ML_AMZN_Polyfit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import time\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('all_stocks_5yr.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-02-08</td>\n",
       "      <td>15.07</td>\n",
       "      <td>15.12</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.75</td>\n",
       "      <td>8407500</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-02-11</td>\n",
       "      <td>14.89</td>\n",
       "      <td>15.01</td>\n",
       "      <td>14.26</td>\n",
       "      <td>14.46</td>\n",
       "      <td>8882000</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-02-12</td>\n",
       "      <td>14.45</td>\n",
       "      <td>14.51</td>\n",
       "      <td>14.10</td>\n",
       "      <td>14.27</td>\n",
       "      <td>8126000</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-02-13</td>\n",
       "      <td>14.30</td>\n",
       "      <td>14.94</td>\n",
       "      <td>14.25</td>\n",
       "      <td>14.66</td>\n",
       "      <td>10259500</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-02-14</td>\n",
       "      <td>14.94</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.16</td>\n",
       "      <td>13.99</td>\n",
       "      <td>31879900</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close    volume Name\n",
       "0  2013-02-08  15.07  15.12  14.63  14.75   8407500  AAL\n",
       "1  2013-02-11  14.89  15.01  14.26  14.46   8882000  AAL\n",
       "2  2013-02-12  14.45  14.51  14.10  14.27   8126000  AAL\n",
       "3  2013-02-13  14.30  14.94  14.25  14.66  10259500  AAL\n",
       "4  2013-02-14  14.94  14.96  13.16  13.99  31879900  AAL"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(619040, 7)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "amzn = df.loc[df['Name'] == 'AMZN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1259, 7)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amzn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "amzn=amzn.drop('Name',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>46387</th>\n",
       "      <td>2013-02-08</td>\n",
       "      <td>261.40</td>\n",
       "      <td>265.25</td>\n",
       "      <td>260.555</td>\n",
       "      <td>261.95</td>\n",
       "      <td>3879078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46388</th>\n",
       "      <td>2013-02-11</td>\n",
       "      <td>263.20</td>\n",
       "      <td>263.25</td>\n",
       "      <td>256.600</td>\n",
       "      <td>257.21</td>\n",
       "      <td>3403403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46389</th>\n",
       "      <td>2013-02-12</td>\n",
       "      <td>259.19</td>\n",
       "      <td>260.16</td>\n",
       "      <td>257.000</td>\n",
       "      <td>258.70</td>\n",
       "      <td>2938660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46390</th>\n",
       "      <td>2013-02-13</td>\n",
       "      <td>261.53</td>\n",
       "      <td>269.96</td>\n",
       "      <td>260.300</td>\n",
       "      <td>269.47</td>\n",
       "      <td>5292996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46391</th>\n",
       "      <td>2013-02-14</td>\n",
       "      <td>267.37</td>\n",
       "      <td>270.65</td>\n",
       "      <td>265.400</td>\n",
       "      <td>269.24</td>\n",
       "      <td>3462780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    open    high      low   close   volume\n",
       "46387 2013-02-08  261.40  265.25  260.555  261.95  3879078\n",
       "46388 2013-02-11  263.20  263.25  256.600  257.21  3403403\n",
       "46389 2013-02-12  259.19  260.16  257.000  258.70  2938660\n",
       "46390 2013-02-13  261.53  269.96  260.300  269.47  5292996\n",
       "46391 2013-02-14  267.37  270.65  265.400  269.24  3462780"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amzn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\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>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1.259000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>576.867264</td>\n",
       "      <td>582.017221</td>\n",
       "      <td>571.113517</td>\n",
       "      <td>576.880041</td>\n",
       "      <td>3.730465e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>282.500019</td>\n",
       "      <td>284.417123</td>\n",
       "      <td>280.215237</td>\n",
       "      <td>282.500395</td>\n",
       "      <td>2.166506e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>248.940000</td>\n",
       "      <td>252.930000</td>\n",
       "      <td>245.750000</td>\n",
       "      <td>248.230000</td>\n",
       "      <td>1.092970e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>325.870000</td>\n",
       "      <td>329.485000</td>\n",
       "      <td>322.185000</td>\n",
       "      <td>325.800000</td>\n",
       "      <td>2.511165e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>512.330000</td>\n",
       "      <td>495.640000</td>\n",
       "      <td>503.820000</td>\n",
       "      <td>3.144719e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>777.620000</td>\n",
       "      <td>781.845000</td>\n",
       "      <td>770.720000</td>\n",
       "      <td>777.420000</td>\n",
       "      <td>4.220246e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1477.390000</td>\n",
       "      <td>1498.000000</td>\n",
       "      <td>1450.040000</td>\n",
       "      <td>1450.890000</td>\n",
       "      <td>2.385606e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open         high          low        close        volume\n",
       "count  1259.000000  1259.000000  1259.000000  1259.000000  1.259000e+03\n",
       "mean    576.867264   582.017221   571.113517   576.880041  3.730465e+06\n",
       "std     282.500019   284.417123   280.215237   282.500395  2.166506e+06\n",
       "min     248.940000   252.930000   245.750000   248.230000  1.092970e+06\n",
       "25%     325.870000   329.485000   322.185000   325.800000  2.511165e+06\n",
       "50%     506.000000   512.330000   495.640000   503.820000  3.144719e+06\n",
       "75%     777.620000   781.845000   770.720000   777.420000  4.220246e+06\n",
       "max    1477.390000  1498.000000  1450.040000  1450.890000  2.385606e+07"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amzn.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1259 entries, 46387 to 47645\n",
      "Data columns (total 6 columns):\n",
      "date      1259 non-null datetime64[ns]\n",
      "open      1259 non-null float64\n",
      "high      1259 non-null float64\n",
      "low       1259 non-null float64\n",
      "close     1259 non-null float64\n",
      "volume    1259 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(4), int64(1)\n",
      "memory usage: 68.9 KB\n"
     ]
    }
   ],
   "source": [
    "#print(amzn.info())\n",
    "amzn['date']=pd.to_datetime(amzn['date'])\n",
    "amzn.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "#amzn_close=amzn.drop(['open','high','low','volume'],axis=1)\n",
    "date=amzn.drop(['open','high','low','volume','close'],axis=1)\n",
    "close=amzn.drop(['open','high','low','volume','date'],axis=1)\n",
    "X_date = mdates.date2num(date)\n",
    "print(type(x))\n",
    "y_close=close.values\n",
    "print(type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[734907.],\n",
       "       [734910.],\n",
       "       [734911.],\n",
       "       ...,\n",
       "       [736730.],\n",
       "       [736731.],\n",
       "       [736732.]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=[]\n",
    "y=[]\n",
    "for sublist in X_date:\n",
    "    for item in sublist:\n",
    "        X.append(item)\n",
    "for sublist in y_close:\n",
    "    for item in sublist:\n",
    "        y.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coeff  [ 3.23991113e-04 -4.76294646e+02  1.75048758e+08]\n",
      "          2\n",
      "0.000324 t - 476.3 t + 1.75e+08\n"
     ]
    }
   ],
   "source": [
    "# Polynomial fit using Numpy\n",
    "Order = 2 ###You can try different order, calculate the y_calculated \n",
    "        #####and then find the MSE to determine which is a better fit\n",
    "fit = np.polyfit(X,y,Order)\n",
    "print(\"Coeff \", fit)\n",
    "fit1 = np.poly1d(fit,variable=\"t\")\n",
    "print(fit1)\n",
    "y1 = fit1(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "734907.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "736732.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Equation for Order 2\n",
    "#0.000324 t**2- 476.3 t + 1.75e+08\n",
    "print(X[0])\n",
    "X[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "xx = np.linspace(min(X), max(X), 100)\n",
    "dd = mdates.num2date(xx)\n",
    "print(type(dd))\n",
    "y_calculated=[]\n",
    "y_calculated=fit1(xx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, cx = plt.subplots()\n",
    "cx.plot(date, y, 'ro')\n",
    "cx.plot(dd, y_calculated, 'b+')\n",
    "#cx.axis(2013,2018,240,1500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1609.99946454]\n",
      "[2130.26688686]\n"
     ]
    }
   ],
   "source": [
    "# OK, now predict values for 2019 and 2020\n",
    "predict_date1=['2019-01-01']\n",
    "predict_date2=['2020-01-01']\n",
    "predict_date1=pd.to_datetime(predict_date1)\n",
    "predict_date2=pd.to_datetime(predict_date2)\n",
    "predict_X1 = mdates.date2num(predict_date1)\n",
    "predict_X2=mdates.date2num(predict_date2)\n",
    "y1=[]\n",
    "y2=[]\n",
    "y1=fit1(predict_X1)\n",
    "print(y1)\n",
    "y2=fit1(predict_X2)\n",
    "print(y2)\n",
    "y_calculated=np.append(y_calculated,y1)\n",
    "y_calculated=np.append(y_calculated,y2)\n",
    "#print(fit1(60000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xx1 = np.linspace(min(X), max(X), 100)\n",
    "dd1 = mdates.num2date(predict_X1)\n",
    "dd2=mdates.num2date(predict_X2)\n",
    "dd.extend(dd1)\n",
    "dd.extend(dd2)\n",
    "len(dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, cx = plt.subplots()\n",
    "cx.plot(date, y, 'ro')\n",
    "cx.plot(dd, y_calculated, 'b--')\n",
    "#cx.axis(2013,2018,240,1500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}