machine_learn/mix/lstm_predict_everyday.py

# -*- encoding:utf-8 -*-
import numpy as np
from keras.models import load_model
import joblib


holder_stock_list = [
                        '000063.SZ', '002093.SZ'
                        '300253.SZ', '300807.SZ',

                         # b账户

    ]


def read_data(path):
    lines = []
    with open(path) as f:
        for line in f.readlines()[:]:
            line = eval(line.strip())
            if line[-2][0].startswith('0') or line[-2][0].startswith('3'):
                lines.append(line)

    size = len(lines[0])
    train_x=[s[:size - 2] for s in lines]
    train_y=[s[size-1] for s in lines]
    return np.array(train_x),np.array(train_y),lines


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({}))


industry = ['家用电器', '元器件', 'IT设备', '汽车服务',
            '汽车配件', '软件服务',
            '互联网', '纺织',
            '塑料', '半导体',]

A_concept_code_list = [   'TS2', # 5G
                        'TS24', # OLED
                        'TS26', #健康中国
                        'TS43',  #新能源整车
                        'TS59', # 特斯拉
                        'TS65', #汽车整车
                        'TS142', # 物联网
                        'TS153', # 无人驾驶
                        'TS163', # 雄安板块-智慧城市
                        'TS175', # 工业自动化
                        'TS232', # 新能源汽车
                        'TS254', # 人工智能
                        'TS258', # 互联网医疗
                        'TS264', # 工业互联网
                        'TS266', # 半导体
                        'TS269', # 智慧城市
                        'TS271', # 3D玻璃
                        'TS295', # 国产芯片
                        'TS303', # 医疗信息化
                        'TS323', # 充电桩
                        'TS328', # 虹膜识别
                        'TS361', # 病毒
    ]


gainian_map = {}
hangye_map = {}

def predict_today(file, day, model='10_18d', log=True):
    lines = []
    with open(file) as f:
        for line in f.readlines()[:]:
            line = eval(line.strip())
            # if line[-1][0].startswith('0') or line[-1][0].startswith('3'):
            lines.append(line)

    size = len(lines[0])

    model=load_model(model)

    for line in lines:
        train_x = np.array([line[:size - 1]])
        train_x_a = train_x[:,:18*24]
        train_x_a = train_x_a.reshape(train_x.shape[0], 18, 24)
        # train_x_b = train_x[:, 18*18:18*18+2*18]
        # train_x_b = train_x_b.reshape(train_x.shape[0], 18, 2, 1)
        train_x_c = train_x[:,18*24:]

        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] + result[0][1] > 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 holder_stock_list:
                print(stock['ts_code'], stock['name'], '维持买入评级')

            # 跌的
            k_table_list = list(k_table.find({'code':line[-1][0], 'tradeDate':{'$lte':day}}).sort("tradeDate", pymongo.DESCENDING).limit(5))
            # if k_table_list[0]['close'] > k_table_list[-1]['close']*1.20:
            #     continue
            # if k_table_list[0]['close'] < k_table_list[-1]['close']*0.90:
            #     continue
            # if k_table_list[-1]['close'] > 80:
            #     continue

            # 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 = []

            # 处理行业
            if stock['sw_industry'] in hangye_map:
                i_c = hangye_map[stock['sw_industry']]
                hangye_map[stock['sw_industry']] = i_c + 1
            else:
                hangye_map[stock['sw_industry']] = 1

            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 c['name'] in gainian_map:
                                g_c = gainian_map[c['name']]
                                gainian_map[c['name']] = g_c + 1
                            else:
                                gainian_map[c['name']] = 1

            print(line[-1], stock['name'], stock['sw_industry'], str(concept_detail_list), 'buy', k_table_list[0]['pct_chg'])

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

        elif result[0][2] > 0.5:
            if stock['ts_code'] in holder_stock_list:
                print(stock['ts_code'], stock['name'], '震荡评级')

        elif result[0][3] + result[0][4] > 0.5:
            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:
                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)

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\\stock160_18d_20200312.log", 20200313, model='160_18d_lstm_5D_ma5_s_seq.h5', log=True)
    # join_two_day(20200305, 20200305)
    # check_everyday(20200311, 20200312)