v4.0_RNN_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNN model for stock price forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "########Load required Modules\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "######import functions \n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "###### Set device: GPU or CPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess dataset\n",
    "use close to forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>1418.030029</td>\n",
       "      <td>1429.420044</td>\n",
       "      <td>1407.859985</td>\n",
       "      <td>1416.599976</td>\n",
       "      <td>1416.599976</td>\n",
       "      <td>3429160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>1416.599976</td>\n",
       "      <td>1421.839966</td>\n",
       "      <td>1408.430054</td>\n",
       "      <td>1418.339966</td>\n",
       "      <td>1418.339966</td>\n",
       "      <td>3004460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-01-05</td>\n",
       "      <td>1418.339966</td>\n",
       "      <td>1418.339966</td>\n",
       "      <td>1405.750000</td>\n",
       "      <td>1409.709961</td>\n",
       "      <td>1409.709961</td>\n",
       "      <td>2919400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>1409.260010</td>\n",
       "      <td>1414.979980</td>\n",
       "      <td>1403.969971</td>\n",
       "      <td>1412.839966</td>\n",
       "      <td>1412.839966</td>\n",
       "      <td>2763340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-01-09</td>\n",
       "      <td>1412.839966</td>\n",
       "      <td>1415.609985</td>\n",
       "      <td>1405.420044</td>\n",
       "      <td>1412.109985</td>\n",
       "      <td>1412.109985</td>\n",
       "      <td>3038380000</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>2173.149902</td>\n",
       "      <td>2178.290039</td>\n",
       "      <td>2166.209961</td>\n",
       "      <td>2170.840088</td>\n",
       "      <td>2170.840088</td>\n",
       "      <td>3505990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2016-08-02</td>\n",
       "      <td>2169.939941</td>\n",
       "      <td>2170.199951</td>\n",
       "      <td>2147.580078</td>\n",
       "      <td>2157.030029</td>\n",
       "      <td>2157.030029</td>\n",
       "      <td>3848750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>2016-08-03</td>\n",
       "      <td>2156.810059</td>\n",
       "      <td>2163.790039</td>\n",
       "      <td>2152.560059</td>\n",
       "      <td>2163.790039</td>\n",
       "      <td>2163.790039</td>\n",
       "      <td>3786530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2016-08-04</td>\n",
       "      <td>2163.510010</td>\n",
       "      <td>2168.189941</td>\n",
       "      <td>2159.070068</td>\n",
       "      <td>2164.250000</td>\n",
       "      <td>2164.250000</td>\n",
       "      <td>3709200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>2168.790039</td>\n",
       "      <td>2182.870117</td>\n",
       "      <td>2168.790039</td>\n",
       "      <td>2182.870117</td>\n",
       "      <td>2182.870117</td>\n",
       "      <td>3663070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2416 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date         Open         High          Low        Close  \\\n",
       "0     2007-01-03  1418.030029  1429.420044  1407.859985  1416.599976   \n",
       "1     2007-01-04  1416.599976  1421.839966  1408.430054  1418.339966   \n",
       "2     2007-01-05  1418.339966  1418.339966  1405.750000  1409.709961   \n",
       "3     2007-01-08  1409.260010  1414.979980  1403.969971  1412.839966   \n",
       "4     2007-01-09  1412.839966  1415.609985  1405.420044  1412.109985   \n",
       "...          ...          ...          ...          ...          ...   \n",
       "2411  2016-08-01  2173.149902  2178.290039  2166.209961  2170.840088   \n",
       "2412  2016-08-02  2169.939941  2170.199951  2147.580078  2157.030029   \n",
       "2413  2016-08-03  2156.810059  2163.790039  2152.560059  2163.790039   \n",
       "2414  2016-08-04  2163.510010  2168.189941  2159.070068  2164.250000   \n",
       "2415  2016-08-05  2168.790039  2182.870117  2168.790039  2182.870117   \n",
       "\n",
       "        Adj Close      Volume  \n",
       "0     1416.599976  3429160000  \n",
       "1     1418.339966  3004460000  \n",
       "2     1409.709961  2919400000  \n",
       "3     1412.839966  2763340000  \n",
       "4     1412.109985  3038380000  \n",
       "...           ...         ...  \n",
       "2411  2170.840088  3505990000  \n",
       "2412  2157.030029  3848750000  \n",
       "2413  2163.790039  3786530000  \n",
       "2414  2164.250000  3709200000  \n",
       "2415  2182.870117  3663070000  \n",
       "\n",
       "[2416 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train\n",
    "dataset_train = pd.read_csv(\"train.csv\")\n",
    "dataset_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_train['Date'] = pd.to_datetime(dataset_train['Date'])\n",
    "dataset_train = dataset_train[['Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1416.599976</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>1409.709961</td>\n",
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       "      <th>3</th>\n",
       "      <td>1412.839966</td>\n",
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       "      <th>4</th>\n",
       "      <td>1412.109985</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2170.840088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2157.030029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>2163.790039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2164.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2182.870117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2416 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "            Close\n",
       "0     1416.599976\n",
       "1     1418.339966\n",
       "2     1409.709961\n",
       "3     1412.839966\n",
       "4     1412.109985\n",
       "...           ...\n",
       "2411  2170.840088\n",
       "2412  2157.030029\n",
       "2413  2163.790039\n",
       "2414  2164.250000\n",
       "2415  2182.870117\n",
       "\n",
       "[2416 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-08</td>\n",
       "      <td>2183.760010</td>\n",
       "      <td>2185.439941</td>\n",
       "      <td>2177.850098</td>\n",
       "      <td>2180.889893</td>\n",
       "      <td>2180.889893</td>\n",
       "      <td>3327550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-09</td>\n",
       "      <td>2182.239990</td>\n",
       "      <td>2187.659912</td>\n",
       "      <td>2178.610107</td>\n",
       "      <td>2181.739990</td>\n",
       "      <td>2181.739990</td>\n",
       "      <td>3334300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-10</td>\n",
       "      <td>2182.810059</td>\n",
       "      <td>2183.409912</td>\n",
       "      <td>2172.000000</td>\n",
       "      <td>2175.489990</td>\n",
       "      <td>2175.489990</td>\n",
       "      <td>3254950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-11</td>\n",
       "      <td>2177.969971</td>\n",
       "      <td>2188.449951</td>\n",
       "      <td>2177.969971</td>\n",
       "      <td>2185.790039</td>\n",
       "      <td>2185.790039</td>\n",
       "      <td>3423160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-12</td>\n",
       "      <td>2183.739990</td>\n",
       "      <td>2186.280029</td>\n",
       "      <td>2179.419922</td>\n",
       "      <td>2184.050049</td>\n",
       "      <td>2184.050049</td>\n",
       "      <td>3000660000</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>2965.810059</td>\n",
       "      <td>2972.840088</td>\n",
       "      <td>2962.939941</td>\n",
       "      <td>2966.149902</td>\n",
       "      <td>2966.149902</td>\n",
       "      <td>2559270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2973.610107</td>\n",
       "      <td>3003.280029</td>\n",
       "      <td>2973.610107</td>\n",
       "      <td>2995.679932</td>\n",
       "      <td>2995.679932</td>\n",
       "      <td>3345090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>2989.679932</td>\n",
       "      <td>2997.540039</td>\n",
       "      <td>2985.199951</td>\n",
       "      <td>2989.689941</td>\n",
       "      <td>2989.689941</td>\n",
       "      <td>3230320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>3000.770020</td>\n",
       "      <td>3008.290039</td>\n",
       "      <td>2991.790039</td>\n",
       "      <td>2997.949951</td>\n",
       "      <td>2997.949951</td>\n",
       "      <td>3103470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>2996.840088</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2976.310059</td>\n",
       "      <td>2986.199951</td>\n",
       "      <td>2986.199951</td>\n",
       "      <td>3268970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>806 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date         Open         High          Low        Close  \\\n",
       "0    2016-08-08  2183.760010  2185.439941  2177.850098  2180.889893   \n",
       "1    2016-08-09  2182.239990  2187.659912  2178.610107  2181.739990   \n",
       "2    2016-08-10  2182.810059  2183.409912  2172.000000  2175.489990   \n",
       "3    2016-08-11  2177.969971  2188.449951  2177.969971  2185.790039   \n",
       "4    2016-08-12  2183.739990  2186.280029  2179.419922  2184.050049   \n",
       "..          ...          ...          ...          ...          ...   \n",
       "801  2019-10-14  2965.810059  2972.840088  2962.939941  2966.149902   \n",
       "802  2019-10-15  2973.610107  3003.280029  2973.610107  2995.679932   \n",
       "803  2019-10-16  2989.679932  2997.540039  2985.199951  2989.689941   \n",
       "804  2019-10-17  3000.770020  3008.290039  2991.790039  2997.949951   \n",
       "805  2019-10-18  2996.840088  3000.000000  2976.310059  2986.199951   \n",
       "\n",
       "       Adj Close      Volume  \n",
       "0    2180.889893  3327550000  \n",
       "1    2181.739990  3334300000  \n",
       "2    2175.489990  3254950000  \n",
       "3    2185.790039  3423160000  \n",
       "4    2184.050049  3000660000  \n",
       "..           ...         ...  \n",
       "801  2966.149902  2559270000  \n",
       "802  2995.679932  3345090000  \n",
       "803  2989.689941  3230320000  \n",
       "804  2997.949951  3103470000  \n",
       "805  2986.199951  3268970000  \n",
       "\n",
       "[806 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Val\n",
    "dataset_val = pd.read_csv(\"val.csv\")\n",
    "dataset_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_val['Date'] = pd.to_datetime(dataset_val['Date'])\n",
    "dataset_val = dataset_val[['Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2180.889893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2181.739990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2175.489990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2185.790039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2184.050049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>2966.149902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>2995.679932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>2989.689941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>2997.949951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>2986.199951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>806 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Close\n",
       "0    2180.889893\n",
       "1    2181.739990\n",
       "2    2175.489990\n",
       "3    2185.790039\n",
       "4    2184.050049\n",
       "..           ...\n",
       "801  2966.149902\n",
       "802  2995.679932\n",
       "803  2989.689941\n",
       "804  2997.949951\n",
       "805  2986.199951\n",
       "\n",
       "[806 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2416, 1), (806, 1))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.shape, dataset_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# use Minimax strategic to rescale data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_train = MinMaxScaler()\n",
    "scaler_val = MinMaxScaler()\n",
    "dataset_train_scaled = scaler_train.fit_transform(dataset_train.Close.values.reshape(-1,1))\n",
    "dataset_val_scaled = scaler_val.fit_transform(dataset_val.Close.values.reshape(-1,1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# use a window of 20 days on close price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "for index in range(len(dataset_train_scaled) - window):\n",
    "    need_data = dataset_train_scaled[index: index + window]\n",
    "    x_train.append(need_data[:-1])\n",
    "    y_train.append(need_data[-1])\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = []\n",
    "y_val = []\n",
    "for index in range(len(dataset_val_scaled) - window):\n",
    "    need_data = dataset_val_scaled[index: index + window]\n",
    "    x_val.append(need_data[:-1])\n",
    "    y_val.append(need_data[-1])\n",
    "x_val = np.array(x_val)\n",
    "y_val = np.array(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2395, 20, 1), (2395, 1), (785, 20, 1), (785, 1))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape, x_val.shape, y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_torch = torch.from_numpy(x_train).type(torch.Tensor)\n",
    "y_train_torch = torch.from_numpy(y_train).type(torch.Tensor)\n",
    "\n",
    "x_val_torch = torch.from_numpy(x_val).type(torch.Tensor)\n",
    "y_val_torch = torch.from_numpy(y_val).type(torch.Tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### DEFINE RNN net as described above: \n",
    "class StackedRNN(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, num_layers=1):\n",
    "        super(StackedRNN, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.num_layers = num_layers\n",
    "        \n",
    "        # Create a list to hold multiple RNN layers\n",
    "        self.rnn_layers = nn.ModuleList([nn.RNN(input_dim if i == 0 else hidden_dim,\n",
    "                                                hidden_dim,\n",
    "                                                batch_first=True)\n",
    "                                         for i in range(num_layers)])\n",
    "        \n",
    "        # Final fully connected layer\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Initialize hidden states\n",
    "        hiddens = [torch.zeros(1, x.size(0), self.hidden_dim).to(x.device) for _ in range(self.num_layers)]\n",
    "        \n",
    "        # Forward pass through each RNN layer\n",
    "        for i, rnn_layer in enumerate(self.rnn_layers):\n",
    "            out, hiddens[i] = rnn_layer(x if i == 0 else out, hiddens[i])\n",
    "        \n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set hyperparameter\n",
    "num_epochs = 200# number of iterations\n",
    "input_dim = 1 # dimension of input data\n",
    "hidden_dim = 64 # dimension of hidden layers\n",
    "num_layers = 3 # number of RNN\n",
    "output_dim = 1# dimension of output data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set the Loss & Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "StackedRNN(\n",
       "  (rnn_layers): ModuleList(\n",
       "    (0): RNN(1, 64, batch_first=True)\n",
       "    (1-2): 2 x RNN(64, 64, batch_first=True)\n",
       "  )\n",
       "  (fc): Linear(in_features=64, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####### Declare a net\n",
    "stacked_rnn_model = StackedRNN(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers).to(device)\n",
    "\n",
    "#######define LOSS and Optimizer\n",
    "criterion = torch.nn.MSELoss(reduction='mean')\n",
    "#define the Mean Squared Error (MSE) loss function for regression tasks\n",
    "# optimizer = optim.SGD(model.parameters(), lr = 0.01)\n",
    "optimiser = torch.optim.Adam(stacked_rnn_model.parameters(), lr=0.005)\n",
    "####### show the net frame\n",
    "stacked_rnn_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  0 MSE:  0.39723432064056396\n",
      "Epoch  10 MSE:  0.009627292864024639\n",
      "Epoch  20 MSE:  0.015525929629802704\n",
      "Epoch  30 MSE:  0.0042466954328119755\n",
      "Epoch  40 MSE:  0.0028321866411715746\n",
      "Epoch  50 MSE:  0.0008995297248475254\n",
      "Epoch  60 MSE:  0.0002851290046237409\n",
      "Epoch  70 MSE:  0.00031141185900196433\n",
      "Epoch  80 MSE:  0.00023807678371667862\n",
      "Epoch  90 MSE:  0.00023524987045675516\n",
      "Epoch  100 MSE:  0.00021721457596868277\n",
      "Epoch  110 MSE:  0.00020808661065530032\n",
      "Epoch  120 MSE:  0.0002022856060648337\n",
      "Epoch  130 MSE:  0.00019701718701981008\n",
      "Epoch  140 MSE:  0.00019263314607087523\n",
      "Epoch  150 MSE:  0.00018876079411711544\n",
      "Epoch  160 MSE:  0.00018517978605814278\n",
      "Epoch  170 MSE:  0.00018187021487392485\n",
      "Epoch  180 MSE:  0.00017877272330224514\n",
      "Epoch  190 MSE:  0.00017585977911949158\n",
      "Training time: 23.545005798339844\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# Ensure the stacked_rnn_model is in training mode\n",
    "stacked_rnn_model.train()\n",
    "hist = []  # Using a list instead of np.zeros for dynamic storage of losses\n",
    "start_time = time.time()\n",
    "\n",
    "for t in range(num_epochs):\n",
    "    y_train_pred = stacked_rnn_model(x_train_torch.to(device))\n",
    "    loss = criterion(y_train_pred, y_train_torch.to(device))\n",
    "    if t % 10 == 0:\n",
    "        print(\"Epoch \", t, \"MSE: \", loss.item())\n",
    "    hist.append(loss.item())\n",
    "    # Zero the parameter gradients\n",
    "    optimiser.zero_grad()\n",
    "    loss.backward()\n",
    "    optimiser.step()\n",
    "\n",
    "torch.save(stacked_rnn_model.state_dict(), 'stacked_rnn_model.pth')  # Save the model state_dict\n",
    "training_time = time.time() - start_time\n",
    "print(\"Training time: {}\".format(training_time))\n",
    "print('Finished Training')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evalue the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "########Load pretrain model\n",
    "# Create an instance of the StackedRNN model\n",
    "final_model = StackedRNN(input_dim, hidden_dim, output_dim, num_layers)\n",
    "\n",
    "# Load the model's state dictionary\n",
    "final_model.load_state_dict(torch.load('stacked_rnn_model.pth'))\n",
    "\n",
    "# Move the model to the specified device\n",
    "final_model = final_model.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "########Evaluate model\n",
    "final_model.eval()\n",
    "y_train_model = scaler_train.inverse_transform(y_train_pred.detach().cpu().numpy())\n",
    "y_train_original = scaler_train.inverse_transform(y_train)\n",
    "\n",
    "\n",
    "y_val_pred = final_model(x_val_torch.to(device))\n",
    "y_val_model = scaler_val.inverse_transform(y_val_pred.detach().cpu().numpy())\n",
    "y_val_original = scaler_val.inverse_transform(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((785, 1), (785, 1))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val_model.shape, y_val_original.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2560x1920 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "######visualization for single test\n",
    "N_original,N_val = len(y_train_original),len(y_val_model)\n",
    "index_val = [i for i in range(0,N_val)]\n",
    "index_val = np.array(index_val).reshape(-1,1) + N_original\n",
    "\n",
    "## Show all curve\n",
    "plt.figure(dpi=400)\n",
    "plt.figure(figsize=(20, 5))\n",
    "#plt.plot(y_train_original, color = 'blue', label = 'Price')\n",
    "#plt.plot(y_train_model, color = 'green', label = 'Training Set Prediction')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Price')\n",
    "plt.plot(index_val, y_val_model, color = 'purple', label = 'Val data Prediction')\n",
    "plt.plot(index_val, y_val_original, color = 'orange', label = 'Val Price')\n",
    "plt.legend()\n",
    "plt.savefig(\"RNN-Val-Result1.png\", dpi=500)\n",
    "plt.show()\n",
    "df_Stacked_RNN_result = pd.DataFrame({'Original':y_val_original.flatten(),'Val Prediction':y_val_model.flatten()})\n",
    "df_Stacked_RNN_result.to_csv(r\"RNN-Val-Result.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2560x1920 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_val = len(y_val_model)\n",
    "index_val = [i for i in range(0,N_val)]\n",
    "index_val = np.array(index_val).reshape(-1,1)  #+ N_original\n",
    "\n",
    "## Show all curve\n",
    "plt.figure(dpi=400)\n",
    "plt.figure(figsize=(20, 5))\n",
    "#plt.plot(y_train_original, color = 'blue', label = 'Price')\n",
    "#plt.plot(y_train_model, color = 'green', label = 'Training Set Prediction')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Price')\n",
    "plt.plot(index_val, y_val_model, color = 'purple', label = 'Val data Prediction')\n",
    "plt.plot(index_val, y_val_original, color = 'orange', label = 'Val Price')\n",
    "plt.legend()\n",
    "plt.savefig(\"RNN-Val-Result1.png\", dpi=500)\n",
    "plt.show()\n",
    "# df_Stacked_RNN_result = pd.DataFrame({'Original':y_val_original.flatten(),'Val Prediction':y_val_model.flatten()})\n",
    "# df_Stacked_RNN_result.to_csv(r\"RNN-Val-Result.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 583.854184028753\n",
      "R-squared (R²): 0.9903647830795296\n",
      "Mean Absolute Percentage Error (MAPE): 0.6245872237953842\n",
      "Directional symmetry (DS): 32.6530612244898\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "# Calculate the mean squared_error\n",
    "mse = mean_squared_error(y_val_original, y_val_model)\n",
    "\n",
    "# calculate the r2 score\n",
    "r2 = r2_score(y_val_original, y_val_model)\n",
    "\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"R-squared (R²):\", r2)\n",
    "\n",
    "# Calculate Mean Absolute Percentage Error (MAPE)\n",
    "def calculate_mape(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "# Calculate Determination Coefficient (DS)\n",
    "def calculate_ds(y_true, y_pred):\n",
    "    d = []\n",
    "    for i in range(1,len(y_true)):\n",
    "        if (y_true[i]-y_pred[i-1])*(y_pred[i] - y_true[i-1])>=0:\n",
    "            d.append(1)\n",
    "        else:\n",
    "            d.append(0)\n",
    "    DS = (1/(len(y_true)-1))*np.sum(d)*100  \n",
    "    return DS\n",
    "\n",
    "# Calculate MAPE\n",
    "mape = calculate_mape(y_val_original, y_val_model)\n",
    "\n",
    "# Calculate DS\n",
    "DS = calculate_ds(y_val_original, y_val_model)\n",
    "\n",
    "print(\"Mean Absolute Percentage Error (MAPE):\", mape)\n",
    "print(\"Directional symmetry (DS):\", DS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(predictions, original_prices, threshold=0.015, initial_cash=500000):\n",
    "    cash = initial_cash\n",
    "    shares = 0\n",
    "    total_assets = initial_cash\n",
    "    # make sure each time only do one strategy （state machine）\n",
    "    position = 0  # 0 for no position, 1 for long, -1 for short\n",
    "\n",
    "    for i in range(1, len(predictions)):\n",
    "        predicted_change = (predictions[i] - original_prices[i - 1]) / original_prices[i - 1]\n",
    "        actual_change = (original_prices[i] - original_prices[i - 1]) / original_prices[i - 1]\n",
    "\n",
    "        if position == 0:  # No position\n",
    "            # long buy\n",
    "            if predicted_change > threshold and cash >= original_prices[i]:\n",
    "                shares_bought = cash // original_prices[i]\n",
    "                shares += shares_bought\n",
    "                cash -= shares_bought * original_prices[i]\n",
    "                position = 1  # Long position opened\n",
    "            # short buy\n",
    "            elif predicted_change < -threshold and cash >= original_prices[i]:\n",
    "                shares_sold_short = cash // original_prices[i]\n",
    "                shares += shares_sold_short\n",
    "                cash -= shares_sold_short * original_prices[i]\n",
    "                position = -1  # Short position opened\n",
    "\n",
    "        elif position == 1:  # Long position\n",
    "            # long sell\n",
    "            if predicted_change < -threshold:\n",
    "                cash += shares * original_prices[i]\n",
    "                shares = 0\n",
    "                position = 0  # Exit long position\n",
    "            # short sell\n",
    "        elif position == -1:  # Short position\n",
    "            if predicted_change > threshold:\n",
    "                cash += shares * original_prices[i]\n",
    "                shares = 0\n",
    "                position = 0  # Exit short position\n",
    "\n",
    "        total_assets = cash + shares * original_prices[i]\n",
    "\n",
    "    return total_assets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Assets: 617952.865234375 Total Return: 117952.865234375 Return Rate: 23.590573046875\n"
     ]
    }
   ],
   "source": [
    "initial_cash = 500000\n",
    "threshold = 0.0007\n",
    "# Applying the backtest function to the RNN model's predictions\n",
    "final_assets = backtest(y_val_model[:, 0], y_val_original[:, 0], threshold=threshold, initial_cash=initial_cash)\n",
    "total_return = final_assets - initial_cash\n",
    "return_rate = (total_return / initial_cash) * 100\n",
    "print(\"Final Assets:\", final_assets, \"Total Return:\", total_return, \"Return Rate:\", return_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold: 0.0041 Final Assets: 661161.3198242188 Total Return: 161161.31982421875 Best Return Rate: 46.960853125\n"
     ]
    }
   ],
   "source": [
    "initial_cash = 500000\n",
    "best_threshold = 0\n",
    "best_return_rate = 0\n",
    "\n",
    "# seeking best threshold between 0.0001 to 0.009\n",
    "for threshold in range(1, 100):\n",
    "    threshold /= 10000  # 将范围调整为0.0001到0.0099\n",
    "    # get the \"final_assets\" from backtest function \n",
    "    final_assets = backtest(y_val_model[:, 0], y_val_original[:, 0], threshold=threshold, initial_cash=initial_cash)\n",
    "    total_return = final_assets - initial_cash\n",
    "    return_rate = (total_return / initial_cash) * 100\n",
    "    # sorting return rate find the best one\n",
    "    if return_rate > best_return_rate:\n",
    "        best_return_rate = return_rate\n",
    "        best_threshold = threshold\n",
    "\n",
    "#print(\"Best Threshold:\", best_threshold, \"Best Return Rate:\", best_return_rate)\n",
    "print(\"Best Threshold:\", best_threshold, \"Final Assets:\", final_assets, \"Total Return:\", total_return,\"Best Return Rate:\", best_return_rate,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
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