machine_learn/mix/mix_predict_everyday_600.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[:,:30*19]
        train_x_a = train_x_tmp.reshape(train_x.shape[0], 30, 19, 1)
        # train_x_b = train_x_tmp.reshape(train_x.shape[0], 18, 24)
        train_x_c = train_x[:,30*19:]

        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 zixuan_stock_list:
                # print(line[-1], stock['name'], stock['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])
                print(stock['ts_code'], stock['name'], '买入评级', k_table_list[0]['pct_chg'])
                Z_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
            elif stock['ts_code'] in ROE_stock_list:
                print(stock['ts_code'], stock['name'], '买入评级', k_table_list[0]['pct_chg'])
                R_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])
            else:
                O_list.append([stock['name'], stock['sw_industry'], k_table_list[0]['pct_chg']])

            if log is True:
                with open('D:\\data\\quantization\\predict\\' + str(day) + '_mix.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:
        #     if stock['ts_code'] in holder_stock_list:
        #         print(stock['ts_code'], stock['name'], '震荡评级')
        # elif result[0][2] > 0.4:
        #     if stock['ts_code'] in holder_stock_list:
        #         print(stock['ts_code'], stock['name'], '赶紧卖出')
        # else:
        #     if stock['ts_code'] in holder_stock_list or stock['ts_code'] in ROE_stock_list:
        #         print(stock['ts_code'], stock['name'], result[0],)

    # print(gainian_map)
    # print(hangye_map)

    # gainian_list = [(key, gainian_map[key])for key in gainian_map]
    # gainian_list = sorted(gainian_list, key=lambda x:x[1], reverse=True)
    #
    # hangye_list = [(key, hangye_map[key])for key in hangye_map]
    # hangye_list = sorted(hangye_list, key=lambda x:x[1], reverse=True)

    # print(gainian_list)
    # print(hangye_list)

    print('-----买入列表---------')
    print(Z_list)
    print(R_list)
    print(O_list)

    print('------随机结果--------')
    # random.shuffle(Z_list)
    # print('自选')
    # print(Z_list[:3])

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

    O_list.extend(Z_list)
    O_list.extend(Z_list)
    random.shuffle(O_list)
    print('其他')
    print(O_list[:3])


def _read_pfile_map(path):
    s_list = []
    with open(path, encoding='utf-8') as f:
        for line in f.readlines()[:]:
            s_list.append(line)
    return s_list


def join_two_day(a, b):
    a_list = _read_pfile_map('D:\\data\\quantization\\predict\\' + str(a) + '.txt')
    b_list = _read_pfile_map('D:\\data\\quantization\\predict\\dmi_' + str(b) + '.txt')
    for a in a_list:
        for b in b_list:
            if a[2:11] == b[2:11]:
                print(a)


def check_everyday(day, today):
    a_list = _read_pfile_map('D:\\data\\quantization\\predict\\' + str(day) + '.txt')
    x = 0
    for a in a_list:
        print(a[:-1])
        k_day_list = list(k_table.find({'code':a[2:11], 'tradeDate':{'$lte':int(today)}}).sort('tradeDate', pymongo.DESCENDING).limit(5))
        if k_day_list is not None and len(k_day_list) > 0:
            k_day = k_day_list[0]
            k_day_0 = k_day_list[-1]
            k_day_last = k_day_list[1]
            if ((k_day_last['close'] - k_day_0['pre_close'])/k_day_0['pre_close']) < 0.2:
                print(k_day['open'], k_day['close'], 100*(k_day['close'] - k_day_last['close'])/k_day_last['close'])
                x = x + 100*(k_day['close'] - k_day_last['close'])/k_day_last['close']

    print(x/len(a_list))


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()
    # predict_today("D:\\data\\quantization\\stock405_30d_20200413.log", 20200413, model='405_30d_mix_5D_ma5_s_seq.h5', log=True)
    # 模型A
    predict_today("D:\\data\\quantization\\stock603_30d_20200415.log", 20200415, model='603_30d_mix_5D_ma5_s_seq.h5', log=True)
    # join_two_day(20200305, 20200305)
    # check_everyday(20200311, 20200312)