diff --git "a/troubleshoot_day_model.ipynb" "b/troubleshoot_day_model.ipynb"
--- "a/troubleshoot_day_model.ipynb"
+++ "b/troubleshoot_day_model.ipynb"
@@ -8,7 +8,8 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "from model_intra import get_data, walk_forward_validation_seq"
+    "from model_intra import get_data, walk_forward_validation\n",
+    "import lightgbm as lgb"
    ]
   },
   {
@@ -20,16 +21,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "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",
+      "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00,  3.10it/s]\n",
+      "Getting release dates: 100%|██████████| 8/8 [00:01<00:00,  4.87it/s]\n",
+      "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 3997.91it/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, 999.09it/s]\n"
+      "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 799.22it/s]\n"
      ]
     }
    ],
    "source": [
-    "data, df_final, final_row = get_data(2)"
+    "data, df_final, final_row = get_data(5)"
    ]
   },
   {
@@ -46,32 +47,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate the rolling likelihood\n",
+    "rolling_likelihood = (data['H1Break'] & data['H1Touch']==True).rolling(window=100).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['H1BreakTouchPct'] = data['H1Break'].expanding().sum() / data['H1Touch'].expanding().sum()\n",
+    "data['H2BreakTouchPct'] = data['H2Break'].expanding().sum() / data['H2Touch'].expanding().sum()\n",
+    "data['H1BreakTouchPct'] = data['L1Break'].expanding().sum() / data['L1Touch'].expanding().sum()\n",
+    "data['H2BreakTouchPct'] = data['L2Break'].expanding().sum() / data['L2Touch'].expanding().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "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",
-      "  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"
-     ]
+     "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>H2Touch</th>\n",
+       "      <th>H2Break</th>\n",
+       "      <th>H2BreakTouch</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-07-02</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-03</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-05</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-06</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-09</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-10-10</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-10-11</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-10-12</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-10-13</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-10-16</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1332 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            H2Touch  H2Break  H2BreakTouch\n",
+       "2018-07-02    False    False           NaN\n",
+       "2018-07-03    False    False           NaN\n",
+       "2018-07-05    False    False           NaN\n",
+       "2018-07-06    False    False           NaN\n",
+       "2018-07-09    False    False           NaN\n",
+       "...             ...      ...           ...\n",
+       "2023-10-10     True    False      0.588235\n",
+       "2023-10-11    False    False      0.588235\n",
+       "2023-10-12    False    False      0.588235\n",
+       "2023-10-13    False    False      0.588235\n",
+       "2023-10-16     True    False      0.571429\n",
+       "\n",
+       "[1332 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "res1, model1, model2 = walk_forward_validation_seq(df_final.dropna(axis=0), 'Target_clf', 'Target', 100, 1)"
+    "data[['H2Touch','H2Break','H2BreakTouch']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2018-07-02     NaN\n",
+       "2018-07-03     NaN\n",
+       "2018-07-05     NaN\n",
+       "2018-07-06     NaN\n",
+       "2018-07-09     NaN\n",
+       "              ... \n",
+       "2023-10-10    0.22\n",
+       "2023-10-11    0.21\n",
+       "2023-10-12    0.21\n",
+       "2023-10-13    0.21\n",
+       "2023-10-16    0.22\n",
+       "Name: H1BreakPct, Length: 1332, dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['H1BreakPct']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res1, model1 = walk_forward_validation(df_final.dropna(axis=0), 'Target_clf', 100, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot feature importances\n",
+    "plt.figure(figsize=(10, 12))\n",
+    "lgb.plot_importance(model1)  # Adjust max_num_features as needed\n",
+    "plt.title(\"Feature Importances\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yfinance as yf\n",
+    "\n",
+    "vix = yf.Ticker('^TNX')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vix.history(interval='30m')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_final.groupby(pd.qcut(df_final['CurrentGap'], 10))['Target_clf'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,258 +377,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    <tr style=\"text-align: right;\">\n",
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-       "      <th>True</th>\n",
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-       "      <td>0.471917</td>\n",
-       "      <td>0.422018</td>\n",
-       "      <td>🟨</td>\n",
-       "      <td>&amp;#11036;</td>\n",
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-       "    <tr>\n",
-       "      <th>2023-09-13</th>\n",
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-       "      <td>0.876106</td>\n",
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-       "      <td>0.224466</td>\n",
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-       "      <td>True</td>\n",
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-       "      <td>🟨</td>\n",
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-       "      <th>2023-09-18</th>\n",
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-       "      <td>0.218419</td>\n",
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-       "      <td>&amp;#9989;</td>\n",
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-       "    <tr>\n",
-       "      <th>2023-09-19</th>\n",
-       "      <td>False</td>\n",
-       "      <td>0.495749</td>\n",
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-       "      <td>🟨</td>\n",
-       "      <td>&amp;#11036;</td>\n",
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-       "    <tr>\n",
-       "      <th>2023-09-20</th>\n",
-       "      <td>False</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>0.441842</td>\n",
-       "      <td>0.427273</td>\n",
-       "      <td>🟨</td>\n",
-       "      <td>&amp;#11036;</td>\n",
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-       "    <tr>\n",
-       "      <th>2023-09-22</th>\n",
-       "      <td>True</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>0.196610</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>0.400516</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>0.719858</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>0.778499</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>0.547333</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>0.199027</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>0.528719</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>0.240551</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>0.232103</td>\n",
-       "      <td>0.116129</td>\n",
-       "      <td>❌</td>\n",
-       "      <td>&amp;#10060;</td>\n",
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-       "    <tr>\n",
-       "      <th>2023-10-06</th>\n",
-       "      <td>True</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",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             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",
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-       "2023-09-15   True   0.484659        0.604651        🟨  &#11036;\n",
-       "2023-09-18  False   0.218419        0.120000        ✅   &#9989;\n",
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-       "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": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "perf_daily.tail(20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "147"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_probas.loc[df_probas.index[0], 'NumObs']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,9310 +413,27 @@
   },
   {
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-       "    <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",
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-      "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_levels"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>All</th>\n",
-       "      <th>High Confidence</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>N</th>\n",
-       "      <td>1029.00</td>\n",
-       "      <td>756.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ROC AUC</th>\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>0.81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Precision</th>\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>0.87</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-      ],
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-       "                All  High Confidence\n",
-       "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": 8,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
+   "outputs": [],
    "source": [
     "df_performance"
    ]
   },
   {
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-          "2023-07-31T00:00:00",
-          "2023-10-09T00:00:00"
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-            "sat",
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-        "yaxis": {
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-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import plotly.graph_objs as go\n",
@@ -9811,21 +506,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'XXX' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32md:\\Projects\\gamedayspx\\troubleshoot_day_model.ipynb Cell 11\u001b[0m line \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/Projects/gamedayspx/troubleshoot_day_model.ipynb#X13sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m XXX\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'XXX' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "XXX"
    ]