incline/examples/incline_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Incline\n",
    "\n",
    "We showcase how to use **incline** using data from the stock market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Google Daily Stock Price Data\n",
    "\n",
    "Convert the date column to date, sort by date, and index the dataframe by date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>49.676899</td>\n",
       "      <td>51.693783</td>\n",
       "      <td>47.669952</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>44994500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>50.178635</td>\n",
       "      <td>54.187561</td>\n",
       "      <td>49.925285</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>23005800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>55.017166</td>\n",
       "      <td>56.373344</td>\n",
       "      <td>54.172661</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>18393200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.260582</td>\n",
       "      <td>55.439419</td>\n",
       "      <td>51.450363</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>15361800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.140873</td>\n",
       "      <td>53.651051</td>\n",
       "      <td>51.604362</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>9257400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2004-08-19  49.676899  51.693783  47.669952  49.845802  49.845802  44994500\n",
       "2004-08-20  50.178635  54.187561  49.925285  53.805050  53.805050  23005800\n",
       "2004-08-23  55.017166  56.373344  54.172661  54.346527  54.346527  18393200\n",
       "2004-08-24  55.260582  55.439419  51.450363  52.096165  52.096165  15361800\n",
       "2004-08-25  52.140873  53.651051  51.604362  52.657513  52.657513   9257400"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/GOOG.csv')\n",
    "df.Date = pd.to_datetime(df.Date)\n",
    "df.sort_values('Date', inplace=True)\n",
    "df.set_index('Date', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using incline\n",
    "\n",
    "We start by importing the functions and then fit spline_trend and sgolay_trend to the Closing value of the stock "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from incline import naive_trend, spline_trend, sgolay_trend, trending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>smoothed_value</th>\n",
       "      <th>derivative_value</th>\n",
       "      <th>function_order</th>\n",
       "      <th>derivative_method</th>\n",
       "      <th>derivative_order</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>49.676899</td>\n",
       "      <td>51.693783</td>\n",
       "      <td>47.669952</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>44994500</td>\n",
       "      <td>53.577715</td>\n",
       "      <td>-0.717595</td>\n",
       "      <td>3</td>\n",
       "      <td>spline</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-spline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>50.178635</td>\n",
       "      <td>54.187561</td>\n",
       "      <td>49.925285</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>23005800</td>\n",
       "      <td>52.912576</td>\n",
       "      <td>-0.613191</td>\n",
       "      <td>3</td>\n",
       "      <td>spline</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-spline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>55.017166</td>\n",
       "      <td>56.373344</td>\n",
       "      <td>54.172661</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>18393200</td>\n",
       "      <td>52.350316</td>\n",
       "      <td>-0.511836</td>\n",
       "      <td>3</td>\n",
       "      <td>spline</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-spline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.260582</td>\n",
       "      <td>55.439419</td>\n",
       "      <td>51.450363</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>15361800</td>\n",
       "      <td>51.887887</td>\n",
       "      <td>-0.413530</td>\n",
       "      <td>3</td>\n",
       "      <td>spline</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-spline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.140873</td>\n",
       "      <td>53.651051</td>\n",
       "      <td>51.604362</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>9257400</td>\n",
       "      <td>51.522239</td>\n",
       "      <td>-0.318274</td>\n",
       "      <td>3</td>\n",
       "      <td>spline</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-spline</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
       "Date                                                                          \n",
       "2004-08-19  49.676899  51.693783  47.669952  49.845802  49.845802  44994500   \n",
       "2004-08-20  50.178635  54.187561  49.925285  53.805050  53.805050  23005800   \n",
       "2004-08-23  55.017166  56.373344  54.172661  54.346527  54.346527  18393200   \n",
       "2004-08-24  55.260582  55.439419  51.450363  52.096165  52.096165  15361800   \n",
       "2004-08-25  52.140873  53.651051  51.604362  52.657513  52.657513   9257400   \n",
       "\n",
       "            smoothed_value  derivative_value  function_order  \\\n",
       "Date                                                           \n",
       "2004-08-19       53.577715         -0.717595               3   \n",
       "2004-08-20       52.912576         -0.613191               3   \n",
       "2004-08-23       52.350316         -0.511836               3   \n",
       "2004-08-24       51.887887         -0.413530               3   \n",
       "2004-08-25       51.522239         -0.318274               3   \n",
       "\n",
       "           derivative_method  derivative_order                 id  \n",
       "Date                                                               \n",
       "2004-08-19            spline                 1  GOOG-Close-spline  \n",
       "2004-08-20            spline                 1  GOOG-Close-spline  \n",
       "2004-08-23            spline                 1  GOOG-Close-spline  \n",
       "2004-08-24            spline                 1  GOOG-Close-spline  \n",
       "2004-08-25            spline                 1  GOOG-Close-spline  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = spline_trend(df, column_value='Close',  function_order=3, derivative_order=1, s=50000)\n",
    "df1['id'] = 'GOOG-Close-spline'\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>smoothed_value</th>\n",
       "      <th>derivative_value</th>\n",
       "      <th>function_order</th>\n",
       "      <th>derivative_method</th>\n",
       "      <th>derivative_order</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>49.676899</td>\n",
       "      <td>51.693783</td>\n",
       "      <td>47.669952</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>44994500</td>\n",
       "      <td>51.099678</td>\n",
       "      <td>1.563686</td>\n",
       "      <td>3</td>\n",
       "      <td>sgolay</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-sgolay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>50.178635</td>\n",
       "      <td>54.187561</td>\n",
       "      <td>49.925285</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>23005800</td>\n",
       "      <td>52.363147</td>\n",
       "      <td>0.977906</td>\n",
       "      <td>3</td>\n",
       "      <td>sgolay</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-sgolay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>55.017166</td>\n",
       "      <td>56.373344</td>\n",
       "      <td>54.172661</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>18393200</td>\n",
       "      <td>53.084794</td>\n",
       "      <td>0.480041</td>\n",
       "      <td>3</td>\n",
       "      <td>sgolay</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-sgolay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.260582</td>\n",
       "      <td>55.439419</td>\n",
       "      <td>51.450363</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>15361800</td>\n",
       "      <td>53.352533</td>\n",
       "      <td>0.070091</td>\n",
       "      <td>3</td>\n",
       "      <td>sgolay</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-sgolay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.140873</td>\n",
       "      <td>53.651051</td>\n",
       "      <td>51.604362</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>9257400</td>\n",
       "      <td>53.254281</td>\n",
       "      <td>-0.251943</td>\n",
       "      <td>3</td>\n",
       "      <td>sgolay</td>\n",
       "      <td>1</td>\n",
       "      <td>GOOG-Close-sgolay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
       "Date                                                                          \n",
       "2004-08-19  49.676899  51.693783  47.669952  49.845802  49.845802  44994500   \n",
       "2004-08-20  50.178635  54.187561  49.925285  53.805050  53.805050  23005800   \n",
       "2004-08-23  55.017166  56.373344  54.172661  54.346527  54.346527  18393200   \n",
       "2004-08-24  55.260582  55.439419  51.450363  52.096165  52.096165  15361800   \n",
       "2004-08-25  52.140873  53.651051  51.604362  52.657513  52.657513   9257400   \n",
       "\n",
       "            smoothed_value  derivative_value  function_order  \\\n",
       "Date                                                           \n",
       "2004-08-19       51.099678          1.563686               3   \n",
       "2004-08-20       52.363147          0.977906               3   \n",
       "2004-08-23       53.084794          0.480041               3   \n",
       "2004-08-24       53.352533          0.070091               3   \n",
       "2004-08-25       53.254281         -0.251943               3   \n",
       "\n",
       "           derivative_method  derivative_order                 id  \n",
       "Date                                                               \n",
       "2004-08-19            sgolay                 1  GOOG-Close-sgolay  \n",
       "2004-08-20            sgolay                 1  GOOG-Close-sgolay  \n",
       "2004-08-23            sgolay                 1  GOOG-Close-sgolay  \n",
       "2004-08-24            sgolay                 1  GOOG-Close-sgolay  \n",
       "2004-08-25            sgolay                 1  GOOG-Close-sgolay  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = sgolay_trend(df, column_value='Close',  function_order=3, derivative_order=1)\n",
    "df2['id'] = 'GOOG-Close-sgolay'\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].set_title('GOOG-Close-spline');\n",
    "axes[1].set_title('GOOG-Close-sgolay');\n",
    "df1[['Close', 'smoothed_value']][:100].plot(figsize=(12, 16), secondary_y='smoothed_value', ax=axes[0], style=['-', '--']);\n",
    "df2[['Close', 'smoothed_value']][:100].plot(figsize=(12, 16), secondary_y='smoothed_value', ax=axes[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>max_or_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOG-Close-sgolay</td>\n",
       "      <td>13.407763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG-Close-spline</td>\n",
       "      <td>15.277732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  max_or_avg\n",
       "0  GOOG-Close-sgolay   13.407763\n",
       "1  GOOG-Close-spline   15.277732"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdf = trending([df1, df2], derivative_order=1, max_or_avg='max')\n",
    "tdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>derivative_value</th>\n",
       "      <th>derivative_method</th>\n",
       "      <th>function_order</th>\n",
       "      <th>derivative_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>49.676899</td>\n",
       "      <td>51.693783</td>\n",
       "      <td>47.669952</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>49.845802</td>\n",
       "      <td>44994500</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>naive</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>50.178635</td>\n",
       "      <td>54.187561</td>\n",
       "      <td>49.925285</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>53.805050</td>\n",
       "      <td>23005800</td>\n",
       "      <td>-2.979624</td>\n",
       "      <td>naive</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>55.017166</td>\n",
       "      <td>56.373344</td>\n",
       "      <td>54.172661</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>54.346527</td>\n",
       "      <td>18393200</td>\n",
       "      <td>-1.270739</td>\n",
       "      <td>naive</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.260582</td>\n",
       "      <td>55.439419</td>\n",
       "      <td>51.450363</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>52.096165</td>\n",
       "      <td>15361800</td>\n",
       "      <td>0.125181</td>\n",
       "      <td>naive</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.140873</td>\n",
       "      <td>53.651051</td>\n",
       "      <td>51.604362</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>52.657513</td>\n",
       "      <td>9257400</td>\n",
       "      <td>-1.280674</td>\n",
       "      <td>naive</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
       "Date                                                                          \n",
       "2004-08-19  49.676899  51.693783  47.669952  49.845802  49.845802  44994500   \n",
       "2004-08-20  50.178635  54.187561  49.925285  53.805050  53.805050  23005800   \n",
       "2004-08-23  55.017166  56.373344  54.172661  54.346527  54.346527  18393200   \n",
       "2004-08-24  55.260582  55.439419  51.450363  52.096165  52.096165  15361800   \n",
       "2004-08-25  52.140873  53.651051  51.604362  52.657513  52.657513   9257400   \n",
       "\n",
       "            derivative_value derivative_method function_order  \\\n",
       "Date                                                            \n",
       "2004-08-19         -2.000000             naive           None   \n",
       "2004-08-20         -2.979624             naive           None   \n",
       "2004-08-23         -1.270739             naive           None   \n",
       "2004-08-24          0.125181             naive           None   \n",
       "2004-08-25         -1.280674             naive           None   \n",
       "\n",
       "            derivative_order  \n",
       "Date                          \n",
       "2004-08-19                 1  \n",
       "2004-08-20                 1  \n",
       "2004-08-23                 1  \n",
       "2004-08-24                 1  \n",
       "2004-08-25                 1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = naive_trend(df, column_value='Close')\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f709a1cf358>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "plt.plot(df3[['derivative_value']][:100], color = \"blue\", label = \"naive\")\n",
    "plt.plot(df2[['derivative_value']][:100], color = \"green\", label = \"SG\")\n",
    "plt.plot(df1[['derivative_value']][:100], color = \"black\", label = \"Spline\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations Between Naive, Spline, and Savitzky-Golay Derivative Est."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "derivative_value    0.699171\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[['derivative_value']].corrwith(df2[['derivative_value']], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "derivative_value   -0.474181\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['derivative_value']].corrwith(df3[['derivative_value']], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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