init

hts/HTS.py

@@ -1,11 +1,15 @@
 import os
 import re
-import win32com.client
+#import win32com.client
 from datetime import datetime
-#import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import mplfinance as mpf
 import pandas as pd
 
-#import mplfinance as mpf
+import cufflinks as cf
+import plotly.graph_objects as go
+import matplotlib.pyplot as plt
 
 class HTS:
 
@@ -270,9 +274,13 @@ class HTS:
         size = objStockChart.GetHeaderValue(3)
 
         #print("날짜", "시간", "시가", "고가", "저가", "종가", "거래량")
+        start_time = datetime.strptime(day+" 093000", '%Y%m%d %H%M%S')
         for i in range(size-1, -1, -1):
             #day = objStockChart.GetDataValue(0, i)
-            time = objStockChart.GetDataValue(1, i)
+            time = datetime.strptime(day+" "+objStockChart.GetDataValue(1, i), '%Y%m%d %H%M%S')
+            if time < start_time:
+                continue
+
             #open = objStockChart.GetDataValue(2, i)
             #high = objStockChart.GetDataValue(3, i)
             #low = objStockChart.GetDataValue(4, i)
@@ -287,44 +295,76 @@ class HTS:
                 result["vol"].append(vol)
         return
 
-    def getCSV(self, fileName, result):
+    def getCSV(self, fileName, day, result):
         data = pd.read_csv(fileName)
 
+        days = data.날짜
         time = data.시간
         close = data.종가
-        for i in range(len(data)-1, -1, -1):
-            if time[i] not in result["check"]:
-                result["check"].add(time[i])
-                result["time"].append(time[i])
+        vol = data.거래량
+        start_time = datetime.strptime(day + " 093000", '%Y%m%d %H%M%S')
+
+        for i in range(len(data)):
+            temp = datetime.strptime(str(days[i]) + " " + str(time[i]), '%Y%m%d %H%M%S')
+            if temp < start_time:
+                continue
+
+            if temp not in result["check"]:
+                result["check"].add(temp)
+                result["time"].append(temp)
                 result["close"].append(close[i])
-                result["vol"].append(0)
+                result["vol"].append(vol[i])
         return
 
     def analyze(self, result):
-        df = pd.DataFrame(result["close"])
         y_value = result["close"]
+        #x_value = [i for i in range(len(y_value))]
         vol = result["vol"]
+        df = pd.DataFrame(y_value)
+        point = result["time"]
 
         max20 = df.rolling(window=20).mean()
         stddev20 = df.rolling(window=20).std()
-        upper = max20 + (stddev20 * 2)                  # 상단 볼린저 밴드
-        lower = max20 - (stddev20 * 2)                  # 하단 볼린저 밴드
-        middle = (upper + lower) / 2                    # 중심 블린저 밴드
+        upper_df = max20 + (stddev20 * 2)                  # 상단 볼린저 밴드
+        lower_df = max20 - (stddev20 * 2)                  # 하단 볼린저 밴드
 
-        x_value = [i for i in range(len(y_value))]
-
-        window = 3
+        window = 60
         open = y_value[:len(y_value)-window] + [y_value[len(y_value)-window] for i in range(window)]    # 시가
         close = [y_value[window-1] for i in range(window-1)] + y_value[window-1:]                       # 종가
-        high = df.rolling(window=window).max()          # 고가
-        low = df.rolling(window=window).min()           # 저가
-        mean5 = df.rolling(window=window).mean()        # 이동평균선
-        mean20 = df.rolling(window=window*4).mean()     # 이동평균선
-        mean60 = df.rolling(window=window*12).mean()    # 이동평균선
+        high_df = df.rolling(window=window).max()          # 고가
+        low_df = df.rolling(window=window).min()           # 저가
 
-        temp = {'Open': open, 'High': high.values.tolist(), 'Low': low.values.tolist(), 'Close': close, 'Volume': vol}
-        data = pd.DataFrame(temp, index=x_value)
-        return data
+        high, low, upper, lower = [], [], [], []
+        for i in range(len(high_df)):
+            if i < window:
+                high.append(high_df.values[window - 1][0])
+                low.append(low_df.values[window - 1][0])
+
+                upper.append(upper_df.values[window - 1][0])
+                lower.append(lower_df.values[window - 1][0])
+            else:
+                high.append(high_df.values[i][0])
+                low.append(low_df.values[i][0])
+
+                upper.append(upper_df.values[i][0])
+                lower.append(lower_df.values[i][0])
+
+        open_temp = [open[i] for i in range(len(open)) if i % window == 0]
+        high_temp = [high[i] for i in range(len(high)) if i % window == 0]
+        low_temp = [low[i] for i in range(len(low)) if i % window == 0]
+        close_temp = [close[i] for i in range(len(close)) if i % window == 0]
+        vol_temp = [vol[i] for i in range(len(vol)) if i % window == 0]
+        point_temp = [point[i] for i in range(len(point)) if i % window == 0]
+        upper_temp = [upper[i] for i in range(len(upper)) if i % window == 0]
+        lower_temp = [lower[i] for i in range(len(lower)) if i % window == 0]
+
+
+        temp = {"Open": open_temp, "High": high_temp, "Low": low_temp, "Close": close_temp, "Volume": vol_temp, "Date": point_temp}
+        data = pd.DataFrame(temp)
+        df_final_time = pd.DatetimeIndex(point_temp)
+        data.index = df_final_time
+
+        return data, upper_temp, lower_temp
 
     def buyRealTime(self, stock_code, day):
         result = {"check": set(),
@@ -334,16 +374,40 @@ class HTS:
 
 
         #self.getRealTime(stock_code, day, result)
-        self.getCSV("data_s_1.csv", result)
-        self.getCSV("data_s_2.csv", result)
-        data = self.analyze(result)
+        self.getCSV("data_s_1.csv", day, result)
+        self.getCSV("data_s_2.csv", day, result)
+        data, upper, lower = self.analyze(result)
 
-        mpf.plot(data, type='candle', mav=(3, 6, 9))
+        data['Open'] = pd.to_numeric(data['Open'])
+        data['High'] = pd.to_numeric(data['High'])
+        data['Low'] = pd.to_numeric(data['Low'])
+        data['Close'] = pd.to_numeric(data['Close'])
+        data['Volume'] = pd.to_numeric(data['Volume'])
+
+        fig = plt.figure(figsize=(10, 10))
+        ax = fig.add_subplot(111)
+
+        """
+        # 이동평균선 그리기
+        ax.plot(upper, label='upper', linewidth=1.5)
+        ax.plot(lower, label='lower', linewidth=1.5)
+
+        # 참고) https://pypi.org/project/mplfinance/
+        mc = mpf.make_marketcolors(up='red', down='blue', inherit=True)
+        style_final = mpf.make_mpf_style(marketcolors=mc)
+        mpf.plot(data, ax=ax, style=style_final)
+        plt.grid()
         plt.show()
+        """
 
-        print(result)
+        bolinger_upper = go.Scatter(x=data['Date'], y=upper, name="upper", line_color='#8B4513')
+        bolinger_lower = go.Scatter(x=data['Date'], y=lower, name="lower", line_color='#8B4513')
+        candle_stick = go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'])
+        fig = go.Figure(data=[candle_stick, bolinger_upper, bolinger_lower])
+        fig.update_layout(title="2x")
+        fig.show()
 
-        return result
+        return
 
 if __name__ == "__main__":
 
@@ -358,9 +422,9 @@ if __name__ == "__main__":
     #hts.all_stocks()
     #hts.getChartData(stock_code)
     #hts.currentStock(stock_code)
+    #hts.printStockData(stock_code, day)
 
     day = datetime.today().strftime("%Y%m%d")
     day = '20210917'
-    #hts.buyRealTime(stock_code, day)
-    hts.printStockData(stock_code, day)
+    hts.buyRealTime(stock_code, day)
     print ("done...")