fix ohlc issues

app.py

@@ -222,11 +222,15 @@ with st.form("choose_model"):
                 return df.to_csv()
 
             csv = convert_df(perf_daily)
+            
+            check = data.tail(1)
 
             with tab1:
-                st.subheader(f'Pred for {curr_date} as of 6:30AM PST')
+                st.subheader(f'Pred for {curr_date} as of 7AM PST')
                 st.write(results)
                 st.write(df_probas)
+                st.text('For checking only 👇🏽')
+                st.write(check)
             with tab2:
                 st.subheader('Latest Data for Pred')
                 st.write(new_pred)
@@ -445,10 +449,14 @@ with st.form("choose_model"):
 
             csv = convert_df(perf_daily)
 
+            check = data.tail(1)
+
             with tab1:
                 st.subheader(f'Pred for {curr_date} as of 7AM PST')
                 st.write(results)
                 st.write(df_probas)
+                st.text('For checking only 👇🏽')
+                st.write(check)
             with tab2:
                 st.subheader('Latest Data for Pred')
                 st.write(new_pred)
@@ -665,11 +673,15 @@ with st.form("choose_model"):
                 return df.to_csv()
 
             csv = convert_df(perf_daily)
+            
+            check = data.tail(1)
 
             with tab1:
                 st.subheader(f'Pred for {curr_date} as of 7:30AM PST')
                 st.write(results)
                 st.write(df_probas)
+                st.text('For checking only 👇🏽')
+                st.write(check)
             with tab2:
                 st.subheader('Latest Data for Pred')
                 st.write(new_pred)
@@ -886,11 +898,15 @@ with st.form("choose_model"):
                 return df.to_csv()
 
             csv = convert_df(perf_daily)
+            
+            check = data.tail(1)
 
             with tab1:
                 st.subheader(f'Pred for {curr_date} as of 8AM PST')
                 st.write(results)
                 st.write(df_probas)
+                st.text('For checking only 👇🏽')
+                st.write(check)
             with tab2:
                 st.subheader('Latest Data for Pred')
                 st.write(new_pred)

model_1h.py

@@ -248,15 +248,15 @@ def get_data():
     df_30m = df_30m[['Open','High','Low','Close']]
 
     opens_1h = df_30m.groupby('Datetime')['Open'].head(1)
-    closes_1h = df_30m.groupby('Datetime')['Close'].tail(1)
     highs_1h = df_30m.groupby('Datetime')['High'].max()
     lows_1h = df_30m.groupby('Datetime')['Low'].min()
+    closes_1h = df_30m.groupby('Datetime')['Close'].tail(1)
     
     df_1h = pd.DataFrame(index=df_30m.index.unique())
     df_1h['Open'] = opens_1h
-    df_1h['Close'] = closes_1h
     df_1h['High'] = highs_1h
     df_1h['Low'] = lows_1h
+    df_1h['Close'] = closes_1h
 
     df_1h.columns = ['Open30','High30','Low30','Close30']
 

model_90m.py

@@ -248,15 +248,15 @@ def get_data():
     df_30m = df_30m[['Open','High','Low','Close']]
 
     opens_1h = df_30m.groupby('Datetime')['Open'].head(1)
-    closes_1h = df_30m.groupby('Datetime')['Close'].tail(1)
     highs_1h = df_30m.groupby('Datetime')['High'].max()
     lows_1h = df_30m.groupby('Datetime')['Low'].min()
+    closes_1h = df_30m.groupby('Datetime')['Close'].tail(1)
     
     df_1h = pd.DataFrame(index=df_30m.index.unique())
     df_1h['Open'] = opens_1h
-    df_1h['Close'] = closes_1h
     df_1h['High'] = highs_1h
     df_1h['Low'] = lows_1h
+    df_1h['Close'] = closes_1h
 
     df_1h.columns = ['Open30','High30','Low30','Close30']