diff --git a/stock/util/Stock2Vector.py b/stock/util/Stock2Vector.py
index 5244a57..f5500c4 100644
--- a/stock/util/Stock2Vector.py
+++ b/stock/util/Stock2Vector.py
@@ -285,8 +285,8 @@ class Stock2Vector(HTS):
         Y = np.asarray(Y, dtype='int64')
         return X, Y
 
-    def getVectorData(self, data, VECTOR_SIZE = 32):
-        return self.getVectorData_2(data, VECTOR_SIZE)
+    def getVectorData(self, data, VECTOR_SIZE = 32, H_VECTOR_SIZE = 32, W_VECTOR_SIZE = 200):
+        return self.getVectorData_2(data, H_VECTOR_SIZE, W_VECTOR_SIZE)
 
     def getVectorData_1(self, data, VECTOR_SIZE):
         df = self.buySellChecker.analyze(data)
@@ -329,39 +329,83 @@ class Stock2Vector(HTS):
         batch_Y = np.asarray(batch_Y, dtype='int64')
         return batch_X, batch_Y
 
-    def getVectorData_2(self, data, VECTOR_SIZE = 32):
+    def getVectorData_2(self, data, H_VECTOR_SIZE = 32, W_VECTOR_SIZE = 200):
         df = self.buySellChecker.analyze(data)
 
-        # avg10, 볼린져밴드 상단과 하단의 차이, rsi, avg3만 이용한다.
-        # channel1: avg10, channel2: diff_upper_lower, channel3: abs_avg_2, channel4: abs_avg_3
-        avg3 = df['avg3'].tolist()
-        avg9 = df['avg9'].tolist()
-        avg12 = df['avg9'].tolist()
-        avg24 = df['avg9'].tolist()
-
-        diff_upper_lower = df['diff_upper_lower'].tolist()
+        macd = df['macd'].tolist()
+        diff_avg27 = df['diff_avg27'].tolist()
+        diff_avg3_avg27 = df['diff_avg3_avg27'].tolist()
+        diff_avg3_avg54 = df['diff_avg3_avg54'].tolist()
+        diff_avg6_avg27 = df['diff_avg6_avg27'].tolist()
+        diff_avg6_avg54 = df['diff_avg6_avg54'].tolist()
+        diff_avg9_avg27 = df['diff_avg9_avg27'].tolist()
+        diff_avg9_avg54 = df['diff_avg9_avg54'].tolist()
+        diff_avg12_avg27 = df['diff_avg12_avg27'].tolist()
+        diff_avg12_avg54 = df['diff_avg12_avg54'].tolist()
+        diff_change_lead1 = df['diff_change_lead1'].tolist()
+        diff_open_lead1 = df['diff_open_lead1'].tolist()
+        diff_close_lead1 = df['diff_close_lead1'].tolist()
+        diff_high_lead1 = df['diff_high_lead1'].tolist()
+        diff_low_lead1 = df['diff_low_lead1'].tolist()
         rsi = df['rsi'].tolist()
+        rsis = df['rsis'].tolist()
+        diff_avg54 = df['diff_avg54'].tolist()
+        diff_change_base = df['diff_change_base'].tolist()
+        diff_base_lead1 = df['diff_base_lead1'].tolist()
+        diff_open_base = df['diff_open_base'].tolist()
+        diff_close_base = df['diff_close_base'].tolist()
+        diff_high_base = df['diff_high_base'].tolist()
+        diff_low_base = df['diff_low_base'].tolist()
+        abs_avg_1 = df['abs_avg_1'].tolist()
+        abs_avg_2 = df['abs_avg_2'].tolist()
         abs_avg_3 = df['abs_avg_3'].tolist()
-
-        size = len(avg10)
-        batch_X, batch_Y = [], []
-        X = np.zeros((4, VECTOR_SIZE, VECTOR_SIZE))
+        abs_avg_4 = df['abs_avg_4'].tolist()
+        abs_avg_5 = df['abs_avg_5'].tolist()
+        diff_upper_lower = df['diff_upper_lower'].tolist()
+        diff_open_lower = df['diff_open_lower'].tolist()
+        diff_close_upper = df['diff_close_upper'].tolist()
         label = df['label'].tolist()
-        for i in range(VECTOR_SIZE * VECTOR_SIZE - 1, size):
-            w, h = 0, 0
-            for j in range(i - VECTOR_SIZE * VECTOR_SIZE + 1, i + 1):
-                X[0, h, w] = avg10[j]
-                X[1, h, w] = diff_upper_lower[j]
-                X[2, h, w] = abs_avg_3[j]
-                X[3, h, w] = rsi[j]
-                w += 1
-                if w >= VECTOR_SIZE:
-                    w = 0
-                    h += 1
-                    if h >= VECTOR_SIZE:
-                        h = 0
-                        batch_X.append(X)
-            batch_Y.append(label[i])
+
+        size = len(label)
+        batch_X, batch_Y = [], []
+        X = np.zeros((H_VECTOR_SIZE, W_VECTOR_SIZE))
+        for i in range(W_VECTOR_SIZE - 1, size):
+            for w in range(i - W_VECTOR_SIZE + 1, i + 1):
+                X[0, w] = macd[w]
+                X[1, w] = diff_avg27[w]
+                X[2, w] = diff_avg3_avg27[w]
+                X[3, w] = diff_avg3_avg54[w]
+                X[4, w] = diff_avg6_avg27[w]
+                X[5, w] = diff_avg6_avg54[w]
+                X[6, w] = diff_avg9_avg27[w]
+                X[7, w] = diff_avg9_avg54[w]
+                X[8, w] = diff_avg12_avg27[w]
+                X[9, w] = diff_avg12_avg54[w]
+                X[10, w] = diff_change_lead1[w]
+                X[11, w] = diff_open_lead1[w]
+                X[12, w] = diff_close_lead1[w]
+                X[13, w] = diff_high_lead1[w]
+                X[14, w] = diff_low_lead1[w]
+                X[15, w] = rsi[w]
+                X[16, w] = rsis[w]
+                X[17, w] = diff_avg54[w]
+                X[18, w] = diff_change_base[w]
+                X[19, w] = diff_base_lead1[w]
+                X[20, w] = diff_open_base[w]
+                X[21, w] = diff_close_base[w]
+                X[22, w] = diff_high_base[w]
+                X[23, w] = diff_low_base[w]
+                X[24, w] = abs_avg_1[w]
+                X[25, w] = abs_avg_2[w]
+                X[26, w] = abs_avg_3[w]
+                X[27, w] = abs_avg_4[w]
+                X[28, w] = abs_avg_5[w]
+                X[29, w] = diff_upper_lower[w]
+                X[30, w] = diff_open_lower[w]
+                X[31, w] = diff_close_upper[w]
+
+                batch_X.append(X)
+                batch_Y.append(label[i])
 
         batch_X = np.asarray(batch_X)
         batch_Y = np.asarray(batch_Y, dtype='int64')
@@ -407,7 +451,7 @@ if __name__ == "__main__":
     stock_codes = {
         # 252670
         # 122630
-        "252670": ['20220729'],
+        "252670": ['20220801', '20220802', '20220803', '20220804', '20220805']
     }
 
     for stock_code in stock_codes: