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- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import load_model
- import random
- from mix.stock_source import *
- import pymongo
- from util.mongodb import get_mongo_table_instance
- code_table = get_mongo_table_instance('tushare_code')
- k_table = get_mongo_table_instance('stock_day_k')
- stock_concept_table = get_mongo_table_instance('tushare_concept_detail')
- all_concept_code_list = list(get_mongo_table_instance('tushare_concept').find({}))
- gainian_map = {}
- hangye_map = {}
- Z_list = [] # 自选
- R_list = [] # ROE
- O_list = [] # 其他
- def predict_today(file, day, model='10_18d', log=True):
- industry_list = get_hot_industry(day)
- lines = []
- with open(file) as f:
- for line in f.readlines()[:]:
- line = eval(line.strip())
- lines.append(line)
- size = len(lines[0])
- model=load_model(model)
- for line in lines:
- train_x = np.array([line[:size - 1]])
- train_x_tmp = train_x[:,:11*11]
- train_x_a = train_x_tmp.reshape(train_x.shape[0], 11, 11, 1)
- # train_x_b = train_x_tmp.reshape(train_x.shape[0], 18, 24)
- train_x_c = train_x[:,11*11:]
- result = model.predict([train_x_c, train_x_a, ])
- # print(result, line[-1])
- stock = code_table.find_one({'ts_code':line[-1][0]})
- with open('D:\\data\\quantization\\predict\\' + str(day) + '_industry100.txt', mode='a', encoding="utf-8") as f:
- if result[0][0] > 0.5:
- print(line[-1], '大涨')
- O_list.append(line[-1])
- f.write(str(line[-1]) + ',大涨\n')
- elif result[0][1] > 0.5:
- print(line[-1], '涨')
- O_list.append(line[-1])
- f.write(str(line[-1]) + ',涨\n')
- elif result[0][2] > 0.5:
- print(line[-1], '跌')
- f.write(str(line[-1]) + ',跌\n')
- elif result[0][3] > 0.5:
- print(line[-1], '大跌')
- f.write(str(line[-1]) + ',大跌\n')
- random.shuffle(O_list)
- print(O_list[:3])
- if __name__ == '__main__':
- predict_today("D:\\data\\quantization\\industry\\stock15_10d_3D_20200417.log", 20200417, model='111_10d_mix_3D_s_seq.h5', log=True)
- # join_two_day(20200305, 20200305)
- # check_everyday(20200311, 20200312)
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