machine_learn/week/predict_everyweek_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)

    row = 18
    col = 11

    for line in lines:
        train_x = np.array([line[:size - 1]])

        train_x_a = train_x[:,:row*col]
        train_x_a = train_x_a.reshape(train_x.shape[0], row, col, 1)
        train_x_b = train_x[:, row*col:row*col + 11*16]
        train_x_b = train_x_b.reshape(train_x.shape[0], 11, 16, 1)
        train_x_c = train_x[:,row*col + 11*16:]

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

        if result[0][0] > 0.5:
            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

            if stock['ts_code'] in ROE_stock_list or stock['ts_code'] in zeng_stock_list:
                R_list.append([stock['ts_code'], stock['name']])

            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 log is True:
                with open('D:\\data\\quantization\\predict\\' + str(day) + '_week_103A.txt', mode='a', encoding="utf-8") as f:
                    f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' A ' + str(concept_detail_list) + ' ' + str(result[0][0]) + '\n')

        elif result[0][1] > 0.5:
            with open('D:\\data\\quantization\\predict\\' + str(day) + '_week_103A.txt', mode='a', encoding="utf-8") as f:
                f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' B ' + str(result[0][1]) + '\n')
        elif result[0][2] > 0.5:
            if stock['ts_code'] in holder_stock_list:
                print(stock['ts_code'], stock['name'], '警告危险')
        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])

    random.shuffle(R_list)
    print('----ROE----')
    print(R_list[:3])


if __name__ == '__main__':
    # 策略B
    # predict_today("D:\\data\\quantization\\stock505_28d_20200416.log", 20200416, model='505_28d_mix_5D_ma5_s_seq.h5', log=True)
    predict_today("D:\\data\\quantization\\week101_18d_20191213.log", 20191213, model='103_18d_mix_3W_s_seqA.h5', log=True)