machine_learn/mix/mix_predict_everyday_500.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[:,:28*20]
        train_x_a = train_x_tmp.reshape(train_x.shape[0], 28, 20, 1)
        # train_x_b = train_x_tmp.reshape(train_x.shape[0], 18, 24)
        train_x_c = train_x[:,28*20:]

        result = model.predict([train_x_c, train_x_a, ])
        # print(result, line[-1])
        stock = code_table.find_one({'ts_code':line[-1][0]})

        if result[0][0] > 0.5 and stock['sw_industry'] in industry_list:
            if line[-1][0].startswith('688'):
                continue
            # 去掉ST
            if stock['name'].startswith('ST') or stock['name'].startswith('N') or stock['name'].startswith('*'):
                continue
            k_table_list = list(k_table.find({'code':line[-1][0], 'tradeDate':{'$lte':day}}).sort("tradeDate", pymongo.DESCENDING).limit(5))

            # 指定某几个行业
            # if stock['industry'] in industry:
            concept_code_list = list(stock_concept_table.find({'ts_code':stock['ts_code']}))
            concept_detail_list = []

            if len(concept_code_list) > 0:
                for concept in concept_code_list:
                    for c in all_concept_code_list:
                        if c['code'] == concept['concept_code']:
                            concept_detail_list.append(c['name'])
            # if stock['ts_code'] in ROE_stock_list:
            print(stock['ts_code'], stock['name'], '买入')
            O_list.append([stock['ts_code'], stock['name']])

            if log is True:
                with open('D:\\data\\quantization\\predict\\' + str(day) + '_mix500.txt', mode='a', encoding="utf-8") as f:
                    f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' ' + str(concept_detail_list) + ' ' + str(result[0][0]) + '\n')

        elif result[0][1] > 0.5 or result[0][2] > 0.5 :
            pass
        elif result[0][3] > 0.5:
            if stock['ts_code'] in holder_stock_list or stock['ts_code'] in zixuan_stock_list:
                print(stock['ts_code'], stock['name'], '赶紧卖出')
        else:
            pass

    # print(gainian_map)
    # print(hangye_map)
    random.shuffle(O_list)
    print(O_list[:3])


if __name__ == '__main__':
    # predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
    # predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
    # multi_predict()
    # 策略B
    predict_today("D:\\data\\quantization\\stock505_28d_20200415.log", 20200415, model='505_28d_mix_5D_ma5_s_seq.h5', log=True)
    # join_two_day(20200305, 20200305)
    # check_everyday(20200311, 20200312)