init

hts/BuySellChecker.py

@@ -2,6 +2,7 @@ import pandas as pd
 from stockpredictor.analysis.Common import Common
 from stockpredictor.analysis.Stochastic import Stochastic
 from stockpredictor.analysis.RSI import RSI
+from stockpredictor.analysis.MACD import MACD
 
 class BuySellChecker:
 
@@ -13,6 +14,7 @@ class BuySellChecker:
         self.common = Common()
         self.stochastic = Stochastic()
         self.rsi = RSI()
+        self.macd = MACD()
 
         return
 
@@ -20,7 +22,7 @@ class BuySellChecker:
         status = set()
 
         # 정배열 체크
-        temp_status = self.common.check_RightArrange(STOCK, last_index)
+        temp_status = self.common.check_RightArrange(STOCK)
         if temp_status != "":
             status.add(temp_status)
 
@@ -183,60 +185,52 @@ class BuySellChecker:
     def getPriceAndWeight1(self, data, i):
         buy, weight, sell = -1, -1, -1
 
-        ################
+        if i >= 3:
-        ### sell 분석 ###
+            ################
-        ################
+            ### sell 분석 ###
-        # 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
+            ################
-        if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
+            # 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
-            sell = data["high"][i]
+            #if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
-        """
+            #    sell = data["high"][i]
-        # 2. slow_k가 90이 넘으면 매도한다.
+
-        if data["slow_k"][i] >= 90:
+            # 2. slow_k가 90이 넘으면 매도한다.
-            sell = data["high"][i]
+            if data["slow_k"][i] > 90:
-        """
-        if data["slow_k"][i] >= 85:
-            if data["slow_d"][i-1] < data["slow_k"][i-1] and data["slow_k"][i] < data["slow_d"][i]:
                 sell = data["high"][i]
 
-        # 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
+            #if data["slow_k"][i] >= 85:
-        if i > 300 and data["high"][i] > data["upper"][i]:
+            #    if data["slow_d"][i-1] < data["slow_k"][i-1] and data["slow_k"][i] < data["slow_d"][i]:
-                sell = data["high"][i]
+            #        sell = data["high"][i]
 
-        ##########################
+            # 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
-        ### STOCHASTIC buy 분석 ###
+            if i > 300 and data["high"][i] > data["upper"][i]:
-        ##########################
+                    sell = data["high"][i]
-        if i < 40:
+
-            if data["low"][i] < data["lower"][i]+5:
+            ##########################
-                if data["slow_k"][i-1] < 50 and data["slow_k"][i] < 55:
+            ### buy 분석 ###
-                    if data["slow_k"][i-1] < data["slow_d"][i-1] and data["slow_d"][i] < data["slow_k"][i] and data["slow_k"][i-1] < data["slow_k"][i]:
+            ##########################
-                        buy = data["low"][i]
+            if data["low"][i] < data["lower"][i] + 5 and data["open"][i] <= data["close"][i]:
-        else:
-            if data["low"][i] < data["lower"][i] + 5:
                 if data["slow_k"][i-1] < 30 and data["slow_k"][i] < 30:
                     if data["slow_k"][i-1] < data["slow_k"][i]:
                         buy = data["low"][i]
 
-        #############################
+            if data["rsi"][i] < 25:
-        ### STOCHASTIC weight 분석 ###
+                if data["rsi"][i - 2] < data["rsis"][i - 2] and data["rsi"][i - 1] < data["rsis"][i - 1] and data["rsis"][i] < data["rsi"][i]:
-        #############################
+                    if data["close"][i] < data["avg5"][i]:
-        if data["slow_k"][i] in (0, 1, 2, 3):
+                        buy = data["close"][i]
-            weight = 1
+                    else:
-        if data["slow_k"][i] in (4, 5, 6, 7, 8):
+                        buy = data["low"][i]
-            weight = 1
+                    weight = 1
-        elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
-            weight = 1
-        elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
-            weight = 1
 
-        ###################
+            #############################
-        ### RSI buy 분석 ###
+            ### STOCHASTIC weight 분석 ###
-        ###################
+            #############################
-        if data["rsi"][i] < 25:
+            if data["slow_k"][i] in (0, 1, 2, 3):
-            if data["rsi"][i - 2] < data["rsis"][i - 2] and data["rsi"][i - 1] < data["rsis"][i - 1] and data["rsis"][i] < data["rsi"][i]:
+                weight = 1
-                if data["close"][i] < data["avg5"][i]:
+            if data["slow_k"][i] in (4, 5, 6, 7, 8):
-                    buy = data["close"][i]
+                weight = 1
-                else:
+            elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
-                    buy = data["low"][i]
+                weight = 1
+            elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
                 weight = 1
 
         return buy, weight, sell
@@ -301,63 +295,14 @@ class BuySellChecker:
 
         return buy, weight, sell
 
-    def getPriceAndWeight_1minute(self, data, i):
+    def getPriceAndWeight3(self, data, i):
         buy, weight, sell = -1, -1, -1
 
-        ################
+        # 381: 어제 날짜 데이터 개수
-        ### sell 분석 ###
+        if i >= 381 + 5:
-        ################
+            if data["macdo"][i] < 0 and data["macd"][i] < -5:
-        # 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
+                if data["macd"][i-3] > data["macd"][i-2] and data["macd"][i-2] > data["macd"][i-1] and data["macd"][i-1] < data["macd"][i]:
-        if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
+                    buy = data["close"][i]
-            sell = data["high"][i]
-
-        if data["slow_k"][i] >= 85:
-            if data["slow_d"][i - 1] < data["slow_k"][i - 1] and data["slow_k"][i] < data["slow_d"][i]:
-                sell = data["high"][i]
-
-        # 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
-        if i > 300 and data["high"][i] > data["upper"][i]:
-            sell = data["high"][i]
-
-        ##########################
-        ### STOCHASTIC buy 분석 ###
-        ##########################
-        if i < 40:
-            pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
-            now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
-            if pre_slow < 0 and 0 < now_slow:
-                if data["slow_k"][i] <= 35:
-                    if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
-                        if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
-                            if  data['avg3'][i] <= data['avg2'][i]:
-                                if data["open"][i] < data["close"][i]:
-                                    buy = data["close"][i]
-                                else:
-                                    buy = data["low"][i]
-        else:
-            pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
-            now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
-            if pre_slow < 0 and pre_slow < now_slow and -0.15 < now_slow:
-                if data["slow_k"][i] <= 10:
-                    if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
-                        if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
-                            if  data['avg3'][i] <= data['avg2'][i]:
-                                if data["close"][i] < data["avg5"][i]:
-                                    buy = data["close"][i]
-                                else:
-                                    buy = data["low"][i]
-
-        #############################
-        ### STOCHASTIC weight 분석 ###
-        #############################
-        if data["slow_k"][i] in (0, 1, 2, 3):
-            weight = 1
-        if data["slow_k"][i] in (4, 5, 6, 7, 8):
-            weight = 1
-        elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
-            weight = 1
-        elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
-            weight = 1
 
         return buy, weight, sell
 
@@ -370,8 +315,8 @@ class BuySellChecker:
         vol = result["vol"]
 
         close_df = pd.DataFrame(close)
-        avg2_list = close_df.rolling(window=2).mean().fillna(close[0]).values.tolist()
+        avg3_list = close_df.rolling(window=3).mean().fillna(close[0]).values.tolist()
-        avg2 = [item[0] for item in avg2_list]
+        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()
@@ -382,8 +327,8 @@ class BuySellChecker:
         avg60 = [item[0] for item in avg60_list]
 
         df = pd.DataFrame(close)
-        max20 = df.rolling(window=10).mean()
+        max20 = df.rolling(window=20).mean()
-        stddev20 = df.rolling(window=10).std()
+        stddev20 = df.rolling(window=20).std()
         upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드
         lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드
 
@@ -401,7 +346,7 @@ class BuySellChecker:
         STOCK = []
         for i in range(len(open)):
             STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
-                          'avg2': avg2[i], 'avg5': avg5[i],'avg10': avg10[i],'avg30': avg30[i],'avg60': avg60[i]})
+                          'avg3': avg3[i], 'avg5': avg5[i],'avg10': avg10[i],'avg30': avg30[i],'avg60': avg60[i]})
 
         # stochastic 계산
         stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
@@ -410,6 +355,13 @@ class BuySellChecker:
         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)
@@ -418,10 +370,32 @@ class BuySellChecker:
 
         temp = {"date": point_temp,
                 "open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower,
-                "avg2": avg2, "avg5": avg5, "avg10": avg10, "avg30": avg30, "avg60": avg60,
+                "avg3": avg3, "avg5": avg5, "avg10": avg10, "avg30": avg30, "avg60": avg60,
-                "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "rsi": rsi, "rsis": rsis}
+                "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
 
-        return data
+        return data
+
+    def checkTransaction(self, data, stock_code):
+        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)]
+
+        for i in range(size):
+            if stock_code == "252670":
+                buy, weight, sell = self.getPriceAndWeight3(data, i)
+            else:
+                buy, weight, sell = self.getPriceAndWeight3(data, i)
+
+            bsLine['buy'][i] = buy
+            bsLine['weight'][i] = weight
+            bsLine['sell'][i] = sell
+
+        return bsLine

hts/HTS.py

@@ -1,5 +1,7 @@
 import win32com.client
+import csv
 import time
+from datetime import datetime
 from hts.OrderItem import OrderItem
 
 class HTS:
@@ -371,7 +373,7 @@ class HTS:
         return
 
     # 주식 현재가 조회
-    def writeStockData(self, stock_code, given_day):
+    def writeStockData(self, stock_code, today):
         objCpCybos = win32com.client.Dispatch("CpUtil.CpCybos")
         bConnect = objCpCybos.IsConnect
         if (bConnect == 0):
@@ -381,11 +383,11 @@ class HTS:
         # 차트 객체 구하기
         objStockChart = win32com.client.Dispatch("CpSysDib.StockChart")
 
-        outfp = open("./data/"+stock_code+"_"+given_day+".csv", mode="w", encoding="utf-8")
+        outfp = open("./data/"+stock_code+"_"+today+".csv", mode="w", encoding="utf-8")
         objStockChart.SetInputValue(0, 'A' + stock_code)  # 종목 코드
         objStockChart.SetInputValue(1, ord('1'))  # 1: 기간으로 조회, 2: 개수로 조회
-        objStockChart.SetInputValue(2, given_day)  # 기간 조회 시, 시작일
+        objStockChart.SetInputValue(2, today)  # 기간 조회 시, 시작일
-        objStockChart.SetInputValue(3, given_day)             # 기간 조회 시, 종료일
+        objStockChart.SetInputValue(3, today)             # 기간 조회 시, 종료일
         objStockChart.SetInputValue(4, 400)  # 조회 시 가져오는 Line 개수
         objStockChart.SetInputValue(5, [0, 1, 2, 3, 4, 5, 8])  # 날짜,시간,시가,고가,저가,종가,거래량
         objStockChart.SetInputValue(6, ord('m'))  # '차트 주가 - 월(M), 주(W), 일(D), 시(H), 분(m), 초(S) 차트 요청
@@ -428,9 +430,47 @@ class HTS:
         outFp.close()
         return
 
+    def getCSV(self, fileName, given_day, result):
+        with open(fileName, 'r') as infp:
+            reader = csv.reader(infp)
+            next(reader)
+
+            for rows in reader:
+                days = rows[0]  # data.날짜
+                time = rows[1]  # data.시간
+                open_v = rows[2]  # data.시가
+                high = rows[3]  # data.고가
+                low = rows[4]  # data.저가
+                close = rows[5]  # data.종가
+                vol = rows[6]  # data.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
+
     # 주식 현재가 조회
-    def getRealTime(self, stock_code, given_day, result):
+    def getRealTime(self, stock_code, lastday, today):
-        int_given_day = int(given_day)
+        result = {"check": set(),
+                  "time": [],
+                  "open": [],
+                  "close": [],
+                  "high": [],
+                  "low": [],
+                  "vol": []}
+
+        self.getCSV("./data/" + stock_code + "_" + lastday + ".csv", today, result)
+
+        int_given_day = int(today)
         objCpCybos = win32com.client.Dispatch("CpUtil.CpCybos")
         bConnect = objCpCybos.IsConnect
         if (bConnect == 0):
@@ -442,8 +482,8 @@ class HTS:
 
         objStockChart.SetInputValue(0, 'A'+stock_code)          # 종목 코드
         objStockChart.SetInputValue(1, ord('1'))                # 1: 기간으로 조회, 2: 개수로 조회
-        objStockChart.SetInputValue(2, given_day)               # 기간 조회 시, 시작일
+        objStockChart.SetInputValue(2, today)                   # 기간 조회 시, 시작일
-        objStockChart.SetInputValue(3, given_day)               # 기간 조회 시, 종료일
+        objStockChart.SetInputValue(3, today)                   # 기간 조회 시, 종료일
         objStockChart.SetInputValue(4, 400)                     # 조회 시 가져오는 Line 개수
         objStockChart.SetInputValue(5, [0, 1, 2, 3, 4, 5, 8])   # 날짜,시간,시가,고가,저가,종가,거래량
         objStockChart.SetInputValue(6, ord('m'))                # '차트 주가 - 월(M), 주(W), 일(D), 시(H), 분(m), 초(S) 차트 요청
@@ -454,7 +494,7 @@ class HTS:
         size = objStockChart.GetHeaderValue(3)
 
         #print("날짜", "시간", "시가", "고가", "저가", "종가", "거래량")
-        start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
+        start_time = datetime.strptime(today + " 090000", '%Y%m%d %H%M%S')
         for i in range(size-1, -1, -1):
             int_day = objStockChart.GetDataValue(0, i)
             int_time = objStockChart.GetDataValue(1, i)

hts/HTS_122630.py

@@ -1,180 +0,0 @@
-import time
-import os
-from datetime import datetime
-import pandas as pd
-
-from hts.HTS import HTS
-from hts.OrderType import OrderType
-
-from hts.BuySellChecker import BuySellChecker
-from hts.OrderChecker import OrderChecker
-
-
-class HTS_122630 (HTS):
-
-    buySellChecker = None
-    stock_code = None
-    buy_count = None
-
-    def __init__(self, stock_code, buy_count):
-        super().__init__()
-
-        self.buySellChecker = BuySellChecker()
-        self.stock_code = stock_code
-        self.buy_count = buy_count
-        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)]
-
-        i = size - 1
-        if i < 5:
-            return -1, -1, -1
-
-        buy, weight, sell = self.buySellChecker.getPriceAndWeight2(data, i)
-        return buy, weight, sell
-
-    def getSellingPrice(self, final_price):
-        # 만약 잔고가 있으면 장부가보다 5원 높게 매도한다.
-        jangoDic = self.requstJango()
-        if jangoDic and len(jangoDic.keys()) > 0:
-            for code in jangoDic:
-                if jangoDic[code]['매도가능'] > 0:
-                    if final_price >= jangoDic[code]['장부가'] + 5:
-                        return jangoDic[code]['매도가능'], final_price
-                    else:
-                        # 장부가 가격의 마지막 자리를 0으로 만든다. (2090 -> 2090, 2092 -> 2090, 2098 -> 2090)
-                        sell_price = int(jangoDic[code]['장부가'] / 10) * 10
-                        # 장부가의 마지막 자리수를 가져온다.
-                        last_number = int(jangoDic[code]['장부가']) % 10
-                        if last_number in [0, 1, 2, 3]:
-                            # 장부가의 마지막 자리수가 0,1,2,3 이라면 (2090, 2091, 2092 -> 2095 에 매도)
-                            return jangoDic[code]['매도가능'], sell_price + 5
-                        elif last_number in [4, 5, 6, 7]:
-                            # 장부가의 마지막 자리수가 4,5,6,7 이라면 (2093, 2094, 2095, 2096 -> 2100 에 매도)
-                            return jangoDic[code]['매도가능'], sell_price + 10
-                        else:
-                            # 장부가의 마지막 자리수가 8,9 라면 (2098, 2099 -> 2105 에 매도)
-                            return jangoDic[code]['매도가능'], sell_price + 15
-
-        return 0, 0
-
-    def buyRealTime(self, GIVEN_DAY):
-        orderChecker = OrderChecker(self.stock_code)
-
-        timecheckList = pd.read_csv("timecheck.csv").values.tolist()
-        timecheck = {GIVEN_DAY + " " + str(second).zfill(6):False for second, check in timecheckList}
-
-        result = {"check": set(),
-                  "time": [],
-                  "open": [],
-                  "close": [],
-                  "high": [],
-                  "low": [],
-                  "vol": []}
-
-        final_price = 0
-        print ("START...")
-        THIS_TIME = datetime.now()
-        while datetime.strptime(GIVEN_DAY + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 15200", '%Y%m%d %H%M%S'):
-
-            if datetime.strptime(GIVEN_DAY + " 090100", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 151500", '%Y%m%d %H%M%S'):
-                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.
-                    self.getRealTime(self.stock_code, GIVEN_DAY, result)
-
-                    # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
-                    data = self.buySellChecker.analyze(result)
-                    # 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
-                    bs_buy_price, bs_weight, bs_sell_price = self.checkTransaction(data)
-                    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)
-
-                        # 미체결 기록을 가져온다.
-                        ORDER_LIST = self.requestOrderList()
-                        # 매수 주문을 기록한다.
-                        orderListToCancel = orderChecker.add(self.stock_code, OrderType.buy, orderNum, BUY_COUNT, bs_buy_price, ORDER_LIST)
-                        # 두 시간 이전 미체결을 모두 취소한다.
-                        self.cancelOrderList(orderListToCancel)
-                        # 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()
-                        # 매도 주문을 기록을 가져온다.
-                        orderListToCancel = orderChecker.remove(self.stock_code, OrderType.sell, ORDER_LIST)
-                        # 매도 미체결을 모두 취소한다.
-                        self.cancelOrderList(orderListToCancel)
-
-                        # 매도 가격을 가져온다.
-                        selling_count, selling_price = self.getSellingPrice(final_price)
-                        # 분석된 가격으로 매도 요청한다.
-                        if selling_count != 0 and selling_price != 0:
-                            # 매도를 요청한다.
-                            orderNum = self.requestOrder(OrderType.sell, self.stock_code, selling_count, selling_price)
-
-                            # 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("TIMECHECK", THIS_TIME, final_price, data["low"][data_size-1], data["slow_k"][data_size-1], data["slow_d"][data_size-1])
-                    timecheck[THIS_TIME] = True
-
-            if datetime.strptime(GIVEN_DAY + " 151530", '%Y%m%d %H%M%S') < THIS_TIME:
-                ####
-                # 손해 보지 않는 가격에 매도한다.
-                ####
-
-                # 주문 리스트를 가져온다.
-                orderList = self.requestOrderList()
-                # 15:10:00 이후라면 모든 미체결 취소한다.
-                self.cancelOrderList(orderList)
-
-                # 매도 가격을 가져온다.
-                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)
-                    # 로그 출력
-                    print("SELL", THIS_TIME, selling_count, selling_price)
-
-                break
-
-            time.sleep(0.9)
-            THIS_TIME = datetime.now()
-
-        return
-
-if __name__ == "__main__":
-
-    today = datetime.today()
-
-    PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))
-    RESOURCE_DIR = PROJECT_HOME + "/resources/analysis/"+today.strftime("%Y%m%d")
-
-    # KODEX 인버스 * 2
-    stock_code = "122630"
-    buy_count = 120
-
-    hts = HTS_122630(stock_code, buy_count)
-    given_day = datetime.today().strftime('%Y%m%d')
-
-    hts.writeStockData(stock_code, "20220620")
-    #hts.buyRealTime(given_day)
-
-    print ("done...")

hts/HTS_ETF.py

@@ -1,6 +1,6 @@
 import time
 import os
-from datetime import datetime
+from datetime import datetime, timedelta
 import pandas as pd
 
 from hts.HTS import HTS
@@ -32,11 +32,8 @@ class HTS_252670 (HTS):
         bsLine['weight'] = [-1 for i in range(size)]
         bsLine['sell'] = [-1 for i in range(size)]
 
-        i = size - 1
+        last_index = size - 1
-        if i < 5:
+        buy, weight, sell = self.buySellChecker.getPriceAndWeight1(data, last_index)
-            return -1, -1, -1
-
-        buy, weight, sell = self.buySellChecker.getPriceAndWeight1(data, i)
         return buy, weight, sell
 
     def getSellingPrice(self, final_price):
@@ -64,32 +61,25 @@ class HTS_252670 (HTS):
 
         return 0, 0
 
-    def buyRealTime(self, GIVEN_DAY):
+    def buyRealTime(self, lastday, today):
         orderChecker = OrderChecker(self.stock_code)
 
         timecheckList = pd.read_csv("timecheck.csv").values.tolist()
-        timecheck = {GIVEN_DAY + " " + str(second).zfill(6):False for second, check in timecheckList}
+        timecheck = {today + " " + str(second).zfill(6):False for second, check in timecheckList}
-
-        result = {"check": set(),
-                  "time": [],
-                  "open": [],
-                  "close": [],
-                  "high": [],
-                  "low": [],
-                  "vol": []}
 
         print ("START...")
         THIS_TIME = datetime.now()
-        while datetime.strptime(GIVEN_DAY + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 15200", '%Y%m%d %H%M%S'):
+        while datetime.strptime(today + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 15200", '%Y%m%d %H%M%S'):
 
-            if datetime.strptime(GIVEN_DAY + " 090100", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 151500", '%Y%m%d %H%M%S'):
+            if datetime.strptime(today + " 090100", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 151500", '%Y%m%d %H%M%S'):
                 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.
-                    self.getRealTime(self.stock_code, GIVEN_DAY, result)
+                    result = self.getRealTime(self.stock_code, lastday, today)
 
                     # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
                     data = self.buySellChecker.analyze(result)
+
                     # 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
                     bs_buy_price, bs_weight, bs_sell_price = self.checkTransaction(data)
                     data_size = len(data["close"])
@@ -140,7 +130,7 @@ class HTS_252670 (HTS):
                            data["rsi"][data_size-1], data["rsis"][data_size-1]))
                     timecheck[THIS_TIME] = True
 
-            if datetime.strptime(GIVEN_DAY + " 151530", '%Y%m%d %H%M%S') < THIS_TIME:
+            if datetime.strptime(today + " 151530", '%Y%m%d %H%M%S') < THIS_TIME:
                 ####
                 # 손해 보지 않는 가격에 매도한다.
                 ####
@@ -177,9 +167,14 @@ if __name__ == "__main__":
     buy_count = 120
 
     hts = HTS_252670(stock_code, buy_count)
-    given_day = datetime.today().strftime('%Y%m%d')
+    today_str = today.strftime('%Y%m%d')
+    lastday_str = ""
+    for i in range(1, 10):
+        lastday_str = (today - timedelta(i)).strftime('%Y%m%d')
+        if os.path.isfile("./data/" + stock_code + "_" + lastday_str + ".csv"):
+            break
 
-    hts.buyRealTime(given_day)
+    hts.buyRealTime(lastday_str, today_str)
-    hts.writeStockData(stock_code, "20220620")
+    hts.writeStockData(stock_code, today_str)
 
     print ("done...")

hts/Simulation.py

@@ -1,5 +1,6 @@
-import os
+from math import nan
 from datetime import datetime, timedelta
+import csv
 import pandas as pd
 import plotly.graph_objects as go
 from plotly import subplots
@@ -19,110 +20,102 @@ class Simulation:
         return
 
     def getCSV(self, fileName, given_day, result):
-        data = pd.read_csv(fileName)
+        with open(fileName, 'r') as infp:
+            reader = csv.reader(infp)
+            next(reader)
 
-        days = data.날짜
+            for rows in reader:
-        time = data.시간
+                days = rows[0] # data.날짜
-        open = data.시가
+                time = rows[1] # data.시간
-        close = data.종가
+                open_v = rows[2] # data.시가
-        high = data.고가
+                high = rows[3] # data.고가
-        low = data.저가
+                low = rows[4] # data.저가
-        vol = data.거래량
+                close = rows[5] # data.종가
-        start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
+                vol = rows[6] # data.거래량
+                start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
 
-        for i in range(len(data)):
+                temp = datetime.strptime(str(days) + " " + str(time).zfill(4)+"00", '%Y%m%d %H%M%S')
-            temp = datetime.strptime(str(days[i]) + " " + str(time[i]).zfill(4)+"00", '%Y%m%d %H%M%S')
+                if temp < start_time:
-            if temp < start_time:
+                    continue
-                continue
 
-            if temp not in result["check"]:
-                result["check"].add(temp)
                 result["time"].append(temp)
-                result["open"].append(open[i])
+                result["open"].append(int(open_v))
-                result["close"].append(close[i])
+                result["close"].append(int(close))
-                result["high"].append(high[i])
+                result["high"].append(int(high))
-                result["low"].append(low[i])
+                result["low"].append(int(low))
-                result["vol"].append(vol[i])
+                result["vol"].append(int(vol))
         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)]
-
-        for i in range(6, size-5):
-            if self.stock_code == "252670":
-                buy, weight, sell = self.buySellChecker.getPriceAndWeight1(data, i)
-            else:
-                buy, weight, sell = self.buySellChecker.getPriceAndWeight2(data, i)
-            bsLine['buy'][i] = buy
-            bsLine['weight'][i] = weight
-            bsLine['sell'][i] = sell
-
-        return bsLine
-
     def draw(self, stock_code, given_day, data, bsLine):
-        buy_line = bsLine['buy']
+        # 어제 데이터는 지운다.
-        sell_line = bsLine['sell']
+        data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
+        buy_line = bsLine['buy'][381:]
+        sell_line = bsLine['sell'][381:]
+        #buy_line = bsLine['buy']
+        #sell_line = bsLine['sell']
 
         # 그래프 설정을 위한 변수를 생성한다.
-        data['open'] = pd.to_numeric(data['open'])
+        data = data.astype({'open': 'int',
-        data['high'] = pd.to_numeric(data['high'])
+                            'high': 'int',
-        data['low'] = pd.to_numeric(data['low'])
+                            'low': 'int',
-        data['close'] = pd.to_numeric(data['close'])
+                            'close': 'int',
-        data['volume'] = pd.to_numeric(data['volume'])
+                            'volume': 'int',
-        data['avg2'] = pd.to_numeric(data['avg2'])
+                            'avg3': 'float',
-        data['avg5'] = pd.to_numeric(data['avg5'])
+                            'avg5': 'float',
-        data['avg10'] = pd.to_numeric(data['avg10'])
+                            'avg10': 'float',
-        data['avg30'] = pd.to_numeric(data['avg30'])
+                            'avg30': 'float',
-        data['avg60'] = pd.to_numeric(data['avg60'])
+                            'avg60': 'float',
-        data["fast_k"] = pd.to_numeric(data['fast_k'])
+                            'fast_k': 'float',
-        data["slow_k"] = pd.to_numeric(data['slow_k'])
+                            'slow_k': 'float',
-        data["slow_d"] = pd.to_numeric(data['slow_d'])
+                            'slow_d': 'float',
-        data["rsi"] = pd.to_numeric(data['rsi'])
+                            'rsi': 'float',
-        data["rsis"] = pd.to_numeric(data['rsis'])
+                            'rsis': 'float'
+                            })
 
         buy_colors = []
         for i in range(len(buy_line)):
             if buy_line[i] < 0:
                 buy_colors.append("#ffffff")
-                buy_line[i] = data["lower"][0]
+                buy_line[i] = nan
             else:
                 buy_colors.append("#ff00ff")
         sell_colors = []
         for i in range(len(sell_line)):
             if sell_line[i] < 0:
                 sell_colors.append("#ffffff")
-                sell_line[i] = data["lower"][0]
+                sell_line[i] = nan
             else:
                 sell_colors.append("#00ced1")
 
         # 그래프를 설정한다.
         buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=14, color=buy_colors, line_width=0))
         sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
-        bolinger_upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#8B4513')
+        upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
-        bolinger_lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#8B4513')
+        lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
-        avg2 = go.Scatter(x=data['date'], y=data["avg2"], name="avg2", line_color='#800080')
+        avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#000000')
-        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#800080')
+        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#00FF00')
         avg10 = go.Scatter(x=data['date'], y=data["avg10"], name="avg10", line_color='#ff00ff')
-        avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#00ffff')
+        avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#FFA500')
-        avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#008000')
+        #avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#008000')
 
         candle_stick = go.Candlestick(x=data['date'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue')
         volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
-        fast_k_line = go.Scatter(x=data['date'], y=data["fast_k"], mode='lines', name='fast_k')
+        #fast_k_line = go.Scatter(x=data['date'], y=data["fast_k"], mode='lines', name='fast_k')
+
+        macd_line = go.Scatter(x=data['date'], y=data["macd"], mode='lines', name='macd')
+        macd_s_line = go.Scatter(x=data['date'], y=data["macds"], mode='lines', name='macds')
+        macd_o_line = go.Scatter(x=data['date'], y=data["macdo"], mode='lines', name='macdo')
+
         slow_k_line = go.Scatter(x=data['date'], y=data["slow_k"], mode='lines', name='slow_k')
         slow_d_line = go.Scatter(x=data['date'], y=data["slow_d"], mode='lines', name='slow_d')
+
         rsi_line = go.Scatter(x=data['date'], y=data["rsi"], mode='lines', name='rsi')
         rsis_line = go.Scatter(x=data['date'], y=data["rsis"], mode='lines', name='rsis')
 
-        #candle_data = [candle_stick, bolinger_upper, bolinger_lower, buy_check, sell_check, avg1, avg2, avg5, avg10, avg20, avg30, avg40, avg50, avg60]
+        candle_data = [candle_stick, upper, lower, avg3, avg5, avg10, avg30, buy_check, sell_check]
-        candle_data = [candle_stick, bolinger_upper, bolinger_lower, avg2, avg5, avg10, avg30, avg60, buy_check, sell_check]
         volume_data = [volume_line]
-        stochastic_data = [fast_k_line, slow_k_line, slow_d_line]
+        macd_data = [macd_line, macd_s_line, macd_o_line]
+        stochastic_data = [slow_k_line, slow_d_line]
         rsi_data = [rsi_line, rsis_line]
 
         # 그래프를 그린다.
@@ -132,15 +125,17 @@ class Simulation:
         fig.show()
         """
 
-        fig = subplots.make_subplots(rows=4, cols=1, subplot_titles=('캔들', "거래량", "스토캐스틱", "RSI"))
+        fig = subplots.make_subplots(rows=5, cols=1, subplot_titles=('캔들', "거래량", "MACD", "스토캐스틱", "RSI"))
         for trace in candle_data:
             fig.append_trace(trace, 1, 1)
         for trace in volume_data:
             fig.append_trace(trace, 2, 1)
-        for trace in stochastic_data:
+        for trace in macd_data:
             fig.append_trace(trace, 3, 1)
-        for trace in rsi_data:
+        for trace in stochastic_data:
             fig.append_trace(trace, 4, 1)
+        for trace in rsi_data:
+            fig.append_trace(trace, 5, 1)
         #fig.update_xaxes(nticks=5)
         #fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
 
@@ -149,12 +144,12 @@ class Simulation:
         buy_count = len(df.loc[df["buy"] > 0])
         sell_count = len(df.loc[df["sell"] > 0])
 
-        fig.update_layout(height=1800, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
+        fig.update_layout(height=5000, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
         fig.show()
 
         return
 
-    def simulate(self, GIVEN_DAY):
+    def simulate(self, days):
         result = {"check": set(),
                   "time": [],
                   "open": [],
@@ -163,30 +158,31 @@ class Simulation:
                   "low": [],
                   "vol": []}
 
+        last_day = days[0]
+        today = days[1]
+
         # 데이터를 가지고 온다.
-        self.getCSV("./data/"+self.stock_code+"_"+GIVEN_DAY+".csv", GIVEN_DAY, result)
+        self.getCSV("./data/" + self.stock_code + "_" + last_day + ".csv", last_day, result)
+        self.getCSV("./data/" + self.stock_code + "_" + today + ".csv", today, result)
 
         # 분석을 통해서 볼린저밴드 상/하단을 계산한다.
         data = self.buySellChecker.analyze(result)
 
         # 사야 할 시점과 팔아야 할 시점을 체크한다.
-        bsLine = self.checkTransaction(data)
+        bsLine = self.buySellChecker.checkTransaction(data, self.stock_code)
 
         # 그래프를 그린다.
-        self.draw(self.stock_code, GIVEN_DAY, data, bsLine)
+        self.draw(self.stock_code, today, data, bsLine)
 
         return
 
 if __name__ == "__main__":
 
-    today = datetime.today()
-
-    PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))
-    RESOURCE_DIR = PROJECT_HOME + "/resources/analysis/"+today.strftime("%Y%m%d")
-
     stock_codes = {
-        "252670": ['20220520', '20220128', '20220121', '20220120', '20211026'],
+        # "252670": ['20220620', '20220621', '20220622', '20220623', '20220624'],
-        "122630": ['20211026', '20211025', '20211022', '20211021', '20211020']
+        # "122630": ['20220620', '20220621', '20220622', '20220623', '20220624']
+        "252670": [('20220620', '20220621')],
+        "122630": [('20220620', '20220621')]
     }
 
     for stock_code in stock_codes: