init

HTS_122630.py

@@ -28,14 +28,6 @@ class HTS_122630 (HTS):
         self.buySellChecker = BuySellChecker()
         return
 
-    def checkTransaction(self, data):
-        size = len(data["close"])
-
-        last_index = size - 1
-        sell, weight = self.buySellChecker.getSellPriceAndWeight_15000(data, last_index)
-        buy, weight = self.buySellChecker.getBuyPriceAndWeight_15000(data, last_index)
-        return buy, weight, sell
-
     def getSellingPrice(self, final_price):
         # 만약 잔고가 있으면 장부가보다 5원 높게 매도한다.
         jangoDic = self.requstJango()
@@ -92,20 +84,20 @@ class HTS_122630 (HTS):
                     result = self.getRealTime(self.stock_code, today, LAST_DATA)
 
                     # 규칙 기반의 분석을 통해서 볼린저밴드 상/하단을 계산한다.
-                    data = self.buySellChecker.analyzeByRule(result)
+                    data = self.buySellChecker.analyze(result)
 
                     # 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
-                    bsLine, data = self.buySellChecker.checkTransaction(data, self.stock_code, True)
+                    bsLine, data = self.buySellChecker.checkTransaction(data, self.stock_code, isRealTime=True)
-                    bs_buy_price = bsLine['buy']
+                    bs_buy_price = bsLine['buy'][0]
-                    bs_weight = bsLine['buy_weight']
+                    bs_buy_weight = bsLine['buy_weight'][0]
-                    bs_sell_price = bsLine['sell']
+                    bs_sell_price = bsLine['sell'][0]
 
                     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 * bs_buy_weight)
 
                         # 매수를 주문한다.
                         orderNum = self.requestOrder(OrderType.buy, self.stock_code, BUY_COUNT, bs_buy_price)

HTS_252670.py

@@ -85,20 +85,20 @@ class HTS_252670 (HTS):
                     result = self.getRealTime(self.stock_code, today, LAST_DATA)
 
                     # 규칙 기반의 분석을 통해서 볼린저밴드 상/하단을 계산한다.
-                    data = self.buySellChecker.analyzeByRule(result)
+                    data = self.buySellChecker.analyze(result)
 
                     # 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
-                    bsLine, data = self.buySellChecker.checkTransaction(data, self.stock_code, True)
+                    bsLine, data = self.buySellChecker.checkTransaction(data, self.stock_code, isRealTime=True)
-                    bs_buy_price = bsLine['buy']
+                    bs_buy_price = bsLine['buy'][0]
-                    bs_weight = bsLine['buy_weight']
+                    bs_buy_weight = bsLine['buy_weight'][0]
-                    bs_sell_price = bsLine['sell']
+                    bs_sell_price = bsLine['sell'][0]
 
                     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 * bs_buy_weight)
 
                         # 매수를 주문한다.
                         orderNum = self.requestOrder(OrderType.buy, self.stock_code, BUY_COUNT , bs_buy_price)

Simulation.py

@@ -3,7 +3,6 @@ import pandas as pd
 import plotly.graph_objects as go
 from plotly import subplots
 import os
-import sqlite3
 
 from hts.HTS import HTS
 from stock.util.Stock2Vector import Stock2Vector
@@ -98,7 +97,7 @@ class Simulation (HTS):
 
         # 그래프를 그린다.
         """
-        fig = go.Figure(hts=candle_data)
+        fig = go.Figure(data=candle_data)
         fig.update_layout(title=stock_code + "_" + given_day)
         fig.show()
         """
@@ -127,8 +126,8 @@ class Simulation (HTS):
 
         return
 
-    def getData(self):
+    def analyzeAutoMode(self, data):
-        return
+        return data, None
 
     def simulate(self, stock_code, today, type="rule"):
         LAST_DATA = self.stock2Vector.getLastData(stock_code, today)
@@ -136,14 +135,14 @@ class Simulation (HTS):
         result = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA)
 
         if type == "rule":
-            # 규칙 기반의 분석을 통해서 볼린저밴드 상/하단을 계산한다.
+            # 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
-            data = self.buySellChecker.analyzeByRule(result)
+            data = self.buySellChecker.analyze(result)
             # 사야 할 시점과 팔아야 할 시점을 체크한다.
-            bsLine, data = self.buySellChecker.checkTransaction(data, stock_code, False)
+            bsLine, data = self.buySellChecker.checkTransaction(data, stock_code, isRealTime=False)
-        elif type == "labeltest":
+        elif type == "auto":
-            data = self.getData(result)
+            data, bsLine = self.analyzeAutoMode(result)
         else:
-            data = None
+            data, bsLine = None, None
 
         if data is not None:
             # 그래프를 그린다.

hts/BuySellChecker.py

@@ -746,14 +746,7 @@ class BuySellChecker:
 
         return sell, weight
 
-
+    def analyze(self, result):
-
-
-
-
-
-
-    def analyzeByRule(self, result):
         open = result["open"]
         close = result["close"]
         high = result["high"]
@@ -1161,7 +1154,7 @@ class BuySellChecker:
         data = data.fillna(close[0])
         return data
 
-    def checkTransaction(self, data, stock_code, type=True):
+    def checkTransaction(self, data, stock_code, isRealTime=True):
         # 4일치 중에서 앞에 2일은 제거한다.
         date = data['date'].dt.date.unique().tolist()
         data = data[data['date'].dt.date != date[0]]
@@ -1170,7 +1163,8 @@ class BuySellChecker:
         # 어제 오늘 데이터로 분석
         bsLine = {}
         size = len(data["close"])
-        if type:
+        if isRealTime:
+            # isRealTime=True, 실시간 적용
             last_index = size - 1
 
             if stock_code == "252670":
@@ -1180,25 +1174,28 @@ class BuySellChecker:
                 sell, weight = self.getSellPriceAndWeight_15000(data, last_index)
                 buy, weight = self.getBuyPriceAndWeight_15000(data, last_index)
 
-            bsLine['buy'] = buy
+            bsLine['buy'] = [buy]
-            bsLine['buy_weight'] = weight
+            bsLine['buy_weight'] = [weight]
-            bsLine['sell'] = sell
+            bsLine['sell'] = [sell]
-            bsLine['sell_weight'] = weight
+            bsLine['sell_weight'] = [weight]
         else:
+            # Type=False, 시뮬레이션 적용
             bsLine['buy'] = [-1 for i in range(size)]
-            bsLine['weight'] = [-1 for i in range(size)]
+            bsLine['buy_weight'] = [-1 for i in range(size)]
             bsLine['sell'] = [-1 for i in range(size)]
+            bsLine['sell_weight'] = [-1 for i in range(size)]
 
             for i in range(size):
                 if stock_code == "252670":
-                    sell, weight = self.getSellPriceAndWeight_3000(data, i)
+                    sell, sell_weight = self.getSellPriceAndWeight_3000(data, i)
-                    buy, weight = self.getBuyPriceAndWeight_3000(data, i)
+                    buy, buy_weight = self.getBuyPriceAndWeight_3000(data, i)
                 else:
-                    sell, weight = self.getSellPriceAndWeight_15000(data, i)
+                    sell, sell_weight = self.getSellPriceAndWeight_15000(data, i)
-                    buy, weight = self.getBuyPriceAndWeight_15000(data, i)
+                    buy, buy_weight = self.getBuyPriceAndWeight_15000(data, i)
 
                 bsLine['buy'][i] = buy
-                bsLine['weight'][i] = weight
+                bsLine['buy_weight'][i] = buy_weight
                 bsLine['sell'][i] = sell
+                bsLine['sell_weight'][i] = sell_weight
 
         return bsLine, data

resources/tmp/20220727.buy.csv

@@ -0,0 +1,13 @@
+2022-07-27 09:19:00,3060
+2022-07-27 09:20:00,3070
+2022-07-27 09:21:00,3070
+2022-07-27 09:53:00,3075
+2022-07-27 09:57:00,3075
+2022-07-27 09:58:00,3075
+2022-07-27 12:26:00,3090
+2022-07-27 12:27:00,3090
+2022-07-27 12:31:00,3090
+2022-07-27 12:32:00,3090
+2022-07-27 12:33:00,3090
+2022-07-27 12:36:00,3090
+2022-07-27 12:38:00,3090

resources/tmp/20220727.sell.csv

@@ -0,0 +1,8 @@
+2022-07-27 09:05:00,3085
+2022-07-27 09:06:00,3085
+2022-07-27 09:08:00,3085
+2022-07-27 09:34:00,3085
+2022-07-27 10:29:00,3105
+2022-07-27 10:30:00,3105
+2022-07-27 13:07:00,3105
+2022-07-27 13:24:00,3105

resources/tmp/20220728.buy.csv

@@ -0,0 +1,14 @@
+2022-07-28 09:07:00,2975
+2022-07-28 09:08:00,2985
+2022-07-28 09:09:00,2995
+2022-07-28 09:10:00,2995
+2022-07-28 09:51:00,2995
+2022-07-28 09:52:00,3000
+2022-07-28 09:53:00,3000
+2022-07-28 09:55:00,3000
+2022-07-28 09:57:00,3000
+2022-07-28 09:58:00,3005
+2022-07-28 09:59:00,3005
+2022-07-28 11:59:00,3005
+2022-07-28 12:00:00,3010
+2022-07-28 12:01:00,3010

resources/tmp/20220728.sell.csv

@@ -0,0 +1,9 @@
+2022-07-28 09:32:00,3025
+2022-07-28 10:23:00,3035
+2022-07-28 10:37:00,3035
+2022-07-28 10:38:00,3035
+2022-07-28 10:41:00,3035
+2022-07-28 10:44:00,3035
+2022-07-28 10:46:00,3035
+2022-07-28 12:24:00,3030
+2022-07-28 14:27:00,3040

resources/tmp/20220729.buy.csv

@@ -0,0 +1,8 @@
+2022-07-29 09:34:00,2945
+2022-07-29 09:35:00,2945
+2022-07-29 09:36:00,2945
+2022-07-29 09:37:00,2945
+2022-07-29 09:38:00,2950
+2022-07-29 09:39:00,2950
+2022-07-29 09:40:00,2950
+2022-07-29 12:31:00,2990

resources/tmp/20220729.sell.csv

@@ -0,0 +1,5 @@
+2022-07-29 10:07:00,2985
+2022-07-29 10:49:00,2990
+2022-07-29 12:53:00,3010
+2022-07-29 14:15:00,3015
+2022-07-29 14:16:00,3015

stock/util/LabelMaker.py

@@ -1,8 +1,189 @@
-import sqlite3
+import os
+import csv
+from math import nan
+import pandas as pd
+import plotly.graph_objects as go
+from plotly import subplots
+
+from hts.HTS import HTS
+from hts.BuySellChecker import BuySellChecker
+
+class LabelMaker (HTS):
+    buySellChecker = None
 
-class LabelMaker:
     def __init__(self):
+        super().__init__(RESOURCE_PATH)
+        self.buySellChecker = BuySellChecker()
+        return
+
+    def checkTransaction(self, data):
+        bsLine = {}
+        size = len(data["close"])
+
+        # Type=False, 시뮬레이션 적용
+        bsLine['buy'] = [-1 for i in range(size)]
+        bsLine['buy_weight'] = [-1 for i in range(size)]
+        bsLine['sell'] = [-1 for i in range(size)]
+        bsLine['sell_weight'] = [-1 for i in range(size)]
+
+        for i in range(size-60):
+            min_price, min_price_c, max_price, max_price_c = 9999999, -1, 0, -1
+            for c in range(i, i+60):
+                if data["close"][c] > max_price:
+                    max_price = data["close"][c]
+                    max_price_c = c
+                if data["close"][c] < min_price:
+                    min_price = data["close"][c]
+                    min_price_c = c
+
+            if min_price_c > 0:
+                bsLine['buy'][min_price_c] = min_price
+                bsLine['buy_weight'][min_price_c] = 1
+            if max_price_c > 0:
+                bsLine['sell'][max_price_c] = max_price
+                bsLine['sell_weight'][min_price_c] = 1
+
+        return bsLine, data
+
+    def draw(self, stock_code, given_day, data, bsLine):
+        buy_line = bsLine['buy']
+        sell_line = bsLine['sell']
+
+        # 그래프 설정을 위한 변수를 생성한다.
+        data = data.astype({'open': 'int',
+                            'high': 'int',
+                            'low': 'int',
+                            'close': 'int',
+                            'volume': 'int',
+                            'avg3': 'float',
+                            'avg5': 'float',
+                            'avg10': 'float',
+                            'avg20': 'float',
+                            'avg30': 'float',
+                            'avg60': 'float',
+                            'fast_k': 'float',
+                            'slow_k': 'float',
+                            'slow_d': 'float',
+                            'rsi': 'float',
+                            'rsis': 'float'
+                            })
+
+        buy_colors = []
+        for i in range(len(buy_line)):
+            if buy_line[i] < 0:
+                buy_colors.append("#ffffff")
+                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] = 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))
+        upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
+        lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
+        avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#1469F4')
+        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#089B5B')
+        avg10 = go.Scatter(x=data['date'], y=data["avg10"], name="avg10", line_color='#ff00ff')
+        avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#8F8203')
+        avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#000000')
+
+        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=hts['date'], y=hts["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, upper, lower, avg3, avg5, avg10, avg20, avg30, buy_check, sell_check]
+        volume_data = [volume_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]
+
+        """
+        fig = go.Figure(data=candle_data)
+        df = pd.DataFrame(bsLine)
+        df = df.fillna(-1)
+        buy_count = len(df.loc[df["buy"] > 0])
+        sell_count = len(df.loc[df["sell"] > 0])
+
+        fig.update_layout(height=1000, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
+        fig.show()
+        """
+
+        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 macd_data:
+            fig.append_trace(trace, 3, 1)
+        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)
+
+        df = pd.DataFrame(bsLine)
+        df = df.fillna(-1)
+        buy_count = len(df.loc[df["buy"] > 0])
+        sell_count = len(df.loc[df["sell"] > 0])
+
+        fig.update_layout(height=3000, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
+        fig.show()
+
+
+        return
+
+    def writeLabelFile(self, bsLine, data, ymd):
+        outFileName = os.path.join(self.RESOURCE_PATH, "tmp", ymd+".sell.csv")
+        with open(outFileName, "w", encoding="utf-8") as outFp:
+            writer = csv.writer(outFp)
+            for i, price in enumerate(bsLine["sell"]):
+                if price != -1:
+                    writer.writerow([data['date'][i], bsLine["sell"][i]])
+
+        outFileName = os.path.join(self.RESOURCE_PATH, "tmp", ymd + ".buy.csv")
+        with open(outFileName, "w", encoding="utf-8") as outFp:
+            writer = csv.writer(outFp)
+            for i, price in enumerate(bsLine["buy"]):
+                if price != -1:
+                    writer.writerow([data['date'][i], bsLine["buy"][i]])
+        return
+
+    def makeCandidate(self, stock_code, ymd="20220727"):
+        result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": [], "label": []}
+        self.getDBData(stock_code, ymd, result)
+
+        data = self.buySellChecker.analyze(result)
+        bsLine, data = self.checkTransaction(data)
+
+        self.writeLabelFile(bsLine, data, ymd)
+        self.draw(stock_code, ymd, data, bsLine)
         return
 
 if __name__ == "__main__":
+    PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))))
+    RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
+
     labelMaker = LabelMaker()
+
+    stock_code = "252670"
+    #stock_code = "122630"
+
+    labelMaker.makeCandidate(stock_code, "20220727")