{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63e84164-3f22-4718-aa50-8c1587985e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Prevent scientific notation in pandas output.\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "\n",
    "\n",
    "# Add the parent directory to sys.path\n",
    "sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), '..')))\n",
    "\n",
    "\n",
    "from database import Database\n",
    "from model.ticker import Ticker\n",
    "\n",
    "\n",
    "DB_PATH = \"sqlite:///../finance.db\"\n",
    "\n",
    "\n",
    "db = Database(database_url=DB_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59cb5f64-18d3-4f0a-950a-123a6e7bc6f7",
   "metadata": {},
   "source": [
    "# Let's Do Some High Level Queries On The Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cc8fb31-406d-4635-98e7-4a2b7ca19086",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_tickers = db.session.query(Ticker.symbol).distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7540c4ec-0ec5-4536-80dc-b222e37f43d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_tickers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69153cc0-58ed-4fe1-bac1-ea4179f17a70",
   "metadata": {},
   "source": [
    "# Load A Sample Of Data Into Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20650f2c-7bdd-4663-bab1-124a667be5a6",
   "metadata": {},
   "source": [
    "We want to load a sample since the database has roughly 25 million rows. Sampling makes the demo easier to present without crashing the browser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3153db1b-bc97-4d8c-be3a-c1524d34d07c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eada2911-1eef-4447-83f5-48471463d5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2010\n",
    "query = f\"SELECT * FROM ticker WHERE strftime('%Y', ticker.date) = '{year}'\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4997978f-c1ea-4b51-b329-8d7fa97c1b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql(query, db.engine)\n",
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d %H:%M:%S.%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "796a5ed7-1255-4e6b-aa1d-28a3cedfc71e",
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1044000.000</td>\n",
       "      <td>1044000</td>\n",
       "      <td>1044000.000</td>\n",
       "      <td>1044000.000</td>\n",
       "      <td>1044000.000</td>\n",
       "      <td>1044000.000</td>\n",
       "      <td>1044000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12521609.500</td>\n",
       "      <td>2010-07-02 19:07:35.172413440</td>\n",
       "      <td>249.979</td>\n",
       "      <td>263.009</td>\n",
       "      <td>235.933</td>\n",
       "      <td>249.988</td>\n",
       "      <td>1075619.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2610.000</td>\n",
       "      <td>2010-01-01 00:00:00</td>\n",
       "      <td>0.985</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.936</td>\n",
       "      <td>1.034</td>\n",
       "      <td>49077.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6262109.750</td>\n",
       "      <td>2010-04-02 00:00:00</td>\n",
       "      <td>86.348</td>\n",
       "      <td>90.865</td>\n",
       "      <td>81.526</td>\n",
       "      <td>86.464</td>\n",
       "      <td>564369.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12521609.500</td>\n",
       "      <td>2010-07-02 00:00:00</td>\n",
       "      <td>181.516</td>\n",
       "      <td>191.091</td>\n",
       "      <td>171.390</td>\n",
       "      <td>181.713</td>\n",
       "      <td>1087653.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18781109.250</td>\n",
       "      <td>2010-10-01 00:00:00</td>\n",
       "      <td>336.361</td>\n",
       "      <td>353.789</td>\n",
       "      <td>317.443</td>\n",
       "      <td>336.434</td>\n",
       "      <td>1581562.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25040609.000</td>\n",
       "      <td>2010-12-31 00:00:00</td>\n",
       "      <td>2520.380</td>\n",
       "      <td>2631.542</td>\n",
       "      <td>2335.396</td>\n",
       "      <td>2461.570</td>\n",
       "      <td>2299658.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7229583.308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237.887</td>\n",
       "      <td>250.233</td>\n",
       "      <td>224.430</td>\n",
       "      <td>237.659</td>\n",
       "      <td>591062.757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id                           date        open        high  \\\n",
       "count  1044000.000                        1044000 1044000.000 1044000.000   \n",
       "mean  12521609.500  2010-07-02 19:07:35.172413440     249.979     263.009   \n",
       "min       2610.000            2010-01-01 00:00:00       0.985       1.079   \n",
       "25%    6262109.750            2010-04-02 00:00:00      86.348      90.865   \n",
       "50%   12521609.500            2010-07-02 00:00:00     181.516     191.091   \n",
       "75%   18781109.250            2010-10-01 00:00:00     336.361     353.789   \n",
       "max   25040609.000            2010-12-31 00:00:00    2520.380    2631.542   \n",
       "std    7229583.308                            NaN     237.887     250.233   \n",
       "\n",
       "              low       close      volume  \n",
       "count 1044000.000 1044000.000 1044000.000  \n",
       "mean      235.933     249.988 1075619.915  \n",
       "min         0.936       1.034   49077.000  \n",
       "25%        81.526      86.464  564369.000  \n",
       "50%       171.390     181.713 1087653.000  \n",
       "75%       317.443     336.434 1581562.500  \n",
       "max      2335.396    2461.570 2299658.000  \n",
       "std       224.430     237.659  591062.757  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "edcada31-c631-4a3c-9b26-ad3055f9700e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hpar = df[df['symbol'] == 'HPAR-fake']\n",
    "vhex = df[df['symbol'] == 'RVBL-fake']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "46a23f6d-2b88-46fa-a340-9ed03723b08e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "\n",
    "# Plot the filtered data\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(vhex['date'], vhex['close'], linestyle='-')\n",
    "plt.title('Value over Time for Symbol HPAR-fake')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Value')\n",
    "plt.grid(True)\n",
    "\n",
    "# Formatting the date\n",
    "plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "\n",
    "# Rotate date labels for better readability\n",
    "plt.gcf().autofmt_xdate()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "232e3d02-36de-4ef0-bef0-23a2aa09e953",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}