ui

@@ -59,7 +59,7 @@ def lstm_algorithm(dataset,ai_setting):
         msg += f'    {ratio:.2f}% ⯆ '
     print(msg, end=' ')
 
-    return ai_setting['ratio_cut']
+    return ai_setting['ratio_cut']>=ratio
 
 
 def create_training_engine(db_name):
@@ -78,7 +78,7 @@ def ai_filter(ai_filter_num,engine,until=datetime.datetime.today()):
     if ai_filter_num == 1:
         ai_setting = {
             "n_steps": 100,                         # 시퀀스 데이터를 몇개씩 담을지 설정
-            "lookup_step": 30,                      # 단위 : 몇 일(분) 뒤의 종가를 예측 할 것 인지
+            "lookup_step": 1,                       # 단위 : 몇 일(분) 뒤의 종가를 예측 할 것 인지
             "test_size": 0.2,                       # train 범위
             "n_layers": 4,                          # LSTM layer 개수
             "units": 50,                            # LSTM neurons 개수
@@ -87,7 +87,7 @@ def ai_filter(ai_filter_num,engine,until=datetime.datetime.today()):
             "optimizer": "adam",                    # optimizer : 최적화 알고리즘 선택
             "batch_size": 64,                       # 각 학습 반복에 사용할 데이터 샘플 수
             "epochs": 400,                          # 몇 번 테스트 할지
-            "ratio_cut": 3,                         # 단위:(%) lookup_step 기간 뒤 ratio_cut(%) 만큼 증가 할 것이 예측 된다면 매수
+            "ratio_cut": 2,                         # 단위:(%) lookup_step 기간 뒤 ratio_cut(%) 만큼 증가 할 것이 예측 된다면 매수
             "table": "daily_craw",                  # 분석 시 daily_craw(일별데이터)를 이용 할지 min_craw(분별데이터)를 이용 할지
             "is_used_predicted_close" : True        # ratio(예상 상승률) 계산 시 예측 그래프의 close 값을 이용 할 경우 True,
                                                     # 실제 close 값을 이용할 시 False
@@ -109,6 +109,7 @@ def ai_filter(ai_filter_num,engine,until=datetime.datetime.today()):
         feature_columns = ["close", "volume", "open", "high", "low"]
         filtered_list = []
         for code_name in buy_list:
+            code_name=code_name[0]
             sql = """
                 SELECT {} FROM `{}`
                 WHERE STR_TO_DATE(date, '%Y%m%d%H%i') <= '{}'

 import pymysql
 pymysql.install_as_MySQLdb()
-import sys
 
 from library.collector_api import *
+import library.cf
 
 class Collector:
-    def __init__(self):
+    def __init__(self,num=1):
+        self.set_simul_num(num)
         self.collector_api=collector_api()
 
     def collecting(self):
         self.collector_api.code_update_check()
 
+    def set_simul_num(self,num):
+        library.cf.test_num=num
+
+    def set_real_num(self,num=1):
+        library.cf.real_num=num
+
 if __name__=="__main__":
     app=QApplication(sys.argv)
     c=Collector()

+import sys
+from PyQt5.QtWidgets import *
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
+
+from sqlalchemy import create_engine
+from library.cf import *
+
+class Graph(QDialog):
+    def __init__(self,num=1):
+        QDialog.__init__(self,None)
+        self.setGeometry(200,200,1000,800)
+
+        self.engine1 = create_engine("mysql+pymysql://" + db_id + ":" + db_pw + "@" +
+                                        db_ip + ":" + db_port + "/simul1", encoding='utf-8')
+        self.engine2 = create_engine("mysql+pymysql://" + db_id + ":" + db_pw + "@" +
+                                        db_ip + ":" + db_port + "/simul2", encoding='utf-8')
+        self.engine3 = create_engine("mysql+pymysql://" + db_id + ":" + db_pw + "@" +
+                                        db_ip + ":" + db_port + "/simul3", encoding='utf-8')
+        self.engine4 = create_engine("mysql+pymysql://" + db_id + ":" + db_pw + "@" +
+                                        db_ip + ":" + db_port + "/simul4", encoding='utf-8')
+
+        date,data=self.getAlgorithm(num)
+
+        col=""
+        if num==1:
+            col="red"
+        elif num==2:
+            col="blue"
+        elif num==3:
+            col="green"
+        elif num==4:
+            col="purple"
+
+        N = len(data)
+        value = data
+        ind = np.arange(N)
+        width = 0.35
+
+        self.fig = plt.Figure()
+        ax = self.fig.add_subplot(111)
+        ax.bar(ind, value, width,color=col)
+        ax.set_xticks(ind + width / 20)
+        ax.set_xticklabels(date)
+
+        canvas = FigureCanvas(self.fig)
+        canvas.draw()
+
+        lay=QVBoxLayout()
+        self.setLayout(lay)
+        lay.addWidget(canvas)
+        canvas.show()
+
+
+    def getdata(self,engine):
+        query="select date, sum_valuation_profit from jango_data order by date"
+        result=engine.execute(query).fetchall()
+        date=list()
+        data=list()
+        for i in range(len(result)):
+            date.append(result[i][0])
+            data.append(int(result[i][1]))
+        return date,data
+
+    def getAlgorithm(self,num):
+        if num==1:
+            engine=self.engine1
+        elif num==2:
+            engine=self.engine2
+        elif num==3:
+            engine=self.engine3
+        elif num==4:
+            engine=self.engine4
+        else:
+            sys.exit()
+        date,data=self.getdata(engine)
+        return date,data
+
+if __name__=="__main__":
+    app=QApplication(sys.argv)
+    main_dialog=Graph()
+    main_dialog.show()
+    app.exec_()

@@ -75,7 +75,7 @@ def load_data(df,n_steps=100,lookup_step=1,test_size=0.2,shuffle=True):
 
     last_sequence=list(sequences)+list(last_sequence)
     last_sequence=np.array(pd.DataFrame(last_sequence).shift(-1).dropna())
-
+    result['last_sequence']=last_sequence
     X,y=[],[]
 
     for seq,target in sequence_data:

@@ -564,28 +564,101 @@ class simulator_api:
 
     # 다음날 매수 할 종목의 리스트를 선정하는 함수
     def db_to_realtime_daily_buy_list(self,date_rows_today,date_rows_yesterday,i):
-        realtime_daily_buy_list=None            # 다음날 매수할 종목 리스트를 저장하는 변수
+        realtime_daily_buy_list=list()           # 다음날 매수할 종목 리스트를 저장하는 변수
+        if self.db_to_realtime_daily_buy_list_num in [5,6,7,8]:
+            self.use_ai=True
+
         # (5,20) 골든크로스
-        if self.db_to_realtime_daily_buy_list_num == 1:
+        if self.db_to_realtime_daily_buy_list_num in [1,5]:
             query=f"select * from `{date_rows_yesterday}` " \
                   f"where yes_clo20>yes_clo5 and clo5>clo20 and close<'{self.invest_unit}' group by code"
             realtime_daily_buy_list=self.engine_daily_buy_list.execute(query).fetchall()
 
         # (20,60) 골든크로스
-        elif self.db_to_realtime_daily_buy_list_num == 2:
+        elif self.db_to_realtime_daily_buy_list_num in [2,6]:
             query = f"select * from `{date_rows_yesterday}` " \
-                    f"where yes_clo40 > yes_clo5 and clo5 > clo40 and close < '{self.invest_unit}' group by code"
+                    f"where yes_clo60 > yes_clo20 and clo20 > clo60 and close < '{self.invest_unit}' group by code"
             realtime_daily_buy_list = self.engine_daily_buy_list.execute(query).fetchall()
-        """ 
+
         # RSI
-        elif self.db_to_realtime_daily_buy_list_num==3:
-            au_query=f"select "
+        elif self.db_to_realtime_daily_buy_list_num in [3,7]:
+            realtime_daily_buy_list=list()
+            self.sell_list=list()
+
+            date_form=datetime.datetime.strptime(date_rows_yesterday,"%Y%m%d").date()
+            weeks_ago=date_form-datetime.timedelta(14)
+            weeks_ago=weeks_ago.strftime("%Y%m%d")
+            query="SELECT table_name as tname FROM information_schema.tables " \
+                  "WHERE table_schema ='daily_craw'"
+            name_list=self.engine_daily_craw.execute(query).fetchall()
+            for i in range(len(name_list)):
+                name=name_list[i]
+                name=name[0]
+                au_query=f"select sum(d1_diff) from `{name}` where d1_diff>0 and " \
+                         f"{weeks_ago}<=date and date<={date_rows_yesterday} " \
+                         f"group by code"
+                au=self.engine_daily_craw.execute(au_query).fetchall()
+                ad_query=f"select sum(d1_diff) from `{name}` where d1_diff<0 and " \
+                         f"{weeks_ago}<=date and date<={date_rows_yesterday} " \
+                         f"group by code"
+                ad=self.engine_daily_craw.execute(ad_query).fetchall()
+
+                if len(au)==0 or len(ad)==0:
+                    continue
+
+                au=au[0][0]
+                ad=ad[0][0]
+
+                au=float(au/14)
+                ad=float(ad/14)*(-1)
+                rsi=float(au/(au+ad))
+
+                if rsi<0.3:
+                    query=f"select * from `{date_rows_yesterday}` where code_name='{name}'"
+                    val=self.engine_daily_buy_list.execute(query).fetchall()
+                    realtime_daily_buy_list.append(val[0])
+
         # RMI
-        elif self.db_to_realtime_daily_buy_list_num==4:
+        elif self.db_to_realtime_daily_buy_list_num in [4,8]:
+            realtime_daily_buy_list = list()
+            self.sell_list = list()
+
+            date_form = datetime.datetime.strptime(date_rows_yesterday, "%Y%m%d").date()
+            weeks_ago = date_form - datetime.timedelta(14)
+            weeks_ago = weeks_ago.strftime("%Y%m%d")
+            query = "SELECT table_name as tname FROM information_schema.tables " \
+                    "WHERE table_schema ='daily_craw'"
+            name_list = self.engine_daily_craw.execute(query).fetchall()
+            for i in range(len(name_list)):
+                name = name_list[i]
+                name = name[0]
+                mu_query = f"select sum(dn_diff) from `{name}` where dn_diff>0 and " \
+                           f"{weeks_ago}<=date and date<={date_rows_yesterday} " \
+                           f"group by code"
+                mu = self.engine_daily_craw.execute(mu_query).fetchall()
+                md_query = f"select sum(dn_diff) from `{name}` where dn_diff<0 and " \
+                           f"{weeks_ago}<=date and date<={date_rows_yesterday} " \
+                           f"group by code"
+                md = self.engine_daily_craw.execute(md_query).fetchall()
+
+                if len(mu) == 0 or len(md) == 0:
+                    continue
+
+                mu = mu[0][0]
+                md = md[0][0]
+
+                mu = float(mu / 14)
+                md = float(md / 14) * (-1)
+                rmi = float(mu / (mu + md))
+
+                if rmi < 0.3:
+                    query = f"select * from `{date_rows_yesterday}` where code_name='{name}'"
+                    val = self.engine_daily_buy_list.execute(query).fetchall()
+                    realtime_daily_buy_list.append(val[0])
 
         else:
             logger.error("Invalid Algorithm Setting...")
-        """
+
         # 알고리즘에 의해 선택된 항목이 존재한다면 데이터베이스에 해당 항목 업데이트
         if len(realtime_daily_buy_list) > 0:
             df_realtime_daily_buy_list = DataFrame(realtime_daily_buy_list,
@@ -620,10 +693,10 @@ class simulator_api:
 
                 # 딥러닝 알고리즘을 이용
                 if self.use_ai:
+                    print(date_rows_today)
                     from ai_trader import ai_filter
                     ai_filter(self.ai_num, engine=self.engine_simul, until=date_rows_yesterday)
 
-
             # 모의투자 / 실전투자
             else:
                 df_realtime_daily_buy_list['check_item'] = int(0)
@@ -851,6 +924,7 @@ class simulator_api:
     def auto_trade_sell_stock(self,date,i):
         # 매도할 리스트를 저장
         sell_list=self.get_sell_list(i)
+        print(sell_list)
 
         for j in range(len(sell_list)):
             sell_code=sell_list[j][0]
@@ -905,9 +979,16 @@ class simulator_api:
             sell_list=self.engine_simul.execute(query).fetchall()
 
         # RSI
-        #elif self.sell_algorithm==3:
+        elif self.sell_list_num==3:
+            query=f"select code,rate,present_price,valuation_price from all_stocks " \
+                  f"where sell_date='0' and (clo5<clo20 or rate<='{self.losscut_point}') group by code"
+            sell_list=self.engine_simul.execute(query).fetchall()
+
         # RMI
-        #elif self.sell_algorithm==4:
+        elif self.sell_list_num==4:
+            query=f"select code,rate, present_price,valuation_price from all_stocks " \
+                  f"where sell_date='0' and (clo20<clo60 or rate<='{self.losscut_point}') group by code"
+            sell_list=self.engine_simul.execute(query).fetchall()
 
         else:
             logger.error("Invalid sell algorithm setting...")

@@ -21,7 +21,7 @@ real_daily_craw_db_name = "daily_craw"
 real_daily_buy_list_db_name = "daily_buy_list"
 
 # 시뮬레이션을 돌리기 시작할 날짜
-start_daily_buy_list='20190102'
+start_daily_buy_list='20150101'
 
 # api를 최대 몇 번까지 호출 하고 봇을 끌지 설정 하는 옵션
 max_api_call=998

@@ -293,7 +293,7 @@ class collector_api():
 
         # 특정 종목명(code_name)의 테이블이 존재하는 경우
         if self.engine_bot.dialect.has_table(self.open_api.engine_daily_craw, code_name):
-            query=f"select * from {code_name} where date='{oldest_row['date']}' limit 1"
+            query=f"select * from `{code_name}` where date='{oldest_row['date']}' limit 1"
             check_row = self.open_api.engine_daily_craw.execute(query).fetchall()
         # 종목명 테이블이 존재하지 않는 경우, 종목 리스트(stock_item_all) 테이블에 check_daily_crawler=4 업데이트
         else:

@@ -282,7 +282,6 @@ class open_api(QAxWidget):
         # 국내주식 잔고전달
         elif sGubun=="1":
             chegyul_fail_amount=self.get_chejan_data(902)
-            logger.debug("미체결 수량 : "+chegyul_fail_amount)
 
         else:
             logger.debug("Invlid _receive_chejan_data")
@@ -325,7 +324,7 @@ class open_api(QAxWidget):
     def sell_chegyul_fail_check(self,code):
         query = f"UPDATE all_stocks SET chegyul_check='1' WHERE code='{code}' and sell_date = '0' " \
                 f"ORDER BY buy_date desc LIMIT 1"
-        self.engine_JB.execute(query)
+        self.engine_bot.execute(query)
 
     # OnReceiveChejan()이벤트가 호출될때 체결정보나 잔고정보를 얻어오는 함수
     # param     :   nFid    -   실시간 타입에 포함된 FID
@@ -767,7 +766,7 @@ class open_api(QAxWidget):
     # daily_craw의 특정 종목 테이블에서 마지막으로 저장된 날짜를 가져오는 함수
     # 저장된 데이터가 없다면 '0'문자를 반환
     def get_daily_craw_db_last_date(self,code_name):
-        query="select date from {} order by date desc limit 1"
+        query="select date from `{}` order by date desc limit 1"
         result=self.engine_daily_craw.execute(query.format(code_name)).fetchall()
         if len(result):
             return result[0][0]
@@ -895,7 +894,7 @@ class open_api(QAxWidget):
 
     # min_craw 테이블에서 마지막에 저장한 행의 시간(분) 정보를 가져오는 함수
     def get_craw_db_last_min(self,code_name):
-        query = f"SELECT date from {code_name} order by date desc limit 1"
+        query = f"SELECT date from `{code_name}` order by date desc limit 1"
         result = self.engine_craw.execute(query).fetchall()
         if len(result):
             return result[0][0]

@@ -14,7 +14,7 @@ class Trader(QMainWindow):
 
         self.market_start_time=QTime(9,0,0)     # 장시작 시간
         self.market_end_time=QTime(15,30,0)     # 장마감 시간
-        self.buy_end_time=QTime(9,30,0)        # 매수를 몇 시까지 할지
+        self.buy_end_time=QTime(15,31,0)        # 매수를 몇 시까지 할지
 
     # 매수 함수
     def auto_trade_stock(self):

+from collector import *
+from trader import *
+from graph import *
+
+
+class Ui(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("Menu Page")
+        self.resize(820,465)
+
+        self.using_dl=QLabel("----------*  Deep Learning  *------------",self)
+        self.using_dl.setGeometry(QRect(50,20,800,30))
+        self.using_dl=QRadioButton("Using Deep Learning",self)
+        self.using_dl.clicked.connect(self.__Rdl_Clicked)
+        self.using_dl.setGeometry(QRect(50,70,800,30))
+        self.not_using_dl=QRadioButton("Not Using Deep Learning",self)
+        self.not_using_dl.setGeometry(QRect(50,120,800,30))
+        self.not_using_dl.clicked.connect(self.__Rndl_Clicked)
+
+        self.line=QLabel("----------*      Algorithm     *------------",self)
+        self.line.setGeometry(QRect(50,170,800,30))
+        self.input=QLabel("Selection    :   ",self)
+        self.input.setGeometry(QRect(50,200,150,40))
+        self.al=QLineEdit(self)
+        self.al.setGeometry(QRect(200,200,150,40))
+        self.al1=QLabel("1. Golden/Dead Cross(5,20)",self)
+        self.al1.setGeometry(QRect(50,250,800,30))
+        self.al2=QLabel("2. Golden/Dead Cross(20,60)",self)
+        self.al2.setGeometry(QRect(50,300,800,30))
+        self.al3=QLabel("3. RSI",self)
+        self.al3.setGeometry(QRect(50,350,800,30))
+        self.al4=QLabel("4. RMI",self)
+        self.al4.setGeometry(QRect(50,400,800,30))
+
+        # 매매 프로그램 시작
+        self.btn_run=QPushButton("Trade",self)
+        self.btn_run.setGeometry(QRect(600,50,200,70))
+        self.btn_run.clicked.connect(self.__bRun_Clicked)
+        # 매매 후 결과 화면
+        self.btn_report=QPushButton("Report",self)
+        self.btn_report.setGeometry(QRect(600,150,200,70))
+        self.btn_report.clicked.connect(self.__bReport_Clicked)
+        # 컬렉터
+        self.btn_collector=QPushButton("Collector",self)
+        self.btn_collector.setGeometry(QRect(600,250,200,70))
+        self.btn_collector.clicked.connect(self.__bCollector_Clicked)
+        # 프로그램 종료
+        self.btn_exit=QPushButton("Exit",self)
+        self.btn_exit.setGeometry(QRect(600,350,200,70))
+        self.btn_exit.clicked.connect(self.__bExit_Clicked)
+
+        self.option=-1
+
+    def __Rdl_Clicked(self):
+        if self.using_dl.isChecked():
+            self.option=0
+
+    def __Rndl_Clicked(self):
+        if self.not_using_dl.isChecked():
+            self.option=4
+
+    # open selection page for rpa
+    def __bRun_Clicked(self):
+        trader = Trader()
+        trader.run()
+
+    def __bReport_Clicked(self):
+        if self.al.text() not in ['1','2','3','4']:
+            print("error")
+        else:
+            g=Graph(int(self.al.text()))
+            g.exec()
+            g.show()
+
+    def __bCollector_Clicked(self):
+        algorithm_num=0
+        if self.al.text()=="" or self.option==-1:
+            print("error")
+        else:
+            algorithm_num=self.option+int(self.al.text())
+
+        c = Collector(algorithm_num)
+        c.collecting()
+
+    def __bExit_Clicked(self):
+        self.close()
+        sys.exit()
+
+if __name__=="__main__":
+    app = QApplication(sys.argv)
+    ui=Ui()
+    ui.show()
+    app.exec_()
\ No newline at end of file