#在时间序列下计算最后三个月的预测值，以此作为决策信号
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TypeError Traceback (most recent call last)
< ipython-input-17-2ad4c2a88be8> in < module>
15 print('time cost:%0.3f'%clofk_diff)
16 return l
---> 17 pre_res_l = get_pre_signal(ml,sd,ed)
< ipython-input-17-2ad4c2a88be8> in get_pre_signal(ml, sd, ed)
9 month_l = ml[i]
10 end_d = ed[i]
---> 11 pre = monthly_fun(month_l,sd,end_d)
12 l.append(pre)
13 end_clock = time.clock()
< ipython-input-16-ce16c308c5e8> in monthly_fun(month_list, start_date, end_date)
1 def monthly_fun(month_list, start_date, end_date):
----> 2 macro_data_o = get_macro_data(month_list,start_date, end_date)
3 res = macro_data_o[0]
4 res_a = macro_data_o[1]
5 price = macro_data_o[2]
< ipython-input-6-569b9af28df0> in get_macro_data(month_list, start_date, end_date)
93 industry_indicator['enterprise_value_acc_diff'] = industry_indicator_diff/industry_indicator['enterprise_value_acc'].shift().fillna(method='bfill')
94 industry_indicator['loss_enterprise_ratio_acc_diff'] = industry_indicator['loss_enterprise_ratio_acc'].diff()
---> 95 industry_indicator['total_interest_ratio_acc_diff'] = industry_indicator['total_interest_ratio_acc'].diff()
96 industry_indicator = industry_indicator.fillna(method='bfill')
97 l = ['stat_month','enterprise_value_acc_diff','loss_enterprise_ratio_acc_diff','total_interest_ratio_acc_diff']
/opt/conda/lib/python3.6/site-packages/pandas/core/series.py in diff(self, periods)
1993 dtype: float64
1994 """
-> 1995 result = algorithms.diff(com._values_from_object(self), periods)
1996 return self._constructor(result, index=self.index).__finalize__(self)
1997
/opt/conda/lib/python3.6/site-packages/pandas/core/algorithms.py in diff(arr, n, axis)
1821 out_arr[res_indexer] = result
1822 else:
-> 1823 out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer]
1824
1825 if is_timedelta:
TypeError: unsupported operand type(s) for -: 'NoneType' and 'NoneType'
2024-02-20
雪球
