machine_learn/stock/cnn_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 + '.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)

            train_x = np.array([line[:-2]])
            train_x = train_x.reshape(train_x.shape[0], 1,6,77)
            result = models[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] > 0.6 or result[0][1] > 0.6: #and (r[0] not in [2,6,8,10]):
                chiyou_0 = 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['close'])
                    buy = 1
                    if last_price > high_price:
                        high_price = last_price
            elif buy == 1:
                chiyou_0 = chiyou_0 + 1
                last_price = today_price['close']
                if chiyou_0 > 2 and today_price['close'] < last_price:
                    print('卖出', stock_name, today_price['close'])
                    init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
                    buy = 0
                    chiyou_0 = 0

                elif init_money < 9000:
                    print('止损卖出', stock_name, today_price['close'])
                    init_money = init_money * (today_price['close'] - last_price)/last_price + init_money
                    buy = 0
                    chiyou_0 = 0

        print(key, init_money)

        with open('D:\\data\\quantization\\stock_16_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\\stock16_18d_test.log', model_path='16_18d_cnn_seq')
    # predict(file_path='D:\\data\\quantization\\stock12_18d_20190103_20190604.log', model_path='13_18d_dnn_seq')