Quant/Q6_DC.py at master · kzil88/Quant · GitHub

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# -*- coding:utf8 -*-
import tushare as ts
import re
import numpy as np
from pandas import DataFrame
import matplotlib.pyplot as plt
import pandas as pd
import datetime
import talib as ta
import pymysql


class data_collect2(object):
    code = ''
    date_seq = []
    open_list = []
    close_list = []
    high_list = []
    low_list = []
    vol_list = []
    amount_list = []
    tor_list = []
    vr_list = []
    ma5_list = []
    ma10_list = []
    ma20_list = []
    ma30_list = []
    ma60_list = []
    avg = []
    good_factor = 0.02
    bad_factor = 0.05
    cnt_bad_sell = 0
    cnt_good_buy = 0
    cnt_good_sell = 0
    cnt_risk = 0
    af = []
    fq = []
    process = []
    dd_list = []
    dd_list_show = []
    macd_list = []
    kdj_list = []
    bool_up = 0.00
    bool_mid = 0.00
    bool_dn = 0.00
    data_train = []
    data_target = []
    test_case = []
    cnt_pos = 0

    def __init__(self, in_code,start_dt,end_dt,year):
        self.collectDATA(in_code,start_dt,end_dt,year)

    def collectDATA(self,in_code,start_dt,end_dt,year):
        # 建立数据库连接,剔除已入库的部分
        db = pymysql.connect(host='127.0.0.1', user='root', passwd='admin', db='stock', charset='utf8')
        cursor = db.cursor()
        sql_done_set = "SELECT * FROM stock_%s a where stock_code = '%s' and state_dt >= '%s' and state_dt <= '%s' order by state_dt asc" % (str(year),in_code, start_dt,end_dt)
        cursor.execute(sql_done_set)
        done_set = cursor.fetchall()
        if len(done_set) == 0:
            raise Exception
        self.date_seq = []
        self.open_list = []
        self.close_list = []
        self.high_list = []
        self.low_list = []
        self.vol_list = []
        self.amount_list = []
        self.tor_list = []
        self.vr_list = []
        self.ma5_list = []
        self.ma10_list = []
        self.ma20_list = []
        self.ma30_list = []
        self.ma60_list = []
        for i in range(len(done_set)):
            self.date_seq.append(done_set[i][0])
            self.open_list.append(float(done_set[i][2]))
            self.close_list.append(float(done_set[i][3]))
            self.high_list.append(float(done_set[i][4]))
            self.low_list.append(float(done_set[i][5]))
            self.vol_list.append(float(done_set[i][6]))
            self.amount_list.append(float(done_set[i][7]))
            self.tor_list.append(float(done_set[i][8]))
            self.vr_list.append(float(done_set[i][9]))
            self.ma5_list.append(float(done_set[i][10]))
            self.ma10_list.append(float(done_set[i][11]))
            self.ma20_list.append(float(done_set[i][12]))
            self.ma30_list.append(float(done_set[i][13]))
            self.ma60_list.append(float(done_set[i][14]))
        db.close()

        self.data_train = []
        self.data_target = []
        self.data_target_onehot = []
        for i in range(len(self.close_list)-5):
            train = []
            self.data_train.append(np.array(train))
            # after_max_price = max(self.close_list[i+1:i + 5])
            # after_min_price = min(self.close_list[i+1:i+5])
            # if after_max_price / self.close_list[i] >= 1.01:
            #     self.data_target.append(float(1.00))
            #     self.data_target_onehot.append([1,0,0])
            # elif after_min_price / self.close_list[i] < 0.99:
            #     self.data_target.append(float(-1.00))
            #     self.data_target_onehot.append([0,1,0])
            # else:
            #     self.data_target.append(float(0.00))
            #     self.data_target_onehot.append([0,0,1])

            after_mean_price = np.array(self.close_list[i+1:i+5]).mean()
            if after_mean_price/self.close_list[i] > 1.03:
                self.data_target.append(float(1.00))
                self.data_target_onehot.append([1,0,0])
            else:
                self.data_target.append(float(-1.00))
                self.data_target_onehot.append([0,1,0])
        self.cnt_pos = 0
        self.cnt_pos =len([x for x in self.data_target if x == 1.00])

        self.test_case = []
        self.test_case = np.array(
            []
        )
        self.data_train = np.array(self.data_train)
        self.data_target = np.array(self.data_target)