과거 10년간 n% 상승을 경험한 종목의 비중은 어떻게 될까?(KOSPI)

매해 4월 1일 종가를 기준으로 다음 해 3월 31일까지 n% 이상 상승을 경험한 종목의 비중을 분석해 봄.

- KOSPI

- 10%, 20%, 30%, 40%, 50%

- 수정종가 사용.

- 파생상품 제외함.

- 2017년 2월 상장된 회사의 종목코드들로 10년치 종가 data를 불러 왔기 때문에 과거로 갈수록 정확도가 떨어짐. 

import sqlite3
from Kiwoom import *
from PyQt5.QtWidgets import *
import matplotlib.pyplot as plt

MARKET_KOSPI = 0


class Up30:
    def run(self):
        #self.check_up30()
        self.draw_chart()

    def check_up30(self, success = 0, fail = 0, sf = 0):

        # Database connect
        con = sqlite3.connect("C:\\Users\\Kyoungsoo\\PycharmProjects\\Analysis\\stock_info.db")
        cursor = con.cursor()
        # cursor.execute(("DROP TABLE up30"))
        #cursor.execute("CREATE TABLE up30_3(Code text, StartDate text, EndDate text, SDPrice int, "
        #               "MaxPrice int, SF50 int, SF40 int, SF30 int, SF20 int, SF10 int)")

        for year in range(2015, 2016):
            yr1 = str(year)
            yr2 = str(year + 1)
            start_day = yr1 +'-04-01'
            end_day = yr2 + '-03-31'

            cursor.execute("SELECT Code From Kospi_Codes1702")
            rows = cursor.fetchall()

            codes = []
            for row in rows:
                codes.append(row[0])

            for cd in codes:
                code = '"' + cd + '"'

                cursor.execute("SELECT Adj_Close FROM kospi "
                               "WHERE kospi.Code = " + code + " and Date BETWEEN " + '"' + start_day + '"' + " AND " + "'" + end_day + " '"
                               "ORDER BY kospi.Date asc")

                rows = cursor.fetchall()

                try:
                    price = []
                    for row in rows:
                        price.append(row[0])

                    start_day_price = price[0]
                    max_price = max(price)

                    if start_day_price * 1.5 <= max_price:
                        sf50 = 1
                        sf40 = 1
                        sf30 = 1
                        sf20 = 1
                        sf10 = 1
                    elif start_day_price * 1.4 <= max_price:
                        sf50 = 0
                        sf40 = 1
                        sf30 = 1
                        sf20 = 1
                        sf10 = 1
                    elif start_day_price * 1.3 <= max_price:
                        sf50 = 0
                        sf40 = 0
                        sf30 = 1
                        sf20 = 1
                        sf10 = 1
                    elif start_day_price * 1.2 <= max_price:
                        sf50 = 0
                        sf40 = 0
                        sf30 = 0
                        sf20 = 1
                        sf10 = 1
                    elif start_day_price * 1.1 <= max_price:
                        sf50 = 0
                        sf40 = 0
                        sf30 = 0
                        sf20 = 0
                        sf10 = 1
                    else:
                        sf50 = 0
                        sf40 = 0
                        sf30 = 0
                        sf20 = 0
                        sf10 = 0
                except:
                    start_day_price = 0
                    max_price = 0
                    sf50 = 2
                    sf40 = 2
                    sf30 = 2
                    sf20 = 2
                    sf10 = 2

                # Insert data
                cursor.execute("INSERT INTO up30_3(Code, StartDate, EndDate, SDPrice, MaxPrice, SF50, SF40, SF30, SF20, SF10) "
                               "VALUES(?,?,?,?,?,?,?,?,?,?)",
                               (cd, start_day, end_day, start_day_price, max_price, sf50, sf40, sf30, sf20, sf10))

                print(cd, start_day, end_day, start_day_price, max_price, sf50, sf40, sf30, sf20, sf10)

            con.commit()
        con.close()

    def draw_chart(self):
        suc_rates = {'10':[], '20':[], '30':[], '40':[], '50':[]}
        sfs = {'2006': [], '2007': [], '2008': [], '2009': [], '2010': [], '2011': [], '2012': [], '2013': [],
               '2014': [], '2015': []}
        # Database connect
        con = sqlite3.connect("C:\\Users\\Kyoungsoo\\PycharmProjects\\Analysis\\stock_info.db")
        cursor = con.cursor()

        for year in range(2006, 2016):
            yr = str(year)
            start_day = yr + '-04-01'

            for rate in range(10, 60, 10):
                rt = str(rate)
                cursor.execute("SELECT SF" + rt + " FROM up30_3 WHERE StartDate= " + '"' + start_day + '"')
                rows = cursor.fetchall()

                for row in rows:
                    sfs[yr].append(row[0])

                s_count = sfs[yr].count(1)
                f_count = sfs[yr].count(0)

                s_rate = s_count / (s_count + f_count)
                s_rate = round(s_rate * 100, 2)
                suc_rates[rt].append(s_rate)

        plt.style.use('ggplot')
        fig = plt.figure()
        ax = fig.add_subplot(1,1,1)

        ax.set_xlim([2006, 2015])
        ax.set_ylim([0, 100])
        ax.set_xticks([2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015])
        ax.set_xticklabels(['$06.4-07.3$','$07.4-08.3$','$08.4-09.3$','$09.4-10.3$','$10.4-11.3$','$11.4-12.3$','$12.4-13.3$','$13.4-14.3$','$14.4-15.3$','$15.4-16.3$'], rotation=45)

        term = range(2006, 2016)
        plt.plot(term, suc_rates['10'], 'ro-', label='10%')
        plt.plot(term, suc_rates['20'], 'co-', label='20%')
        plt.plot(term, suc_rates['30'], 'go-', label='30%')
        plt.plot(term, suc_rates['40'], 'mo-', label='40%')
        plt.plot(term, suc_rates['50'], 'bo-', label='50%')
        plt.xlabel('period')
        plt.ylabel('rate(%)')
        plt.legend(loc = 'best')
        plt.show()

if __name__ == "__main__":
    app = QApplication(sys.argv)
    up30 = Up30()
    up30.run()