Swap-Profitability .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aab7e815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cUM+xAgC+02F6DvKqP/dw345YVdVbq+qLVXW6qk7u1+MAAKyLfQmrqnpJkn+f5G1Jrkjyzqq6Yj8eCwBgXezXEaurkpzu7i919zeTfCLJdfv0WAAAa2G/nmN1aZIntlx/Msnf37pBVZ1IcmK5+v+q6ov7NMt+uCjJn656iAPIfjt/9tnu2G+7Y7/tjv22O/uy3+pfTf/Ec/obL3TDyp683t03J7l5VY+/F1V1qruPr3qOg8Z+O3/22e7Yb7tjv+2O/bY7h3W/7depwDNJjm25ftmyBgBwaO1XWP1Bksur6tVV9T1Jrk9y5z49FgDAWtiXU4Hd/WxVvTfJf0vykiS3dvcj+/FYK3IgT2GuAfvt/Nlnu2O/7Y79tjv22+4cyv1W3b3qGQAADgUfaQMAMERYAQAMEVa7VFXvq6o/qqpHqupfr3qeg6SqPlhVXVUXrXqWg6Cq/s3yd+2hqvp0Vb1i1TOtMx+ndf6q6lhVfbaqHl1+p71/1TMdFFX1kqr6H1X1G6ue5SCpqldU1a8vv9seq6p/sOqZpgirXaiqt2TzneRf192vTfJzKx7pwKiqY0l+KMn/WvUsB8g9Sf5Od/+9JP8zyYdWPM/a8nFau/Zskg929xVJ3pjkRvttx96f5LFVD3EA/UKS3+ruv53kdTlE+1BY7c5PJbmpu7+RJN39zIrnOUg+luRfJPGqiR3q7t/u7meXq5/L5vvCcW4+TmsXuvup7n5gufy1bP5P7tLVTrX+quqyJNcm+eVVz3KQVNVfTfKPk9ySJN39ze7+vysdapCw2p3XJPlHVXVfVf1uVf3gqgc6CKrquiRnuvsPVz3LAfbPkvzmqodYY+f6OC2BcB6qaiPJ65Pct+JRDoJ/m81/KH5rxXMcNK9OcjbJf1xOo/5yVX3vqoeasrKPtFl3VfU7Sf7aOW766Wzut1dl85D5Dya5o6r+Znvviu3224ezeRqQb/Ni+627P7Ns89PZPGVz+3dzNi4cVfXyJJ9M8oHu/uqq51lnVfUjSZ7p7vur6s0rHuegOZLkDUne1933VdUvJDmZ5F+udqwZwuoFdPc/eaHbquqnknxqCanPV9W3svlhkme/W/Otqxfab1X1d7P5r5Q/rKpk83TWA1V1VXd/5bs44lp6sb9vSVJV/zTJjyS5WsC/KB+ntUtV9dJsRtXt3f2pVc9zALwpyY9W1TVJ/nKSv1JV/6m7f3zFcx0ETyZ5srufPyr669kMq0PBqcDd+a9J3pIkVfWaJN8Tn2z+orr7C939/d290d0b2fwP6w2iantV9dZsnm740e7++qrnWXM+TmsXavNfO7ckeay7P7rqeQ6C7v5Qd1+2/D67Psl/F1U7s/zef6Kq/taydHWSR1c40ihHrHbn1iS3VtXDSb6Z5AZHEdhH/y7Jy5Lcsxzt+1x3//PVjrSeLoCP09ovb0ryriRfqKoHl7UPd/fdqxuJQ+59SW5f/gH0pSTvXvE8Y3ykDQDAEKcCAQCGCCsAgCHCCgBgiLACABgirAAAhggrAIAhwgoAYMj/BzU9kPAyt1kGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = np.random.normal(0.0000001, 1.89, 1000)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "var_X = np.array(X)\n",
    " \n",
    "# Creating histogram\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(var_X)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e27b8d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c0844bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Ratio_a (HDFCLTD)</th>\n",
       "      <th>Ratio_b (HDFCBANK)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.989524</td>\n",
       "      <td>1.000473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>1.042616</td>\n",
       "      <td>1.013917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0.986774</td>\n",
       "      <td>0.991557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>0.952914</td>\n",
       "      <td>0.984433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>0.997550</td>\n",
       "      <td>1.002919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Ratio_a (HDFCLTD)  Ratio_b (HDFCBANK)\n",
       "0  2015-01-01           0.989524            1.000473\n",
       "1  2015-01-02           1.042616            1.013917\n",
       "2  2015-01-05           0.986774            0.991557\n",
       "3  2015-01-06           0.952914            0.984433\n",
       "4  2015-01-07           0.997550            1.002919"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dataset/Ratio-Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f218827",
   "metadata": {},
   "source": [
    "Here: \n",
    "This is calculated from [NSE Website](https://www1.nseindia.com/products/content/equities/equities/eq_security.htm). Aboout 1700 data samples have been collected for daily close and previous close. \n",
    "\n",
    "$Ratio-a (HDFCLTD) = \\frac{Prev Close}{Close Price} $\n",
    "\n",
    "$Ratio-b (HDFCBANK) = \\frac{Prev Close}{Close Price} $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28e67cad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ratio_a (HDFCLTD)</th>\n",
       "      <th>Ratio_b (HDFCBANK)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.000655</td>\n",
       "      <td>1.000524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.018831</td>\n",
       "      <td>0.018877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.867014</td>\n",
       "      <td>0.503280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.990644</td>\n",
       "      <td>0.993577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000182</td>\n",
       "      <td>1.000494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.010443</td>\n",
       "      <td>1.007571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.101882</td>\n",
       "      <td>1.115996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ratio_a (HDFCLTD)  Ratio_b (HDFCBANK)\n",
       "count        1797.000000         1797.000000\n",
       "mean            1.000655            1.000524\n",
       "std             0.018831            0.018877\n",
       "min             0.867014            0.503280\n",
       "25%             0.990644            0.993577\n",
       "50%             1.000182            1.000494\n",
       "75%             1.010443            1.007571\n",
       "max             1.101882            1.115996"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "50f19c77",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_a = df[\"Ratio_a (HDFCLTD)\"].to_numpy()\n",
    "ratio_b = df[\"Ratio_b (HDFCBANK)\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dacecb88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Creating histogram\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(ratio_a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c38f5502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating histogram\n",
    "fig, by = plt.subplots(figsize =(10, 7))\n",
    "by.hist(ratio_a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eac2be4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3-dev",
   "language": "python",
   "name": "python3-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}