{
 "cells": [
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   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xlrd\n",
    "from plotly.graph_objs import Scatter, layout\n",
    "import plotly\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "import cufflinks as cf\n",
    "from urllib.request import urlopen\n",
    "import json\n",
    "import re\n",
    "import pandas as pd\n",
    "cf.go_offline()###这两句是离线生成图片的设置\n",
    "cf.set_config_file(offline=True, world_readable=True)\n",
    "plotly.offline.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 比特币预测数据\n",
    "df_features = pd.read_csv('df_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features.index = df_features['Unnamed: 0']\n",
    "df_features.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "bitcoin_value = df_features['value'].values\n",
    "df_features.drop('value', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .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>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag3</th>\n",
       "      <th>lag4</th>\n",
       "      <th>lag5</th>\n",
       "      <th>lag6</th>\n",
       "      <th>lag7</th>\n",
       "      <th>lag8</th>\n",
       "      <th>lag9</th>\n",
       "      <th>lag10</th>\n",
       "      <th>day_of_week_0</th>\n",
       "      <th>day_of_week_1</th>\n",
       "      <th>day_of_week_2</th>\n",
       "      <th>day_of_week_3</th>\n",
       "      <th>day_of_week_4</th>\n",
       "      <th>day_of_week_5</th>\n",
       "      <th>day_of_week_6</th>\n",
       "      <th>is_holiday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</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",
       "      <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>2016-09-21</th>\n",
       "      <td>608.66</td>\n",
       "      <td>610.19</td>\n",
       "      <td>611.58</td>\n",
       "      <td>607.04</td>\n",
       "      <td>609.11</td>\n",
       "      <td>610.38</td>\n",
       "      <td>608.82</td>\n",
       "      <td>610.92</td>\n",
       "      <td>609.67</td>\n",
       "      <td>621.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-22</th>\n",
       "      <td>598.88</td>\n",
       "      <td>608.66</td>\n",
       "      <td>610.19</td>\n",
       "      <td>611.58</td>\n",
       "      <td>607.04</td>\n",
       "      <td>609.11</td>\n",
       "      <td>610.38</td>\n",
       "      <td>608.82</td>\n",
       "      <td>610.92</td>\n",
       "      <td>609.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-23</th>\n",
       "      <td>597.42</td>\n",
       "      <td>598.88</td>\n",
       "      <td>608.66</td>\n",
       "      <td>610.19</td>\n",
       "      <td>611.58</td>\n",
       "      <td>607.04</td>\n",
       "      <td>609.11</td>\n",
       "      <td>610.38</td>\n",
       "      <td>608.82</td>\n",
       "      <td>610.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-24</th>\n",
       "      <td>594.08</td>\n",
       "      <td>597.42</td>\n",
       "      <td>598.88</td>\n",
       "      <td>608.66</td>\n",
       "      <td>610.19</td>\n",
       "      <td>611.58</td>\n",
       "      <td>607.04</td>\n",
       "      <td>609.11</td>\n",
       "      <td>610.38</td>\n",
       "      <td>608.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>603.88</td>\n",
       "      <td>594.08</td>\n",
       "      <td>597.42</td>\n",
       "      <td>598.88</td>\n",
       "      <td>608.66</td>\n",
       "      <td>610.19</td>\n",
       "      <td>611.58</td>\n",
       "      <td>607.04</td>\n",
       "      <td>609.11</td>\n",
       "      <td>610.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              lag1    lag2    lag3    lag4    lag5    lag6    lag7    lag8  \\\n",
       "Unnamed: 0                                                                   \n",
       "2016-09-21  608.66  610.19  611.58  607.04  609.11  610.38  608.82  610.92   \n",
       "2016-09-22  598.88  608.66  610.19  611.58  607.04  609.11  610.38  608.82   \n",
       "2016-09-23  597.42  598.88  608.66  610.19  611.58  607.04  609.11  610.38   \n",
       "2016-09-24  594.08  597.42  598.88  608.66  610.19  611.58  607.04  609.11   \n",
       "2016-09-25  603.88  594.08  597.42  598.88  608.66  610.19  611.58  607.04   \n",
       "\n",
       "              lag9   lag10  day_of_week_0  day_of_week_1  day_of_week_2  \\\n",
       "Unnamed: 0                                                                \n",
       "2016-09-21  609.67  621.65              0              0              1   \n",
       "2016-09-22  610.92  609.67              0              0              0   \n",
       "2016-09-23  608.82  610.92              0              0              0   \n",
       "2016-09-24  610.38  608.82              0              0              0   \n",
       "2016-09-25  609.11  610.38              0              0              0   \n",
       "\n",
       "            day_of_week_3  day_of_week_4  day_of_week_5  day_of_week_6  \\\n",
       "Unnamed: 0                                                               \n",
       "2016-09-21              0              0              0              0   \n",
       "2016-09-22              1              0              0              0   \n",
       "2016-09-23              0              1              0              0   \n",
       "2016-09-24              0              0              1              0   \n",
       "2016-09-25              0              0              0              1   \n",
       "\n",
       "            is_holiday  \n",
       "Unnamed: 0              \n",
       "2016-09-21           0  \n",
       "2016-09-22           0  \n",
       "2016-09-23           0  \n",
       "2016-09-24           0  \n",
       "2016-09-25           0  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "prediction = np.array([])\n",
    "model = LinearRegression()\n",
    "start, end = 10, 0\n",
    "length = df_features.shape[0]\n",
    "while True:\n",
    "    model.fit(df_features[:start], bitcoin_value[:start])\n",
    "    end = min(start + round(start * 0.3), length)\n",
    "    prediction = np.append(prediction, model.predict(df_features[start : end]))\n",
    "    if end == length:\n",
    "        break\n",
    "    else:\n",
    "        start = end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.append(prediction[1:], (prediction[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = deepcopy(df_features)\n",
    "tmp = tmp.iloc[10:]\n",
    "tmp['prediction'] = prediction\n",
    "tmp['bitcoinValue'] = np.array(bitcoin_value[10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig2 = go.Figure()\n",
    "fig2.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['prediction'][-100:],\n",
    "    legendgroup='group0',\n",
    "    mode='lines',\n",
    "    name='prediction',\n",
    "    marker=dict(color='rgb(180, 151, 231)')\n",
    "))\n",
    "fig2.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['bitcoinValue'][-100:],\n",
    "    legendgroup='group1',\n",
    "    mode='lines',\n",
    "    name='true value',\n",
    "    marker=dict(color='rgb(139, 224, 164)')\n",
    "))\n",
    "fig2.update_layout(title={'text':'Lag of LR on Bitcoin Data','y' : 0.9,'x' : 0.5,  },\n",
    "    xaxis_title='date',\n",
    "    yaxis_title='U.S. dollars($)',\n",
    ")\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  390.2197975220207\n",
      "mse:  676047.8406085104\n",
      "rmse 822.2212844535894\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "print('mae: ', mean_absolute_error(bitcoin_value[10:], prediction))\n",
    "print('mse: ', mean_squared_error(bitcoin_value[10:], prediction))\n",
    "print('rmse', mean_squared_error(bitcoin_value[10:], prediction) ** 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "move = deepcopy(prediction)\n",
    "move = [move[i] for i in range(1, len(move))]\n",
    "move = np.concatenate((move, np.array([move[-1]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(move)):\n",
    "    move[i] += (move[i] - bitcoin_value[i + 10]) * 3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  356.31250130843\n",
      "mse:  414338.2848372418\n",
      "rmse 643.6911408721126\n"
     ]
    }
   ],
   "source": [
    "print('mae: ', mean_absolute_error(bitcoin_value[10:], move))\n",
    "print('mse: ', mean_squared_error(bitcoin_value[10:], move))\n",
    "print('rmse', mean_squared_error(bitcoin_value[10:], move) ** 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
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  {
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   "source": [
    "fig2 = go.Figure()\n",
    "fig2.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['prediction1'][-100:],\n",
    "    legendgroup='group0',\n",
    "    mode='lines',\n",
    "    name='prediction',\n",
    "    marker=dict(color='rgb(180, 151, 231)')\n",
    "))\n",
    "fig2.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['bitcoinValue'][-100:],\n",
    "    legendgroup='group1',\n",
    "    mode='lines',\n",
    "    name='true value',\n",
    "    marker=dict(color='rgb(139, 224, 164)')\n",
    "))\n",
    "fig2.update_layout(title={'text':'Prediction of LR-LSTM Model on Bitcoin Data','y' : 0.9,'x' : 0.5,  },\n",
    "    xaxis_title='date',\n",
    "    yaxis_title='U.S. dollars($)',\n",
    ")\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1806"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(move)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_of_bitcoin = pd.DataFrame({'date' : df_features.index[-1806:], 'data' : move})\n",
    "prediction_of_bitcoin.to_csv('prediction_of_bitcoin.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = pd.read_csv('df_features_gold.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features.index = df_features['Unnamed: 0']\n",
    "df_features.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "gold_value = df_features['value'].values\n",
    "df_features.drop('value', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "prediction = np.array([])\n",
    "model = LinearRegression()\n",
    "start, end = 20, 0\n",
    "length = df_features.shape[0]\n",
    "while True:\n",
    "    model.fit(df_features[:start], gold_value[:start])\n",
    "    end = min(start + round(start * 0.3), length)\n",
    "    prediction = np.append(prediction, model.predict(df_features[start : end]))\n",
    "    if end == length:\n",
    "        break\n",
    "    else:\n",
    "        start = end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = deepcopy(df_features)\n",
    "tmp = tmp.iloc[20:]\n",
    "tmp['prediction'] = prediction\n",
    "tmp['goldValue'] = np.array(gold_value[20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  7.7242867954081245\n",
      "mse:  172.02935980408267\n",
      "rmse 13.115996332878515\n"
     ]
    }
   ],
   "source": [
    "print('mae: ', mean_absolute_error(gold_value[20:], prediction))\n",
    "print('mse: ', mean_squared_error(gold_value[20:], prediction))\n",
    "print('rmse', mean_squared_error(gold_value[20:], prediction) ** 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig3 = go.Figure()\n",
    "fig3.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['prediction'][-100:],\n",
    "    legendgroup='group0',\n",
    "    mode='lines',\n",
    "    name='prediction',\n",
    "    marker=dict(color='rgb(207, 28, 144)')\n",
    "))\n",
    "fig3.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['goldValue'][-100:],\n",
    "    legendgroup='group1',\n",
    "    mode='lines',\n",
    "    name='true value',\n",
    "    marker=dict(color='rgb(47, 138, 196)')\n",
    "))\n",
    "fig3.update_layout(title={'text':'Lag of LR on Gold Data','y' : 0.9,'x' : 0.5,  },\n",
    "    xaxis_title='date',\n",
    "    yaxis_title='U.S. dollars($)',\n",
    ")\n",
    "fig3.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "move = deepcopy(prediction)\n",
    "move = [move[i] for i in range(1, len(move))]\n",
    "move = np.concatenate((move, np.array([move[-1]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(move)):\n",
    "    move[i] += (move[i] - gold_value[i + 20]) * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  8.47971804696774\n",
      "mse:  351.03621940486664\n",
      "rmse 18.73596059466572\n"
     ]
    }
   ],
   "source": [
    "print('mae: ', mean_absolute_error(gold_value[20:], move))\n",
    "print('mse: ', mean_squared_error(gold_value[20:], move))\n",
    "print('rmse', mean_squared_error(gold_value[20:], move) ** 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp['prediction1'] = move"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig2 = go.Figure()\n",
    "fig2.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['prediction1'][-100:],\n",
    "    legendgroup='group0',\n",
    "    mode='lines',\n",
    "    name='prediction',\n",
    "    marker=dict(color='rgb(207, 28, 144)')\n",
    "))\n",
    "fig2.add_trace(Scatter(\n",
    "    x=tmp.index[-100:],\n",
    "    y=tmp['goldValue'][-100:],\n",
    "    legendgroup='group1',\n",
    "    mode='lines',\n",
    "    name='true value',\n",
    "    marker=dict(color='rgb(47, 138, 196)')\n",
    "))\n",
    "fig2.update_layout(title={'text':'Prediction of LR-LSTM Model on Gold Data','y' : 0.9,'x' : 0.5,  },\n",
    "    xaxis_title='date',\n",
    "    yaxis_title='U.S. dollars($)',\n",
    ")\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_of_gold = pd.DataFrame({'date' : df_features.index[-1785:], 'data' : move})\n",
    "prediction_of_gold.to_csv('prediction_of_gold.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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