init

hts/BuySellChecker.py

@@ -366,14 +366,14 @@ class BuySellChecker:
                           'avg20': avg20[i], 'avg60': avg60[i], 'avg120': avg120[i],'avg240': avg240[i]})
 
         # stochastic 계산
-        stochastic_df = self.stochastic.apply(pd.DataFrame(STOCK))
+        stochastic_df = self.stochastic.apply(STOCK)
         stochastic_df = stochastic_df.fillna(100)
         fast_k = stochastic_df['fast_k'].values.tolist()
         slow_k = stochastic_df['slow_k'].values.tolist()
         slow_d = stochastic_df['slow_d'].values.tolist()
 
         # rsi 계산
-        rsi_df = self.rsi.apply(pd.DataFrame(STOCK))
+        rsi_df = self.rsi.apply(STOCK)
         rsi_df = rsi_df.fillna(100)
         rsi = rsi_df['rsi'].values.tolist()
         rsis = rsi_df['rsis'].values.tolist()

stockpredictor/analysis/RSI.py

@@ -18,6 +18,8 @@ class RSI:
         return
 
     def apply(sefl, df, period=10):
+        df = pd.DataFrame(df)
+
         # df.diff를 통해 (기준일 종가 - 기준일 전일 종가)를 계산하여 0보다 크면 증가분을 감소했으면 0을 넣어줌
         U = np.where(df.close.diff(1) > 0, df.close.diff(1), 0)