tgs part 3

app.py

@@ -66,17 +66,13 @@ def get_time_emojis(_times, _pst_now):
     return emojis
 
 # Example usage:
-times_list = ['06:30', '07:00', '07:30', '08:00']
+times_list = ['06:30', '07:00', '07:30', '08:00', '08:30', '09:00', '09:30', '10:00', '10:30', '11:00', '11:30', '12:00', '12:30']
 emojis_list = get_time_emojis(times_list, pst_now)
 
 
 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'))
-
 
     col1, col2 = st.columns(2)
         
@@ -98,10 +94,11 @@ with st.form("choose_model"):
         st.write('No model selected.')
 
     if submitted:
+        
+        fname=f'performance_for_{option}_model.csv'
 
         if option == '06:30':
-        # runday = st.button('🏃🏽‍♂️ Run')
-        # if runday:
+
             from model_day import *
 
             fname='performance_for_open_model.csv'
@@ -155,142 +152,14 @@ with st.form("choose_model"):
                 new_pred['H2BreakPct'] = new_pred['H2BreakPct'].astype(float)
 
                 seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
-
-        elif option == '07:00':
-        # run30 = st.button('🏃🏽‍♂️ Run')
-        # if run30:
-            from model_intra import *
-
-            fname='performance_for_30m_model.csv'
-
-            with st.spinner('Loading data...'):
-                data, df_final, final_row = get_data(1)
-            # st.success("✅ Historical data")
-
-            with st.spinner("Training models..."):
-                def train_models():
-                    res1, xgbr, seq2 = walk_forward_validation_seq(df_final.dropna(), 'Target_clf', 'Target', 100, 1)
-                    return res1, xgbr, seq2
-                res1, xgbr, seq2 = train_models()
-            # st.success("✅ Models trained")
-
-            with st.spinner("Getting new prediction..."):
-
-                # Get last row
-                new_pred = data.loc[final_row, model_cols]
-
-                new_pred = pd.DataFrame(new_pred).T
-                # new_pred_show = pd.DataFrame(index=[new_pred.columns], columns=[new_pred.index], data=[[v] for v in new_pred.values])
-                # last_date = datetime.datetime.strptime(data.loc[final_row], '%Y-%m-%d')
-                curr_date = final_row + BDay(1)
-                curr_date = curr_date.strftime('%Y-%m-%d')
-
-                new_pred['BigNewsDay'] = new_pred['BigNewsDay'].astype(float)
-                new_pred['Quarter'] = new_pred['Quarter'].astype(int)
-                new_pred['Perf5Day'] = new_pred['Perf5Day'].astype(bool)
-                new_pred['Perf5Day_n1'] = new_pred['Perf5Day_n1'].astype(bool)
-                new_pred['DaysGreen'] = new_pred['DaysGreen'].astype(float)
-                new_pred['DaysRed'] = new_pred['DaysRed'].astype(float)
-                new_pred['CurrentHigh30toClose'] = new_pred['CurrentHigh30toClose'].astype(float)
-                new_pred['CurrentLow30toClose'] = new_pred['CurrentLow30toClose'].astype(float)
-                new_pred['CurrentClose30toClose'] = new_pred['CurrentClose30toClose'].astype(float)
-                new_pred['CurrentRange30'] = new_pred['CurrentRange30'].astype(float)
-                new_pred['GapFill30'] = new_pred['GapFill30'].astype(float)  
-                new_pred['CurrentGap'] = new_pred['CurrentGap'].astype(float)
-                new_pred['RangePct'] = new_pred['RangePct'].astype(float)
-                new_pred['RangePct_n1'] = new_pred['RangePct_n1'].astype(float)
-                new_pred['RangePct_n2'] = new_pred['RangePct_n2'].astype(float)
-                new_pred['OHLC4_VIX'] = new_pred['OHLC4_VIX'].astype(float)
-                new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float)
-                new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float)
-                new_pred['OpenL1'] = new_pred['OpenL1'].astype(float)
-                new_pred['OpenL2'] = new_pred['OpenL2'].astype(float)
-                new_pred['OpenH1'] = new_pred['OpenH1'].astype(float)
-                new_pred['OpenH2'] = new_pred['OpenH2'].astype(float)
-                new_pred['L1TouchPct'] = new_pred['L1TouchPct'].astype(float)
-                new_pred['L2TouchPct'] = new_pred['L2TouchPct'].astype(float)
-                new_pred['H1TouchPct'] = new_pred['H1TouchPct'].astype(float)
-                new_pred['H2TouchPct'] = new_pred['H2TouchPct'].astype(float)
-                new_pred['L1BreakPct'] = new_pred['L1BreakPct'].astype(float)
-                new_pred['L2BreakPct'] = new_pred['L2BreakPct'].astype(float)
-                new_pred['H1BreakPct'] = new_pred['H1BreakPct'].astype(float)
-                new_pred['H2BreakPct'] = new_pred['H2BreakPct'].astype(float)
-                new_pred['GreenProbas'] = new_pred['GreenProbas'].astype(float)
-
-                seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
         
-        elif option == '07:30':
-        # run60 = st.button('🏃🏽‍♂️ Run')
-        # if run60:
-            from model_intra import *
-            
-            fname='performance_for_1h_model.csv'
-
-            with st.spinner('Loading data...'):
-                data, df_final, final_row = get_data(2)
-            # st.success("✅ Historical data")
-
-            with st.spinner("Training models..."):
-                def train_models():
-                    res1, xgbr, seq2 = walk_forward_validation_seq(df_final.dropna(), 'Target_clf', 'Target', 100, 1)
-                    return res1, xgbr, seq2
-                res1, xgbr, seq2 = train_models()
-            # st.success("✅ Models trained")
-
-            with st.spinner("Getting new prediction..."):
-
-                # Get last row
-                new_pred = data.loc[final_row, model_cols]
-
-                new_pred = pd.DataFrame(new_pred).T
-                # new_pred_show = pd.DataFrame(index=[new_pred.columns], columns=[new_pred.index], data=[[v] for v in new_pred.values])
-                # last_date = datetime.datetime.strptime(data.loc[final_row], '%Y-%m-%d')
-                curr_date = final_row + BDay(1)
-                curr_date = curr_date.strftime('%Y-%m-%d')
-
-                new_pred['BigNewsDay'] = new_pred['BigNewsDay'].astype(float)
-                new_pred['Quarter'] = new_pred['Quarter'].astype(int)
-                new_pred['Perf5Day'] = new_pred['Perf5Day'].astype(bool)
-                new_pred['Perf5Day_n1'] = new_pred['Perf5Day_n1'].astype(bool)
-                new_pred['DaysGreen'] = new_pred['DaysGreen'].astype(float)
-                new_pred['DaysRed'] = new_pred['DaysRed'].astype(float)
-                new_pred['CurrentHigh30toClose'] = new_pred['CurrentHigh30toClose'].astype(float)
-                new_pred['CurrentLow30toClose'] = new_pred['CurrentLow30toClose'].astype(float)
-                new_pred['CurrentClose30toClose'] = new_pred['CurrentClose30toClose'].astype(float)
-                new_pred['CurrentRange30'] = new_pred['CurrentRange30'].astype(float)
-                new_pred['GapFill30'] = new_pred['GapFill30'].astype(float)  
-                new_pred['CurrentGap'] = new_pred['CurrentGap'].astype(float)
-                new_pred['RangePct'] = new_pred['RangePct'].astype(float)
-                new_pred['RangePct_n1'] = new_pred['RangePct_n1'].astype(float)
-                new_pred['RangePct_n2'] = new_pred['RangePct_n2'].astype(float)
-                new_pred['OHLC4_VIX'] = new_pred['OHLC4_VIX'].astype(float)
-                new_pred['OHLC4_VIX_n1'] = new_pred['OHLC4_VIX_n1'].astype(float)
-                new_pred['OHLC4_VIX_n2'] = new_pred['OHLC4_VIX_n2'].astype(float)
-                new_pred['OpenL1'] = new_pred['OpenL1'].astype(float)
-                new_pred['OpenL2'] = new_pred['OpenL2'].astype(float)
-                new_pred['OpenH1'] = new_pred['OpenH1'].astype(float)
-                new_pred['OpenH2'] = new_pred['OpenH2'].astype(float)
-                new_pred['L1TouchPct'] = new_pred['L1TouchPct'].astype(float)
-                new_pred['L2TouchPct'] = new_pred['L2TouchPct'].astype(float)
-                new_pred['H1TouchPct'] = new_pred['H1TouchPct'].astype(float)
-                new_pred['H2TouchPct'] = new_pred['H2TouchPct'].astype(float)
-                new_pred['L1BreakPct'] = new_pred['L1BreakPct'].astype(float)
-                new_pred['L2BreakPct'] = new_pred['L2BreakPct'].astype(float)
-                new_pred['H1BreakPct'] = new_pred['H1BreakPct'].astype(float)
-                new_pred['H2BreakPct'] = new_pred['H2BreakPct'].astype(float)
-                new_pred['GreenProbas'] = new_pred['GreenProbas'].astype(float)
+        else:
 
-                seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
-        
-        elif option == '08:00':
-        # run60 = st.button('🏃🏽‍♂️ Run')
-        # if run60:
             from model_intra import *
-
-            fname='performance_for_90m_model.csv'
+            idx = times_list.index(option)
 
             with st.spinner('Loading data...'):
-                data, df_final, final_row = get_data(3)
+                data, df_final, final_row = get_data(idx)
             # st.success("✅ Historical data")
 
             with st.spinner("Training models..."):
@@ -448,7 +317,7 @@ with st.form("choose_model"):
         prices.columns = ['']
 
         roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
-        roc_auc_score_calib = roc_auc_score(res1.dropna(subset='CalibGreenProba')['True'].astype(int), res1.dropna(subset='CalibGreenProba')['CalibGreenProba'].values)
+        roc_auc_score_calib = roc_auc_score(res1.dropna(subset='CalibPredicted')['True'].astype(int), res1.dropna(subset='CalibPredicted')['CalibPredicted'].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)
@@ -456,7 +325,7 @@ with st.form("choose_model"):
         res2_filtered = res1.loc[(res1['Predicted'] > hi_thres) | (res1['Predicted'] <= lo_thres)]
 
         roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)
-        roc_auc_score_hi_calib = roc_auc_score(res2_filtered.dropna(subset='CalibGreenProba')['True'].astype(int), res2_filtered.dropna(subset='CalibGreenProba')['CalibGreenProba'].values)
+        roc_auc_score_hi_calib = roc_auc_score(res2_filtered.dropna(subset='CalibPredicted')['True'].astype(int), res2_filtered.dropna(subset='CalibPredicted')['CalibPredicted'].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)
@@ -533,32 +402,16 @@ with st.form("choose_model"):
             
         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_levels = pd.DataFrame(
+            index=['H2','H1','L1','L2'],
+            columns=['Level','BreakPct(100)','TouchPct(100)'],
+            data=[
+                [f"{data['H2'].iloc[-1]:.2f}",f"{data['H2BreakPct'].iloc[-2]:.1%}",f"{data['H2TouchPct'].iloc[-2]:.1%}"],
+                [f"{data['H1'].iloc[-1]:.2f}",f"{data['H1BreakPct'].iloc[-2]:.1%}",f"{data['H1TouchPct'].iloc[-2]:.1%}"],
+                [f"{data['L1'].iloc[-1]:.2f}",f"{data['L1BreakPct'].iloc[-2]:.1%}",f"{data['L1TouchPct'].iloc[-2]:.1%}"],
+                [f"{data['L2'].iloc[-1]:.2f}",f"{data['L2BreakPct'].iloc[-2]:.1%}",f"{data['L2TouchPct'].iloc[-2]:.1%}"]
+            ]
+        )
         
         # Cache all DFs
         all_dfs = []
@@ -571,33 +424,8 @@ with st.form("choose_model"):
             # st.dataframe(prices.T.set_index('PrevClose', drop=True))
             st.write(df_probas)
 
-            for close_use, em_use, lbl in zip([prev_close, curr_close], [curr_em, fwd_em], ['Current','Forward']):
-
-                st.write(f'🌊 VIX EM {lbl}: {em_use:.2f}')
-                df_em = pd.DataFrame(columns=['EM','Low','High','InRange','Tested'])
-                df_em.index.name = f'{close_use:.2f}'
-                df_em.loc['EM 1X'] = [
-                    f"{em_use:.2f} ({em_use / close_use:.1%})",
-                    f"{close_use - em_use:.2f}",
-                    f"{close_use + em_use:.2f}",
-                    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'] = [
-                    f"{curr_em * 1.25:.2f} ({(em_use * 1.25) / close_use:.1%})",
-                    f"{close_use - (em_use * 1.25):.2f}",
-                    f"{close_use + (em_use * 1.25):.2f}",
-                    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"] = [
-                    f"{curr_em * 1.5:.2f} ({(em_use * 1.5) / close_use:.1%})",
-                    f"{close_use - (em_use * 1.5):.2f}",
-                    f"{close_use + (em_use * 1.5):.2f}",
-                    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%}"
-                    ]
-                st.write(df_em)
+            st.write(f'🌊 JC Levels')
+            st.write(df_levels)
 
         with tab2:
             st.subheader('Latest Data for Pred')

model_day.py

@@ -126,7 +126,7 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
 
     greenprobas = []
     meanprobas = []
-    for i, pct in enumerate(df_results['Predicted']):
+    for i, pct in tqdm(enumerate(df_results['Predicted'], desc='Calibrating Probas')):
         try:
             df_q = get_quantiles(df_results.iloc[:i], 'Predicted', 7)
             for q in df_q.index:
@@ -140,8 +140,7 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
         greenprobas.append(p)
         meanprobas.append(c)
 
-    df_results['CalibPredicted'] = meanprobas
-    df_results['CalibGreenProba'] = greenprobas
+    df_results['CalibPredicted'] = greenprobas
     
     return df_results, model1, model2
 

model_intra.py

@@ -135,7 +135,7 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
 
     greenprobas = []
     meanprobas = []
-    for i, pct in enumerate(df_results['Predicted']):
+    for i, pct in tqdm(enumerate(df_results['Predicted']), desc='Calibrating Probas'):
         try:
             df_q = get_quantiles(df_results.iloc[:i], 'Predicted', 7)
             for q in df_q.index:
@@ -149,8 +149,7 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
         greenprobas.append(p)
         meanprobas.append(c)
 
-    df_results['CalibPredicted'] = meanprobas
-    df_results['CalibGreenProba'] = greenprobas
+    df_results['CalibPredicted'] = greenprobas
 
     return df_results, model1, model2
 

troubleshoot_day_model.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,18 +13,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00,  6.07it/s]\n",
-      "Getting release dates: 100%|██████████| 8/8 [00:01<00:00,  4.53it/s]\n",
-      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 3997.43it/s]\n",
+      "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00,  4.27it/s]\n",
+      "Getting release dates: 100%|██████████| 8/8 [00:01<00:00,  4.30it/s]\n",
+      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 2663.26it/s]\n",
       "Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--spx_intra-e0e5e7af8fd43022/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n",
-      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 999.12it/s]\n"
+      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 999.09it/s]\n"
      ]
     }
    ],
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,23 +46,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "LR Model: 100%|██████████| 1126/1126 [00:03<00:00, 332.50it/s]\n",
-      "d:\\Projects\\gamedayspx\\model_intra.py:97: SettingWithCopyWarning:\n",
-      "\n",
-      "\n",
+      "LR Model: 100%|██████████| 1129/1129 [00:03<00:00, 300.05it/s]\n",
+      "d:\\Projects\\gamedayspx\\model_intra.py:97: 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",
-      "\n",
-      "CLF Model: 100%|██████████| 1026/1026 [00:07<00:00, 141.65it/s]\n"
+      "  for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0\n",
+      "CLF Model: 100%|██████████| 1029/1029 [00:08<00:00, 123.30it/s]\n",
+      "Calibrating Probas: 1029it [00:02, 415.09it/s]\n"
      ]
     }
    ],
@@ -72,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +100,7 @@
     "hi_thres = 0.6 # res1.loc[middle_quantiles, 'Predicted'].max()\n",
     "\n",
     "roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)\n",
-    "roc_auc_score_calib = roc_auc_score(res1.dropna(subset='CalibGreenProba')['True'].astype(int), res1.dropna(subset='CalibGreenProba')['CalibGreenProba'].values)\n",
+    "roc_auc_score_calib = roc_auc_score(res1.dropna(subset='CalibPredicted')['True'].astype(int), res1.dropna(subset='CalibPredicted')['CalibPredicted'].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",
@@ -109,7 +108,7 @@
     "res2_filtered = res1.loc[(res1['Predicted'] > hi_thres) | (res1['Predicted'] <= lo_thres)]\n",
     "\n",
     "roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)\n",
-    "roc_auc_score_hi_calib = roc_auc_score(res2_filtered.dropna(subset='CalibGreenProba')['True'].astype(int), res2_filtered.dropna(subset='CalibGreenProba')['CalibGreenProba'].values)\n",
+    "roc_auc_score_hi_calib = roc_auc_score(res2_filtered.dropna(subset='CalibPredicted')['True'].astype(int), res2_filtered.dropna(subset='CalibPredicted')['CalibPredicted'].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",
@@ -167,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -194,189 +193,168 @@
        "      <th>True</th>\n",
        "      <th>Predicted</th>\n",
        "      <th>CalibPredicted</th>\n",
-       "      <th>CalibGreenProba</th>\n",
        "      <th>Accuracy</th>\n",
        "      <th>HTML</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2023-09-11</th>\n",
-       "      <td>False</td>\n",
-       "      <td>0.207403</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>0.120567</td>\n",
-       "      <td>✅</td>\n",
-       "      <td>&amp;#9989;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>2023-09-12</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.568866</td>\n",
-       "      <td>0.6140</td>\n",
-       "      <td>0.556391</td>\n",
+       "      <td>0.471917</td>\n",
+       "      <td>0.422018</td>\n",
        "      <td>🟨</td>\n",
        "      <td>&amp;#11036;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-13</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.766335</td>\n",
-       "      <td>0.8030</td>\n",
-       "      <td>0.870690</td>\n",
+       "      <td>0.788835</td>\n",
+       "      <td>0.876106</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-14</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.252581</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>0.119718</td>\n",
+       "      <td>0.224466</td>\n",
+       "      <td>0.120805</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-15</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.587229</td>\n",
-       "      <td>0.6140</td>\n",
-       "      <td>0.559701</td>\n",
+       "      <td>0.484659</td>\n",
+       "      <td>0.604651</td>\n",
        "      <td>🟨</td>\n",
        "      <td>&amp;#11036;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-18</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.223258</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>0.118881</td>\n",
+       "      <td>0.218419</td>\n",
+       "      <td>0.120000</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-19</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.606953</td>\n",
-       "      <td>0.6140</td>\n",
-       "      <td>0.562963</td>\n",
-       "      <td>❌</td>\n",
-       "      <td>&amp;#10060;</td>\n",
+       "      <td>0.495749</td>\n",
+       "      <td>0.607692</td>\n",
+       "      <td>🟨</td>\n",
+       "      <td>&amp;#11036;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-20</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.196109</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>0.118056</td>\n",
+       "      <td>0.193814</td>\n",
+       "      <td>0.119205</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-21</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.431239</td>\n",
-       "      <td>0.4245</td>\n",
-       "      <td>0.420168</td>\n",
+       "      <td>0.441842</td>\n",
+       "      <td>0.427273</td>\n",
        "      <td>🟨</td>\n",
        "      <td>&amp;#11036;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-22</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.411964</td>\n",
-       "      <td>0.4245</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>🟨</td>\n",
-       "      <td>&amp;#11036;</td>\n",
+       "      <td>0.377387</td>\n",
+       "      <td>0.196850</td>\n",
+       "      <td>❌</td>\n",
+       "      <td>&amp;#10060;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-25</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.199644</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>0.117241</td>\n",
+       "      <td>0.196610</td>\n",
+       "      <td>0.118421</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-26</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.446124</td>\n",
-       "      <td>0.4245</td>\n",
-       "      <td>0.421488</td>\n",
+       "      <td>0.400516</td>\n",
+       "      <td>0.423423</td>\n",
        "      <td>🟨</td>\n",
        "      <td>&amp;#11036;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-27</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.637091</td>\n",
-       "      <td>0.6140</td>\n",
-       "      <td>0.558824</td>\n",
+       "      <td>0.719858</td>\n",
+       "      <td>0.761194</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-28</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.776567</td>\n",
-       "      <td>0.8030</td>\n",
-       "      <td>0.871245</td>\n",
+       "      <td>0.778499</td>\n",
+       "      <td>0.876652</td>\n",
        "      <td>❌</td>\n",
        "      <td>&amp;#10060;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-29</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.502801</td>\n",
-       "      <td>0.5195</td>\n",
-       "      <td>0.557377</td>\n",
+       "      <td>0.547333</td>\n",
+       "      <td>0.603053</td>\n",
        "      <td>🟨</td>\n",
        "      <td>&amp;#11036;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-10-02</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.198343</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>0.116438</td>\n",
+       "      <td>0.199027</td>\n",
+       "      <td>0.117647</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-10-03</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.610792</td>\n",
-       "      <td>0.6140</td>\n",
-       "      <td>0.562044</td>\n",
-       "      <td>✅</td>\n",
-       "      <td>&amp;#9989;</td>\n",
+       "      <td>0.528719</td>\n",
+       "      <td>0.606061</td>\n",
+       "      <td>🟨</td>\n",
+       "      <td>&amp;#11036;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-10-04</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.250420</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>0.115646</td>\n",
+       "      <td>0.240551</td>\n",
+       "      <td>0.116883</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-10-05</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.303259</td>\n",
-       "      <td>0.3300</td>\n",
-       "      <td>0.204724</td>\n",
+       "      <td>0.232103</td>\n",
+       "      <td>0.116129</td>\n",
        "      <td>❌</td>\n",
        "      <td>&amp;#10060;</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-10-06</th>\n",
        "      <td>True</td>\n",
-       "      <td>0.628482</td>\n",
-       "      <td>0.6140</td>\n",
-       "      <td>0.565217</td>\n",
+       "      <td>0.638131</td>\n",
+       "      <td>0.511111</td>\n",
+       "      <td>✅</td>\n",
+       "      <td>&amp;#9989;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-10-09</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0.785267</td>\n",
+       "      <td>0.872807</td>\n",
        "      <td>✅</td>\n",
        "      <td>&amp;#9989;</td>\n",
        "    </tr>\n",
@@ -385,52 +363,30 @@
        "</div>"
       ],
       "text/plain": [
-       "             True  Predicted  CalibPredicted  CalibGreenProba Accuracy  \\\n",
-       "2023-09-11  False   0.207403          0.2350         0.120567        ✅   \n",
-       "2023-09-12   True   0.568866          0.6140         0.556391        🟨   \n",
-       "2023-09-13   True   0.766335          0.8030         0.870690        ✅   \n",
-       "2023-09-14  False   0.252581          0.2350         0.119718        ✅   \n",
-       "2023-09-15   True   0.587229          0.6140         0.559701        🟨   \n",
-       "2023-09-18  False   0.223258          0.2350         0.118881        ✅   \n",
-       "2023-09-19  False   0.606953          0.6140         0.562963        ❌   \n",
-       "2023-09-20  False   0.196109          0.2350         0.118056        ✅   \n",
-       "2023-09-21  False   0.431239          0.4245         0.420168        🟨   \n",
-       "2023-09-22   True   0.411964          0.4245         0.416667        🟨   \n",
-       "2023-09-25  False   0.199644          0.2350         0.117241        ✅   \n",
-       "2023-09-26   True   0.446124          0.4245         0.421488        🟨   \n",
-       "2023-09-27   True   0.637091          0.6140         0.558824        ✅   \n",
-       "2023-09-28  False   0.776567          0.8030         0.871245        ❌   \n",
-       "2023-09-29   True   0.502801          0.5195         0.557377        🟨   \n",
-       "2023-10-02  False   0.198343          0.2350         0.116438        ✅   \n",
-       "2023-10-03   True   0.610792          0.6140         0.562044        ✅   \n",
-       "2023-10-04  False   0.250420          0.2350         0.115646        ✅   \n",
-       "2023-10-05   True   0.303259          0.3300         0.204724        ❌   \n",
-       "2023-10-06   True   0.628482          0.6140         0.565217        ✅   \n",
-       "\n",
-       "                HTML  \n",
-       "2023-09-11   &#9989;  \n",
-       "2023-09-12  &#11036;  \n",
-       "2023-09-13   &#9989;  \n",
-       "2023-09-14   &#9989;  \n",
-       "2023-09-15  &#11036;  \n",
-       "2023-09-18   &#9989;  \n",
-       "2023-09-19  &#10060;  \n",
-       "2023-09-20   &#9989;  \n",
-       "2023-09-21  &#11036;  \n",
-       "2023-09-22  &#11036;  \n",
-       "2023-09-25   &#9989;  \n",
-       "2023-09-26  &#11036;  \n",
-       "2023-09-27   &#9989;  \n",
-       "2023-09-28  &#10060;  \n",
-       "2023-09-29  &#11036;  \n",
-       "2023-10-02   &#9989;  \n",
-       "2023-10-03   &#9989;  \n",
-       "2023-10-04   &#9989;  \n",
-       "2023-10-05  &#10060;  \n",
-       "2023-10-06   &#9989;  "
+       "             True  Predicted  CalibPredicted Accuracy      HTML\n",
+       "2023-09-12   True   0.471917        0.422018        🟨  &#11036;\n",
+       "2023-09-13   True   0.788835        0.876106        ✅   &#9989;\n",
+       "2023-09-14  False   0.224466        0.120805        ✅   &#9989;\n",
+       "2023-09-15   True   0.484659        0.604651        🟨  &#11036;\n",
+       "2023-09-18  False   0.218419        0.120000        ✅   &#9989;\n",
+       "2023-09-19  False   0.495749        0.607692        🟨  &#11036;\n",
+       "2023-09-20  False   0.193814        0.119205        ✅   &#9989;\n",
+       "2023-09-21  False   0.441842        0.427273        🟨  &#11036;\n",
+       "2023-09-22   True   0.377387        0.196850        ❌  &#10060;\n",
+       "2023-09-25  False   0.196610        0.118421        ✅   &#9989;\n",
+       "2023-09-26   True   0.400516        0.423423        🟨  &#11036;\n",
+       "2023-09-27   True   0.719858        0.761194        ✅   &#9989;\n",
+       "2023-09-28  False   0.778499        0.876652        ❌  &#10060;\n",
+       "2023-09-29   True   0.547333        0.603053        🟨  &#11036;\n",
+       "2023-10-02  False   0.199027        0.117647        ✅   &#9989;\n",
+       "2023-10-03   True   0.528719        0.606061        🟨  &#11036;\n",
+       "2023-10-04  False   0.240551        0.116883        ✅   &#9989;\n",
+       "2023-10-05   True   0.232103        0.116129        ❌  &#10060;\n",
+       "2023-10-06   True   0.638131        0.511111        ✅   &#9989;\n",
+       "2023-10-09   True   0.785267        0.872807        ✅   &#9989;"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -450,7 +406,7 @@
        "147"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -461,7 +417,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_levels = pd.DataFrame(\n",
+    "    index=['H2','H1','L1','L2'],\n",
+    "    columns=['Level','BreakPct(100)','TouchPct(100)'],\n",
+    "    data=[\n",
+    "        [f\"{data['H2'].iloc[-1]:.2f}\",f\"{data['H2BreakPct'].iloc[-2]:.1%}\",f\"{data['H2TouchPct'].iloc[-2]:.1%}\"],\n",
+    "        [f\"{data['H1'].iloc[-1]:.2f}\",f\"{data['H1BreakPct'].iloc[-2]:.1%}\",f\"{data['H1TouchPct'].iloc[-2]:.1%}\"],\n",
+    "        [f\"{data['L1'].iloc[-1]:.2f}\",f\"{data['L1BreakPct'].iloc[-2]:.1%}\",f\"{data['L1TouchPct'].iloc[-2]:.1%}\"],\n",
+    "        [f\"{data['L2'].iloc[-1]:.2f}\",f\"{data['L2BreakPct'].iloc[-2]:.1%}\",f\"{data['L2TouchPct'].iloc[-2]:.1%}\"]\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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>Level</th>\n",
+       "      <th>BreakPct</th>\n",
+       "      <th>TouchPct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>H2</th>\n",
+       "      <td>4379.56</td>\n",
+       "      <td>11.0%</td>\n",
+       "      <td>17.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H1</th>\n",
+       "      <td>4359.40</td>\n",
+       "      <td>22.0%</td>\n",
+       "      <td>41.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L1</th>\n",
+       "      <td>4317.15</td>\n",
+       "      <td>21.0%</td>\n",
+       "      <td>41.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L2</th>\n",
+       "      <td>4296.99</td>\n",
+       "      <td>14.0%</td>\n",
+       "      <td>21.0%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Level BreakPct TouchPct\n",
+       "H2  4379.56    11.0%    17.0%\n",
+       "H1  4359.40    22.0%    41.0%\n",
+       "L1  4317.15    21.0%    41.0%\n",
+       "L2  4296.99    14.0%    21.0%"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_levels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -492,28 +543,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>N</th>\n",
-       "      <td>1026.00</td>\n",
-       "      <td>761.00</td>\n",
+       "      <td>1029.00</td>\n",
+       "      <td>756.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROC AUC</th>\n",
-       "      <td>0.80</td>\n",
+       "      <td>0.81</td>\n",
        "      <td>0.85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Calib. AUC</th>\n",
        "      <td>0.77</td>\n",
-       "      <td>0.82</td>\n",
+       "      <td>0.81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
-       "      <td>0.72</td>\n",
-       "      <td>0.76</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>0.78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.79</td>\n",
-       "      <td>0.86</td>\n",
+       "      <td>0.87</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -521,14 +572,14 @@
       ],
       "text/plain": [
        "                All  High Confidence\n",
-       "N           1026.00           761.00\n",
-       "ROC AUC        0.80             0.85\n",
-       "Calib. AUC     0.77             0.82\n",
-       "Precision      0.72             0.76\n",
-       "Recall         0.79             0.86"
+       "N           1029.00           756.00\n",
+       "ROC AUC        0.81             0.85\n",
+       "Calib. AUC     0.77             0.81\n",
+       "Precision      0.73             0.78\n",
+       "Recall         0.79             0.87"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -539,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
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        "data": [
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+          "2019-09-10T00:00:00",
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-          "2023-10-06T00:00:00"
+          "2023-10-06T00:00:00",
+          "2023-10-09T00:00:00"
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         {
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@@ -9630,8 +9705,8 @@
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@@ -9736,7 +9811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {