diff --git a/hts/BuySellChecker.py b/hts/BuySellChecker.py
index 835ace3..34bfc98 100644
--- a/hts/BuySellChecker.py
+++ b/hts/BuySellChecker.py
@@ -272,24 +272,26 @@ class BuySellChecker:
             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] <= 20:
-                    if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.1:
+                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["close"][i] < data["avg5"][i]:
-                                buy = data["close"][i]
-                            else:
-                                buy = data["low"][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] <= 20:
+                if data["slow_k"][i] <= 30:
                     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["close"][i] < data["avg5"][i]:
-                                buy = data["close"][i]
-                            else:
-                                buy = data["low"][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 ###
@@ -305,72 +307,6 @@ class BuySellChecker:
 
         return buy, weight, sell
 
-    def getPriceAndWeight3(self, data, i):
-        buy, weight, sell = -1, -1, -1
-
-        ################
-        ### 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]:
-            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 data["slow_d"][i - 2] > data["slow_d"][i - 1] and data["slow_d"][i - 1] < data["slow_d"][i]:
-                if abs(data["slow_d"][i]-data["slow_k"][i]) < abs(data["slow_d"][i-1]-data["slow_k"][i-1]):
-                    if now_slow < 0.15:
-                        if data["close"][i] < data["avg5"][i]:
-                            buy = data["close"][i]
-                        else:
-                            buy = data["low"][i]
-            if data["slow_k"][i-1] < data["slow_d"][i-1] and data["slow_d"][i] < data["slow_k"][i]:
-                if abs(now_slow) < 0.001:
-                    if now_slow < 0.15:
-                        if data["close"][i] < data["avg5"][i]:
-                            buy = data["close"][i]
-                        else:
-                            buy = data["low"][i]
-        else:
-            if i > 60:
-                print (1)
-            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] <= 20:
-                    if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
-                        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
-
-
     def analyze(self, result):
         df = pd.DataFrame(result["close"])
 
@@ -391,6 +327,8 @@ class BuySellChecker:
         avg1 = [item[0] for item in avg1_list]
         avg2_list = close_df.rolling(window=2).mean().fillna(close[0]).values.tolist()
         avg2 = [item[0] for item in avg2_list]
+        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()
@@ -405,6 +343,10 @@ class BuySellChecker:
         avg50 = [item[0] for item in avg50_list]
         avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist()
         avg60 = [item[0] for item in avg60_list]
+        avg120_list = close_df.rolling(window=120).mean().fillna(close[0]).values.tolist()
+        avg120 = [item[0] for item in avg120_list]
+        avg240_list = close_df.rolling(window=240).mean().fillna(close[0]).values.tolist()
+        avg240 = [item[0] for item in avg240_list]
 
         upper, lower = [], []
         for i in range(len(upper_df)):
@@ -419,10 +361,9 @@ class BuySellChecker:
 
         STOCK = []
         for i in range(len(result["open"])):
-            STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i],
-                          'high': high[i], 'low': low[i], 'avg5': avg2[i],
-                          'avg20': avg5[i], 'avg60': avg10[i], 'avg120': avg20[i],
-                          'avg240': avg30[i]})
+            STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
+                          'avg1': avg1[i],'avg2': avg2[i],'avg3': avg3[i],'avg5': avg5[i],'avg10': avg10[i],
+                          'avg20': avg20[i], 'avg60': avg60[i], 'avg120': avg120[i],'avg240': avg240[i]})
 
         # stochastic 계산
         stochastic_df = self.stochastic.apply(pd.DataFrame(STOCK))
@@ -438,11 +379,9 @@ class BuySellChecker:
         rsis = rsi_df['rsis'].values.tolist()
 
         temp = {"date": point_temp,
-                "open": open, "high": high, "low": low, "close": close, "volume": vol,
-                "upper": upper, "lower": lower,
-                "avg1": avg1, "avg2": avg2, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg40": avg40, "avg50": avg50, "avg60": avg60,
-                "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
-                "rsi": rsi, "rsis": rsis}
+                "open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower,
+                "avg1": avg1, "avg2": avg2, "avg3": avg3, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg40": avg40, "avg50": avg50, "avg60": avg60, "avg120": avg120, "avg240": avg240,
+                "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
diff --git a/hts/Simulation.py b/hts/Simulation.py
index 11f340f..780bfa6 100644
--- a/hts/Simulation.py
+++ b/hts/Simulation.py
@@ -101,7 +101,12 @@ class Simulation:
         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')
         bolinger_lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#8B4513')
-        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#000000')
+        avg2 = go.Scatter(x=data['date'], y=data["avg2"], name="avg2", line_color='#000000')
+        avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#c0c0c0')
+        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#800080')
+        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='#00ffff')
+        avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", 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')
@@ -112,7 +117,7 @@ class Simulation:
         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, bolinger_upper, bolinger_lower, avg5, buy_check, sell_check]
+        candle_data = [candle_stick, bolinger_upper, bolinger_lower, avg2, avg3, avg5, avg10, buy_check, sell_check]
         volume_data = [volume_line]
         stochastic_data = [slow_k_line, slow_d_line]
         rsi_data = [rsi_line, rsis_line]
@@ -180,9 +185,9 @@ if __name__ == "__main__":
     given_days = ['20210901','20210902','20210903','20210906','20210907','20210908','20210909','20210910','20210913',
                   '20210914','20210915','20210916','20210917','20210923','20210924','20210927','20210928','20210929',
                   '20210930','20211001','20211005','20211006','20211007','20211008','20211012','20211013','20211014',
-                  '20211018', '20211019','20211020','20211021','20211022']
-
-    simulation = Simulation(stock_codes[1])
+                  '20211018', '20211019','20211020','20211021','20211022','20211025']
+    #given_days = ['20211006']
+    simulation = Simulation(stock_codes[0])
 
     given_days = sorted(given_days, reverse=True)
     for given_day in given_days: