br-2-pesuit-579-601-618.ipynb

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     "text": [
      "/kaggle/input/ue21cs342aa2/sample_submission.csv\n",
      "/kaggle/input/ue21cs342aa2/train.csv\n",
      "/kaggle/input/ue21cs342aa2/test.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260a5f4c",
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   "source": [
    "## 1. Data Collection"
   ]
  },
  {
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   },
   "source": [
    "### 1.1 Importing Necessary Libraries"
   ]
  },
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   "id": "b7d5dc22",
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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "from subprocess import check_output\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from pandas.plotting import lag_plot\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4332354",
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     "start_time": "2023-11-03T14:08:54.537292",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 1.2 Loading Datasets\n",
    "#### It's essential first to visualize the **top rows** of the dataset to understand its structure. We can identify column names, data types, and general content."
   ]
  },
  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>66.208486</td>\n",
       "      <td>75.609978</td>\n",
       "      <td>298506300</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-06-08</td>\n",
       "      <td>64.116235</td>\n",
       "      <td>74.443331</td>\n",
       "      <td>227974800</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>47.701942</td>\n",
       "      <td>71.140831</td>\n",
       "      <td>250670900</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-06-22</td>\n",
       "      <td>54.754816</td>\n",
       "      <td>72.881344</td>\n",
       "      <td>223614300</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-06-29</td>\n",
       "      <td>48.031899</td>\n",
       "      <td>66.284718</td>\n",
       "      <td>406814900</td>\n",
       "      <td>Buy</td>\n",
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       "          Date       Open      Close     Volume Strategy\n",
       "id                                                      \n",
       "0   2015-06-01  66.208486  75.609978  298506300     Hold\n",
       "1   2015-06-08  64.116235  74.443331  227974800     Hold\n",
       "2   2015-06-15  47.701942  71.140831  250670900      Buy\n",
       "3   2015-06-22  54.754816  72.881344  223614300     Hold\n",
       "4   2015-06-29  48.031899  66.284718  406814900      Buy"
      ]
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   ],
   "source": [
    "train_data = pd.read_csv(\"/kaggle/input/ue21cs342aa2/train.csv\",index_col = 0)\n",
    "train_data.head()\n"
   ]
  },
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>72.708331</td>\n",
       "      <td>205897600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-08</td>\n",
       "      <td>73.310886</td>\n",
       "      <td>214095600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>70.610119</td>\n",
       "      <td>151601700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-22</td>\n",
       "      <td>70.256017</td>\n",
       "      <td>193982500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>68.258324</td>\n",
       "      <td>220238400</td>\n",
       "    </tr>\n",
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       "          Date       Open     Volume\n",
       "id                                  \n",
       "0   2021-03-01  72.708331  205897600\n",
       "1   2021-03-08  73.310886  214095600\n",
       "2   2021-03-15  70.610119  151601700\n",
       "3   2021-03-22  70.256017  193982500\n",
       "4   2021-03-29  68.258324  220238400"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "test_data = pd.read_csv(\"/kaggle/input/ue21cs342aa2/test.csv\", index_col = 0)\n",
    "test_data.head()"
   ]
  },
  {
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   },
   "source": [
    "### 1.3 Checking for missing values"
   ]
  },
  {
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   "id": "26a506ee",
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    {
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     "text": [
      "Empty DataFrame\n",
      "Columns: [Count, Percentage]\n",
      "Index: []\n",
      "Empty DataFrame\n",
      "Columns: [Count, Percentage]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "missing_count = train_data.isnull().sum()\n",
    "missing_percentage = (train_data.isnull().sum() / len(train_data)) * 100\n",
    "missing_data = pd.DataFrame({'Count': missing_count, 'Percentage': missing_percentage})\n",
    "print(missing_data[missing_data['Count'] > 0])\n",
    "\n",
    "missing_count2 = test_data.isnull().sum()\n",
    "missing_percentage2 = (test_data.isnull().sum() / len(test_data)) * 100\n",
    "missing_data2 = pd.DataFrame({'Count': missing_count, 'Percentage': missing_percentage})\n",
    "print(missing_data2[missing_data2['Count'] > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c968b3a8",
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    "tags": []
   },
   "source": [
    "#### Dataset is complete, and there are no missing values to report."
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "### 1.4 Checking for outliers"
   ]
  },
  {
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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "numeric_cols = train_data.select_dtypes(include=['float64', 'int64']).columns\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "for i, col in enumerate(numeric_cols):\n",
    "    sns.boxplot(data=train_data, y=col, ax=axes[i])  \n",
    "    axes[i].set_title(f\"Boxplot of {col}\")\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab62c88",
   "metadata": {
    "papermill": {
     "duration": 0.018194,
     "end_time": "2023-11-03T14:08:55.625394",
     "exception": false,
     "start_time": "2023-11-03T14:08:55.607200",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    \n",
    "#### There are multiple dots or points above the top whisker, representing outlier values. These indicate that there were certain days/times when the trading volume was significantly higher than typical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8a407de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:55.666733Z",
     "iopub.status.busy": "2023-11-03T14:08:55.666008Z",
     "iopub.status.idle": "2023-11-03T14:08:56.052930Z",
     "shell.execute_reply": "2023-11-03T14:08:56.051740Z"
    },
    "papermill": {
     "duration": 0.411074,
     "end_time": "2023-11-03T14:08:56.055349",
     "exception": false,
     "start_time": "2023-11-03T14:08:55.644275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_cols = test_data.select_dtypes(include=['float64', 'int64']).columns\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "for i, col in enumerate(numeric_cols):\n",
    "    sns.boxplot(data=test_data, y=col, ax=axes[i])  \n",
    "    axes[i].set_title(f\"Boxplot of {col}\")\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc1629d",
   "metadata": {
    "papermill": {
     "duration": 0.01739,
     "end_time": "2023-11-03T14:08:56.090292",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.072902",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2. DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6accf106",
   "metadata": {
    "papermill": {
     "duration": 0.017017,
     "end_time": "2023-11-03T14:08:56.124973",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.107956",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.1 Removing outliers for 'Volume'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbea7533",
   "metadata": {
    "papermill": {
     "duration": 0.017692,
     "end_time": "2023-11-03T14:08:56.160310",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.142618",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### We will not be removing any outliers here as they might hold a significant value during a particular peak or low state but let's see how the outliers can be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60039fc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:56.198654Z",
     "iopub.status.busy": "2023-11-03T14:08:56.197994Z",
     "iopub.status.idle": "2023-11-03T14:08:56.206734Z",
     "shell.execute_reply": "2023-11-03T14:08:56.205926Z"
    },
    "papermill": {
     "duration": 0.031397,
     "end_time": "2023-11-03T14:08:56.209446",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.178049",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Q1 = train_data['Volume'].quantile(0.25)\n",
    "Q3 = train_data['Volume'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "filtered_data = train_data[(train_data['Volume'] >= lower_bound) & (train_data['Volume'] <= upper_bound)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5034a346",
   "metadata": {
    "papermill": {
     "duration": 0.01851,
     "end_time": "2023-11-03T14:08:56.246259",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.227749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### High trading volumes can be associated with significant market crashes, major company announcements, or other factors that could have driven a large number of trades -> valuable information about the stock's behavior during those times, whereas if we're interested in the typical behavior of the stock, these outliers might introduce noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e7623be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:56.285727Z",
     "iopub.status.busy": "2023-11-03T14:08:56.284615Z",
     "iopub.status.idle": "2023-11-03T14:08:56.454662Z",
     "shell.execute_reply": "2023-11-03T14:08:56.452958Z"
    },
    "papermill": {
     "duration": 0.193947,
     "end_time": "2023-11-03T14:08:56.458017",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.264070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(filtered_data['Volume'])\n",
    "plt.title(\"Boxplot of Volume\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f8cdc34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:56.496693Z",
     "iopub.status.busy": "2023-11-03T14:08:56.496292Z",
     "iopub.status.idle": "2023-11-03T14:08:56.507990Z",
     "shell.execute_reply": "2023-11-03T14:08:56.506673Z"
    },
    "papermill": {
     "duration": 0.034595,
     "end_time": "2023-11-03T14:08:56.511098",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.476503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Q1 = test_data['Volume'].quantile(0.25)\n",
    "Q3 = test_data['Volume'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "filtered_data2 = test_data[(test_data['Volume'] >= lower_bound) & (test_data['Volume'] <= upper_bound)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a63188e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:56.550718Z",
     "iopub.status.busy": "2023-11-03T14:08:56.550321Z",
     "iopub.status.idle": "2023-11-03T14:08:56.720279Z",
     "shell.execute_reply": "2023-11-03T14:08:56.718396Z"
    },
    "papermill": {
     "duration": 0.192915,
     "end_time": "2023-11-03T14:08:56.723214",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.530299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(filtered_data2['Volume'])\n",
    "plt.title(\"Boxplot of Volume\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680d5e8e",
   "metadata": {
    "papermill": {
     "duration": 0.018706,
     "end_time": "2023-11-03T14:08:56.760766",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.742060",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### 2.2 Convert time-related columns into datetime format if they aren't already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9788c5ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:56.804163Z",
     "iopub.status.busy": "2023-11-03T14:08:56.802990Z",
     "iopub.status.idle": "2023-11-03T14:08:56.817034Z",
     "shell.execute_reply": "2023-11-03T14:08:56.816012Z"
    },
    "papermill": {
     "duration": 0.037298,
     "end_time": "2023-11-03T14:08:56.819569",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.782271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data['Date'] = pd.to_datetime(train_data['Date'])\n",
    "test_data['Date'] = pd.to_datetime(test_data['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2fe6e17",
   "metadata": {
    "papermill": {
     "duration": 0.019163,
     "end_time": "2023-11-03T14:08:56.857310",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.838147",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### 2.3 Scaling ensures each feature has equal weight, initially -> we will not be doing this as the absolute values have meaning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54df4a54",
   "metadata": {
    "papermill": {
     "duration": 0.021092,
     "end_time": "2023-11-03T14:08:56.897401",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.876309",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02239ce8",
   "metadata": {
    "papermill": {
     "duration": 0.020041,
     "end_time": "2023-11-03T14:08:56.936652",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.916611",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.1 Time Series Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd4ed6ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:56.981122Z",
     "iopub.status.busy": "2023-11-03T14:08:56.980715Z",
     "iopub.status.idle": "2023-11-03T14:08:57.290612Z",
     "shell.execute_reply": "2023-11-03T14:08:57.289850Z"
    },
    "papermill": {
     "duration": 0.334279,
     "end_time": "2023-11-03T14:08:57.292428",
     "exception": false,
     "start_time": "2023-11-03T14:08:56.958149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iNjYWc+fOLW9lnz17NmrUqIHevXujffv25bd766230KVLF7z99tt4+OGHERcXh0aNGuHaa68tn6ivX79+WL16NT777DMcOXIEaWlpuOCCCzBjxgw0bty40uOYNWsWPvroI7z77rt49NFHUVxcjKZNm+Lxxx/Hvffei5SUlAr3S0xMxKWXXooZM2ZgwIABdifLe/DBB9GiRQu89NJL5XMTZGVlYdCgQbj00ku9+fEFTSR+T0REVJFJ88fsOEREROSWyZMn4+2330ZeXp7DSciIiIgo+nBMOxERUYCdO3fO6usTJ07g448/Ru/evRnYiYiIyArb44mIiAKsR48e6N+/P1q3bo0jR47gvffeQ25uLh599NFgHxoRERGFGIZ2IiKiABs6dCi+/PJLTJ8+HSaTCeeddx7ee+899O3bN9iHRkRERCGGY9qJiIiIiIiIQhTHtBMRERERERGFKIZ2IiIiIiIiohDFMe0AysrKcPjwYVStWhUmkynYh0NEREREREQRTtM0nD17FpmZmYiJcVxPZ2gHcPjwYWRlZQX7MIiIiIiIiCjKHDhwAPXr13d4PUM7gKpVqwKQH1ZqamqQj4YCzWKxYMGCBRg0aBDMZnOwD4fCHN9P5Et8P5Gv8L1EvsT3E/lKtL+XcnNzkZWVVZ5HHWFoB8pb4lNTUxnao5DFYkFycjJSU1Oj8o8F+RbfT+RLfD+Rr/C9RL7E9xP5Ct9LorIh2pyIjoiIiIiIiChEMbQTERERERERhSiGdiIiIiIiIqIQxTHtREREREREAVJaWgqLxRLswwgJFosFcXFxKCwsRGlpabAPx+diY2MRFxfn9bLiDO1EREREREQBkJeXh4MHD0LTtGAfSkjQNA116tTBgQMHvA62oSo5ORl169ZFfHy8x4/B0E5ERERERORnpaWlOHjwIJKTk1GrVq2IDanuKCsrQ15eHqpUqYKYmMgaua1pGoqLi3Hs2DFkZ2ejefPmHn+PDO1ERERERER+ZrFYoGkaatWqhaSkpGAfTkgoKytDcXExEhMTIy60A0BSUhLMZjP27dtX/n16IvJ+MkRERERERCGKFfbo4ouTEQztRERERERERCGKoZ2IiIiIiIgoRDG0ExERERERkVdMJhO++eabYB+G2/r374/JkycH+zCcYmgnIiIiIiIih3JycjBp0iQ0adIECQkJyMrKwogRI/DTTz8F+9DKPfHEEzCZTDCZTIiLi0OjRo1w9913Iy8vz+n9vvrqK0ydOjVAR+kZzh5PREREREREdu3duxe9evVCeno6nn/+ebRv3x4WiwXz58/H7bffjm3btgX7EMu1bdsWixYtQklJCZYvX46bbroJBQUFePvttyvctri4GPHx8ahevXoQjtQ9rLQTEREREREFmKYB+fnB+adprh/nbbfdBpPJhNWrV2PMmDFo0aIF2rZti3vuuQcrV650eL9NmzbhoosuQlJSEmrUqIEJEyZYVb1/+eUXdO/eHfXq1UP16tXRq1cv7Nu3r/z6b7/9Fueddx4SExPRpEkTTJkyBSUlJU6PNS4uDnXq1EH9+vUxbtw4XHPNNfjuu+8ASCW+U6dOePfdd9G4cePy5dds2+OLiorwwAMPICsrCwkJCWjWrBnee++98us3b96MIUOGoEqVKsjIyMB1112H48ePu/4D9QAr7URERERERAFWUABUqRKc587LA1JSKr/dyZMnMW/ePDz99NNIsXOH9PR0u/fLz8/H4MGD0aNHD6xZswZHjx7FLbfcgjvuuAMffPABSkpKMGrUKNxyyy14++23ER8fj99//718ObylS5di/PjxePXVV9GnTx/s3r0bEyZMAAA8/vjjLn+fSUlJKC4uLv96165dmD17Nr766ivExsbavc/48eOxYsUKvPrqq+jYsSOys7PLQ/np06dx0UUX4ZZbbsFLL72Ec+fO4YEHHsDYsWPx888/u3xc7mJoJyIiIiIiogp27doFTdPQqlUrt+43c+ZMFBYW4qOPPioP+9OmTcOIESPw7LPPwmw248yZMxg2bBgaN26M1NRUtG3btvz+U6ZMwYMPPojrr78eANCkSRNMnToV999/v8uhfe3atZg5cyYuuuii8suKi4vx0UcfoVatWnbvs2PHDsyaNQsLFy7EgAEDyp9bmTZtGjp37oz//Oc/5Zf973//Q1ZWFnbs2IEWLVq4+BNyD0M7ERERRYXiYuD334GuXQGzOdhHQ0TRLjlZKt7Bem5XaO700Rts3boVHTt2tKrO9+rVC2VlZdi+fTv69u2LG264AUOGDEH//v1xySWXYNy4cahbty4AYMOGDVi+fDmefvrp8vuXlpaisLAQBQUFSHbwDWzatAlVqlRBaWkpiouLMWzYMEybNq38+oYNGzoM7ACwfv16xMbGol+/fnav37BhAxYvXowqdlokdu/ezdBORERE5I0XXwQeegh45RXgzjuDfTREFO1MJtda1IOpefPmMJlMfpls7v3338cdd9yBb7/9FrNmzcKjjz6KhQsXonv37sjLy8OUKVMwevToCvdTY9HtadmyJb777jvExcUhMzMT8fHxVtfba/E3SkpKcnp9Xl5eebeALXXCwR84ER0RERFFhc2brbdERORc9erVMXjwYLz++uvIz8+vcP3p06ft3q9169bYsGGD1X2WL1+OmJgYtGzZsvyyzp0745577sGyZcvQrl07zJw5EwBw3nnnYfv27WjWrFmFfzExjiNsfHw8mjVrhkaNGlUI7K5o3749ysrK8Ouvv9q9/rzzzsOWLVvQqFGjCsdV2QkBbzC0ExERUUgqKpLq+Natvnm8I0dk+9dfvnk8IqJo8Prrr6O0tBQXXHABZs+ejZ07d2Lr1q149dVX0aNHD7v3ueaaa5CYmIjrr78emzdvxuLFizFp0iRcd911yMjIQHZ2Nh566CGsWLEC+/fvx4IFC7Bz5060bt0aAPDYY4/ho48+wpQpU7BlyxZs3boVn332GR555BG/fq+NGjXC9ddfj5tuugnffPMNsrOz8csvv2DWrFkAgNtvvx0nT57EVVddhTVr1mD37t2YP38+brzxRpSWlvrtuBjaiYiIKCR9/z1w773AXXf55vFUaM/J8c3jERFFgyZNmuCPP/7AhRdeiHvvvRft2rXDwIED8dNPP+HNN9+0e5/k5GTMnz8fJ0+eRNeuXXH55Zfj4osvLh9fnpycjG3btuGKK65A165d8Y9//AO33347Jk6cCAAYPHgwvv/+eyxYsABdu3ZF9+7d8dJLL6Fhw4Z+/37ffPNNXH755bjtttvQqlUr3HrrreUdA5mZmVi+fDlKS0sxaNAgtG/fHpMnT0Z6errTDgBvmTRPZxeIILm5uUhLS8OZM2eQmpoa7MOhALNYLJg7dy6GDh0KM2cmIi/x/US+FO3vp9dek7HnNWoAx47J+E9vZGQAR48C9esDBw745hjDRbS/l8i3+H7yTGFhIbKzs63WCI92ZWVlyM3NRWpqql9DbzA5e91dzaGR+ZMhIiKisHfqlGxPnJCw7Y3SUuDvZXaRkwOUlXn3eERERIHC0E5EREQhSYV2ANi0ybvHOnFCD+olJcDJk949HhERUaAwtBMREVFIMoZ2b2d8V+PZFU5GR0RE4YKhnYiIiEKSP0M7J6MjIqJwwdBOREREIYmVdiKKRJwHPLr44vVmaCciIqKQZBvavZk8jpV2Igq22NhYAEBxcXGQj4QCqaCgAAC8WmkhzlcHQ0RERORLxtCenw/s2wc0buzZYzG0E1GwxcXFITk5GceOHYPZbI7YJc7cUVZWhuLiYhQWFkbcz0PTNBQUFODo0aNIT08vP2njCYZ2IiIiCkkqtKemArm5Um33NrTXqiVrvrM9nogCzWQyoW7dusjOzsa+ffuCfTghQdM0nDt3DklJSTCZTME+HL9IT09HnTp1vHoMhnYiIiIKOcXFwN8dhejdG5g7V0L7iBGePZ4K7R07AosWsdJORMERHx+P5s2bs0X+bxaLBUuWLEHfvn29ah8PVWaz2asKu8LQTkRERCFHVdlNJqBXLz20e8o2tLPSTkTBEhMTg8TExGAfRkiIjY1FSUkJEhMTIzK0+0pkDRwgIiKiiHD6tGzT0oAOHWR/0ybPH+/oUdl27ChbVtqJiChcMLQTERFRyFGV9mrVgHbtZH/bNsBicf+xNE0P7Z06yfbMGeDcOa8Pk4iIyO8Y2omIKGrddhtw6aXeLSVG/qFCe3o60LAhUKWKBPadOz17LBX2mzcHVFcqq+1ERBQOGNqJiCgqFRcDb74JzJkD7NkT7KMhW8ZKu8mkV9s9GdeuxrOnpUlgV5P4clw7ERGFA4Z2IiKKSsYq64kTwTsOss8Y2gE9tK9b5/g+778PfPBBxctVaM/IkG3durJlpZ2IiMIBQzsREUUlY5WVoT302Ib2Pn1k+9139m9/7Bhw883y78wZ6+tsQ7uqtDO0ExFROAhqaF+yZAlGjBiBzMxMmEwmfPPNN1bXa5qGxx57DHXr1kVSUhIGDBiAnTaD2U6ePIlrrrkGqampSE9Px80334y8vLwAfhdERBSOGNpDm21ov/RSID4e+PNPYMuWirdfv14mnCsrqzjcwVFoZ3s8ERGFg6CG9vz8fHTs2BGvv/663eufe+45vPrqq3jrrbewatUqpKSkYPDgwSgsLCy/zTXXXIMtW7Zg4cKF+P7777FkyRJMmDAhUN8CERGFKYb20GYb2tPTgcGDZX/WrIq3N7bNVxba2R5PREThJC6YTz5kyBAMGTLE7nWapuHll1/GI488gpEjRwIAPvroI2RkZOCbb77BlVdeia1bt2LevHlYs2YNzj//fADAa6+9hqFDh+L//u//kJmZafexi4qKUFRUVP51bm4uAMBiscDiyVoyFNbUa87XnnyB76fwcfBgDIBYAMDRo6WwWEJvCvlofj+dOBELIAapqfprM3q0CXPmxOHzzzX8+98lMJn02//xh9weAHbutH49//pLrqtZUy6vVcsEIA6HD5fBYikN2PcUTNH8XiLf4/uJfCXa30uuft9BDe3OZGdnIycnBwMGDCi/LC0tDd26dcOKFStw5ZVXYsWKFUhPTy8P7AAwYMAAxMTEYNWqVbjsssvsPvYzzzyDKVOmVLh8wYIFSE5O9v03Q2Fh4cKFwT4EiiB8P4W+NWs6AmgEAFi3bj/mzt0Y1ONxJhrfT7t39wJQE3v3/oG5cw8DABIS4mA2X4Lt22Px1ltL0bDh2fLbL19+EYCqAIBff92P1q3113Pz5gsA1MXRo5swd+4+HDyYAaA7tm/Pxdy5vwbumwoB0fheIv/h+4l8JVrfSwUFBS7dLmRDe87fPWsZqpftbxkZGeXX5eTkoHbt2lbXx8XFoXr16uW3seehhx7CPffcU/51bm4usrKyMGjQIKSmpvrqW6AwYbFYsHDhQgwcOBBmsznYh0Nhju+n8DF9emz5fkpKQwwdWj+IR2NfNL+fHn1UPqJceGFnDBzYqfzyTz81Yc4c4MiRfvjnP6Wanp8PHDqkf6QpLbV+PZ9+Wl7riy9uh6FD2yIjw4SnnwbOnUvD0KFDA/DdBF80v5fI9/h+Il+J9veS6viuTMiGdn9KSEhAQkJChcvNZnNUvllI8PUnX+L7KfSpcc4AcOpUDMzm0F1QJRrfT6dPy7ZWrTgYv/UrrwTmzAFmz47FU0/FwmQCtm+XSeiU7Gzr1/PoUdnWqyePVf/vPH/kiAmxsWbEhO5L73PR+F4i/+H7iXwlWt9Lrn7PIfvfVJ2/p3Y9YvxU9ffX6ro6dergqPqf+G8lJSU4efJk+W2IiIjs4UR0oc12IjplxAggIUGC+sa/O+DVJHRt28p2716g9O+h6pqmn6BRzXmqia+0lK+9J+bNA5o0ARYvDvaREBFFh5AN7Y0bN0adOnXw008/lV+Wm5uLVatWoUePHgCAHj164PTp01i7dm35bX7++WeUlZWhW7duAT9mIiIKD6Wl1pV2BrfQYrEAavVW29BetSowbJjsf/ihbNevl+2wYbIsXEkJcOCAXHb2LKAWnVFh3WwGataUfS775r5vvwWys4Evvwz2kRARRYeghva8vDysX78e6//+3zY7Oxvr16/H/v37YTKZMHnyZDz11FP47rvvsGnTJowfPx6ZmZkYNWoUAKB169a45JJLcOutt2L16tVYvnw57rjjDlx55ZUOZ44nIiI6cUKvxKqvKXScOaPvp6dXvP7mm2X74YcSyFWl/bzzgMaNZV8t+6ZOzqSkyD+Fy755Tg3B3LcvuMdBRBQtghraf//9d3Tu3BmdO3cGANxzzz3o3LkzHnvsMQDA/fffj0mTJmHChAno2rUr8vLyMG/ePCQmJpY/xowZM9CqVStcfPHFGDp0KHr37o3p06cH5fshIqLwoKqrSUmyPXcOcHECVwoA1RqfmgrExla8fvBgICsLOHlS1mxXbfKdO0vbNlAxtNvMa1v+tc0oPHKBCu379wf3OIiIokVQJ6Lr378/NOPMMTZMJhOefPJJPPnkkw5vU716dcycOdMfh0dERH7yr38Bq1YBCxfK+ORAU6G9eXPgzz+lnfrECYCrfoYGFdrtVdkBCfK33AI8/jjw6KNSbU9JAZo2dT2016ghW3ZZVKRpgMnk+HrVCcFKOxFRYITsmHYiIopcb78NLF0K/PFHcJ5fhfa6dRneQpGjSeiMbroJiInRq70dOkiYtw3tmzfLtkED6/vzdbfPYgG6dQP69AHKyuzfRlXac3P1Wf6JiMh/GNqJiCigCgtlcjAgeJOAMbSHNldCe/36+oR0gLTGAxVD+5w5sh00yPr+1avL9uRJ74410vz0E7BmDbBsmeNAbpxzgNV2IiL/Y2gnIqKAOnZM3w+F0K5mEWdoDx2uhHYAmDhR3+/USbbG0H7oELB2rbR6GwM+wJM1jsyape+rk2u2VKUdYGgnIgoEhnYiIgqoo0f1/cOHg3MMasZwVtpDk6uh/ZJLJKSbTECvXnKZmj3+xAlgxgzZ797d8Zh2Vtp1xcXA11/rX9sL7ZrG0E5EFGgM7UREFFD+DO25ucB111W+frSqtNep4zy0FxUBc+dahxTyP1dDe2ws8PPP0srdpo1cVrUqULu27E+bJtsRIyreV7XH82SNbtEi65Z4e+/7c+dk4kaFM8gTEfkfQzsREQWUP9vjp08HPvkEmDrV+e3sjWk/frzi7d55R9qqO3cO3qR50cjV0A4ADRsCPXtaX6Za5A8ckO2ll1a8HzssKvriC+uv7VXabYM8K+1ERP7H0E5ERAHlr0q7pgEffljxOezdztWJ6Hbvlu2ePRIMp0+X+5N/uRPa7VGhXe2rKrwRJ6KzZmyNT0mRrb1Ku3ESOoChnYgoEBjaiYgooIyB2peV9j/+0Jf3On7ccbjOzZUWX6Dy0K4CXY0a0io/cSIwZozzkwLkPV+G9ksvtb/muHrdz56VwBrtFi6UQF63riz3BrDSTkQUKhjaiYgooIyB9/hx3wUmVWUHZMyto+Wq1CR0qalAcrLz2eNVaP/Pf4BnnwXi4qQa2bat9YRd5Fu+DO32xrMDQHq6HubV80UzNWv8FVcAaWmy7yy0160r2yNHZBlHIiLyH4Z2IiIKKOOYdkAP0d4oLgZmznT+PIqxNR5wXmlXYa5GDeD++2X96g4d5GTDFVcAu3Z5f+xUkbehvXlz2aal6VVjW7GxEtwBjms/flyfvHHsWJnMD3DeHt+okd5Gz8noiIj8i6GdiIgCyra13NNx7Xl5sg43APzwgwSvzEyZmAzwTWhXlXY1/rlTJwnubdoApaXAn396duzknOqS8DS09+oFPPmkTEpoNju+HSejE9OmAQUFwHnnydwNqalyubNKe1qa/rvGFnkiIv9iaCciooBSoT0+Xraehvb+/YH69YELLgCeeEIuu/ZaWcYNsA7te/cC//2vXOYotJ8+bb2UFVAxtKvjVmEl2sOeP5SW6tVcT0O7yQQ8+igwfLjz23EyOiA/H3jtNdl/8EH52blSaU9LAxo0kH1W2omI/IuhnYiIAkbT9DDdtq1sPZmM7uRJYO1a2V+zBti4Ufavvx6oVUv2jaF96lTgoYck6G/YIJepcG8Mhsbwpmn2QzvgfJk48o5xdnLVvu4vrLQD774r7/NmzYDRo+UyVyrtqamstBMRBUpcsA+AiIiiR36+PnN7x47AunWeVdq3bpVtZqZUB2fPlrXU27SxH9r37JHtn3/qLe2q0h4XJ+Hw9GkJb7Vr68dqsci+o9AezWHPX3bulG1KivPWdl9Qr2O0VtqLi4EXXpD9f/1LxvkDeqW9svZ49XvB0E5E5F+stBMRUcCo1vjkZH2yME8q7Vu2yLZDB2DSJOCXX4CXXpLL7IV2dWIgMVG/TIV2wH4IV0EuPl6O18jZjPPkGU0D3nwTuOgi+bpTJ/8/pwqd0fo6fvopcOCAdJ2MH69f7kp7PCvtRESBw9BOREQBo0J7rVp6aPak0q6q5W3aVLzONrRrmj5h3ddfyzh4AGjfXr+PvRBunMHcdp1vVtp969QpGX9+220yIdpFF0mg9Ldor7R/9plsJ02yPqHF9ngiotDC9ngiIgoYFaRr15bWdsCzSrs7of3sWWl1B2T5r02bgN27pT1fcVZpt22Nd3R78syWLcDIkfKaJCYCzz4L3HEHEBOAskK0V9rVUITeva0vd7U9XoX2gwdlAkHVXg/IEJb4eKBpU98eMxFRNGJoJyKigFGV9tq1A1dpV1X2tDR9XekuXazvY29iOVdCOyei886PP8p69/n5su73N99Yn0zxt2g++WKxyKoKgExCZ6Qq7ZW1x9etK3NClJTI73FWllyXlyerOiQlye+fv+cmICKKdGyPJyKigDG2x6tK+/HjMiGWq86c0YN469YVr3cU2uvVc/yY7lbaOabdeyUlwI03SmC/6CJZBSCQgR2I7vb4ffukOp6UZD2/A+BapT01VSrrariJcdm37GwJ7seOATt2+P7YiYiiDUM7EREFjLE9vkYNvQKXk+P6Yxhnjre3JJgxtGuaXslXJwns8aY9XtNcPnQyWLAAOHJEXq8ff9RPhARSNLfH79ol22bNKs7ZoEJ7QYGcXDEyrtMO2B/XfvCgvr9+vU8Ol4goqjG0ExFRwBjb400mz1rknbXGA3poLyqSKq4/Ku3q9iUl9quRVLmPPpLt1VfL2OdgiOZKuzG021Lt8YBUzI2MlXbAfmg/cEDf37DBu+MkIiKGdiIiCiBjezzg2WR0lYX2lBRp+QWk2u5ppV3NHm8vtCcl6c/Bce3uO31axq8D1kuNBZp63c+dk3/RxFloT0jQu2CMJ6U0zXoiOoChnYgoEBjaiYgoYIzt8YAepH1ZaQesW+RdqbTbG6Ouqq/Vqrl2n9JS4OabZa1xcu7LL6UTom1boHPn4B1H1aoykRoQfS3yauZ4e6EdsD8ZXV6ePhzEWaWd7fFERL7F0E5ERAFjbI8H/NMeD1iHdncq7a7OHm+8jwp7v/8O/O9/wNSpjp+HxIcfynb8+IrjqQPJZNJfX9sW+dJS4PHHgUWLAn9cgeCs0g7Yn4xOBfjYWL3TpEED2TqqtB896t6cFUREVBFDOxERBYSmed8en5enhwNfVtrVcRw9KhVgwP3Qro6LY9yd270bWLZMAvM11wT7aBxPRrdgAfDkk8B99wX+mPytpERmeAcqD+3GSrtxEjp1skVV2vfv16vwxtAOsEWeiMhbDO1ERBQQp0/rM1GrUO1upX3bNtmq2ecdUY9/5Ih+QsBZpb1mTSA5WfZV+HY1tKvqvFryqqCAM8rbs2MH8NprwHXXydcDBjg/kRIojiajW7NGtvbWKg93Bw7IOu0JCfqSbbZU+7u9SrtxojpVac/Pl5+hpunt8V26yJahnYjIOwztREQUEGo8e2oqkJgo++5W2l1pjQf00P7nn9LmbDIBdeo4vr3JBDRuLPt798q2stBuO6Zdhf2yMvfWnY8GX34JtGwJ3HknsGKFXDZxYnCPSbE3CSEA/PGHbAsLA3s8gaBa45s0AWIcfBJ01h6vJqED5Hc5I0P29+2TCRwLCuTrYcNky3HtRETeYWgnIqKAsG2NB9yfiM7V0K4CtQoLGRn6hGOONGok2+xsCd35+fK1q+3xqtIO6KGFxOLFsm3bFvjvf6XyOmZMcI9JcdQev3atbCM5tDdv7vg29iaiU+3xxko7YD0Znaqy16wJdOsm+6y0ExF5h6GdiIgCwnYSOkBvjz9+XNp1K+NJpR1wrQ3bWGlXy72ZTNZVRSOGdtep8dN33QU88ADQoUNwj8fIXnv80aN6+Izk0O5oPDvgvNLuLLSr8exZWUDHjrK/fXvFJfUOHQKef56T1BERuYKhnYiIAsJ2uTdAqpyqPdeV9c5VIGjSxPntVGhXY+idjWdXjJV2FeDS0x23Dzsa0w4wtNtSoV2dGAkl9irtqjUekNAeaXMUeBrajRPRGdkL7fXry+9djRoyRGXLFuv7PPMMcP/9Mu591SrPvg8iomjB0E5ERH6zcCFw4YXAV1/Zr7THxNhfbs0RdRtji709tte7W2mvbDw7YD2mPS/PulLL0K7TNH2egFAM7fYq7ao1HpDjd6ULJJy4Etrttcc7qrQbl31THQpZWdKp0qmTfG3bIq/WiT98GOjbF/jgA3e+AyKi6MLQTkREfrFtm4xb/uUX2b77rlxuG6hV+FWVeGdUaHc2c7y95/C00u4stBvb422XuGJo1+XkSLU6JkYPd6HE3kR0xtAORFaLfFmZLLsH+L49fv9+6/Z4QG+Rtw3tqjOlbVuZQ+LGGyv+3ImISDC0ExGRz505A4waJR/4VZVbfUg3VtoB6zXVnSko0MOTCvqOeFNpN45ndjW0G1vjAX0SO9Jb47OyALM5uMdiT2Xt8UBkhfZDh4CiInktVLC2x9lEdM7a49XvjlpKToV24wzymqavtvD110D//rK/erU730nk+usvYOvWYB8FEYUShnYiIvKpsjJg/HiZfKp+fQlA778PxMfL9bbVVldDu6qyx8cDVao4v21amnVAdKXSnp6uB5V162TrSmgvKJDv1YiVdl0oj2cHKrbHnzihB0o1n0Ekhfbdu00A5PVwtqKCJ5X248f13wV1QqBdO9kaf0dOnNAnpmvQAGjfXvbVzz2aaRowYADQubPrq2oQUeRjaCciIp966ingu++AhAQZy167NnDDDTLZ1Kuv6ms3K+6G9po1ZaysMyaTdTXelUq7ca12VWl1FtpTU/XQo0K+wtCuC/XQbqy0a5reot2smR5OIym0uzKeHXBvIjrjCa9Dh2SrKu1Nm8r26FH9sVQ4r1NH/k4YK/XRbutWWfWiqAjYsSPYR0NEoYKhnYiIfGbOHODxx2X/rbeArl316zp1AiZN0ivuiieh3RX21oOvjBrXvmmTbKtVc3xbk0mv0jK0O7Znj2xDNbSr17CkRCYUVCdszjsPSEyU/UgK7fv26ZV2Z9yZiA7Qg7eiQntamv47q8bSq+Ek6j4M7boFC/R9VybnJKLowNBOREQ+sX07cO21sn/HHVJdd4W7ob2ySehsHzchwXnF3EgFmeJi2VZ2P3Usajkr1U7N0K4L9Up7crIezpcv1yvtXbpEZmhX8y2kpzu/nTvt8YD1sJfateX3TlHVdlXlV6Fd3YehXbdwob7vyuScRBQdGNqJiMhrxcXAZZfJh/o+fYAXX3T9vqoKV1lVSU0U5m6lPTOz8nZ6RVXaFVdDu1oPXq0fH06h/b77gKFD9e/B10I9tAN6q/iQIcA338h+pIb2ggL5ZUhOdn47FdpdmYgOsK60qyq7on6+qtKuwrltpf3wYWkLj1ZFRbLahsLQTkQKQzsREXlt7VoZi5meDnzxhXuzhPu7Pd6V8eyKbbB0NbQrrVrJNlxCe1kZ8MorwI8/+me2aotFXwIslEP73LnATTcBsbH6yYtIbY9XlfbKQruqpufny/sEcL093nZWehXaHVXaa9UCkpJk33b5xGjy22/WfzsY2olIYWgnIiKvqQ/zjRsDGRnu3ddfoV0tLefqeHbA/Uq78VhiYvRwEi6h/fhxPaT6Y/zsgQMS+BISZNKxUJWVBbz3HrBtm8y78NprMp9BJIZ29d50tdIOyFj/0lLZAu5X2lV7vG2lXYV2k4kt8oA+nj02VrYM7USkOFnsg4iIyDXqw3xlS7HZo0L7iRMS8GIcnE52d0z7uHHAsmXAbbe5fiyetscDUtE3VifDgXFJKX8EBNUa36iR49c1lDRrJiscKJEY2tVSa5WF9sREWR2hpEROyqlqO+C7SrvxPg0bykkThnZg4EBg3jyGdiLShcF/oUREFOq8Ce2qWl1aCpw65fh27lbamzeXD779+rl+LKmp1kHdndDeoIEehMKl0v7XX/q+Pyrt4TCe3RkV2lXQjQSuVtpNJuvJ6FQ3TXy89SRzirPQrirtBw8Cp0/L8m+A9eR10V5pP3ZMX7ng6qv1y4iIAIZ2IiLyAW9Ce3y83m7r7EOquxPRecpYbXe25BsQ/qE9UJX2cA/tkVRpVxPRpaRUflvjZHTOJqEDZFiMWs7Rtj2+Vi15LE0DlixB+fMbf7+iPbQvWiTbjh2Bdu1kn6GdiBSGdiIi8ppaFsqT0A7oQdzZh1R3K+2eUgEzJaXimvK2jMcSjqGdlXbnIjO0y7aySjugt8EbK+32WuMBGf4wapT8HnTubH2dyaS3yP/8s2wbNrRe1SHaQ7uxNV4NGTp+XE50EBExtBMRkde8qbQD1h9S7dG0wIV2VWl3ZW1320q7ql6GS2j3d6V9zx7ZMrSHDndCu7E9XlXaHYV2APj8czlRY5zETlEt8j/9JFtjazzA0L5+vWz79NH/HpaUyHACIiKGdiIi8pqvQruj4Jifr6/f7OpEdJ5SAdOT0M5KuzVVaVfr14cbtQxZtIZ2FdBzc/VKu6P2eMXRhIOq0r55s2wdhfYDB2R+i2iiafokfS1ayJwB6sQHW+SJCGBoJyIiH1Ch3V6FzRWVhXY1nj0hwbWxuN7o0UO2HTtWfttwD+3eVNrnzpXl0dTJFFv5+fqEY6y0hw5PK+07dsi++l11l6q0K8aJ6wBZmlHNVm88mRQNjhyRv6ExMfrviqtLYRJRdGBoJyIir/m70m5sjTeOg/WH886TJanefbfy21avLv+Sk6WaHG6h3ZtK+wMPANOmAQsX2r9+717ZpqVVPqFfqIq00F5WBhQWyi+Qu6H9q69k/5JLPHtuVWlXbCvtsbH6BHbR1iKvquwNGugz87syzwcRRQ+GdiIi8logQ3sgZGUBZnPlt4uNldmwly2T710FoXBYp72szDq0Hzvm+qRXmqaPV1fh3Fa4j2cHIi+0FxfHlu+70rGi2uPXrQM2bJD3+8iRnj13ZaEdiN5x7Sq0G39GrLQTkVFcsA+AiIjCn79nj1eh3d/j2T3Rtq2+H06V9uPHpRVZsVjkdXQ20Zhy7Jj+PToKWGpiLePPJ9xEWmgvLNRDu/renFGV9m+/le2FF3r+O5iZKVVkNZzCtj3eeJmjE0GRaudO2TK0E5EjrLQTEZHXIq3S7qlwCu2qyl6rln7crgYEY6jav9/+bdaulW2XLh4dXkgIRGh/5hmgffvAhLOiIgntSUmOJ4wzUidwiotle/nlnj93TIw+rj0mRkK8rWivtDdvrl/G0E5ERgztRETkNX8v+aYmoguX0G6xyL9Qpiahy8x0PyCoWeEBhnZv/e9/MqP6/Pn+ew6luFgaLF0Zzw5YTyxpMsk67N5QoT0z0/7wk2gP7ay0E5EjDO1EROQ1X1ba7Y2rDrdKOwCcOxe843CFqrTXrVv5SRNbxkq7vYB19Chw8KAEvc6dvTrMoPJ3aNc0/eTJn3/65zmMVKXdk9Dety+QkeHd86tQaq813nh5NIV243JvDO1E5AhDOxERec1XS74VFemPZRTKY9qNEhL0tuNQb5E3VtrdnanaWGn/66+Ky76pKnuLFp6/J0KBv0P7mTP6+2TLFv88h5G7od04v8GYMd4/vzqB066d/euNod3VSRHD3bFjQG6unOBq0kS/nKGdiIw4ER0REXlF07yvtKekyDjbc+fkQ6pt0AuXSrvJJIEoLy/0Q7uqtGdm6q38nlTaAeDQIevAoUL7+ed7dYhB5+/QfuiQvh/ISrsrM8cD1r+Ho0d7//xXXSVLJPbsaf/6rCzZqr8DtWt7/5yhTlXZs7KsJwd0t/uFiCIbK+1EROSVc+dk+TDA89AOWFd7//xTxkJ/+aVcFi5j2oHwmYxOVdrr1vWu0g5UbGeOhPHsQGBD+549/h9SoWaPd7XS3rGjvD+uuQaoV8/754+LA4YNA6pVs399YiLQurXs//CD988XDuy1xgOVDxkioujC0E5ERF4xtrO7GgbsMX5IffJJ4I8/gKeeksvCpdIOhM9a7fYmonOlqldWpod0FTRsJ6P7/XfZMrQ7ZwztZWXA9u3+eR7F3fb4atXkGD/+2I8HZWP8eNn+73+Be85gsrfcG6D/ThYWhv7fEiLyP4Z2IiLyigrtKSmuLSPliPqQumUL8NVXsr9hA3DgQHiGdncq7R9/DHzyiX+OxxHjRHTuVNpzcmQMe2ws0KuXXGYM7ZEyCR3g/9CuTpwo/m6RLy52L7QD8jqaTH46IDvGj5e/I8uWATt2BO55g8Xecm+A/D1V7z+OaycihnYiIvKKt+PZFRXaX33Verm0zz7T14kO9YnoAPdD++nTwA03ANdfL/uBUFZmPabdnUq7Gs+elaWPYze2x0fKJHRAYCvtgP9Du7uV9mDIzASGDJH9aKi2O2qPN5k4GR0R6RjaiYjIK97OHK+oD6iq+ti+vWw/+EC2iYmhHTYUd0P7zp0SogPRHq2cOAGUlMh+nTruVdrVePZGjYAGDWTfWGmPlPHsgB7a/TXWXIV2NY7b3zPIFxW5t057sNx0k2w//FB/n0YiTXPcHg8wtBORjqGdiIi84utKOwCkpQHvviv7qvpYs2Zg23Q95W5oV5U2ANi2zffHY486MVKrFmA2e1Zpb9xYX6LLXmgP95njgcBV2gcOlK2/K+1qIjpXZ48PluHD5T2ZkwPMmxfso/GfEydk2T8AaNq04vUM7USkMLQTEZFXzp6Vrbeh3Theffx4oGtXPRTaXh/KVCDyJLQHqtJubI0H9HBw5ow+FMERR5V2NcN1JFbai4r8M4O3bWjftavimve+FA7t8QAQHw9cd53sP/ccsHJlZFbc1e9+/fqy5KUtd1d1IKLIxdBORERe8UelfeJEqaoPH65fFg7j2YHwqrTXrSvb9HSZWA6Q6l9uLnDFFcCMGRXva6y0168v++fOSZX+yBGZODASJqEDrIOUr8N0SYn8vADpSkhP9/8QiXAJ7YDeIr90KdCjh6zv/tprwT0mX3PWGg+w0k5EOoZ2IiLyiq9C+/nnS2Vz1CigbVu5bNgw/fpwqbSHU2hXlfaYGP2kyLFjwMyZwJdfAo8+WvG+xkp7QoIe/PfvB779VvY7dw7/SegAvdIO+L5FPidHqvdxcUDt2vp73p8t8p7MHh8sbdsCn38OjB4tS8+dPQu88EKwj8q3HM0crzC0E5HC0E5ERF7xVWivX1+qtZ9/rl924YV6wAi30O7q2srG0L5rV2DagG3b4wH953v8ODB/vuxnZ+vDHwCgtFQfv964sWyNLfKffSb748b557gDLS5OX8bQ16FdtcbXrSvP0aaNfO3P0B5OlXYAGDsWmD0b2LNHvt63Dzh1KrjH5EsqjKsTX7YY2olICfnQfvbsWUyePBkNGzZEUlISevbsiTVr1pRfr2kaHnvsMdStWxdJSUkYMGAAdqp+IyIi8jtfhXZAxoPHx+tfJyYCAwbIfp063j9+ILhTac/NlXXNAZkQzmLRK9n+ZNseD1jP3v/TT/rlmzfr+4cOyUkFs1m/rwrtq1YBv/4q+2PH+ue4A81k8t9kdCq0qxMnKrT7cwb5cAvtSnq6dHYAwIYNwTwS31In9hz97WRoJyIl5EP7LbfcgoULF+Ljjz/Gpk2bMGjQIAwYMACH/v7f7rnnnsOrr76Kt956C6tWrUJKSgoGDx6MQn9N9UpERFZ8teSbIy+8ANx2G3Drrf55fF9zJ7Tv3i3bWrX00BaIyehU5bJePf0yVWmfM8e6ur5xo76vxrM3bKiPgVeh/e23ZUx29+56wIoE/g7t6jVwpdK+ZAmQlSXVZ08UFsqSb6E+e7w9nTrJdv36YB6Fb6m/nY5eD4Z2IlJCOrSfO3cOs2fPxnPPPYe+ffuiWbNmeOKJJ9CsWTO8+eab0DQNL7/8Mh555BGMHDkSHTp0wEcffYTDhw/jm2++CfbhExGFtXnzpJr6/ffOb+fLSrs9zZoBr78OZGT45/F9zZ3QrlrjmzUDWrWSfX+Paz9zBti0Sfa7dtUvVwHhu++sb69uC1iPZ1fUDP+nT8s2UlrjFX+FdtXtoEK7GtO+c6fjGfy//x44eBD45BPPnjNcK+1AZIZ2VWlnaCeiysQF+wCcKSkpQWlpKRKNM8EASEpKwrJly5CdnY2cnBwMUL2TANLS0tCtWzesWLECV155pd3HLSoqQpFhGtjc3FwAgMVigcVi8cN3QqFMveZ87ckXIun99N57scjJicF335Vi8OAyh7c7cyYWQAySkkphsTi+XbRISDABiENeXhksllKnt92+PQZALJo0KUOjRhqAWPz5p34/f7yfli41QdPi0KSJhlq1SqAeulo1ORYVTgcMKMOiRTHYsEE/nt275TYNG+qXZWbK9wsAJpOGUaP0x4wEiYlxAEzIyyuBxeK7dd8OHJDfmzp15PemVi2gSpU45OWZsGOHBS1bVrzPX3/JfTZu1GCxuDf5gcViKZ+ILj7et99LILRrJ++zdevc/95DVV6evJ6JifZfj2rVAMCMvDwgN9did1m4YImk/+souKL9veTq9x3Sob1q1aro0aMHpk6ditatWyMjIwOffvopVqxYgWbNmiEnJwcAkGFTfsnIyCi/zp5nnnkGU6ZMqXD5ggULkByOp5/JJxYuXBjsQ6AIEgnvp19+GQggGZs2HcHcuWsc3i47uxuAOtizZyPmzt0fsOMLVTt21AfQBfv3H8fcuSuc3vaXXzoBaIiysu04dy4fwPlYufIU5s5dZnU7d99Pp0/Ho0oVC+LiKgaBTz5pDaAFGjY8gLlz15VffuxYEwDty7/u1WslFi3qiT/+KMUPP8yFyQQsXtwFQH0UFm7D3Lkyf8z+/WkA+gMA2rQ5gQ0blkfUuGOL5UIAqfjll5U4ceKEzx5348aeAGrh2LH1mDv3IACgWrULkZeXiq++WoOOHSuWV7ds6Q4gA3v2mDB79gIkJbkXXouKZEH4deuWIy/vtJffQWCdPJkEYBD+/FPDt9/+CLPZ8UmHQ4dSUFQUiyZNcgN3gB74669+ANLx559rkJh4tML1mgaYzcNhscTis88WIyPjXOAPshKR8H8dhYZofS8VuLjUjEnTtJA+1bp7927cdNNNWLJkCWJjY3HeeeehRYsWWLt2Ld577z306tULhw8fRl3DbDpjx46FyWTC58YpiA3sVdqzsrJw/PhxpKam+v17otBisViwcOFCDBw4EGazOdiHQ2EuUt5Phw4BjRvL8ffqVYbFix1XjAcMiMWSJTGYMaMEV1wR0v+lBMQ335gwdmwcevYswy+/OK+0X3xxLJYujcFHH5WgZUsN3bqZUbOmhsOHJYx58n6aP9+EMWNiccklGr78suLzq+d8660S3HST/np9+qkJ118v5/I7dtSwfHkJ0tPjUFJiwq5dFmRmAvXqxeHUKRMWLy5Br15y35MngTp15NhefbUU//hHZHVbdOsWh3XrTJgzpwSDB/vu/d2uXRx27DBh/vwSXHihPO6IEbGYPz8G06eX4IYbKj5X165x2LDBBABYsqQE3bu7fjwWiwV168YiNzcB69ZZytvxw4WmARkZcTh92oTVqy3o1Ekuy8mxnlCxrAxo1CgOJ04A69aVoEWLoB1ypdq0icOuXSb8/HMJeve2/1o2axaH/ftNWLq0BN26hc7f10j5v46CL9rfS7m5uahZsybOnDnjNIeGdKUdAJo2bYpff/0V+fn5yM3NRd26dTFu3Dg0adIEdf6eSvjIkSNWof3IkSPopAY/2ZGQkICEhIQKl5vN5qh8s5Dg60++FO7vp99/1/ePH4+B2ex4ChQ1LjM9PQ5h/C37jPo/99w55z83QJ+IrlWruPKJyI4fNyE311y+bjrg+vtpzx5g/HgZE71unanC8xcVAWoBlv79rV8vY/C55BITUlLMaN1axrRv3WrGoUOy3FaNGkCfPnHlE9HVri3j8Y8cAcaNi4XZHFvpcYYT1ZJcUuLb97ca096wof64an6AQ4fsP5dxbPOff8ahTx/3nrOoSAJ/Wpo5LH9XO3UCfvkF2LLFjK5dgUceAZ5+WuZhGDFCbrN7twR5AJg2zYw33wzW0VZOFdic/e2sU0eWUzx+PDT/vob7/3UUOqL1veTq9xzSE9EZpaSkoG7dujh16hTmz5+PkSNHonHjxqhTpw5+MqxNk5ubi1WrVqFHjx5BPFoiovC2wtDVfbRi16YVf09EF25cXac9P18Pbs2ayWRUWVnytSczyBcUAKNH6+tYHz9e8TZr18qEarVqoUIFUk16BQCDB8u2/d/d8hs3yqzyADB0qD5zPCDLoi1fLkvD1a7t/nGHOn9MRHf2rP57Y5zBX83Ef+BAxfuUlVn/Lhpn9XdFWRlQVBS+s8cD1pPRnT4NvPyyfD13rn4b45J5H3wQ2pO4VTYRHaAvdXnkiP+Ph4hCV8iH9vnz52PevHnIzs7GwoULceGFF6JVq1a48cYbYTKZMHnyZDz11FP47rvvsGnTJowfPx6ZmZkYNWpUsA+diChsGUP7qVNwOrGYv5d8Czeuzh6vll2rXl1NOIXyycc8mUH+tttkDevq1eXrc+cqHsOyv4fK9+4tYduoQQOpKteqBfTqJZd16CBbY2hXFU2j6tX19cYjjT9Cu1ruLTXV+mSXOmmz387UEKdOASWGIezuhnbj8Yfr9D2dO8t2/XrgnXf00GucQ8EY2gsLgTfeCNjhuUXT3AvtTqZqIqIoEPKh/cyZM7j99tvRqlUrjB8/Hr1798b8+fPLWwnuv/9+TJo0CRMmTEDXrl2Rl5eHefPmVZhxnoiIXFNUJBVZI2fzb6k1vVlpF66GduNyb4pa9s3dSvuJE8CHH8r+7NlAfLzs21bbly6Vbe/eFR+jenW5fskS/f4qtC9YIMdkNutV+Gjhz9BurLIDeqXdXmi3rbRu3CjBz1XG92MozULuDmOl/bXX9Ms3bZJOAkA6PgDgvPNk+/rrcgIr1BQX6ydhGNqJqDIhH9rHjh2L3bt3o6ioCH/99RemTZuGtLS08utNJhOefPJJ5OTkoLCwEIsWLUKLUJ51hIgoxK1bJx8oa9aUf4DjFlNNY3u8LV+EdmeV9nXrgCeftA6R6gN9jRpA//7662YM7WVl0sYOwOFY6C5d9GMA9NCuWu779dPH7EcLf4Z22+4EY2i3DeQqtDdpIidPcnPth3tH1PsxIUGzGt4QTlq1khNKZ87IEIJatYCEBPkblJ0tt1GV9n//W+YIOHYM+Pjj4B2zI8bhM85Cu1ogiaGdKLqFfGgnIqLAUq3x3bvrY5QdjWsvLNQrXAztQoV248/GHnuhXbXHO6u033sv8Pjjers6oJ9UUePS1SR2xtC+dauE7+RkvWJZmcxMvd0esN8aH+lUaPdltdZRpb1ePRm2UFhYsUtChfb69YHWrWXfnRZ5FdrDtTUekMBunPX+ttv0rzduBEpL9RNeHTsCd98t+6++GtjjdIUK7fHxcDrBHCvtRAQwtBMRkQ0V2nv00EOgo0q7qrID4R0GfMn4c3AW9OyFdjV7+IEDjlufVSVRBT9AD3iqwq62xmENqjW+e3fnIcHIZNKr7UB0h3ZfVtrVBIS2oT0hQQ9ptlV0deIsI8N6rgFXRUJoB/QTTvHxwD//qf8sNmyQmeOLiqT9v3FjYNw4ue7PP6V7KJS4Mp4d4ER0RCQY2omIyIoK7T176pX2ykJ7cjLCtuXW14zjhR21yC9dqreqG9vRVYgrKJAWYFunTunhzdj9oF4f29BurNb+8Ydse/as/HswUjPIt20rQSja+CO0qxM26iSNkaMZ5FVoq13b09AuMw+Ge2i/+GLZ3nyznMDo2FG+3rBBP6HVpg0QEyPXJyTICTB1oiRUqL+drob2nBz35jAgosjC0E5EROUOHpR/sbFA1656pd1RezzHs1cUE6MHd3uhfcsW4NJLpSI4ciRw/vn6dcnJ+kzyxkq6YmybN74mKpyr18teaFePZy8oOjNunLy+qtU42qjX0pehXU2Wpk6IGDmaQV6FdnuV9j//BGbNkveUI5FSab/6amDNGr3l3fizUD9X1TJvMjmfkT+YVKW9sr+dakz7uXP6pJ+uuP12YNgw6xUHiCh8MbQTEVG5NWtk2769VIAqa49XHyK53Js1R2u1HzwIXHKJrDHdsyfw6acVl15T1fbKQrvxNXHUHm8M7X/9JVt3l2br1Ute55tvdu9+kcLXlfZTp+R9AFiPz1YczSBvL7Tv2CGzo593npxcadECePdd+0s06qE9vMu1JpOc6IqTJefLK+179gArV8q+8efqbEb+YHK1PT4lRQ/2ro5rX7tWlrqbO9d6CTzyL01zfIKbyFsM7UREVE4FuyZNZOtqezwr7dYczSD/8MMS2Fq1Ar77zv7SW66Gdnvt8c4q7eq1rVvXte+BhK9DuwpRWVmAYTGccq6E9jp15DUuKwPuuEMq7MnJcp9bb5VZ/m0rrJFSabdVo4b+O7NggWzbtdOvD9VKu6vt8YDzyeiOHQPeftt6fhHjcniO/naT7z31lPx+GicJJfIVhnYiIip38qRs1ezjbI/3jKPQ/uefsn3mGf1nbMuT0G5babedPb6kRL89Q7t7fB3aVQu3MVgaOQrtxonojBMExsQA//mPhLOXXpL33ooVUm01UpMihusa7c6on4U6UWGv0m47R0CwuVppB5xPRvfYY8A//gFcf71e6f30U/16Vn4D58svZbtuXXCPgyITQzsREZVToV0t8+Xq7PEM7dYchXYVxFX1zx5P2uMrm4ju6FGpysbE6K8pucbXoX3TJtnaG88O2A/tmmZdaQeAyZOBPn2A+fOBhx6S99zkycDgwXL9L79YP25+fmRMRGePapEH5G+R+hkCod8e78rfTmeVdrUqxFdfATNnAu+8Yz1TPkN7YJw8qf9u25tElMhbDO1ERFROLRGmQjvb4z1jL7RbLHrwsl3qy8hRaC8t1WcdB2ScuaqeOpqITr2eqjU+I4Oz/LsrWJX2nBw9fJ09qz+/+p0cMQJYsgQYMMD6/v37y9Y2tKv3oiuV3XBjXJawbVvreSJCPbR70x6fm6t37wAyVGLaNNlXf0e4VFxgLF2qz+7P0E7+wNBORETlHLXHnzxpf3Irhnb77IV2tWRTXJwevOxxFNr37ZOxywkJ+jrrx47JYzqbiE7TOJ7dG74M7ZpWeWivWVOeU9P094AKXikplYc8FdqXLbP+nY2UiejsMVbabSf3U10todYe786YdtVdYRva16yR90mDBkCXLjLBZU6O3F5NHMlKe2D8+qu+z9BO/sDQTkRE5Wzb46tX16tWqmprxNBun73QrgJY3brSpu6Io9CuWuObN7fugMjP1wOlbaW9qEiuZ2j3nC9D+19/ye9YTAzQurX929hbpsy2Nd6Zdu3k9zYvD/jjD/3ySB7T3qKFnMwCHIf2M2fcC1M5Ofos//7gizHtq1bJtkcP4MMP9Z/BxIlA/fqyz9AeGEuW6PsM7eQPDO1ERFTOtj0+NlYPgPZa5Lnkm33qg7i90K4+TDtibGs1jk1Vob1lS+sJAlWVPTFRP1mQnKyHzePHGdq94cvQrqrszZvrj2uPbUu3O6E9Jgbo21f2jS3ykTp7PCDdKz16yL7aKlWq6H/PXK2279sHtGkj//zVVu+LMe1qibtu3eRkxUcfyTr2d9+tn9hjaPe/M2esJ5/LzQ3esVDkYmgnIqJytpV2wPkM8qy022dvnXYV2p2NZwfkJIlqf1dhGwC2bZNty5bWH8iNy72prgiTybpFnqHdc/4I7Y5a4xXb0G6cOd4V9sa1R/JEdADw8cfATz9VDO2Ae8u+lZYC110HnDolJyUffti3x6l4O6Zd0/RKe/fush07FpgxA0hPZ2gPpOXLZaJPhZV28geGdiIiAiAfAm3HtAPOZ5BnaLfPXnu8arWtLLTHxACZmbJ/+LA+o5ax0m5sj7cdz64Yl31jaPecP0K7o5njFUeVdmdzIRjZG9ceyZV2QDpYLrrI/nXuLPv23//KpGLJyXLya8YMYPVq3x2n4sk67UeO6OFw3z4J5GYz0LlzxfswtAeOGs9+3nmyZWgnf2BoJyIiAFJVKi2V/WrV9MudzSDP0G6fszHtlYV2422M49odtcfbLvemGGeQZ2j3nC9Du1oSyt1Kuzvt8YCcFKhWzXpcuxrTHokT0VXGlRnky8qAhQuBxx+Xr994Axg/XvbvvlufGdxX3Km0q7/BJSX6iVXVGt+xo/2hFuo+BQXWHT/ke2o8+4gRsmVoJ39gaCciIgD6ePakJOvJqtge7z5fhXZVac/N1YO3bXu87XJvCtvjfcNXob2sDNiyRfYrC+22M567G9pjYoB+/WRftchHeqXdGWeh/Y8/gKFDZUjQoEFy4nLcOAns//mP/Lx++w2YNcu3x+TOmPb4eH3Iknov2LbG26pSRX/vstruP/n5wO+/y74K7QUF9ldbIfIGQzsREQGw3xoPsD3eE74L7bLduVPCe0aG9XhVZ+3x6uujR/WxsAzt7vNVaM/Olmp3QgLQrJnz2zZpItsdOySkuRvagYrj2qM5tDtb9u3RR4Eff5TqaHIyMHo08Oab0hqfmQk88IDc7qmnfHtM7lTagYrj2lVo79bN/u1NJrbIB8Jvv0kHRIMGQIcO+uWcjI58jaGdiIgA2J+EDnAe2jl7vH22od245rYroV3NMH/okIR1Y2s8YL893lGlfft2veqjPviT61RoLy62nmzKXao1vk0bWZXBmSZNJIxZLNKm7UloV5X25culelxQENkT0TnjqNKuafp49S++kOA+e7b18KBrr5Xtrl2+bZF3Z0w7YB3ai4v1YQ+OQjugv18Y2v1nzRrZ9u4t8wuoLjWGdvI1hnYiIgJQcbk3xVm1hpV2+9QHcfXB7fRpfUyxJ5X2bdskcKnQbq893lGlXYXFGjWkzZbcYxwv7E21fccO2Tpan93IZALuvVf233hDH97gTmhv104C+tmzcuImmivtKrQfPGh94mXfPvn9MZultTkuruJ91cmwwkLrzhlvudMeD1iH9g0bgKIi+VvtrGtD/Z2wXd892h096rv29exs2bZoIdu0NNlyXDv5GkM7EREB8KzSztBun/ogvXmzdZW9enXr+QIcsR3TPm+e/Hd9wQVyubE93lGlXQ1z2LVLtmpGenKPr0K7OinmavC+7DKgUSMJlSosujp7PCAB9PzzZX/1av0xkpKibyK6unVlnL/FYh1gVZW0QwcZtmBPlSr6dfb+BnrK3fZ49b45dAh45hnZ795dX+bRHrbHV/TLL3ISZ+xY3zyeCu2NGsmWoZ38haGdiIgAuD+mXdMY2h3p1ElaoHNy5EO2O63xxtsdOgT89VcK1q83ITYWGDVKLlevyblzwN69su+o0q4qixzP7pm4OL2d3ZvQ7uikmLPnvesu/ev4eD0QuEqd5Fm1Sg/trobESBIXp/9OGVvkVWt8166O72sy6b9vqqvFW5rm+Zj2N94Avv5a3g8PPuj8PqEW2ouKfD8LvztOnQKuu06OQ82+7y3195ehnfyNoZ2IiABU3h5/8qR1S+G5c/oScQzt1pKT9bW416xxP7SrqnhhoQkLFjQEAFx4oR7EU1L0ir16bEehXWFo95z6WfsitNueFHPm5pv1EJCR4byqao+90B6N7fGA/XHtqtLuLLQD+u+Sryrt587p4dXd0F5UJF0Dn34K9Onj/D6uhvY1a6TyrIbj+MOZMzIh4MiR/nsOWydOAFu36j/r22+XIRKA/EzU/1+eKi3V30+NG8uWoZ38haGdiIgAOK4EVq+uhwUV7AFg927ZpqVxIjp7VBBYvVr/oOhqaE9M1MOdCu1XXKFfb6z+KY4molMY2j3nixnk3a20A/J7NWGC7HsyiaCapGzDBkDTonciOqBiaC8tBdaulf3KQruzIUKeMK6b7urrYRzeMn26zHJfGVdD+913y0R8M2e6diye2LZNfn4LFwam2q5pcqKzTRsZ/vCPf8iJjthY+ftZVub963n4sJzIjovTX5/UVNkytJOvMbQTEREAx5XA2Fj9MuOHHLXmdNu27lcAo4EKAp5U2o23zc+PR2yshssus77ednyzbRi0fR0Z2j3ni9DuqJOlMg88IFXQylqh7cnKkgq9cfI1V+ZUiES2y75t3y7De5KTK58cUJ0A81V7vArtSUmVrySg9OsnwfPDD6UDwxWuhPZDh2SFAUAf7uQPaqWRwkKZmNPf/vhDn4Rz82bg7bdl/7HH9PkB1ASPnlKt8VlZ+iSGqtIeqbPHHz8emNePKmJoJyIiAM4rgfY+/P35p2zbtvXvcYUrFdp//939Srvtbfv31ypU0o2hvXr1ijNfJydbV/EY2j0XrEo7ICdfPv/ctcqqLZNJb5EHgLi4MpjN7j9OJFCV9n37ZKta4887z/6s8Ua+rrS7u9wbIDPcv/kmMH686/dxJbR/+aW+r1a48AdjiPVnG76ivq/hw4HXX5dJ+8aOBR5+WP9b6KvQrlrjgchujy8slBNcnToFd26CaMXQTkREAJxXAo3rgiuq0t6mjX+PK1y1bSth78wZvZLlaWgfM6biAuHGEG/bCm/vcoZ2zwUztHvLuI53fLyXg3jDmPo7NX++tMi7Op4d8P1EdO5OQucp4yoTZRX/hACQtnjFn6FdVdoB/4d2TdND+3XXAbfdBqxYISe/4uKsl8/zhu0kdEBkh/a//pLfgX37rF9PCgyGdiIiAuA8VKhKgqquG/dZabfPbAY6d5Z99QGufn3X769Ce0xMGUaOrFjWMFbaHYV2Y4s8Q7vnvA3t587pgSjQod1YaU9IiN7Q3r+/TNx27hxw333uhXZfT0Tn7hrtnjKuIKH+vhsZW+OByKm0b9okS10mJgJDh1a83leVdtvl3oDIDu3GtvhTp4J2GFGLoZ2IiKBpzme37t5dtitWyLaoCNi5U/ZZaXfMNhC4U2lv1Uq2HTocr9AaD1iHdnvXA6y0+4q3oV19wI2N1SeqChTjezAxsSSwTx5CTCZg2jSZef2LL2TYChDZlfb4eKBaNdk3rk+vzJ5t/XWgKu3ehuXKqCr7JZfYPzHC9njPGIN6sEP7l18CS5cG9xgCjaGdiIiQm6svf6M+5Bmp0L56tdxu507ZpqVZz2pM1qyrnO4t93XFFcC775bgrrv+sHu9O+3xqanRO2u4L3gb2tUJsWrVAj9pY3o60LKl7EdzpR2QWcRvv132y8rk9WjatPL7+brS7smYdk+pSdfsjWtXrfHq/REp7fHqZMSYMfav92d7fCTPHm8M6vY6NwJlxw75/3HUKMfDPiIRQzsREZX/B5yUZH926Xbt5APm2bOy7q1xPDtnjnfMWMXLzHTvZxUbC4wfr6FatSK717tTaWeV3TvehnY1X4Q7J218SZ08ivbQDgBPPqn/vpx/vmu/k/5a8i0Qod3RZHTG1vhrr5VtJLTHb90qQ7fMZmDECPu38UWlvaSk4hrtQGTPHh8qlfbVq2V78iSwZ0/wjiPQGNqJiMhpazwgAVJ98F+xwnq5N3KsWTP9Q5w7rfGucGVMO0O7b/iq0h7o8eyKmowumtvjlfR0aZNPTASuvtq1+6jfo1OnJKx5K1Bj2gHHoX3WLBkW1bMn0Ly5XFZQ4L/jCFSlXVXZBw7U//ba8kVoP3RIus3MZuu/r2yP978/DM1nGzYE7zgCjaGdiIhcChU9esh25Up9EjqOZ3cuJkaqeYDvQ7uxuu6o0q7aXnlyxTvhHtqvvRa47royjB69MzgHEGLGjpUW9RtucO32NWroFXnVNeGNYFfaNQ145x3Zv+YavbsqEtrj1Xj2yy93fBvVHv/XX54vXaZa4xs2lL/zSiSH9lCZiG7dOn2foZ2IiKKKs+XeFONkdKy0u65vX9mqieV8xZUx7ZdfDixeDPz3v7597mgT7qE9LQ14771SdOzoo5nUIkBsrHu3Va+dLyajC+SYdnuhfelSaSNPSZETOoEI7bbt8f5Y53vXLglxsbHApZc6vp2qjBcW2m9jf/55OeF58KDjx1Azxxtb4wE9tJ89G3njrf0xpj0vz733gqZZh/aNG31zHOGAoZ2IiFwKFSq0b93KmePdce+90op6772+fdykJKBqVdl3FNpjY2Wpq0C04UaycA/t5D1fTkYX7Er722/L9qqrrCepDFSl3WLxTceCLdUaf9FFzuePSErSw7W9Fvnp02WyM2ezk9ubhA7QH1fTIm8tc1+3x+/ZIyefJ0xw/QxadrZ1FwMr7UREFFUqG9MOyH+uaqbl0lL5sOfrlu9IlJIiM92qgO1LN98sJ1M6dPD9Y5OOoZ18uexbMMe0Hz+ut5BPnCjbQFfaAf+0yKvvy9Gs8UbGFnmjggJg927Zd9bi7ii0JybKUnuV3T8c+Tq0r14tf1OXLq04G6Sjx1fj2dVnkb17I+/n7AhDOxERudQeD+jj2gHOHB8KXnpJhiuoUEn+oX6+noYahvbw58tKezDb4z/8ECguBs47T59vI5Bj2tWwBBXaDx0C/vlPPQR7at8+4PffZXz5qFGV3161yNsu+/bnn3q7tnEMty17a7Qratm3SJtB3teh/cgR2drOLfD881JAePHFivdRrfEXXghkZcl+tLTIM7QTEZHLoUK1yAMcz07Rw1dLvjG0hy9fLvsWjPb43buBSy4BXnlFvv7HP/TbBDK0N2kiWxXan3kGeOst4NlnvXt81Rrft6++Nr0zjmaQ37xZ33dWwVVj2m0r7UDkTkZnPInhizHt6oTJuXMmnDsXV375kiUS4u+7D/jqK+v7qEr7eecBHTvKfrS0yDO0ExGRS+3xQMVKO1E08FV7fLDWaSfv+aM9PhChvVkzoF8/CUHz5wMHDshQnauu0m9jDO3+mCCurEwP7WpFCxXaV62SrVqRxFVHjwLXXy/j88vK9NDuSms84Lg9ftMmfd9Rpd1i0Sepi6bQ7q9Kuzye3i526JBsNU0mSlyzRv9ahfbOnRnaiYgoCrlaaW/fXv+Ax0o7RYv0dNnarnXtKrbHhz9P2uNXrgR++63i5YEc0x4bC/zyC7Btm1S1L75YhtUYn1v9TQc8PzHljPp+AevQXlioB66tW917zCeeAD76SDoGevTQf86jR7t2f0ft8a5U2g8elBMFiYl6+DeKxNCuad6F9t9+A77+2voyY2g/eTKhfF+d0GnTRk4kXXqpBPm//pK/wTExMo+LCu1sjycioqjhavuu2QxMmSJjBvv18/thEYWE1q1l6241UGFoD3/uVtp//x3o1Uv+vfyy9XWBHNOutGwJPPggsGiRTGBpZAzt/miRV2O7Y2L0CcQOH5bAbrHI18eOuT6j/IkTwAcfyH5iokxoBgA9ewKZma49hivt8Y4q7Wo8e4MG9ud1icTQnp8PlJToX58+7fqSdkVFwJAh0gVhnLvAeMLk9GmptFss+snR776TQkFODjB+PLB2rVzeqpWseKBC+6ZNMjlupGNoJyIit0LFv/4lZ8w5+RlFCzUU5OBB9z+IFxXplUaG9vDlTqXdYpFgrELN3XcDjz+ut54Hsj3eFWYzEPf3kGJ/hHbVGm9cceTwYb3tWdm2zbXHe+stOc7OnWVpthEjJDzfdZfrx2SvPf7kSetZ7R2FdnWb+vXtXx+JoV1V1mP+To6a5vpEe0uXym01TZ+ZH7Btj5dKe06O3M5slkn+vvxSAvrPP8vvESDj2QE5AZScLO+FXbu8+ObCBEM7EVGU0zSOuSVyJj1dr+C5W21XH3ZNJv3DPIUfdyaie+45admtWVOq2wDw5JMS3IHAtse7yp+T0anQXrWq/nt0+LBeIVdcCe1FRcC0abJ/770yg/h338lxjx3r+jHZq7Qbq+yA49CtKsT2WuMBffb4SArt6gRGjRoSlAHXJ6P78Ud9/8AB2ZaV2bbHSxVAjWevW1dOELRoAbz2mlymAn/nzrKNjQXatZP9aBjXztBORBTlcnP11rJq1YJ7LEShSs3h4G5oVx9sq1XTq1QUflSl/fhx55O1bd0qAR2QmdqfeUafsf2FF6w7L0Kl0g74N7Srimxqqh7ac3JkuUoAaN5ctq6Ma//0U7lvvXrWIT0hwfF97FGh/dQpeU0APbSrY3RUaVdh01FoVyfnImnJN3XyMT1d/5zg6rh2Y2hXE/idOmXbbi+hXXUxqI4MALjxRuDyy/WvVWgHomsyOv73QUQUpf7v/4ABA4Dhw+XrpCTrsY1EpFOhfcsW9+7H5d4ig6q0FxfrlWNbRUXADTfIbYYN02donzRJQmJBAfDrr3owjpbQbqy0164tJ69KS/WW5uuuk21llXZN09fuvvNOaaH2VLVqQHy87KvKuZo5vndv2ToK7er2jpaWi+T2+GrV3Avt+/ZZn4xRlXZjlV0eS866qEq7cW4CkwmYPl1O7mRkAOefr1/H0E5ERBGtsBC4/37gp5+AZcvkshYtgntMRKFMjWt3N7RzErrIkJystwXbm4xO04Dbb5eW7/R04M039UnKTCZZIx3QlyYDQjO0FxT4/rGNoT0uzjrsNm6sh+TKKu0bN0qwTk4Gbr3Vu2MymSqOa1eVdnU8BQX6RHlGlbXHR3poV3/LXAnt8+ZZf60q7baz9qsl3+xV2tXzbtgA7Nkj7yOlfXvZuvt3ORwxtBMRRaETJ+RDZmwsMGMGMHMm8MMPwT4qotDlbXs854sIf84mo3vjDeC996SK/PnnMtbaSIV2teyVyRRanU2Bao8HrKuoF1ygr86Qne18yTkV+Fq18s1QLuOyb5qmh/ZevfTb2Ave0R7a1c/elTHtqjVenQixrbSrDhYV2u1V2pWkJP3EmaL+Lu/dq6/KEKkY2omIopCxZffqq6WN0/bMNhHpPJ1BnpX2yOFo2belS4HJk2X/2WeBQYMq3nfAAAn0KvCnpNhfLixYAtUeD1QM7RkZ0p2gacDOnY4fR/3c1evgLWOl/dAhaYePjZUgqCYJtNci7+qY9kgK7ern4E57fHGxdPMBwIQJsrWttHfqJNuzZ+NRVKSHdlc/j9SoIUMuANdXHwhXDO1ERFGI1T8i93g6gzxDe+RwVGmfOlUm1brqKpnR3J7q1YHu3fWvQ6k1Hghepb1rVzl50aqVfO2sRV6FdvU6eMs4g7yqsrdoIZPapafL17bBu6REf/05pt35fZYvl+p37dqyLJ+6T36+fuKjdWsgPl5mdjxyRG+Pt1dpd8TTLqhww9BORBSFODkWkfs8+XDI0B457C37VloKrFwp+w8+6Lx6rlrkgdAL7artOJCV9pgYfc1t1SLvrFrqr9D+9tt6p4QaI62Ct22l/dgx6QiIiXF8HOrkRKTOHu/qmHbVGn/JJXI/9fofPGjdraA6FnJyTG5X2gHP5xsJNwztRERRSIV2VtqJXGc7g3xuLrBqlfP7MLRHDnvt8Vu3SihNSdHfH44YQ3sordEOBKc9vl07/eSFK5V2dbLEV6H9ggtke/QosH277HftKltVabcN7aqtu3ZtaaW3x1hpd7Y8YDjxZEy7WtJvwADZqnkeDhywnoE/I0N+SDt36u8VdyrtKrRHeqU9LtgHQEREgcfQTuQ+Y0XHYgH69QPWr5dKa7du9u/DrpbIYa89XlXZL7jAcYhTunSRxzh+PPQq7YFsjx86FOjTRx/nDLhXaffVmPZLLpGguHu3VH5LSoArr5TrHLXHVzYJHaCH9tJSmYE+1F5rTxjHtKt17SurtKu/fSqs168vwdpRpX39emlTqVrVeob4yni6HGe4YWgnIopCDO1E7jO2x7/4ogR29bWj0M5Ke+RQYdG4xrQK7cbx6o7ExACDB8uKHaEW5AK15BsgVdQlS6xvoyrt27cDZWXys7Ll6/Z4AGjWTP7ZctQer157R+PZAXltY2MltJ85E3qvtSeMlXZXQ7uxpR5wXGmvW1cq7WvXSmh3d1JcdTJ1714ZLx8JP2972B5PRBSFOBEdkfuMM8g/8YR+ub11uxWG9sihxjsvWaJXpN0J7QBw+eWybdLEt8fmrUBW2u1p3BiIj5fn37/f/m183R7vjDeVdpNJ/14jZTK6yiaiszcMwHgfQCrtgLy+R4/Kvr1Kuzut8YCcTKtVS44hkmeQZ2gnIopCbNklcp9xBnnjetKuhHaeIAt/F1wg1cK8PGD+fAlkahyto04LWyNHAr//Drzyiv+O0xOBHNNuT1wc0Ly57G/caP82vm6Pd8ZRpd2V0G68fySGdtuJ6Naskdfk7bf12587p1fkVWhXlfYNG6QLAZC5AerUkcSfl+dZpR2IjnHtDO1ERFGI7fFEnlEt8omJwA03yL6j0G6x6IGFJ8jCX0wMcMUVsj9rFrB6tVT3Gjd23i5tZDLJ2PbERP8dpyeCHdoBvVvhrrushyAAMt5chcRAVtq9De3OTuiFi8JC/SRleroews+ckfD92WfymeL77/X7qNcqJkZ/3VWlfcMG2VavDpjNFX+W7lbageiYQZ6hnYgoCjG0E3lm4EDZPv20HjIcfTA3to+qEEDhbexY2X73HbB4sez36BG84/GVYLfHA8B//yvjy/fulXW9jePrT57UW7ADcQLMUXu8K2PaAZkZH5AqdLhTJy5U27/xb9np0/os8epzhfE+6en6Moiq0l5cLFsV1tXSe4onlfZoWKudoZ2IKAqxZZfIM/feKxMp3XOPXvFzFNrV71l6euUzi1N4uOACoEEDmfBq2jS5zNXx7KHMX+u0a5oMJwAqr7TXrAnMnSv/L61ZA1xzjR7U1e9Y9erSSu9v3rbH9+4t22XLfHpYQWGcUC4mRqrjasnCI0eAtWtl3xjabcezA3qlXVEnPlR7vMJKu30M7UREUUbTGNqJPBUTo3/4dBTaT5+WMc/Tp8vXbI2PHCaTXm1Xbd+RENr9VWkvKJDZ4IHKK+2AjGv/5huZlO6bb/RA6I+Z453xtj2+Tx/Zrlwpw2TCmb0ArvZ//lmvnBvXbbd3n9RU6/eA+hnWrg2YTHpw96bSnp3tnxUQQgFDOxFRlMnNlfGBAMMEkTfshfaCAqBFC1kD+qWX5LKGDQN/bOQ/alw7ACQkAB07Bu9YfMVfS76p1viYGL2aX5nevfWJ/XbulG0gZ44H7LfHFxbqIb6y0N66tQTWggJ9achwZS+Aq88Oc+fql508qZ+gsXcfwLrarirtZjOQmlpcfrknlfZataQIEckzyDO0ExFFGdXClpSkf1AjIvepAHHqlD4b8q5dEjDi4yXcTZ0KvP9+8I6RfK9rV/1ETJcu8lqHO39V2lU3QpUq+thmVzRqJNu9e2Ub6Eq7vfZ4tUxZfHzlc1TExAC9esn+0qW+PrrAsl1vHdDDuJrXAZDArk5yGMe0G6lx7YD1vADVqunLcdiOcXeFyRT549oZ2omIogwnoSPyDVVt0jT9g+3hw7Jt3VpmGH/kEVbaI43JBIwfL/sDBgT3WHzFX6Hd1UnobKnQvm+fbAO53BtgXWlX1WPVGp+R4doJCNUiH+7j2lUAt9ceb1z6EtBb5F2ptBu7FdLTZX242rWl8u6JSB/XztBORBRlOJ6dyDfMZv3DvQoVKrR70uJJ4ePRR4E5c4CHHgr2kfiGvyvtlU1CZ8u20h6s9njjRHqujmdXjJPRaZrz24YyZ2PaAekqUJ8nVFHAUWivrNLuyXh2RYV2tscTEVFEYKWdyHdsx7X/9ZdsPWnxpPBhNgPDh4feeuue8ndo97TSHqz2+MREfdiDqjQbK+2u6NJF5jw4dkwfmx+OKgvt7drpYbyy0O6o0l69uoR2b052tmol261bPX+MUMbQTkQUZdR/qpyEjsh7tqGdlXYKR/5a8k21x3taad+3T6rUgW6PByrOIK/WaHe10p6QoE+o58m49q++Au68M/izzzubiA4AevSoWGl3d0x7s2Zyh65dPT9OFdp37w7+z8wfGNqJiKIMK+1EvqN+jxjaKZyFWnt8/foybrywUCaAC3R7PFBxBnl32+MB79ZrnzwZeO014Ntv3b+vL1VWae/eXf87WNmYdmNoN56A6dHjL+zcacGjj3p+nPXrAykpsjrO7t2eP06oCunQXlpaikcffRSNGzdGUlISmjZtiqlTp0IzDAzRNA2PPfYY6tati6SkJAwYMAA7w7kHhYjIzxjaiXzHUXs8QzuFE2No9+X4a08noouP18c3790b+PZ4oOIM8oEM7bm5wIEDnt3X1+xVzY1h3F6l3VFob9FChpX8858VJ5xr2FDGx3vKZIrsFnmPfzS7du3C/Pnzce7vU3KaH2ZYePbZZ/Hmm29i2rRp2Lp1K5599lk899xzeO2118pv89xzz+HVV1/FW2+9hVWrViElJQWDBw9Goe10hkREBIAT0RH5kqP2eI5pp3BiXP7Tlx+hPa20A9bj2oPZHm9baXd1TDsA9OwpYXLXLn3JOFcYly0Ldmh31h5frRrQvLn+dWWhPTZWJnB84w3/HKsK7ZE4GZ3bof3EiRMYMGAAWrRogaFDh+Kvv08p33zzzbj33nt9enC//fYbRo4ciWHDhqFRo0a4/PLLMWjQIKxevRqAnCh4+eWX8cgjj2DkyJHo0KEDPvroIxw+fBjffPONT4+FiChSsNJO5DvG0F5Wxko7hSdjaPdli7ynE9EBemj/80+goED2g1lpd3dMu3qMJk1k3531w43Llq1bp/8cg8FeAO/ZE7j4YllFwd7s8Y7GtPtb69ayjcTQHufuHe6++27ExcVh//79aK1+MgDGjRuHe+65By+88ILPDq5nz56YPn06duzYgRYtWmDDhg1YtmwZXnzxRQBAdnY2cnJyMMCwSGZaWhq6deuGFStW4Morr7T7uEVFRSgqKir/Ovfv3h2LxQJLJM5cQE6p15yvPflCOLyfjh+PBRCDtLQSWCxhvA5NFAiH91O0q1bNBCAOx46VISenFCUlZphMGqpXLwmpyZD4XqLKxMbGobTUhNxcS6WVcVffT6dPy/83ycmlsFjK3DqerKwYALH4/fcyADEwmzUkJgbu9yo1VY79xAk59pycOAAm1KhhcesYWraMxe7dMdi8uRS9ern2M9i8Wb53QE4GLl9egosvDvz/15oGHDki33e1avr3nZAA/Pij7FssQFqa/B08caIMBQWlyMuT3vcqVSr/Wfnyb1OzZnIcf/5ZBoul1OvHCwRXv2+3Q/uCBQswf/581DfO2Q+gefPm2Ldvn7sP59SDDz6I3NxctGrVCrGxsSgtLcXTTz+Na665BgCQ83efSoZNn0pGRkb5dfY888wzmDJlSoXLFyxYgGQ1fSZFnYULFwb7ECiChPL76eDBAQBSsH37bzCZTgX7cMgFofx+inZ799YB0A27dp3GrFkbAFyI1NQiLFw4P9iHZhffS+SI2TwMpaVxmDfvV9Stm+/SfSp7P+3c2RVAJvbv34K5c7PdOp7c3AYAOmP58mIAiahSpQg//hi436uTJ9sAaI716/dg9uztyM8fDgDYsGE+duxwPRDGx8vjzJ+/F1lZm126z6+/dgeQAbO5FBZLLD74YBeKira7/0146cyZeBQXDwEArF//I7ZssX/iYM+eDADdsWdPLr78cgUAuc9vv81FbKxrz+WLv01Hj1YFcBG2bCnFDz/Mhcnk9UP6XYFqI6mE26E9Pz/fbrA9efIkEhIS3H04p2bNmoUZM2Zg5syZaNu2LdavX4/JkycjMzMT119/vceP+9BDD+Gee+4p/zo3NxdZWVkYNGgQUj3p36GwZrFYsHDhQgwcOBBm21kxiNwUDu+nc+fkT/+wYT3QsmWQD4acCof3U7RLSzPhv/8FSkuroWnTPgCARo0SMHTo0CAfmTW+l6gyVavGorAQuOCCfmjf3vltXX0/vf66JLbu3dtg6NDWDm9nT0KCCa+/Dpw5kwgAqF8/sL9X69fH4JtvgOrVm6JOncYAgGrVNIwZM9itxzl61IRvvgEKCxtj6NAG5ZcfOSLt4/bi06RJ8v/0FVeYMHMmcOxYCwwd2tTD78SxrVuBESPicN99ZfjHPyp2AaxfL9uMDA0jRw5x+DjVq5vw9NNAaWkaunSRDujUVA0jRlT+evnyb1NREXD33RrOnTOjc+ehYTFMSXV8V8bt0N6nTx989NFHmDp1KgDAZDKhrKwMzz33HC688EJ3H86pf/3rX3jwwQfL29zbt2+Pffv24ZlnnsH111+POn8PKjly5AjqGmZ8OXLkCDp16uTwcRMSEuyeYDCbzfyPLIrx9SdfCtX3k8Wiz+Zbp465wuytFJpC9f1E+vjW48dNOHpUPlbVq2cK2deL7yVyRNXkSkpc/7+hsveTGotdrVqc2//fNGtm/XWtWoH9vVLjtHNzY/D99zIN2NCh7h9D27ay3bYtBmazPM4ff8ga7uPHA++9Z31748zx//hHDGbOBFaujIEMEfD0u7Fv6VJg/37g449jMWlSxZK4Gsdf2d801fR88qQJ+flyu/R0935WvvjbZDbLHAI7dwK7dpnRsKFXDxcQrn7Pbk9E99xzz2H69OkYMmQIiouLcf/996Ndu3ZYsmQJnn32WbcP1JmCggLE2Mz9Hxsbi7IyORPUuHFj1KlTBz/99FP59bm5uVi1ahV69Ojh02MhIooEakIZk6nirK5E5D41MdaZM/LhF+AkdBSe/LFWuzcT0WVlwaq9OZCT0AH6JGqnTwNqfutRo9x/HDWj+cGD+s/ju+9kPfG5cyveXi1XVrcu0KuXHEdBgV719iX1mWDLFhk7b+vQIdmq5fcc0U9wAMeOyX6wPmNE6mR0bof2du3aYceOHejduzdGjhyJ/Px8jB49GuvWrUPTpr5t2xgxYgSefvpp/PDDD9i7dy++/vprvPjii7jssssASJV/8uTJeOqpp/Ddd99h06ZNGD9+PDIzMzHKk98qIqIIp2Z2TU+Hy+PMiMixatX0YLH57+GqXO6NwpEK7S4OsXWJ6uzyZMm3hATrE2CBXO4N0GeP//13YPduOZ5LLnH/capXB2rXlv3tfw9LX7lStjk5eshV1MzxbdvKzOy9esnX/lj6TYX2ggLA3tRkrob29HT97+Du3bINVmiP1GXf3G6PB2SG9n//+9++PpYKXnvtNTz66KO47bbbcPToUWRmZmLixIl47LHHym9z//33Iz8/HxMmTMDp06fRu3dvzJs3D4mJiX4/PiKicKNCu1pTlYi8Exsrv08nTgAbN8plrLRTOPJnpd2T0A4ADRvqwTGYlXYAGDgQqFLFs8dq3VrWad+6FTjvPD20A8CmTcBFF+lfq6Xh2rSRbZ8+wA8/SGi/+27Pnt8R9b0BctKxcWPr610N7bGx8vM6dUrWpAeCH9pVx0KkcLvS/v777+OLL76ocPkXX3yBDz/80CcHpVStWhUvv/wy9u3bh3PnzmH37t146qmnEB8fX34bk8mEJ598Ejk5OSgsLMSiRYvQokULnx4HEVGk4BrtRL6nwoSqMDG0UzjydWjXNO/a4wF9rXYgeKFd8aaJ19iyvW2bDKdR1Mk+xVhpB4DevWW7dKn9FnZvnDIsILPZzsT2roZ2QP9coUJ7oNdoV9ge/7dnnnkGNe381tSuXRv/+c9/fHJQRETkHydPypahnch31Mci7e/VkNgeT+HI16H9zBmg9O+V0TytuhpDe7Da4wFp/R4xwvPHMlZ/V6ywvq6y0H7++VLhP3YMeOcdz4/BHn+E9mC3x6tVcQ4d0k8aRQK3Q/v+/fvR2LZ3AkDDhg2xX83AQkREIYmVdiLfs/19YqWdwpGvQ3tOjmzT0vTHdleoVNp79dLHpXtCVX+NoV01Bm/apN/OOHO8ao9PSACeekr277vP/thzT9m2x9s6eFC29etX/lhq2F12tmyDFdqrVdNns4+karvbob127drYaHtKCMCGDRtQg58CiYhCGkM7ke8Zw4TJpH9gJAon/grtallETwQztFepIhPBAd61xgN6pX3XLmDJEtmfOFG2mzfrHQnGmeONoXfSJGmTz8sDbrlF7+qxWOSf+tpdxkr7tm3yWEpBgR7q3am0q8cI5go1kdgi73Zov+qqq3DnnXdi8eLFKC0tRWlpKX7++Wfcdddd5eupExFRaOJEdES+ZwwTtWsDcR5N80sUXGqd9lAN7YFuj4+JkVbrxERg9GjvHqt+fSAlRZZ527lTLrv6avmZFxbq48BtJ6EzHsv//ifHsmgRMGwY0LGjnGiJj5fr09OBOXPcOy5jaC8u1o8D0FvjU1Jcm5PAthgQrDHtgJwkSUzUhwRGArdD+9SpU9GtWzdcfPHFSEpKQlJSEgYNGoSLLrqIY9qJiEIcx7QT+Z4xtLM1nsKVr5d880Vob9hQfr9q1gx8aAeA+fNlyTc7I4Pdok4AKE2bys+lXTv5WjUx245nN2reHFBR68cf5T6qQg/IHALujHkvLdWX5FPfn7FF3jieXS3n5ozt54pgVtqff166Eu66K3jH4GtunwuOj4/H559/jqlTp2LDhg1ISkpC+/bt0bBhQ38cHxER+RDb44l8j6GdIoG/2uO9mZgxPh5Yv17av81mnxyWW7KyfPdYrVsDf/wh+z16yLZ9e2D1ahnXPmaMXinv3Nn+Y9x5J1BUJIG0a1dZPq5KFXncAQNkhvnSUlmCTdE0+6HbOIN9794yFn3zZuCKK+QydyahAyp28AUztHu6NF8o87iBq0WLFlxajYgozKgPUQztRL7D0E6RIBTHtAOuh8ZQp8ZZA3po79BBths3AnPnAjt2yMR9l19u/zFiY4EHH6x4eb9+QNWqMgZ940Y99I8dC/z6q4R625+jao1PSZHbf/yx40q7K0Kp0h6JXArt99xzD6ZOnYqUlBTcc889Tm/74osv+uTAiIjIt3bulElZYmOBTp2CfTREkcMY2rncG4WrUA3tkUJNRgfYD+0vvST7Eya4XymOiwP69JHg/+uvEsIPHAC++EKu//jjimFfhfZq1fQ2fdWeD+gzx3sa2oM5pj0SuRTa161bB8vfUwH+8ccfMDkY2ODociIiCr7PP5ftgAHBGRtIFKlYaadIwNDuXx07yjYtTdriAX2bnS3/YmNlpnhP9Osnof2XX4DJk4Evv9SvmzmzYmhXM8Onp+uhfedOmRgvMVGvtLuy3BvASru/uRTaFy9eXL7/yy+/+OtYiIjITzQN+PRT2b/qquAeC1GkYWinSODr0P7XX7JlaBfNmsnJ8zp19BUmatSQvxmHD8vXV1zh+Tj6/v1lu3QpUFamV9kBGTO/aZN+kgCwrrTXqSNj0k+elI68Tp28G9OelCTry5PvuDV7vMViQVxcHDYbBzwQEVHI27RJlpJJSPB+vVkispaWpk/8xPZ4Cle+XPKtpAQ4dkz2Gdp1Y8cCfftaX6Za5AHg7rs9f2w1Kd3JkzK7/IoVMgFd9+5yvTpxrxhDu8mkV9tVzPNmTDur7L7nVmg3m81o0KABSo3rCxARUcj77DPZDh0qAYOIfCcmBrjoIvlwa5xsiiic+HLJt2PHpMMrJsa6E4UqUm3zvXoBF1zg+ePExcks8ABw7736Y6oTAZ9+Kq+JotrjVcBWVXg1A73qlHA1tFepos/wz/Hsvuf2Ou3//ve/8fDDD+NkJK1WT0QUwTRND+1XXhncYyGKVPPmAXv2yEzMROHIl+3xajx77drWy49RRZMmATfdBEyf7v1j9esn2+3bZXvFFcDw4RKo9+4FVq7Ub6sq7Spgjx4t208/lb9lpaVy0iUjw7XnNpn0FnlW2n3P7SXfpk2bhl27diEzMxMNGzZEis3/Tn+oBQiJiCgkrFolE9ykpMh/3kTkezExsqY0UbjyR2hna3zl6tUD3nvPN4+lxrUrY8bIsIfLLpMZ5GfO1GeuN7bHA8CFFwLNm8tkdM8/L5cZx9+7okYN4MgRhnZ/cDu0jxw5krPEExGFETWObeRIfcwiERGREUN7+OvSRU7Q5+dLa7xqbb/6agntn38OvPKKnGS0De0mEzBxInDffcD778tlrrbGK2pcO0O777kd2p944gk/HAYREflDYaH8Rw0A114b3GMhIqLQxdAe/sxmqbb/8AMwbpx++cUXS8X82DGZqb5+fesl35TrrwcefhgoLpavXV3uTVGhnWPafc/lMe35+fn45z//iXr16qFWrVq48sorcUxNC0lERCFp9mw5m96gATBoULCPhoiIQhVDe2R4/XXg7beBf/5Tv8xs1gP4vn2yta20AzJp4BVX6F+7W2mvXVt/HPItl0P7o48+io8//hjDhw/H1VdfjZ9//hkTJkzw57EREYUsi0UmfBk1yno21lCjJra55RZOBkRERI4ZQ7u3/6+p0M4lEAOvYUNgwoSKY9EbNpSts9AOSIu84m5ov+MO4MYbpWJPvuVye/zXX3+N999/H1f8ffpl/Pjx6N69O0pKShDnzgwFRERhpqRE2siMZ45XrQKWLJH9o0ddn101kLZtk2OMiZGZaYmIiBwxznlSWKiHeE+w0h56bEO7vfZ4QJaNa9MG+PNPoGlT956jfXvgf//z5ijJEZcr7QcPHkSvXr3Kv+7SpQvMZjMOHz7slwMjIgoV990n1YIVK/TLFi/W93fsCPwxueKdd2Q7fLj7Z8uJiCi6GEO6ty3yDO2hxxjaNc1xpd1kkqF1zz8vs85TaHA5tJeVlcFsNltdFhcXh9LSUp8fFBFRKPntN6m2f/ihfpkxtKv1UENJYaF+vBzJRERElTGb9WFUDO2Rxxja8/JkHXbA/kzvrVpJwYLN1KHD5ZdC0zRcfPHFVq3wBQUFGDFiBOINC5NynXYiijQnTsh2zhzgjTdkVtXfftOvD2Zo1zTg/vuBli1l3Lry7bdy3FlZwCWXBO/4iIgofCQlSaDzJrQXFAC5ubLP0B46jKFdtcbHx3s3DIICx+XQ/vjjj1e4bOTIkT49GCKiUHT8uGwPHwb++EM+0BQV6dcHM7Tv2AH83/8BqanWoX3dOtmOHMkJ6IiIyDW+CO1HjuiPVbWqb46LvNeggWz37dNb49PTpR2eQp9XoZ2IKNJZLHrFAJBqu5KZKUE+mKFddQHk5sqJhIQE+frkSdmq5VeIiIgq44tl3/76S7Z16jAQhhIV2gsKgF27ZN9eazyFJpfHtFNoCOWlpYgikQq/ynff6ePZb71Vtnv2SLgPBuMJBXXmHNDDfI0agT0eIiIKX74I7RzPHpoSE/WVbtavly1De/hgaA8T27bJDNAXXxzsIyGKLqo1PilJKgbr1+uzyF9zDZCSIpPU7dkTnOM7c0bfV0Ed0E82VK8e2OMhIqLwlZIi27w8zx+DoT10qXHtGzbI1na5NwpdDO1hIjUV+OEHqfBxlT2iwFFBOCsL6NlT9ktKgPr1gWbNgBYt5LJgtcgbQ7uxK4ChnYiI3KWGVKlx6Z5gaA9dKrSz0h5+GNrDRGYm0KOH7H/7bXCPhSiaGNvMR4zQL7/wQqm8h1JoN1ba2R5PRETuqltXtmpcuicY2kOXCu3798uWoT18eBXaCwsLfXUc5ILLLpPtV18F9ziIoolqj69ZE7j0Uv3yCy+UbcuWsvVXaD95UrpsHI2ZZ6WdiIh8RQVtFbw9wdAeulRoVxjaw4fbob2srAxTp05FvXr1UKVKFez5eyDno48+ivfee8/nB0g6Fdp/+aXi5FhE5B/GinWrVkD37kBamr72uT9D+w8/AG3ayHwWH35o/zb2Ku3nzumTCDG0ExGRq3xRaVfDONVjUeiwDe0c0x4+3A7tTz31FD744AM899xziI+PL7+8Xbt2ePfdd316cGStWTOgfXsZT/v998E+GqLoYAztJhOwaBGwe7f+YcQfob2oCJgwQcK6Glf4xx/2b2uv0q5mkY+NlfkwiIiIXOFtpX3/fmDdOtlv08Y3x0S+w0p7+HI7tH/00UeYPn06rrnmGsTGxpZf3rFjR2zbts2nB0cVqWr7118H9ziIooWxPR6QmXWN48TVmPZjx6yXXPPGtGnAO+/ISYJu3eSyHTvs39ZepV1tq1fnGrlEROQ6byvt06cDZWXARRcBTZv67rjINxjaw5fbof3QoUNo1qxZhcvLyspgCdZCxVFk9GjZzpsH5OcH91iIokFlE7pVrSoTRQKOg7W71GSTzz8PvPii88e2V2nneHYiIvKEsdKuae7dt7hYTjgDwG23+fa4yDfS0uSfwvb48OF2aG/Tpg2WLl1a4fIvv/wSnTt39slBkWMdOgCNGwOFhcD8+cE+GqLIpyrtzmZh9+UM8qdPA7/9JvtjxuiPfeCAPk7dyF6lXYV2zhxPRETuUJX2ggLg7Fn37vv118DRo/IYxolbKbQ0aKDvs9IePtwO7Y899hjuuOMOPPvssygrK8NXX32FW2+9FU8//TQee+wxfxwjGZhMerWds8gT+Z8Kwqo93h5fjmtftAgoLZVJ7xo1kuCt/lPdtavi7e1V2o3t8URERK5KSZEOMsD9ce1vvCHbCRMAs9m3x0W+Y2yRZ2gPH26H9pEjR2LOnDlYtGgRUlJS8Nhjj2Hr1q2YM2cOBg4c6I9jJBuDB8v299+DexxE0cCV9c59GdrnzpXt0KGyNa4Fb69Fnu3xRETkS6pF3p1x7Zs3A0uWyASot97qn+Mi3zCGdrbHh484T+7Up08fLFy40NfHQi5q1Ei2Bw/KeCNONEXkH6WlrrWat2ol2z/+cH8MoJGmyXwVADBkiH558+bAqlUVQ7umsT2eiIh8q25dYOdO9yrtatXnUaOAevX8cljkIyq0m0xcYSacuF1pP3DgAA4ePFj+9erVqzF58mRMnz7dpwdGjtWvL9v8fBn/SkT+cfq0HsKdBeDevYGkJCA7G/j9d8/Pom3YIJWNlBSgTx/9clVp37nT+vbnzsmJBePX586xPZ6IiDznSaVdLfM2apTPD4d8TIX29HQgxu0kSMHi9kt19dVXY/HixQCAnJwcDBgwAKtXr8a///1vPPnkkz4/QKooKUkPEAcOBPdYiCKZCr+pqc7H51Wtqi/HOGOG56H9xx9le9FFQEKCfrmj9nhVZY+JkZZEQKrsbI8nIiJPebLsW3a2bLnMW+hr21a2xgnpKPS5Hdo3b96MCy64AAAwa9YstG/fHr/99htmzJiBDz74wNfHRw5kZcnW0PRARD7myszxyrXXynbWrBiUlHgW3NV4dmNrPFB5aE9N1QO6MbSzPZ6IiNxlXPbNFcXFehGpSRP/HBP5Ttu2wJw5wGefBftIyB1uh3aLxYKEv0tAixYtwqV/r+nQqlUr/OXOKTnyigrtrLQT+Y8rM8crAwcCGRnA8eMmrFtX2+3nOn0aWLFC9m1De/Pmsj12zHpIjArtaWl6QD9xgu3xRETkOXcr7fv3y1Cy5GSgtvv//VEQDB+uz8dD4cHt0N62bVu89dZbWLp0KRYuXIhLLrkEAHD48GHUYFknYBjaifzPlZnjlbg44KqrZP+XX7Lcfq4VK2R8eosW+mSTSpUq+oco47h2Y2i3V2lnaCciIne5W2nfs0e2jRpxcmQif3E7tD/77LN4++230b9/f1x11VXo2LEjAOC7774rb5sn/1OT0TG0E/mPO+3xAHDddbJds6aO1azurlAfjho3tn+9vRZ5e6H9xAm2xxMRkefcrbSr8exsjSfyH7eXfOvfvz+OHz+O3NxcVKtWrfzyCRMmIDk52acHR45xTDuR/7nTHg8AnTsDrVpp2LYtFl9/rbm1Vq06QVCrlv3rW7QAfv3VcWhXAf3QIZlBHmClnYiI3KdC+/HjgMXifCJWQA/tjk46E5H3PJroPzY2FiUlJVi2bBmWLVuGY8eOoVGjRqjNgSwBw/Z4Iv9zpz0ekLbAa68tAwA891wsCgtdf65jx2TrLLQD9kO7cSK6XbtkGxcns9oTERG5o0YN+T8EAI4cqfz2qj2elXYi/3E7tOfn5+Omm25C3bp10bdvX/Tt2xeZmZm4+eabUVBQ4I9jJDuMoV2tI01EvuVuezwATJxYhmrVCrFrlwnPPuv6/SoL7Woyusoq7WrMe/XqHFtIRETui4mRiVUB18a1s9JO5H9uh/Z77rkHv/76K+bMmYPTp0/j9OnT+Pbbb/Hrr7/i3nvv9ccxkh316sm2sFCvBn74IZCZCaxaFbzjIook7rbHAxKgb755EwDgP//RQ/TmzcBXXwFlZfbvp0K7o+dSlfadO/UTdfbGtKtKO1vjiYjIU2oyOlfGtatKO0M7kf+4Hdpnz56N9957D0OGDEFqaipSU1MxdOhQvPPOO/jyyy/9cYxkR0KCvqyGGtf+3nvyx/Wnn4J3XESRxN32eKVXr8MYNKgMxcXAjTcCo0cD7dsDY8YAixfbv09llfYmTaT6cfas3q7oaPZ4T46ZiIhIUePaK6u05+bq/+8wtBP5j9uhvaCgABmqZ8agdu3abI8PMGOLfEkJsHatfO3urNVEZJ9qj3en0g5IW/qrr5YiMRFYvhz4+mv9uv37nT+Xo9CekKAvBada5O21xyustBMRkadcrbSr1viaNTmPCpE/uR3ae/TogccffxyFhhmWzp07hylTpqBHjx4+PThyzhjat2wB1DmT3NzgHRNRpNA0zyvtgFTGX3gBSEyU9dv79ZPLVUXCVmWVdgBo2lS2qhXRXqVdYWgnIiJPGZd9O3sWGDgQ+Oc/K94uO1smT2GVnci/3F7y7ZVXXsHgwYNRv3798jXaN2zYgMTERMyfP9/nB0iOGUO7cRw7QzuR986elQ4WwPNW89tukw85JhMwaZIs2WYvtBcVyfMBzkO7+lCkKhvqd91epZ3t8URE5Clje/x//gMsWiT/xowBBgzQb7d3r4R2zhxP5F9uh/Z27dph586dmDFjBrZt2wYAuOqqq3DNNdcgKSnJ5wdIjtWvL9uDB/UqHcD2eCJfUO3qycmAN3/a1AzuqvJ96lTF26jf39hYID3d8WPZhnZW2omIyB9Ue/wffwBz5+qXP/wwcPHF+tecOZ4oMNwO7QCQnJyMW2+91dfHQm4yVtpVGy/ASjuRL3jTGm+P7URxRsaZ450t0+YstFepApjNgMVi/XxERETuUpV2NQ9Lr17A+vXAmjUyT8uIEXI5K+1EgeFSaP/uu+9cfsBLL73U44Mh96jQvm0bcPSofjkr7UTeC0Zod9YaDzgP7SaTPIeaWZ7t8URE5ClVaQeAuDjgnXeAmTOBp54CHnkEGDpUruOYdqLAcCm0jxo1yqUHM5lMKC0t9eZ4yA0qtKsP6Qor7UTe83TmeEechfbKZo5X1Ieiw4clsBcXy9dpafpzqL8HrLQTEZGnjKH9jjuA1q2B++4DXn8d2LoVmDHDhBo1gL175TYM7UT+5dLs8WVlZS79Y2APrMxM61baNm1ky9BO5L1QrLTXrClj7DUN2LRJLjOZ9GV2jMfK0E5ERJ5KTARGjQI6dAAef1wuS0sDHnxQ9u++OxZr19ZGYaEJMTFAgwZBO1SiqOD2km8UOsxm6zOhAwfK9swZ+VBPRJ4LxdBuMunVjPXrZVu1KhATY/0cANvjiYjIO19/Lf/XGCdIvesuoH9/4OxZE/773wsASOen2RyMIySKHi6H9p9//hlt2rRBrp0y7pkzZ9C2bVssWbLEpwdHlVMt8gAwaJBsS0uBc+eCczxEkcLX7fHVqsn2zBn5HTVyNbQDFUO7ao0HWGknIiLfsp0cNSEB+PZboFMnDSUlsQDYGk8UCC6H9pdffhm33norUlNTK1yXlpaGiRMn4qWXXvLpwVHlVGiPiQH69tX/uHIyOvdoGrBzZ8UwRdHL15V2FdoB4PRp6+t8FdpVUI+Lk9nkiYiIfC01FZgzpwR16+YBAJo3D/IBEUUBl0P7hg0bcMkllzi8ftCgQVi7dq1PDopcp0J7u3byIV2dU+G4dvcsX56Jtm3NePLJYB8JhQpfh3azWR97btsib1zyrTIqtG/eLFt7ob1GDedLxxEREXkjIwN48snf8K9/leKBB4J9NESRz+V12o8cOQKzkwErcXFxOKY+eVLAtG8v24svlm1qqlTZGdrds2+fnO1YvjzIB0Ihw9ft8YCE6rNnK4Z2V2ePB/TQXlQkW3vt8WyNJyIif6tV6xyuv74MZnNssA+FKOK5XGmvV68eNqvSjh0bN25E3bp1fXJQ5Lrx44HFi4GpU+Vr9QGe7fHuyc+XE1I7dwb5QChk+LrSDjiejM6T9njFGNozM623RERERBT+XA7tQ4cOxaOPPorCwsIK1507dw6PP/44hg8f7tODo8rFxcksnikp8jXb4z2jQvuBA4CdtzhFoUCF9tJS/WtvQ/sllwDPPgv83/95d5xEREREFDpcbo9/5JFH8NVXX6FFixa444470LJlSwDAtm3b8Prrr6O0tBT//ve//Xag5BpW2j2jQrumAbt3A23bBvmAKKgKCvQVGHzdHg9Yh/YTJ/QlGl05QZCaKo+jHsMY2s1m4P77fXOsRERERBQaXA7tGRkZ+O233/DPf/4TDz30ELS/P2WaTCYMHjwYr7/+OjIyMvx2oOQaVto9k5enz9ewcydDe7RTVXaz2bezsNsL7ao1vnp16ZxxRePG+mPYWdCDiIiIiCKIy+3xANCwYUPMnTsXx48fx6pVq7By5UocP34cc+fORWM/LdLYqFEjmEymCv9uv/12AEBhYSFuv/121KhRA1WqVMGYMWNw5MgRvxxLOGBo90xeXnz5Pse1k7E13pezsKvQfuqUfpk7M8crxj+3xko7EREREUUelyvtRtWqVUPXrl19fSx2rVmzBqWGxbM3b96MgQMH4oorrgAA3H333fjhhx/wxRdfIC0tDXfccQdGjx6N5VE6DTjb4z2j2uMBhnbyz8zxgP1KuzszxysM7URERETRw6PQHki1bD7J/ve//0XTpk3Rr18/nDlzBu+99x5mzpyJiy66CADw/vvvo3Xr1li5ciW6d+8ejEMOKlbaPcPQTkb+mIQOcN4ez9BORERERPaEfGg3Ki4uxieffIJ77rkHJpMJa9euhcViwYABA8pv06pVKzRo0AArVqxwGNqLiopQpBY5BpD7d8K1WCywWCz+/Sb8rEqVGACxOHWqDBZLaaW3J+DsWQuKi5PLv965U4PFUhLEI6JgO3pUfo+qVXP/90j9DbH3t6RqVROAOJw4oT9uTo48V40arj9XVpY8DgCkpJTAYtHcOkYKH87eT0Tu4HuJfInvJ/KVaH8vufp9h1Vo/+abb3D69GnccMMNAICcnBzEx8cjPT3d6nYZGRnIyclx+DjPPPMMpkyZUuHyBQsWIDk52c49wkd2dhaA87BnzzHMnbsy2IcTFk6dSgBwSfnXhw6Z8PXX85GQwJMe0WrFihYAWqOgYD/mzt3g0WMsXLiwwmU7dtQA0BsHDuRj7tyfAQC//94eQBOcObMLc+dudemxDx2qAuBiAMDGjUtx9ixbayKdvfcTkSf4XiJf4vuJfCVa30sFBQUu3S6sQvt7772HIUOGIDMz06vHeeihh3DPPfeUf52bm4usrCwMGjQIqWE+FXNxsQmvvQYkJNTC0KFDg304YWHTJqmqV6umwWQCTp40oWnTwejQIcgHRkGzaJHM0dmpUxaGDq3n1n0tFgsWLlyIgQMHwmw2W12XlQU88ghQXFyl/PdzxoxYAED37k0xdKhrE3oWFgJ33KFB00wYPrw3GjZ06xApjDh7PxG5g+8l8iW+n8hXov29lOvimOawCe379u3DokWL8NVXX5VfVqdOHRQXF+P06dNW1fYjR46gTp06Dh8rISEBCQkJFS43m81h/2ZRY2Zzc2NgNru1OEDUysuT6cHT04HatU1YtQrYu9eMLl2Ce1wUPGp299q1Y2E2x3r0GPb+nqhVMU+eNCEuzvz3SSJ1nevPZTYDU6YAhw4BTZuafTrDPYWmSPj/iUID30vkS3w/ka9E63vJ1e85bFLd+++/j9q1a2PYsGHll3Xp0gVmsxk//fRT+WXbt2/H/v370aNHj2AcZtBxIjr3nT4t2/R0oHlz2Xd1MrpvvwXOP1/+jRsHPPYYEMUrDkYMf88eX1oK5OXJvicT0QHAo48Cb73l2yXpiIiIiCj0hEWlvaysDO+//z6uv/56xMXph5yWloabb74Z99xzD6pXr47U1FRMmjQJPXr0iMqZ4wEu+eYJVVWtVk1D8+aSgCoL7Tk5wKRJwJdf6petXSvbw4eBd9/1w4FSwPhr9vikJCAxUdrbT54Eqlb1PLQTERERUXQIi9C+aNEi7N+/HzfddFOF61566SXExMRgzJgxKCoqwuDBg/HGG28E4ShDg6q0nz0LlJUBMWHTSxE8Z85IUE9Lc63SfuoUcN55wF9/AbGxwH33AT16AEuXAi+8AMydC2gaK6DhzF+hHZBq++HDEtobNPBsnXYiIiIiih5hEdoHDRoETbO/pFFiYiJef/11vP766wE+qtCkKu2aJu23YT6vXkDolfaKoX32bODXX4Fnn5UqKQB88YUE9oYNga+/Bjp3lssHDwbefFOu27gR6NgxsN8H+Y6/2uMB69B+5gygVvpgaCciIiIie1iHjTCJiYAaQcBx7fbZnv/Rx7Rr5aE9Jwd45x3giiuA116zbnf/9FPZ3nabHtgB+dlfeKHsz5vnl0OnACgulk4VwH+VdkBC+44d+mWJib5/LiIiIiIKfwztEcZk4mR0zhQXS2v7ZZfpl50+rc8en5amVzwnTNAD/htvyP7hw1J5B2TiOVuX/L3cO0N7+FKzucfEyHvC14yhfelS2e/Z0/fPQ0RERESRgaE9AnEyOsd27wbWrwe++UYmAwOs2+MBvUUeAMaOBapUAbZtAxYvBmbNkvDesyfsro09ZIhsly3Tq7UUXlRrfPXq/pkTQr3PjKG9Tx/fPw8RERERRQaG9gjESrtjKpABssY1YN0eD0glHgBGjgQ++QQYP16+fv114LPPZP/KK+0/ftOmQLNmQEkJ8PPPvj12Cgx/TkIH6JX2Eyfk5A7A0E5EREREjjG0RyBW2h1Ty2sBwIEDsjW2xwPA1KnAd99JVd1slrHrgFTnV62S6uvYsY6fQ7XI//ijTw+dAiRQof233+S5kpKALl3881xEREREFP4Y2iMQK+2OGSvtemiXrWpbTk8HRowA4uPl67ZtgX79ZAk9ALjoIiAjw/FzGMe1a5qs375qla++A/I3f84cD+ihfeVK2Xbrpr/XiIiIiIhsMbRHIIZ2x+xV2tWYdtUeb4+qtgOOW+OV/v0lhO3bB9SrB5x/vqzj/scfnh0zBVagKu1qkkO2xhMRERGRMwztEYjt8Y4ZK+0HDwKlpUBurnV7vD2XXQa0bg3UqQOMHu38OVJSJLgDsmY7IAGN7fLhIVChXenb1z/PQ0RERESRgaE9ArHS7phtpd14YkO1x9tjNgNr1si62s5up7z6KvDww8CcOcDzz8tlaqk4Cm2Bao8HgNhYoHt3/zwPEREREUWGuGAfAPkeK+2O2Y5pV63xiYklMJud3zclxfXnadkSePpp2W/UCPjXv4DlywGLBZU+DwVXICvt550nSwoSERERETnCSnsEYqXdMWOl/eBBfRK6lBSL356zTRsJgAUFwO+/++1pyEcCGdo5np2IiIiIKsPQHoEY2h0zVtpPnAAOH5Z9f4b2mBiZfR4AfvnFb09DPuLv9viqVaUtHmBoJyIiIqLKMbRHILbHO2YM7QCwaZNsq1Qp9uvzqtDOce2hz9+VdpMJ6NkTqFVLn7CQiIiIiMgRjmmPQKy021dQIP8AIDNTquwqtPuz0g7o4WzZMo5rD2Wlpfo8B/4K7QDw009ASQmQlOS/5yAiIiKiyMBKewRipd0+VWWPj5dx5oCx0u7f0N6unYxlzs/neu2h7NQpff1026XZfMlsZmAnIiIiItcwtEcgVtrtU5PQ1aoFZGXJ/rZtsvV3pT0mRl+Pm+PaQ5dqjU9LYzcEEREREYUGhvYIpCrt+fnS7kvCOMFY/fqyr34+/g7tAMe1hwP1HvFnazwRERERkTsY2iNQ1ar6PqvtOlVpr1lTr7Qr/m6PB6zHtZeU+P3pyAOq0u6vmeOJiIiIiNzF0B6BEhLkH8DQbqSqqMb2eCUQob1DByAxETh7Fti/3+9PRx7w98zxRERERETuYmiPUJyMriJ77fFKINrjY2KARo1kf+9evz8deeDIEdnWrh3c4yAiIiIiUhjaIxQno6vI3kR0SkqKf9dpV1Roz84OyNORm1Roz8gI7nEQERERESkM7RGKob0iY6U9Lc167H8g2uMBoHFj2TK0hyaGdiIiIiIKNQztEUoF0ry84B5HKDFW2gHrFvlAtMcDbI8PdQztRERERBRqGNojVJUqsmVo1xkr7YB1izwr7QQwtBMRERFR6GFoj1AM7RXZVtpVaI+L05CQEJgF7VlpD20qtNepE9zjICIiIiJSGNojlArtZ88G9zhCRWkpcPKk7KtKu2qPr1YNMJkCcxyq0n74MFBYGJjnJNdYLPqSb6y0ExEREVGoYGiPUKy0Wzt1Cigrk321BreqtKenB+44atQAUlJkn2u1h5ZjxwBNA2JjuU47EREREYUOhvYIxYnorKnx7OnpgNks+x07yrZ5cy1gx2EycVx7qFKt8bVqATH8y0hEREREIYIfTSMUK+3WbCehA4DzzwdWrgT+97/AjGdXOK49NHESOiIiIiIKRXHBPgDyD4Z2a7aT0CnduslY5kBipT00MbQTERERUShipT1CcSI6a/Yq7cHCSntoYmgnIiIiolDE0B6hQrHSvn49sHVrcJ7bUaU9GFhpD00M7UREREQUihjaI1QoTUS3ciUweDDQuTPQvTtQXBz4Y2ClnSrD0E5EREREoYihPUKFSqV98mSgRw9gwQL5OjdXXws7kEKx0n70KJCfH9xjIZ0K7XXqBPc4iIiIiIiMGNojVKiE9k8+ke211wKpqbIfjNAeSpX29HQgLU32WW0PHay0ExEREVEoYmiPUKEQ2gsL9YD+yit6BfPkycAfi6q0h0JoB/RqO0N76MjJkS1DOxERERGFEob2CKVCe34+UFYWnGM4fFi2iYlAtWpA9erydTBCu6q0h0J7PKCPa+dkdKGhpER/jzC0ExEREVEoYWiPUGoiOiB446YPHZJtvXqAyQTUqCFfB7o9/scfgX37ZL9+/cA+tyOstAfHc88BI0YABQXWlx8/DmgaEBMTOt0YREREREQAQ3vESkyUAAIEr0XeGNqB4FTa9+2T8fQAcNtt+rEEGyvtwfHf/wLffw/MnWt9uRrPXrMmEBsb+OMiIiIiInKEoT1CmUzBH9duG9oDXWkvKgLGjpWTBF27Ai++GJjndQUr7YF35gxw6pTs//qr9XWchI6IiIiIQhVDewQLtdAe6Er7k08Cq1fLePovvgASEgLzvK5gpT3wjCdIGNqJiIiIKFwwtEcwFdrPng3O8we70j5/vmxfeAFo2DAwz+kqFdpPnZIKMPmf8QTJpk3W70Ou0U5EREREoYqhPYKpyeiCXWnPzJRtoCvtubmybd48MM/njqpV9ZMYxgpwWRmwZUvwZvyPZLZdDUuX6vustBMRERFRqGJoj2DBbo9XS74Fq9KuKtipqYF5PnfZG9f+5ptAu3bAa68F5ZAimm1oN7bIM7QTERERUahiaI9gwQztmlYxtAer0p6WFpjnc5cK7cYwuXy5bP/8M/DHE+nUz7l/f9kytBMRERFROGBoj2DBDO0nTsjs7YDeHh/ISntxMVBYKPuhWmlX49qNlfbt22XLce6+p37O118v2/XrgdOnZT8nR7YM7UREREQUahjaI1gwJ6JT49lr1QLi42VfVdoLC4Fz5/z7/KrKDuhj+0ONbaVd04AdO2Sfod23NE3/OffsKfMcaBqwbJlcxko7EREREYUqhvYIFsyJ6GxnjlfHExcn+/6utqvQnpKiP2eosa20//WX/loxtPvW8eNAfr7sN2wI9Osn+7/+CpSWAseOydcM7UREREQUahjaI1gw2+PthXaTKXDj2kN9EjrAutJurLIDets2+YaqsmdmAgkJemj/8Ufgp59ktn6TSTpDiIiIiIhCCUN7BAu10A4Ebly7qrSHcmhXa8efPSsnMdR4doCVdl9ToV2dKFGhfcsWYPBg2a9ZM3S7MoiIiIgoejG0R7BQDO2BrrSH6szxAJCUBNSpI/t791pX2hnafcs2tGdlAf/3f0CfPkDdunLZsGHBOTYiIiIiImdYV4pgoTARHSvtzjVqJDOXZ2dbV9rz84GSElZ+fUXNG6BCOwDce6/8A2Slg4SEgB8WEREREVGlWGmPYKEwEZ1a7k0JVKU91NdoV1SItK20A6y2+5KqtKvJ/2wxsBMRERFRqGJoj2Ch2B4fqEp7OExEB+ghcscOYM8e6+sY2n3Htj2eiIiIiChcMLRHsGCF9qIiPZQHa0x7uLTHqxD588+y9FiVKvo4d4Z23ygrA/btk32GdiIiIiIKNwztESxYof3wYdkmJOghXQn0mPZQb49Xlfbdu2XbogWQni77XPbNNw4fBoqLgdhYoH79YB8NEREREZF7GNojWLBCu7E13mSyvo7rtFuzrfwaQzsr7b6hJqFr0IAT+xERERFR+GFoj2BqIrr8fGkRDhRH49kBzh5vKyvL+sRGy5Z6dwBDu29UNgkdEREREVEoY2iPYKrSDkhwDxRnoZ3rtFtLSLD+ObVowdDua5yEjoiIiIjCGUN7BEtMBGL+foUD2SLvaqVd0/x3DOFSaQesK8DG0M4x7b7B0E5ERERE4YyhPYKZTMEZ1+5Kpd1i8W/1P1wmogOswyTHtPuemjme7fFEREREFI4Y2iNcMEJ7To5s1dJlRsnJ0hIO+LdFPlwmogP0MFmnjhwv2+N968AB2WZlBfc4iIiIiIg8wdAe4dRkdGfPBu45jx6VbUZGxetMJr3a7q/J6DQtvNrjmzeXbZs2smVo90xZGTB3LnDkiH6ZpgEHD8o+QzsRERERhSOG9ggXjEq7s9AO6OPa/VVpLyqS9nsgPNrjR48GHngA+O9/5WuOaffM/fcDw4YBkybplx0/DhQWyr694RpERERERKGOqxZHuECH9pISvYJeu7b92/i70m6sUBtn0A9VKSl6YAc4pt0TM2cCL7wg+xs26JerKnvt2vqwDCIiIiKicBLylfZDhw7h2muvRY0aNZCUlIT27dvj999/L79e0zQ89thjqFu3LpKSkjBgwADs3LkziEccWgId2o8fl21MjB7Obf1/e3ceJUV573/807PCDMwgMM4w7GiGVdlkGfmpAUG4SiKKxi1HNC5RMQJGvVfiiZoYORIlXnOJnBwNxMRg5Mb9uADDYgyLihCGRRbZVGYQRGbYZq/fH8+tqe6hZ+hpqrurp9+vczhV3V1d/RT9HPTT3+d56kwq7b/4hTR7dtPH2EPj27Z1Vs+PJwyPb57PPpNuu815vGePGSovMZ8dAAAA8c/Tkea7777TqFGjlJqaqvfee09btmzRM888o7POOqv+mNmzZ+u5557TvHnztHbtWmVmZmr8+PGqsMfEJjj/0F5RIX3/+4HDh91mzyfu2FFKTg5+TLiV9i+/lJ58Unr4YWf4ezDxco/2xhDaQ3fypHTVVaZvT5ggpaRIVVXS/v3mdUI7AAAA4p2nh8c/9dRT6tq1q+bPn1//XE+/+2NZlqVnn31WjzzyiK688kpJ0ksvvaTc3Fy98cYbuv7666PeZq/xX4hu1Spp5Upp3Trp97+PzOfZ89kbGxovhV9pt++3XVdnAm3HjsGPi6dF6IJhTnvoNmyQ9u0zfWrhQmnoUGnXLtNXunQhtAMAACD+eTq0v/XWWxo/fryuvfZarVy5Up07d9Y999yjO+64Q5K0e/dulZaWauzYsfXvyc7O1ogRI7R69epGQ3tlZaUqKyvrH5f/X8qrrq5WdVMl3DiUkZEkKVllZbVavVqSknXsmFRZWR2RoeP79/skpSgnp07V1bVBj8nONm06eLDxY4L54gtzbkk6eLC60Ur64cPmuLZtQzu//Z175bvPzJSkVFVWSseOVTMXuwlffWW+63PPrVNmZq169EjWrl1J2r69RiNHWtq7N1lSkvLza1VdXReVNnmtPyG+0Z/gFvoS3ER/glsSvS+Fet2eDu27du3S888/r/vvv18zZ87UJ598ovvuu09paWmaMmWKSv/vhuC5DZYpz83NrX8tmFmzZunxxx8/5fnFixcrIyPD3YuIsZKSPpJ6a/PmvTp8uJWkfEnS668vVuvWNa5/3ocf9pJ0nmpq9uvdd9cFPWb//m6SBuvzzw/o3Xc/DvncS5YUSOorSXr33VU699wjQY/75z+7ShqiqqqDevfdNc04/5KQj42k2lpJMiNH/vGPpWrXriqm7fGyoqIekgZKMn0pOXmgpB5asmSnOnbcpo0bR0nqqEOH1uvdd7+Oatu80p/QMtCf4Bb6EtxEf4JbErUvnThxIqTjPB3a6+rqdMEFF+jJJ5+UJA0ePFibNm3SvHnzNGXKlLDP+/DDD+v++++vf1xeXq6uXbvqsssuU1a8jqluxObNSVq0SOrYsYc2bvTVP3/hhZepUyf3P+9f/zLl+0GDOunyyy8PekxlpU9z50opKbmNHhPMm286k+T79x+lSy+1gh63e7dpQ69eOSGdv7q6WkuWLNG4ceOUmpoacnsiqW1bS0eP+jRs2Nj6+7g3R0WF1KqV++3ymk8+Md/1+eefrcsvv1zFxUlaskRKTS3Q5ZefoxkzzD9xP/jBIF144cCotMmL/Qnxi/4Et9CX4Cb6E9yS6H3JHvF9Op4O7Z06dVK/fv0Cnuvbt6/+8Y9/SJLy8vIkSQcOHFAnvwR64MABDRo0qNHzpqenKz3ImOPU1NQW11nsIeRffJFUf/srSaqoSFUkLtVeXC4vL1mpqcFXorMHRuzfn6SUlCT5fEEPO8W+fc7+8eMpjbb/+HGzPeusJKWmhj4HwEvff7t2Zh2C48eb/z39+c9mNfVXXpGuuSYizfMM+24F+fmmv517rnm8Z0+SkpOdPt+zZ+P9JVK81J8Q/+hPcAt9CW6iP8EtidqXQr1mT68eP2rUKG3bti3gue3bt6t79+6SzKJ0eXl5Kioqqn+9vLxca9euVWFhYVTb6lX2QnRr1wY+f/RoZD7PXj2+qYXoBg6UMjKkvXslv6/utPbudfabWqTNXnU9ngdNnMkK8kVFZoh9c/5u45U9C+b/fr9Tr15mu3u3WRSxulry+RSRUSUAAABANHg6tM+YMUNr1qzRk08+qZ07d+pvf/ub/vjHP2rq1KmSJJ/Pp+nTp+uJJ57QW2+9peLiYt18883Kz8/XpEmTYtt4j7Bv+VbTYPp6pO7bHsrq8e3aOffVfvrp0M5bVxdYaW8qtMf76vHSmYV2u7psr5zekjUM7fbNJb7+Wtq50+x36qSoV9kBAAAAt3g6tA8bNkyvv/66Fi5cqAEDBujXv/61nn32Wd100031xzz00EP62c9+pjvvvFPDhg3TsWPH9P7776tVIkzoDYEd2huKVKU9lNAuSdOnS0lJ0gcfSBs3nv68paXm/tu2UCrt8Xqfdsmd0O7/I0dLZY/ssKdcdOzo9PmPPjJbbvcGAACAeObp0C5JEydOVHFxsSoqKrR169b6273ZfD6ffvWrX6m0tFQVFRVaunSpCgoKYtRa72kY2tu1M9tIVNotK/TQ3quXNHmy2Z8z5/Tn3rMn8HFLr7Tb31Nz79VuWYkT2i3r1Eq7z+dU21euNFtCOwAAAOKZ50M7zox/aE9Lk0aONPuRqLQfPy6dPGn2G9yFL6if/9xs//Y3M5y5Kf7z2aWWH9rDrbR/953zHZSVOX8XLVF5uVklXwrsb3Zo/9e/zJbQDgAAgHhGaG/h7IXoJGnwYKl9e7MfidBuV9kzMqTMzNMfP2KEdNFFZrGwuXObPtYO7Un/12MZHh+c/x0CpJY9r92usmdlmT5nsxejs/s4oR0AAADxjNDewvlX2ocPd0J8JIbHhzo03p+9IJ09/7gx9vD4Pn3MtmFo377dhH8psSvtDUcstOTQbs9nt4fG2+xKu43QDgAAgHhGaG/h/EP7sGFOaI9EpT2U2701ZIfwXbuaPs6utA8caLb+of3NN6XevaX/+i/zuCVU2sOd096w0t6S57XblfaGUzEI7QAAAGhJCO0tXKtWUnq62R8+3AnxkRwe35zQfs45Zvv1185c7GDsSvugQWbrH2Y3bDDbt94yi5MlUqXdsgIfJ+Lw+IaVdnt4vK1Ll+i0BwAAAIgEQnsL5/NJf/iDNHu2qUZ7bXh8hw5OmxquEG+zrKYr7fbn7txp/tTVmcctPbR/952pKt91l/OcHdrtv9NEqLQ3DO09ejj7KSmnvg4AAADEE0J7AvjJT6QHHzT7kRweH05o9/mcymhjQ+QPHjRVeJ9POu8889yxY1JNjdm3h+VL0jvvmG1SUmiL4XlVKKH944/Njxl/+YvzQ4Ud2keMMNuWXGlvbE57ZqYzZD4/X0pOjm67AAAAADcR2hOMPTw+kpX2UG735s8eIv/FF8Fft6vs+flSTo7zvD0MPlhoz8oyIT9ehTKnvaTEbE+ccH7wsBeiKyw020SotAfrb/a8duazAwAAIN4R2hOM1yrt0ukr7faw+e7dpdRUp4JuB1r7cyXpww/NNp6HxkuBlfaG89ZtdmiXpI0bzdautNuh/auvnCp8S9PY8HiJ0A4AAICWg9CeYCK5EF04q8dLpw/tdqXdnqvcsArtX2m3h8zH88rxktP+mprGF+jzD+3FxeY7tYfTjxhhRhpUVprpBV5SXCz9859nfp6mQvvgwWZrT6cAAAAA4hWhPcF4bSE66fTD4/0r7VJgaK+ocIJqfr7znnivtLdpY+blS0613f5Bwtaw0m4Pjc/Kktq3lzp1Mo+9NES+pkYaPVq6+GLpxhulb78N7zx1dU5/Cxbaf/YzafFi6f77w28rAAAA4AWE9gQTqeHxtbXSoUNm/0wq7cGGgjdVaberyKmp0sSJznviPbT7fE61vbRUGjdO6tw5MOQ2rLTbQ+PtW5x162a2XlqMbutW5xoWLpT695eWLWv+eQ4fdn7ECNbfWrUyf2etWoXfVgAAAMALCO0Jxn94fGNzpcPx7bfO+Tp2bN57u3UzVeWKCmfIs7+mKu3+Q/IvucR5T7wPj5eca5gyRSoqMpXljz92Xt+/39nfuVPavt3s26Hdns/tpUr7p5+abZ8+Ut++5vu79dbmn8fuJx06mB9sAAAAgJaK0J5g7Ep7XZ0JyW6xhyp36GDujd0caWlOVTjYEHm7gmwfEyy05+aaIde2eK+0S05oLy52nrN/wLAsp9KenGweL15sHnfubLZerLTbof2KK6Tly83+vn2nDv0/ncZu9wYAAAC0NIT2BON/73I3h8iHe7s3W2OL0VVUOAvO2QHNP7T7f26XLs55WlKlXZIKCszWnipQVub86GLfk33pUrONh0r7BReYERn2bfmaO7e9qUXoAAAAgJaE0J5gkpKc4O7mYnThLkJnsxejaxja7fOmpTlhvbHh8ZI0ZozZ2ouwxbM+fcx25kzppz81+3al3a6yZ2c7of34cbP16pz2qirp3/82+xdcYEYItG9vHtvrIYSK0A4AAIBE0cyBzGgJ2rY1Ac/NSnu4t3uz2RXyhsPj7XCWm+tUZf1Duz0U367wP/GENGSIdNNN4bXDS+bMke64Qxo6VHr9dfNcw9DeqdOptzXzaqV982ZzC7rsbOdHmo4dTZU93NAe7sgOAAAAIF5QaU9AkbhXu10Rz8kJ7/2NDY/3n7Nus0N7WdmpPxbk5kp3390y5rRnZpqKtM/nLMJnD4+3F6Hr1Ek6//zA9zWstJeWmip3rPkPjbd/gLEXLWxuaGdOOwAAABIFoT0BReJe7YcPm21zV463NTY8Plg4s+d6N1yIriWzb3dXWiqdPBlYae/Xz7mnu+QsRJeTI6Wnm0Xq7Hu4x5J/aLeFG9oZHg8AAIBEQWhPQMHu1W5XysNlLyRmz1FuLrvSXlrqzM22H0vBK+0NF6Jrydq3d0ZI7NvnhPb8fKl1a+l73zOPW7VyvgOfzxki74V57YR2AAAAoPkI7Qmo4fD4v//dhN5bbjG3gguHXWnv0CG89591lhPGd+92nm9qeHywhehaKp/Pqbbv3RtYaZecee1dujhDzyVnWH2wW+lFU0WFc+s6QjsAAAAQOkJ7Amo4PH7VKrP985/NfHDLav457dAebqVdCj5EPlg4s0O7/wJmLb3SLjkBfM+epkO7v379zHbTpog3r0nFxVJ1tflRx74OyVkDoTmhvbjYOb4l3CUAAAAAaAqhPQE1HB5vL2omSX/8o3T//c0P7m6E9mAryDdVaT9+3IwM8PnCn0sfT+xKe7DQfvXVZv/aawPfY4d5u8odK8EWoZOaX2mvqpJuvtn0z0mTEuN7BwAAQGIjtCcge3i8XWm3Fym7+mqzffZZ5xZjtsOHnXnrwbgZ2v0r7cFCu70Qna1DB+fWby2Z//B4/9XjJWnAAPPcPfcEvsdeWd5Lod2fHboPHgztPE88IW3YYL7zefNcax4AAADgWYT2BNRYpf3BB6Wf/czsv/22c3xVlQlb558vnThx6vmqq51zRWN4fFqalJHhPE6EofGSE9o3bXJ+cMnPb/o9/fubbWlp8+eNu2njRrMdMiTw+eZU2j/5RHrySbP//POJ870DAAAgsRHaE5D/QnSW5YT2/Hxp4kSzv2yZM0R+1SqzONz+/U748vfdd2br8zlD18PRcHh8RYVUXm72GwY0/89p6YvQ2ey54Pb89MxM5weYxrRp4/y9xrLabo/SaDgHvTmhfdo0qbZWuv76U6cBAAAAAC0VoT0B+S9E9+23plIumUA1apSUmmpuK2av4v7++857168/9Xx2IGvXTkpODr9ddrjcvdvMVbeHxqelnTok3j+0J0rF1a602yv8h7oImz2vPdgPLtFi/7Bz1lmBz9uh/fhxc/95yfw4dPvt0vbtznGHD0tr1pj93/42sm0FAAAAvITQnoD8h8fb89nPPtuE9cxMacQI89yyZWZ7utDuxnx2ydxTPCXFDMffvz9waLz/4mVSYob2jh0DpwU0N7THqtJeVyeVlZn9hqE9K8tZj8D+8WfuXOnFF6WHHnKO+/BDM/KjT59TV8gHAAAAWjJCewLyHx7vPzTeNnq02S5fblYp//e/ndc2bDj1fG6F9pSUwPuKB1uEzpaIw+N9vsDbpYUa2mO9GF15uTPVouH0Cf+V/+0h8jt3mu3SpVJlpdlfvtxs7b4JAAAAJApCewLyHx7fVGhftkz64AOzby8EV1ws1dQEns+t0C4FriAfbBE6WyJW2iVniLx0+kXobHalfdMmZ2h9NNlD41u3ltLTT329YWi3FyI8flxaudLsE9oBAACQqAjtCch/eHyw0F5YaMJVaan03HPmudtuM++rqJC2bQs8n5uh3X8F+aYq7f5z3BM1tIdaaT/3XPN9njgRuDJ/tBw5YraNLVLYWGiXpHfeMbeDs0cJfP/7EWggAAAA4GGE9gTkf592e057587O661aSRdeaPbtOez/8R/SwIGBz9kiUWlneHxw4YT2lBSpXz+zH4sh8o0tQmfzD+1Hjjj9STKh3a62Dxgg5eRErJkAAACAJxHaE9DpKu2SNGaMs5+dbRanGzTIPG4Y2u0FxBgeH3nhzGmXYrsYXaiV9oMHnSp7u3bmrgG7d0t/+IN5jqHxAAAASESE9gRkV9qrqqQ9e8x+w9DuH5DGjTPV2sGDzeOGi9HZldEOHc68baEOj6fSHvqcdim2i9E1p9Juh/a+fQMXRJQI7QAAAEhMhPYEZFfaJWnHDrNtGACHDXNuLzZhgtnaoX39emc1cMnd4fE9e5rtwYPOKuJNhfa2bc0CZ4kinOHxUmwr7eGE9l69pCuucI7x+aRLLolcGwEAAACvIrQnoJQUM29dMgvLSYFz2iUzNPkXvzBBafJk81y/fua9330nffmlc6yboT0726nYNzU83g6AiTQ0XjLXO2OG9POfNx6Cg7FD+44d0smTkWlbY043PN6ep95UaB80yJ3+BQAAAMQbQnuCsofIS1JycvAFvmbOlFascMJWerrUv7/Z95/X7mZol5wh8rZgwfz//T9p0iTpP//Tnc+MJ3PmSE8/3bz35OWZH0Pq6qQtWyLTrsaEW2nv1csMk5cYGg8AAIDERWhPUP5D5Dt1kpJC7An+Q+Rtbod2ezE6yfxQkJV16jGtW0uvvy7dfrs7n9nS+Xxm9XVJ2rw5up/dnFu++Yd2yYz26NdPuuOOSLYQAAAA8C5Ce4LyD+3NWdDMXkHeXoyupkYqKzP7kQjteXkmcOLM2aMkoh3aQ620Hzwo7d1r9u0+cNNNpr19+kS2jQAAAIBXpcS6AYgN/+HxzQntDVeQtwOZ1Lw51k3xHx6faHPWIylWoT3USnt1tdmmpTWvTwIAAAAtGZX2BOVfaW+4CF1T7OC3d690/LgzND472yxS54aGlXa4w6uV9oyMwDsA9OwZ+nQNAAAAoKXjf40TVLiV9g4dnEXrtm1zfz67FBjaqbS7xw7te/ZIx45F73NPF9olp9ouBX7/AAAAQKIjtCeocOe0S8784s8/j0xo79zZDJGWCO1u6thROvtss791a/Q+93TD4yVCOwAAANAYQnuCOpPQbt+Ga+vWyIT25GSpRw+zz/B4d9kryG/aFJ3PO3lSqqw0+1TaAQAAgOYjtCeocIfHS5GvtEvSiBFme9557p430UV7XrtdZU9KCuxzDRHaAQAAgOAI7Qkq3IXoJKfSHsnQPm+eVFwsXXSRu+dNdJEO7ZYlrVolnThhHtvz2du1a3pxOUI7AAAAEByhPUHZob1Vq6bnGgdjV9q3b5e++cbsd+jgWtMkmRXFBwzgHu1ui3Ro/9OfpFGjpCeeMI9Dmc8uBYb2nj0j0TIAAAAgPhHaE5Q9VDk/v/nBuFs3c4uuqipp3TrznNuVdkSGHdq//FIqL3f//G+8YbYff2y2oawcLzmh/eyzA0eBAAAAAImO0J6gOnUy23POaf57k5Kk3r3N/vr1Zktojw9nneV891u2uHvumhpp5Uqzv2uX2YYa2u0pGgUF7rYJAAAAiHeE9gR16aXSH/8o/f734b3fHiJfU2O2hPb4EakV5D/7TDp61Ozv22f6RqjD4ydMkB5/XJozx902AQAAAPEuJdYNQGykpEh33BH+++3F6GyE9vjRv7+0ZIn789qXLXP2a2vNEPxQK+3p6dIvf+luewAAAICWgEo7wmJX2m2E9vgRqcXo/EO7ZIbIh1ppBwAAABAcoR1haVhpd3v1eEROJEJ7VZX00Udmv3t3s929O/RKOwAAAIDgCO0Iy/e+F7jqPKEsfgwYYBYT3L/fDGF3w9q10smTZvX3yy83z1FpBwAAAM4coR1hadXKuZ9227ZSamps24PQtW0rDRtm9ouKwj/Ppk3OKvHLl5vt6NHOHQmotAMAAABnjtCOsNlD5JnPHn/GjjXbJUvCe//hw9IFF5i1DZ55xgn/Y8Y4P+b4V9oJ7QAAAEB4CO0Im70YHaE9/tihfelSybKa//4dO6TKSqm6WnrgAenDD83zo0dLvXqZ/V27nEo7w+MBAACA8BDaETb7ft+5ubFtB5qvsFDKyJC++Sa8+7Xbc+FzcqTWrc1+587Suec6lfZDh6SSErNPpR0AAAAID6EdYfvRj6QHH5R+9atYtwTNlZ4uXXyx2V+6tPnvt0P76NHSp59KV18tzZ5tFifMznZGX1RVmS2VdgAAACA8hHaELSPDBDV7UTPEF/8h8s1lh/auXaV+/aR//EO68UbndXuIvI3QDgAAAISH0A4kKDu0r1zpVMRD5R/ag7GHyEvmx520tOa3DwAAAAChHUhY551n7qt+/Li0Zk3z3nu60O5faWc+OwAAABA+QjuQoJKSpEsvNfvNHSLfnEo7oR0AAAAIH6EdSGD2EPnFi0N/T3W1syp8KJV25rMDAAAA4SO0Awls/Hiz/fhjqbQ0tPfs32/u7Z6aaobXB0OlHQAAAHCHp0P7Y489Jp/PF/CnT58+9a9XVFRo6tSp6tChg9q0aaPJkyfrwIEDMWwxEF86d5aGDzch/O23Q3uPPTS+SxczxD6Ybt2c16i0AwAAAOHzdGiXpP79+6ukpKT+z0cffVT/2owZM/T2229r0aJFWrlypfbv36+rr746hq0F4s+kSWb7xhuhHX+6+eySWS3efp1KOwAAABA+z4f2lJQU5eXl1f/p2LGjJKmsrEwvvvii5syZozFjxmjo0KGaP3++Vq1apTXNXQobSGB2aF+6VDp69PTHhxLaJWeIPJV2AAAAIHwpsW7A6ezYsUP5+flq1aqVCgsLNWvWLHXr1k3r1q1TdXW1xtoraUnq06ePunXrptWrV2vkyJGNnrOyslKVlZX1j8vLyyVJ1dXVqq6ujtzFwJPs7zxRv/tzzpG+970U7djh0zvv1Oiaa6wmj9+7N0lSsvLza1VdXdfocUOGJGnFimSdc06NqqubPmdLkuj9Ce6iP8Et9CW4if4EtyR6Xwr1uj0d2keMGKEFCxaod+/eKikp0eOPP66LLrpImzZtUmlpqdLS0tSuQRkvNzdXpadZUWvWrFl6/PHHT3l+8eLFysjIcPMSEEeWLFkS6ybETP/+/bRjx/c0b16JMjI+a/LYTz8dLqmTyso26d139zR63MiRSfrtb7PUtu0Rvfuuu+2NB4ncn+A++hPcQl+Cm+hPcEui9qUTJ06EdJzPsqy4KYEdOXJE3bt315w5c9S6dWvdeuutARVzSRo+fLhGjx6tp556qtHzBKu0d+3aVYcOHVJWVlbE2g9vqq6u1pIlSzRu3DilpqbGujkxsXq1T5dckqLsbEtff12jtLTGjx0xIkXr1/v02ms1mjgxbv75iBr6E9xEf4Jb6EtwE/0Jbkn0vlReXq6OHTuqrKysyRzq6Up7Q+3atVNBQYF27typcePGqaqqSkeOHAmoth84cEB5eXlNnic9PV3p6emnPJ+ampqQnQVGIn//o0ZJubnSgQM+rVqVqnHjGj/266/NtmfPFCXoX1dIErk/wX30J7iFvgQ30Z/glkTtS6Fes+cXovN37NgxffHFF+rUqZOGDh2q1NRUFRUV1b++bds27du3T4WFhTFsJRB/kpOlH/7Q7E+dKi1cKNXWnnpcZaX0zTdm/3QL0QEAAAA4c54O7Q888IBWrlypPXv2aNWqVbrqqquUnJysG264QdnZ2brtttt0//33a/ny5Vq3bp1uvfVWFRYWNrkIHYDgpk0zt2fbsUO68Uapf39p797AY776ymxbt5bat49+GwEAAIBE4+nQ/tVXX+mGG25Q79699aMf/UgdOnTQmjVrlJOTI0n63e9+p4kTJ2ry5Mm6+OKLlZeXp9deey3GrQbiU//+0q5d0q9/bQL5tm1Sw6Uh/G/35vNFv40AAABAovH0nPZXXnmlyddbtWqluXPnau7cuVFqEdCytWsnPfKIdN555v7tDRfyDPUe7QAAAADc4elKO4DYGD3azHPfuVPas8d5ntAOAAAARBehHcApsrIke2kI/2o7oR0AAACILkI7gKDs274tXuw8R2gHAAAAoovQDiCoyy4z26Ii5/ZvhHYAAAAgujy9EB2A2Bk2TMrOlr77TvrsMyk/39wOTiK0AwAAANFCpR1AUCkpZkE6SfrgA+mWW6STJ6WhQ6W+fWPaNAAAACBhENoBNMqe1z5rlrR0qdS6tfTyy1IS/3IAAAAAUcH/egNolD2v/cQJs50zR+rdO3btAQAAABINoR1Ao845R+rRw+xPnCj99KcxbQ4AAACQcAjtABrl85nq+o9/LP3pT+YxAAAAgOhh9XgATbrqKvMHAAAAQPRRaQcAAAAAwKMI7QAAAAAAeBShHQAAAAAAjyK0AwAAAADgUYR2AAAAAAA8itAOAAAAAIBHEdoBAAAAAPAoQjsAAAAAAB5FaAcAAAAAwKMI7QAAAAAAeBShHQAAAAAAjyK0AwAAAADgUYR2AAAAAAA8itAOAAAAAIBHEdoBAAAAAPAoQjsAAAAAAB5FaAcAAAAAwKMI7QAAAAAAeFRKrBvgBZZlSZLKy8tj3BLEQnV1tU6cOKHy8nKlpqbGujmIc/QnuIn+BLfQl+Am+hPckuh9yc6fdh5tDKFd0tGjRyVJXbt2jXFLAAAAAACJ5OjRo8rOzm70dZ91ulifAOrq6rR//361bdtWPp8v1s1BlJWXl6tr16768ssvlZWVFevmIM7Rn+Am+hPcQl+Cm+hPcEui9yXLsnT06FHl5+crKanxmetU2iUlJSWpS5cusW4GYiwrKysh/7FAZNCf4Cb6E9xCX4Kb6E9wSyL3paYq7DYWogMAAAAAwKMI7QAAAAAAeBShHQkvPT1djz76qNLT02PdFLQA9Ce4if4Et9CX4Cb6E9xCXwoNC9EBAAAAAOBRVNoBAAAAAPAoQjsAAAAAAB5FaAcAAAAAwKMI7QAAAAAAeBShHS3CrFmzNGzYMLVt21Znn322Jk2apG3btgUcU1FRoalTp6pDhw5q06aNJk+erAMHDgQcc99992no0KFKT0/XoEGDgn6WZVl6+umnVVBQoPT0dHXu3Fm/+c1vInVpiIFo9afHHntMPp/vlD+ZmZmRvDxEUTT/bfrggw80cuRItW3bVjk5OZo8ebL27NkToStDtEWzL7366qsaNGiQMjIy1L17d/32t7+N1GUhRtzoT//+9791ww03qGvXrmrdurX69u2r//7v/z7ls1asWKEhQ4YoPT1d5557rhYsWBDpy0OURas/lZSU6MYbb1RBQYGSkpI0ffr0aFyeJxDa0SKsXLlSU6dO1Zo1a7RkyRJVV1frsssu0/Hjx+uPmTFjht5++20tWrRIK1eu1P79+3X11Vefcq6f/OQnuu666xr9rGnTpumFF17Q008/rc8//1xvvfWWhg8fHpHrQmxEqz898MADKikpCfjTr18/XXvttRG7NkRXtPrS7t27deWVV2rMmDHasGGDPvjgAx06dCjoeRCfotWX3nvvPd1000266667tGnTJv3hD3/Q7373O/3P//xPxK4N0edGf1q3bp3OPvts/fWvf9XmzZv1i1/8Qg8//HBAX9m9e7euuOIKjR49Whs2bND06dN1++2364MPPojq9SKyotWfKisrlZOTo0ceeUQDBw6M6jXGnAW0QN98840lyVq5cqVlWZZ15MgRKzU11Vq0aFH9MVu3brUkWatXrz7l/Y8++qg1cODAU57fsmWLlZKSYn3++ecRazu8J1L9qaENGzZYkqwPP/zQtbbDWyLVlxYtWmSlpKRYtbW19c+99dZbls/ns6qqqty/EMRcpPrSDTfcYF1zzTUBzz333HNWly5drLq6OncvAp5xpv3Jds8991ijR4+uf/zQQw9Z/fv3Dzjmuuuus8aPH+/yFcBLItWf/F1yySXWtGnTXG23l1FpR4tUVlYmSWrfvr0k8+tddXW1xo4dW39Mnz591K1bN61evTrk87799tvq1auX3nnnHfXs2VM9evTQ7bffrsOHD7t7AfCUSPWnhl544QUVFBTooosuOrMGw7Mi1ZeGDh2qpKQkzZ8/X7W1tSorK9Nf/vIXjR07Vqmpqe5eBDwhUn2psrJSrVq1CniudevW+uqrr7R3714XWg4vcqs/lZWV1Z9DklavXh1wDkkaP378Gf23Et4Xqf6UyAjtaHHq6uo0ffp0jRo1SgMGDJAklZaWKi0tTe3atQs4Njc3V6WlpSGfe9euXdq7d68WLVqkl156SQsWLNC6det0zTXXuHkJ8JBI9id/FRUVevnll3XbbbedaZPhUZHsSz179tTixYs1c+ZMpaenq127dvrqq6/06quvunkJ8IhI9qXx48frtddeU1FRkerq6rR9+3Y988wzksx8UrQ8bvWnVatW6e9//7vuvPPO+udKS0uVm5t7yjnKy8t18uRJdy8EnhDJ/pTIUmLdAMBtU6dO1aZNm/TRRx+5fu66ujpVVlbqpZdeUkFBgSTpxRdf1NChQ7Vt2zb17t3b9c9EbEWyP/l7/fXXdfToUU2ZMiWin4PYiWRfKi0t1R133KEpU6bohhtu0NGjR/XLX/5S11xzjZYsWSKfz+f6ZyJ2ItmX7rjjDn3xxReaOHGiqqurlZWVpWnTpumxxx5TUhK1npbIjf60adMmXXnllXr00Ud12WWXudg6xBv6U2Twry9alHvvvVfvvPOOli9fri5dutQ/n5eXp6qqKh05ciTg+AMHDigvLy/k83fq1EkpKSn1gV2S+vbtK0nat2/fmTUenhPp/uTvhRde0MSJE0+pSKBliHRfmjt3rrKzszV79mwNHjxYF198sf7617+qqKhIa9eudesy4AGR7ks+n09PPfWUjh07pr1796q0tLR+sdVevXq5cg3wDjf605YtW3TppZfqzjvv1COPPBLwWl5e3il3MDhw4ICysrLUunVrdy8GMRfp/pTICO1oESzL0r333qvXX39dy5YtU8+ePQNeHzp0qFJTU1VUVFT/3LZt27Rv3z4VFhaG/DmjRo1STU2Nvvjii/rntm/fLknq3r37GV4FvCJa/cm2e/duLV++nKHxLVC0+tKJEydOqYImJydLMiOEEP+i/e9ScnKyOnfurLS0NC1cuFCFhYXKyck54+uAN7jVnzZv3qzRo0drypQpQW9/W1hYGHAOSVqyZElYfRLeFa3+lNBiuQoe4Ja7777bys7OtlasWGGVlJTU/zlx4kT9MXfddZfVrVs3a9myZdann35qFRYWWoWFhQHn2bFjh7V+/Xrrpz/9qVVQUGCtX7/eWr9+vVVZWWlZlmXV1tZaQ4YMsS6++GLrs88+sz799FNrxIgR1rhx46J6vYisaPUn2yOPPGLl5+dbNTU1Ubk+RE+0+lJRUZHl8/msxx9/3Nq+fbu1bt06a/z48Vb37t0DPgvxK1p96eDBg9bzzz9vbd261Vq/fr113333Wa1atbLWrl0b1etFZLnRn4qLi62cnBzrxz/+ccA5vvnmm/pjdu3aZWVkZFgPPvigtXXrVmvu3LlWcnKy9f7770f1ehFZ0epPlmXV/5s1dOhQ68Ybb7TWr19vbd68OWrXGiuEdrQIkoL+mT9/fv0xJ0+etO655x7rrLPOsjIyMqyrrrrKKikpCTjPJZdcEvQ8u3fvrj/m66+/tq6++mqrTZs2Vm5urnXLLbdY3377bZSuFNEQzf5UW1trdenSxZo5c2aUrg7RFM2+tHDhQmvw4MFWZmamlZOTY/3whz+0tm7dGqUrRaRFqy8dPHjQGjlypJWZmWllZGRYl156qbVmzZooXimiwY3+9OijjwY9R/fu3QM+a/ny5dagQYOstLQ0q1evXgGfgZYhmv0plGNaIp9lWdYZFOoBAAAAAECEMKcdAAAAAACPIrQDAAAAAOBRhHYAAAAAADyK0A4AAAAAgEcR2gEAAAAA8ChCOwAAAAAAHkVoBwAAAADAowjtAAAAAAB4FKEdAAAAAACPIrQDAJDgbrnlFvl8Pvl8PqWmpio3N1fjxo3Tn/70J9XV1YV8ngULFqhdu3aRaygAAAmI0A4AADRhwgSVlJRoz549eu+99zR69GhNmzZNEydOVE1NTaybBwBAwiK0AwAApaenKy8vT507d9aQIUM0c+ZMvfnmm3rvvfe0YMECSdKcOXN03nnnKTMzU127dtU999yjY8eOSZJWrFihW2+9VWVlZfVV+8cee0ySVFlZqQceeECdO3dWZmamRowYoRUrVsTmQgEAiDOEdgAAENSYMWM0cOBAvfbaa5KkpKQkPffcc9q8ebP+/Oc/a9myZXrooYckSRdeeKGeffZZZWVlqaSkRCUlJXrggQckSffee69Wr16tV155RRs3btS1116rCRMmaMeOHTG7NgAA4oXPsiwr1o0AAACxc8stt+jIkSN64403Tnnt+uuv18aNG7Vly5ZTXvvf//1f3XXXXTp06JAkM6d9+vTpOnLkSP0x+/btU69evbRv3z7l5+fXPz927FgNHz5cTz75pOvXAwBAS5IS6wYAAADvsixLPp9PkrR06VLNmjVLn3/+ucrLy1VTU6OKigqdOHFCGRkZQd9fXFys2tpaFRQUBDxfWVmpDh06RLz9AADEO0I7AABo1NatW9WzZ0/t2bNHEydO1N13363f/OY3at++vT766CPddtttqqqqajS0Hzt2TMnJyVq3bp2Sk5MDXmvTpk00LgEAgLhGaAcAAEEtW7ZMxcXFmjFjhtatW6e6ujo988wzSkoyS+K8+uqrAcenpaWptrY24LnBgwertrZW33zzjS666KKotR0AgJaC0A4AAFRZWanS0lLV1tbqwIEDev/99zVr1ixNnDhRN998szZt2qTq6mr9/ve/1w9+8AP961//0rx58wLO0aNHDx07dkxFRUUaOHCgMjIyVFBQoJtuukk333yznnnmGQ0ePFgHDx5UUVGRzj//fF1xxRUxumIAAOIDq8cDAAC9//776tSpk3r06KEJEyZo+fLleu655/Tmm28qOTlZAwcO1Jw5c/TUU09pwIABevnllzVr1qyAc1x44YW66667dN111yknJ0ezZ8+WJM2fP18333yzfv7zn6t3796aNGmSPvnkE3Xr1i0WlwoAQFxh9XgAAAAAADyKSjsAAAAAAB5FaAcAAAAAwKMI7QAAAAAAeBShHQAAAAAAjyK0AwAAAADgUYR2AAAAAAA8itAOAAAAAIBHEdoBAAAAAPAoQjsAAAAAAB5FaAcAAAAAwKMI7QAAAAAAeNT/B3EHceaEB/hzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "train_data = train_data.sort_values(by='Date')\n",
    "\n",
    "# Create a time series plot for the closing prices\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(train_data['Date'], train_data['Close'], label='Close Price', color='blue')\n",
    "plt.title('Closing Prices Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Price')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514570f9",
   "metadata": {
    "papermill": {
     "duration": 0.019301,
     "end_time": "2023-11-03T14:08:57.330946",
     "exception": false,
     "start_time": "2023-11-03T14:08:57.311645",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The time series plot displays the closing prices from 2016 to 2021. It shows an upward trend from mid-2016 to early 2018, with notable volatility post-2018."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929ca8c2",
   "metadata": {
    "papermill": {
     "duration": 0.01859,
     "end_time": "2023-11-03T14:08:57.368336",
     "exception": false,
     "start_time": "2023-11-03T14:08:57.349746",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.2 Data Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f183c9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:57.412913Z",
     "iopub.status.busy": "2023-11-03T14:08:57.412137Z",
     "iopub.status.idle": "2023-11-03T14:08:57.429313Z",
     "shell.execute_reply": "2023-11-03T14:08:57.428058Z"
    },
    "papermill": {
     "duration": 0.042954,
     "end_time": "2023-11-03T14:08:57.431924",
     "exception": false,
     "start_time": "2023-11-03T14:08:57.388970",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Date        Open       Close        Volume\n",
      "count                  300  300.000000  300.000000  3.000000e+02\n",
      "mean   2018-04-12 12:00:00   75.977813   79.430489  1.866300e+08\n",
      "min    2015-06-01 00:00:00   40.269435   47.979353  5.991330e+07\n",
      "25%    2016-11-05 06:00:00   64.872395   69.689227  1.324862e+08\n",
      "50%    2018-04-12 12:00:00   79.805683   82.991900  1.715536e+08\n",
      "75%    2019-09-17 18:00:00   88.342927   89.719145  2.201842e+08\n",
      "max    2021-02-22 00:00:00  102.558322  103.119373  6.935702e+08\n",
      "std                    NaN   16.158343   13.878395  7.952792e+07\n"
     ]
    }
   ],
   "source": [
    "data_summary = train_data.describe()\n",
    "print(data_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d6d8aa",
   "metadata": {
    "papermill": {
     "duration": 0.021053,
     "end_time": "2023-11-03T14:08:57.472281",
     "exception": false,
     "start_time": "2023-11-03T14:08:57.451228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The data provides insights into the opening and closing prices of a certain financial instrument or stock, as well as its trading volume, over a specified period. The prices have shown fluctuations, as indicated by the standard deviation, and the volume has varied considerably as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb9afe5",
   "metadata": {
    "papermill": {
     "duration": 0.02065,
     "end_time": "2023-11-03T14:08:57.514733",
     "exception": false,
     "start_time": "2023-11-03T14:08:57.494083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.2 Data Distribution plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0fffd8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:57.560913Z",
     "iopub.status.busy": "2023-11-03T14:08:57.560465Z",
     "iopub.status.idle": "2023-11-03T14:08:58.297597Z",
     "shell.execute_reply": "2023-11-03T14:08:58.295756Z"
    },
    "papermill": {
     "duration": 0.763663,
     "end_time": "2023-11-03T14:08:58.300572",
     "exception": false,
     "start_time": "2023-11-03T14:08:57.536909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data distribution for numerical columns\n",
    "plt.figure(figsize=(14, 5))\n",
    "\n",
    "# Plot histograms for 'Open,' 'Close,' and 'Volume'\n",
    "plt.subplot(131)\n",
    "plt.hist(train_data['Open'], bins=20, color='blue', alpha=0.7)\n",
    "plt.title('Distribution of Open Price')\n",
    "plt.xlabel('Open Price')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.hist(train_data['Close'], bins=20, color='green', alpha=0.7)\n",
    "plt.title('Distribution of Close Price')\n",
    "plt.xlabel('Close Price')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.hist(train_data['Volume'], bins=20, color='red', alpha=0.7)\n",
    "plt.title('Distribution of Volume')\n",
    "plt.xlabel('Volume')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625a1547",
   "metadata": {
    "papermill": {
     "duration": 0.019797,
     "end_time": "2023-11-03T14:08:58.340338",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.320541",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The histograms display the distribution of Open Price, Close Price, and Volume:\n",
    "\n",
    "1. Open Price: Most frequently falls in the range of approximately 70 to 90.\n",
    "2. Close Price: Peaks around 70 to 80.\n",
    "3. Volume: Predominantly lies between 1e8 to 4e8 with a sharp decrease thereafter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de087f45",
   "metadata": {
    "papermill": {
     "duration": 0.020156,
     "end_time": "2023-11-03T14:08:58.380595",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.360439",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.3 Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f05e8f71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:58.423017Z",
     "iopub.status.busy": "2023-11-03T14:08:58.422397Z",
     "iopub.status.idle": "2023-11-03T14:08:58.432208Z",
     "shell.execute_reply": "2023-11-03T14:08:58.430451Z"
    },
    "papermill": {
     "duration": 0.034551,
     "end_time": "2023-11-03T14:08:58.434822",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.400271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 'Close' prices are not normally distributed (reject the null hypothesis).\n",
      "Test Statistic: 0.941137433052063\n",
      "P-Value: 1.4571368556204334e-09\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "# Extract the 'Close' prices\n",
    "close_prices = train_data['Close']\n",
    "\n",
    "# Perform the Shapiro-Wilk test for normality\n",
    "statistic, p_value = shapiro(close_prices)\n",
    "\n",
    "# Set the significance level\n",
    "alpha = 0.05\n",
    "\n",
    "# Inference\n",
    "if p_value > alpha:\n",
    "    print(\"The 'Close' prices are normally distributed (fail to reject the null hypothesis).\")\n",
    "else:\n",
    "    print(\"The 'Close' prices are not normally distributed (reject the null hypothesis).\")\n",
    "\n",
    "# Output the test statistic and p-value\n",
    "print(f\"Test Statistic: {statistic}\")\n",
    "print(f\"P-Value: {p_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc737c0d",
   "metadata": {
    "papermill": {
     "duration": 0.019859,
     "end_time": "2023-11-03T14:08:58.474673",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.454814",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The Shapiro-Wilk test was conducted to check the normality of 'Close' prices. The result indicates that the 'Close' prices are not normally distributed with a Test Statistic of 0.9411 and a p-value close to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395f4559",
   "metadata": {
    "papermill": {
     "duration": 0.019338,
     "end_time": "2023-11-03T14:08:58.514008",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.494670",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.4 Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "928be389",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:58.557344Z",
     "iopub.status.busy": "2023-11-03T14:08:58.556958Z",
     "iopub.status.idle": "2023-11-03T14:08:58.781894Z",
     "shell.execute_reply": "2023-11-03T14:08:58.780926Z"
    },
    "papermill": {
     "duration": 0.251342,
     "end_time": "2023-11-03T14:08:58.784786",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.533444",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.11876288e+08 -3.86184626e+00]\n",
      " [ 4.13447877e+07  7.17960821e+00]\n",
      " [ 6.40408877e+07  1.90419545e+01]\n",
      " [ 3.69842877e+07  1.58694245e+01]\n",
      " [ 2.20184888e+08  2.68894183e+00]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "features = train_data[['Open', 'Close', 'Volume']]\n",
    "\n",
    "# Initialize PCA with the desired number of components\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "# Fit and transform the data\n",
    "reduced_data = pca.fit_transform(features)\n",
    "# Output the first few rows of the reduced data\n",
    "print(reduced_data[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6561a4",
   "metadata": {
    "papermill": {
     "duration": 0.019634,
     "end_time": "2023-11-03T14:08:58.824843",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.805209",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Principal Component Analysis (PCA) was applied to reduce the dimensionality of the 'Open', 'Close', and 'Volume' features. The data was transformed into 2 principal components. The displayed output shows the first few rows of the reduced data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f71dfd4",
   "metadata": {
    "papermill": {
     "duration": 0.019631,
     "end_time": "2023-11-03T14:08:58.864304",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.844673",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.5 Pattern and Anamoly Detection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6959fb4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:58.908420Z",
     "iopub.status.busy": "2023-11-03T14:08:58.907030Z",
     "iopub.status.idle": "2023-11-03T14:08:59.625116Z",
     "shell.execute_reply": "2023-11-03T14:08:59.624360Z"
    },
    "papermill": {
     "duration": 0.742866,
     "end_time": "2023-11-03T14:08:59.627048",
     "exception": false,
     "start_time": "2023-11-03T14:08:58.884182",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date       Open      Close     Volume Strategy  Cluster\n",
      "id                                                              \n",
      "0  2015-06-01  66.208486  75.609978  298506300     Hold        2\n",
      "1  2015-06-08  64.116235  74.443331  227974800     Hold        2\n",
      "2  2015-06-15  47.701942  71.140831  250670900      Buy        2\n",
      "3  2015-06-22  54.754816  72.881344  223614300     Hold        2\n",
      "4  2015-06-29  48.031899  66.284718  406814900      Buy        1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "feature_for_clustering = train_data[['Close']]\n",
    "\n",
    "# Initialize K-Means with the desired number of clusters (e.g., 3) and set n_init explicitly\n",
    "kmeans = KMeans(n_clusters=3, random_state=0, n_init=10)\n",
    "\n",
    "# Fit the data to the K-Means model\n",
    "train_data['Cluster'] = kmeans.fit_predict(feature_for_clustering)\n",
    "# Output the first few rows of the data with cluster assignments\n",
    "print(train_data.head())\n",
    "\n",
    "# Visualize the clusters\n",
    "plt.scatter(train_data.index, train_data['Close'], c=train_data['Cluster'], cmap='viridis')\n",
    "plt.title('K-Means Clustering of Close Prices')\n",
    "plt.xlabel('Data Point Index')\n",
    "plt.ylabel('Close Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97b4e2e",
   "metadata": {
    "papermill": {
     "duration": 0.019748,
     "end_time": "2023-11-03T14:08:59.667653",
     "exception": false,
     "start_time": "2023-11-03T14:08:59.647905",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The K-Means clustering of 'Close Prices' has grouped the data into three distinct clusters, represented by different colors. The scatter plot shows the distribution of these clusters across the data point indices. The table snippet above provides a glimpse of the data, indicating the strategy ('Hold' or 'Buy') and associated cluster number for specific data points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854bc95a",
   "metadata": {
    "papermill": {
     "duration": 0.019892,
     "end_time": "2023-11-03T14:08:59.707632",
     "exception": false,
     "start_time": "2023-11-03T14:08:59.687740",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.6 Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71c1d069",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:08:59.752278Z",
     "iopub.status.busy": "2023-11-03T14:08:59.750450Z",
     "iopub.status.idle": "2023-11-03T14:09:00.824529Z",
     "shell.execute_reply": "2023-11-03T14:09:00.822996Z"
    },
    "papermill": {
     "duration": 1.099272,
     "end_time": "2023-11-03T14:09:00.826917",
     "exception": false,
     "start_time": "2023-11-03T14:08:59.727645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert 'Date' column to datetime\n",
    "train_data['Date'] = pd.to_datetime(train_data['Date'])\n",
    "\n",
    "# Set the date as the index for time series analysis\n",
    "train_data.set_index('Date', inplace=True)\n",
    "\n",
    "# Create subplots\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 10))\n",
    "fig.suptitle('Stock Price EDA', fontsize=16)\n",
    "\n",
    "# Plot 1: Closing Price over Time\n",
    "axes[0, 0].plot(train_data.index, train_data['Close'], color='blue')\n",
    "axes[0, 0].set_title('Closing Price Over Time')\n",
    "axes[0, 0].set_xlabel('Date')\n",
    "axes[0, 0].set_ylabel('Close Price')\n",
    "\n",
    "# Plot 2: Volume over Time\n",
    "axes[0, 1].plot(train_data.index, train_data['Volume'], color='green')\n",
    "axes[0, 1].set_title('Volume Over Time')\n",
    "axes[0, 1].set_xlabel('Date')\n",
    "axes[0, 1].set_ylabel('Volume')\n",
    "\n",
    "# Plot 3: Strategy Distribution\n",
    "sns.countplot(data=train_data, x='Strategy', ax=axes[1, 0])\n",
    "axes[1, 0].set_title('Distribution of Strategies')\n",
    "axes[1, 0].set_xlabel('Strategy')\n",
    "axes[1, 0].set_ylabel('Count')\n",
    "\n",
    "# Plot 4: Weekly Mean Close Price\n",
    "weekly_mean = train_data['Close'].resample('W').mean()\n",
    "weekly_mean.plot(ax=axes[1, 1], color='red')\n",
    "axes[1, 1].set_title('Weekly Mean Close Price')\n",
    "axes[1, 1].set_xlabel('Week')\n",
    "axes[1, 1].set_ylabel('Mean Close Price')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb5f216",
   "metadata": {
    "papermill": {
     "duration": 0.022257,
     "end_time": "2023-11-03T14:09:00.871691",
     "exception": false,
     "start_time": "2023-11-03T14:09:00.849434",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The provided charts offer an exploratory data analysis (EDA) of stock prices:\n",
    "\n",
    "1. *Closing Price Over Time*: Shows fluctuations in stock prices from 2016 to 2021 with noticeable highs around 2018 and lows in 2016 and 2020.\n",
    "2. *Volume Over Time*: Indicates the traded volume of the stock over the same period. There's a pronounced spike around 2017 and overall variability throughout the years.\n",
    "3. *Distribution of Strategies*: Displays the frequency of trading strategies used. 'Hold' is the most frequently adopted strategy, followed by 'Buy' and 'Sell'.\n",
    "4. *Weekly Mean Close Price*: Represents the average weekly closing prices of the stock, depicting a rise until 2018, followed by a decline and then stabilization. \n",
    "\n",
    "Overall, the stock experienced notable price changes over the years, with trading strategies predominantly favoring 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11bcf634",
   "metadata": {
    "papermill": {
     "duration": 0.02155,
     "end_time": "2023-11-03T14:09:00.915286",
     "exception": false,
     "start_time": "2023-11-03T14:09:00.893736",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.6 Trend Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "58a7aa1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:00.961015Z",
     "iopub.status.busy": "2023-11-03T14:09:00.960632Z",
     "iopub.status.idle": "2023-11-03T14:09:01.247370Z",
     "shell.execute_reply": "2023-11-03T14:09:01.246238Z"
    },
    "papermill": {
     "duration": 0.313314,
     "end_time": "2023-11-03T14:09:01.250463",
     "exception": false,
     "start_time": "2023-11-03T14:09:00.937149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trend Analysis\n",
    "# Calculate returns\n",
    "train_data['Returns'] = train_data['Close'].pct_change()\n",
    "\n",
    "# Visualize Returns\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(train_data.index, train_data['Returns'], color='purple')\n",
    "plt.title('Daily Returns Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Daily Returns')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "969f4bc1",
   "metadata": {
    "papermill": {
     "duration": 0.02338,
     "end_time": "2023-11-03T14:09:01.297645",
     "exception": false,
     "start_time": "2023-11-03T14:09:01.274265",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The chart displays the daily returns of a stock from 2016 to 2021. The returns fluctuate frequently between positive and negative values, indicating volatility. Notably, there are several instances of sharp spikes and drops throughout the period, suggesting days with significant price changes. Overall, the stock has experienced consistent variability in its daily returns over the years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66d8f93b",
   "metadata": {
    "papermill": {
     "duration": 0.024801,
     "end_time": "2023-11-03T14:09:01.346048",
     "exception": false,
     "start_time": "2023-11-03T14:09:01.321247",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.7 Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "89d360fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:01.397127Z",
     "iopub.status.busy": "2023-11-03T14:09:01.396452Z",
     "iopub.status.idle": "2023-11-03T14:09:01.824820Z",
     "shell.execute_reply": "2023-11-03T14:09:01.824104Z"
    },
    "papermill": {
     "duration": 0.455235,
     "end_time": "2023-11-03T14:09:01.826717",
     "exception": false,
     "start_time": "2023-11-03T14:09:01.371482",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One-hot encoding for the 'Strategy' column\n",
    "train_data = pd.get_dummies(train_data, columns=['Strategy'])\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix = train_data.corr()\n",
    "\n",
    "# Create a heatmap to visualize the correlations\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f11f01",
   "metadata": {
    "papermill": {
     "duration": 0.024534,
     "end_time": "2023-11-03T14:09:01.876305",
     "exception": false,
     "start_time": "2023-11-03T14:09:01.851771",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The heatmap displays the correlation matrix for various metrics. Key observations:\n",
    "\n",
    "1. \"Open\" and \"Close\" prices are highly correlated (0.93), indicating they often move together.\n",
    "2. Both \"Open\" and \"Close\" prices have a negative correlation with \"Volume\" (around -0.46), suggesting that as prices increase, trading volume might decrease.\n",
    "3. \"Cluster\" and \"Volume\" have a positive correlation of 0.23, implying some relation between clustering patterns and trading volume.\n",
    "4. \"Returns\" exhibit low correlation with most other metrics, with the most negative correlation being with \"Strategy_Buy\" at -0.32.\n",
    "5. Strategies \"Buy\", \"Hold\", and \"Sell\" have negative correlations among them, with \"Buy\" and \"Hold\" having the highest negative correlation (-0.57).\n",
    "\n",
    "Overall, the matrix gives insights into how certain metrics relate to each other in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ed57afc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:01.925642Z",
     "iopub.status.busy": "2023-11-03T14:09:01.924901Z",
     "iopub.status.idle": "2023-11-03T14:09:02.221173Z",
     "shell.execute_reply": "2023-11-03T14:09:02.219773Z"
    },
    "papermill": {
     "duration": 0.32425,
     "end_time": "2023-11-03T14:09:02.224212",
     "exception": false,
     "start_time": "2023-11-03T14:09:01.899962",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load your dataset (replace 'your_dataset.csv' with the actual file path)\n",
    "data = pd.read_csv('/kaggle/input/ue21cs342aa2/train.csv')\n",
    "\n",
    "# Convert 'Date' to datetime\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "\n",
    "# Calculate the 10-day moving average of the 'Close' column\n",
    "data['10_Day_MA'] = data['Close'].rolling(window=10).mean()\n",
    "\n",
    "# Create a line chart to visualize the closing prices and moving average\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(data['Date'], data['Close'], label='Closing Prices', color='blue')\n",
    "plt.plot(data['Date'], data['10_Day_MA'], label='10-Day Moving Average', color='red')\n",
    "\n",
    "# Add labels and legend\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Closing Prices and 10-Day Moving Average')\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d705807",
   "metadata": {
    "papermill": {
     "duration": 0.025079,
     "end_time": "2023-11-03T14:09:02.274057",
     "exception": false,
     "start_time": "2023-11-03T14:09:02.248978",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The graph displays the closing prices of an asset over time from 2016 to 2021. Additionally, a 10-day moving average is plotted. The asset experienced notable fluctuations, with a peak around late 2017 or early 2018. After this peak, there was a decline followed by stabilization. The 10-day moving average generally follows the trend of the closing prices, smoothing out short-term fluctuations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2167c0a4",
   "metadata": {
    "papermill": {
     "duration": 0.024378,
     "end_time": "2023-11-03T14:09:02.323385",
     "exception": false,
     "start_time": "2023-11-03T14:09:02.299007",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4. ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d194d74e",
   "metadata": {
    "papermill": {
     "duration": 0.026286,
     "end_time": "2023-11-03T14:09:02.377213",
     "exception": false,
     "start_time": "2023-11-03T14:09:02.350927",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.1 Checking if data is stationary -> Dickey Fuller Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78d8332c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:02.428973Z",
     "iopub.status.busy": "2023-11-03T14:09:02.428614Z",
     "iopub.status.idle": "2023-11-03T14:09:02.694523Z",
     "shell.execute_reply": "2023-11-03T14:09:02.692412Z"
    },
    "papermill": {
     "duration": 0.29576,
     "end_time": "2023-11-03T14:09:02.697525",
     "exception": false,
     "start_time": "2023-11-03T14:09:02.401765",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -1.808006\n",
      "p-value                          0.376494\n",
      "#Lags Used                       1.000000\n",
      "Number of Observations Used    298.000000\n",
      "Critical Value (1%)             -3.452486\n",
      "Critical Value (5%)             -2.871288\n",
      "Critical Value (10%)            -2.571964\n",
      "dtype: float64\n",
      "\n",
      "Not Stationary!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def check_stationarity(timeseries):\n",
    "    print('Results of Dickey-Fuller Test:')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\n",
    "\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)' % key] = value\n",
    "    \n",
    "    print(dfoutput)\n",
    "\n",
    "    if dfoutput['p-value'] <= 0.05:\n",
    "        print(\"\\nStationary!\")\n",
    "    else:\n",
    "        print(\"\\nNot Stationary!\")\n",
    "\n",
    "check_stationarity(train_data['Close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d353b48e",
   "metadata": {
    "papermill": {
     "duration": 0.025331,
     "end_time": "2023-11-03T14:09:02.748489",
     "exception": false,
     "start_time": "2023-11-03T14:09:02.723158",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The Dickey-Fuller test results indicate that the data is not stationary, as the test statistic (-1.808006) is greater than the critical values at all confidence levels (1%, 5%, 10%) and the p-value (0.376494) is greater than common significance levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e13aa91",
   "metadata": {
    "papermill": {
     "duration": 0.025477,
     "end_time": "2023-11-03T14:09:02.799323",
     "exception": false,
     "start_time": "2023-11-03T14:09:02.773846",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.2 Differencing to make it stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72dc7e73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:02.851721Z",
     "iopub.status.busy": "2023-11-03T14:09:02.851075Z",
     "iopub.status.idle": "2023-11-03T14:09:03.140641Z",
     "shell.execute_reply": "2023-11-03T14:09:03.138947Z"
    },
    "papermill": {
     "duration": 0.318764,
     "end_time": "2023-11-03T14:09:03.143554",
     "exception": false,
     "start_time": "2023-11-03T14:09:02.824790",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.arima.model import ARIMA  # Updated import\n",
    "\n",
    "# Load your dataset (replace 'your_dataset.csv' with the actual file path)\n",
    "data = pd.read_csv('/kaggle/input/ue21cs342aa2/train.csv')\n",
    "\n",
    "# Convert 'Date' to datetime\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "\n",
    "# Set 'Date' as the index\n",
    "data.set_index('Date', inplace=True)\n",
    "\n",
    "# Differencing to make the data stationary\n",
    "data['Close_diff'] = data['Close'].diff().dropna()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355dd3f5",
   "metadata": {
    "papermill": {
     "duration": 0.025683,
     "end_time": "2023-11-03T14:09:03.195460",
     "exception": false,
     "start_time": "2023-11-03T14:09:03.169777",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.3 ACF and PACF plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1dfa4087",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:03.248408Z",
     "iopub.status.busy": "2023-11-03T14:09:03.248049Z",
     "iopub.status.idle": "2023-11-03T14:09:03.709709Z",
     "shell.execute_reply": "2023-11-03T14:09:03.708954Z"
    },
    "papermill": {
     "duration": 0.490498,
     "end_time": "2023-11-03T14:09:03.711498",
     "exception": false,
     "start_time": "2023-11-03T14:09:03.221000",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_diff = data['Close'].diff().dropna()\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "plot_acf(data_diff, ax=ax[0], lags=20)\n",
    "plot_pacf(data_diff, ax=ax[1], lags=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0030898",
   "metadata": {
    "papermill": {
     "duration": 0.024215,
     "end_time": "2023-11-03T14:09:03.760643",
     "exception": false,
     "start_time": "2023-11-03T14:09:03.736428",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The autocorrelation plot shows a significant spike at lag 1 and then declines towards zero for subsequent lags. The partial autocorrelation plot also has a significant spike at lag 1 but drops off sharply afterwards. This suggests that the data may be best described using an AR(1) model, as there's a strong autocorrelation at the first lag and minimal correlations thereafter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eaf4713",
   "metadata": {
    "papermill": {
     "duration": 0.024434,
     "end_time": "2023-11-03T14:09:03.810115",
     "exception": false,
     "start_time": "2023-11-03T14:09:03.785681",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.4 Training ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae25bdbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:03.863745Z",
     "iopub.status.busy": "2023-11-03T14:09:03.863061Z",
     "iopub.status.idle": "2023-11-03T14:09:03.971281Z",
     "shell.execute_reply": "2023-11-03T14:09:03.970336Z"
    },
    "papermill": {
     "duration": 0.138913,
     "end_time": "2023-11-03T14:09:03.973510",
     "exception": false,
     "start_time": "2023-11-03T14:09:03.834597",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-03-01    79.088733\n",
       "2021-03-08    79.150514\n",
       "2021-03-15    79.159673\n",
       "2021-03-22    79.161031\n",
       "2021-03-29    79.161232\n",
       "2021-04-05    79.161262\n",
       "2021-04-12    79.161267\n",
       "2021-04-19    79.161267\n",
       "2021-04-26    79.161267\n",
       "2021-05-03    79.161267\n",
       "Freq: W-MON, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assuming data is a DataFrame with a DateTimeIndex\n",
    "data.index.freq = 'W-MON'  # Set the frequency to daily\n",
    "\n",
    "# Choose p and q values based on ACF and PACF plots\n",
    "p, d, q = 1, 1, 1  # Example values, adjust as needed\n",
    "\n",
    "# Train the ARIMA model\n",
    "model = ARIMA(data['Close'], order=(p, d, q))\n",
    "model_fit = model.fit()\n",
    "\n",
    "# Forecast future stock prices\n",
    "# Forecast future stock prices\n",
    "forecast_steps = 10  # Number of time steps to forecast\n",
    "forecast = model_fit.forecast(steps=forecast_steps)\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e9127a86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.026557Z",
     "iopub.status.busy": "2023-11-03T14:09:04.026158Z",
     "iopub.status.idle": "2023-11-03T14:09:04.030745Z",
     "shell.execute_reply": "2023-11-03T14:09:04.029835Z"
    },
    "papermill": {
     "duration": 0.033702,
     "end_time": "2023-11-03T14:09:04.032742",
     "exception": false,
     "start_time": "2023-11-03T14:09:03.999040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def smape_kun(y_true, y_pred):\n",
    "    return np.mean((np.abs(y_pred - y_true) * 200/ (np.abs(y_pred) + np.abs(y_true))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19cf53ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.085969Z",
     "iopub.status.busy": "2023-11-03T14:09:04.085067Z",
     "iopub.status.idle": "2023-11-03T14:09:04.089666Z",
     "shell.execute_reply": "2023-11-03T14:09:04.089044Z"
    },
    "papermill": {
     "duration": 0.032822,
     "end_time": "2023-11-03T14:09:04.091532",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.058710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train_ar = data['Open'].values\n",
    "# test_ar = data['Open'].values\n",
    "\n",
    "# history = [x for x in train_ar]\n",
    "# print(type(history))\n",
    "# predictions = list()\n",
    "# for t in range(len(test_ar)):\n",
    "#     model = ARIMA(history, order=(5,1,0))\n",
    "#     model_fit = model.fit()\n",
    "#     output = model_fit.forecast()\n",
    "#     yhat = output[0]\n",
    "#     predictions.append(yhat)\n",
    "#     obs = test_ar[t]\n",
    "#     history.append(obs)\n",
    "#     #print('predicted=%f, expected=%f' % (yhat, obs))\n",
    "# error = mean_squared_error(test_ar, predictions)\n",
    "# print('Testing Mean Squared Error: %.3f' % error)\n",
    "# error2 = smape_kun(test_ar, predictions)\n",
    "# print('Symmetric mean absolute percentage error: %.3f' % error2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819e1d3a",
   "metadata": {
    "papermill": {
     "duration": 0.024812,
     "end_time": "2023-11-03T14:09:04.141697",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.116885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### <class 'list'>, Testing Mean Squared Error: 14.684, Symmetric mean absolute percentage error: 4.122"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4922a981",
   "metadata": {
    "papermill": {
     "duration": 0.026234,
     "end_time": "2023-11-03T14:09:04.193934",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.167700",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Arima gives SATISFACTORY results but linear regression gives better results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78df972f",
   "metadata": {
    "papermill": {
     "duration": 0.026821,
     "end_time": "2023-11-03T14:09:04.245670",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.218849",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 5. Linear Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a72148",
   "metadata": {
    "papermill": {
     "duration": 0.026562,
     "end_time": "2023-11-03T14:09:04.299326",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.272764",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.1 Loading Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0137747d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.353039Z",
     "iopub.status.busy": "2023-11-03T14:09:04.352280Z",
     "iopub.status.idle": "2023-11-03T14:09:04.375135Z",
     "shell.execute_reply": "2023-11-03T14:09:04.373329Z"
    },
    "papermill": {
     "duration": 0.053148,
     "end_time": "2023-11-03T14:09:04.378071",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.324923",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Load the training data\n",
    "train_data = pd.read_csv(\"/kaggle/input/ue21cs342aa2/train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0336dfd9",
   "metadata": {
    "papermill": {
     "duration": 0.02502,
     "end_time": "2023-11-03T14:09:04.429570",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.404550",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.2 Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "00c9364f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.483693Z",
     "iopub.status.busy": "2023-11-03T14:09:04.483315Z",
     "iopub.status.idle": "2023-11-03T14:09:04.494803Z",
     "shell.execute_reply": "2023-11-03T14:09:04.493875Z"
    },
    "papermill": {
     "duration": 0.040493,
     "end_time": "2023-11-03T14:09:04.496739",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.456246",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 1. Lag features for 'Open' prices\n",
    "train_data['Open_Lag1'] = train_data['Open'].shift(1)\n",
    "train_data['Open_Lag2'] = train_data['Open'].shift(2)\n",
    "\n",
    "# 2. Rolling statistics (e.g., 5-day moving average) for 'Volume'\n",
    "train_data['Rolling_Volume_Mean'] = train_data['Volume'].rolling(window=5).mean()\n",
    "train_data['Rolling_Volume_Std'] = train_data['Volume'].rolling(window=5).std()\n",
    "\n",
    "# Add more lag features\n",
    "train_data['Open_Lag3'] = train_data['Open'].shift(3)\n",
    "train_data['Open_Lag4'] = train_data['Open'].shift(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35341352",
   "metadata": {
    "papermill": {
     "duration": 0.025294,
     "end_time": "2023-11-03T14:09:04.547352",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.522058",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.3 Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "67f2d83e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.600341Z",
     "iopub.status.busy": "2023-11-03T14:09:04.599908Z",
     "iopub.status.idle": "2023-11-03T14:09:04.619251Z",
     "shell.execute_reply": "2023-11-03T14:09:04.616700Z"
    },
    "papermill": {
     "duration": 0.049704,
     "end_time": "2023-11-03T14:09:04.622323",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.572619",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train_data[['Open', 'Volume', 'Open_Lag1', 'Open_Lag2', 'Rolling_Volume_Mean', 'Rolling_Volume_Std','Open_Lag3','Open_Lag4']]\n",
    "y_close = train_data['Close']\n",
    "\n",
    "# Impute missing values\n",
    "imputer = SimpleImputer(strategy='mean')  # You can choose a different strategy based on your data\n",
    "X = imputer.fit_transform(X)\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "X_train, X_valid, y_close_train, y_close_valid = train_test_split(\n",
    "    X, y_close, test_size=0.2, random_state=42)\n",
    "\n",
    "# Normalize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_valid = scaler.transform(X_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7320a2",
   "metadata": {
    "papermill": {
     "duration": 0.026461,
     "end_time": "2023-11-03T14:09:04.676070",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.649609",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.4 Training LR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d95b5b53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.730991Z",
     "iopub.status.busy": "2023-11-03T14:09:04.729938Z",
     "iopub.status.idle": "2023-11-03T14:09:04.745521Z",
     "shell.execute_reply": "2023-11-03T14:09:04.744352Z"
    },
    "papermill": {
     "duration": 0.045439,
     "end_time": "2023-11-03T14:09:04.748031",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.702592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Train a Linear Regressor to predict 'Close' prices\n",
    "close_model = LinearRegression()\n",
    "close_model.fit(X_train, y_close_train)\n",
    "\n",
    "# Make predictions on the validation set\n",
    "close_preds = close_model.predict(X_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7690f484",
   "metadata": {
    "papermill": {
     "duration": 0.026883,
     "end_time": "2023-11-03T14:09:04.801491",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.774608",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.5 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8a6c4318",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.859546Z",
     "iopub.status.busy": "2023-11-03T14:09:04.858404Z",
     "iopub.status.idle": "2023-11-03T14:09:04.865728Z",
     "shell.execute_reply": "2023-11-03T14:09:04.864080Z"
    },
    "papermill": {
     "duration": 0.037385,
     "end_time": "2023-11-03T14:09:04.868091",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.830706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error for Close Price Prediction: 1.5995838092700714\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "mae = mean_absolute_error(y_close_valid, close_preds)\n",
    "\n",
    "print(f\"Mean Absolute Error for Close Price Prediction: {mae}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fe1b3f",
   "metadata": {
    "papermill": {
     "duration": 0.025989,
     "end_time": "2023-11-03T14:09:04.919986",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.893997",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.6 Applying on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b4fae459",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:04.974020Z",
     "iopub.status.busy": "2023-11-03T14:09:04.972877Z",
     "iopub.status.idle": "2023-11-03T14:09:04.995840Z",
     "shell.execute_reply": "2023-11-03T14:09:04.994903Z"
    },
    "papermill": {
     "duration": 0.052718,
     "end_time": "2023-11-03T14:09:04.998347",
     "exception": false,
     "start_time": "2023-11-03T14:09:04.945629",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load the test data\n",
    "test_data = pd.read_csv(\"/kaggle/input/ue21cs342aa2/test.csv\")\n",
    "\n",
    "# Feature engineering for the test data (similar to the training data)\n",
    "test_data['Open_Lag1'] = test_data['Open'].shift(1)\n",
    "test_data['Open_Lag2'] = test_data['Open'].shift(2)\n",
    "test_data['Rolling_Volume_Mean'] = test_data['Volume'].rolling(window=5).mean()\n",
    "test_data['Rolling_Volume_Std'] = test_data['Volume'].rolling(window=5).std()\n",
    "test_data['Open_Lag3'] = test_data['Open'].shift(3)\n",
    "test_data['Open_Lag4'] = test_data['Open'].shift(4)\n",
    "# Select the same features as used in the training data\n",
    "X_test = test_data[['Open', 'Volume', 'Open_Lag1', 'Open_Lag2', 'Rolling_Volume_Mean', 'Rolling_Volume_Std','Open_Lag3','Open_Lag4']]\n",
    "\n",
    "# Impute missing values in the test data\n",
    "X_test = imputer.transform(X_test)\n",
    "\n",
    "# Normalize test data\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Make predictions on the test data\n",
    "test_close_preds = close_model.predict(X_test)\n",
    "\n",
    "# Add the predictions to the test data\n",
    "test_data['Close'] = test_close_preds\n",
    "columns_to_drop = ['Open_Lag1','Open_Lag2','Rolling_Volume_Mean','Rolling_Volume_Std','Open_Lag3','Open_Lag4']\n",
    "test_data = test_data.drop(columns=columns_to_drop)\n",
    "test_data.to_csv('test.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96de4f1",
   "metadata": {
    "papermill": {
     "duration": 0.024964,
     "end_time": "2023-11-03T14:09:05.050400",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.025436",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.7 Hyperparameter Tuning -> Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "330f0a2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:05.102796Z",
     "iopub.status.busy": "2023-11-03T14:09:05.101951Z",
     "iopub.status.idle": "2023-11-03T14:09:05.107839Z",
     "shell.execute_reply": "2023-11-03T14:09:05.106915Z"
    },
    "papermill": {
     "duration": 0.03481,
     "end_time": "2023-11-03T14:09:05.110519",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.075709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# from sklearn.linear_model import LinearRegression\n",
    "# from sklearn.model_selection import GridSearchCV\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "# from sklearn.impute import SimpleImputer\n",
    "# from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# # Load the training data\n",
    "# train_data = pd.read_csv(\"/kaggle/input/ue21cs342aa2/train.csv\")\n",
    "\n",
    "# # Data preprocessing and feature engineering (same as in your code)\n",
    "# # Split the data into training and validation sets\n",
    "# X_train, X_valid, y_close_train, y_close_valid = train_test_split(\n",
    "#     X, y_close, test_size=0.2, random_state=42)\n",
    "\n",
    "# # Define the model\n",
    "# lr = LinearRegression()\n",
    "\n",
    "# # Define a parameter grid to search for the best hyperparameters\n",
    "# param_grid = {\n",
    "#     'fit_intercept': [True, False],  # Whether to calculate the intercept\n",
    "#     'n_jobs': [None, -1],  # Number of CPU cores to use for computation\n",
    "#     'positive': [True, False],  # Constrain the coefficients to be positive\n",
    "# }\n",
    "\n",
    "# # Create a GridSearchCV object\n",
    "# grid_search = GridSearchCV(lr, param_grid, cv=5, scoring='neg_mean_absolute_error')\n",
    "\n",
    "# # Fit the grid search to the data\n",
    "# grid_search.fit(X_train, y_close_train)\n",
    "\n",
    "# # Get the best hyperparameters\n",
    "# best_params = grid_search.best_params_\n",
    "# print(\"Best Hyperparameters:\", best_params)\n",
    "\n",
    "# # Create a Linear Regression model with the best hyperparameters\n",
    "# best_lr = LinearRegression(**best_params)\n",
    "\n",
    "# # Train the model\n",
    "# best_lr.fit(X_train, y_close_train)\n",
    "\n",
    "# # Make predictions on the validation set\n",
    "# close_preds = best_lr.predict(X_valid)\n",
    "\n",
    "# # Evaluate the model\n",
    "# mae = mean_absolute_error(y_close_valid, close_preds)\n",
    "\n",
    "# print(f\"Mean Absolute Error for Close Price Prediction: {mae}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e997f74e",
   "metadata": {
    "papermill": {
     "duration": 0.025094,
     "end_time": "2023-11-03T14:09:05.160798",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.135704",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5.8 Test data output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "02a1428d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:05.216283Z",
     "iopub.status.busy": "2023-11-03T14:09:05.215912Z",
     "iopub.status.idle": "2023-11-03T14:09:05.228192Z",
     "shell.execute_reply": "2023-11-03T14:09:05.227193Z"
    },
    "papermill": {
     "duration": 0.044366,
     "end_time": "2023-11-03T14:09:05.230555",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.186189",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id        Date       Open     Volume      Close\n",
      "0   0  2021-03-01  72.708331  205897600  79.323597\n",
      "1   1  2021-03-08  73.310886  214095600  79.224478\n",
      "2   2  2021-03-15  70.610119  151601700  78.982273\n",
      "3   3  2021-03-22  70.256017  193982500  77.142385\n",
      "4   4  2021-03-29  68.258324  220238400  76.620517\n"
     ]
    }
   ],
   "source": [
    "data  = pd.read_csv(\"/kaggle/working/test.csv\")\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84d2c33",
   "metadata": {
    "papermill": {
     "duration": 0.027155,
     "end_time": "2023-11-03T14:09:05.285159",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.258004",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 6. Ensemble Learning with XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec99fda",
   "metadata": {
    "papermill": {
     "duration": 0.026478,
     "end_time": "2023-11-03T14:09:05.340102",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.313624",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 6.1 Importing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b59578fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:05.396327Z",
     "iopub.status.busy": "2023-11-03T14:09:05.395921Z",
     "iopub.status.idle": "2023-11-03T14:09:05.733590Z",
     "shell.execute_reply": "2023-11-03T14:09:05.732493Z"
    },
    "papermill": {
     "duration": 0.368886,
     "end_time": "2023-11-03T14:09:05.736010",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.367124",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score, precision_score, recall_score\n",
    "\n",
    "data = pd.read_csv('/kaggle/input/ue21cs342aa2/train.csv')\n",
    "\n",
    "# Encode the 'Strategy' column into numerical labels\n",
    "label_encoder = LabelEncoder()\n",
    "data['Strategy_encoded'] = label_encoder.fit_transform(data['Strategy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb65fd2c",
   "metadata": {
    "papermill": {
     "duration": 0.026212,
     "end_time": "2023-11-03T14:09:05.788455",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.762243",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 6.2 Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2f641cf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:05.843365Z",
     "iopub.status.busy": "2023-11-03T14:09:05.841778Z",
     "iopub.status.idle": "2023-11-03T14:09:05.877720Z",
     "shell.execute_reply": "2023-11-03T14:09:05.876179Z"
    },
    "papermill": {
     "duration": 0.065561,
     "end_time": "2023-11-03T14:09:05.880212",
     "exception": false,
     "start_time": "2023-11-03T14:09:05.814651",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_lags = 10\n",
    "for lag in range(1, n_lags + 1):\n",
    "    data[f'Open_Lag_{lag}'] = data['Open'].shift(lag)\n",
    "    data[f'Close_Lag_{lag}'] = data['Close'].shift(lag)\n",
    "    data[f'Volume_Lag_{lag}'] = data['Volume'].shift(lag)\n",
    "\n",
    "# Calculate MACD (12, 26, 9) - Example parameters, you can adjust them\n",
    "short_period = 12\n",
    "long_period = 26\n",
    "signal_period = 9\n",
    "\n",
    "data['EMA_12'] = data['Close'].ewm(span=short_period, adjust=False).mean()\n",
    "data['EMA_26'] = data['Close'].ewm(span=long_period, adjust=False).mean()\n",
    "data['MACD'] = data['EMA_12'] - data['EMA_26']\n",
    "data['Signal_Line'] = data['MACD'].ewm(span=signal_period, adjust=False).mean()\n",
    "\n",
    "# Calculate RSI for a 14-day period\n",
    "period = 14\n",
    "delta = data['Close'].diff()\n",
    "gain = delta.where(delta > 0, 0)\n",
    "loss = -delta.where(delta < 0, 0)\n",
    "average_gain = gain.rolling(window=period).mean()\n",
    "average_loss = loss.rolling(window=period).mean()\n",
    "rs = average_gain / average_loss\n",
    "rsi = 100 - (100 / (1 + rs))\n",
    "data['RSI'] = rsi\n",
    "\n",
    "# Additional Features\n",
    "n_sma = 20\n",
    "data['SMA_Close'] = data['Close'].rolling(window=n_sma).mean()\n",
    "\n",
    "# Define features and target\n",
    "features = ['Open', 'Close', 'Volume'] + [f'Open_Lag_{lag}' for lag in range(1, n_lags + 1)] + \\\n",
    "           [f'Close_Lag_{lag}' for lag in range(1, n_lags + 1)] + [f'Volume_Lag_{lag}' for lag in range(1, n_lags + 1)] + ['SMA_Close', 'MACD', 'Signal_Line', 'RSI']\n",
    "\n",
    "X = data[features]\n",
    "y = data['Strategy_encoded']\n",
    "\n",
    "# Handle missing values using an imputer\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "X = imputer.fit_transform(X)\n",
    "\n",
    "# Define individual models\n",
    "xgb_model = XGBClassifier(n_estimators=100, max_depth=3, learning_rate=0.05, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04674446",
   "metadata": {
    "papermill": {
     "duration": 0.024476,
     "end_time": "2023-11-03T14:09:05.929306",
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     "start_time": "2023-11-03T14:09:05.904830",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 6.3 Making the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6e2d3265",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:05.985120Z",
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     "start_time": "2023-11-03T14:09:05.956308",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;XGBoost&#x27;,\n",
       "                              XGBClassifier(base_score=None, booster=None,\n",
       "                                            callbacks=None,\n",
       "                                            colsample_bylevel=None,\n",
       "                                            colsample_bynode=None,\n",
       "                                            colsample_bytree=None,\n",
       "                                            early_stopping_rounds=None,\n",
       "                                            enable_categorical=False,\n",
       "                                            eval_metric=None,\n",
       "                                            feature_types=None, gamma=None,\n",
       "                                            gpu_id=None, grow_policy=None,\n",
       "                                            importance_type=None,\n",
       "                                            interaction_constraints=None,\n",
       "                                            learning_rate=0.05, max_bin=None,\n",
       "                                            max_cat_threshold=None,\n",
       "                                            max_cat_to_onehot=None,\n",
       "                                            max_delta_step=None, max_depth=3,\n",
       "                                            max_leaves=None,\n",
       "                                            min_child_weight=None, missing=nan,\n",
       "                                            monotone_constraints=None,\n",
       "                                            n_estimators=100, n_jobs=None,\n",
       "                                            num_parallel_tree=None,\n",
       "                                            predictor=None, random_state=42, ...))],\n",
       "                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;XGBoost&#x27;,\n",
       "                              XGBClassifier(base_score=None, booster=None,\n",
       "                                            callbacks=None,\n",
       "                                            colsample_bylevel=None,\n",
       "                                            colsample_bynode=None,\n",
       "                                            colsample_bytree=None,\n",
       "                                            early_stopping_rounds=None,\n",
       "                                            enable_categorical=False,\n",
       "                                            eval_metric=None,\n",
       "                                            feature_types=None, gamma=None,\n",
       "                                            gpu_id=None, grow_policy=None,\n",
       "                                            importance_type=None,\n",
       "                                            interaction_constraints=None,\n",
       "                                            learning_rate=0.05, max_bin=None,\n",
       "                                            max_cat_threshold=None,\n",
       "                                            max_cat_to_onehot=None,\n",
       "                                            max_delta_step=None, max_depth=3,\n",
       "                                            max_leaves=None,\n",
       "                                            min_child_weight=None, missing=nan,\n",
       "                                            monotone_constraints=None,\n",
       "                                            n_estimators=100, n_jobs=None,\n",
       "                                            num_parallel_tree=None,\n",
       "                                            predictor=None, random_state=42, ...))],\n",
       "                 voting=&#x27;soft&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>XGBoost</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('XGBoost',\n",
       "                              XGBClassifier(base_score=None, booster=None,\n",
       "                                            callbacks=None,\n",
       "                                            colsample_bylevel=None,\n",
       "                                            colsample_bynode=None,\n",
       "                                            colsample_bytree=None,\n",
       "                                            early_stopping_rounds=None,\n",
       "                                            enable_categorical=False,\n",
       "                                            eval_metric=None,\n",
       "                                            feature_types=None, gamma=None,\n",
       "                                            gpu_id=None, grow_policy=None,\n",
       "                                            importance_type=None,\n",
       "                                            interaction_constraints=None,\n",
       "                                            learning_rate=0.05, max_bin=None,\n",
       "                                            max_cat_threshold=None,\n",
       "                                            max_cat_to_onehot=None,\n",
       "                                            max_delta_step=None, max_depth=3,\n",
       "                                            max_leaves=None,\n",
       "                                            min_child_weight=None, missing=nan,\n",
       "                                            monotone_constraints=None,\n",
       "                                            n_estimators=100, n_jobs=None,\n",
       "                                            num_parallel_tree=None,\n",
       "                                            predictor=None, random_state=42, ...))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an ensemble model using a VotingClassifier\n",
    "ensemble_model = VotingClassifier(estimators=[\n",
    "    ('XGBoost', xgb_model)\n",
    "], voting='soft')\n",
    "# Split the data into training and testing sets\n",
    "X_train = X\n",
    "y_train = y\n",
    "\n",
    "# Train the ensemble model on the entire dataset\n",
    "ensemble_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73be86af",
   "metadata": {
    "papermill": {
     "duration": 0.02646,
     "end_time": "2023-11-03T14:09:06.289602",
     "exception": false,
     "start_time": "2023-11-03T14:09:06.263142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 6.4 Applying on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2984c249",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:06.344830Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load your test dataset (replace 'test.csv' with the file path to your test data)\n",
    "test_data = pd.read_csv('/kaggle/working/test.csv')\n",
    "\n",
    "# Perform the same feature engineering and preprocessing on the test dataset\n",
    "n_lags = 10\n",
    "for lag in range(1, n_lags + 1):\n",
    "    test_data[f'Open_Lag_{lag}'] = test_data['Open'].shift(lag)\n",
    "    test_data[f'Close_Lag_{lag}'] = test_data['Close'].shift(lag)\n",
    "    test_data[f'Volume_Lag_{lag}'] = test_data['Volume'].shift(lag)\n",
    "\n",
    "# Calculate MACD (12, 26, 9) - Example parameters, you can adjust them\n",
    "short_period = 12\n",
    "long_period = 26\n",
    "signal_period = 9\n",
    "\n",
    "test_data['EMA_12'] = test_data['Close'].ewm(span=short_period, adjust=False).mean()\n",
    "test_data['EMA_26'] = test_data['Close'].ewm(span=long_period, adjust=False).mean()\n",
    "test_data['MACD'] = test_data['EMA_12'] - test_data['EMA_26']\n",
    "test_data['Signal_Line'] = test_data['MACD'].ewm(span=signal_period, adjust=False).mean()\n",
    "\n",
    "# Calculate RSI for a 14-day period\n",
    "period = 14\n",
    "delta = test_data['Close'].diff()\n",
    "gain = delta.where(delta > 0, 0)\n",
    "loss = -delta.where(delta < 0, 0)\n",
    "average_gain = gain.rolling(window=period).mean()\n",
    "average_loss = loss.rolling(window=period).mean()\n",
    "rs = average_gain / average_loss\n",
    "rsi = 100 - (100 / (1 + rs))\n",
    "test_data['RSI'] = rsi\n",
    "\n",
    "# Additional Features\n",
    "\n",
    "n_sma = 20\n",
    "test_data['SMA_Close'] = test_data['Close'].rolling(window=n_sma).mean()\n",
    "\n",
    "# Define features for the test dataset\n",
    "test_features = ['Open', 'Close', 'Volume'] + [f'Open_Lag_{lag}' for lag in range(1, n_lags + 1)] + \\\n",
    "                [f'Close_Lag_{lag}' for lag in range(1, n_lags + 1)] + [f'Volume_Lag_{lag}' for lag in range(1, n_lags + 1)] + ['SMA_Close', 'MACD', 'Signal_Line', 'RSI']\n",
    "\n",
    "# Handle missing values in the test dataset using the same imputer\n",
    "test_X = test_data[test_features]\n",
    "test_X = imputer.transform(test_X)\n",
    "\n",
    "# Predict 'Strategy' labels on the test dataset\n",
    "test_predictions = ensemble_model.predict(test_X)\n",
    "\n",
    "# Decode the numerical labels back to the original 'Strategy' labels\n",
    "test_predictions = label_encoder.inverse_transform(test_predictions)\n",
    "\n",
    "# Add the predicted 'Strategy' labels to the test dataset\n",
    "test_data['Strategy'] = test_predictions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cf57fc",
   "metadata": {
    "papermill": {
     "duration": 0.027693,
     "end_time": "2023-11-03T14:09:06.435029",
     "exception": false,
     "start_time": "2023-11-03T14:09:06.407336",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 6.5 HyperParameter Tuning -> Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ba237177",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:06.490719Z",
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     "shell.execute_reply": "2023-11-03T14:09:06.494397Z"
    },
    "papermill": {
     "duration": 0.035064,
     "end_time": "2023-11-03T14:09:06.497551",
     "exception": false,
     "start_time": "2023-11-03T14:09:06.462487",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # Define hyperparameters to search over\n",
    "# param_grid = {\n",
    "#     'XGBoost__n_estimators': [100, 200, 300],\n",
    "#     'XGBoost__max_depth': [3, 4, 5],\n",
    "#     'XGBoost__learning_rate': [0.05, 0.1, 0.2]\n",
    "# }\n",
    "\n",
    "# # Create a grid search object\n",
    "# grid_search = GridSearchCV(ensemble_model, param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# # Fit the grid search to your data\n",
    "# grid_search.fit(X, y)\n",
    "\n",
    "# # Get the best hyperparameters from the grid search\n",
    "# best_params = grid_search.best_params_\n",
    "\n",
    "# # Print the best hyperparameters\n",
    "# print(\"Best Hyperparameters:\", best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "271fe421",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:09:06.552557Z",
     "iopub.status.busy": "2023-11-03T14:09:06.552207Z",
     "iopub.status.idle": "2023-11-03T14:09:06.560105Z",
     "shell.execute_reply": "2023-11-03T14:09:06.558232Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hold', 'Hold', 'Hold', 'Hold', 'Hold', 'Hold', 'Buy', 'Buy',\n",
       "       'Hold', 'Buy', 'Hold', 'Hold', 'Hold', 'Buy', 'Buy', 'Hold',\n",
       "       'Hold', 'Hold', 'Hold', 'Hold', 'Hold', 'Hold', 'Hold', 'Hold',\n",
       "       'Hold', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell',\n",
       "       'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell',\n",
       "       'Sell', 'Sell', 'Hold', 'Hold', 'Buy', 'Buy', 'Buy', 'Buy', 'Buy',\n",
       "       'Buy', 'Buy', 'Buy', 'Buy', 'Buy', 'Buy', 'Buy', 'Hold', 'Sell',\n",
       "       'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell', 'Sell',\n",
       "       'Hold', 'Hold', 'Hold', 'Buy', 'Buy', 'Buy', 'Buy', 'Buy', 'Buy',\n",
       "       'Buy', 'Buy', 'Buy', 'Buy', 'Buy', 'Hold', 'Buy', 'Buy', 'Hold',\n",
       "       'Hold', 'Buy', 'Hold', 'Hold', 'Hold', 'Hold', 'Buy', 'Buy',\n",
       "       'Hold', 'Hold', 'Hold', 'Hold', 'Hold', 'Buy', 'Hold', 'Hold'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e61dc10d",
   "metadata": {
    "execution": {
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    {
     "data": {
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       "<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>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>79.323597</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-08</td>\n",
       "      <td>79.224478</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>78.982273</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2021-03-22</td>\n",
       "      <td>77.142385</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>76.620517</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>2022-12-26</td>\n",
       "      <td>64.629792</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>64.175998</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>2023-01-09</td>\n",
       "      <td>62.570091</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>2023-01-16</td>\n",
       "      <td>63.221223</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>2023-01-23</td>\n",
       "      <td>65.466264</td>\n",
       "      <td>Hold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id        Date      Close Strategy\n",
       "0    0  2021-03-01  79.323597     Hold\n",
       "1    1  2021-03-08  79.224478     Hold\n",
       "2    2  2021-03-15  78.982273     Hold\n",
       "3    3  2021-03-22  77.142385     Hold\n",
       "4    4  2021-03-29  76.620517     Hold\n",
       "..  ..         ...        ...      ...\n",
       "95  95  2022-12-26  64.629792     Hold\n",
       "96  96  2023-01-02  64.175998     Hold\n",
       "97  97  2023-01-09  62.570091      Buy\n",
       "98  98  2023-01-16  63.221223     Hold\n",
       "99  99  2023-01-23  65.466264     Hold\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = test_data[['id',\"Date\",\"Close\",\"Strategy\"]]\n",
    "\n",
    "selected_columns.to_csv('submission.csv', index=False)\n",
    "selected_columns"
   ]
  }
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