_revenue_pre_year_4']].fillna(0.0).mean(axis=1)
+        historical_growth['mean'] = historical_growth[['operating_revenue', 'operating_revenue_pre_year_1',
+                                                       'operating_revenue_pre_year_2', 'operating_revenue_pre_year_3',
+                                                       'operating_revenue_pre_year_4']].fillna(0.0).mean(axis=1)
 
         fun1 = lambda x: x[0] / abs(x[1]) if x[1] is not None and x[0] is not None and x[1] != 0 else None
         historical_growth['Sales5YChgTTM'] = historical_growth[['coefficient', 'mean']].apply(fun1, axis=1)
@@ -564,7 +558,8 @@ class Growth(FactorBase):
         return factor_historical_growth
 
     # 分析师预期增长
-    def fsalesg(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def fsalesg(tp_historical_growth, factor_historical_growth):
         """
         未来预期营收增长
         :param tp_historical_growth:
@@ -583,7 +578,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def fearng(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def fearng(tp_historical_growth, factor_historical_growth):
         """
         未来预期盈利增长
         :param tp_historical_growth:
@@ -602,7 +598,8 @@ class Growth(FactorBase):
         factor_historical_growth = pd.merge(factor_historical_growth, historical_growth, on='symbol')
         return factor_historical_growth
 
-    def egibs_long(self, tp_historical_growth, factor_historical_growth):
+    @staticmethod
+    def egibs_long(tp_historical_growth, factor_historical_growth):
         """
         净利润增长率
         :param tp_historical_growth:
@@ -633,7 +630,6 @@ def calculate(trade_date, growth_sets, growth):
         print("%s has no data" % trade_date)
         return
 
-    # psindu
     factor_historical_growth = growth.historical_net_asset_grow_rate(growth_sets)
     factor_historical_growth = growth.historical_total_asset_grow_rate(growth_sets, factor_historical_growth)
     factor_historical_growth = growth.historical_operating_revenue_grow_rate(growth_sets, factor_historical_growth)

factor/factor_per_share_indicators.py

@@ -10,11 +10,10 @@
 """
 
 import sys
+sys.path.append("..")
+
 import json
 from pandas.io.json import json_normalize
-
-
-sys.path.append("..")
 from factor.ttm_fundamental import *
 from factor.factor_base import FactorBase
 from vision.fm.signletion_engine import *
@@ -62,7 +61,8 @@ class PerShareIndicators(FactorBase):
                     )ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name)
         super(PerShareIndicators, self)._create_tables(create_sql, drop_sql)
 
-    def eps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def eps(tp_share_indicators, factor_share_indicators):
         """
         基本每股收益
         :param tp_share_indicators:
@@ -77,7 +77,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def diluted_eps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def diluted_eps(tp_share_indicators, factor_share_indicators):
         """
         稀释每股收益
         :param tp_share_indicators:
@@ -93,7 +94,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def cash_equivalent_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def cash_equivalent_ps(tp_share_indicators, factor_share_indicators):
         """
         每股现金及现金等价物余额
         :param tp_share_indicators:
@@ -112,7 +114,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def dividend_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def dividend_ps(tp_share_indicators, factor_share_indicators):
         """
         每股股利（税前）
         :param tp_share_indicators:
@@ -128,7 +131,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def eps_ttm(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def eps_ttm(tp_share_indicators, factor_share_indicators):
         """
         每股收益 TTM
         :param tp_share_indicators:
@@ -147,7 +151,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def net_asset_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def net_asset_ps(tp_share_indicators, factor_share_indicators):
         """
         每股净资产
         :param tp_share_indicators:
@@ -165,7 +170,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def tor_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def tor_ps(tp_share_indicators, factor_share_indicators):
         """
         每股营业总收入
         :param tp_share_indicators:
@@ -184,7 +190,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def tor_ps_latest(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def tor_ps_latest(tp_share_indicators, factor_share_indicators):
         """
         每股营业总收入
         :param tp_share_indicators:
@@ -202,7 +209,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def operating_revenue_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def operating_revenue_ps(tp_share_indicators, factor_share_indicators):
         """
         每股营业收入
         :param tp_share_indicators:
@@ -222,7 +230,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def operating_revenue_ps_latest(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def operating_revenue_ps_latest(tp_share_indicators, factor_share_indicators):
         """
         每股营业收入(最新)
         :param tp_share_indicators:
@@ -243,7 +252,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def operating_profit_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def operating_profit_ps(tp_share_indicators, factor_share_indicators):
         """
         每股营业利润
         :param tp_share_indicators:
@@ -264,7 +274,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def operating_profit_ps_latest(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def operating_profit_ps_latest(tp_share_indicators, factor_share_indicators):
         """
         每股营业利润（最新）
         :param tp_share_indicators:
@@ -281,7 +292,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def capital_surplus_fund_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def capital_surplus_fund_ps(tp_share_indicators, factor_share_indicators):
         """
         每股资本公积金
         :param tp_share_indicators:
@@ -301,7 +313,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def surplus_reserve_fund_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def surplus_reserve_fund_ps(tp_share_indicators, factor_share_indicators):
         """
         每股盈余公积金
         :param tp_share_indicators:
@@ -319,7 +332,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def undivided_pro_fit_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def undivided_pro_fit_ps(tp_share_indicators, factor_share_indicators):
         """
         每股未分配利润
         :param tp_share_indicators:
@@ -339,7 +353,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def retained_earnings_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def retained_earnings_ps(tp_share_indicators, factor_share_indicators):
         """
         每股留存收益
         :param tp_share_indicators:
@@ -355,7 +370,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def oper_cash_flow_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def oper_cash_flow_ps(tp_share_indicators, factor_share_indicators):
         """
         每股经营活动产生的现金流量净额
         :param tp_share_indicators:
@@ -376,7 +392,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def cash_flow_ps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def cash_flow_ps(tp_share_indicators, factor_share_indicators):
         """
         每股现金流量净额
         :param tp_share_indicators:
@@ -394,7 +411,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def enterprise_fcfps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def enterprise_fcfps(tp_share_indicators, factor_share_indicators):
         """
         每股企业自由现金流量
         缺每股企业自由现金流量
@@ -410,7 +428,8 @@ class PerShareIndicators(FactorBase):
         factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
         return factor_share_indicators
 
-    def shareholder_fcfps(self, tp_share_indicators, factor_share_indicators):
+    @staticmethod
+    def shareholder_fcfps(tp_share_indicators, factor_share_indicators):
         """
         每股股东自由现金流量
         缺每股股东自由现金流量
@@ -459,14 +478,6 @@ def calculate(trade_date, valuation_sets, per_share):
     factor_share_indicators = per_share.oper_cash_flow_ps(factor_share_indicators, factor_share_indicators)  # memorydrror
     factor_share_indicators = per_share.cash_flow_ps(factor_share_indicators, factor_share_indicators)  # memorydrror
 
-    # factor_historical_value = factor_historical_value.drop(columns=['pb', 'pe', 'ps', 'pcf', 'market_cap',
-    #                                                                 'circulating_market_cap', 'isymbol',
-    #                                                                 'np_parent_company_owners',
-    #                                                                 'np_parent_company_owners_3',
-    #                                                                 'np_parent_company_owners_5',
-    #                                                                 'net_operate_cash_flow', 'net_profit',
-    #                                                                 'goods_sale_and_service_render_cash'])
-
     # factor_share_indicators = factor_share_indicators[['symbol',
     #                                                    'EPS',
     #                                                    'DilutedEPSTTM',
@@ -514,15 +525,6 @@ def calculate(trade_date, valuation_sets, per_share):
     per_share._storage_data(factor_share_indicators, trade_date)
 
 
-def do_update(self, start_date, end_date, count):
-    # 读取本地交易日
-    trade_date_sets = self._trade_date.trade_date_sets_ago(start_date, end_date, count)
-    for trade_date in trade_date_sets:
-        print('当前交易日： %s' % trade_date)
-        self.calculate(trade_date)
-    print('----->')
-
-
 # @app.task()
 def factor_calculate(**kwargs):
     print("per_share_kwargs: {}".format(kwargs))
@@ -530,6 +532,6 @@ def factor_calculate(**kwargs):
     session = kwargs['session']
     per_share = PerShareIndicators('factor_per_share')  # 注意, 这里的name要与client中新建table时的name一致, 不然回报错
     content = cache_data.get_cache(session, date_index)
-    total_growth_data = json_normalize(json.loads(str(content, encoding='utf8')))
-    print("len_total_per_share_data {}".format(len(total_growth_data)))
-    calculate(date_index, total_growth_data, per_share)
+    total_pre_share_data = json_normalize(json.loads(str(content, encoding='utf8')))
+    print("len_total_per_share_data {}".format(len(total_pre_share_data)))
+    calculate(date_index, total_pre_share_data, per_share)

factor/historical_value.py

@@ -60,7 +60,8 @@ class HistoricalValue(FactorBase):
                     )ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name)
         super(HistoricalValue, self)._create_tables(create_sql, drop_sql)
 
-    def ps_indu(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def ps_indu(tp_historical_value, factor_historical_value):
         """
         PEIndu， 市销率，以及同行业所有的公司的市销率
         # (PS – PS 的行业均值)/PS 的行业标准差
@@ -84,7 +85,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def etop(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def etop(tp_historical_value, factor_historical_value):
         """
         收益市值比
         # 收益市值比= 净利润/总市值
@@ -105,7 +107,8 @@ class HistoricalValue(FactorBase):
         return factor_historical_value
 
     # 5年平均收益市值比 = 近5年净利润 / 近5年总市值
-    def etp5(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def etp5(tp_historical_value, factor_historical_value):
         columns_lists = ['symbol', 'net_profit_5', 'circulating_market_cap_5', 'market_cap_5']
         historical_value = tp_historical_value.loc[:, columns_lists]
 
@@ -117,7 +120,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def pe_indu(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def pe_indu(tp_historical_value, factor_historical_value):
         """
         # (PE – PE 的行业均值)/PE 的行业标准差
         :param tp_historical_value:
@@ -138,7 +142,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def peg_3y(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def peg_3y(tp_historical_value, factor_historical_value):
         """
         # 市盈率/归属于母公司所有者净利润 3 年复合增长率
         :param tp_historical_value:
@@ -157,7 +162,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def peg_5y(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def peg_5y(tp_historical_value, factor_historical_value):
         """
         # 市盈率/归属于母公司所有者净利润 5 年复合增长率
         :param tp_historical_value:
@@ -176,7 +182,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def pb_indu(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def pb_indu(tp_historical_value, factor_historical_value):
         """
         # (PB – PB 的行业均值)/PB 的行业标准差
         :param tp_historical_value:
@@ -198,7 +205,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def lcap(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def lcap(tp_historical_value, factor_historical_value):
         """
         总市值的对数
         # 对数市值 即市值的对数
@@ -213,7 +221,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def lflo(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def lflo(tp_historical_value, factor_historical_value):
         """
         流通总市值的对数
         # 对数市值 即流通市值的对数
@@ -229,7 +238,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def nlsize(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def nlsize(tp_historical_value, factor_historical_value):
         """
         对数市值开立方
         :param tp_historical_value:
@@ -243,7 +253,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def pcf_indu(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def pcf_indu(tp_historical_value, factor_historical_value):
         """
         # (PCF – PCF 的行业均值)/PCF 的行业标准差
         :param tp_historical_value:
@@ -267,7 +278,8 @@ class HistoricalValue(FactorBase):
         factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
         return factor_historical_value
 
-    def cetop(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def cetop(tp_historical_value, factor_historical_value):
         """
         # 经营活动产生的现金流量净额与市值比
         :param tp_historical_value:
@@ -287,7 +299,8 @@ class HistoricalValue(FactorBase):
         return factor_historical_value
 
     # 现金流市值比 = 每股派现 * 分红前总股本/总市值
-    def ctop(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def ctop(tp_historical_value, factor_historical_value):
         columns_lists = ['symbol', 'pcd', 'sbd', 'circulating_market_cap', 'market_cap']
         historical_value = tp_historical_value.loc[:, columns_lists]
 
@@ -300,7 +313,8 @@ class HistoricalValue(FactorBase):
         return factor_historical_value
 
     # 5 年平均现金流市值比  = 近5年每股派现 * 分红前总股本/近5年总市值
-    def ctop5(self, tp_historical_value, factor_historical_value):
+    @staticmethod
+    def ctop5(tp_historical_value, factor_historical_value):
         columns_lists = ['symbol', 'pcd', 'sbd', 'circulating_market_cap_5', 'market_cap_5']
         historical_value = tp_historical_value.loc[:, columns_lists]
 
@@ -318,14 +332,11 @@ class HistoricalValue(FactorBase):
 
 def calculate(trade_date, valuation_sets, historical_value):
     """
-
+    :param historical_value:
+    :param valuation_sets:
     :param trade_date:
     :return:
     """
-    # valuation_sets, ttm_factor_sets, cash_flow_sets, income_sets = self.get_basic_data(trade_date)
-    # valuation_sets = pd.merge(valuation_sets, income_sets, on='symbol')
-    # valuation_sets = pd.merge(valuation_sets, ttm_factor_sets, on='symbol')
-    # valuation_sets = pd.merge(valuation_sets, cash_flow_sets, on='symbol')
     if len(valuation_sets) <= 0:
         print("%s has no data" % trade_date)
         return
@@ -377,15 +388,6 @@ def calculate(trade_date, valuation_sets, historical_value):
     historical_value._storage_data(factor_historical_value, trade_date)
 
 
-def do_update(self, start_date, end_date, count):
-    # 读取本地交易日
-    trade_date_sets = self._trade_date.trade_date_sets_ago(start_date, end_date, count)
-    for trade_date in trade_date_sets:
-        print('因子计算日期： %s' % trade_date)
-        self.calculate(trade_date)
-    print('----->')
-
-
 @app.task()
 def factor_calculate(**kwargs):
     print("history_value_kwargs: {}".format(kwargs))