{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7fae3f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This code generates long term, outer and inner trend lines\n",
    "\n",
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f67519f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "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",
       "    </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>2019-07-08</th>\n",
       "      <td>97.739998</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>97.309998</td>\n",
       "      <td>103.250000</td>\n",
       "      <td>103.250000</td>\n",
       "      <td>12268300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-09</th>\n",
       "      <td>102.669998</td>\n",
       "      <td>104.440002</td>\n",
       "      <td>100.620003</td>\n",
       "      <td>103.400002</td>\n",
       "      <td>103.400002</td>\n",
       "      <td>10727300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-10</th>\n",
       "      <td>104.279999</td>\n",
       "      <td>105.889999</td>\n",
       "      <td>102.089996</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>8519300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-11</th>\n",
       "      <td>103.900002</td>\n",
       "      <td>104.900002</td>\n",
       "      <td>101.559998</td>\n",
       "      <td>104.540001</td>\n",
       "      <td>104.540001</td>\n",
       "      <td>9091800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-12</th>\n",
       "      <td>104.220001</td>\n",
       "      <td>105.455002</td>\n",
       "      <td>102.120003</td>\n",
       "      <td>102.470001</td>\n",
       "      <td>102.470001</td>\n",
       "      <td>7524200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-13</th>\n",
       "      <td>148.619003</td>\n",
       "      <td>152.005005</td>\n",
       "      <td>146.809998</td>\n",
       "      <td>149.970001</td>\n",
       "      <td>149.970001</td>\n",
       "      <td>5544700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-14</th>\n",
       "      <td>150.850006</td>\n",
       "      <td>152.500000</td>\n",
       "      <td>146.199997</td>\n",
       "      <td>146.850006</td>\n",
       "      <td>146.850006</td>\n",
       "      <td>3855000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>147.770004</td>\n",
       "      <td>148.270004</td>\n",
       "      <td>143.360001</td>\n",
       "      <td>145.710007</td>\n",
       "      <td>145.710007</td>\n",
       "      <td>4907400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-18</th>\n",
       "      <td>147.300003</td>\n",
       "      <td>152.190002</td>\n",
       "      <td>146.509995</td>\n",
       "      <td>149.850006</td>\n",
       "      <td>149.850006</td>\n",
       "      <td>5945800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-19</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>150.889999</td>\n",
       "      <td>146.173004</td>\n",
       "      <td>147.369995</td>\n",
       "      <td>147.369995</td>\n",
       "      <td>4333700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2019-07-08   97.739998  104.000000   97.309998  103.250000  103.250000   \n",
       "2019-07-09  102.669998  104.440002  100.620003  103.400002  103.400002   \n",
       "2019-07-10  104.279999  105.889999  102.089996  103.500000  103.500000   \n",
       "2019-07-11  103.900002  104.900002  101.559998  104.540001  104.540001   \n",
       "2019-07-12  104.220001  105.455002  102.120003  102.470001  102.470001   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-08-13  148.619003  152.005005  146.809998  149.970001  149.970001   \n",
       "2020-08-14  150.850006  152.500000  146.199997  146.850006  146.850006   \n",
       "2020-08-17  147.770004  148.270004  143.360001  145.710007  145.710007   \n",
       "2020-08-18  147.300003  152.190002  146.509995  149.850006  149.850006   \n",
       "2020-08-19  149.000000  150.889999  146.173004  147.369995  147.369995   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2019-07-08  12268300  \n",
       "2019-07-09  10727300  \n",
       "2019-07-10   8519300  \n",
       "2019-07-11   9091800  \n",
       "2019-07-12   7524200  \n",
       "...              ...  \n",
       "2020-08-13   5544700  \n",
       "2020-08-14   3855000  \n",
       "2020-08-17   4907400  \n",
       "2020-08-18   5945800  \n",
       "2020-08-19   4333700  \n",
       "\n",
       "[284 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_name_list = ['ROKU']\n",
    "df = yf.download(\"ROKU\", '2019-07-06', '2020-08-20')#df.to_excel('output.xls\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "511c1ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23260699d90>,\n",
       " <matplotlib.lines.Line2D at 0x23260699c70>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VtOzcJZXDzn1uvM5dUpHs3E9tCJdlJBXEzn1uXJaRVCQ795llq8WKSK9zl6Rpig73VvtjvHbukkrhsszsJifrG/D4BdmSSuOyzMxa7XC3c5dUCjv32bU793DNXVJp7NxnNjE5WT0w3CVpiqLDPSdOVA+G+vuNJpLUMy7LzM5lGUnFcllmZtlelvGEqiRNUXS4T7bs3CUVxmWZ2bVclpFUqqW8LBMRZ0fEZyPiexHxWET8bEScGxH3RcTj9facXk12utZk/QlVw11SKQrp3P8z8KXM/DvAG4HHgFuAPZm5GdhT/9wXLZdlJJVqqXbuEfFa4OeATwJk5kuZ+TxwNXBXvdtdwDWLm+LMWl7nLqk0BXTuPw2MA5+KiIci4r9FxKuB12Xm0wD1dm0P5tlV2rlLKk073Jdq5w6sBC4F/mtmvgl4kXkswUTEDRExGhGj4+PjC5rAkfs/B8Cl9/9bnvnw6/n27k8s6DiSdNoUEO5jwFhm3l///FmqsH82ItYB1Nuj3V6cmXdk5khmjgwPD8/7zb+9+xNceqgK8wg4j3Eu3vshA15SGZZquGfmM8DhiLiwHroCeBTYDWyvx7YDX1jUDGew8cHbeFWcmDK2Ol5i44O39ePtJKk3TtOa+2JvyvJrwGci4kzgL4F/TfUXxq6IuB54Erh2ke/R1dochy5/8a3NH/Tj7SSpN17/erj2Wli1qq9vs6hwz8zvACNdnrpiMcedi6MxzHmcvFZ/NNZwXr/fXJIW6sorq199VuwnVA9fejPH8swpY8fyTA5fevOAZiRJS0ex4f6Wq36VfW/+HZ5hmFYGzzDMvjf/Dm+56lcHPTVJGrjI07S4fyojIyM5Ojo66GlIUlEiYm9mdlsaL7dzlyTNzHCXpAYy3CWpgQx3SWogw12SGmhJXC0TEePAX8/zZWuA5fRx1OVWL1jzcrDc6oXe1vy3MrPrzbmWRLgvRESMznQJUBMtt3rBmpeD5VYvnL6aXZaRpAYy3CWpgUoO9zsGPYHTbLnVC9a8HCy3euE01VzsmrskaWYld+6SpBkY7pLUQEsm3CPizog4GhH7OsbeGBF/ERHfjYgvRsRr6/F/GRHf6fjVioifqZ97c73/wYj4o4g+f1HhIsyz5jMi4q56/LGI2NHxmiJqnme9Z0bEp+rxhyPi8o7XFFEvQERsjIiv1//P9kfEr9fj50bEfRHxeL09p+M1O+raDkTE1o7xJV/3fOuNiJ+q9/9RRHxs2rGWfL2woJrfGRF769r2RsQ7Oo7Vu5ozc0n8An6O6gu293WMfRv4R/Xj9wL/scvr/h7wlx0/PwD8LNWX8P0p8E8HXVsvagZ+CbinfnwW8FfAppJqnme9NwKfqh+vBfYCQyXVW891HXBp/fgngO8DW4DfA26px28Bfrd+vAV4GFgFXAA8Aawope4F1Ptq4O3A+4CPTTvWkq93gTW/CTi/fnwx8FQ/ah74f5hp/5E2TfuD/0NeOem7EXi0y2v+E7Cz4z/y9zqeuw74xKDr6kXNdS1fpPpqxJ+qfwOdW1rN86j3vwDv6dhvD3BZafV2qf8LwDuBA8C6emwdcKB+vAPY0bH/l+s/7EXWPVu9Hfv9q85wL7Xe+dRcjwfwf6j+Mu9pzUtmWWYG+4Cr6sfXUv3hn+7dwN314/XAWMdzY/VYSWaq+bPAi8DTVF88/tHMfI7ya56p3oeBqyNiZURcALy5fq7YeiNiE1XXdj/wusx8GqDerq13Ww8c7nhZu77i6p5jvTMprl5YUM3vAh7KzOP0uOalHu7vBW6MiL1U/9x5qfPJiPj7wN9kZnsNt9v6VGnXes5U82XAJHA+1T/Xfysifprya56p3jupfnOPAn8I/DkwQaH1RsRrgD8BfiMzf3iqXbuM5SnGl6R51DvjIbqMLdl6Yf41R8RFwO8C7e8G7WnNKxf6wtMhM78H/BOAiHgD8M+n7bKNV7p2qMJgQ8fPG4Aj/Zxjr52i5l8CvpSZJ4CjEfFNYAT43xRc80z1ZuYE8Jvt/SLiz4HHgf9LYfVGxBlUf+g/k5n31sPPRsS6zHw6ItYBR+vxMab+C7VdXzG/t+dZ70yKqRfmX3NEbAA+B/xyZj5RD/e05iXduUfE2no7BHwIuL3juSGqf8bf0x6r/+nz/yLirfVZ5l+mWv8qxilqfhJ4R1ReDbyVan2u6JpnqjcizqrrJCLeCUxk5qOl1VvP8ZPAY5n5+x1P7Qa214+380oNu4FtEbGqXo7aDDxQSt0LqLerUuqF+dccEWcD/4Pq3Mo32zv3vOZBn3zoOHlwN9V68gmqv8GuB36d6sTh94GPUJ94q/e/HPhWl+OMUK3jPgF8rPM1S+3XfGoGXgP8MbAfeBS4ubSa51nvJqoTUo8BX6W6tWlR9dZzfTvVP60fAb5T//pnVCfF91D9a2QPcG7Haz5Y13aAjqslSqh7gfX+FfAc8KP698WWUupdSM1UTcyLHft+B1jb65q9/YAkNdCSXpaRJC2M4S5JDWS4S1IDGe6S1ECGuyQ1kOEuSQ1kuEtSA/1/bINI22fxnRUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_ = np.min(df[\"Low\"])\n",
    "idx_min = np.argmin(df[\"Low\"])\n",
    "new_df = df[\"Low\"][np.argmin(df[\"Low\"]):]\n",
    "index = np.arange(np.size(new_df)+1)[1:]\n",
    "index = np.reshape(index, (np.size(index), -1))\n",
    "numerator = (new_df - min_)\n",
    "numerator = numerator.values\n",
    "numerator = np.reshape(numerator, (np.size(numerator), -1))\n",
    "ans = numerator/index\n",
    "slope = np.arctan(ans)\n",
    "np.argmin(slope[1:])\n",
    "idx_min_slope = np.argmin(slope[1:])\n",
    "minimum_slope = np.min(slope[1:])\n",
    "\n",
    "\n",
    "# Note about the calculation done below :-\n",
    "# Here minimum_slope value and value of printed line's slopw will be diffrerent because printed line\n",
    "# is based on \"df\" data frame index and calculated slope is based on new_df index, So in order to extent\n",
    "# the line we need to calculate the slope according to \"df\" data frame's index, in order to do that hrere\n",
    "# as shown below I am calcu;ating slope value using x and y values given below and finally, \n",
    "# There calculated slope value \"m\" will be used to extend the trendline (line shown in the figure below)\n",
    "\n",
    "# y = (m*x) + c\n",
    "\n",
    "x = idx_min\n",
    "x1 = (idx_min + idx_min_slope + 1)  # x value in order to calulate slope \"m\"\n",
    "y = new_df.values[0]  # x value in order to calulate slope \"m\"\n",
    "y1 = new_df.values[idx_min_slope+1]\n",
    "\n",
    "c = y\n",
    "\n",
    "Y = (y - y1)\n",
    "X = x - x1\n",
    "\n",
    "m = Y / X  #  y   =  mx  + c\n",
    "           #  y1  =  mx1 + c\n",
    "           # -    -    -\n",
    "           # y-y1 =  m*(x-x1)\n",
    "           # m = (y-y1)/(x-x1)\n",
    "c = y - (m*x)  # from the above given illustration we can calculate the value of c using this equation (c = y - mx)\n",
    "# print(m)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# ax.legend(loc=df[i])\n",
    "ax.plot(df[\"Low\"][idx_min:], color='r')\n",
    "\n",
    "x1, y1 = [idx_min, (idx_min + idx_min_slope + 1)], [new_df.values[0], new_df.values[idx_min_slope+1]]\n",
    "# x1, y1 = [idx_min,(idx_min + idx_min_slope + 1)], [new_df.values[0], (idx_min + idx_min_slope + 1)]\n",
    "x2, y2 = [idx_min, np.size(df[\"Low\"])], [new_df.values[0], (m*np.size(df[\"Low\"]) + c)]\n",
    "\n",
    "plt.plot(x1, y1, x2, y2, marker = 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f16a506",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "103.94000244140625\n",
      "0\n",
      "112.11000061035156\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# ax.legend(loc=df[i])\n",
    "ax.plot(df[\"Low\"][idx_min:], color='r')\n",
    "\n",
    "for _ in range(0,2):\n",
    "    first_index = idx_min + idx_min_slope + 1\n",
    "    new_df_ = new_df[idx_min_slope+1:]\n",
    "    # print(new_df_)\n",
    "    index_ = np.arange(np.size(new_df_)+1)[1:]\n",
    "    index_ = np.reshape(index_, (np.size(index_), -1))\n",
    "    # print(index_)\n",
    "    numerator_ = (new_df_ - new_df_.values[0])\n",
    "    numerator_ = numerator_.values\n",
    "    numerator_ = np.reshape(numerator_, (np.size(numerator_), -1))\n",
    "    ans_ = numerator_/index_\n",
    "    slope_ = np.arctan(ans_)\n",
    "    np.argmin(slope_[1:])\n",
    "    idx_min_slope_ = np.argmin(slope_[1:])\n",
    "    minimum_slope_ = np.min(slope_[1:])\n",
    "    print(idx_min_slope_)\n",
    "    print(new_df_.values[0])\n",
    "    \n",
    "    \n",
    "    x = first_index\n",
    "    x1 = (first_index + idx_min_slope_ + 1)  # x value in order to calulate slope \"m\"\n",
    "    y = new_df_.values[0]  # x value in order to calulate slope \"m\"\n",
    "    y1 = new_df_.values[idx_min_slope_+1]\n",
    "\n",
    "    c = y\n",
    "\n",
    "    Y = (y - y1)\n",
    "    X = x - x1\n",
    "\n",
    "    m = Y / X  #  y   =  mx  + c\n",
    "               #  y1  =  mx1 + c\n",
    "               # -    -    -\n",
    "               # y-y1 =  m*(x-x1)\n",
    "               # m = (y-y1)/(x-x1)\n",
    "    c = y - (m*x)  # from the above given illustration we can calculate the value of c using this equation (c = y - mx)\n",
    "    # print(m)\n",
    "    \n",
    "    \n",
    "#     x1, y1 = [first_index, (first_index + idx_min_slope_ + 1)], [new_df_.values[0], new_df_.values[idx_min_slope_+1]]\n",
    "    x2, y2 = [first_index, np.size(df[\"Low\"])], [new_df_.values[0], (m*np.size(df[\"Low\"]) + c)]\n",
    "    \n",
    "    \n",
    "    plt.plot(x2, y2, marker = 'o')\n",
    "    \n",
    "    idx_min_slope = idx_min_slope + idx_min_slope_ + 1\n",
    "    \n",
    "# TODO: Have to add a slope condition whi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "770992c1",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}