machine_learn/stock/dnn_predict_by_stock.py

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


def read_data(path):
    stock_lines = {}
    with open(path) as f:
        for line in f.readlines()[:]:
            line = eval(line.strip())
            stock = str(line[-2][0])

            if stock in stock_lines:
                stock_lines[stock].append(line)
            else:
                stock_lines[stock] = [line]
    # print(len(day_lines['20191230']))
    return stock_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')


def predict(file_path='', model_path='15min_dnn_seq'):
    stock_lines = read_data(file_path)
    print('数据读取完毕')

    models = []
    for x in range(0, 12):
        models.append(load_model(model_path + '_' + str(x) + '.h5'))
    estimator = joblib.load('km_dmi_18.pkl')
    print('模型加载完毕')

    total_money = 0
    total_num = 0
    items = sorted(stock_lines.keys())
    for key in items:
        # print(day)
        lines = stock_lines[key]
        init_money = 10000
        last_price = 1

        if lines[0][-2][0].startswith('6'):
            continue

        buy = 0 # 0空 1买入 2卖出
        chiyou_0 = 0
        high_price = 0

        x = 24 # 每条数据项数
        k = 18 # 周期
        for line in lines:
            v = line[1:x*k + 1]
            v = np.array(v)
            v = v.reshape(k, x)
            v = v[:,6:10]
            v = v.reshape(1, 4*k)
            # print(v)
            r = estimator.predict(v)

            train_x = np.array([line[:-2]])
            result = models[r[0]].predict(train_x)

            stock_name = line[-2]
            today_price = list(k_table.find({'code':line[-2][0], 'tradeDate':{'$gt':int(line[-2][1])}}).sort('tradeDate',pymongo.ASCENDING).limit(1))
            today_price = today_price[0]

            if result[0][0] > 0.5 or result[0][1] > 0.5: #and (r[0] not in [2,6,8,10]):
                chiyou_0 = 0
                print(r[0])
                if buy == 0:
                    last_price = today_price['open']
                    high_price = last_price
                    print('首次买入', stock_name, today_price['open'])
                    buy = 1
                else:
                    init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
                    last_price = today_price['close']
                    print('买入+买入', stock_name, today_price['open'])
                    buy = 1
                    if last_price > high_price:
                        high_price = last_price
            elif result[0][3] > 0.5 or result[0][4] > 0.5:#and (r[0] not in [5,8]):
                if buy == 1:
                    if chiyou_0 > 2 or init_money < 9000:
                        init_money = init_money * (today_price['open'] - last_price)/last_price + init_money
                        print('卖出', stock_name, today_price['open'])
                        buy = 0
                        chiyou_0 = 0
                    # elif init_money > 15000 and 100*(today_price['close'] - high_price)/high_price < -15:
                    #     init_money = init_money * (today_price['open'] - last_price)/last_price + init_money
                    #     print('最高点回撤卖出', stock_name, today_price['open'])
                    #     buy = 0
                    #     chiyou_0 = 0
                    else:
                        init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
                        print('继续持有，不卖出', stock_name, today_price['close'])
                        buy = 1
                        chiyou_0 = chiyou_0 + 1

                        if today_price['close'] > high_price:
                            high_price = today_price['close']

            else:
                if buy == 1:
                    init_money = (init_money * (today_price['close'] - last_price)/last_price) + init_money
                    if init_money < 8500:
                        print('止损卖出', stock_name, today_price['close'])
                        buy = 0
                    else:
                        chiyou_0 = chiyou_0 + 1
                        if init_money < 10500 and chiyou_0 > 1 and today_price['close'] < last_price:
                            print('连续持有次数太多-- 卖出', stock_name, today_price['close'])
                            buy = 0
                            chiyou_0 = 0
                        elif chiyou_0 > 2 and today_price['close'] < last_price:
                            print('连续持有次数太多++ 卖出', stock_name, today_price['close'])
                            buy = 0
                            chiyou_0 = 0
                        else:
                            buy = 1
                            print('持有', stock_name, today_price['close'])

                            if today_price['close'] > high_price:
                                high_price = today_price['close']

                    last_price = today_price['close']
                else:
                    # print('忽略')
                    pass

            # 具有后验知识的存在，
            # if result[0][1] > 0.5 or result[0][2] > 0.5:
            #     chiyou_0 = 0
            #     if r[0] in [2,5,9,10,11]:
            #         if buy == 0:
            #             last_price = today_price['open']
            #             print('首次买入', line[-2], today_price['open'])
            #             buy = 1
            #         elif buy == 1:
            #             init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
            #             last_price = today_price['close']
            #             print('买入+买入', line[-2], today_price['open'])
            #             buy = 1
            #         else:
            #             last_price = today_price['close']
            #             print('卖出后买入', line[-2], today_price['open'])
            #             buy = 1
            #     else:
            #         if buy == 1:
            #             init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
            #             last_price = today_price['close']
            #             print('买入+买入', line[-2], today_price['open'])
            #             buy = 1
            # elif result[0][1] > 0.5 or result[0][2] > 0.5:
            #     # if r[0] in [0,1,3,4,6,7] and buy in [0,1]:
            #     buy = 0
            #     init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
            #     print('卖出', line[-2], today_price['close'])
            # else:
            #     if buy == 1:
            #         init_money = (init_money * (today_price['close'] - last_price)/last_price) + init_money
            #         if init_money < 9000:
            #             print('止损卖出', line[-2], today_price['close'])
            #             buy = 0
            #         else:
            #             chiyou_0 = chiyou_0 + 1
            #             if chiyou_0 > 5 and today_price['close'] < last_price:
            #                 print('连续持有次数太多 卖出', line[-2], today_price['close'])
            #                 buy = 0
            #             else:
            #                 buy = 1
            #                 print('持有', line[-2], today_price['close'])
            #         last_price = today_price['close']

        print(key, init_money)

        with open('D:\\data\\quantization\\stock_15_18d' + '_' +  'profit.log', 'a') as f:
            if init_money > 10000:
                f.write(str(key) + ' ' + str(init_money) + '\n')
            elif init_money < 10000:
                f.write(str(key) + ' ' + str(init_money) + '\n')

        if init_money != 10000:
            total_money = total_money + init_money
            total_num = total_num + 1

    print(total_money, total_num, total_money/total_num/10000)


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\\stock12_18d_test.log', model_path='12_18d_dnn_seq')
    predict(file_path='D:\\data\\quantization\\stock15_18d_test.log', model_path='15_18d_dnn_seq')
    # predict(file_path='D:\\data\\quantization\\stock12_18d_20190103_20190604.log', model_path='13_18d_dnn_seq')