init

HTS_122630.py

@@ -31,12 +31,6 @@ class HTS_122630 (HTS):
     def checkTransaction(self, data):
         size = len(data["close"])
 
-        bsLine = {}
-        bsLine['buy']  = [-1 for i in range(size)]
-        bsLine['weight'] = [-1 for i in range(size)]
-        bsLine['sell'] = [-1 for i in range(size)]
-
-        buy, weight, sell = -1, -1, -1
         last_index = size - 1
         sell, weight = self.buySellChecker.getSellPriceAndWeight_15000(data, last_index)
         buy, weight = self.buySellChecker.getBuyPriceAndWeight_15000(data, last_index)
@@ -77,34 +71,41 @@ class HTS_122630 (HTS):
 
         return 0, 0
 
-    def buyRealTime(self, lastday, today):
+    def buyRealTime(self, today):
+
         timecheckList = pd.read_csv("hts/timecheck.csv").values.tolist()
         timecheck = {today + " " + str(second).zfill(6):False for second, check in timecheckList}
 
-        print("START...")
+        print ("START...")
         THIS_TIME = datetime.now()
         final_sell_check = False
-        while datetime.strptime(today + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 153100",
+        LAST_DATA = self.getLastData(self.stock_code, today)
-                                                                                                    '%Y%m%d %H%M%S'):
+
+        while datetime.strptime(today + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 153100", '%Y%m%d %H%M%S'):
 
             if datetime.strptime(today + " 090000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 151500", '%Y%m%d %H%M%S'):
+                # 3시 까지만 매수를 시도한다.
+
                 if THIS_TIME.strftime('%Y%m%d %H%M%S') in timecheck and not timecheck[THIS_TIME.strftime('%Y%m%d %H%M%S')]:
 
                     # 데이터를 가지고 온다.
-                    result = self.getRealTime(self.stock_code, lastday, today)
+                    result = self.getRealTime(self.stock_code, today, LAST_DATA)
 
-                    # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
+                    # 규칙 기반의 분석을 통해서 볼린저밴드 상/하단을 계산한다.
-                    data = self.buySellChecker.analyze(result)
+                    data = self.buySellChecker.analyzeByRule(result)
 
                     # 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
-                    bs_buy_price, bs_weight, bs_sell_price = self.checkTransaction(data)
+                    bsLine, data = self.buySellChecker.checkTransaction(data, self.stock_code, True)
+                    bs_buy_price = bsLine['buy']
+                    bs_weight = bsLine['buy_weight']
+                    bs_sell_price = bsLine['sell']
+
                     data_size = len(data["close"])
-                    final_price = data["close"][data_size - 1]
+                    final_price = data["close"][data_size-1]
 
                     if bs_buy_price > 0:
                         # 기본 100 주에 가중치를 추가해서 매수한다.
-                        #33BUY_COUNT = int(self.buy_count * bs_weight)
+                        BUY_COUNT = int(self.buy_count * bs_weight)
-                        BUY_COUNT = int(self.buy_count * 1)
 
                         # 매수를 주문한다.
                         orderNum = self.requestOrder(OrderType.buy, self.stock_code, BUY_COUNT, bs_buy_price)
@@ -118,6 +119,7 @@ class HTS_122630 (HTS):
                         # 로그 출력
                         print("BUY", THIS_TIME.strftime('%Y%m%d %H%M%S'), BUY_COUNT, bs_buy_price, len(orderListToCancel), len(ORDER_LIST))
 
+
                     if bs_sell_price > 0:
                         # 미체결 기록을 가져온다.
                         ORDER_LIST = self.requestOrderList()
@@ -136,9 +138,9 @@ class HTS_122630 (HTS):
                             # 로그 출력
                             print("SELL", THIS_TIME.strftime('%Y%m%d %H%M%S'), selling_count, selling_price, len(orderListToCancel), len(ORDER_LIST))
 
+
                     # 로그 출력
-                    print(
+                    print("TIMECHECK: %s, price: %d, open: %d, high: %d, low: %d, avg3: %.2f, avg5: %.2f, avg10: %.2f, avg20: %.2f, avg30: %.2f, avg60: %.2f,\n           lower: %.2f, upper: %.2f, macd: %.2f, macds: %.2f, macdo: %.2f,\n           rsi: %.2f, rsis: %.2f, fast_k: %.2f, slow_k: %.2f, slow_d: %.2f" %
-                        "TIMECHECK: %s, price: %d, open: %d, high: %d, low: %d, avg3: %.2f, avg5: %.2f, avg10: %.2f, avg20: %.2f, avg30: %.2f, avg60: %.2f,\n           lower: %.2f, upper: %.2f, macd: %.2f, macds: %.2f, macdo: %.2f,\n           rsi: %.2f, rsis: %.2f, fast_k: %.2f, slow_k: %.2f, slow_d: %.2f" %
                         (str(THIS_TIME), final_price, data["open"][data_size - 1], data["high"][data_size - 1],
                          data["low"][data_size - 1],
                          data["avg3"][data_size - 1], data["avg5"][data_size - 1], data["avg10"][data_size - 1],
@@ -151,10 +153,11 @@ class HTS_122630 (HTS):
                     timecheck[THIS_TIME] = True
 
             elif datetime.strptime(today + " 151530", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 151600", '%Y%m%d %H%M%S'):
+                # 3시 15분 30초부터 3시 16분 사이는 잔량을 매도한다.
 
                 if not final_sell_check:
                     ####
-                    # 손해 보지 않는 가격에 매도한다.
+                    # 손해봐도 매도한다.
                     ####
 
                     # 주문 리스트를 가져온다.
@@ -163,9 +166,12 @@ class HTS_122630 (HTS):
                     self.cancelOrderList(orderList)
 
                     # 매도 가격을 가져온다.
-                    result = self.getRealTime(self.stock_code, lastday, today)
+                    result = self.getRealTime(self.stock_code, today, LAST_DATA)
                     final_price = result["close"][len(result["close"]) - 1]
+
                     selling_count, selling_price = self.getFinalSellingPrice(final_price)
+                    # selling_count, selling_price = self.getSellingPrice(final_price)
+
                     # 분석되 가격으로 매도 요청한다.
                     if selling_count != 0 and selling_price != 0:
                         orderNum = self.requestOrder(OrderType.sell, self.stock_code, selling_count, selling_price)
@@ -193,15 +199,8 @@ if __name__ == "__main__":
 
     hts = HTS_122630(RESOURCE_PATH, stock_code, buy_count)
     today_str = today.strftime('%Y%m%d')
-    lastday_str = ""
-    for i in range(1, 10):
-        lastday_str = (today - timedelta(i)).strftime('%Y%m%d')
-        last_hts_data_filename = os.path.join(RESOURCE_PATH, "hts", stock_code + "_" + lastday_str + ".csv")
-        if os.path.isfile(last_hts_data_filename):
-            break
 
-    hts.buyRealTime(lastday_str, today_str)
+    hts.buyRealTime(today_str)
-    #hts.writeStockData(stock_code, today_str)
 
     db_filename = os.path.join(RESOURCE_PATH, "hts.db")
     hts.insertStockData(db_filename, stock_code, stock_name, today_str)

HTS_252670.py

@@ -29,20 +29,6 @@ class HTS_252670 (HTS):
         self.buySellChecker = BuySellChecker()
         return
 
-    def checkTransaction(self, data):
-        size = len(data["close"])
-
-        bsLine = {}
-        bsLine['buy']  = [-1 for i in range(size)]
-        bsLine['weight'] = [-1 for i in range(size)]
-        bsLine['sell'] = [-1 for i in range(size)]
-
-        last_index = size - 1
-        #buy, weight, sell = self.buySellChecker.getPriceAndWeight3(hts, last_index)
-        sell, weight = self.buySellChecker.getSellPriceAndWeight_3000(data, last_index)
-        buy, weight = self.buySellChecker.getBuyPriceAndWeight_3000(data, last_index)
-        return buy, weight, sell
-
     def getSellingPrice(self, final_price):
         # 만약 잔고가 있으면 장부가보다 5원 높게 매도한다.
         jangoDic = self.requstJango()
@@ -78,7 +64,7 @@ class HTS_252670 (HTS):
 
         return 0, 0
 
-    def buyRealTime(self, lastday, today):
+    def buyRealTime(self, today):
 
         timecheckList = pd.read_csv("hts/timecheck.csv").values.tolist()
         timecheck = {today + " " + str(second).zfill(6):False for second, check in timecheckList}
@@ -86,29 +72,33 @@ class HTS_252670 (HTS):
         print ("START...")
         THIS_TIME = datetime.now()
         final_sell_check = False
+        LAST_DATA = self.getLastData(self.stock_code, today)
+
         while datetime.strptime(today + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 153100", '%Y%m%d %H%M%S'):
 
-            if datetime.strptime(today + " 090000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 150000", '%Y%m%d %H%M%S'):
+            if datetime.strptime(today + " 090000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 151500", '%Y%m%d %H%M%S'):
                 # 3시 까지만 매수를 시도한다.
 
                 if THIS_TIME.strftime('%Y%m%d %H%M%S') in timecheck and not timecheck[THIS_TIME.strftime('%Y%m%d %H%M%S')]:
 
                     # 데이터를 가지고 온다.
-                    result = self.getRealTime(self.stock_code, lastday, today)
+                    result = self.getRealTime(self.stock_code, today, LAST_DATA)
 
-                    # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
+                    # 규칙 기반의 분석을 통해서 볼린저밴드 상/하단을 계산한다.
-                    data = self.buySellChecker.analyze(result)
+                    data = self.buySellChecker.analyzeByRule(result)
 
                     # 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
-                    bs_buy_price, bs_weight, bs_sell_price = self.checkTransaction(data)
+                    bsLine, data = self.buySellChecker.checkTransaction(data, self.stock_code, True)
+                    bs_buy_price = bsLine['buy']
+                    bs_weight = bsLine['buy_weight']
+                    bs_sell_price = bsLine['sell']
+
                     data_size = len(data["close"])
                     final_price = data["close"][data_size-1]
 
                     if bs_buy_price > 0:
                         # 기본 100 주에 가중치를 추가해서 매수한다.
                         BUY_COUNT = int(self.buy_count * bs_weight)
-                        #BUY_COUNT = int(self.buy_count * 1)
-
 
                         # 매수를 주문한다.
                         orderNum = self.requestOrder(OrderType.buy, self.stock_code, BUY_COUNT , bs_buy_price)
@@ -159,6 +149,9 @@ class HTS_252670 (HTS):
                 # 3시 15분 30초부터 3시 16분 사이는 잔량을 매도한다.
 
                 if not final_sell_check:
+                    ####
+                    # 손해 보지 않는 가격에 매도한다.
+                    ####
 
                     # 주문 리스트를 가져온다.
                     orderList = self.requestOrderList()
@@ -166,10 +159,12 @@ class HTS_252670 (HTS):
                     self.cancelOrderList(orderList)
 
                     # 매도 가격을 가져온다.
-                    result = self.getRealTime(self.stock_code, lastday, today)
+                    result = self.getRealTime(self.stock_code, today, LAST_DATA)
                     final_price = result["close"][len(result["close"]) - 1]
+
                     #selling_count, selling_price = self.getFinalSellingPrice(final_price)
                     selling_count, selling_price = self.getSellingPrice(final_price)
+
                     # 분석된 가격으로 매도 요청한다.
                     if selling_count != 0 and selling_price != 0:
                         orderNum = self.requestOrder(OrderType.sell, self.stock_code, selling_count, selling_price)
@@ -197,15 +192,8 @@ if __name__ == "__main__":
 
     hts = HTS_252670(RESOURCE_PATH, stock_code, buy_count)
     today_str = today.strftime('%Y%m%d')
-    lastday_str = ""
-    for i in range(1, 10):
-        lastday_str = (today - timedelta(i)).strftime('%Y%m%d')
-        last_hts_data_filename = os.path.join(RESOURCE_PATH, "hts", stock_code + "_" + lastday_str + ".csv")
-        if os.path.isfile(last_hts_data_filename):
-            break
 
-    hts.buyRealTime(lastday_str, today_str)
+    hts.buyRealTime(today_str)
-    #hts.writeStockData(stock_code, today_str)
 
     db_filename = os.path.join(RESOURCE_PATH, "hts.db")
     hts.insertStockData(db_filename, stock_code, stock_name, today_str)

Simulation.py

@@ -1,20 +1,22 @@
 from math import nan
 from datetime import datetime, timedelta
-import csv
+import copy
 import pandas as pd
 import plotly.graph_objects as go
 from plotly import subplots
 import sqlite3
 import os
 
+from hts.HTS import HTS
 from hts.BuySellChecker import BuySellChecker
 
-class Simulation:
+class Simulation (HTS):
 
     buySellChecker = None
     stock_code = None
 
     def __init__(self, RESOURCE_PATH, stock_code):
+        super().__init__(RESOURCE_PATH)
 
         self.buySellChecker = BuySellChecker()
         self.stock_code = stock_code
@@ -22,59 +24,6 @@ class Simulation:
         #self.connect()
         return
 
-    def getCSV(self, fileName, given_day, result):
-        with open(fileName, 'r', encoding='utf-8') as infp:
-            reader = csv.reader(infp)
-            next(reader)
-
-            for rows in reader:
-                days = rows[0] # hts.날짜
-                time = rows[1] # hts.시간
-                open_v = rows[2] # hts.시가
-                high = rows[3] # hts.고가
-                low = rows[4] # hts.저가
-                close = rows[5] # hts.종가
-                vol = rows[6] # hts.거래량
-                start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
-
-                temp = datetime.strptime(str(days) + " " + str(time).zfill(4)+"00", '%Y%m%d %H%M%S')
-                #if temp < start_time:
-                #    continue
-
-                result["time"].append(temp)
-                result["open"].append(int(open_v))
-                result["close"].append(int(close))
-                result["high"].append(int(high))
-                result["low"].append(int(low))
-                result["vol"].append(int(vol))
-        return
-
-    def getDBData(self, stock_code, lastday, result):
-        tableName = 'hts'
-        conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "hts.db"))
-        cursor = conn.cursor()
-
-        cursor.execute('SELECT ymd, hms, open, high, low, close, volume FROM ' + tableName + ' WHERE CODE=? and ymd=? order by ymd, hms', (stock_code, lastday,))
-        db_result = cursor.fetchall()
-        for rows in db_result:
-            ymd = rows[0]  # hts.날짜
-            hms = rows[1]  # hts.시간
-            open = rows[2]  # hts.시가
-            high = rows[3]  # hts.고가
-            low = rows[4]  # hts.저가
-            close = rows[5]  # hts.종가
-            vol = rows[6]  # hts.거래량
-
-            temp = datetime.strptime(str(ymd) + " " + str(hms).zfill(4) + "00", '%Y%m%d %H%M%S')
-
-            result["time"].append(temp)
-            result["open"].append(int(open))
-            result["close"].append(int(close))
-            result["high"].append(int(high))
-            result["low"].append(int(low))
-            result["vol"].append(int(vol))
-        return
-
     def draw(self, stock_code, given_day, data, bsLine):
         # 어제 데이터는 지운다.
         data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
@@ -180,30 +129,27 @@ class Simulation:
 
         return
 
+    def getRealTime(self, stock_code, today, LAST_DATA=None):
+        if LAST_DATA is not None:
+            result = copy.deepcopy(LAST_DATA)
+        else:
+            result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": []}
+
+        self.getDBData(stock_code, today, result)
+
+        return result
+
+
     def simulate(self, today):
-        result = {"check": set(),
+        LAST_DATA = self.getLastData(self.stock_code, today)
-                  "time": [],
-                  "open": [],
-                  "close": [],
-                  "high": [],
-                  "low": [],
-                  "vol": []}
 
-        # 데이터를 가지고 온다.
+        result = self.getRealTime(self.stock_code, today, LAST_DATA)
-        #self.getCSV(path + "/" + self.stock_code + "_" + last_day + ".csv", last_day, result)
-        #self.getCSV(path + "/" + self.stock_code + "_" + today + ".csv", last_day, result)
-        for i in range(1, 10):
-            last_day = (datetime.strptime(today, '%Y%m%d') - timedelta(i)).strftime('%Y%m%d')
-            self.getDBData(self.stock_code, last_day, result)
-            if len(result['time']) > 0:
-                break
-        self.getDBData(self.stock_code, today, result)
 
-        # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
+        # 규칙 기반의 분석을 통해서 볼린저밴드 상/하단을 계산한다.
-        data = self.buySellChecker.analyze(result)
+        data = self.buySellChecker.analyzeByRule(result)
 
         # 사야 할 시점과 팔아야 할 시점을 체크한다.
-        bsLine = self.buySellChecker.checkTransaction(data, self.stock_code)
+        bsLine, data = self.buySellChecker.checkTransaction(data, self.stock_code, False)
 
         # 그래프를 그린다.
         self.draw(self.stock_code, today, data, bsLine)

hts/BuySellChecker.py

@@ -10,6 +10,7 @@ class BuySellChecker:
     common = None
     stochastic = None
     rsi = None
+    macd = None
     ichimokuCloud = None
 
     def __init__(self):
@@ -752,7 +753,7 @@ class BuySellChecker:
 
 
 
-    def analyze(self, result):
+    def analyzeByRule(self, result):
         open = result["open"]
         close = result["close"]
         high = result["high"]
@@ -835,24 +836,44 @@ class BuySellChecker:
 
         return data
 
-    def checkTransaction(self, data, stock_code):
+    def checkTransaction(self, data, stock_code, type=True):
-        size = len(data["close"])
+        # 4일치 중에서 앞에 2일은 제거한다.
+        date = data['date'].dt.date.unique().tolist()
+        data = data[data['date'].dt.date != date[0]]
+        data = data[data['date'].dt.date != date[1]]
 
+        # 어제 오늘 데이터로 분석
         bsLine = {}
-        bsLine['buy'] = [-1 for i in range(size)]
+        size = len(data["close"])
-        bsLine['weight'] = [-1 for i in range(size)]
+        if type:
-        bsLine['sell'] = [-1 for i in range(size)]
+            last_index = size - 1
 
-        for i in range(size):
             if stock_code == "252670":
-                sell, weight = self.getSellPriceAndWeight_3000(data, i)
+                sell, weight = self.getSellPriceAndWeight_3000(data, last_index)
-                buy, weight = self.getBuyPriceAndWeight_3000(data, i)
+                buy, weight = self.getBuyPriceAndWeight_3000(data, last_index)
             else:
-                sell, weight = self.getSellPriceAndWeight_15000(data, i)
+                sell, weight = self.getSellPriceAndWeight_15000(data, last_index)
-                buy, weight = self.getBuyPriceAndWeight_15000(data, i)
+                buy, weight = self.getBuyPriceAndWeight_15000(data, last_index)
 
-            bsLine['buy'][i] = buy
+            bsLine['buy'] = buy
-            bsLine['weight'][i] = weight
+            bsLine['buy_weight'] = weight
-            bsLine['sell'][i] = sell
+            bsLine['sell'] = sell
+            bsLine['sell_weight'] = weight
+        else:
+            bsLine['buy'] = [-1 for i in range(size)]
+            bsLine['weight'] = [-1 for i in range(size)]
+            bsLine['sell'] = [-1 for i in range(size)]
 
-        return bsLine
+            for i in range(size):
+                if stock_code == "252670":
+                    sell, weight = self.getSellPriceAndWeight_3000(data, i)
+                    buy, weight = self.getBuyPriceAndWeight_3000(data, i)
+                else:
+                    sell, weight = self.getSellPriceAndWeight_15000(data, i)
+                    buy, weight = self.getBuyPriceAndWeight_15000(data, i)
+
+                bsLine['buy'][i] = buy
+                bsLine['weight'][i] = weight
+                bsLine['sell'][i] = sell
+
+        return bsLine, data

hts/HTS.py

@@ -1,10 +1,12 @@
 import os.path
-
+import copy
-import win32com.client
+import platform
+if platform.system().lower().find("window") >= 0:
+    import win32com.client
 import csv
 import time
 import sqlite3
-from datetime import datetime
+from datetime import datetime, timedelta
 from hts.OrderItem import OrderItem
 
 class HTS:
@@ -546,12 +548,12 @@ class HTS:
                 result["vol"].append(int(vol))
         return
 
-    def getDBData(self, stock_code, lastday, result):
+    def getDBData(self, stock_code, day, result):
         tableName = 'hts'
         conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "hts.db"))
         cursor = conn.cursor()
 
-        cursor.execute('SELECT ymd, hms, open, high, low, close, volume FROM ' + tableName + ' WHERE CODE=? and ymd=? order by ymd, hms', (stock_code, lastday,))
+        cursor.execute('SELECT ymd, hms, open, high, low, close, volume FROM ' + tableName + ' WHERE CODE=? and ymd=? order by ymd, hms', (stock_code, day,))
         db_result = cursor.fetchall()
         for rows in db_result:
             ymd = rows[0]  # hts.날짜
@@ -570,10 +572,21 @@ class HTS:
             result["high"].append(int(high))
             result["low"].append(int(low))
             result["vol"].append(int(vol))
+
         return
 
-    # 주식 현재가 조회
+    def isValidYMD(self, stock_code, day):
-    def getRealTime(self, stock_code, lastday, today):
+        tableName = 'hts'
+        conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "hts.db"))
+        cursor = conn.cursor()
+
+        cursor.execute('SELECT ymd, count(*) as cnt FROM ' + tableName + ' WHERE CODE=? and ymd=?', (stock_code, day,))
+        db_result = cursor.fetchone()
+        if db_result[1] > 0:
+            return True
+        return False
+
+    def getLastData(self, stock_code, today, n=3):
         result = {"check": set(),
                   "time": [],
                   "open": [],
@@ -582,9 +595,29 @@ class HTS:
                   "low": [],
                   "vol": []}
 
-        #last_stock_filename = os.path.join(self.RESOURCE_PATH, "hts", stock_code + "_" + lastday + ".csv")
+        days = []
-        #self.getCSV(last_stock_filename, result)
+        for i in range(1, 10):
-        self.getDBData(stock_code, lastday, result)
+            last_day = (datetime.strptime(today, '%Y%m%d') - timedelta(i)).strftime('%Y%m%d')
+            isValid = self.isValidYMD(stock_code, last_day)
+            if isValid:
+                days.append(last_day)
+                if len(days) >= 3:
+                    break
+
+        days.sort()
+        for day in days:
+            self.getDBData(stock_code, day, result)
+
+        return result
+
+    # 주식 현재가 조회
+    def getRealTime(self, stock_code, today, LAST_DATA=None):
+        if LAST_DATA is not None:
+            result = copy.deepcopy(LAST_DATA)
+        else:
+            result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": []}
+
+        self.getDBData(stock_code, today, result)
 
         int_given_day = int(today)
         objCpCybos = win32com.client.Dispatch("CpUtil.CpCybos")

stock/util/Stock2Vector.py

@@ -1,15 +1,184 @@
+import os
+import sqlite3
+from datetime import datetime, timedelta
+import pandas as pd
+
+from stock.analysis.Common import Common
+from stock.analysis.Stochastic import Stochastic
+from stock.analysis.RSI import RSI
+from stock.analysis.MACD import MACD
+from stock.analysis.IchimokuCloud import IchimokuCloud
+
 class Stock2Vector:
 
-    def __init__(self):
+    RESOURCE_PATH = None
+
+    common = None
+    stochastic = None
+    rsi = None
+    macd = None
+    ichimokuCloud = None
+
+    def __init__(self, RESOURCE_PATH):
+        self.RESOURCE_PATH = RESOURCE_PATH
+
+        self.common = Common()
+        self.stochastic = Stochastic()
+        self.rsi = RSI()
+        self.macd = MACD()
+        self.ichimokuCloud = IchimokuCloud()
         return
 
-    def read(self, fileName):
+    def analyze(self, result):
-        return None
+        open = result["open"]
+        close = result["close"]
+        high = result["high"]
+        low = result["low"]
+        vol = result["vol"]
 
-    def process(self, vector):
+        close_df = pd.DataFrame(close)
+        avg3_list = close_df.rolling(window=3).mean().fillna(close[0]).values.tolist()
+        avg3 = [item[0] for item in avg3_list]
+        avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist()
+        avg5 = [item[0] for item in avg5_list]
+        avg10_list = close_df.rolling(window=10).mean().fillna(close[0]).values.tolist()
+        avg10 = [item[0] for item in avg10_list]
+        avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist()
+        avg20 = [item[0] for item in avg20_list]
+        avg30_list = close_df.rolling(window=30).mean().fillna(close[0]).values.tolist()
+        avg30 = [item[0] for item in avg30_list]
+        avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist()
+        avg60 = [item[0] for item in avg60_list]
+
+        df = pd.DataFrame(close)
+        max20 = df.rolling(window=20).mean()
+        stddev20 = df.rolling(window=20).std()
+        upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드
+        lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드
+
+        upper, lower = [], []
+        for i in range(len(upper_df)):
+            if i < 10:
+                upper.append(upper_df.values[0][0])
+                lower.append(lower_df.values[0][0])
+            else:
+                upper.append(upper_df.values[i][0])
+                lower.append(lower_df.values[i][0])
+
+        point_temp = result["time"]
+
+        STOCK = []
+        for i in range(len(open)):
+            STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
+                          'avg3': avg3[i], 'avg5': avg5[i],'avg10': avg10[i],'avg20': avg20[i],'avg30': avg30[i],'avg60': avg60[i]})
+
+        # stochastic 계산
+        stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
+        stochastic_df = stochastic_df.fillna(100)
+        fast_k = stochastic_df['fast_k'].values.tolist()
+        slow_k = stochastic_df['slow_k'].values.tolist()
+        slow_d = stochastic_df['slow_d'].values.tolist()
+
+        # macd 계산
+        macd_df = self.macd.apply(STOCK, short=12, long=26, t=9)
+        macd_df = macd_df.fillna(100)
+        macd = macd_df['macd'].values.tolist()
+        macds = macd_df['macds'].values.tolist()
+        macdo = macd_df['macdo'].values.tolist()
+
+        # rsi 계산
+        rsi_df = self.rsi.apply(STOCK, period=30, window=5)
+        rsi_df = rsi_df.fillna(100)
+        rsi = rsi_df['rsi'].values.tolist()
+        rsis = rsi_df['rsis'].values.tolist()
+
+        # ichimokuCloud 계산
+        # ichimokuCloud_df = self.ichimokuCloud.apply(STOCK, c=9, b=26, l=52)
+        # ichimokuCloud_df = rsi_df.fillna(100)
+        # changeLine = rsi_df['changeLine'].values.tolist()
+        # baseLine = rsi_df['baseLine'].values.tolist()
+        # leadingSpan1 = rsi_df['leadingSpan1'].values.tolist()
+        # leadingSpan2 = rsi_df['leadingSpan2'].values.tolist()
+
+        temp = {"date": point_temp,
+                "open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower,
+                "avg3": avg3, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg60": avg60,
+                "macd": macd, "macds": macds, "macdo": macdo,
+                "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
+                "rsi": rsi, "rsis": rsis}
+        data = pd.DataFrame(temp)
+        df_final_time = pd.DatetimeIndex(point_temp)
+        data.index = df_final_time
+
+        data.fillna(0)
+
+        return data
+
+    def getDBData(self, stock_code, lastday, result):
+        tableName = 'hts'
+        conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "hts.db"))
+        cursor = conn.cursor()
+
+        cursor.execute('SELECT ymd, hms, open, high, low, close, volume FROM ' + tableName + ' WHERE CODE=? and ymd=? order by ymd, hms', (stock_code, lastday,))
+        db_result = cursor.fetchall()
+        for rows in db_result:
+            ymd = rows[0]  # hts.날짜
+            hms = rows[1]  # hts.시간
+            open = rows[2]  # hts.시가
+            high = rows[3]  # hts.고가
+            low = rows[4]  # hts.저가
+            close = rows[5]  # hts.종가
+            vol = rows[6]  # hts.거래량
+
+            temp = datetime.strptime(str(ymd) + " " + str(hms).zfill(4) + "00", '%Y%m%d %H%M%S')
+
+            result["time"].append(temp)
+            result["open"].append(int(open))
+            result["close"].append(int(close))
+            result["high"].append(int(high))
+            result["low"].append(int(low))
+            result["vol"].append(int(vol))
         return
 
+    def vectorize(self, stock_code, given_day):
+        result = {"check": set(),
+                  "time": [],
+                  "open": [],
+                  "close": [],
+                  "high": [],
+                  "low": [],
+                  "vol": []}
+
+        for i in range(1, 10):
+            last_day = (datetime.strptime(given_day, '%Y%m%d') - timedelta(i)).strftime('%Y%m%d')
+            self.getDBData(stock_code, last_day, result)
+            if len(result['time']) > 0:
+                break
+        self.getDBData(stock_code, given_day, result)
+
+        # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
+        data = self.analyze(result)
+
+        return data
+
 if __name__ == "__main__":
-    stock2Vector = Stock2Vector()
+    PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))))
-    fileName = "./resources/hts/252670_20220726.csv"
+    RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
+
+    stock2Vector = Stock2Vector(RESOURCE_PATH)
+
+    # to check bying
+    stock_codes = {
+        # 252670
+        # 122630
+        "122630": ['20220725'],
+    }
+
+    for stock_code in stock_codes:
+
+        for given_day in stock_codes[stock_code]:
+            stock2Vector.vectorize(stock_code, given_day)
+
+    print ("done...")
+