added download history button and some other QOL stuff

app.py

@@ -10,16 +10,18 @@ st.set_page_config(
 )
 
 st.title('🎮 Gameday Model for $SPX')
-st.markdown('**PLEASE NOTE:** Model should be run at or after market open. Documentation and information about model coming soon.')
-
-if st.button("🧹 Clear All"):
-    st.cache_data.clear()
 col1, col2 = st.columns(2)
+with col1:
+    st.markdown('**PLEASE NOTE:** Model should be run at or after market open. Documentation on the model and its features [can be found here.](https://huggingface.co/spaces/boomsss/gamedayspx/blob/main/README.md)')
+with col2:
+    st.caption('Clear before next run.')
+    if st.button("🧹 Clear All"):
+        st.cache_data.clear()
 
 option = st.selectbox(
     'Select a model, then run.',
     ('', '🌞 At Open', '⌚ 30 Mins', '⏳ 60 Mins'))
-
+    
 if option == '':
     st.write('Gotta pick one.')
 
@@ -188,23 +190,51 @@ elif option == '🌞 At Open':
                 return '❌'
             else:
                 return '🟨'
+            
+        def get_acc_text(t, p):
+            if t == False and p <= 0.4:
+                return 'Correct'
+            elif t == True and p > 0.6:
+                return 'Correct'
+            elif t == False and p > 0.6:
+                return 'Incorrect'
+            elif t == True and p <= 0.4:
+                return 'Incorrect'
+            else:
+                return 'No Action'
 
         perf_daily = res1.copy()
+        perf_daily['TargetDate'] = perf_daily.index + BDay(1)
         perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]
-        
-        tab1.subheader(f'Pred for {curr_date} as of 6:30AM PST')
-        tab1.write(results)
-        tab1.write(df_probas)
-
-        tab2.subheader('Latest Data for Pred')
-        tab2.write(new_pred)
-
-        tab3.subheader('Historical Data')
-        tab3.write(df_final)
-
-        tab4.subheader('Performance')
-        tab4.write(df_performance)
-        tab4.write(perf_daily)
+        perf_daily['AccuracyText'] = [get_acc_text(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]
+        perf_daily['ConfidenceScore'] = [x if x > 0.6 else 1-x if x <= 0.4 else x for x in perf_daily['Predicted']]
+        perf_daily = perf_daily[['TargetDate','Predicted','True','Accuracy','AccuracyText','ConfidenceScore']]
+
+        def convert_df(df):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return df.to_csv()
+
+        csv = convert_df(perf_daily)
+
+        with tab1:
+            st.subheader(f'Pred for {curr_date} as of 6:30AM PST')
+            st.write(results)
+            st.write(df_probas)
+        with tab2:
+            st.subheader('Latest Data for Pred')
+            st.write(new_pred)
+        with tab3:
+            st.subheader('Historical Data')
+            st.write(df_final)
+        with tab4:
+            st.subheader('Performance')
+            st.write(df_performance)
+            st.write(perf_daily[['TargetDate','Predicted','True','Accuracy']])
+            st.download_button(
+                label="Download Historical Performance",
+                data=csv,
+                file_name='performance_for_at_open_model.csv',
+            )
 
 elif option == '⌚ 30 Mins':
     if st.button('🏃🏽‍♂️ Run'):
@@ -381,23 +411,51 @@ elif option == '⌚ 30 Mins':
                 return '❌'
             else:
                 return '🟨'
+            
+        def get_acc_text(t, p):
+            if t == False and p <= 0.4:
+                return 'Correct'
+            elif t == True and p > 0.6:
+                return 'Correct'
+            elif t == False and p > 0.6:
+                return 'Incorrect'
+            elif t == True and p <= 0.4:
+                return 'Incorrect'
+            else:
+                return 'No Action'
 
         perf_daily = res1.copy()
+        perf_daily['TargetDate'] = perf_daily.index + BDay(1)
         perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]
-        
-        tab1.subheader(f'Pred for {curr_date} as of 7AM PST')
-        tab1.write(results)
-        tab1.write(df_probas)
-
-        tab2.subheader('Latest Data for Pred')
-        tab2.write(new_pred)
-
-        tab3.subheader('Historical Data')
-        tab3.write(df_final)
-
-        tab4.subheader('Performance')
-        tab4.write(df_performance)
-        tab4.write(perf_daily.sort_index(ascending=False))
+        perf_daily['AccuracyText'] = [get_acc_text(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]
+        perf_daily['ConfidenceScore'] = [x if x > 0.6 else 1-x if x <= 0.4 else x for x in perf_daily['Predicted']]
+        perf_daily = perf_daily[['TargetDate','Predicted','True','Accuracy','AccuracyText','ConfidenceScore']]
+
+        def convert_df(df):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return df.to_csv()
+
+        csv = convert_df(perf_daily)
+
+        with tab1:
+            st.subheader(f'Pred for {curr_date} as of 7AM PST')
+            st.write(results)
+            st.write(df_probas)
+        with tab2:
+            st.subheader('Latest Data for Pred')
+            st.write(new_pred)
+        with tab3:
+            st.subheader('Historical Data')
+            st.write(df_final)
+        with tab4:
+            st.subheader('Performance')
+            st.write(df_performance)
+            st.write(perf_daily[['TargetDate','Predicted','True','Accuracy']])
+            st.download_button(
+                label="Download Historical Performance",
+                data=csv,
+                file_name='performance_for_30m_model.csv',
+            )
 
 elif option == '⏳ 60 Mins':
     if st.button('🏃🏽‍♂️ Run'):
@@ -573,20 +631,48 @@ elif option == '⏳ 60 Mins':
                 return '❌'
             else:
                 return '🟨'
+            
+        def get_acc_text(t, p):
+            if t == False and p <= 0.4:
+                return 'Correct'
+            elif t == True and p > 0.6:
+                return 'Correct'
+            elif t == False and p > 0.6:
+                return 'Incorrect'
+            elif t == True and p <= 0.4:
+                return 'Incorrect'
+            else:
+                return 'No Action'
 
         perf_daily = res1.copy()
+        perf_daily['TargetDate'] = perf_daily.index + BDay(1)
         perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]
-        
-        tab1.subheader(f'Pred for {curr_date} as of 7:30AM PST')
-        tab1.write(results)
-        tab1.write(df_probas)
-
-        tab2.subheader('Latest Data for Pred')
-        tab2.write(new_pred)
-
-        tab3.subheader('Historical Data')
-        tab3.write(df_final)
-
-        tab4.subheader('Performance')
-        tab4.write(df_performance)
-        tab4.write(perf_daily)
+        perf_daily['AccuracyText'] = [get_acc_text(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]
+        perf_daily['ConfidenceScore'] = [x if x > 0.6 else 1-x if x <= 0.4 else x for x in perf_daily['Predicted']]
+        perf_daily = perf_daily[['TargetDate','Predicted','True','Accuracy','AccuracyText','ConfidenceScore']]
+
+        def convert_df(df):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return df.to_csv()
+
+        csv = convert_df(perf_daily)
+
+        with tab1:
+            st.subheader(f'Pred for {curr_date} as of 7:30AM PST')
+            st.write(results)
+            st.write(df_probas)
+        with tab2:
+            st.subheader('Latest Data for Pred')
+            st.write(new_pred)
+        with tab3:
+            st.subheader('Historical Data')
+            st.write(df_final)
+        with tab4:
+            st.subheader('Performance')
+            st.write(df_performance)
+            st.write(perf_daily[['TargetDate','Predicted','True','Accuracy']])
+            st.download_button(
+                label="Download Historical Performance",
+                data=csv,
+                file_name='performance_for_60m_model.csv',
+            )

model_30m.py

@@ -305,7 +305,6 @@ def get_data():
     data['CurrentHigh30toClose'] = (data['CurrentHigh30'] / data['Close']) - 1
     data['CurrentLow30toClose'] = (data['CurrentLow30'] / data['Close']) - 1
     data['CurrentClose30toClose'] = (data['CurrentClose30'] / data['Close']) - 1
-
     data['CurrentRange30'] = (data['CurrentHigh30'] - data['CurrentLow30']) / data['Close']
     data['GapFill30'] = [low <= prev_close if gap > 0 else high >= prev_close for high, low, prev_close, gap in zip(data['CurrentHigh30'], data['CurrentLow30'], data['Close'], data['CurrentGap'])]