big changes, shorter code

.streamlit/config.toml

@@ -0,0 +1,3 @@
+[theme]
+base="dark"
+primaryColor="#3399cc"

app.py

@@ -5,22 +5,27 @@ from sklearn.metrics import roc_auc_score, precision_score, recall_score
 from pandas.tseries.offsets import BDay
 
 st.set_page_config(
-    page_title="Gameday Model for $SPX",
+    page_title="Gameday $SPX",
     page_icon="🎮"
 )
 
 st.title('🎮 Gameday Model for $SPX')
 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 st.form("choose_model"):
+    # option = st.selectbox(
+    #     'Select a model, then run.',
+    #     ('', '🌞 At Open', '⌚ 30 Mins', '⏳ 60 Mins', '🕰 90 Mins'))
+
 
-    option = st.selectbox(
-        'Select a model, then run.',
-        ('', '🌞 At Open', '⌚ 30 Mins', '⏳ 60 Mins', '🕰 90 Mins'))
     col1, col2 = st.columns(2)
-    with col1:
-        submitted = st.form_submit_button('🏃🏽‍♂️ Run',use_container_width=True)
         
+    with col1:
+        option = st.select_slider(
+            'Slide the scale based on PST, then run.',
+            ['06:30', '07:00', '07:30', '08:00']
+        )
     with col2:
+        submitted = st.form_submit_button('🏃🏽‍♂️ Run',use_container_width=True)
         cleared = st.form_submit_button('🧹 Clear All',use_container_width=True)
 
     if cleared:
@@ -31,10 +36,13 @@ with st.form("choose_model"):
 
     if submitted:
 
-        if option == '🌞 At Open':
+        if option == '06:30':
         # runday = st.button('🏃🏽‍♂️ Run')
         # if runday:
             from model_day import *
+
+            fname='performance_for_open_model.csv'
+
             with st.spinner('Loading data...'):
                 data, df_final, final_row = get_data()
             # st.success("✅ Historical data")
@@ -83,226 +91,15 @@ with st.form("choose_model"):
                 new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float)
                 new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float)
 
-            st.success("✅ All done!")
-            tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"])
-
-            seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
-
-            green_proba = seq_proba[0]
-            red_proba = 1 - green_proba
-            do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) 
-            stdev = 0.01
-            score = None
-            num_obs = None
-            cond = None
-            historical_proba = None
-            text_cond = None
-            operator = None
-
-            if do_not_play:
-                text_cond = '🟨'
-                operator = ''
-                score = seq_proba[0]
-                cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6)
-                num_obs = len(res1.loc[cond])
-                historical_proba = res1.loc[cond, 'True'].mean()
-
-                
-            elif green_proba > red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟩'
-                operator = '>='
-                score = green_proba
-                # How many with this score?
-                cond = (res1['Predicted'] >= green_proba)
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            elif green_proba <= red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟥'
-                operator = '<='
-                score = red_proba
-                # How many with this score?
-                cond = (res1['Predicted'] <= seq_proba[0])
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = 1 - res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            score_fmt = f'{score:.1%}'
-
-            results = pd.DataFrame(index=[
-                'PrevClose',
-                'Confidence Score',
-                'Success Rate',
-                f'NumObs {operator} {"" if do_not_play else score_fmt}',
-            ], data = [
-                f"{data.loc[final_row,'Close']:.2f}",
-                f'{text_cond} {score:.1%}',
-                f'{historical_proba:.1%}', 
-                num_obs,
-                ])
-
-            results.columns = ['Outputs']
-
-            # st.subheader('New Prediction')
-
-            int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
-            # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
-
-            data['ClosePct'] = (data['Close'] / data['PrevClose']) - 1
-            data['ClosePct'] = data['ClosePct'].shift(-1)
-            res1 = res1.merge(data['ClosePct'], left_index=True,right_index=True)
-            df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean]})
-            df_probas.columns = ['PctGreen','NumObs','NumGreen','AvgPerf']
-            df_probas['AvgPerf'] = df_probas['AvgPerf'].apply(lambda x: f'{x:.2%}')
-
-            roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
-            precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            len_all = len(res1)
-
-            res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)]
-
-            roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)
-            precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            len_hi = len(res2_filtered)
-
-            df_performance = pd.DataFrame(
-                index=[
-                    'N',
-                    'ROC AUC',
-                    'Precision',
-                    'Recall'
-                ],
-                columns = [
-                    'All',
-                    'High Confidence'
-                ],
-                data = [
-                    [len_all, len_hi],
-                    [roc_auc_score_all, roc_auc_score_hi],
-                    [precision_score_all, precision_score_hi],
-                    [recall_score_all, recall_score_hi]
-                ]
-            ).round(2)
-
-            def get_acc(t, p):
-                if t == False and p <= 0.4:
-                    return '✅'
-                elif t == True and p > 0.6:
-                    return '✅'
-                elif t == False and p > 0.6:
-                    return '❌'
-                elif t == True and p <= 0.4:
-                    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'])]
-            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)
-            
-            check = data.tail(1)
-
-            data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))
-            data['VIX_EM_High'] = data['Close'] + data['VIX_EM']
-            data['VIX_EM_Low'] = data['Close'] - data['VIX_EM']
-
-            data['VIX_EM_125'] = data['VIX_EM'] * 1.25
-            data['VIX_EM_125_High'] = data['Close'] + data['VIX_EM_125']
-            data['VIX_EM_125_Low'] = data['Close'] - data['VIX_EM_125']
-
-            data['VIX_EM_15'] = data['VIX_EM'] * 1.5
-            data['VIX_EM_15_High'] = data['Close'] + data['VIX_EM_15']
-            data['VIX_EM_15_Low'] = data['Close'] - data['VIX_EM_15']
-
-            data['VIX_EM'] = data['VIX_EM'].shift(1)
-            data['VIX_EM_High'] = data['VIX_EM_High'].shift(1)
-            data['VIX_EM_Low'] = data['VIX_EM_Low'].shift(1)
-
-            data['VIX_EM_15'] = data['VIX_EM_15'].shift(1)
-            data['VIX_EM_15_High'] = data['VIX_EM_15_High'].shift(1)
-            data['VIX_EM_15_Low'] = data['VIX_EM_15_Low'].shift(1)
-
-            data['VIX_EM_125'] = data['VIX_EM_125'].shift(1)
-            data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)
-            data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)
-
-            df_em = pd.DataFrame(columns=['EM','Low','High','WithinRange','Tested'])
-            df_em.loc['EM 1X'] = [
-                data['VIX_EM'].iloc[-1].round(2),
-                data['VIX_EM_Low'].iloc[-1].round(2), 
-                data['VIX_EM_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data):.1%}"
-                ]
-            df_em.loc['EM 1.25X'] = [
-                data['VIX_EM_125'].iloc[-1].round(2),
-                data['VIX_EM_125_Low'].iloc[-1].round(2), 
-                data['VIX_EM_125_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data):.1%}"
-                ]
-            df_em.loc[f"EM 1.5X"] = [
-                data['VIX_EM_15'].iloc[-1].round(2),
-                data['VIX_EM_15_Low'].iloc[-1].round(2), 
-                data['VIX_EM_15_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data):.1%}"
-                ]
-            
-            with tab1:
-                st.subheader(f'Pred for {curr_date} as of 6:30AM PST')
-                st.write(results)
-                st.write(df_probas)
-                st.text('VIX EM')
-                st.write(df_em)
-            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,
-                fname='performance_for_at_open_model.csv'
-                # )
-
-        elif option == '⌚ 30 Mins':
+                seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
+
+        elif option == '07:00':
         # run30 = st.button('🏃🏽‍♂️ Run')
         # if run30:
             from model_30m import *
+
+            fname='performance_for_30m_model.csv'
+
             with st.spinner('Loading data...'):
                 data, df_final, final_row = get_data()
             # st.success("✅ Historical data")
@@ -361,226 +158,15 @@ with st.form("choose_model"):
                 new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float)
                 new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float)
 
-            st.success("✅ All done!")
-            tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"])
-
-            seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
-
-            green_proba = seq_proba[0]
-            red_proba = 1 - green_proba
-            do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) 
-            stdev = 0.01
-            score = None
-            num_obs = None
-            cond = None
-            historical_proba = None
-            text_cond = None
-            operator = None
-
-            if do_not_play:
-                text_cond = '🟨'
-                operator = ''
-                score = seq_proba[0]
-                cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6)
-                num_obs = len(res1.loc[cond])
-                historical_proba = res1.loc[cond, 'True'].mean()
-
-                
-            elif green_proba > red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟩'
-                operator = '>='
-                score = green_proba
-                # How many with this score?
-                cond = (res1['Predicted'] >= green_proba)
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            elif green_proba <= red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟥'
-                operator = '<='
-                score = red_proba
-                # How many with this score?
-                cond = (res1['Predicted'] <= seq_proba[0])
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = 1 - res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            score_fmt = f'{score:.1%}'
-
-            results = pd.DataFrame(index=[
-                'PrevClose',
-                'Confidence Score',
-                'Success Rate',
-                f'NumObs {operator} {"" if do_not_play else score_fmt}',
-            ], data = [
-                f"{data.loc[final_row,'Close']:.2f}",
-                f'{text_cond} {score:.1%}',
-                f'{historical_proba:.1%}', 
-                num_obs,
-                ])
-
-            results.columns = ['Outputs']
-
-            # st.subheader('New Prediction')
-
-            int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
-            # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
-            
-            data['ClosePct'] = (data['Close'] / data['PrevClose']) - 1
-            data['ClosePct'] = data['ClosePct'].shift(-1)
-            res1 = res1.merge(data['ClosePct'], left_index=True,right_index=True)
-            df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean]})
-            df_probas.columns = ['PctGreen','NumObs','NumGreen','AvgPerf']
-            df_probas['AvgPerf'] = df_probas['AvgPerf'].apply(lambda x: f'{x:.2%}')
-            
-            roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
-            precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            len_all = len(res1)
-
-            res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)]
-
-            roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)
-            precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            len_hi = len(res2_filtered)
-
-            df_performance = pd.DataFrame(
-                index=[
-                    'N',
-                    'ROC AUC',
-                    'Precision',
-                    'Recall'
-                ],
-                columns = [
-                    'All',
-                    'High Confidence'
-                ],
-                data = [
-                    [len_all, len_hi],
-                    [roc_auc_score_all, roc_auc_score_hi],
-                    [precision_score_all, precision_score_hi],
-                    [recall_score_all, recall_score_hi]
-                ]
-            ).round(2)
-
-            def get_acc(t, p):
-                if t == False and p <= 0.4:
-                    return '✅'
-                elif t == True and p > 0.6:
-                    return '✅'
-                elif t == False and p > 0.6:
-                    return '❌'
-                elif t == True and p <= 0.4:
-                    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'])]
-            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)
-
-            check = data.tail(1)
-
-            data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))
-            data['VIX_EM_High'] = data['Close'] + data['VIX_EM']
-            data['VIX_EM_Low'] = data['Close'] - data['VIX_EM']
-
-            data['VIX_EM_125'] = data['VIX_EM'] * 1.25
-            data['VIX_EM_125_High'] = data['Close'] + data['VIX_EM_125']
-            data['VIX_EM_125_Low'] = data['Close'] - data['VIX_EM_125']
-
-            data['VIX_EM_15'] = data['VIX_EM'] * 1.5
-            data['VIX_EM_15_High'] = data['Close'] + data['VIX_EM_15']
-            data['VIX_EM_15_Low'] = data['Close'] - data['VIX_EM_15']
-
-            data['VIX_EM'] = data['VIX_EM'].shift(1)
-            data['VIX_EM_High'] = data['VIX_EM_High'].shift(1)
-            data['VIX_EM_Low'] = data['VIX_EM_Low'].shift(1)
-
-            data['VIX_EM_15'] = data['VIX_EM_15'].shift(1)
-            data['VIX_EM_15_High'] = data['VIX_EM_15_High'].shift(1)
-            data['VIX_EM_15_Low'] = data['VIX_EM_15_Low'].shift(1)
-
-            data['VIX_EM_125'] = data['VIX_EM_125'].shift(1)
-            data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)
-            data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)
-
-            df_em = pd.DataFrame(columns=['EM','Low','High','WithinRange','Tested'])
-            df_em.loc['EM 1X'] = [
-                data['VIX_EM'].iloc[-1].round(2),
-                data['VIX_EM_Low'].iloc[-1].round(2), 
-                data['VIX_EM_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data):.1%}"
-                ]
-            df_em.loc['EM 1.25X'] = [
-                data['VIX_EM_125'].iloc[-1].round(2),
-                data['VIX_EM_125_Low'].iloc[-1].round(2), 
-                data['VIX_EM_125_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data):.1%}"
-                ]
-            df_em.loc[f"EM 1.5X"] = [
-                data['VIX_EM_15'].iloc[-1].round(2),
-                data['VIX_EM_15_Low'].iloc[-1].round(2), 
-                data['VIX_EM_15_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data):.1%}"
-                ]
-            
-            with tab1:
-                st.subheader(f'Pred for {curr_date} as of 7AM PST')
-                st.write(results)
-                st.write(df_probas)
-                st.text('VIX EM')
-                st.write(df_em)
-            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,
-                fname='performance_for_30m_model.csv'
-                # )
-
-        elif option == '⏳ 60 Mins':
+                seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
+        
+        elif option == '07:30':
         # run60 = st.button('🏃🏽‍♂️ Run')
         # if run60:
             from model_1h import *
+            
+            fname='performance_for_1h_model.csv'
+
             with st.spinner('Loading data...'):
                 data, df_final, final_row = get_data()
             # st.success("✅ Historical data")
@@ -639,225 +225,15 @@ with st.form("choose_model"):
                 new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float)
                 new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float)
 
-            st.success("✅ All done!")
-            tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"])
-
-            seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
-
-            green_proba = seq_proba[0]
-            red_proba = 1 - green_proba
-            do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) 
-            stdev = 0.01
-            score = None
-            num_obs = None
-            cond = None
-            historical_proba = None
-            text_cond = None
-            operator = None
-
-            if do_not_play:
-                text_cond = '🟨'
-                operator = ''
-                score = seq_proba[0]
-                cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6)
-                num_obs = len(res1.loc[cond])
-                historical_proba = res1.loc[cond, 'True'].mean()
-
-                
-            elif green_proba > red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟩'
-                operator = '>='
-                score = green_proba
-                # How many with this score?
-                cond = (res1['Predicted'] >= green_proba)
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            elif green_proba <= red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟥'
-                operator = '<='
-                score = red_proba
-                # How many with this score?
-                cond = (res1['Predicted'] <= seq_proba[0])
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = 1 - res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            score_fmt = f'{score:.1%}'
-
-            results = pd.DataFrame(index=[
-                'PrevClose',
-                'Confidence Score',
-                'Success Rate',
-                f'NumObs {operator} {"" if do_not_play else score_fmt}',
-            ], data = [
-                f"{data.loc[final_row,'Close']:.2f}",
-                f'{text_cond} {score:.1%}',
-                f'{historical_proba:.1%}', 
-                num_obs,
-                ])
-
-            results.columns = ['Outputs']
-
-            # st.subheader('New Prediction')
-            int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
-            # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
-            
-            data['ClosePct'] = (data['Close'] / data['PrevClose']) - 1
-            data['ClosePct'] = data['ClosePct'].shift(-1)
-            res1 = res1.merge(data['ClosePct'], left_index=True,right_index=True)
-            df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean]})
-            df_probas.columns = ['PctGreen','NumObs','NumGreen','AvgPerf']
-            df_probas['AvgPerf'] = df_probas['AvgPerf'].apply(lambda x: f'{x:.2%}')
-
-            roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
-            precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            len_all = len(res1)
-
-            res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)]
-
-            roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)
-            precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            len_hi = len(res2_filtered)
-
-            df_performance = pd.DataFrame(
-                index=[
-                    'N',
-                    'ROC AUC',
-                    'Precision',
-                    'Recall'
-                ],
-                columns = [
-                    'All',
-                    'High Confidence'
-                ],
-                data = [
-                    [len_all, len_hi],
-                    [roc_auc_score_all, roc_auc_score_hi],
-                    [precision_score_all, precision_score_hi],
-                    [recall_score_all, recall_score_hi]
-                ]
-            ).round(2)
-
-            def get_acc(t, p):
-                if t == False and p <= 0.4:
-                    return '✅'
-                elif t == True and p > 0.6:
-                    return '✅'
-                elif t == False and p > 0.6:
-                    return '❌'
-                elif t == True and p <= 0.4:
-                    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'])]
-            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)
-            
-            check = data.tail(1)
-
-            data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))
-            data['VIX_EM_High'] = data['Close'] + data['VIX_EM']
-            data['VIX_EM_Low'] = data['Close'] - data['VIX_EM']
-
-            data['VIX_EM_125'] = data['VIX_EM'] * 1.25
-            data['VIX_EM_125_High'] = data['Close'] + data['VIX_EM_125']
-            data['VIX_EM_125_Low'] = data['Close'] - data['VIX_EM_125']
-
-            data['VIX_EM_15'] = data['VIX_EM'] * 1.5
-            data['VIX_EM_15_High'] = data['Close'] + data['VIX_EM_15']
-            data['VIX_EM_15_Low'] = data['Close'] - data['VIX_EM_15']
-
-            data['VIX_EM'] = data['VIX_EM'].shift(1)
-            data['VIX_EM_High'] = data['VIX_EM_High'].shift(1)
-            data['VIX_EM_Low'] = data['VIX_EM_Low'].shift(1)
-
-            data['VIX_EM_15'] = data['VIX_EM_15'].shift(1)
-            data['VIX_EM_15_High'] = data['VIX_EM_15_High'].shift(1)
-            data['VIX_EM_15_Low'] = data['VIX_EM_15_Low'].shift(1)
-
-            data['VIX_EM_125'] = data['VIX_EM_125'].shift(1)
-            data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)
-            data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)
-
-            df_em = pd.DataFrame(columns=['EM','Low','High','WithinRange','Tested'])
-            df_em.loc['EM 1X'] = [
-                data['VIX_EM'].iloc[-1].round(2),
-                data['VIX_EM_Low'].iloc[-1].round(2), 
-                data['VIX_EM_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data):.1%}"
-                ]
-            df_em.loc['EM 1.25X'] = [
-                data['VIX_EM_125'].iloc[-1].round(2),
-                data['VIX_EM_125_Low'].iloc[-1].round(2), 
-                data['VIX_EM_125_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data):.1%}"
-                ]
-            df_em.loc[f"EM 1.5X"] = [
-                data['VIX_EM_15'].iloc[-1].round(2),
-                data['VIX_EM_15_Low'].iloc[-1].round(2), 
-                data['VIX_EM_15_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data):.1%}"
-                ]
-            
-            with tab1:
-                st.subheader(f'Pred for {curr_date} as of 7:30AM PST')
-                st.write(results)
-                st.write(df_probas)
-                st.text('VIX EM')
-                st.write(df_em)
-            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,
-                fname='performance_for_60m_model.csv'
-                # )                
-
-        elif option == '🕰 90 Mins':
+                seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
+        
+        elif option == '08:00':
         # run60 = st.button('🏃🏽‍♂️ Run')
         # if run60:
             from model_90m import *
+
+            fname='performance_for_90m_model.csv'
+
             with st.spinner('Loading data...'):
                 data, df_final, final_row = get_data()
             # st.success("✅ Historical data")
@@ -916,220 +292,225 @@ with st.form("choose_model"):
                 new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float)
                 new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float)
 
-            st.success("✅ All done!")
-            tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"])
-
-            seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
-
-            green_proba = seq_proba[0]
-            red_proba = 1 - green_proba
-            do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) 
-            stdev = 0.01
-            score = None
-            num_obs = None
-            cond = None
-            historical_proba = None
-            text_cond = None
-            operator = None
-
-            if do_not_play:
-                text_cond = '🟨'
-                operator = ''
-                score = seq_proba[0]
-                cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6)
-                num_obs = len(res1.loc[cond])
-                historical_proba = res1.loc[cond, 'True'].mean()
-
-                
-            elif green_proba > red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟩'
-                operator = '>='
-                score = green_proba
-                # How many with this score?
-                cond = (res1['Predicted'] >= green_proba)
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            elif green_proba <= red_proba:
-                # If the day is predicted to be green, say so
-                text_cond = '🟥'
-                operator = '<='
-                score = red_proba
-                # How many with this score?
-                cond = (res1['Predicted'] <= seq_proba[0])
-                num_obs = len(res1.loc[cond])
-                # How often green?
-                historical_proba = 1 - res1.loc[cond, 'True'].mean()
-                # print(cond)
-
-            score_fmt = f'{score:.1%}'
-
-            results = pd.DataFrame(index=[
-                'PrevClose',
-                'Confidence Score',
-                'Success Rate',
-                f'NumObs {operator} {"" if do_not_play else score_fmt}',
-            ], data = [
-                f"{data.loc[final_row,'Close']:.2f}",
-                f'{text_cond} {score:.1%}',
-                f'{historical_proba:.1%}', 
-                num_obs,
-                ])
-
-            results.columns = ['Outputs']
-
-            # st.subheader('New Prediction')
-            int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
-            # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
+                seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
+
+        st.success(f"All done for {option}!", icon="✅")
+
+        green_proba = seq_proba[0]
+        red_proba = 1 - green_proba
+        do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) 
+        stdev = 0.01
+        score = None
+        num_obs = None
+        cond = None
+        historical_proba = None
+        text_cond = None
+        operator = None
+
+        if do_not_play:
+            text_cond = '🟨'
+            operator = ''
+            score = seq_proba[0]
+            cond = (res1['Predicted'] > 0.4) & (res1['Predicted'] <= 0.6)
+            num_obs = len(res1.loc[cond])
+            historical_proba = res1.loc[cond, 'True'].mean()
+
             
-            data['ClosePct'] = (data['Close'] / data['PrevClose']) - 1
-            data['ClosePct'] = data['ClosePct'].shift(-1)
-            res1 = res1.merge(data['ClosePct'], left_index=True,right_index=True)
-            df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean]})
-            df_probas.columns = ['PctGreen','NumObs','NumGreen','AvgPerf']
-            df_probas['AvgPerf'] = df_probas['AvgPerf'].apply(lambda x: f'{x:.2%}')
-
-            roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
-            precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
-            len_all = len(res1)
-
-            res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)]
-
-            roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)
-            precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
-            len_hi = len(res2_filtered)
-
-            df_performance = pd.DataFrame(
-                index=[
-                    'N',
-                    'ROC AUC',
-                    'Precision',
-                    'Recall'
-                ],
-                columns = [
-                    'All',
-                    'High Confidence'
-                ],
-                data = [
-                    [len_all, len_hi],
-                    [roc_auc_score_all, roc_auc_score_hi],
-                    [precision_score_all, precision_score_hi],
-                    [recall_score_all, recall_score_hi]
-                ]
-            ).round(2)
-
-            def get_acc(t, p):
-                if t == False and p <= 0.4:
-                    return '✅'
-                elif t == True and p > 0.6:
-                    return '✅'
-                elif t == False and p > 0.6:
-                    return '❌'
-                elif t == True and p <= 0.4:
-                    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'])]
-            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)
+        elif green_proba > red_proba:
+            # If the day is predicted to be green, say so
+            text_cond = '🟩'
+            operator = '>='
+            score = green_proba
+            # How many with this score?
+            cond = (res1['Predicted'] >= green_proba)
+            num_obs = len(res1.loc[cond])
+            # How often green?
+            historical_proba = res1.loc[cond, 'True'].mean()
+            # print(cond)
+
+        elif green_proba <= red_proba:
+            # If the day is predicted to be green, say so
+            text_cond = '🟥'
+            operator = '<='
+            score = red_proba
+            # How many with this score?
+            cond = (res1['Predicted'] <= seq_proba[0])
+            num_obs = len(res1.loc[cond])
+            # How often green?
+            historical_proba = 1 - res1.loc[cond, 'True'].mean()
+            # print(cond)
+
+        score_fmt = f'{score:.1%}'
+
+        results = pd.DataFrame(index=[
+            'PrevClose',
+            'Confidence Score',
+            'Success Rate',
+            f'NumObs {operator} {"" if do_not_play else score_fmt}',
+        ], data = [
+            f"{data.loc[final_row,'Close']:.2f}",
+            f'{text_cond} {score:.1%}',
+            f'{historical_proba:.1%}', 
+            num_obs,
+            ])
+
+        results.columns = ['Outputs']
+
+        # st.subheader('New Prediction')
+
+        int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
+        # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
+
+        data['ClosePct'] = (data['Close'] / data['PrevClose']) - 1
+        data['ClosePct'] = data['ClosePct'].shift(-1)
+        res1 = res1.merge(data['ClosePct'], left_index=True,right_index=True)
+        df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean]})
+        df_probas.columns = ['PctGreen','NumObs','NumGreen','AvgPerf']
+        df_probas['AvgPerf'] = df_probas['AvgPerf'].apply(lambda x: f'{x:.2%}')
+
+        roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
+        precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
+        recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)
+        len_all = len(res1)
+
+        res2_filtered = res1.loc[(res1['Predicted'] > 0.6) | (res1['Predicted'] <= 0.4)]
+
+        roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)
+        precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
+        recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)
+        len_hi = len(res2_filtered)
+
+        df_performance = pd.DataFrame(
+            index=[
+                'N',
+                'ROC AUC',
+                'Precision',
+                'Recall'
+            ],
+            columns = [
+                'All',
+                'High Confidence'
+            ],
+            data = [
+                [len_all, len_hi],
+                [roc_auc_score_all, roc_auc_score_hi],
+                [precision_score_all, precision_score_hi],
+                [recall_score_all, recall_score_hi]
+            ]
+        ).round(2)
+
+        def get_acc(t, p):
+            if t == False and p <= 0.4:
+                return '✅'
+            elif t == True and p > 0.6:
+                return '✅'
+            elif t == False and p > 0.6:
+                return '❌'
+            elif t == True and p <= 0.4:
+                return '❌'
+            else:
+                return '🟨'
             
-            check = data.tail(1)
-
-            data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))
-            data['VIX_EM_High'] = data['Close'] + data['VIX_EM']
-            data['VIX_EM_Low'] = data['Close'] - data['VIX_EM']
-
-            data['VIX_EM_125'] = data['VIX_EM'] * 1.25
-            data['VIX_EM_125_High'] = data['Close'] + data['VIX_EM_125']
-            data['VIX_EM_125_Low'] = data['Close'] - data['VIX_EM_125']
-
-            data['VIX_EM_15'] = data['VIX_EM'] * 1.5
-            data['VIX_EM_15_High'] = data['Close'] + data['VIX_EM_15']
-            data['VIX_EM_15_Low'] = data['Close'] - data['VIX_EM_15']
-
-            data['VIX_EM'] = data['VIX_EM'].shift(1)
-            data['VIX_EM_High'] = data['VIX_EM_High'].shift(1)
-            data['VIX_EM_Low'] = data['VIX_EM_Low'].shift(1)
-
-            data['VIX_EM_15'] = data['VIX_EM_15'].shift(1)
-            data['VIX_EM_15_High'] = data['VIX_EM_15_High'].shift(1)
-            data['VIX_EM_15_Low'] = data['VIX_EM_15_Low'].shift(1)
-
-            data['VIX_EM_125'] = data['VIX_EM_125'].shift(1)
-            data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)
-            data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)
-
-            df_em = pd.DataFrame(columns=['EM','Low','High','WithinRange','Tested'])
-            df_em.loc['EM 1X'] = [
-                data['VIX_EM'].iloc[-1].round(2),
-                data['VIX_EM_Low'].iloc[-1].round(2), 
-                data['VIX_EM_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data):.1%}"
-                ]
-            df_em.loc['EM 1.25X'] = [
-                data['VIX_EM_125'].iloc[-1].round(2),
-                data['VIX_EM_125_Low'].iloc[-1].round(2), 
-                data['VIX_EM_125_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data):.1%}"
-                ]
-            df_em.loc[f"EM 1.5X"] = [
-                data['VIX_EM_15'].iloc[-1].round(2),
-                data['VIX_EM_15_Low'].iloc[-1].round(2), 
-                data['VIX_EM_15_High'].iloc[-1].round(2), 
-                f"{len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data):.1%}"
-                ]
+        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'])]
+        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 8AM PST')
-                st.write(results)
-                st.write(df_probas)
-                st.text('VIX EM')
-                st.write(df_em)
-            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,
-                fname='performance_for_90m_model.csv'
-                # )                
+        tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"])
+
+        check = data.tail(1)
+
+        data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))
+        data['VIX_EM_High'] = data['Close'] + data['VIX_EM']
+        data['VIX_EM_Low'] = data['Close'] - data['VIX_EM']
+
+        # Tomorrrow's EM and Today's EM
+        fwd_em, curr_em = data['VIX_EM'].iloc[-1], data['VIX_EM'].iloc[-2]
+
+        data['VIX_EM_125'] = data['VIX_EM'] * 1.25
+        data['VIX_EM_125_High'] = data['Close'] + data['VIX_EM_125']
+        data['VIX_EM_125_Low'] = data['Close'] - data['VIX_EM_125']
+
+        data['VIX_EM_15'] = data['VIX_EM'] * 1.5
+        data['VIX_EM_15_High'] = data['Close'] + data['VIX_EM_15']
+        data['VIX_EM_15_Low'] = data['Close'] - data['VIX_EM_15']
+
+        data['VIX_EM'] = data['VIX_EM'].shift(1)
+        data['VIX_EM_High'] = data['VIX_EM_High'].shift(1)
+        data['VIX_EM_Low'] = data['VIX_EM_Low'].shift(1)
+
+        data['VIX_EM_15'] = data['VIX_EM_15'].shift(1)
+        data['VIX_EM_15_High'] = data['VIX_EM_15_High'].shift(1)
+        data['VIX_EM_15_Low'] = data['VIX_EM_15_Low'].shift(1)
+
+        data['VIX_EM_125'] = data['VIX_EM_125'].shift(1)
+        data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)
+        data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)
+
+        df_em = pd.DataFrame(columns=['EM','Low','High','WithinRange','Tested'])
+        df_em.loc['EM 1X'] = [
+            data['VIX_EM'].iloc[-1].round(2),
+            data['VIX_EM_Low'].iloc[-1].round(2), 
+            data['VIX_EM_High'].iloc[-1].round(2), 
+            f"{len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data):.1%}",
+            f"{len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data):.1%}"
+            ]
+        df_em.loc['EM 1.25X'] = [
+            data['VIX_EM_125'].iloc[-1].round(2),
+            data['VIX_EM_125_Low'].iloc[-1].round(2), 
+            data['VIX_EM_125_High'].iloc[-1].round(2), 
+            f"{len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data):.1%}",
+            f"{len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data):.1%}"
+            ]
+        df_em.loc[f"EM 1.5X"] = [
+            data['VIX_EM_15'].iloc[-1].round(2),
+            data['VIX_EM_15_Low'].iloc[-1].round(2), 
+            data['VIX_EM_15_High'].iloc[-1].round(2), 
+            f"{len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data):.1%}",
+            f"{len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data):.1%}"
+            ]
+        
+        with tab1:
+            st.subheader(f'{option} on {curr_date}')
+            st.write(results)
+            st.write(df_probas)
+            st.text(f'VIX EM ({curr_em:.2f} / {fwd_em:.2f})')
+            st.write(df_em)
+        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.text('Performance last 10 days (download for all)')
+            st.write(perf_daily[['TargetDate','Predicted','True','Accuracy']].iloc[-10:])
+            # st.download_button(
+            #     label="Download Historical Performance",
+            #     data=csv,
+            # )
 
 if submitted:
     st.download_button(

mas_analysis.ipynb

@@ -0,0 +1,954 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--SPX_full_30min-37ae67efd8a1cc91/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import model_day\n",
+    "import model_30m\n",
+    "import model_1h\n",
+    "import model_90m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00,  3.22it/s]\n",
+      "Getting release dates: 100%|██████████| 8/8 [00:02<00:00,  3.78it/s]\n",
+      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 3996.48it/s]\n",
+      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 888.11it/s]\n",
+      "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00,  4.14it/s]\n",
+      "Getting release dates: 100%|██████████| 8/8 [00:01<00:00,  4.32it/s]\n",
+      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 7985.35it/s]\n",
+      "Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--SPX_full_30min-37ae67efd8a1cc91/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n",
+      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 999.03it/s]\n",
+      "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00,  4.55it/s]\n",
+      "Getting release dates: 100%|██████████| 8/8 [00:02<00:00,  3.26it/s]\n",
+      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 3995.05it/s]\n",
+      "Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--SPX_full_30min-37ae67efd8a1cc91/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n",
+      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 930.93it/s]\n",
+      "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00,  5.78it/s]\n",
+      "Getting release dates: 100%|██████████| 8/8 [00:01<00:00,  5.24it/s]\n",
+      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 3996.00it/s]\n",
+      "Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--SPX_full_30min-37ae67efd8a1cc91/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n",
+      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 999.18it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, df_final_day, _ = model_day.get_data()\n",
+    "_, df_final_30m, _ = model_30m.get_data()\n",
+    "_, df_final_1h, _ = model_1h.get_data()\n",
+    "_, df_final_90m, _ = model_90m.get_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "LR Model: 100%|██████████| 1177/1177 [00:03<00:00, 391.99it/s]\n",
+      "d:\\Projects\\gamedayspx\\model_day.py:63: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0\n",
+      "CLF Model: 100%|██████████| 1077/1077 [00:08<00:00, 120.80it/s]\n",
+      "LR Model: 100%|██████████| 1177/1177 [00:03<00:00, 367.13it/s]\n",
+      "d:\\Projects\\gamedayspx\\model_30m.py:61: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0\n",
+      "CLF Model: 100%|██████████| 1077/1077 [00:10<00:00, 105.72it/s]\n",
+      "LR Model: 100%|██████████| 1177/1177 [00:03<00:00, 351.68it/s]\n",
+      "d:\\Projects\\gamedayspx\\model_1h.py:60: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0\n",
+      "CLF Model: 100%|██████████| 1077/1077 [00:10<00:00, 102.81it/s]\n",
+      "LR Model: 100%|██████████| 1177/1177 [00:03<00:00, 368.34it/s]\n",
+      "d:\\Projects\\gamedayspx\\model_90m.py:60: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0\n",
+      "CLF Model: 100%|██████████| 1077/1077 [00:10<00:00, 106.84it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_day, _, _ = model_day.walk_forward_validation_seq(df_final_day.dropna(axis=0), 'Target_clf', 'Target', 100, 1)\n",
+    "res_30m, _, _ = model_30m.walk_forward_validation_seq(df_final_30m.dropna(axis=0), 'Target_clf', 'Target', 100, 1)\n",
+    "res_1h, _, _ = model_1h.walk_forward_validation_seq(df_final_1h.dropna(axis=0), 'Target_clf', 'Target', 100, 1)\n",
+    "res_90m, _, _ = model_90m.walk_forward_validation_seq(df_final_90m.dropna(axis=0), 'Target_clf', 'Target', 100, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>True</th>\n",
+       "      <th>Predicted</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2019-04-23</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.798423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-24</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.235411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-25</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.479671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-26</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.180924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-29</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.457531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-26</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.863622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-27</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.875761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-28</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.506219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-31</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.273154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-08-01</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.238163</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1077 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             True  Predicted\n",
+       "index                       \n",
+       "2019-04-23  False   0.798423\n",
+       "2019-04-24  False   0.235411\n",
+       "2019-04-25   True   0.479671\n",
+       "2019-04-26   True   0.180924\n",
+       "2019-04-29   True   0.457531\n",
+       "...           ...        ...\n",
+       "2023-07-26  False   0.863622\n",
+       "2023-07-27   True   0.875761\n",
+       "2023-07-28   True   0.506219\n",
+       "2023-07-31  False   0.273154\n",
+       "2023-08-01  False   0.238163\n",
+       "\n",
+       "[1077 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for df in [res_day, res_30m, res_1h, res_90m]:\n",
+    "    df['HighConfidence'] = [True if x > 0.6 or x <= 0.4 else False for x in df['Predicted']]\n",
+    "    df['PredDirection'] = df['Predicted'] > 0.5\n",
+    "    df['Correct'] = df['PredDirection'] == df['True']\n",
+    "    df['RedDays'] = df['True'] == False\n",
+    "    df['GreenDays'] = df['True'] == True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>True</th>\n",
+       "      <th>Predicted</th>\n",
+       "      <th>HighConfidence</th>\n",
+       "      <th>PredDirection</th>\n",
+       "      <th>RedDays</th>\n",
+       "      <th>GreenDays</th>\n",
+       "      <th>Correct</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2019-04-23</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.798423</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-24</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.235411</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-25</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.479671</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-26</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.180924</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-29</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.457531</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-26</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.863622</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-27</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.875761</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-28</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.506219</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-31</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.273154</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-08-01</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0.238163</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1077 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             True  Predicted  HighConfidence  PredDirection  RedDays  \\\n",
+       "index                                                                  \n",
+       "2019-04-23  False   0.798423            True           True     True   \n",
+       "2019-04-24  False   0.235411            True          False     True   \n",
+       "2019-04-25   True   0.479671           False          False    False   \n",
+       "2019-04-26   True   0.180924            True          False    False   \n",
+       "2019-04-29   True   0.457531           False          False    False   \n",
+       "...           ...        ...             ...            ...      ...   \n",
+       "2023-07-26  False   0.863622            True           True     True   \n",
+       "2023-07-27   True   0.875761            True           True    False   \n",
+       "2023-07-28   True   0.506219           False           True    False   \n",
+       "2023-07-31  False   0.273154            True          False     True   \n",
+       "2023-08-01  False   0.238163            True          False     True   \n",
+       "\n",
+       "            GreenDays  Correct  \n",
+       "index                           \n",
+       "2019-04-23      False    False  \n",
+       "2019-04-24      False     True  \n",
+       "2019-04-25       True    False  \n",
+       "2019-04-26       True    False  \n",
+       "2019-04-29       True    False  \n",
+       "...               ...      ...  \n",
+       "2023-07-26      False    False  \n",
+       "2023-07-27       True     True  \n",
+       "2023-07-28       True     True  \n",
+       "2023-07-31      False     True  \n",
+       "2023-08-01      False     True  \n",
+       "\n",
+       "[1077 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_res = pd.concat([res_day.drop(columns=['GreenDays','RedDays']), res_30m.drop(columns=['GreenDays','RedDays']), res_90m.drop(columns=['GreenDays','RedDays']), res_1h.drop(columns=['GreenDays','RedDays']), res_day[['GreenDays','RedDays']]], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_res1 = all_res[['HighConfidence','PredDirection','Correct','GreenDays','RedDays']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HighConfidence</th>\n",
+       "      <th>HighConfidence</th>\n",
+       "      <th>HighConfidence</th>\n",
+       "      <th>HighConfidence</th>\n",
+       "      <th>PredDirection</th>\n",
+       "      <th>PredDirection</th>\n",
+       "      <th>PredDirection</th>\n",
+       "      <th>PredDirection</th>\n",
+       "      <th>Correct</th>\n",
+       "      <th>Correct</th>\n",
+       "      <th>Correct</th>\n",
+       "      <th>Correct</th>\n",
+       "      <th>GreenDays</th>\n",
+       "      <th>RedDays</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2019-04-23</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-24</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-25</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-26</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-29</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-26</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-27</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-28</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-31</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-08-01</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1077 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            HighConfidence  HighConfidence  HighConfidence  HighConfidence  \\\n",
+       "2019-04-23            True            True            True            True   \n",
+       "2019-04-24            True            True            True            True   \n",
+       "2019-04-25           False            True            True           False   \n",
+       "2019-04-26            True            True            True            True   \n",
+       "2019-04-29           False            True            True            True   \n",
+       "...                    ...             ...             ...             ...   \n",
+       "2023-07-26            True            True            True            True   \n",
+       "2023-07-27            True            True            True            True   \n",
+       "2023-07-28           False           False            True            True   \n",
+       "2023-07-31            True            True            True            True   \n",
+       "2023-08-01            True            True            True            True   \n",
+       "\n",
+       "            PredDirection  PredDirection  PredDirection  PredDirection  \\\n",
+       "2019-04-23           True          False           True           True   \n",
+       "2019-04-24          False          False          False          False   \n",
+       "2019-04-25          False          False           True           True   \n",
+       "2019-04-26          False           True           True           True   \n",
+       "2019-04-29          False          False          False          False   \n",
+       "...                   ...            ...            ...            ...   \n",
+       "2023-07-26           True           True           True           True   \n",
+       "2023-07-27           True           True           True           True   \n",
+       "2023-07-28           True           True           True           True   \n",
+       "2023-07-31          False          False          False          False   \n",
+       "2023-08-01          False          False          False          False   \n",
+       "\n",
+       "            Correct  Correct  Correct  Correct  GreenDays  RedDays  \n",
+       "2019-04-23    False     True    False    False      False     True  \n",
+       "2019-04-24     True     True     True     True      False     True  \n",
+       "2019-04-25    False    False     True     True       True    False  \n",
+       "2019-04-26    False     True     True     True       True    False  \n",
+       "2019-04-29    False    False    False    False       True    False  \n",
+       "...             ...      ...      ...      ...        ...      ...  \n",
+       "2023-07-26    False    False    False    False      False     True  \n",
+       "2023-07-27     True     True     True     True       True    False  \n",
+       "2023-07-28     True     True     True     True       True    False  \n",
+       "2023-07-31     True     True     True     True      False     True  \n",
+       "2023-08-01     True     True     True     True      False     True  \n",
+       "\n",
+       "[1077 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_res1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_res1.columns = [\n",
+    "    'HighConfidence_day',\n",
+    "    'HighConfidence_30m',\n",
+    "    'HighConfidence_1h',\n",
+    "    'HighConfidence_90m',\n",
+    "    'PredDirection_day',\n",
+    "    'PredDirection_30m',\n",
+    "    'PredDirection_1h',\n",
+    "    'PredDirection_90m',\n",
+    "    'Correct_day',\n",
+    "    'Correct_30m',\n",
+    "    'Correct_1h',\n",
+    "    'Correct_90m',\n",
+    "    'GreenDays',\n",
+    "    'RedDays'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8133333333333334"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# When all models pred green, how often was it green?\n",
+    "all_res1.query('''\n",
+    "    PredDirection_day == True & PredDirection_30m == True & PredDirection_1h == True & PredDirection_90m == True\n",
+    "''')['GreenDays'].sum() / len(all_res1.query('''\n",
+    "    PredDirection_day == True & PredDirection_30m == True & PredDirection_1h == True & PredDirection_90m == True\n",
+    "'''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8638297872340426"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# When all models pred red, how often was it red?\n",
+    "all_res1.query('''\n",
+    "    PredDirection_day == False & PredDirection_30m == False & PredDirection_1h == False & PredDirection_90m == False\n",
+    "''')['RedDays'].sum() / len(all_res1.query('''\n",
+    "    PredDirection_day == False & PredDirection_30m == False & PredDirection_1h == False & PredDirection_90m == False\n",
+    "'''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8508474576271187\n",
+      "251\n",
+      "295\n"
+     ]
+    }
+   ],
+   "source": [
+    "# When all models are pred green with high confidendce, how often was it green?\n",
+    "print(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == True & PredDirection_30m == True & PredDirection_1h == True & PredDirection_90m == True\n",
+    "''')['GreenDays'].sum()  / len(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == True & PredDirection_30m == True & PredDirection_1h == True & PredDirection_90m == True\n",
+    " ''')))\n",
+    "\n",
+    "print(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == True & PredDirection_30m == True & PredDirection_1h == True & PredDirection_90m == True\n",
+    "''')['GreenDays'].sum())\n",
+    "\n",
+    "print(len(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == True & PredDirection_30m == True & PredDirection_1h == True & PredDirection_90m == True\n",
+    " ''')))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9090909090909091\n",
+      "150\n",
+      "165\n"
+     ]
+    }
+   ],
+   "source": [
+    "# When all models are pred red with high confidendce, how often was it red?\n",
+    "print(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == False & PredDirection_30m == False & PredDirection_1h == False & PredDirection_90m == False\n",
+    "''')['RedDays'].sum()  / len(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == False & PredDirection_30m == False & PredDirection_1h == False & PredDirection_90m == False\n",
+    " ''')))\n",
+    "\n",
+    "print(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == False & PredDirection_30m == False & PredDirection_1h == False & PredDirection_90m == False\n",
+    " ''')['RedDays'].sum())\n",
+    "\n",
+    "print(len(all_res1.query('''\n",
+    "    HighConfidence_day == True & HighConfidence_30m == True & HighConfidence_1h == True & HighConfidence_90m == True & \\\n",
+    "    PredDirection_day == False & PredDirection_30m == False & PredDirection_1h == False & PredDirection_90m == False\n",
+    " ''')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4271123491179202"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(165 + 295) / 1077"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

troubleshoot_day_model.ipynb

@@ -0,0 +1,707 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--SPX_full_30min-37ae67efd8a1cc91/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from model_day import get_data, walk_forward_validation_seq\n",
+    "import xgboost as xgb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "getting econ tickers: 100%|██████████| 3/3 [00:01<00:00,  2.62it/s]\n",
+      "Getting release dates: 100%|██████████| 8/8 [00:02<00:00,  3.85it/s]\n",
+      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 2664.95it/s]\n",
+      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 999.15it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data, df_final, final_row = get_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['ClosePct'] = (data['Close'] / data['PrevClose']) - 1\n",
+    "data['HighPct'] = (data['High'] / data['PrevClose']) - 1\n",
+    "data['LowPct'] = (data['Low'] / data['PrevClose']) - 1\n",
+    "data['ClosePct'] = data['ClosePct'].shift(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "LR Model: 100%|██████████| 1178/1178 [00:03<00:00, 385.55it/s]\n",
+      "d:\\Projects\\gamedayspx\\model_day.py:63: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0\n",
+      "CLF Model: 100%|██████████| 1078/1078 [00:09<00:00, 119.55it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "res1, model1, model2 = walk_forward_validation_seq(df_final.dropna(axis=0), 'Target_clf', 'Target', 100, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgb.plot_importance(model2, importance_type='gain')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import roc_auc_score, precision_score, recall_score\n",
+    "\n",
+    "# st.subheader('New Prediction')\n",
+    "\n",
+    "# df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})\n",
+    "df_probas = res1.groupby(pd.cut(res1['Predicted'],[-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf])).agg({'True':[np.mean,len,np.sum]})\n",
+    "df_probas.columns = ['PctGreen','NumObs','NumGreen']\n",
+    "\n",
+    "roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)\n",
+    "precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)\n",
+    "recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)\n",
+    "len_all = len(res1)\n",
+    "\n",
+    "res2_filtered = res1.loc[(res1['Predicted'] > 0.625) | (res1['Predicted'] <= 0.375)]\n",
+    "\n",
+    "roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)\n",
+    "precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)\n",
+    "recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)\n",
+    "len_hi = len(res2_filtered)\n",
+    "\n",
+    "df_performance = pd.DataFrame(\n",
+    "    index=[\n",
+    "        'N',\n",
+    "        'ROC AUC',\n",
+    "        'Precision',\n",
+    "        'Recall'\n",
+    "    ],\n",
+    "    columns = [\n",
+    "        'All',\n",
+    "        'High Confidence'\n",
+    "    ],\n",
+    "    data = [\n",
+    "        [len_all, len_hi],\n",
+    "        [roc_auc_score_all, roc_auc_score_hi],\n",
+    "        [precision_score_all, precision_score_hi],\n",
+    "        [recall_score_all, recall_score_hi]\n",
+    "    ]\n",
+    ").round(2)\n",
+    "\n",
+    "def get_acc(t, p):\n",
+    "    if t == False and p <= 0.375:\n",
+    "        return '✅'\n",
+    "    elif t == True and p > 0.625:\n",
+    "        return '✅'\n",
+    "    elif t == False and p > 0.625:\n",
+    "        return '❌'\n",
+    "    elif t == True and p <= 0.375:\n",
+    "        return '❌'\n",
+    "    else:\n",
+    "        return '🟨'\n",
+    "\n",
+    "perf_daily = res1.copy()\n",
+    "perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "perf_daily1 = perf_daily.merge(data['ClosePct'], left_index=True, right_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res2 = res1.merge(data[['ClosePct','HighPct','LowPct']], left_index=True, right_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']\n",
+    "# df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})\n",
+    "df_probas = res2.groupby(pd.cut(res2['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum],'ClosePct':[np.mean], 'HighPct':[np.mean], 'LowPct':[np.mean]})\n",
+    "df_probas.columns = ['PctGreen','NumObs','NumGreen','AvgPerf','AvgHigh','AvgLow']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PctGreen</th>\n",
+       "      <th>NumObs</th>\n",
+       "      <th>NumGreen</th>\n",
+       "      <th>AvgPerf</th>\n",
+       "      <th>AvgHigh</th>\n",
+       "      <th>AvgLow</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Predicted</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>(-∞, .20]</th>\n",
+       "      <td>0.214286</td>\n",
+       "      <td>112</td>\n",
+       "      <td>24</td>\n",
+       "      <td>-0.012956</td>\n",
+       "      <td>0.009253</td>\n",
+       "      <td>-0.007881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(.20, .40]</th>\n",
+       "      <td>0.322709</td>\n",
+       "      <td>251</td>\n",
+       "      <td>81</td>\n",
+       "      <td>-0.004048</td>\n",
+       "      <td>0.006433</td>\n",
+       "      <td>-0.005791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(.40, .60]</th>\n",
+       "      <td>0.504630</td>\n",
+       "      <td>216</td>\n",
+       "      <td>109</td>\n",
+       "      <td>-0.000173</td>\n",
+       "      <td>0.006079</td>\n",
+       "      <td>-0.006083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(.60, .80]</th>\n",
+       "      <td>0.645022</td>\n",
+       "      <td>231</td>\n",
+       "      <td>149</td>\n",
+       "      <td>0.002680</td>\n",
+       "      <td>0.006207</td>\n",
+       "      <td>-0.005687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(.80, ∞]</th>\n",
+       "      <td>0.791045</td>\n",
+       "      <td>268</td>\n",
+       "      <td>212</td>\n",
+       "      <td>0.009038</td>\n",
+       "      <td>0.006807</td>\n",
+       "      <td>-0.007949</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            PctGreen  NumObs  NumGreen   AvgPerf   AvgHigh    AvgLow\n",
+       "Predicted                                                           \n",
+       "(-∞, .20]   0.214286     112        24 -0.012956  0.009253 -0.007881\n",
+       "(.20, .40]  0.322709     251        81 -0.004048  0.006433 -0.005791\n",
+       "(.40, .60]  0.504630     216       109 -0.000173  0.006079 -0.006083\n",
+       "(.60, .80]  0.645022     231       149  0.002680  0.006207 -0.005687\n",
+       "(.80, ∞]    0.791045     268       212  0.009038  0.006807 -0.007949"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_probas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res2['Quantile'] = pd.cut(res2['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a DataFrame 'res2' with the columns 'Quantile' and 'ClosePct'\n",
+    "# Assuming you have a list 'int_labels' containing the unique values for 'Quantile'\n",
+    "\n",
+    "# Create a 2x3 grid of subplots\n",
+    "fig, axs = plt.subplots(2, 3, figsize=(15, 8))\n",
+    "\n",
+    "# Loop through the 'int_labels' and plot the histograms in each subplot\n",
+    "for i, lbl in enumerate(int_labels):\n",
+    "    # Get the subplot position based on the index i\n",
+    "    row = i // 3\n",
+    "    col = i % 3\n",
+    "    \n",
+    "    # Filter the DataFrame based on the specified value\n",
+    "    data_subset = res2.loc[res2['Quantile'] == lbl, 'LowPct']\n",
+    "    \n",
+    "    # Plot the histogram in the corresponding subplot\n",
+    "    axs[row, col].hist(data_subset)\n",
+    "    axs[row, col].set_title(lbl)\n",
+    "\n",
+    "# Add some space between the subplots\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Investigate EM\n",
+    "data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))\n",
+    "data['VIX_EM_High'] = data['Close'] + data['VIX_EM']\n",
+    "data['VIX_EM_Low'] = data['Close'] - data['VIX_EM']\n",
+    "\n",
+    "data['VIX_EM_125'] = data['VIX_EM'] * 1.25\n",
+    "data['VIX_EM_125_High'] = data['Close'] + data['VIX_EM_125']\n",
+    "data['VIX_EM_125_Low'] = data['Close'] - data['VIX_EM_125']\n",
+    "\n",
+    "data['VIX_EM_15'] = data['VIX_EM'] * 1.5\n",
+    "data['VIX_EM_15_High'] = data['Close'] + data['VIX_EM_15']\n",
+    "data['VIX_EM_15_Low'] = data['Close'] - data['VIX_EM_15']\n",
+    "\n",
+    "data['VIX_EM'] = data['VIX_EM'].shift(1)\n",
+    "data['VIX_EM_High'] = data['VIX_EM_High'].shift(1)\n",
+    "data['VIX_EM_Low'] = data['VIX_EM_Low'].shift(1)\n",
+    "\n",
+    "data['VIX_EM_15'] = data['VIX_EM_15'].shift(1)\n",
+    "data['VIX_EM_15_High'] = data['VIX_EM_15_High'].shift(1)\n",
+    "data['VIX_EM_15_Low'] = data['VIX_EM_15_Low'].shift(1)\n",
+    "\n",
+    "data['VIX_EM_125'] = data['VIX_EM_125'].shift(1)\n",
+    "data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)\n",
+    "data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIX_EM</th>\n",
+       "      <th>VIX_EM_15</th>\n",
+       "      <th>VIX_EM_15_High</th>\n",
+       "      <th>Close</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-07-02</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2726.709961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-03</th>\n",
+       "      <td>26.795587</td>\n",
+       "      <td>40.193381</td>\n",
+       "      <td>2766.903342</td>\n",
+       "      <td>2713.219971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-05</th>\n",
+       "      <td>27.585969</td>\n",
+       "      <td>41.378954</td>\n",
+       "      <td>2754.598925</td>\n",
+       "      <td>2736.610107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-06</th>\n",
+       "      <td>25.806818</td>\n",
+       "      <td>38.710227</td>\n",
+       "      <td>2775.320335</td>\n",
+       "      <td>2759.820068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-09</th>\n",
+       "      <td>23.244055</td>\n",
+       "      <td>34.866083</td>\n",
+       "      <td>2794.686151</td>\n",
+       "      <td>2784.169922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-28</th>\n",
+       "      <td>41.188099</td>\n",
+       "      <td>61.782148</td>\n",
+       "      <td>4599.192304</td>\n",
+       "      <td>4582.229980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-07-31</th>\n",
+       "      <td>38.477492</td>\n",
+       "      <td>57.716238</td>\n",
+       "      <td>4639.946219</td>\n",
+       "      <td>4588.959961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-08-01</th>\n",
+       "      <td>39.401237</td>\n",
+       "      <td>59.101856</td>\n",
+       "      <td>4648.061817</td>\n",
+       "      <td>4576.729980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-08-02</th>\n",
+       "      <td>40.161151</td>\n",
+       "      <td>60.241726</td>\n",
+       "      <td>4636.971706</td>\n",
+       "      <td>4513.390137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-08-03</th>\n",
+       "      <td>45.746582</td>\n",
+       "      <td>68.619873</td>\n",
+       "      <td>4582.010010</td>\n",
+       "      <td>4501.890137</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1281 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               VIX_EM  VIX_EM_15  VIX_EM_15_High        Close\n",
+       "index                                                        \n",
+       "2018-07-02        NaN        NaN             NaN  2726.709961\n",
+       "2018-07-03  26.795587  40.193381     2766.903342  2713.219971\n",
+       "2018-07-05  27.585969  41.378954     2754.598925  2736.610107\n",
+       "2018-07-06  25.806818  38.710227     2775.320335  2759.820068\n",
+       "2018-07-09  23.244055  34.866083     2794.686151  2784.169922\n",
+       "...               ...        ...             ...          ...\n",
+       "2023-07-28  41.188099  61.782148     4599.192304  4582.229980\n",
+       "2023-07-31  38.477492  57.716238     4639.946219  4588.959961\n",
+       "2023-08-01  39.401237  59.101856     4648.061817  4576.729980\n",
+       "2023-08-02  40.161151  60.241726     4636.971706  4513.390137\n",
+       "2023-08-03  45.746582  68.619873     4582.010010  4501.890137\n",
+       "\n",
+       "[1281 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[['VIX_EM','VIX_EM_15','VIX_EM_15_High','Close']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8032786885245902"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How often did price close within EM?\n",
+    "len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.33099141295862605"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How often was EM tested?\n",
+    "len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8930523028883685"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How often did price close within EM?\n",
+    "len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.19750195160031225"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How often was EM tested?\n",
+    "len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9383294301327089"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How often did price close within EM?\n",
+    "len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.10772833723653395"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How often was EM tested?\n",
+    "len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}