{
 "cells": [
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   "cell_type": "code",
   "execution_count": null,
   "id": "1bbf544a-2be3-4579-ade3-404f4d850ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from DATA225utils import make_connection, dataframe_query\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dcedb2f-42ab-4b8a-bc71-8c9bdeb59793",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = make_connection(config_file='stock-prices.ini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc76337a-e795-4e0d-833e-3d36b20350a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_exponential_smoothing(conn, alpha, label):\n",
    "    \"\"\"\n",
    "    @cursor the database cursor\n",
    "    @alpha the alpha value for exponential smoothing\n",
    "    @label label for the exponentially smoothed values\n",
    "    \"\"\"\n",
    "    conn.cursor().execute('SET @smoothed = NULL')\n",
    "    \n",
    "    _, df = dataframe_query(conn,\n",
    "            f'SELECT days_ago, price, '\n",
    "            f'       @smoothed := ({alpha}*price '\n",
    "            f'                  + (1-{alpha})*(IF (@smoothed IS NULL, price, @smoothed))) '\n",
    "            f'           AS {label} '\n",
    "            f'FROM recent_prices '\n",
    "            f'ORDER BY days_ago'\n",
    "    )\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a30b7c5-2426-4694-93e2-4d2d3984c720",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "first = True\n",
    "\n",
    "for smoother in [5, 2]:\n",
    "    graph_label = f'zero_point_{smoother}_exponentially_smoothed'\n",
    "    alpha = smoother/10\n",
    "    \n",
    "    df = compute_exponential_smoothing(conn, alpha, graph_label)\n",
    "    display(df)\n",
    "\n",
    "    rows = df.values.tolist()\n",
    "    xs   = [row[0] for row in rows]\n",
    "    ys   = [row[1] for row in rows]\n",
    "    exps = [row[2] for row in rows]\n",
    "    \n",
    "    # Initialize the graph.\n",
    "    if first:\n",
    "        plt.xticks(xs)\n",
    "        plt.plot(xs, ys, linewidth=5)\n",
    "        plt.title('Exponentially Smoothed Stock Prices')\n",
    "        plt.xlabel('Days ago')\n",
    "        plt.ylabel('Price')\n",
    "        first = False\n",
    "\n",
    "    # Plot the moving average line.\n",
    "    plt.plot(xs, exps, label=graph_label)\n",
    "    \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84062204-fd04-4dfb-8de9-b20073459435",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "first = True\n",
    "\n",
    "for smoother in [5, 2]:\n",
    "    graph_label = f'zero_point_{smoother}_exponentially_smoothed'\n",
    "    alpha = smoother/10\n",
    "    \n",
    "    df = compute_exponential_smoothing(conn, alpha, graph_label)\n",
    "    display(df)\n",
    "\n",
    "    rows = df.values.tolist()\n",
    "    xs   = [row[0] for row in rows]\n",
    "    ys   = [row[1] for row in rows]\n",
    "    exps = [row[2] for row in rows]\n",
    "    \n",
    "    # Initialize the graph.\n",
    "    if first:\n",
    "        plt.xticks(xs)\n",
    "        plt.plot(xs, ys, linewidth=5)\n",
    "        plt.title('Exponentially Smoothed Stock Prices')\n",
    "        plt.xlabel('Days ago')\n",
    "        plt.ylabel('Price')\n",
    "        first = False\n",
    "\n",
    "    # Plot the moving average line.\n",
    "    plt.plot(xs, exps, label=graph_label)\n",
    "    \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b750ca51-c26e-43df-98b3-ec64a65eff16",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6044e8e5-2c26-4ff4-919f-093576be8e91",
   "metadata": {},
   "source": [
    "#### (c) 2023 by Ronald Mak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e9ace8a-3452-456d-aa22-2c88df721822",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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