machine_learn/industry/industry_predict_everyday_100.py

# -*- 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)