diff --git a/hts/BuySellChecker.py b/hts/BuySellChecker.py
index e4cb97e..009bac6 100644
--- a/hts/BuySellChecker.py
+++ b/hts/BuySellChecker.py
@@ -195,55 +195,47 @@ class BuySellChecker:
         # 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
         if i > 300 and data["High"][i] > data["upper"][i]:
                 sell = data["High"][i]
-
-        ################
-        ### buy 분석 ###
-        ################
-        # 1. slow_k가 10 아래 있으면 매수한다.
-        if i < 30:
+        ##########################
+        ### STOCHASTIC buy 분석 ###
+        ##########################
+        if i < 40:
             if data["slow_k"][i] <= 10:
-                if data["Close"][i] < data["avg5"][i]:
-                    buy = data["Close"][i]
-                else:
-                    buy = data["Low"][i]
-        elif 30 <= i < 60:
-            if data["slow_k"][i] <= 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 data["Close"][i] < data["avg5"][i]:
+                        buy = data["Close"][i]
+                    else:
+                        buy = data["Low"][i]
         else:
-            if data["slow_k"][i] <= 20:
-                if data["Close"][i] < data["avg5"][i]:
-                    buy = data["Close"][i]
-                else:
-                    buy = data["Low"][i]
-
-            if data["rsi"][i-1] < 10 and data["rsi"][i-1] < data["rsis"][i-1] and data["rsis"][i] < data["rsi"][i]:
-                if data["Close"][i] < data["avg5"][i]:
-                    buy = data["Close"][i]
-                else:
-                    buy = data["Low"][i]
-
-        ##################
-        ### weight 분석 ###
-        ##################
-        # rsi가 rsis 위로 올라오며 15 이하일 경우 10배로 주문함 (14:30 이전)
-        if data["rsi"][i] < 15 and data["rsis"][i] < 15 and data["rsi"][i - 1] < data["rsis"][i - 1] and data["rsis"][i] < data["rsi"][i]:
-            buy = data["Low"][i]
-            weight = 4
-        if data["rsi"][i] < 10:
-            weight = 4
+            if data["slow_k"][i] <= 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]
 
+        #############################
+        ### STOCHASTIC weight 분석 ###
+        #############################
         if data["slow_k"][i] in (0, 1, 2, 3):
             weight = 4
-        if data["slow_k"][i] in (4, 5, 6, 7):
+        if data["slow_k"][i] in (4, 5, 6, 7, 8):
             weight = 3
-        elif data["slow_k"][i] in (8, 9, 10, 11, 12, 13):
+        elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
             weight = 2
-        elif data["slow_k"][i] in (14, 15, 16, 17, 18, 19, 20):
+        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 ###
+        ###################
+        if data["rsi"][i - 1] < data["rsis"][i - 1] and data["rsis"][i] < data["rsi"][i]:
+            if data["Close"][i] < data["avg5"][i]:
+                buy = data["Close"][i]
+            else:
+                buy = data["Low"][i]
+            weight = 3
+
+
         return buy, weight, sell
 
     def getPriceAndWeight2(self, data, i):
diff --git a/hts/Simulation.py b/hts/Simulation.py
index f6335ec..c0ee678 100644
--- a/hts/Simulation.py
+++ b/hts/Simulation.py
@@ -169,6 +169,7 @@ if __name__ == "__main__":
                   '20210930','20211001', '20211005','20211006', '20211007','20211008', '20211012','20211013', '20211014']
     simulation = Simulation()
 
+    given_days = sorted(given_days, reverse=True)
     for given_day in given_days:
         simulation.simulate(stock_codes[0], given_day)