diff --git a/notebooks/options-historical-test5.ipynb b/notebooks/options-historical-test5.ipynb
index 152e344..7580364 100644
--- a/notebooks/options-historical-test5.ipynb
+++ b/notebooks/options-historical-test5.ipynb
@@ -11,7 +11,7 @@
      "output_type": "stream",
      "text": [
       "INFO: Pandarallel will run on 12 workers.\n",
-      "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n"
+      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
      ]
     }
    ],
@@ -36,6 +36,643 @@
     "data_directory = \"data/spy-options-data-2020-2022/\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7eb118a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>QUOTE_UNIXTIME</th>\n",
+       "      <th>QUOTE_TIME_HOURS</th>\n",
+       "      <th>UNDERLYING_LAST</th>\n",
+       "      <th>EXPIRE_UNIX</th>\n",
+       "      <th>DTE</th>\n",
+       "      <th>C_DELTA</th>\n",
+       "      <th>C_GAMMA</th>\n",
+       "      <th>C_VEGA</th>\n",
+       "      <th>C_THETA</th>\n",
+       "      <th>C_RHO</th>\n",
+       "      <th>C_IV</th>\n",
+       "      <th>C_VOLUME</th>\n",
+       "      <th>C_LAST</th>\n",
+       "      <th>C_BID</th>\n",
+       "      <th>C_ASK</th>\n",
+       "      <th>STRIKE</th>\n",
+       "      <th>P_BID</th>\n",
+       "      <th>P_ASK</th>\n",
+       "      <th>P_LAST</th>\n",
+       "      <th>P_DELTA</th>\n",
+       "      <th>P_GAMMA</th>\n",
+       "      <th>P_VEGA</th>\n",
+       "      <th>P_THETA</th>\n",
+       "      <th>P_RHO</th>\n",
+       "      <th>P_IV</th>\n",
+       "      <th>P_VOLUME</th>\n",
+       "      <th>STRIKE_DISTANCE</th>\n",
+       "      <th>STRIKE_DISTANCE_PCT</th>\n",
+       "      <th>OPTION_ID</th>\n",
+       "    </tr>\n",
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+       "      <td>2022-12-31 00:00:00+00:00</td>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3589079 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   QUOTE_UNIXTIME  QUOTE_TIME_HOURS  UNDERLYING_LAST  \\\n",
+       "2455495 2020-01-03 00:00:00+00:00              16.0           324.87   \n",
+       "2455496 2020-01-03 00:00:00+00:00              16.0           324.87   \n",
+       "2455497 2020-01-03 00:00:00+00:00              16.0           324.87   \n",
+       "2455498 2020-01-03 00:00:00+00:00              16.0           324.87   \n",
+       "2455499 2020-01-03 00:00:00+00:00              16.0           324.87   \n",
+       "...                           ...               ...              ...   \n",
+       "975120  2022-12-31 00:00:00+00:00              16.0           382.44   \n",
+       "975121  2022-12-31 00:00:00+00:00              16.0           382.44   \n",
+       "975122  2022-12-31 00:00:00+00:00              16.0           382.44   \n",
+       "975123  2022-12-31 00:00:00+00:00              16.0           382.44   \n",
+       "975124  2022-12-31 00:00:00+00:00              16.0           382.44   \n",
+       "\n",
+       "                      EXPIRE_UNIX     DTE  C_DELTA  C_GAMMA   C_VEGA  C_THETA  \\\n",
+       "2455495 2020-01-03 21:00:00+00:00     1.0  1.00000  0.00000  0.00000 -0.00041   \n",
+       "2455496 2020-01-03 21:00:00+00:00     1.0  1.00000  0.00000  0.00000  0.00000   \n",
+       "2455497 2020-01-03 21:00:00+00:00     1.0  1.00000  0.00000  0.00000  0.00000   \n",
+       "2455498 2020-01-03 21:00:00+00:00     1.0  1.00000  0.00000  0.00000 -0.00042   \n",
+       "2455499 2020-01-03 21:00:00+00:00     1.0  1.00000  0.00000  0.00000 -0.00027   \n",
+       "...                           ...     ...      ...      ...      ...      ...   \n",
+       "975120  2025-12-19 21:00:00+00:00  1085.0  0.10595  0.00150  1.19348 -0.01204   \n",
+       "975121  2025-12-19 21:00:00+00:00  1085.0  0.09392  0.00140  1.09723 -0.01082   \n",
+       "975122  2025-12-19 21:00:00+00:00  1085.0  0.09279  0.00137  1.08956 -0.01114   \n",
+       "975123  2025-12-19 21:00:00+00:00  1085.0  0.09938  0.00147  1.14502 -0.01128   \n",
+       "975124  2025-12-19 21:00:00+00:00  1085.0  0.11433  0.00149  1.25917 -0.01324   \n",
+       "\n",
+       "           C_RHO     C_IV  C_VOLUME  C_LAST  C_BID  C_ASK  STRIKE   P_BID  \\\n",
+       "2455495  0.01423      NaN       1.0   53.48  64.56  64.76   260.0    0.00   \n",
+       "2455496  0.01476      NaN       NaN    0.00  59.49  59.73   265.0    0.00   \n",
+       "2455497  0.01467      NaN       NaN    0.00  54.50  54.70   270.0    0.00   \n",
+       "2455498  0.01498      NaN       NaN    0.00  52.00  52.24   272.5    0.00   \n",
+       "2455499  0.01538      NaN       1.0   42.41  49.49  49.67   275.0    0.00   \n",
+       "...          ...      ...       ...     ...    ...    ...     ...     ...   \n",
+       "975120   1.05129  0.17770       2.0    5.71   0.59  10.00   630.0  243.00   \n",
+       "975121   0.94207  0.17304       NaN    0.00   2.00   7.00   635.0  247.66   \n",
+       "975122   0.93199  0.17558       NaN    0.00   2.00   7.00   640.0  253.00   \n",
+       "975123   0.99068  0.18239       3.0    4.33   0.12  10.00   645.0  258.00   \n",
+       "975124   1.11689  0.19561       2.0    4.15   2.60  10.00   650.0  262.75   \n",
+       "\n",
+       "          P_ASK  P_LAST  P_DELTA  P_GAMMA   P_VEGA  P_THETA    P_RHO     P_IV  \\\n",
+       "2455495    0.00    0.03 -0.00049  0.00016  0.00057 -0.00476  0.00000  0.96296   \n",
+       "2455496    0.02    0.01 -0.00086  0.00017  0.00097 -0.00515 -0.00023  0.88540   \n",
+       "2455497    0.01    0.01 -0.00109  0.00016  0.00060 -0.00514  0.00000  0.81007   \n",
+       "2455498    0.01    0.01 -0.00059  0.00013  0.00058 -0.00510 -0.00028  0.77271   \n",
+       "2455499    0.01    0.01 -0.00115  0.00018  0.00033 -0.00535  0.00000  0.73608   \n",
+       "...         ...     ...      ...      ...      ...      ...      ...      ...   \n",
+       "975120   250.50    0.00 -1.00000  0.00000  0.00000  0.00000  0.00000      NaN   \n",
+       "975121   257.50    0.00 -0.89431  0.00307  0.72687 -0.00434 -0.20330  0.29557   \n",
+       "975122   262.50    0.00 -0.88536  0.00298  0.78662 -0.00524 -1.78777  0.30289   \n",
+       "975123   267.50    0.00 -0.88264  0.00287  0.79928 -0.00538 -1.82985  0.30699   \n",
+       "975124   272.50    0.00 -0.88467  0.00288  0.78257 -0.00516 -0.63195  0.31007   \n",
+       "\n",
+       "         P_VOLUME  STRIKE_DISTANCE  STRIKE_DISTANCE_PCT            OPTION_ID  \n",
+       "2455495       NaN             64.9                0.200   -16219963448717015  \n",
+       "2455496       1.0             59.9                0.184  -701381295899092907  \n",
+       "2455497       0.0             54.9                0.169  6061307834470536317  \n",
+       "2455498     100.0             52.4                0.161 -1220682253960080101  \n",
+       "2455499       0.0             49.9                0.154  1653060084193980450  \n",
+       "...           ...              ...                  ...                  ...  \n",
+       "975120        NaN            247.6                0.647  8828685382305019902  \n",
+       "975121        NaN            252.6                0.660  9084648476515366252  \n",
+       "975122        NaN            257.6                0.673 -5359425724938520695  \n",
+       "975123        NaN            262.6                0.687 -9164642606529368658  \n",
+       "975124        NaN            267.6                0.700  6543885475790529709  \n",
+       "\n",
+       "[3589079 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_formatted = pd.read_csv(raw_data_path, low_memory=False)\n",
+    "\n",
+    "columns = df_formatted.columns\n",
+    "columns = [s.replace(\"[\", \"\") for s in columns]\n",
+    "columns = [s.replace(\"]\", \"\") for s in columns]\n",
+    "columns = [s.replace(\" \", \"\") for s in columns]\n",
+    "df_formatted.columns = columns\n",
+    "\n",
+    "date_columns = [\"QUOTE_READTIME\", \"QUOTE_DATE\", \"EXPIRE_DATE\"]\n",
+    "numeric_cols = df_formatted.columns.to_list()\n",
+    "numeric_cols.remove(\"QUOTE_READTIME\")\n",
+    "numeric_cols.remove(\"QUOTE_DATE\")\n",
+    "numeric_cols.remove(\"EXPIRE_DATE\")\n",
+    "\n",
+    "df_formatted.drop(columns=date_columns, inplace=True)\n",
+    "\n",
+    "for i in numeric_cols:\n",
+    "    df_formatted[i] = pd.to_numeric(df_formatted[i], errors=\"coerce\")\n",
+    "\n",
+    "df_formatted.drop(columns=[\"C_SIZE\", \"P_SIZE\"], inplace=True)\n",
+    "df_formatted[\"EXPIRE_UNIX\"] = pd.to_datetime(df_formatted.EXPIRE_UNIX, unit=\"s\", utc=True)\n",
+    "df_formatted[\"QUOTE_UNIXTIME\"] = pd.to_datetime(df_formatted.QUOTE_UNIXTIME, unit=\"s\", utc=True).apply(\n",
+    "    lambda x: pd.Timestamp(x).round(freq=\"D\")\n",
+    ")\n",
+    "df_formatted[\"OPTION_ID\"] = (df_formatted[\"EXPIRE_UNIX\"].astype(str) + df_formatted[\"STRIKE\"].astype(str)).apply(\n",
+    "    lambda x: hash(x)\n",
+    ")\n",
+    "\n",
+    "df_formatted.sort_values(by=[\"QUOTE_UNIXTIME\", \"EXPIRE_UNIX\", \"STRIKE\"], inplace=True)\n",
+    "df_formatted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7fd0ac12",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df_formatted' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m max_column_rename[column] \u001b[38;5;241m=\u001b[39m max_ask_column\n\u001b[1;32m      7\u001b[0m min_column_rename[column] \u001b[38;5;241m=\u001b[39m min_ask_column\n\u001b[0;32m----> 8\u001b[0m \u001b[43mdf_formatted\u001b[49m\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[max_ask_column, min_ask_column], errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      9\u001b[0m df_rolling_max_ask \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     10\u001b[0m     df_formatted\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOPTION_ID\u001b[39m\u001b[38;5;124m\"\u001b[39m, as_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[column]\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;241m.\u001b[39mrolling(window\u001b[38;5;241m=\u001b[39mperiods, min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;66;03m# .drop(columns=[\"OPTION_ID\"])\u001b[39;00m\n\u001b[1;32m     20\u001b[0m )\n\u001b[1;32m     21\u001b[0m df_rolling_min_ask \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     22\u001b[0m     df_formatted\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOPTION_ID\u001b[39m\u001b[38;5;124m\"\u001b[39m, as_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[column]\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;241m.\u001b[39mrolling(window\u001b[38;5;241m=\u001b[39mperiods, min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOPTION_ID\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m     32\u001b[0m )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'df_formatted' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "for column in [\"C_ASK\", \"P_ASK\"]:\n",
+    "    max_ask_column = f\"MAX_{column}_{periods}\"\n",
+    "    min_ask_column = f\"MIN_{column}_{periods}\"\n",
+    "    max_column_rename = {}\n",
+    "    min_column_rename = {}\n",
+    "    max_column_rename[column] = max_ask_column\n",
+    "    min_column_rename[column] = min_ask_column\n",
+    "    df_formatted.drop(columns=[max_ask_column, min_ask_column], errors=\"ignore\", inplace=True)\n",
+    "    df_rolling_max_ask = (\n",
+    "        df_formatted.groupby(\"OPTION_ID\", as_index=False)[column]\n",
+    "        .rolling(window=periods, min_periods=1)\n",
+    "        .max()\n",
+    "        .rename(columns=max_column_rename)\n",
+    "        .drop(columns=[\"OPTION_ID\"])\n",
+    "        .join(df_formatted)[[\"OPTION_ID\", max_ask_column]]\n",
+    "        .groupby(\"OPTION_ID\", as_index=False)\n",
+    "        .shift(periods=-periods)\n",
+    "        .join(df_formatted)[[\"OPTION_ID\", max_ask_column]]\n",
+    "        # .drop(columns=[\"OPTION_ID\"])\n",
+    "    )\n",
+    "    df_rolling_min_ask = (\n",
+    "        df_formatted.groupby(\"OPTION_ID\", as_index=False)[column]\n",
+    "        .rolling(window=periods, min_periods=1)\n",
+    "        .min()\n",
+    "        .rename(columns=min_column_rename)\n",
+    "        .drop(columns=[\"OPTION_ID\"])\n",
+    "        .join(df_formatted)[[\"OPTION_ID\", min_ask_column]]\n",
+    "        .groupby(\"OPTION_ID\", as_index=False)\n",
+    "        .shift(periods=-periods)\n",
+    "        .join(df_formatted)[[\"OPTION_ID\", min_ask_column]]\n",
+    "        .drop(columns=[\"OPTION_ID\"])\n",
+    "    )\n",
+    "    # df_formatted = df_formatted.join(df_rolling_max_ask)\n",
+    "    # df_formatted = df_formatted.join(df_rolling_min_ask)\n",
+    "    break\n",
+    "\n",
+    "df_rolling_max_ask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bedd9ba5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      2457398          NaN\n",
+       "       2461126          NaN\n",
+       "       2464767          NaN\n",
+       "       2468382          NaN\n",
+       "       2472066          NaN\n",
+       "                    ...    \n",
+       "74288  3322270    3317557.0\n",
+       "       3326933    3317557.0\n",
+       "       3331689    3317557.0\n",
+       "       3336433    3317557.0\n",
+       "       3341238    3317557.0\n",
+       "Name: C_ASK, Length: 3589079, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def rolling_idmax(series, n):\n",
+    "    # fist compute the index in the sliding window\n",
+    "    ids = series.rolling(n).apply(np.argmax)\n",
+    "    # 0 <= ids <= n-1\n",
+    "\n",
+    "    # how many rows have past from the sliding window maximum?\n",
+    "    ids = n - 1 - ids\n",
+    "    # 0 <= ids <= n-1\n",
+    "\n",
+    "    # subtract `ids` from the actual positions\n",
+    "    ids = np.arange(len(series)) - ids\n",
+    "    # 0 <= ids <= len(series)-1\n",
+    "\n",
+    "    # convert the positions stored in `ids` with the corrisponding dates (series.index)\n",
+    "    ids.loc[~ids.isna()] = series.index[ids.dropna().astype(int)]\n",
+    "    # \"2005-06-10\" <= ids <= \"2022-03-04\"\n",
+    "\n",
+    "    return ids\n",
+    "\n",
+    "\n",
+    "# df_rolling_max_ask[column + \"_INDEX\"] = df_formatted.groupby(\"OPTION_ID\", as_index=False)[column].apply(rolling_idmax, periods)\n",
+    "# df_rolling_max_ask\n",
+    "\n",
+    "results = df_formatted.groupby(\"OPTION_ID\", as_index=False)[column].apply(rolling_idmax, periods)\n",
+    "results"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -92,53 +729,53 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>8228026009228119508</td>\n",
+       "      <td>4169629807363267195</td>\n",
        "      <td>call</td>\n",
        "      <td>2020-01-03 00:00:00+00:00</td>\n",
-       "      <td>2020-01-31 21:00:00+00:00</td>\n",
-       "      <td>29.00</td>\n",
+       "      <td>2020-02-14 21:00:00+00:00</td>\n",
+       "      <td>43.0</td>\n",
        "      <td>324.87</td>\n",
-       "      <td>255.0</td>\n",
-       "      <td>69.9</td>\n",
-       "      <td>0.215</td>\n",
-       "      <td>0.99254</td>\n",
-       "      <td>0.00067</td>\n",
-       "      <td>0.01952</td>\n",
-       "      <td>-0.01130</td>\n",
-       "      <td>0.20686</td>\n",
-       "      <td>0.35727</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>66.82</td>\n",
-       "      <td>69.87</td>\n",
-       "      <td>70.05</td>\n",
-       "      <td>77.27</td>\n",
-       "      <td>67.22</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>54.9</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.96247</td>\n",
+       "      <td>0.00230</td>\n",
+       "      <td>0.09124</td>\n",
+       "      <td>-0.03148</td>\n",
+       "      <td>0.31026</td>\n",
+       "      <td>0.30852</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>55.31</td>\n",
+       "      <td>55.51</td>\n",
+       "      <td>67.91</td>\n",
+       "      <td>52.30</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>8388765138564423602</td>\n",
+       "      <td>-1427193746298712266</td>\n",
        "      <td>put</td>\n",
        "      <td>2020-01-03 00:00:00+00:00</td>\n",
-       "      <td>2020-01-31 21:00:00+00:00</td>\n",
-       "      <td>29.00</td>\n",
+       "      <td>2020-02-14 21:00:00+00:00</td>\n",
+       "      <td>43.0</td>\n",
        "      <td>324.87</td>\n",
-       "      <td>255.0</td>\n",
-       "      <td>69.9</td>\n",
-       "      <td>0.215</td>\n",
-       "      <td>-0.00379</td>\n",
-       "      <td>0.00033</td>\n",
-       "      <td>0.01031</td>\n",
-       "      <td>-0.00484</td>\n",
-       "      <td>-0.00110</td>\n",
-       "      <td>0.32142</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.04</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>54.9</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>-0.01684</td>\n",
+       "      <td>0.00147</td>\n",
+       "      <td>0.04655</td>\n",
+       "      <td>-0.01293</td>\n",
+       "      <td>-0.00678</td>\n",
+       "      <td>0.25518</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.01</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -146,53 +783,53 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1027550724891153287</td>\n",
+       "      <td>-880651805349778471</td>\n",
        "      <td>call</td>\n",
        "      <td>2020-01-03 00:00:00+00:00</td>\n",
-       "      <td>2020-01-31 21:00:00+00:00</td>\n",
-       "      <td>29.00</td>\n",
+       "      <td>2020-02-14 21:00:00+00:00</td>\n",
+       "      <td>43.0</td>\n",
        "      <td>324.87</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>64.9</td>\n",
-       "      <td>0.200</td>\n",
-       "      <td>0.98654</td>\n",
-       "      <td>0.00101</td>\n",
-       "      <td>0.03238</td>\n",
-       "      <td>-0.01868</td>\n",
-       "      <td>0.20993</td>\n",
-       "      <td>0.36143</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>60.50</td>\n",
-       "      <td>64.94</td>\n",
-       "      <td>65.13</td>\n",
-       "      <td>72.30</td>\n",
-       "      <td>62.23</td>\n",
+       "      <td>275.0</td>\n",
+       "      <td>49.9</td>\n",
+       "      <td>0.154</td>\n",
+       "      <td>0.95833</td>\n",
+       "      <td>0.00278</td>\n",
+       "      <td>0.10050</td>\n",
+       "      <td>-0.03230</td>\n",
+       "      <td>0.31394</td>\n",
+       "      <td>0.28566</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>50.34</td>\n",
+       "      <td>50.53</td>\n",
+       "      <td>62.89</td>\n",
+       "      <td>47.75</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-1534760388604062099</td>\n",
+       "      <td>1419493114519967095</td>\n",
        "      <td>put</td>\n",
        "      <td>2020-01-03 00:00:00+00:00</td>\n",
-       "      <td>2020-01-31 21:00:00+00:00</td>\n",
-       "      <td>29.00</td>\n",
+       "      <td>2020-02-14 21:00:00+00:00</td>\n",
+       "      <td>43.0</td>\n",
        "      <td>324.87</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>64.9</td>\n",
-       "      <td>0.200</td>\n",
-       "      <td>-0.00465</td>\n",
-       "      <td>0.00052</td>\n",
-       "      <td>0.01345</td>\n",
-       "      <td>-0.00650</td>\n",
-       "      <td>-0.00176</td>\n",
-       "      <td>0.30648</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.05</td>\n",
+       "      <td>275.0</td>\n",
+       "      <td>49.9</td>\n",
+       "      <td>0.154</td>\n",
+       "      <td>-0.02157</td>\n",
+       "      <td>0.00193</td>\n",
+       "      <td>0.05840</td>\n",
+       "      <td>-0.01595</td>\n",
+       "      <td>-0.00872</td>\n",
+       "      <td>0.24238</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.30</td>\n",
        "      <td>0.01</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -200,27 +837,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-6171319654666929667</td>\n",
+       "      <td>6851796185238463050</td>\n",
        "      <td>call</td>\n",
        "      <td>2020-01-03 00:00:00+00:00</td>\n",
-       "      <td>2020-01-31 21:00:00+00:00</td>\n",
-       "      <td>29.00</td>\n",
+       "      <td>2020-02-14 21:00:00+00:00</td>\n",
+       "      <td>43.0</td>\n",
        "      <td>324.87</td>\n",
-       "      <td>265.0</td>\n",
-       "      <td>59.9</td>\n",
-       "      <td>0.184</td>\n",
-       "      <td>0.97933</td>\n",
-       "      <td>0.00146</td>\n",
-       "      <td>0.04691</td>\n",
-       "      <td>-0.02728</td>\n",
-       "      <td>0.21135</td>\n",
-       "      <td>0.35875</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>44.9</td>\n",
+       "      <td>0.138</td>\n",
+       "      <td>0.94983</td>\n",
+       "      <td>0.00341</td>\n",
+       "      <td>0.11693</td>\n",
+       "      <td>-0.03563</td>\n",
+       "      <td>0.31725</td>\n",
+       "      <td>0.26822</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>60.01</td>\n",
-       "      <td>60.26</td>\n",
-       "      <td>67.28</td>\n",
-       "      <td>57.21</td>\n",
+       "      <td>45.41</td>\n",
+       "      <td>45.66</td>\n",
+       "      <td>57.90</td>\n",
+       "      <td>42.79</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -253,212 +890,212 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6705099</th>\n",
-       "      <td>2688229576149051334</td>\n",
+       "      <th>5183441</th>\n",
+       "      <td>8337812745454405911</td>\n",
        "      <td>put</td>\n",
        "      <td>2022-12-31 00:00:00+00:00</td>\n",
-       "      <td>2025-03-21 20:00:00+00:00</td>\n",
-       "      <td>811.96</td>\n",
+       "      <td>2025-01-17 21:00:00+00:00</td>\n",
+       "      <td>749.0</td>\n",
        "      <td>382.44</td>\n",
        "      <td>590.0</td>\n",
        "      <td>207.6</td>\n",
        "      <td>0.543</td>\n",
-       "      <td>-1.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>202.75</td>\n",
-       "      <td>210.00</td>\n",
+       "      <td>-0.87442</td>\n",
+       "      <td>0.00305</td>\n",
+       "      <td>0.81016</td>\n",
+       "      <td>-0.00808</td>\n",
+       "      <td>-1.71905</td>\n",
+       "      <td>0.29992</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>193.26</td>\n",
+       "      <td>205.00</td>\n",
+       "      <td>212.60</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>37.0</td>\n",
-       "      <td>76.0</td>\n",
+       "      <td>61.0</td>\n",
        "      <td>36.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6705100</th>\n",
-       "      <td>4933943145329079888</td>\n",
+       "      <th>5183442</th>\n",
+       "      <td>7184827014735111024</td>\n",
        "      <td>call</td>\n",
        "      <td>2022-12-31 00:00:00+00:00</td>\n",
-       "      <td>2025-03-21 20:00:00+00:00</td>\n",
-       "      <td>811.96</td>\n",
+       "      <td>2025-01-17 21:00:00+00:00</td>\n",
+       "      <td>749.0</td>\n",
        "      <td>382.44</td>\n",
        "      <td>595.0</td>\n",
        "      <td>212.6</td>\n",
        "      <td>0.556</td>\n",
-       "      <td>0.09466</td>\n",
-       "      <td>0.00169</td>\n",
-       "      <td>0.96298</td>\n",
-       "      <td>-0.01292</td>\n",
-       "      <td>0.72396</td>\n",
-       "      <td>0.17885</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.73</td>\n",
-       "      <td>6.50</td>\n",
+       "      <td>0.08891</td>\n",
+       "      <td>0.00157</td>\n",
+       "      <td>0.88126</td>\n",
+       "      <td>-0.01276</td>\n",
+       "      <td>0.62712</td>\n",
+       "      <td>0.18258</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.80</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>5.50</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>37.0</td>\n",
-       "      <td>76.0</td>\n",
+       "      <td>61.0</td>\n",
        "      <td>36.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6705101</th>\n",
-       "      <td>-7427531086436162822</td>\n",
+       "      <th>5183443</th>\n",
+       "      <td>5837551170542630138</td>\n",
        "      <td>put</td>\n",
        "      <td>2022-12-31 00:00:00+00:00</td>\n",
-       "      <td>2025-03-21 20:00:00+00:00</td>\n",
-       "      <td>811.96</td>\n",
+       "      <td>2025-01-17 21:00:00+00:00</td>\n",
+       "      <td>749.0</td>\n",
        "      <td>382.44</td>\n",
        "      <td>595.0</td>\n",
        "      <td>212.6</td>\n",
        "      <td>0.556</td>\n",
-       "      <td>-0.90719</td>\n",
-       "      <td>0.00326</td>\n",
-       "      <td>0.61124</td>\n",
-       "      <td>-0.00505</td>\n",
-       "      <td>-1.24251</td>\n",
-       "      <td>0.28500</td>\n",
+       "      <td>-0.86806</td>\n",
+       "      <td>0.00287</td>\n",
+       "      <td>0.84727</td>\n",
+       "      <td>-0.00847</td>\n",
+       "      <td>-1.81885</td>\n",
+       "      <td>0.30789</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>207.82</td>\n",
-       "      <td>217.78</td>\n",
+       "      <td>210.50</td>\n",
+       "      <td>217.50</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>37.0</td>\n",
-       "      <td>76.0</td>\n",
+       "      <td>61.0</td>\n",
        "      <td>36.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6705102</th>\n",
-       "      <td>4838387660868289251</td>\n",
+       "      <th>5183444</th>\n",
+       "      <td>-5828408511425543838</td>\n",
        "      <td>call</td>\n",
        "      <td>2022-12-31 00:00:00+00:00</td>\n",
-       "      <td>2025-03-21 20:00:00+00:00</td>\n",
-       "      <td>811.96</td>\n",
+       "      <td>2025-01-17 21:00:00+00:00</td>\n",
+       "      <td>749.0</td>\n",
        "      <td>382.44</td>\n",
        "      <td>600.0</td>\n",
        "      <td>217.6</td>\n",
        "      <td>0.569</td>\n",
-       "      <td>0.09424</td>\n",
-       "      <td>0.00161</td>\n",
-       "      <td>0.95513</td>\n",
-       "      <td>-0.01327</td>\n",
-       "      <td>0.71483</td>\n",
-       "      <td>0.18161</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.60</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>6.00</td>\n",
+       "      <td>0.07545</td>\n",
+       "      <td>0.00142</td>\n",
+       "      <td>0.77750</td>\n",
+       "      <td>-0.01128</td>\n",
+       "      <td>0.53365</td>\n",
+       "      <td>0.17619</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2.55</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3.45</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>37.0</td>\n",
-       "      <td>76.0</td>\n",
+       "      <td>61.0</td>\n",
        "      <td>36.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6705103</th>\n",
-       "      <td>-1358283876510909960</td>\n",
+       "      <th>5183445</th>\n",
+       "      <td>-4446696192614616184</td>\n",
        "      <td>put</td>\n",
        "      <td>2022-12-31 00:00:00+00:00</td>\n",
-       "      <td>2025-03-21 20:00:00+00:00</td>\n",
-       "      <td>811.96</td>\n",
+       "      <td>2025-01-17 21:00:00+00:00</td>\n",
+       "      <td>749.0</td>\n",
        "      <td>382.44</td>\n",
        "      <td>600.0</td>\n",
        "      <td>217.6</td>\n",
        "      <td>0.569</td>\n",
-       "      <td>-1.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>212.54</td>\n",
-       "      <td>220.50</td>\n",
+       "      <td>-0.86402</td>\n",
+       "      <td>0.00281</td>\n",
+       "      <td>0.86975</td>\n",
+       "      <td>-0.00938</td>\n",
+       "      <td>-1.88504</td>\n",
+       "      <td>0.31386</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>220.69</td>\n",
+       "      <td>215.50</td>\n",
+       "      <td>222.75</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>37.0</td>\n",
-       "      <td>76.0</td>\n",
+       "      <td>61.0</td>\n",
        "      <td>36.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>6705104 rows × 24 columns</p>\n",
+       "<p>5183446 rows × 24 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                   option_id option_type             quote_unixtime  \\\n",
-       "0        8228026009228119508        call  2020-01-03 00:00:00+00:00   \n",
-       "1        8388765138564423602         put  2020-01-03 00:00:00+00:00   \n",
-       "2        1027550724891153287        call  2020-01-03 00:00:00+00:00   \n",
-       "3       -1534760388604062099         put  2020-01-03 00:00:00+00:00   \n",
-       "4       -6171319654666929667        call  2020-01-03 00:00:00+00:00   \n",
+       "0        4169629807363267195        call  2020-01-03 00:00:00+00:00   \n",
+       "1       -1427193746298712266         put  2020-01-03 00:00:00+00:00   \n",
+       "2        -880651805349778471        call  2020-01-03 00:00:00+00:00   \n",
+       "3        1419493114519967095         put  2020-01-03 00:00:00+00:00   \n",
+       "4        6851796185238463050        call  2020-01-03 00:00:00+00:00   \n",
        "...                      ...         ...                        ...   \n",
-       "6705099  2688229576149051334         put  2022-12-31 00:00:00+00:00   \n",
-       "6705100  4933943145329079888        call  2022-12-31 00:00:00+00:00   \n",
-       "6705101 -7427531086436162822         put  2022-12-31 00:00:00+00:00   \n",
-       "6705102  4838387660868289251        call  2022-12-31 00:00:00+00:00   \n",
-       "6705103 -1358283876510909960         put  2022-12-31 00:00:00+00:00   \n",
+       "5183441  8337812745454405911         put  2022-12-31 00:00:00+00:00   \n",
+       "5183442  7184827014735111024        call  2022-12-31 00:00:00+00:00   \n",
+       "5183443  5837551170542630138         put  2022-12-31 00:00:00+00:00   \n",
+       "5183444 -5828408511425543838        call  2022-12-31 00:00:00+00:00   \n",
+       "5183445 -4446696192614616184         put  2022-12-31 00:00:00+00:00   \n",
        "\n",
        "                   expire_unixtime  days_to_expire  underlying_last  strike  \\\n",
-       "0        2020-01-31 21:00:00+00:00           29.00           324.87   255.0   \n",
-       "1        2020-01-31 21:00:00+00:00           29.00           324.87   255.0   \n",
-       "2        2020-01-31 21:00:00+00:00           29.00           324.87   260.0   \n",
-       "3        2020-01-31 21:00:00+00:00           29.00           324.87   260.0   \n",
-       "4        2020-01-31 21:00:00+00:00           29.00           324.87   265.0   \n",
+       "0        2020-02-14 21:00:00+00:00            43.0           324.87   270.0   \n",
+       "1        2020-02-14 21:00:00+00:00            43.0           324.87   270.0   \n",
+       "2        2020-02-14 21:00:00+00:00            43.0           324.87   275.0   \n",
+       "3        2020-02-14 21:00:00+00:00            43.0           324.87   275.0   \n",
+       "4        2020-02-14 21:00:00+00:00            43.0           324.87   280.0   \n",
        "...                            ...             ...              ...     ...   \n",
-       "6705099  2025-03-21 20:00:00+00:00          811.96           382.44   590.0   \n",
-       "6705100  2025-03-21 20:00:00+00:00          811.96           382.44   595.0   \n",
-       "6705101  2025-03-21 20:00:00+00:00          811.96           382.44   595.0   \n",
-       "6705102  2025-03-21 20:00:00+00:00          811.96           382.44   600.0   \n",
-       "6705103  2025-03-21 20:00:00+00:00          811.96           382.44   600.0   \n",
+       "5183441  2025-01-17 21:00:00+00:00           749.0           382.44   590.0   \n",
+       "5183442  2025-01-17 21:00:00+00:00           749.0           382.44   595.0   \n",
+       "5183443  2025-01-17 21:00:00+00:00           749.0           382.44   595.0   \n",
+       "5183444  2025-01-17 21:00:00+00:00           749.0           382.44   600.0   \n",
+       "5183445  2025-01-17 21:00:00+00:00           749.0           382.44   600.0   \n",
        "\n",
        "         strike_distance  strike_distance_pct    delta    gamma     vega  \\\n",
-       "0                   69.9                0.215  0.99254  0.00067  0.01952   \n",
-       "1                   69.9                0.215 -0.00379  0.00033  0.01031   \n",
-       "2                   64.9                0.200  0.98654  0.00101  0.03238   \n",
-       "3                   64.9                0.200 -0.00465  0.00052  0.01345   \n",
-       "4                   59.9                0.184  0.97933  0.00146  0.04691   \n",
+       "0                   54.9                0.169  0.96247  0.00230  0.09124   \n",
+       "1                   54.9                0.169 -0.01684  0.00147  0.04655   \n",
+       "2                   49.9                0.154  0.95833  0.00278  0.10050   \n",
+       "3                   49.9                0.154 -0.02157  0.00193  0.05840   \n",
+       "4                   44.9                0.138  0.94983  0.00341  0.11693   \n",
        "...                  ...                  ...      ...      ...      ...   \n",
-       "6705099            207.6                0.543 -1.00000  0.00000  0.00000   \n",
-       "6705100            212.6                0.556  0.09466  0.00169  0.96298   \n",
-       "6705101            212.6                0.556 -0.90719  0.00326  0.61124   \n",
-       "6705102            217.6                0.569  0.09424  0.00161  0.95513   \n",
-       "6705103            217.6                0.569 -1.00000  0.00000  0.00000   \n",
+       "5183441            207.6                0.543 -0.87442  0.00305  0.81016   \n",
+       "5183442            212.6                0.556  0.08891  0.00157  0.88126   \n",
+       "5183443            212.6                0.556 -0.86806  0.00287  0.84727   \n",
+       "5183444            217.6                0.569  0.07545  0.00142  0.77750   \n",
+       "5183445            217.6                0.569 -0.86402  0.00281  0.86975   \n",
        "\n",
-       "           theta      rho       iv  volume   last     bid     ask  max_ask  \\\n",
-       "0       -0.01130  0.20686  0.35727     1.0  66.82   69.87   70.05    77.27   \n",
-       "1       -0.00484 -0.00110  0.32142    16.0   0.03    0.03    0.04     0.04   \n",
-       "2       -0.01868  0.20993  0.36143    10.0  60.50   64.94   65.13    72.30   \n",
-       "3       -0.00650 -0.00176  0.30648     3.0   0.04    0.04    0.05     0.05   \n",
-       "4       -0.02728  0.21135  0.35875     NaN   0.00   60.01   60.26    67.28   \n",
-       "...          ...      ...      ...     ...    ...     ...     ...      ...   \n",
-       "6705099  0.00000  0.00000      NaN     NaN   0.00  202.75  210.00      NaN   \n",
-       "6705100 -0.01292  0.72396  0.17885     NaN   0.00    1.73    6.50      NaN   \n",
-       "6705101 -0.00505 -1.24251  0.28500     NaN   0.00  207.82  217.78      NaN   \n",
-       "6705102 -0.01327  0.71483  0.18161     0.0   4.60    2.25    6.00      NaN   \n",
-       "6705103  0.00000  0.00000      NaN     NaN   0.00  212.54  220.50      NaN   \n",
+       "           theta      rho       iv  volume    last     bid     ask  max_ask  \\\n",
+       "0       -0.03148  0.31026  0.30852    10.0   53.85   55.31   55.51    67.91   \n",
+       "1       -0.01293 -0.00678  0.25518     NaN    0.00    0.17    0.17     0.23   \n",
+       "2       -0.03230  0.31394  0.28566     NaN    0.00   50.34   50.53    62.89   \n",
+       "3       -0.01595 -0.00872  0.24238     1.0    0.28    0.21    0.23     0.30   \n",
+       "4       -0.03563  0.31725  0.26822     NaN    0.00   45.41   45.66    57.90   \n",
+       "...          ...      ...      ...     ...     ...     ...     ...      ...   \n",
+       "5183441 -0.00808 -1.71905  0.29992     0.0  193.26  205.00  212.60      NaN   \n",
+       "5183442 -0.01276  0.62712  0.18258     0.0    6.80    2.00    5.50      NaN   \n",
+       "5183443 -0.00847 -1.81885  0.30789     NaN    0.00  210.50  217.50      NaN   \n",
+       "5183444 -0.01128  0.53365  0.17619    12.0    2.55    2.50    3.45      NaN   \n",
+       "5183445 -0.00938 -1.88504  0.31386     0.0  220.69  215.50  222.75      NaN   \n",
        "\n",
        "         min_ask  fear_greed_index  fear_greed_index_max  fear_greed_index_min  \n",
-       "0          67.22               NaN                   NaN                   NaN  \n",
+       "0          52.30               NaN                   NaN                   NaN  \n",
        "1           0.01               NaN                   NaN                   NaN  \n",
-       "2          62.23               NaN                   NaN                   NaN  \n",
+       "2          47.75               NaN                   NaN                   NaN  \n",
        "3           0.01               NaN                   NaN                   NaN  \n",
-       "4          57.21               NaN                   NaN                   NaN  \n",
+       "4          42.79               NaN                   NaN                   NaN  \n",
        "...          ...               ...                   ...                   ...  \n",
-       "6705099      NaN              37.0                  76.0                  36.0  \n",
-       "6705100      NaN              37.0                  76.0                  36.0  \n",
-       "6705101      NaN              37.0                  76.0                  36.0  \n",
-       "6705102      NaN              37.0                  76.0                  36.0  \n",
-       "6705103      NaN              37.0                  76.0                  36.0  \n",
+       "5183441      NaN              37.0                  61.0                  36.0  \n",
+       "5183442      NaN              37.0                  61.0                  36.0  \n",
+       "5183443      NaN              37.0                  61.0                  36.0  \n",
+       "5183444      NaN              37.0                  61.0                  36.0  \n",
+       "5183445      NaN              37.0                  61.0                  36.0  \n",
        "\n",
-       "[6705104 rows x 24 columns]"
+       "[5183446 rows x 24 columns]"
       ]
      },
      "execution_count": 2,
@@ -467,16 +1104,16 @@
     }
    ],
    "source": [
-    "periods = 20\n",
-    "# periods = 30  # BEST RESULT\n",
-    "fear_greed_window = 40\n",
+    "# periods = 20\n",
+    "periods = 30  # BEST RESULT\n",
+    "fear_greed_window = 20  # BEST RESULT\n",
     "\n",
     "refresh_formatted_data = False\n",
     "refresh_processed_data = False\n",
     "\n",
     "raw_data_path = data_directory + \"spy_2020_2022.csv\"\n",
-    "formatted_data_path = data_directory + \"spy_2020_2022_formatted.csv\"\n",
-    "processed_data_path = data_directory + \"spy_2020_2022_processed.csv\"\n",
+    "formatted_data_path = data_directory + f\"spy_2020_2022_formatted_{periods}_{fear_greed_window}.csv\"\n",
+    "processed_data_path = data_directory + f\"spy_2020_2022_processed_{periods}_{fear_greed_window}.csv\"\n",
     "\n",
     "if not refresh_processed_data and os.path.isfile(processed_data_path):\n",
     "    df_options = pd.read_csv(processed_data_path, low_memory=False)\n",
@@ -671,6 +1308,59 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "d3d1b213",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='quote_unixtime'>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a029a36aaf8548beb8041fb66342352d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_columns = [\"underlying_last\", \"fear_greed_index\", \"fear_greed_index_min\", \"fear_greed_index_max\"]\n",
+    "df_plot = df_options[[\"quote_unixtime\"] + plot_columns].dropna().drop_duplicates()\n",
+    "df_plot[\"underlying_last\"] = (\n",
+    "    (df_plot.underlying_last - df_plot.underlying_last.min())\n",
+    "    / (df_plot.underlying_last.max() - df_plot.underlying_last.min())\n",
+    "    * 100\n",
+    ")\n",
+    "df_plot.plot(x=\"quote_unixtime\", y=plot_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "3469f106",
    "metadata": {},
    "outputs": [
@@ -721,79 +1411,79 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>6705104.0</td>\n",
-       "      <td>6705104.0</td>\n",
-       "      <td>6705104.0</td>\n",
-       "      <td>6705104.0</td>\n",
-       "      <td>6705104.0</td>\n",
-       "      <td>6705104.0</td>\n",
-       "      <td>6704718.0</td>\n",
-       "      <td>6704718.0</td>\n",
-       "      <td>6704718.0</td>\n",
-       "      <td>6704718.0</td>\n",
-       "      <td>6704718.0</td>\n",
-       "      <td>6379498.0</td>\n",
-       "      <td>5430040.0</td>\n",
-       "      <td>6703192.0</td>\n",
-       "      <td>6703192.0</td>\n",
-       "      <td>6703192.0</td>\n",
-       "      <td>4604612.0</td>\n",
-       "      <td>4604612.0</td>\n",
-       "      <td>3104976.0</td>\n",
-       "      <td>2800326.0</td>\n",
-       "      <td>2800326.0</td>\n",
+       "      <td>5183446.0</td>\n",
+       "      <td>5183446.0</td>\n",
+       "      <td>5183446.0</td>\n",
+       "      <td>5183446.0</td>\n",
+       "      <td>5183446.0</td>\n",
+       "      <td>5183446.0</td>\n",
+       "      <td>5183304.0</td>\n",
+       "      <td>5183304.0</td>\n",
+       "      <td>5183304.0</td>\n",
+       "      <td>5183304.0</td>\n",
+       "      <td>5183304.0</td>\n",
+       "      <td>4995065.0</td>\n",
+       "      <td>4329775.0</td>\n",
+       "      <td>5182658.0</td>\n",
+       "      <td>5182658.0</td>\n",
+       "      <td>5182658.0</td>\n",
+       "      <td>3867274.0</td>\n",
+       "      <td>3867274.0</td>\n",
+       "      <td>2379528.0</td>\n",
+       "      <td>2261992.0</td>\n",
+       "      <td>2261992.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>-4.526365433370833e+16</td>\n",
-       "      <td>165.569171150813</td>\n",
-       "      <td>387.19143309932264</td>\n",
-       "      <td>367.258820146563</td>\n",
-       "      <td>71.87527549162552</td>\n",
-       "      <td>0.18726444332556216</td>\n",
-       "      <td>0.05852685361114359</td>\n",
-       "      <td>-5.539010259172714</td>\n",
-       "      <td>-4.591413383244466</td>\n",
-       "      <td>-0.06525248893689488</td>\n",
-       "      <td>-0.13273299260908508</td>\n",
-       "      <td>0.3319192409606522</td>\n",
-       "      <td>248.14380299224314</td>\n",
-       "      <td>24.879528029332892</td>\n",
-       "      <td>41.13185712567984</td>\n",
-       "      <td>42.06987972297378</td>\n",
-       "      <td>55.65678393966744</td>\n",
-       "      <td>41.49223490491715</td>\n",
-       "      <td>39.144283884964004</td>\n",
-       "      <td>60.48275165105777</td>\n",
-       "      <td>16.130365536012594</td>\n",
+       "      <td>2.9153602750674164e+16</td>\n",
+       "      <td>208.44489484408626</td>\n",
+       "      <td>386.81048857073125</td>\n",
+       "      <td>366.2309153022912</td>\n",
+       "      <td>79.84188649790116</td>\n",
+       "      <td>0.20786633641017963</td>\n",
+       "      <td>0.061377349813941044</td>\n",
+       "      <td>-7.16732997335483</td>\n",
+       "      <td>-4.273652451353425</td>\n",
+       "      <td>-0.05487317240316212</td>\n",
+       "      <td>-0.17676348886926183</td>\n",
+       "      <td>0.325085603106266</td>\n",
+       "      <td>189.24003302712035</td>\n",
+       "      <td>29.880180199040723</td>\n",
+       "      <td>46.262370376358945</td>\n",
+       "      <td>47.367116543287295</td>\n",
+       "      <td>61.246274471371805</td>\n",
+       "      <td>43.06805268258725</td>\n",
+       "      <td>39.16426030708611</td>\n",
+       "      <td>53.758274122985405</td>\n",
+       "      <td>23.277542095639596</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>5.333833272350877e+18</td>\n",
-       "      <td>205.26504897883348</td>\n",
-       "      <td>56.119759445888285</td>\n",
-       "      <td>110.414241734368</td>\n",
-       "      <td>69.74638091674225</td>\n",
-       "      <td>0.17758214171887793</td>\n",
-       "      <td>0.6172408840004929</td>\n",
-       "      <td>7265.928700629256</td>\n",
-       "      <td>65.35914030587458</td>\n",
-       "      <td>0.33726956602471037</td>\n",
-       "      <td>4.943414620970326</td>\n",
-       "      <td>0.3911653863338513</td>\n",
-       "      <td>2996.713572218119</td>\n",
-       "      <td>44.36007231067608</td>\n",
-       "      <td>58.043199584864254</td>\n",
-       "      <td>58.7510679326706</td>\n",
-       "      <td>66.81435376928155</td>\n",
-       "      <td>59.465487793705336</td>\n",
-       "      <td>16.75612826873928</td>\n",
-       "      <td>10.61192060858786</td>\n",
-       "      <td>8.59627846749676</td>\n",
+       "      <td>5.333079846337141e+18</td>\n",
+       "      <td>214.28945070866197</td>\n",
+       "      <td>55.93111870637418</td>\n",
+       "      <td>117.85477394158923</td>\n",
+       "      <td>74.10176115308039</td>\n",
+       "      <td>0.18785161249208368</td>\n",
+       "      <td>0.6097005475830474</td>\n",
+       "      <td>8263.766206348793</td>\n",
+       "      <td>68.27349741470688</td>\n",
+       "      <td>0.2449836036545431</td>\n",
+       "      <td>5.614262441778176</td>\n",
+       "      <td>0.38684982574298166</td>\n",
+       "      <td>2345.348596510615</td>\n",
+       "      <td>48.49748971584839</td>\n",
+       "      <td>62.023225396788206</td>\n",
+       "      <td>62.76557232391271</td>\n",
+       "      <td>70.05371023332876</td>\n",
+       "      <td>60.47939211854961</td>\n",
+       "      <td>16.715752079266753</td>\n",
+       "      <td>13.948223468921501</td>\n",
+       "      <td>13.246103688851774</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>-9.223336797429473e+18</td>\n",
+       "      <td>-9.222950070327091e+18</td>\n",
        "      <td>0.0</td>\n",
        "      <td>222.21</td>\n",
        "      <td>5.0</td>\n",
@@ -812,84 +1502,84 @@
        "      <td>0.01</td>\n",
        "      <td>0.0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>39.0</td>\n",
+       "      <td>22.0</td>\n",
        "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>-4.707991387036329e+18</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>342.65</td>\n",
-       "      <td>299.0</td>\n",
-       "      <td>21.6</td>\n",
-       "      <td>0.056</td>\n",
-       "      <td>-0.34296</td>\n",
-       "      <td>0.00062</td>\n",
-       "      <td>0.07026</td>\n",
-       "      <td>-0.07495</td>\n",
-       "      <td>-0.17122</td>\n",
-       "      <td>0.18541</td>\n",
+       "      <td>-4.5774401300695813e+18</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>342.52</td>\n",
+       "      <td>291.0</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>0.066</td>\n",
+       "      <td>-0.34575</td>\n",
+       "      <td>0.00073</td>\n",
+       "      <td>0.12985</td>\n",
+       "      <td>-0.06511</td>\n",
+       "      <td>-0.33982</td>\n",
+       "      <td>0.18796</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>1.88</td>\n",
-       "      <td>2.08</td>\n",
-       "      <td>6.61</td>\n",
-       "      <td>1.83</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>11.0</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>3.03</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>8.73</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>14.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>-8.316397478915686e+16</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>393.97</td>\n",
-       "      <td>368.0</td>\n",
-       "      <td>49.8</td>\n",
-       "      <td>0.131</td>\n",
+       "      <td>2.6382816117148668e+16</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>393.48</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>58.5</td>\n",
+       "      <td>0.155</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.00297</td>\n",
-       "      <td>0.29822499999999996</td>\n",
-       "      <td>-0.04169</td>\n",
+       "      <td>0.0029</td>\n",
+       "      <td>0.42276</td>\n",
+       "      <td>-0.03984</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.25123</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.32</td>\n",
-       "      <td>16.7</td>\n",
-       "      <td>17.46</td>\n",
-       "      <td>29.95</td>\n",
-       "      <td>15.26</td>\n",
+       "      <td>0.25137</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>20.37</td>\n",
+       "      <td>21.31</td>\n",
+       "      <td>34.95</td>\n",
+       "      <td>16.28</td>\n",
        "      <td>37.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>14.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>19.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>4.563451068124375e+18</td>\n",
-       "      <td>235.04</td>\n",
-       "      <td>434.37</td>\n",
-       "      <td>437.0</td>\n",
-       "      <td>103.6</td>\n",
-       "      <td>0.273</td>\n",
-       "      <td>0.6565274999999999</td>\n",
-       "      <td>0.00649</td>\n",
-       "      <td>0.73064</td>\n",
-       "      <td>-0.01626</td>\n",
-       "      <td>0.17039</td>\n",
-       "      <td>0.35641</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>29.33</td>\n",
-       "      <td>55.64</td>\n",
-       "      <td>57.21</td>\n",
-       "      <td>80.71</td>\n",
-       "      <td>56.47</td>\n",
+       "      <td>4.628489213047513e+18</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>434.01</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>115.4</td>\n",
+       "      <td>0.303</td>\n",
+       "      <td>0.65371</td>\n",
+       "      <td>0.00578</td>\n",
+       "      <td>0.87945</td>\n",
+       "      <td>-0.01762</td>\n",
+       "      <td>0.2984</td>\n",
+       "      <td>0.34917</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>37.32</td>\n",
+       "      <td>64.72</td>\n",
+       "      <td>66.5</td>\n",
+       "      <td>90.53</td>\n",
+       "      <td>59.43</td>\n",
        "      <td>53.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>19.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>29.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>9.223331946358249e+18</td>\n",
+       "      <td>9.223256556356517e+18</td>\n",
        "      <td>1096.0</td>\n",
        "      <td>477.77</td>\n",
        "      <td>4898.0</td>\n",
@@ -909,85 +1599,85 @@
        "      <td>4455.5</td>\n",
        "      <td>77.0</td>\n",
        "      <td>77.0</td>\n",
-       "      <td>52.0</td>\n",
+       "      <td>58.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                    option_id      days_to_expire     underlying_last  \\\n",
-       "count               6705104.0           6705104.0           6705104.0   \n",
-       "mean   -4.526365433370833e+16    165.569171150813  387.19143309932264   \n",
-       "std     5.333833272350877e+18  205.26504897883348  56.119759445888285   \n",
-       "min    -9.223336797429473e+18                 0.0              222.21   \n",
-       "25%    -4.707991387036329e+18                23.0              342.65   \n",
-       "50%    -8.316397478915686e+16                80.0              393.97   \n",
-       "75%     4.563451068124375e+18              235.04              434.37   \n",
-       "max     9.223331946358249e+18              1096.0              477.77   \n",
+       "                     option_id      days_to_expire     underlying_last  \\\n",
+       "count                5183446.0           5183446.0           5183446.0   \n",
+       "mean    2.9153602750674164e+16  208.44489484408626  386.81048857073125   \n",
+       "std      5.333079846337141e+18  214.28945070866197   55.93111870637418   \n",
+       "min     -9.222950070327091e+18                 0.0              222.21   \n",
+       "25%    -4.5774401300695813e+18                45.0              342.52   \n",
+       "50%     2.6382816117148668e+16               140.0              393.48   \n",
+       "75%      4.628489213047513e+18               290.0              434.01   \n",
+       "max      9.223256556356517e+18              1096.0              477.77   \n",
        "\n",
-       "                 strike    strike_distance  strike_distance_pct  \\\n",
-       "count         6705104.0          6705104.0            6705104.0   \n",
-       "mean   367.258820146563  71.87527549162552  0.18726444332556216   \n",
-       "std    110.414241734368  69.74638091674225  0.17758214171887793   \n",
-       "min                 5.0                0.0                  0.0   \n",
-       "25%               299.0               21.6                0.056   \n",
-       "50%               368.0               49.8                0.131   \n",
-       "75%               437.0              103.6                0.273   \n",
-       "max              4898.0             4469.3               10.425   \n",
+       "                   strike    strike_distance  strike_distance_pct  \\\n",
+       "count           5183446.0          5183446.0            5183446.0   \n",
+       "mean    366.2309153022912  79.84188649790116  0.20786633641017963   \n",
+       "std    117.85477394158923  74.10176115308039  0.18785161249208368   \n",
+       "min                   5.0                0.0                  0.0   \n",
+       "25%                 291.0               25.2                0.066   \n",
+       "50%                 365.0               58.5                0.155   \n",
+       "75%                 440.0              115.4                0.303   \n",
+       "max                4898.0             4469.3               10.425   \n",
        "\n",
-       "                     delta               gamma                 vega  \\\n",
-       "count            6704718.0           6704718.0            6704718.0   \n",
-       "mean   0.05852685361114359  -5.539010259172714   -4.591413383244466   \n",
-       "std     0.6172408840004929   7265.928700629256    65.35914030587458   \n",
-       "min                   -1.0     -14950363.96601          -2815.39969   \n",
-       "25%               -0.34296             0.00062              0.07026   \n",
-       "50%                    0.0             0.00297  0.29822499999999996   \n",
-       "75%     0.6565274999999999             0.00649              0.73064   \n",
-       "max                    1.0            10.99989            147.32807   \n",
+       "                      delta              gamma                vega  \\\n",
+       "count             5183304.0          5183304.0           5183304.0   \n",
+       "mean   0.061377349813941044  -7.16732997335483  -4.273652451353425   \n",
+       "std      0.6097005475830474  8263.766206348793   68.27349741470688   \n",
+       "min                    -1.0    -14950363.96601         -2815.39969   \n",
+       "25%                -0.34575            0.00073             0.12985   \n",
+       "50%                     0.0             0.0029             0.42276   \n",
+       "75%                 0.65371            0.00578             0.87945   \n",
+       "max                     1.0           10.99989           147.32807   \n",
        "\n",
-       "                      theta                   rho                  iv  \\\n",
-       "count             6704718.0             6704718.0           6379498.0   \n",
-       "mean   -0.06525248893689488  -0.13273299260908508  0.3319192409606522   \n",
-       "std     0.33726956602471037     4.943414620970326  0.3911653863338513   \n",
-       "min               -45.23137           -2878.27141             -0.0005   \n",
-       "25%                -0.07495              -0.17122             0.18541   \n",
-       "50%                -0.04169                   0.0             0.25123   \n",
-       "75%                -0.01626               0.17039             0.35641   \n",
-       "max                     0.0             357.46355            52.10104   \n",
+       "                      theta                   rho                   iv  \\\n",
+       "count             5183304.0             5183304.0            4995065.0   \n",
+       "mean   -0.05487317240316212  -0.17676348886926183    0.325085603106266   \n",
+       "std      0.2449836036545431     5.614262441778176  0.38684982574298166   \n",
+       "min               -45.23137           -2878.27141              -0.0005   \n",
+       "25%                -0.06511              -0.33982              0.18796   \n",
+       "50%                -0.03984                   0.0              0.25137   \n",
+       "75%                -0.01762                0.2984              0.34917   \n",
+       "max                     0.0             357.46355             52.10104   \n",
        "\n",
        "                   volume                last                 bid  \\\n",
-       "count           5430040.0           6703192.0           6703192.0   \n",
-       "mean   248.14380299224314  24.879528029332892   41.13185712567984   \n",
-       "std     2996.713572218119   44.36007231067608  58.043199584864254   \n",
+       "count           4329775.0           5182658.0           5182658.0   \n",
+       "mean   189.24003302712035  29.880180199040723  46.262370376358945   \n",
+       "std     2345.348596510615   48.49748971584839  62.023225396788206   \n",
        "min                   0.0                 0.0                 0.0   \n",
-       "25%                   0.0                0.06                1.88   \n",
-       "50%                   3.0                5.32                16.7   \n",
-       "75%                  30.0               29.33               55.64   \n",
+       "25%                   0.0                0.25                3.03   \n",
+       "50%                   2.0                 8.9               20.37   \n",
+       "75%                  22.0               37.32               64.72   \n",
        "max              381807.0               444.2             4451.19   \n",
        "\n",
-       "                     ask            max_ask             min_ask  \\\n",
-       "count          6703192.0          4604612.0           4604612.0   \n",
-       "mean   42.06987972297378  55.65678393966744   41.49223490491715   \n",
-       "std     58.7510679326706  66.81435376928155  59.465487793705336   \n",
-       "min                  0.0               0.01                 0.0   \n",
-       "25%                 2.08               6.61                1.83   \n",
-       "50%                17.46              29.95               15.26   \n",
-       "75%                57.21              80.71               56.47   \n",
-       "max               4455.5             4455.5              4455.5   \n",
+       "                      ask             max_ask            min_ask  \\\n",
+       "count           5182658.0           3867274.0          3867274.0   \n",
+       "mean   47.367116543287295  61.246274471371805  43.06805268258725   \n",
+       "std     62.76557232391271   70.05371023332876  60.47939211854961   \n",
+       "min                   0.0                0.01                0.0   \n",
+       "25%                  3.41                8.73               2.36   \n",
+       "50%                 21.31               34.95              16.28   \n",
+       "75%                  66.5               90.53              59.43   \n",
+       "max                4455.5              4455.5             4455.5   \n",
        "\n",
        "         fear_greed_index fear_greed_index_max fear_greed_index_min  \n",
-       "count           3104976.0            2800326.0            2800326.0  \n",
-       "mean   39.144283884964004    60.48275165105777   16.130365536012594  \n",
-       "std     16.75612826873928    10.61192060858786     8.59627846749676  \n",
-       "min                   3.0                 39.0                  3.0  \n",
-       "25%                  26.0                 48.0                 11.0  \n",
-       "50%                  37.0                 62.0                 14.0  \n",
-       "75%                  53.0                 68.0                 19.0  \n",
-       "max                  77.0                 77.0                 52.0  "
+       "count           2379528.0            2261992.0            2261992.0  \n",
+       "mean    39.16426030708611   53.758274122985405   23.277542095639596  \n",
+       "std    16.715752079266753   13.948223468921501   13.246103688851774  \n",
+       "min                   3.0                 22.0                  3.0  \n",
+       "25%                  27.0                 44.0                 14.0  \n",
+       "50%                  37.0                 54.0                 19.0  \n",
+       "75%                  53.0                 65.0                 29.0  \n",
+       "max                  77.0                 77.0                 58.0  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -998,7 +1688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 5,
    "id": "d6bbbe6e",
    "metadata": {},
    "outputs": [
@@ -1006,7 +1696,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/86/qt4jnsg167j7h7mqgbpp44kh0000gn/T/ipykernel_180/3819712389.py:73: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_61923/1226587918.py:73: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\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",
@@ -1062,137 +1752,137 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>3412233</th>\n",
-       "      <td>-7284403565423334204</td>\n",
+       "      <th>3217819</th>\n",
+       "      <td>3008663547997449213</td>\n",
        "      <td>put</td>\n",
-       "      <td>2021-07-14 00:00:00+00:00</td>\n",
-       "      <td>2021-08-13 20:00:00+00:00</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>435.72</td>\n",
-       "      <td>395.0</td>\n",
-       "      <td>40.7</td>\n",
-       "      <td>0.093</td>\n",
-       "      <td>-0.07773</td>\n",
-       "      <td>0.00470</td>\n",
-       "      <td>0.18849</td>\n",
-       "      <td>-0.07067</td>\n",
-       "      <td>-0.02999</td>\n",
-       "      <td>0.24072</td>\n",
-       "      <td>120.0</td>\n",
+       "      <td>2021-06-15 00:00:00+00:00</td>\n",
+       "      <td>2021-07-16 20:00:00+00:00</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>425.33</td>\n",
+       "      <td>385.0</td>\n",
+       "      <td>40.3</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>-0.07572</td>\n",
+       "      <td>0.00487</td>\n",
+       "      <td>0.18310</td>\n",
+       "      <td>-0.06656</td>\n",
+       "      <td>-0.03038</td>\n",
+       "      <td>0.22962</td>\n",
+       "      <td>3686.0</td>\n",
        "      <td>1.08</td>\n",
-       "      <td>1.11</td>\n",
-       "      <td>1.13</td>\n",
-       "      <td>2.28</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>30.0</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>1.97</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>54.0</td>\n",
        "      <td>54.0</td>\n",
        "      <td>27.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3412237</th>\n",
-       "      <td>-6900012193074301262</td>\n",
+       "      <th>3217821</th>\n",
+       "      <td>-6871348143934581290</td>\n",
        "      <td>put</td>\n",
-       "      <td>2021-07-14 00:00:00+00:00</td>\n",
-       "      <td>2021-08-13 20:00:00+00:00</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>435.72</td>\n",
-       "      <td>397.0</td>\n",
-       "      <td>38.7</td>\n",
-       "      <td>0.089</td>\n",
-       "      <td>-0.08554</td>\n",
-       "      <td>0.00515</td>\n",
-       "      <td>0.20168</td>\n",
-       "      <td>-0.07453</td>\n",
-       "      <td>-0.03375</td>\n",
-       "      <td>0.23642</td>\n",
-       "      <td>116.0</td>\n",
+       "      <td>2021-06-15 00:00:00+00:00</td>\n",
+       "      <td>2021-07-16 20:00:00+00:00</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>425.33</td>\n",
+       "      <td>386.0</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>-0.07912</td>\n",
+       "      <td>0.00508</td>\n",
+       "      <td>0.18874</td>\n",
+       "      <td>-0.06780</td>\n",
+       "      <td>-0.03140</td>\n",
+       "      <td>0.22702</td>\n",
+       "      <td>121.0</td>\n",
        "      <td>1.22</td>\n",
-       "      <td>1.21</td>\n",
-       "      <td>1.24</td>\n",
-       "      <td>2.47</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>30.0</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>1.08</td>\n",
+       "      <td>2.06</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>54.0</td>\n",
        "      <td>54.0</td>\n",
        "      <td>27.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3412239</th>\n",
-       "      <td>6126913891936646503</td>\n",
+       "      <th>3217825</th>\n",
+       "      <td>-7643848508642364201</td>\n",
        "      <td>put</td>\n",
-       "      <td>2021-07-14 00:00:00+00:00</td>\n",
-       "      <td>2021-08-13 20:00:00+00:00</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>435.72</td>\n",
-       "      <td>398.0</td>\n",
-       "      <td>37.7</td>\n",
-       "      <td>0.087</td>\n",
-       "      <td>-0.08839</td>\n",
-       "      <td>0.00531</td>\n",
-       "      <td>0.20656</td>\n",
-       "      <td>-0.07547</td>\n",
-       "      <td>-0.03429</td>\n",
-       "      <td>0.23418</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>1.18</td>\n",
-       "      <td>1.26</td>\n",
-       "      <td>1.27</td>\n",
-       "      <td>2.58</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>30.0</td>\n",
+       "      <td>2021-06-15 00:00:00+00:00</td>\n",
+       "      <td>2021-07-16 20:00:00+00:00</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>425.33</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>-0.08590</td>\n",
+       "      <td>0.00551</td>\n",
+       "      <td>0.19974</td>\n",
+       "      <td>-0.07036</td>\n",
+       "      <td>-0.03406</td>\n",
+       "      <td>0.22108</td>\n",
+       "      <td>2414.0</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.14</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>2.23</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>54.0</td>\n",
        "      <td>54.0</td>\n",
        "      <td>27.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3412243</th>\n",
-       "      <td>-8542137146864587244</td>\n",
+       "      <th>3217827</th>\n",
+       "      <td>3936499674942118164</td>\n",
        "      <td>put</td>\n",
-       "      <td>2021-07-14 00:00:00+00:00</td>\n",
-       "      <td>2021-08-13 20:00:00+00:00</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>435.72</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>35.7</td>\n",
-       "      <td>0.082</td>\n",
-       "      <td>-0.09611</td>\n",
-       "      <td>0.00578</td>\n",
-       "      <td>0.22012</td>\n",
-       "      <td>-0.07855</td>\n",
-       "      <td>-0.03787</td>\n",
-       "      <td>0.22859</td>\n",
-       "      <td>153.0</td>\n",
-       "      <td>1.37</td>\n",
-       "      <td>1.36</td>\n",
-       "      <td>1.37</td>\n",
-       "      <td>2.77</td>\n",
+       "      <td>2021-06-15 00:00:00+00:00</td>\n",
+       "      <td>2021-07-16 20:00:00+00:00</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>425.33</td>\n",
+       "      <td>389.0</td>\n",
+       "      <td>36.3</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>-0.08957</td>\n",
+       "      <td>0.00576</td>\n",
+       "      <td>0.20590</td>\n",
+       "      <td>-0.07159</td>\n",
+       "      <td>-0.03513</td>\n",
+       "      <td>0.21832</td>\n",
+       "      <td>243.0</td>\n",
+       "      <td>1.21</td>\n",
+       "      <td>1.18</td>\n",
+       "      <td>1.19</td>\n",
+       "      <td>2.34</td>\n",
        "      <td>0.03</td>\n",
-       "      <td>30.0</td>\n",
+       "      <td>54.0</td>\n",
        "      <td>54.0</td>\n",
        "      <td>27.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3412245</th>\n",
-       "      <td>8889578099934366041</td>\n",
+       "      <th>3217829</th>\n",
+       "      <td>681636405473264716</td>\n",
        "      <td>put</td>\n",
-       "      <td>2021-07-14 00:00:00+00:00</td>\n",
-       "      <td>2021-08-13 20:00:00+00:00</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>435.72</td>\n",
-       "      <td>401.0</td>\n",
-       "      <td>34.7</td>\n",
-       "      <td>0.080</td>\n",
-       "      <td>-0.10012</td>\n",
-       "      <td>0.00609</td>\n",
-       "      <td>0.22682</td>\n",
-       "      <td>-0.07972</td>\n",
-       "      <td>-0.03952</td>\n",
-       "      <td>0.22649</td>\n",
-       "      <td>118.0</td>\n",
-       "      <td>1.42</td>\n",
-       "      <td>1.41</td>\n",
-       "      <td>1.43</td>\n",
-       "      <td>2.88</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>30.0</td>\n",
+       "      <td>2021-06-15 00:00:00+00:00</td>\n",
+       "      <td>2021-07-16 20:00:00+00:00</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>425.33</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>35.3</td>\n",
+       "      <td>0.083</td>\n",
+       "      <td>-0.09293</td>\n",
+       "      <td>0.00601</td>\n",
+       "      <td>0.21253</td>\n",
+       "      <td>-0.07323</td>\n",
+       "      <td>-0.03721</td>\n",
+       "      <td>0.21591</td>\n",
+       "      <td>5567.0</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>1.23</td>\n",
+       "      <td>1.24</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>54.0</td>\n",
        "      <td>54.0</td>\n",
        "      <td>27.0</td>\n",
        "    </tr>\n",
@@ -1225,7 +1915,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6588412</th>\n",
-       "      <td>8926636401208964267</td>\n",
+       "      <td>3074973442818116841</td>\n",
        "      <td>call</td>\n",
        "      <td>2022-12-02 00:00:00+00:00</td>\n",
        "      <td>2023-02-17 21:00:00+00:00</td>\n",
@@ -1248,11 +1938,11 @@
        "      <td>0.24</td>\n",
        "      <td>76.0</td>\n",
        "      <td>76.0</td>\n",
-       "      <td>16.0</td>\n",
+       "      <td>54.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6588414</th>\n",
-       "      <td>-8802341623244967839</td>\n",
+       "      <td>243829186492752650</td>\n",
        "      <td>call</td>\n",
        "      <td>2022-12-02 00:00:00+00:00</td>\n",
        "      <td>2023-02-17 21:00:00+00:00</td>\n",
@@ -1275,11 +1965,11 @@
        "      <td>0.22</td>\n",
        "      <td>76.0</td>\n",
        "      <td>76.0</td>\n",
-       "      <td>16.0</td>\n",
+       "      <td>54.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6588418</th>\n",
-       "      <td>-6999102162049648074</td>\n",
+       "      <td>5941774315107753797</td>\n",
        "      <td>call</td>\n",
        "      <td>2022-12-02 00:00:00+00:00</td>\n",
        "      <td>2023-02-17 21:00:00+00:00</td>\n",
@@ -1302,11 +1992,11 @@
        "      <td>0.19</td>\n",
        "      <td>76.0</td>\n",
        "      <td>76.0</td>\n",
-       "      <td>16.0</td>\n",
+       "      <td>54.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6588426</th>\n",
-       "      <td>-4637813185526554528</td>\n",
+       "      <td>8230302027418419949</td>\n",
        "      <td>call</td>\n",
        "      <td>2022-12-02 00:00:00+00:00</td>\n",
        "      <td>2023-02-17 21:00:00+00:00</td>\n",
@@ -1329,11 +2019,11 @@
        "      <td>0.14</td>\n",
        "      <td>76.0</td>\n",
        "      <td>76.0</td>\n",
-       "      <td>16.0</td>\n",
+       "      <td>54.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6588428</th>\n",
-       "      <td>-7903130375213156883</td>\n",
+       "      <td>-390721989138427125</td>\n",
        "      <td>call</td>\n",
        "      <td>2022-12-02 00:00:00+00:00</td>\n",
        "      <td>2023-02-17 21:00:00+00:00</td>\n",
@@ -1356,33 +2046,33 @@
        "      <td>0.14</td>\n",
        "      <td>76.0</td>\n",
        "      <td>76.0</td>\n",
-       "      <td>16.0</td>\n",
+       "      <td>54.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>36592 rows × 24 columns</p>\n",
+       "<p>39778 rows × 24 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                   option_id option_type             quote_unixtime  \\\n",
-       "3412233 -7284403565423334204         put  2021-07-14 00:00:00+00:00   \n",
-       "3412237 -6900012193074301262         put  2021-07-14 00:00:00+00:00   \n",
-       "3412239  6126913891936646503         put  2021-07-14 00:00:00+00:00   \n",
-       "3412243 -8542137146864587244         put  2021-07-14 00:00:00+00:00   \n",
-       "3412245  8889578099934366041         put  2021-07-14 00:00:00+00:00   \n",
+       "3217819  3008663547997449213         put  2021-06-15 00:00:00+00:00   \n",
+       "3217821 -6871348143934581290         put  2021-06-15 00:00:00+00:00   \n",
+       "3217825 -7643848508642364201         put  2021-06-15 00:00:00+00:00   \n",
+       "3217827  3936499674942118164         put  2021-06-15 00:00:00+00:00   \n",
+       "3217829   681636405473264716         put  2021-06-15 00:00:00+00:00   \n",
        "...                      ...         ...                        ...   \n",
-       "6588412  8926636401208964267        call  2022-12-02 00:00:00+00:00   \n",
-       "6588414 -8802341623244967839        call  2022-12-02 00:00:00+00:00   \n",
-       "6588418 -6999102162049648074        call  2022-12-02 00:00:00+00:00   \n",
-       "6588426 -4637813185526554528        call  2022-12-02 00:00:00+00:00   \n",
-       "6588428 -7903130375213156883        call  2022-12-02 00:00:00+00:00   \n",
+       "6588412  3074973442818116841        call  2022-12-02 00:00:00+00:00   \n",
+       "6588414   243829186492752650        call  2022-12-02 00:00:00+00:00   \n",
+       "6588418  5941774315107753797        call  2022-12-02 00:00:00+00:00   \n",
+       "6588426  8230302027418419949        call  2022-12-02 00:00:00+00:00   \n",
+       "6588428  -390721989138427125        call  2022-12-02 00:00:00+00:00   \n",
        "\n",
        "                   expire_unixtime  days_to_expire  underlying_last  strike  \\\n",
-       "3412233  2021-08-13 20:00:00+00:00            31.0           435.72   395.0   \n",
-       "3412237  2021-08-13 20:00:00+00:00            31.0           435.72   397.0   \n",
-       "3412239  2021-08-13 20:00:00+00:00            31.0           435.72   398.0   \n",
-       "3412243  2021-08-13 20:00:00+00:00            31.0           435.72   400.0   \n",
-       "3412245  2021-08-13 20:00:00+00:00            31.0           435.72   401.0   \n",
+       "3217819  2021-07-16 20:00:00+00:00            32.0           425.33   385.0   \n",
+       "3217821  2021-07-16 20:00:00+00:00            32.0           425.33   386.0   \n",
+       "3217825  2021-07-16 20:00:00+00:00            32.0           425.33   388.0   \n",
+       "3217827  2021-07-16 20:00:00+00:00            32.0           425.33   389.0   \n",
+       "3217829  2021-07-16 20:00:00+00:00            32.0           425.33   390.0   \n",
        "...                            ...             ...              ...     ...   \n",
        "6588412  2023-02-17 21:00:00+00:00            78.0           407.40   437.0   \n",
        "6588414  2023-02-17 21:00:00+00:00            78.0           407.40   438.0   \n",
@@ -1391,11 +2081,11 @@
        "6588428  2023-02-17 21:00:00+00:00            78.0           407.40   445.0   \n",
        "\n",
        "         strike_distance  strike_distance_pct    delta    gamma     vega  \\\n",
-       "3412233             40.7                0.093 -0.07773  0.00470  0.18849   \n",
-       "3412237             38.7                0.089 -0.08554  0.00515  0.20168   \n",
-       "3412239             37.7                0.087 -0.08839  0.00531  0.20656   \n",
-       "3412243             35.7                0.082 -0.09611  0.00578  0.22012   \n",
-       "3412245             34.7                0.080 -0.10012  0.00609  0.22682   \n",
+       "3217819             40.3                0.095 -0.07572  0.00487  0.18310   \n",
+       "3217821             39.3                0.092 -0.07912  0.00508  0.18874   \n",
+       "3217825             37.3                0.088 -0.08590  0.00551  0.19974   \n",
+       "3217827             36.3                0.085 -0.08957  0.00576  0.20590   \n",
+       "3217829             35.3                0.083 -0.09293  0.00601  0.21253   \n",
        "...                  ...                  ...      ...      ...      ...   \n",
        "6588412             29.6                0.073  0.22074  0.00895  0.56191   \n",
        "6588414             30.6                0.075  0.21088  0.00880  0.54746   \n",
@@ -1404,11 +2094,11 @@
        "6588428             37.6                0.092  0.15195  0.00740  0.44562   \n",
        "\n",
        "           theta      rho       iv  volume  last   bid   ask  max_ask  \\\n",
-       "3412233 -0.07067 -0.02999  0.24072   120.0  1.08  1.11  1.13     2.28   \n",
-       "3412237 -0.07453 -0.03375  0.23642   116.0  1.22  1.21  1.24     2.47   \n",
-       "3412239 -0.07547 -0.03429  0.23418   135.0  1.18  1.26  1.27     2.58   \n",
-       "3412243 -0.07855 -0.03787  0.22859   153.0  1.37  1.36  1.37     2.77   \n",
-       "3412245 -0.07972 -0.03952  0.22649   118.0  1.42  1.41  1.43     2.88   \n",
+       "3217819 -0.06656 -0.03038  0.22962  3686.0  1.08  1.03  1.04     1.97   \n",
+       "3217821 -0.06780 -0.03140  0.22702   121.0  1.22  1.06  1.08     2.06   \n",
+       "3217825 -0.07036 -0.03406  0.22108  2414.0  1.15  1.14  1.15     2.23   \n",
+       "3217827 -0.07159 -0.03513  0.21832   243.0  1.21  1.18  1.19     2.34   \n",
+       "3217829 -0.07323 -0.03721  0.21591  5567.0  1.25  1.23  1.24     2.39   \n",
        "...          ...      ...      ...     ...   ...   ...   ...      ...   \n",
        "6588412 -0.06749  0.18591  0.17425   146.0  4.04  4.01  4.05     3.59   \n",
        "6588414 -0.06536  0.17741  0.17288   127.0  3.83  3.77  3.79     3.37   \n",
@@ -1417,22 +2107,22 @@
        "6588428 -0.05121  0.12878  0.16816   137.0  2.41  2.43  2.45     2.10   \n",
        "\n",
        "         min_ask  fear_greed_index  fear_greed_index_max  fear_greed_index_min  \n",
-       "3412233     0.02              30.0                  54.0                  27.0  \n",
-       "3412237     0.02              30.0                  54.0                  27.0  \n",
-       "3412239     0.02              30.0                  54.0                  27.0  \n",
-       "3412243     0.03              30.0                  54.0                  27.0  \n",
-       "3412245     0.03              30.0                  54.0                  27.0  \n",
+       "3217819     0.03              54.0                  54.0                  27.0  \n",
+       "3217821     0.03              54.0                  54.0                  27.0  \n",
+       "3217825     0.03              54.0                  54.0                  27.0  \n",
+       "3217827     0.03              54.0                  54.0                  27.0  \n",
+       "3217829     0.04              54.0                  54.0                  27.0  \n",
        "...          ...               ...                   ...                   ...  \n",
-       "6588412     0.24              76.0                  76.0                  16.0  \n",
-       "6588414     0.22              76.0                  76.0                  16.0  \n",
-       "6588418     0.19              76.0                  76.0                  16.0  \n",
-       "6588426     0.14              76.0                  76.0                  16.0  \n",
-       "6588428     0.14              76.0                  76.0                  16.0  \n",
+       "6588412     0.24              76.0                  76.0                  54.0  \n",
+       "6588414     0.22              76.0                  76.0                  54.0  \n",
+       "6588418     0.19              76.0                  76.0                  54.0  \n",
+       "6588426     0.14              76.0                  76.0                  54.0  \n",
+       "6588428     0.14              76.0                  76.0                  54.0  \n",
        "\n",
-       "[36592 rows x 24 columns]"
+       "[39778 rows x 24 columns]"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1445,10 +2135,10 @@
     "max_dte = 90  # BEST RESULT\n",
     "max_ask = 10\n",
     "# min_ask = max_ask / 10\n",
-    "min_ask = 0.2\n",
+    "min_ask = 0.1\n",
     "min_volume = 100\n",
     "max_strike_distance_pct = 0.1\n",
-    "target_profit = 1.1\n",
+    "target_profit = 1.2\n",
     "\n",
     "df_options_filtered = df_options.loc[\n",
     "    (df_options.fear_greed_index_max.notnull())\n",
@@ -1516,7 +2206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "5f4f3e03",
    "metadata": {},
    "outputs": [],
@@ -1532,7 +2222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "65a3b9cc",
    "metadata": {},
    "outputs": [],
@@ -1544,7 +2234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "fc3ac1db",
    "metadata": {},
    "outputs": [],
@@ -1563,7 +2253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 34,
    "id": "d97801cf",
    "metadata": {},
    "outputs": [
@@ -1571,22 +2261,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total gains: $291584.90\n",
-      "Total losses: $-20822.20\n",
-      "Total profit: $270762.70\n"
+      "Total gains: $577804.00\n",
+      "Total losses: $-54070.80\n",
+      "Total profit: $523733.20\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/86/qt4jnsg167j7h7mqgbpp44kh0000gn/T/ipykernel_180/2931737422.py:19: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_61923/1660768581.py:19: 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",
       "  df_options_filtered[\"did_buy\"] = (\n",
-      "/var/folders/86/qt4jnsg167j7h7mqgbpp44kh0000gn/T/ipykernel_180/2931737422.py:35: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_61923/1660768581.py:44: 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",
@@ -1625,25 +2315,25 @@
        "      <th>0</th>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>9655</td>\n",
+       "      <td>12722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
-       "      <td>19956</td>\n",
+       "      <td>19890</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
-       "      <td>581</td>\n",
+       "      <td>788</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>6400</td>\n",
+       "      <td>6378</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1651,13 +2341,13 @@
       ],
       "text/plain": [
        "   did_buy  should_buy   size\n",
-       "0    False       False   9655\n",
-       "1    False        True  19956\n",
-       "2     True       False    581\n",
-       "3     True        True   6400"
+       "0    False       False  12722\n",
+       "1    False        True  19890\n",
+       "2     True       False    788\n",
+       "3     True        True   6378"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1685,15 +2375,24 @@
     "    (df_options_filtered.option_type == \"call\")\n",
     "    & (\n",
     "        (df_options_filtered.fear_greed_index < min_fear_greed_index_threshold)\n",
-    "        # & (df_options_filtered.fear_greed_index <= (df_options_filtered.fear_greed_index_min + max_diff))\n",
+    "        # | (\n",
+    "        #     # (df_options_filtered.fear_greed_index >= min_fear_greed_index_threshold)\n",
+    "        #     # & (df_options_filtered.fear_greed_index <= max_fear_greed_index_threshold)\n",
+    "        #     (df_options_filtered.fear_greed_index <= (df_options_filtered.fear_greed_index_min - max_diff))\n",
+    "        # )\n",
     "    )\n",
     ") | (\n",
     "    (df_options_filtered.option_type == \"put\")\n",
     "    & (\n",
     "        (df_options_filtered.fear_greed_index > max_fear_greed_index_threshold)\n",
-    "        # & (df_options_filtered.fear_greed_index >= (df_options_filtered.fear_greed_index_max - max_diff))\n",
+    "        # | (\n",
+    "        #     # (df_options_filtered.fear_greed_index >= min_fear_greed_index_threshold)\n",
+    "        #     # & (df_options_filtered.fear_greed_index <= max_fear_greed_index_threshold)\n",
+    "        #     (df_options_filtered.fear_greed_index >= (df_options_filtered.fear_greed_index_max - max_diff))\n",
+    "        # )\n",
     "    )\n",
     ")\n",
+    "\n",
     "# df_options_filtered[\"did_buy\"] = (\n",
     "#     (df_options_filtered.fear_greed_index == df_options_filtered.fear_greed_index_min)\n",
     "# ) | ((df_options_filtered.fear_greed_index == df_options_filtered.fear_greed_index_max))\n",
@@ -1721,255 +2420,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": null,
    "id": "b05bc216",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2021-07-16 $498 1 buy call 2021-08-16 434.65 437.0 $502 -1865698200678983814\n",
-      "2021-07-17 $859 0 sell call 2021-08-16 434.65 437.0 $361 -1865698200678983814\n",
-      "2021-07-20 $68 1 buy call 2021-08-20 424.93 426.0 $791 6092063339491207880\n",
-      "2021-07-21 $1187 0 sell call 2021-08-20 424.93 426.0 $1119 6092063339491207880\n",
-      "2021-07-21 $210 1 buy call 2021-08-20 431.07 428.0 $977 -1185068340927692827\n",
-      "2021-07-22 $1348 0 sell call 2021-08-20 431.07 428.0 $1138 -1185068340927692827\n",
-      "2021-07-22 $409 1 buy call 2021-08-23 434.55 431.0 $939 3385326396673909677\n",
-      "2021-07-23 $291 2 buy call 2021-08-23 435.51 448.0 $118 -5309762386563909929\n",
-      "2021-07-24 $1552 1 sell call 2021-08-23 434.55 431.0 $1261 3385326396673909677\n",
-      "2021-07-24 $1756 0 sell call 2021-08-23 435.51 448.0 $204 -5309762386563909929\n",
-      "2021-07-28 $756 1 buy call 2021-08-27 439.04 435.0 $1000 7246836258143352990\n",
-      "2021-07-29 $49 2 buy call 2021-08-30 438.86 439.0 $707 -8080567427382485175\n",
-      "2021-07-31 $944 1 sell call 2021-08-27 439.04 435.0 $895 7246836258143352990\n",
-      "2021-08-03 $1534 0 sell call 2021-08-30 438.86 439.0 $590 -8080567427382485175\n",
-      "2021-08-03 $587 1 buy call 2021-09-03 437.6 435.0 $947 -2986426380793432629\n",
-      "2021-08-04 $1667 0 sell call 2021-09-03 437.6 435.0 $1080 -2986426380793432629\n",
-      "2021-08-05 $1013 1 buy call 2021-09-07 439.02 440.0 $654 -7167381917856033061\n",
-      "2021-08-06 $1787 0 sell call 2021-09-07 439.02 440.0 $774 -7167381917856033061\n",
-      "2021-08-19 $1172 1 buy call 2021-09-20 439.25 442.0 $615 -5952841570603427991\n",
-      "2021-08-20 $402 2 buy call 2021-09-20 439.87 440.0 $770 2117945670494696352\n",
-      "2021-08-21 $1260 1 sell call 2021-09-20 439.87 440.0 $858 2117945670494696352\n",
-      "2021-08-21 $1978 0 sell call 2021-09-20 439.25 442.0 $718 -5952841570603427991\n",
-      "2021-08-24 $1026 1 buy call 2021-09-24 447.27 442.5 $952 1007818284100143404\n",
-      "2021-08-28 $2075 0 sell call 2021-09-24 447.27 442.5 $1049 1007818284100143404\n",
-      "2021-09-14 $1088 1 buy call 2021-10-15 446.56 442.0 $987 -5056043126330737511\n",
-      "2021-09-15 $115 2 buy call 2021-10-15 444.19 440.0 $973 3077688279994476241\n",
-      "2021-09-15 $944 1 sell call 2021-10-15 446.56 442.0 $829 -5056043126330737511\n",
-      "2021-09-16 $2080 0 sell call 2021-10-15 444.19 440.0 $1136 3077688279994476241\n",
-      "2021-09-21 $1113 1 buy call 2021-10-22 434.01 435.0 $967 4484753239253757658\n",
-      "2021-09-22 $277 2 buy call 2021-10-22 433.74 436.0 $836 -7147423854815175248\n",
-      "2021-09-23 $1239 1 sell call 2021-10-22 433.74 436.0 $962 -7147423854815175248\n",
-      "2021-09-23 $510 2 buy call 2021-10-25 437.78 440.0 $729 -1397838081046108726\n",
-      "2021-09-24 $1802 1 sell call 2021-10-22 434.01 435.0 $1292 4484753239253757658\n",
-      "2021-09-24 $2754 0 sell call 2021-10-25 437.78 440.0 $952 -1397838081046108726\n",
-      "2021-09-24 $1995 1 buy call 2021-10-25 443.13 443.0 $759 -2164725381368397570\n",
-      "2021-09-28 $2643 0 sell call 2021-10-25 443.13 443.0 $648 -2164725381368397570\n",
-      "2021-09-28 $1699 1 buy call 2021-10-29 442.67 440.0 $944 4679128723789441438\n",
-      "2021-09-29 $728 2 buy call 2021-10-29 433.66 434.0 $971 6227944348262442494\n",
-      "2021-09-29 $1329 1 sell call 2021-10-29 442.67 440.0 $601 4679128723789441438\n",
-      "2021-09-30 $1036 2 buy call 2021-11-01 434.37 447.0 $293 -890655637772376661\n",
-      "2021-10-01 $1718 1 sell call 2021-10-29 433.66 434.0 $682 6227944348262442494\n",
-      "2021-10-01 $877 2 buy call 2021-11-01 429.0 432.0 $841 2966695419884744736\n",
-      "2021-10-01 $1019 1 sell call 2021-11-01 434.37 447.0 $142 -890655637772376661\n",
-      "2021-10-02 $2021 0 sell call 2021-11-01 429.0 432.0 $1002 2966695419884744736\n",
-      "2021-10-05 $1032 1 buy call 2021-11-05 428.7 429.0 $989 8402212598011891279\n",
-      "2021-10-06 $2217 0 sell call 2021-11-05 428.7 429.0 $1185 8402212598011891279\n",
-      "2021-10-06 $1235 1 buy call 2021-11-05 433.06 432.0 $982 -8996070546301765505\n",
-      "2021-10-07 $265 2 buy call 2021-11-08 434.9 434.0 $970 879604857806760449\n",
-      "2021-10-08 $1513 1 sell call 2021-11-05 433.06 432.0 $1248 -8996070546301765505\n",
-      "2021-10-08 $2642 0 sell call 2021-11-08 434.9 434.0 $1129 879604857806760449\n",
-      "2021-10-08 $1774 1 buy call 2021-11-08 438.62 438.0 $868 -4799887244984141173\n",
-      "2021-10-09 $2549 0 sell call 2021-11-08 438.62 438.0 $775 -4799887244984141173\n",
-      "2021-10-12 $1966 1 buy call 2021-11-10 434.69 439.0 $583 6252600435450454719\n",
-      "2021-10-13 $2452 0 sell call 2021-11-10 434.69 439.0 $486 6252600435450454719\n",
-      "2021-10-13 $1510 1 buy call 2021-11-12 433.64 432.0 $942 8023676508636502820\n",
-      "2021-10-14 $596 2 buy call 2021-11-12 435.18 433.0 $914 -2005586028692760220\n",
-      "2021-10-15 $2018 1 sell call 2021-11-12 433.64 432.0 $1422 8023676508636502820\n",
-      "2021-10-15 $3360 0 sell call 2021-11-12 435.18 433.0 $1342 -2005586028692760220\n",
-      "2021-11-04 $2732 1 buy put 2021-12-03 464.73 465.0 $628 5266968337383238421\n",
-      "2021-11-05 $3261 0 sell put 2021-12-03 464.73 465.0 $529 5266968337383238421\n",
-      "2021-11-05 $2668 1 buy put 2021-12-06 466.86 466.0 $593 -4809178031147917075\n",
-      "2021-11-09 $3185 0 sell put 2021-12-06 466.86 466.0 $517 -4809178031147917075\n",
-      "2021-11-09 $2399 1 buy put 2021-12-10 468.99 471.0 $786 3452806657830535690\n",
-      "2021-11-10 $1418 2 buy put 2021-12-10 467.38 473.0 $981 4708281349327855410\n",
-      "2021-11-11 $2605 1 sell put 2021-12-10 467.38 473.0 $1187 4708281349327855410\n",
-      "2021-11-11 $3677 0 sell put 2021-12-10 468.99 471.0 $1072 3452806657830535690\n",
-      "2021-11-11 $2950 1 buy put 2021-12-13 463.67 462.0 $727 2403547401208807672\n",
-      "2021-11-12 $2776 2 buy put 2021-12-13 463.81 430.0 $174 3896151830087358092\n",
-      "2021-11-13 $3266 1 sell put 2021-12-13 463.67 462.0 $490 2403547401208807672\n",
-      "2021-11-13 $3385 0 sell put 2021-12-13 463.81 430.0 $119 3896151830087358092\n",
-      "2021-11-16 $2469 1 buy put 2021-12-17 467.47 470.0 $916 3974872326524757179\n",
-      "2021-11-17 $1506 2 buy put 2021-12-17 469.17 473.0 $963 4386833833238591806\n",
-      "2021-11-17 $2306 1 sell put 2021-12-17 467.47 470.0 $800 3974872326524757179\n",
-      "2021-11-27 $4067 0 sell put 2021-12-17 469.17 473.0 $1761 4386833833238591806\n",
-      "2021-12-01 $3068 1 buy call 2021-12-31 456.06 457.0 $999 -2315702792013372266\n",
-      "2021-12-02 $3871 0 sell call 2021-12-31 456.06 457.0 $803 -2315702792013372266\n",
-      "2021-12-02 $3080 1 buy call 2022-01-03 450.44 458.0 $791 8196015329636698313\n",
-      "2021-12-03 $4060 0 sell call 2022-01-03 450.44 458.0 $980 8196015329636698313\n",
-      "2021-12-03 $3158 1 buy call 2022-01-03 457.53 460.0 $902 -1289636431434133754\n",
-      "2021-12-04 $3850 0 sell call 2022-01-03 457.53 460.0 $692 -1289636431434133754\n",
-      "2021-12-07 $2906 1 buy call 2022-01-07 458.82 460.0 $944 -1874786634827731364\n",
-      "2021-12-08 $4282 0 sell call 2022-01-07 458.82 460.0 $1376 -1874786634827731364\n",
-      "2021-12-21 $3286 1 buy call 2022-01-21 454.99 456.0 $996 -4661473952907142915\n",
-      "2021-12-22 $4698 0 sell call 2022-01-21 454.99 456.0 $1412 -4661473952907142915\n",
-      "2022-01-25 $3705 1 buy call 2022-02-25 439.88 445.0 $993 415736499123866744\n",
-      "2022-01-26 $2709 2 buy call 2022-02-25 434.51 440.0 $996 4152422957917560685\n",
-      "2022-01-26 $3445 1 sell call 2022-02-25 439.88 445.0 $736 415736499123866744\n",
-      "2022-01-27 $2479 2 buy call 2022-02-28 433.21 440.0 $966 -6559363897737870013\n",
-      "2022-01-28 $3273 1 sell call 2022-02-25 434.51 440.0 $794 4152422957917560685\n",
-      "2022-01-28 $2289 2 buy call 2022-02-28 431.16 437.0 $984 6322284704477697232\n",
-      "2022-01-28 $3109 1 sell call 2022-02-28 433.21 440.0 $820 -6559363897737870013\n",
-      "2022-01-29 $4510 0 sell call 2022-02-28 431.16 437.0 $1401 6322284704477697232\n",
-      "2022-02-01 $3705 1 buy call 2022-03-04 449.88 455.0 $805 -174576581007648513\n",
-      "2022-02-03 $4732 0 sell call 2022-03-04 449.88 455.0 $1027 -174576581007648513\n",
-      "2022-02-03 $3879 1 buy call 2022-03-07 457.23 460.0 $853 -286438496593537485\n",
-      "2022-02-04 $4304 0 sell call 2022-03-07 457.23 460.0 $425 -286438496593537485\n",
-      "2022-02-04 $4221 1 buy call 2022-03-07 446.66 475.0 $83 889065111311134405\n",
-      "2022-02-08 $4271 0 sell call 2022-03-07 446.66 475.0 $50 889065111311134405\n",
-      "2022-02-08 $3325 1 buy call 2022-03-11 447.24 449.0 $946 8299037952859460061\n",
-      "2022-02-09 $4392 0 sell call 2022-03-11 447.24 449.0 $1067 8299037952859460061\n",
-      "2022-02-09 $3445 1 buy call 2022-03-11 450.82 451.0 $947 6306591902903146369\n",
-      "2022-02-10 $4736 0 sell call 2022-03-11 450.82 451.0 $1291 6306591902903146369\n",
-      "2022-02-15 $3788 1 buy call 2022-03-18 438.98 444.0 $948 7113683748128026974\n",
-      "2022-02-16 $4999 0 sell call 2022-03-18 438.98 444.0 $1211 7113683748128026974\n",
-      "2022-02-23 $4551 1 buy call 2022-03-25 429.65 446.0 $448 6113585466970565958\n",
-      "2022-02-24 $4808 0 sell call 2022-03-25 429.65 446.0 $257 6113585466970565958\n",
-      "2022-02-24 $3846 1 buy call 2022-03-31 421.97 429.0 $962 -6817936444518667840\n",
-      "2022-02-25 $5026 0 sell call 2022-03-31 421.97 429.0 $1180 -6817936444518667840\n",
-      "2022-02-25 $4060 1 buy call 2022-03-31 428.06 433.0 $966 6943739032816645716\n",
-      "2022-02-26 $5517 0 sell call 2022-03-31 428.06 433.0 $1457 6943739032816645716\n",
-      "2022-03-01 $4526 1 buy call 2022-03-31 436.52 440.0 $991 1140089194963828608\n",
-      "2022-03-02 $5335 0 sell call 2022-03-31 436.52 440.0 $809 1140089194963828608\n",
-      "2022-03-02 $4899 1 buy call 2022-04-01 429.99 450.0 $436 5122605772562618555\n",
-      "2022-03-03 $5477 0 sell call 2022-04-01 429.99 450.0 $578 5122605772562618555\n",
-      "2022-03-03 $5205 1 buy call 2022-04-04 437.79 460.0 $272 9107928447585856558\n",
-      "2022-03-04 $4206 2 buy call 2022-04-04 435.67 440.0 $999 -409099046419512565\n",
-      "2022-03-04 $4408 1 sell call 2022-04-04 437.79 460.0 $202 9107928447585856558\n",
-      "2022-03-05 $5237 0 sell call 2022-04-04 435.67 440.0 $829 -409099046419512565\n",
-      "2022-03-08 $4343 1 buy call 2022-04-08 419.55 430.0 $894 -641117648387518929\n",
-      "2022-03-09 $3403 2 buy call 2022-04-08 416.38 425.0 $940 1026071520724195988\n",
-      "2022-03-09 $4104 1 sell call 2022-04-08 419.55 430.0 $701 -641117648387518929\n",
-      "2022-03-10 $5500 0 sell call 2022-04-08 416.38 425.0 $1396 1026071520724195988\n",
-      "2022-03-10 $4550 1 buy call 2022-04-14 427.36 435.0 $950 2418835818104109074\n",
-      "2022-03-11 $3557 2 buy call 2022-04-14 425.49 431.0 $993 -7173151268379317223\n",
-      "2022-03-11 $4340 1 sell call 2022-04-14 427.36 435.0 $783 2418835818104109074\n",
-      "2022-03-12 $5065 0 sell call 2022-04-14 425.49 431.0 $725 -7173151268379317223\n",
-      "2022-03-15 $4104 1 buy call 2022-04-14 416.98 423.0 $961 -1590927762449771011\n",
-      "2022-03-16 $5461 0 sell call 2022-04-14 416.98 423.0 $1357 -1590927762449771011\n",
-      "2022-03-16 $5087 1 buy call 2022-04-18 426.05 445.0 $374 4990819292838603574\n",
-      "2022-03-17 $4265 2 buy call 2022-04-18 435.65 440.0 $822 -1518728398666925257\n",
-      "2022-03-17 $4825 1 sell call 2022-04-18 426.05 445.0 $560 4990819292838603574\n",
-      "2022-03-18 $5779 0 sell call 2022-04-18 435.65 440.0 $954 -1518728398666925257\n",
-      "2022-04-27 $5475 1 buy call 2022-05-27 416.17 440.0 $304 1317095244687628167\n",
-      "2022-04-28 $4948 2 buy call 2022-06-03 417.21 435.0 $527 -2342921089260710762\n",
-      "2022-04-29 $5489 1 sell call 2022-05-27 416.17 440.0 $541 1317095244687628167\n",
-      "2022-04-29 $5342 2 buy call 2022-05-31 427.86 455.0 $147 -454849876825281645\n",
-      "2022-04-29 $6222 1 sell call 2022-06-03 417.21 435.0 $880 -2342921089260710762\n",
-      "2022-04-30 $6266 0 sell call 2022-05-31 427.86 455.0 $44 -454849876825281645\n",
-      "2022-05-03 $5889 1 buy call 2022-06-03 414.55 436.0 $377 2261553131645676161\n",
-      "2022-05-05 $6591 0 sell call 2022-06-03 414.55 436.0 $702 2261553131645676161\n",
-      "2022-05-05 $6225 1 buy call 2022-06-06 428.97 445.0 $366 5441029347918304381\n",
-      "2022-05-06 $6369 0 sell call 2022-06-06 428.97 445.0 $144 5441029347918304381\n",
-      "2022-05-06 $6279 1 buy call 2022-06-06 414.15 450.0 $90 6497616909352270153\n",
-      "2022-05-07 $6338 0 sell call 2022-06-06 414.15 450.0 $59 6497616909352270153\n",
-      "2022-05-10 $6093 1 buy call 2022-06-10 398.33 430.0 $245 7281756982211496225\n",
-      "2022-05-11 $5409 2 buy call 2022-06-10 399.18 414.0 $684 5821012260159690706\n",
-      "2022-05-13 $5798 1 sell call 2022-06-10 399.18 414.0 $389 5821012260159690706\n",
-      "2022-05-13 $5906 0 sell call 2022-06-10 398.33 430.0 $108 7281756982211496225\n",
-      "2022-05-13 $5221 1 buy call 2022-06-13 392.37 406.0 $685 -2847330363861678357\n",
-      "2022-05-14 $6195 0 sell call 2022-06-13 392.37 406.0 $974 -2847330363861678357\n",
-      "2022-05-14 $5685 1 buy call 2022-06-13 401.58 417.0 $510 6334116996163005096\n",
-      "2022-05-17 $6050 0 sell call 2022-06-13 401.58 417.0 $365 6334116996163005096\n",
-      "2022-05-17 $5055 1 buy call 2022-06-17 400.16 404.0 $995 -5158441322056560503\n",
-      "2022-05-18 $6422 0 sell call 2022-06-17 400.16 404.0 $1367 -5158441322056560503\n",
-      "2022-05-18 $5443 1 buy call 2022-06-17 408.26 411.0 $979 -1528480247290725507\n",
-      "2022-05-19 $5854 0 sell call 2022-06-17 408.26 411.0 $411 -1528480247290725507\n",
-      "2022-05-19 $4938 1 buy call 2022-06-24 392.11 400.0 $916 -8081053803200030650\n",
-      "2022-05-20 $5669 0 sell call 2022-06-24 392.11 400.0 $731 -8081053803200030650\n",
-      "2022-05-20 $5177 1 buy call 2022-06-30 389.46 409.0 $492 2690792691865496172\n",
-      "2022-05-21 $5046 2 buy call 2022-06-24 389.59 424.0 $131 -5721098589913395402\n",
-      "2022-05-24 $4566 3 buy call 2022-06-24 396.83 413.0 $480 3833346625187788984\n",
-      "2022-05-24 $4772 2 sell call 2022-06-24 389.59 424.0 $206 -5721098589913395402\n",
-      "2022-05-24 $5472 1 sell call 2022-06-30 389.46 409.0 $700 2690792691865496172\n",
-      "2022-05-25 $5862 0 sell call 2022-06-24 396.83 413.0 $390 3833346625187788984\n",
-      "2022-05-25 $5757 1 buy call 2022-06-24 393.97 430.0 $105 8975661783393494912\n",
-      "2022-05-26 $5879 0 sell call 2022-06-24 393.97 430.0 $122 8975661783393494912\n",
-      "2022-05-26 $5524 1 buy call 2022-06-30 397.36 420.0 $355 -8676004042097833337\n",
-      "2022-05-27 $4525 2 buy call 2022-06-30 405.2 409.0 $999 2690792691865496172\n",
-      "2022-05-27 $5068 1 sell call 2022-06-30 397.36 420.0 $543 -8676004042097833337\n",
-      "2022-05-28 $6553 0 sell call 2022-06-30 405.2 409.0 $1485 2690792691865496172\n",
-      "2022-05-31 $5640 1 buy call 2022-06-30 415.26 420.0 $913 -8676004042097833337\n",
-      "2022-06-01 $6354 0 sell call 2022-06-30 415.26 420.0 $714 -8676004042097833337\n",
-      "2022-06-14 $5361 1 buy call 2022-07-15 375.01 381.0 $993 787958361644213295\n",
-      "2022-06-15 $4379 2 buy call 2022-07-15 373.83 378.0 $982 -1513267786801224305\n",
-      "2022-06-15 $5219 1 sell call 2022-07-15 375.01 381.0 $840 787958361644213295\n",
-      "2022-06-16 $6381 0 sell call 2022-07-15 373.83 378.0 $1162 -1513267786801224305\n",
-      "2022-06-16 $6080 1 buy call 2022-07-22 379.15 404.0 $301 5800337637501664671\n",
-      "2022-06-17 $5643 2 buy call 2022-07-22 366.89 387.0 $437 -664002064277626177\n",
-      "2022-06-17 $5774 1 sell call 2022-07-22 379.15 404.0 $131 5800337637501664671\n",
-      "2022-06-22 $5054 2 buy call 2022-07-22 375.08 385.0 $720 1141112967891694703\n",
-      "2022-06-22 $5685 1 sell call 2022-07-22 366.89 387.0 $631 -664002064277626177\n",
-      "2022-06-23 $4870 2 buy call 2022-07-27 374.35 384.0 $815 -977836438935170061\n",
-      "2022-06-24 $5798 1 sell call 2022-07-27 374.35 384.0 $928 -977836438935170061\n",
-      "2022-06-24 $5410 2 buy call 2022-07-29 378.12 400.0 $388 -6842518803702060287\n",
-      "2022-06-28 $6614 1 sell call 2022-07-22 375.08 385.0 $1204 1141112967891694703\n",
-      "2022-06-28 $7230 0 sell call 2022-07-29 378.12 400.0 $616 -6842518803702060287\n",
-      "2022-06-29 $6276 1 buy call 2022-07-29 380.72 385.0 $954 4290472848141068468\n",
-      "2022-06-30 $5562 2 buy call 2022-08-01 380.37 390.0 $714 -3890423502483596516\n",
-      "2022-07-01 $6304 1 sell call 2022-07-29 380.72 385.0 $742 4290472848141068468\n",
-      "2022-07-01 $6846 0 sell call 2022-08-01 380.37 390.0 $542 -3890423502483596516\n",
-      "2022-07-01 $6673 1 buy call 2022-08-01 377.28 405.0 $173 -6043652149730087164\n",
-      "2022-07-02 $6872 0 sell call 2022-08-01 377.28 405.0 $199 -6043652149730087164\n",
-      "2022-07-02 $6170 1 buy call 2022-08-03 381.2 390.0 $702 4885271388176257916\n",
-      "2022-07-05 $6025 2 buy call 2022-08-12 381.24 415.0 $145 8446800264155035226\n",
-      "2022-07-06 $5286 3 buy call 2022-08-05 381.95 390.0 $739 -3193370669766063555\n",
-      "2022-07-07 $4288 4 buy call 2022-08-19 383.31 390.0 $998 5541315394372987620\n",
-      "2022-07-08 $5260 3 sell call 2022-08-03 381.2 390.0 $972 4885271388176257916\n",
-      "2022-07-08 $6284 2 sell call 2022-08-05 381.95 390.0 $1024 -3193370669766063555\n",
-      "2022-07-08 $6016 3 buy call 2022-08-12 388.97 412.0 $268 2412110746922592790\n",
-      "2022-07-08 $6220 2 sell call 2022-08-12 381.24 415.0 $204 8446800264155035226\n",
-      "2022-07-08 $7479 1 sell call 2022-08-19 383.31 390.0 $1259 5541315394372987620\n",
-      "2022-07-09 $7702 0 sell call 2022-08-12 388.97 412.0 $223 2412110746922592790\n",
-      "2022-07-14 $7116 1 buy call 2022-08-15 378.87 390.0 $586 4131206372619947752\n",
-      "2022-07-15 $7623 0 sell call 2022-08-15 378.87 390.0 $507 4131206372619947752\n",
-      "2022-07-15 $6658 1 buy call 2022-08-19 377.96 382.0 $965 -1558166306032344344\n",
-      "2022-07-16 $7936 0 sell call 2022-08-19 377.96 382.0 $1278 -1558166306032344344\n",
-      "2022-09-22 $7659 1 buy call 2022-10-24 377.4 402.0 $277 1116842650261595831\n",
-      "2022-09-23 $7840 0 sell call 2022-10-24 377.4 402.0 $181 1116842650261595831\n",
-      "2022-09-23 $7753 1 buy call 2022-10-24 374.22 410.0 $87 4579657507739367808\n",
-      "2022-09-24 $7806 0 sell call 2022-10-24 374.22 410.0 $53 4579657507739367808\n",
-      "2022-09-27 $6988 1 buy call 2022-10-28 364.32 374.0 $818 3445166838741207416\n",
-      "2022-09-28 $6033 2 buy call 2022-10-28 363.39 370.0 $955 3699499516693370188\n",
-      "2022-09-29 $7275 1 sell call 2022-10-28 363.39 370.0 $1242 3699499516693370188\n",
-      "2022-09-29 $8297 0 sell call 2022-10-28 364.32 374.0 $1022 3445166838741207416\n",
-      "2022-09-29 $7395 1 buy call 2022-10-31 370.49 377.0 $902 4621023213993556161\n",
-      "2022-09-30 $6528 2 buy call 2022-10-31 362.84 370.0 $867 7550860249701595494\n",
-      "2022-09-30 $7102 1 sell call 2022-10-31 370.49 377.0 $574 4621023213993556161\n",
-      "2022-10-01 $7712 0 sell call 2022-10-31 362.84 370.0 $610 7550860249701595494\n",
-      "2022-10-04 $7121 1 buy call 2022-11-04 366.62 380.0 $591 8416125016534014261\n",
-      "2022-10-05 $8177 0 sell call 2022-11-04 366.62 380.0 $1056 8416125016534014261\n",
-      "2022-10-05 $7364 1 buy call 2022-11-04 377.8 385.0 $813 6101555960250163008\n",
-      "2022-10-07 $7994 0 sell call 2022-11-04 377.8 385.0 $630 6101555960250163008\n",
-      "2022-10-07 $7658 1 buy call 2022-11-07 373.21 395.0 $336 6054498547975392028\n",
-      "2022-10-08 $7798 0 sell call 2022-11-07 373.21 395.0 $140 6054498547975392028\n",
-      "2022-10-11 $7363 1 buy call 2022-11-09 360.07 380.0 $435 2644702717500104802\n",
-      "2022-10-12 $7732 0 sell call 2022-11-09 360.07 380.0 $369 2644702717500104802\n",
-      "2022-10-12 $7030 1 buy call 2022-11-11 357.76 371.0 $702 423711188224674397\n",
-      "2022-10-13 $6498 2 buy call 2022-11-11 356.58 375.0 $532 -5745407638959700536\n",
-      "2022-10-14 $7525 1 sell call 2022-11-11 357.76 371.0 $1027 423711188224674397\n",
-      "2022-10-14 $8372 0 sell call 2022-11-11 356.58 375.0 $847 -5745407638959700536\n",
-      "2022-10-14 $7692 1 buy call 2022-11-14 365.96 380.0 $680 -8897381811219266801\n",
-      "2022-10-15 $8062 0 sell call 2022-11-14 365.96 380.0 $370 -8897381811219266801\n",
-      "2022-10-18 $7074 1 buy call 2022-11-18 366.79 373.0 $988 598368417012148225\n",
-      "2022-10-19 $8252 0 sell call 2022-11-18 366.79 373.0 $1178 598368417012148225\n",
-      "2022-10-19 $7269 1 buy call 2022-11-18 371.15 377.0 $983 747950231604969709\n",
-      "2022-10-20 $6277 2 buy call 2022-11-18 368.51 374.0 $992 5419622031097752721\n",
-      "2022-10-20 $7129 1 sell call 2022-11-18 371.15 377.0 $852 747950231604969709\n",
-      "2022-10-21 $7940 0 sell call 2022-11-18 368.51 374.0 $811 5419622031097752721\n",
-      "100.00% (177933/177933) -- 2022-12-31 (535/535) -- 238 trades\n",
-      "Start money: $1000.00\n",
-      "End money: $7940.00 (694.00%)\n"
+     "ename": "TypeError",
+     "evalue": "Invalid comparison between dtype=datetime64[ns, UTC] and Timestamp",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:531\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin._validate_comparison_value\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    530\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 531\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_compatible_with\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    532\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, IncompatibleFrequency) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;66;03m# e.g. tzawareness mismatch\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arrays/datetimes.py:528\u001b[0m, in \u001b[0;36mDatetimeArray._check_compatible_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 528\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_assert_tzawareness_compat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arrays/datetimes.py:774\u001b[0m, in \u001b[0;36mDatetimeArray._assert_tzawareness_compat\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    773\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m other_tz \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 774\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    775\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot compare tz-naive and tz-aware datetime-like objects\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    776\u001b[0m     )\n",
+      "\u001b[0;31mTypeError\u001b[0m: Cannot compare tz-naive and tz-aware datetime-like objects",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mInvalidComparison\u001b[0m                         Traceback (most recent call last)",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:941\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin._cmp_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 941\u001b[0m     other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_comparison_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    942\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidComparison:\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:534\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin._validate_comparison_value\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, IncompatibleFrequency) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    533\u001b[0m         \u001b[38;5;66;03m# e.g. tzawareness mismatch\u001b[39;00m\n\u001b[0;32m--> 534\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidComparison(other) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    536\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(other):\n",
+      "\u001b[0;31mInvalidComparison\u001b[0m: 2021-06-15 00:00:00",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[26], line 23\u001b[0m\n\u001b[1;32m     18\u001b[0m last_quote_date \u001b[38;5;241m=\u001b[39m last_quote_datetime\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY-\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm-\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# df_options_total = df_options.groupby(\"option_id\", as_index=False).size().rename(columns={\"size\": \"total\"})\u001b[39;00m\n\u001b[1;32m     21\u001b[0m df_options_simulate \u001b[38;5;241m=\u001b[39m df_options\u001b[38;5;241m.\u001b[39mloc[\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;66;03m# df_options.option_id.isin(df_options_total.loc[df_options_total.total > 30].option_id)\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m     (\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquote_unixtime\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfirst_quote_datetime\u001b[49m)\n\u001b[1;32m     24\u001b[0m     \u001b[38;5;241m&\u001b[39m df_options\u001b[38;5;241m.\u001b[39moption_id\u001b[38;5;241m.\u001b[39misin(\n\u001b[1;32m     25\u001b[0m         df_options[\n\u001b[1;32m     26\u001b[0m             \u001b[38;5;66;03m# df_options.probability_of_profit.notna()\u001b[39;00m\n\u001b[1;32m     27\u001b[0m             \u001b[38;5;66;03m# & (df_options.probability_of_profit >= min_probability_of_profit)\u001b[39;00m\n\u001b[1;32m     28\u001b[0m             df_options\u001b[38;5;241m.\u001b[39mfear_greed_index_max\u001b[38;5;241m.\u001b[39mnotnull()\n\u001b[1;32m     29\u001b[0m             \u001b[38;5;66;03m# & (\u001b[39;00m\n\u001b[1;32m     30\u001b[0m             \u001b[38;5;66;03m#     (df_options.fear_greed_index == df_options.fear_greed_index_max)\u001b[39;00m\n\u001b[1;32m     31\u001b[0m             \u001b[38;5;66;03m#     | (df_options.fear_greed_index == df_options.fear_greed_index_min)\u001b[39;00m\n\u001b[1;32m     32\u001b[0m             \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[1;32m     33\u001b[0m             \u001b[38;5;241m&\u001b[39m (\n\u001b[1;32m     34\u001b[0m                 (\n\u001b[1;32m     35\u001b[0m                     (df_options\u001b[38;5;241m.\u001b[39moption_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcall\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     36\u001b[0m                     \u001b[38;5;241m&\u001b[39m (\n\u001b[1;32m     37\u001b[0m                         (df_options\u001b[38;5;241m.\u001b[39mfear_greed_index \u001b[38;5;241m<\u001b[39m min_fear_greed_index_threshold)\n\u001b[1;32m     38\u001b[0m                         \u001b[38;5;241m|\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mfear_greed_index \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mfear_greed_index_min \u001b[38;5;241m+\u001b[39m max_diff))\n\u001b[1;32m     39\u001b[0m                     )\n\u001b[1;32m     40\u001b[0m                 )\n\u001b[1;32m     41\u001b[0m                 \u001b[38;5;241m|\u001b[39m (\n\u001b[1;32m     42\u001b[0m                     (df_options\u001b[38;5;241m.\u001b[39moption_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mput\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     43\u001b[0m                     \u001b[38;5;241m&\u001b[39m (\n\u001b[1;32m     44\u001b[0m                         (df_options\u001b[38;5;241m.\u001b[39mfear_greed_index \u001b[38;5;241m>\u001b[39m max_fear_greed_index_threshold)\n\u001b[1;32m     45\u001b[0m                         \u001b[38;5;241m|\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mfear_greed_index \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mfear_greed_index_max \u001b[38;5;241m-\u001b[39m max_diff))\n\u001b[1;32m     46\u001b[0m                     )\n\u001b[1;32m     47\u001b[0m                 )\n\u001b[1;32m     48\u001b[0m             )\n\u001b[1;32m     49\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mbid \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     50\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mask \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     51\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mask \u001b[38;5;241m<\u001b[39m max_ask)\n\u001b[1;32m     52\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mask \u001b[38;5;241m>\u001b[39m min_ask)\n\u001b[1;32m     53\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mvolume \u001b[38;5;241m>\u001b[39m min_volume)\n\u001b[1;32m     54\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mstrike_distance_pct \u001b[38;5;241m<\u001b[39m max_strike_distance_pct)\n\u001b[1;32m     55\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mdays_to_expire \u001b[38;5;241m>\u001b[39m min_dte)\n\u001b[1;32m     56\u001b[0m             \u001b[38;5;241m&\u001b[39m (df_options\u001b[38;5;241m.\u001b[39mdays_to_expire \u001b[38;5;241m<\u001b[39m max_dte)\n\u001b[1;32m     57\u001b[0m         ]\u001b[38;5;241m.\u001b[39moption_id\n\u001b[1;32m     58\u001b[0m     )\n\u001b[1;32m     59\u001b[0m ]\u001b[38;5;241m.\u001b[39msort_values(\n\u001b[1;32m     60\u001b[0m     by\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquote_unixtime\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexpire_unixtime\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mask\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrike_distance_pct\u001b[39m\u001b[38;5;124m\"\u001b[39m], ascending\u001b[38;5;241m=\u001b[39m[\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m]\n\u001b[1;32m     61\u001b[0m )\n\u001b[1;32m     63\u001b[0m owned_options \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     64\u001b[0m trade_data \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/common.py:76\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m     74\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:60\u001b[0m, in \u001b[0;36mOpsMixin.__ge__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__ge__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__ge__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m---> 60\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cmp_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mge\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/series.py:5799\u001b[0m, in \u001b[0;36mSeries._cmp_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   5796\u001b[0m lvalues \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\n\u001b[1;32m   5797\u001b[0m rvalues \u001b[38;5;241m=\u001b[39m extract_array(other, extract_numpy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, extract_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m-> 5799\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcomparison_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5801\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(res_values, name\u001b[38;5;241m=\u001b[39mres_name)\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/array_ops.py:332\u001b[0m, in \u001b[0;36mcomparison_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    324\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLengths must match to compare\u001b[39m\u001b[38;5;124m\"\u001b[39m, lvalues\u001b[38;5;241m.\u001b[39mshape, rvalues\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    325\u001b[0m         )\n\u001b[1;32m    327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m should_extension_dispatch(lvalues, rvalues) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m    328\u001b[0m     (\u001b[38;5;28misinstance\u001b[39m(rvalues, (Timedelta, BaseOffset, Timestamp)) \u001b[38;5;129;01mor\u001b[39;00m right \u001b[38;5;129;01mis\u001b[39;00m NaT)\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m lvalues\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mobject\u001b[39m\n\u001b[1;32m    330\u001b[0m ):\n\u001b[1;32m    331\u001b[0m     \u001b[38;5;66;03m# Call the method on lvalues\u001b[39;00m\n\u001b[0;32m--> 332\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_scalar(rvalues) \u001b[38;5;129;01mand\u001b[39;00m isna(rvalues):  \u001b[38;5;66;03m# TODO: but not pd.NA?\u001b[39;00m\n\u001b[1;32m    335\u001b[0m     \u001b[38;5;66;03m# numpy does not like comparisons vs None\u001b[39;00m\n\u001b[1;32m    336\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m op \u001b[38;5;129;01mis\u001b[39;00m operator\u001b[38;5;241m.\u001b[39mne:\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/common.py:76\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m     74\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:60\u001b[0m, in \u001b[0;36mOpsMixin.__ge__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__ge__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__ge__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m---> 60\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cmp_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mge\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:943\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin._cmp_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m    941\u001b[0m     other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_comparison_value(other)\n\u001b[1;32m    942\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidComparison:\n\u001b[0;32m--> 943\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minvalid_comparison\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    945\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(other, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    946\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_object_dtype(dtype):\n\u001b[1;32m    947\u001b[0m     \u001b[38;5;66;03m# We have to use comp_method_OBJECT_ARRAY instead of numpy\u001b[39;00m\n\u001b[1;32m    948\u001b[0m     \u001b[38;5;66;03m#  comparison otherwise it would raise when comparing to None\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/invalid.py:40\u001b[0m, in \u001b[0;36minvalid_comparison\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     39\u001b[0m     typ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(right)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[0;32m---> 40\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid comparison between dtype=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mleft\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtyp\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res_values\n",
+      "\u001b[0;31mTypeError\u001b[0m: Invalid comparison between dtype=datetime64[ns, UTC] and Timestamp"
      ]
     }
    ],
@@ -2011,14 +2493,14 @@
     "                    (df_options.option_type == \"call\")\n",
     "                    & (\n",
     "                        (df_options.fear_greed_index < min_fear_greed_index_threshold)\n",
-    "                        # | (df_options.fear_greed_index <= (df_options.fear_greed_index_min + max_diff))\n",
+    "                        | (df_options.fear_greed_index <= (df_options.fear_greed_index_min + max_diff))\n",
     "                    )\n",
     "                )\n",
     "                | (\n",
     "                    (df_options.option_type == \"put\")\n",
     "                    & (\n",
     "                        (df_options.fear_greed_index > max_fear_greed_index_threshold)\n",
-    "                        # | (df_options.fear_greed_index >= (df_options.fear_greed_index_max - max_diff))\n",
+    "                        | (df_options.fear_greed_index >= (df_options.fear_greed_index_max - max_diff))\n",
     "                    )\n",
     "                )\n",
     "            )\n",
@@ -2067,13 +2549,13 @@
     "        (row.option_type == \"call\")\n",
     "        and (\n",
     "            (row.fear_greed_index < min_fear_greed_index_threshold)\n",
-    "            # or (row.fear_greed_index <= (row.fear_greed_index_min + max_diff))\n",
+    "            or (row.fear_greed_index <= (row.fear_greed_index_min + max_diff))\n",
     "        )\n",
     "    ) or (\n",
     "        (row.option_type == \"put\")\n",
     "        and (\n",
     "            (row.fear_greed_index > max_fear_greed_index_threshold)\n",
-    "            # or (row.fear_greed_index >= (row.fear_greed_index_max - max_diff))\n",
+    "            or (row.fear_greed_index >= (row.fear_greed_index_max - max_diff))\n",
     "        )\n",
     "    )\n",
     "\n",
@@ -2120,13 +2602,12 @@
     "\n",
     "    # max_buy_price = money\n",
     "    max_buy_price = 1000\n",
-    "    min_buy_price = 20\n",
+    "    min_buy_price = min_ask * 100\n",
     "\n",
     "    if (\n",
     "        # not np.isnan(row.probability_of_profit)\n",
     "        # and row.probability_of_profit > min_probability_of_profit\n",
     "        pd.notnull(row.fear_greed_index_max)\n",
-    "        # and ((row.fear_greed_index == row.fear_greed_index_max) or (row.fear_greed_index == row.fear_greed_index_min))\n",
     "        and good_buy\n",
     "        and not np.isnan(row.ask)\n",
     "        and row.volume > min_volume\n",
@@ -2144,7 +2625,6 @@
     "        if ask_price <= max_buy_price and ask_price >= min_buy_price and money >= ask_price:\n",
     "            money -= (ask_price + contract_fee) * total_contracts\n",
     "            option_to_buy = row.to_dict()\n",
-    "            option_to_buy[\"side\"] = \"call\"\n",
     "            option_to_buy[\"purchase_date\"] = quote_date\n",
     "            owned_options.append(option_to_buy)\n",
     "            print(\n",
@@ -2170,7 +2650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": null,
    "id": "e7140c8b",
    "metadata": {},
    "outputs": [],
@@ -2181,199 +2661,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": null,
    "id": "9dbb0d3c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "466344eab3ff4264a5a8d19ac1d53858",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
-      "text/html": [
-       "\n",
-       "            <div style=\"display: inline-block;\">\n",
-       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
-       "                    Figure 1\n",
-       "                </div>\n",
-       "                <img 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' width=640.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total trades: 119\n",
-      "Good trades: 66 (55.46%)\n",
-      "Min Diff: $-568.00\n",
-      "Max Diff: $798.00\n",
-      "Min Pct. Change: -70.07%\n",
-      "Max Pct. Change: 86.21%\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>id</th>\n",
-       "      <th>money</th>\n",
-       "      <th>price</th>\n",
-       "      <th>options</th>\n",
-       "      <th>days_to_expire</th>\n",
-       "      <th>diff</th>\n",
-       "      <th>pct_change</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>238.0</td>\n",
-       "      <td>238.0</td>\n",
-       "      <td>238.0</td>\n",
-       "      <td>238.0</td>\n",
-       "      <td>238.0</td>\n",
-       "      <td>121.0</td>\n",
-       "      <td>121.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>1.088679911937857e+18</td>\n",
-       "      <td>4363.226890756303</td>\n",
-       "      <td>748.3781512605042</td>\n",
-       "      <td>0.8865546218487395</td>\n",
-       "      <td>32.564033613445375</td>\n",
-       "      <td>62.88429752066115</td>\n",
-       "      <td>7.358488549399297</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>4.863282538880341e+18</td>\n",
-       "      <td>2241.079703005496</td>\n",
-       "      <td>349.2388104057368</td>\n",
-       "      <td>0.7684083357314562</td>\n",
-       "      <td>2.18777821881706</td>\n",
-       "      <td>248.59576257057174</td>\n",
-       "      <td>37.31522437169087</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-8.996070546301766e+18</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>30.04</td>\n",
-       "      <td>-567.9999999999998</td>\n",
-       "      <td>-70.06802721088435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>-1.9728861802265032e+18</td>\n",
-       "      <td>2496.5</td>\n",
-       "      <td>507.75</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>-151.0</td>\n",
-       "      <td>-21.79627601314349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>1.1411129678916947e+18</td>\n",
-       "      <td>4715.0</td>\n",
-       "      <td>807.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>15.02890173410405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5.089659152631614e+18</td>\n",
-       "      <td>6089.75</td>\n",
-       "      <td>980.75</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>263.0</td>\n",
-       "      <td>38.46153846153848</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>9.107928447585857e+18</td>\n",
-       "      <td>8372.0</td>\n",
-       "      <td>1761.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>797.9999999999999</td>\n",
-       "      <td>86.20689655172413</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            id              money              price  \\\n",
-       "count                    238.0              238.0              238.0   \n",
-       "mean     1.088679911937857e+18  4363.226890756303  748.3781512605042   \n",
-       "std      4.863282538880341e+18  2241.079703005496  349.2388104057368   \n",
-       "min     -8.996070546301766e+18               49.0               44.0   \n",
-       "25%    -1.9728861802265032e+18             2496.5             507.75   \n",
-       "50%     1.1411129678916947e+18             4715.0              807.0   \n",
-       "75%      5.089659152631614e+18            6089.75             980.75   \n",
-       "max      9.107928447585857e+18             8372.0             1761.0   \n",
-       "\n",
-       "                  options      days_to_expire                diff  \\\n",
-       "count               238.0               238.0               121.0   \n",
-       "mean   0.8865546218487395  32.564033613445375   62.88429752066115   \n",
-       "std    0.7684083357314562    2.18777821881706  248.59576257057174   \n",
-       "min                   0.0               30.04  -567.9999999999998   \n",
-       "25%                   0.0                31.0              -151.0   \n",
-       "50%                   1.0                32.0                88.0   \n",
-       "75%                   1.0                33.0               263.0   \n",
-       "max                   4.0                44.0   797.9999999999999   \n",
-       "\n",
-       "               pct_change  \n",
-       "count               121.0  \n",
-       "mean    7.358488549399297  \n",
-       "std     37.31522437169087  \n",
-       "min    -70.06802721088435  \n",
-       "25%    -21.79627601314349  \n",
-       "50%     15.02890173410405  \n",
-       "75%     38.46153846153848  \n",
-       "max     86.20689655172413  "
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_trades = pd.DataFrame(trade_data)\n",
     "# df_trades = df_trades.loc[df_trades[\"type\"] == \"call\"]\n",
@@ -2426,7 +2717,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,