mock_data_generator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns, numpy as np\n",
    "from matplotlib import pyplot as plt, dates\n",
    "import pandas as pd, matplotlib.ticker as mtick"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(columns=['Date','Options','LP'])\n",
    "# Fill the Date column with the dates\n",
    "data['Date'] = pd.date_range('1/1/2021', periods=52, freq='W')\n",
    "# Fill the Options and and LP columns with random numbers with Gaussian distribution between -0.1 and 0.1, 3 decimal places\n",
    "data['Options'] = np.round(np.random.normal(0.0025,0.005,52),3)\n",
    "data['LP'] = np.round(np.random.normal(0.0025, 0.005, 52), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Options</th>\n",
       "      <th>LP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Options     LP\n",
       "0 2021-01-03    0.006  0.005\n",
       "1 2021-01-10    0.000  0.004\n",
       "2 2021-01-17    0.009  0.007\n",
       "3 2021-01-24   -0.004  0.002\n",
       "4 2021-01-31    0.003  0.005"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formatting the data for charting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data['cum_options'] = data['Options'].cumsum()\n",
    "data['cum_lp'] = data['LP'].cumsum()\n",
    "data['date_chart'] = data['Date'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZgGNKkqS2uTSbprExjSQnmT/i7qHA9xq7nAh8oKpWA1vSm7O8ht5cZUmSJGmobXAkOcn5wAHAdklW0Bsxfm6SnekVvrcCfz5i/ycD+1bVqf2mM4GrgHuBwyYwdkmSJGlSbLBIrqqXrqP5n9ez/x3AISPuX0TvBD5JkiRpSvCKe5IkSVKDRbIkSZLUYJEsSZIkNVgkS5IkSQ0WyZIkSVKDRbIkSZLUYJEsSZIkNUzEZaklSZI2jpey1hThSLIkSZLU4EiyJEnTzWijteCIrbSRHEmWJEmSGiySJUmSpAaLZEmSJKnBIlmSJElqsEiWJEmSGlzdQpKkYeZKFVIrHEmWJEmSGiySJUmSpAaLZEmSJKnBIlmSJElqsEiWJEmSGiySJUmSpAaLZEmSJKnBIlmSJElqsEiWJEmSGiySJUmSpAaLZEmSJKnBIlmSJElqsEiWJEmSGiySJUmSpAaLZEmSJKnBIlmSJElq2GCRnOTcJCuT3DCibZsklyf5fv/fJ/bbj0hyY5JvJdm237ZjkgsmLwVJkiRpYm3MSPIS4OBG25uBr1bVfOCr/fsAxwH7AB8FXtZvOw1467gjlSRJkgZkg0VyVX0TuKfRfCjw8f7tjwOH9W+vAbYAZgKrk+wP/KSqvj8h0UqSJEkDsNkYH7d9Vf24f/snwPb92+8CvgLcARwFXAS8ZENPlmQxsBhg7ty5YwxJkiRJmhjjPnGvqgqo/u3Lq2rvqnoevdHmS4FnJPl0knOSzBzlOc6uqkVVtWj27NnjDUmSJEkal7EWyT9N8iSA/r8rR27sF8NHA2cBpwKvAq4AXj7mSCVJkqQBGWuRfAm9wpf+v59vbD8R+EBVrQa2pDfSvIbeXGVJkiRpqG1wTnKS84EDgO2SrADeDpwOXJjkT4FbgSNH7P9kYN+qOrXfdCZwFXAvj5zgJ0mSJA2tDRbJVfXSUTYdOMr+dwCHjLh/Eb0T+CRJkqQpwSvuSZIkSQ1jXQJOkqTp5ZRZo7TfN9g4JA0FR5IlSZKkBotkSZIkqcHpFpIkjcVo0zPAKRrSNOBIsiRJktTgSLIkjdVYTvTy5LBuc/RZmjIcSZYkSZIaLJIlSZKkBqdbSJp+uj6lYbp9pd/136ekVjiSLEmSJDU4kixJ4GjldOPvU9I4OZIsSZIkNVgkS5IkSQ0WyZIkSVKDRbIkSZLUYJEsSZIkNVgkS5IkSQ0uASdpcKbbRS4kSdOWRbKk4db1wnpQ+Y/lOF3/3Uia1pxuIUmSJDVYJEuSJEkNTreQ5FftkiQ1OJIsSZIkNTiSLEkanNG+gfDbB0lDxpFkSZIkqcEiWZIkSWqwSJYkSZIaLJIlSZKkBotkSZIkqcEiWZIkSWqwSJYkSZIaxlUkJ3ljkhuT3JDk/CQzkpyX5Lok7xyx31uTHDbuaCVJkqQBGHORnOQpwOuBRVW1ANgUWAz8sqr2APZJMivJk4DfraqLJyJgSZIkabKNd7rFZsCWSTYDZgLp398E2Bx4CPg74O3jPI4kSZI0MGMukqvqduC9wG3Aj4H7quqfgDuBa4AvADsBm1TVNRMQqyRJkjQQm431gUmeCBwK7ADcC1yU5KiqOn7EPl8Ajk1yMrAncHlVnbOO51pMb6oGc+fOHWtIkiRJ0oQYz3SLPwJ+WFV3VtVq4LPA76/dmORQ4GpgK2DHqjoSeGGSmc0nqqqzq2pRVS2aPXv2OEKSJEmSxm88RfJtwLOSzEwS4EBgOUCSzYHjgXcDWwLVf8ymwOPGcUxJkiRp0o1nTvK3gU/Tm398ff+5zu5v/kvg41W1CrgOmJnkeuDqqrp3XBFLkiRJk2zMc5IBqurtrGPliqp6/4jbBbx0PMeRJEmSBskr7kmSJEkNFsmSJElSg0WyJEmS1GCRLEmSJDVYJEuSJEkNFsmSJElSg0WyJEmS1GCRLEmSJDVYJEuSJEkNFsmSJElSg0WyJEmS1LBZ2wFImgSnzBql/b7BxiFJ0hTlSLIkSZLUYJEsSZIkNVgkS5IkSQ0WyZIkSVKDRbIkSZLUYJEsSZIkNVgkS5IkSQ0WyZIkSVKDRbIkSZLUYJEsSZIkNVgkS5IkSQ0WyZIkSVKDRbIkSZLUsFnbAUjagFNmjdJ+32DjkCSpQyySpUGy4JUkaUpwuoUkSZLUYJEsSZIkNVgkS5IkSQ0WyZIkSVKDRbIkSZLUYJEsSZIkNYyrSE5yS5LrkyxLsrTf9g9JrkvyiRH7HZXk+HHGKkmSJA3ERIwkP7uqFlbVoiSzgN+pqj2AXyfZPcmWwDHAWRNwLEmSJGnSTfR0izXA5kkCzARWAycAZ1bV6gk+liRJkjQpxlskF/DvSa5Osriq7gcuBb4L/Bi4D/jdqrp4fU+SZHGSpUmW3nnnneMMSZIkSRqf8V6W+n9W1e1Jfhu4PMn3qurdwLsBknwM+NskfwYcBFxXVac1n6SqzgbOBli0aFGNMyZJkiRpXMY1klxVt/f/XQl8Dth37bYkewEB/gt4UVUdCeyYZP54jilJkiRNtjEXyUl+K8nWa2/TGym+YcQu7wDeBmwObNpvW0NvrrIkSZI0tMYz3WJ74HO9c/TYDPhkVX0ZIMlhwNKquqN/f1mS6+lNt7h2fCFLkiRJk2vMRXJV3QzsOcq2i4GLR9w/gd4qF5IkSdKkmvfAJ0fddstGPodX3JMkSZIaLJIlSZKkBotkSZIkqcEiWZIkSWqwSJYkSZIaLJIlSZKkBotkSZIkqWE8FxORpo9TZq1n232Di0OSJD3KRKx5PBaOJEuSJEkNFsmSJElSg0WyJEmS1OCcZEmSJA3MaHOMbxlsGBvkSLIkSZLUYJEsSZIkNTjdQhqr0ZaNc8k4SZKmPEeSJUmSpAZHkgfJkcfB8MIgkiRpnBxJliRJkhoskiVJkqQGp1tIkqTOG23tXhh9/d6xPEZThyPJkiRJUoMjyZIkSRqTqXL1vLGwSB52w7wixjDHJkn6jUznYkcaC6dbSJIkSQ2OJGtwXL9YkqSh5bcJj+ZIsiRJktTgSDI4wjnM/N1IkqYRl42bOhxJliRJkhoskiVJkqQGp1uMldMAJEnSAHhCXTscSZYkSZIaHEmWJEmaZhx9Hr8xF8lJngp8AtgeKODsqvqnJP8A/AmwrKpe2d/3KGC7qnr/+EOWJEnDwEJM09l4pls8CLypqnYFngX8ZZI9gd+pqj2AXyfZPcmWwDHAWeMPV5IkSZp8Yx5JrqofAz/u374/yXJgLrB5kgAzgdXACcCZVbV6AuKVJEmSJt2EnLiXZB6wF/AN4FLgu/QK6PuA362qiyfiOJIkSdIgjPvEvSRbAZ8Bjq+qnwPv7v+Q5GPA3yb5M+Ag4LqqOm0dz7EYWAwwd+7c8Yak0ZanW9/SdGN5jCRJ0jQ1riI5yeb0CuTzquqzjW17AQH+C3hXVT0nyb8kmV9V3x+5b1WdDZwNsGjRohpPTJIkqdu89LMmwnhWtwjwz8DyqnrfOnZ5B73R4c2BTftta+jNVZYkSZKG1nhGkvcDXgFcn2RZv+1vqurSJIcBS6vqDoAky5JcT2+6xbXjCViSJK3fdBtJdak5tWE8q1tcQW86xbq2XQxcPOL+CfRWuZAkSZKGnlfckyRJA+OosKYKi2RJmmam21ftXefvU2rHhKyTLEmSJE0njiRLcqRKkqQGR5IlSZKkBotkSZIkqcHpFpIkp9xIUoMjyZIkSVKDI8mSph3XYR2MQY0++/uU1AaLZEnSwFjwSpoqnG4hSZIkNUy/keRTZq1n233rbPaEFUmD4kiqJE0NjiRLkiRJDdNvJHmIOYKkrhvLtzZ+06NB8T1a0kgWyZLGxOJ1MOxnSWqH0y0kSZKkBotkSZIkqcEiWZIkSWoY/jnJoy3pNspyboPiPEFpehnLSVue6DW8BvG78URUaXob/iJZ0m/M4k2SpPFxuoUkSZLUYJEsSZIkNVgkS5IkSQ3OSR5yzi2VJEkaPEeSJUmSpAaLZEmSJKlh6Kdb/KbTDVyDUpIkSePlSLIkSZLUMPQjyRpenlQ4GPazJEmDZ5EsDZAFryRJU4PTLSRJkqQGR5KnobGMVjrCKUmS9AhHkiVJkqSGcY0kJzkY+CdgU+BjVXV6kvOA3YEvVtXf9Pd7K3BDVV08zng1hbk8nyRJmirGXCQn2RQ4C/hjYAVwVZJLgV9W1R5JLk8yC5gJ/G5VnTYhEUuTwAJekiSNNJ6R5H2BH1TVzQBJPgUcAmyZZBNgc+Ah4O+At483UEmSJGlQUlVje2DyQuDgqvqz/v1XAL8LPAgcAPxv4KvAcVX1pxt4rsXAYoC5c+fufeutt44pJk0/8978b6Nuu+X0QybsMZIkqXuSXF1Vi9a1bcJXt6iq40cc+AvAsUlOBvYELq+qc9bxmLOBswEWLVo0tqpdkiRJmiDjKZJvB5464v6cfhsASQ4Frga2AnasqiOTXJbkvKpaNY7jSuvlaLEkSRqv8SwBdxUwP8kOSR4HvAS4BCDJ5sDxwLuBLYG1o8ObAo8bxzElSZKkSTfmIrmqHgReB1wGLAcurKob+5v/Evh4f8T4OmBmkuuBq6vq3vGFLEmSJE2uMZ+4N1kWLVpUS5cubTsMSZIkTXPrO3HPK+5JkiRJDRbJkiRJUoNFsiRJktRgkSxJkiQ1WCRLkiRJDRbJkiRJUoNFsiRJktRgkSxJkiQ1WCRLkiRJDRbJkiRJUoNFsiRJktRgkSxJkiQ1WCRLkiRJDRbJkiRJUkOqqu0YHiXJncCto2zeDrhrgOEMm67nD/aB+Xc7f7APzL/b+YN90PX8YWL74GlVNXtdG4auSF6fJEuralHbcbSl6/mDfWD+3c4f7APz73b+YB90PX8YXB843UKSJElqsEiWJEmSGqZakXx22wG0rOv5g31g/up6H5i/ut4HXc8fBtQHU2pOsiRJkjQIU20kWZIkSZp0FsmSJElSg0WyJEmS1GCRrCkjye+0HUPbkmzXdgxtSrJN2zEMSpIntB2DNCySbJ/kd/o/27cdT5uSvLbtGAYtyaIkL0jy/CTPHNRxNxvUgX5T/U44A1gDvB54G3AYcBPwqqpa3l50ky/JPcBngfOBr1XHzrBcR0Ec4PNJnkfvhNNrWghroJL8CfAh4HbgOOBfgRlJtqD3N/DVNuObbEn2Az5G7z3g1cBpwNOTPA44sqr+vzbjG4C7kvwHvfeAz1TVve2GM1ySfKmq/qTtOCZTkldX1bn923OAjwN7A/8XOLqqbmozvkFIshD4CDCL3nshwJwk9wKvne6fBUn+qtkEvCXJDICqet/goxqcJH8A/CNwL73X/n8CT0yyGnhFVf1oUo8/rLVXkm8C7wG2Ak4H/hq4APhfwPFVdWCL4U26JP8FnAm8FJgHfBo4v6qubDOuQUmyBrgS+NWI5mf126qq/rCVwAYoyTJ6v/8nAF8EDqmqK5PsApxXVdN6ZD3Jd4A/pfce8AXgsKq6ov8fqDOrar9WA5xkSa4H3kLvNXAwcAW9gvnzVfXLNmMblPV8exTgi1X1pEHGM2hJrln7d57kQuAr9P7jeCjwuun+OQgPvw8eW1XfbrQ/C/hoVe3ZSmADkuR+4FLgRnqve4DjgfcDVNWprQQ2IEm+CxxUVXcm2QF4X1W9IMkfAydW1UGTevwhLpK/W1V79W//oKp2GrHtmg4UCCPfHOcCL+n/PAH4VFX9TYvhTbokR9D7BuH0qvpSv+2HVbVDu5ENTuM18KOqeuqIbcuqamFrwQ1A4z1geVXtMmJb194DtgSeR+894A+Ay6rqZW3GNwhJHgK+wSPFwUjPqqotBxzSQDVeA4/6mx/59zGdJfl+Vc0fZdujaoPpqP/5/4/AzcCpVbUqyc1V9fSWQxuIJNdV1R7925sCV434m7ixqnabzOMP7XQLYNMRt5tfJzxukIG05OEPhaq6DXg38O7+NJQXtxbVgFTVZ5JcBrwjyauBNwHD+T+6yXNvkmOBxwM/S/JG4ELgj4BftBrZYIw8Z+ItjW1dew/4Jb3f/YVJZtGbetYFy+mNIn6/uSHJpH7NOiTmJPkAvdfC7CSbV9Xq/rbNW4xrkL6U5N+ATwBrf+dPBV4JfLm1qAak//n/oiSHApcnOaPtmAZsaZJ/Br4GPB/4D4AkM3l0nTgphrlIPivJVlX1i6r60NrGJDvR+8ppuvv6uhqr6nvAtP56Za2q+gXwxiR70ZuLt1XLIQ3aq4C30puTexC9r90vA24FXtNiXIPytiQzq2pVVV28tjHJjvQ+MKe789bVWFX30ft76IJTGP0E8+MGGEdbThxxeym998CfJfkfwCXthDRYVfX6/vkZhwJP6TffDpxVVZe2F9lgVdXnk3yF3t/EipbDGaRj6X3e/R692u/cfnsBz5nsgw/tdAtppCQBtq6qn7cdiyRJmv6Gdgm4JJslOTbJl5Nc1//5UpI/TzLtv2bqev5N1fNzgCR/23Y8betCHyTZtP838I7+Shcjt721rbgGpev5r5XkOUk+nOSS/s+HkxzcdlyDMOJz4Et+DjxWkrPbjqFNSb7UdgxtGkT+QzuSnOR8ekt+fJxHvlqYQ+8r6G2qalrPy+16/uuT5Laqmtt2HG3qQh8k+RgwE/gO8ArgG1X1V/1tXThxr9P5AyR5P/AMetNrRr4PvhL4flW9oaXQBsLPgfWujR7g2qqaM8h4Bs0VXtrNf5iL5Juq6hm/6bbpwvwz2rSKAFtW1TDPp58QXe+DxlnNm9FbM3o7enOzr5zuZ/Z3PX8Y/b2uP/3qptFWPZguuv45AA+vcHIrj17hpPr3n1JV0/okXld4aTf/Yf6QvSfJi+gtor8GIMkmwIuAn7Ua2WB0Pf97gX2q6qfNDR05qx3sg4c//KrqQWBxf5rJ1+jGSZxdzx/ggST7VNVVjfZ9gAfaCGjAuv45AL2lzw7sr/LwKB15H+z6Ci+t5j+0c5LprQf6QuCnSW5KchPwU+Dw/rbpruv5fwJ42ijbPjnIQFrU9T5Y2px7WlV/B/wLvQvsTHddzx/gaOCDSf5vkn/v/ywHPtDfNt11/XMAehfNeOIo2949wDjacgrdXuHlFFrMf2inW4yUZFuAqrq77Vja0PX8JXVbf8mzh5f/qqqftBlPG/wckAZvmEeSH1ZVd1fV3V09k7Xr+a+V5JS2Y2hb1/vAv4Fu5l9VP6mqq6vqauDP246nDX4OPKLrfZDki23H0KZB5j8liuQRFrUdQMu6nv/z2w5gCHS9D7r+N9D1/MG/AV8D9sFTNrzLtDaw/Kdakbyy7QBa1vX813V2a9d0vQ+6/jfQ9fzBvwFfA/bBd9sOoGUDy39KzEmWoHdW99ozvLvKPlDXJUn5wSVpAKbaSDLQjflIXb/aVpKZSU5KcmKSGUmOBi5O8u4kXVn+al2+13YAg5JkjxG3N0/y1v4V196ZZGabsQ1Cktcl2a5/e6ck30zysyTfTrJ72/ENQpKnJzk3yWlJtkpyDnB9kouSzGs7vsmWZJMkr07yb0muTXJNkk8lOaDt2AYlyawkpyf5XpJ7ktydZHm/7Qltx9emLlxxL8njk7wryf9O8rLGtg9N+vGH9T/kXmWn21fbSnIh8CNgS2BnemslXkBvPuL/qKpXtBjeQCS5n96i+fDIV8wzgVX0rtT9+FYCG5CRr/Mk/whsS2/5s8OAbavqlS2GN+mS3FhVu/Vv/xvwsar6XL9A+vuq2m99j58OknwTOB+YBRxF7/d/IXAQ8PKq+sMWw5t0Sf6F3oU0vkJvKbifA98C/hr4fFWd2WJ4A5HkMnprg3987aom/dVOXkVv/eSD2oxvssUr7n0G+D5wJfBqYDXwsqr61SBqoWEukrt+lZ1OX20rybKqWti/staPgSdVVfXvX7u2b6azJB8AngCcuPaCIkl+WFU7tBrYgCT57trXeZJl9C6ssrorr4Ek/1VVO/dvX1VV+4zYdt10zx8e8xp41KXYR26brpq/5yRXVtWzkmwBLKuqXVoMbyBG/h38Jtumi3jFvWVVtXDE/ZOB59IbMLt8sovkYb7iXtevsuPVtugNlya5dO0cxP794fyf3QSrqtcn2Rs4P8nFwAd5ZGS5C2YleQG9aWFbVNVq6NRr4NNJlgB/B3wuyfHA54A/BB7zvjhNrUnyDHojyTOTLKqqpUl2AjZtObZBWJ1kx6r67/6I4q8B+qNoXfgbALg1yUn0RpLXDhZsT+9iMl2oBbp+xb0tRp6LU1V/n+R24JsMoBYa5iL5/fSusrOuD4MuXGVnaZKDq+rLaxuq6u+S3AF8uMW4BmVpkq2q6hdV9eq1jUl2BO5vMa6Bqqqrk/wR8Dp6owkzWg5pkL7BI8t9XZlk+6r6af+r1rtajGsgqurk/lz884EdgS2AxcDFwMvbi2ygTgK+AKyhN83mLUn2BB4PvKbFuAblRODrSX5N7z8FLwFIMhvoylq5LwbeDHwjyW/3234KXAIc2VpUg3MK3b7i3hfoDQx8ZW1DVS1J8hNg0qcbDe10C2k0STfPbk/yJGCvqrq07ViktvRPZvxZVT3UdiyD0J9etG1VTfv/GErDZphHkknyTOBQRlyOFLikqpa3F9XgmH+384d190F/XnIn+qDrr4Gu5w+j9sHn6c5KLzsDhybp7GtgNEmOqap/aTuOtpj/5Oc/tEvAJflr4FP0Jqt/p/8TevMz39xmbINg/t3OH+wD8+92/rDePvhUF/rA18AGndp2AC0z/0k2tNMtktwE7Lb2ZJ0R7Y8Dbqyq+e1ENhjm3+38wT4w/27nD/ZB1/OH3gofo20CnlFVWwwynkEz/3bzH+bpFmuAJ9NbBm6kJ/W3TXfm3+38wT4w/27nD/ZB1/MH2B54DvCzRnuA/zP4cAbO/FvMf5iL5OOBryb5Po8s8zIX2Inemf7T3fGYf5fzB/vgeMy/y/mDfXA83c4feqt4bFVVy5obkvzHwKMZPPNvMf+hnW4BvUtyAvvy6BM2rurQWc3m3+H8wT4w/27nD/ZB1/OX2jTURXJTksVVdXbbcbTF/LudP9gH5t/t/ME+6Hr+YB+Y/+DyH9rVLUbx520H0DLzV9f7wPzV9T7oev5gH5j/gEy1Inld1y7vEvNX1/vA/NX1Puh6/mAfmP+gDjTFplvMqaoVbcfRFvPvdv5gH5h/t/MH+6Dr+YN9YP6Dy39KjSSv7ZQkx7QdSxvMv9v5g31g/t3OH+yDrucP9oH5Dy7/KTWSvFaS26pqbttxtMX8u50/2Afm3+38wT7oev5gH5j/5Oc/tOskb+AqK9sPMpY2mH+38wf7wPy7nT/YB13PH+wD8283/6EtkvEqM+bf7fzBPjD/bucP9kHX8wf7wPy94t46eZUZ8+9y/mAfmH+38wf7oOv5g31g/l5xT5IkSRoeU2p1C0mSJGkQhrZITrJHkiuT/CjJ2UmeOGLbd9qMbRDMv9v5g31g/t3OH+yDrucP9oH5t5v/0BbJwIeAU4DdgZuAK5Ls2N+2eVtBDZD5dzt/sA/Mv9v5g33Q9fzBPjD/FvMf5hP3tq6qL/dvvzfJ1cCXk7wC6MJEavPvdv5gH5h/t/MH+6Dr+YN9YP4t5j/MRTJJZlXVfQBV9fUkRwCfAbZpN7LBMP9u5w/2gfl3O3+wD7qeP9gH5t9e/sM83eIfgF1GNlTVdcCBwGdbiWiwzL/b+YN9YP7dzh/sg67nD/aB+beYv0vASZIkSQ1DO5KcZFaS05N8L8k9Se5Osrzf9oS245ts5t/t/ME+MP9u5w/2QdfzB/vA/NvNf2iLZOBCepchPKCqtqmqbYFn99subDWywTD/bucP9oH5dzt/sA+6nj/YB+bfYv5DO90iyX9V1c6/6bbpwvy7nT/YB+bf7fzBPuh6/mAfmH+7+Q/zSPKtSU5Ksv3ahiTbJ/lr4EctxjUo5t/t/ME+MP9u5w/2QdfzB/vA/FvMf5iL5BcD2wLfSPKzJPcA/0FvyY8j2wxsQMy/2/mDfWD+3c4f7IOu5w/2gfm3mP/QTrcASPJMYA5wZVX9YkT7wSMWl562zL/b+YN9YP7dzh/sg67nD/aB+beX/9COJCd5PfB54HXADUkOHbH5ne1ENTjm3+38wT4w/27nD/ZB1/MH+8D8281/mK+49xpg76r6RZJ5wKeTzKuqfwLSbmgDYf7dzh/sA/Pvdv5gH3Q9f7APzL/F/Ie5SN5k7bB6Vd2S5AB6nfM0uvHCMP9u5w/2gfl3O3+wD7qeP9gH5t9i/kM73QL4aZKFa+/0O+l/AdsBu7cV1ACZf7fzB/vA/LudP9gHXc8f7APzbzH/oT1xL8kc4MGq+sk6tu1XVf/ZQlgDY/7dzh/sA/Pvdv5gH3Q9f7APzL/d/Ie2SJYkSZLaMszTLSRJkqRWWCRLkiRJDRbJkiRJUoNFsiQNUJJTkpywnu2HJdl1Ao83L8kN43yOA5L8/kTFJElTgUWyJA2Xw4AJK5LHK8lmwAGARbKkTnF1C0maZElOBl4FrAR+BFwN3AcsBh4H/AB4BbAQ+GJ/233AEf2nOAuYDawCXlNV3xvlONsDHwGe3m/6C+AO4EvAFfQK3duBQ6vql0le04yhqlYlWQI8AOzV3//3gYeAO4Hjqupb4+0TSRp2jiRL0iRKsjfwEnoF8HOBffqbPltV+1TVnsBy4E+r6v8AlwAnVtXCqvpv4Gx6henewAnAh9ZzuA8A3+g/5+8AN/bb5wNnVdVuwL08Unw/JoYRzzUH+P2qOpxe4X1GPyYLZEmdMMyXpZak6WB/4HNVtQogySX99gVJTgOeAGwFXNZ8YJKt6I3iXpQ8fAXWLdZzrD8EXglQVQ8B9yV5IvDDqlrW3+dqYN5GxHBR/zkkqZMskiWpHUuAw6rq2iRH05v327QJcG9VLRznsX414vZDwJYbEcP/G+cxJWlKc7qFJE2ubwKHJdkyydbA8/rtWwM/TrI58PIR+9/f30ZV/Rz4YZIXAaRnz/Uc66v05iGTZNMkszYQ22gxND0ckyR1hUWyJE2iqroGuAC4lt4JdFf1N70N+Dbwn8DIE/E+BZyY5LtJdqRXvP5pkmvpzTE+dD2HewPw7CTX05tWsaFVMkaLoekLwAuSLEuy/waeU5KmBVe3kCRJkhocSZYkSZIaPHFPkqaY/rrLL2o0X1RVf99GPJI0HTndQpIkSWpwuoUkSZLUYJEsSZIkNVgkS5IkSQ0WyZIkSVKDRbIkSZLU8P8Dk5EwY44MVT0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw a stacked bar chart of the cumulative sum of the Options and LP columns\n",
    "chart = data.plot(x='date_chart', y=['cum_options','cum_lp'], kind='bar', stacked=True)\n",
    "# set y-axis format to be in percentage with % sign\n",
    "chart.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=0))\n",
    "# set x-axis ticks to be monthly\n",
    "locator = mtick.MultipleLocator(base=5)\n",
    "chart.xaxis.set_major_locator(locator)\n",
    "# Set the legend to be 'Options P&L' and 'LP P&L'\n",
    "chart.legend(['Options P&L', 'LP P&L'])\n",
    "# set the chart size to be 12x6\n",
    "chart.figure.set_size_inches(12,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_plot = sns.lineplot(x='Date', y='cum_options', data=data)\n",
    "sns.lineplot(x='Date', y='cum_lp', data=data)\n",
    "# set the seaborn y axis to be in percentage with % sign\n",
    "line_plot.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=0))\n",
    "# figure size is 12x6\n",
    "line_plot.figure.set_size_inches(12,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sum bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw a stacked bar chart of the cumulative sum of the Options and LP columns\n",
    "data['sum'] = data.cum_lp + data.cum_options\n",
    "chart = data.plot(x='date_chart', y='sum', kind='bar')\n",
    "chart.set_xlabel('Date')\n",
    "# set y-axis format to be in percentage with % sign\n",
    "chart.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=0))\n",
    "# set x-axis ticks to be monthly\n",
    "locator = mtick.MultipleLocator(base=5)\n",
    "chart.xaxis.set_major_locator(locator)\n",
    "# Set the legend to be 'Options P&L' and 'LP P&L'\n",
    "chart.legend(['P&L'])\n",
    "# set the chart size to be 12x6\n",
    "chart.figure.set_size_inches(12,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('data/mock_data.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('data')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "fe091e7701431df76bef8087de3ebd8de2541ecac9ed74009df64fc9dc2ca1eb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}