Task 6 Stock pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('data/SP500.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        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>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>1927-12-30</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1928-01-03</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1928-01-04</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1928-01-05</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1928-01-06</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close  Adj Close  Volume\n",
       "0  1927-12-30  17.660000  17.660000  17.660000  17.660000  17.660000       0\n",
       "1  1928-01-03  17.760000  17.760000  17.760000  17.760000  17.760000       0\n",
       "2  1928-01-04  17.719999  17.719999  17.719999  17.719999  17.719999       0\n",
       "3  1928-01-05  17.549999  17.549999  17.549999  17.549999  17.549999       0\n",
       "4  1928-01-06  17.660000  17.660000  17.660000  17.660000  17.660000       0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "data[['Close']].plot()\n",
    "plt.ylabel(\"stock_price\")\n",
    "plt.title(\"Stock\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shreyasi/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.992158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.992099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.992122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.992223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.992158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Close\n",
       "0 -0.992158\n",
       "1 -0.992099\n",
       "2 -0.992122\n",
       "3 -0.992223\n",
       "4 -0.992158"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=data[['Close']]\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "df['Close'] = scaler.fit_transform(df['Close'].values.reshape(-1,1))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(stock, lookback):\n",
    "    data_raw = stock.to_numpy() # convert to numpy array\n",
    "    data = []\n",
    "    \n",
    "    # create all possible sequences of length seq_len\n",
    "    for index in range(len(data_raw) - lookback): \n",
    "        data.append(data_raw[index: index + lookback])\n",
    "    \n",
    "    data = np.array(data);\n",
    "    test_set_size = int(np.round(0.2*data.shape[0]));\n",
    "    train_set_size = data.shape[0] - (test_set_size);\n",
    "    \n",
    "    x_train = data[:train_set_size,:-1,:]\n",
    "    y_train = data[:train_set_size,-1,:]\n",
    "    \n",
    "    x_test = data[train_set_size:,:-1]\n",
    "    y_test = data[train_set_size:,-1,:]\n",
    "    \n",
    "    return [x_train, y_train, x_test, y_test]\n",
    "lookback = 20 # choose sequence length\n",
    "x_train, y_train, x_test, y_test = split_data(df, lookback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = torch.from_numpy(x_train).type(torch.Tensor)\n",
    "x_test = torch.from_numpy(x_test).type(torch.Tensor)\n",
    "y_train = torch.from_numpy(y_train).type(torch.Tensor)\n",
    "y_test = torch.from_numpy(y_test).type(torch.Tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.num_layers = num_layers\n",
    "        \n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)    \n",
    "    def forward(self, x):\n",
    "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
    "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
    "        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
    "        out = self.fc(out[:, -1, :]) \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LSTM(input_dim=1, hidden_dim=16, output_dim=1, num_layers=2)\n",
    "criterion = torch.nn.MSELoss(reduction='mean')\n",
    "optimiser = torch.optim.Adam(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  0 loss:  1.2289127111434937\n",
      "epoch  1 loss:  1.066201090812683\n",
      "epoch  2 loss:  0.9049499034881592\n",
      "epoch  3 loss:  0.7229641079902649\n",
      "epoch  4 loss:  0.5129319429397583\n",
      "epoch  5 loss:  0.2890082001686096\n",
      "epoch  6 loss:  0.10248866677284241\n",
      "epoch  7 loss:  0.029389789327979088\n",
      "epoch  8 loss:  0.0909225270152092\n",
      "epoch  9 loss:  0.13827595114707947\n",
      "epoch  10 loss:  0.1318044662475586\n",
      "epoch  11 loss:  0.09948302805423737\n",
      "epoch  12 loss:  0.06571262329816818\n",
      "epoch  13 loss:  0.04263751953840256\n",
      "epoch  14 loss:  0.032733842730522156\n",
      "epoch  15 loss:  0.03315083682537079\n",
      "epoch  16 loss:  0.03919782117009163\n",
      "epoch  17 loss:  0.046524204313755035\n",
      "epoch  18 loss:  0.05216178670525551\n",
      "epoch  19 loss:  0.05469134822487831\n",
      "epoch  20 loss:  0.053935084491968155\n",
      "epoch  21 loss:  0.050536055117845535\n",
      "epoch  22 loss:  0.04558081552386284\n",
      "epoch  23 loss:  0.04029124602675438\n",
      "epoch  24 loss:  0.035772766917943954\n",
      "epoch  25 loss:  0.032811492681503296\n",
      "epoch  26 loss:  0.03173559531569481\n",
      "epoch  27 loss:  0.032372623682022095\n",
      "epoch  28 loss:  0.03413090109825134\n",
      "epoch  29 loss:  0.03619911149144173\n",
      "epoch  30 loss:  0.037806436419487\n",
      "epoch  31 loss:  0.03845023363828659\n",
      "epoch  32 loss:  0.03800894320011139\n",
      "epoch  33 loss:  0.03671273961663246\n",
      "epoch  34 loss:  0.03500627353787422\n",
      "epoch  35 loss:  0.03337244689464569\n",
      "epoch  36 loss:  0.032182853668928146\n",
      "epoch  37 loss:  0.03161485865712166\n",
      "epoch  38 loss:  0.03164335712790489\n",
      "epoch  39 loss:  0.03209064155817032\n",
      "epoch  40 loss:  0.03270614892244339\n",
      "epoch  41 loss:  0.033247388899326324\n",
      "epoch  42 loss:  0.033541180193424225\n",
      "epoch  43 loss:  0.033514730632305145\n",
      "epoch  44 loss:  0.033196017146110535\n",
      "epoch  45 loss:  0.03268923610448837\n",
      "epoch  46 loss:  0.032135434448719025\n",
      "epoch  47 loss:  0.03166913613677025\n",
      "epoch  48 loss:  0.031381990760564804\n",
      "epoch  49 loss:  0.0313020758330822\n",
      "epoch  50 loss:  0.03139341250061989\n",
      "epoch  51 loss:  0.03157461807131767\n",
      "epoch  52 loss:  0.031749796122312546\n",
      "epoch  53 loss:  0.03184067830443382\n",
      "epoch  54 loss:  0.03180914744734764\n",
      "epoch  55 loss:  0.031663570553064346\n",
      "epoch  56 loss:  0.031448621302843094\n",
      "epoch  57 loss:  0.031224627047777176\n",
      "epoch  58 loss:  0.03104492649435997\n",
      "epoch  59 loss:  0.030939489603042603\n",
      "epoch  60 loss:  0.03090900555253029\n",
      "epoch  61 loss:  0.030929692089557648\n",
      "epoch  62 loss:  0.03096519224345684\n",
      "epoch  63 loss:  0.0309804305434227\n",
      "epoch  64 loss:  0.030952773988246918\n",
      "epoch  65 loss:  0.03087731823325157\n",
      "epoch  66 loss:  0.03076549805700779\n",
      "epoch  67 loss:  0.030638638883829117\n",
      "epoch  68 loss:  0.030519120395183563\n",
      "epoch  69 loss:  0.03042229823768139\n",
      "epoch  70 loss:  0.030352072790265083\n",
      "epoch  71 loss:  0.030300971120595932\n",
      "epoch  72 loss:  0.03025440312922001\n",
      "epoch  73 loss:  0.03019697777926922\n",
      "epoch  74 loss:  0.030118398368358612\n",
      "epoch  75 loss:  0.03001653403043747\n",
      "epoch  76 loss:  0.02989708073437214\n",
      "epoch  77 loss:  0.02977011539041996\n",
      "epoch  78 loss:  0.029645442962646484\n",
      "epoch  79 loss:  0.02952851541340351\n",
      "epoch  80 loss:  0.029418596997857094\n",
      "epoch  81 loss:  0.029309473931789398\n",
      "epoch  82 loss:  0.029192162677645683\n",
      "epoch  83 loss:  0.02905844710767269\n",
      "epoch  84 loss:  0.02890363708138466\n",
      "epoch  85 loss:  0.02872765064239502\n",
      "epoch  86 loss:  0.02853388898074627\n",
      "epoch  87 loss:  0.028326718136668205\n",
      "epoch  88 loss:  0.028108539059758186\n",
      "epoch  89 loss:  0.02787821926176548\n",
      "epoch  90 loss:  0.027631554752588272\n",
      "epoch  91 loss:  0.02736365981400013\n",
      "epoch  92 loss:  0.027071865275502205\n",
      "epoch  93 loss:  0.026756834238767624\n",
      "epoch  94 loss:  0.026420237496495247\n",
      "epoch  95 loss:  0.026059621945023537\n",
      "epoch  96 loss:  0.025664668530225754\n",
      "epoch  97 loss:  0.025219984352588654\n",
      "epoch  98 loss:  0.02471344918012619\n",
      "epoch  99 loss:  0.0241412166506052\n"
     ]
    }
   ],
   "source": [
    "hist = np.zeros(100)\n",
    "lstm = []\n",
    "for t in range(100):\n",
    "    y_train_pred = model(x_train)    \n",
    "    loss = criterion(y_train_pred, y_train)\n",
    "    print(\"epoch \", t, \"loss: \", loss.item())\n",
    "    hist[t] = loss.item()    \n",
    "    optimiser.zero_grad()\n",
    "    loss.backward()\n",
    "    optimiser.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model(x_test)\n",
    "loss = criterion(y_test_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6533375382423401"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=torch.cat((y_train_pred,y_test_pred),dim=0).detach()\n",
    "y=torch.cat((y_train,y_test),dim=0).detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(12, 5))\n",
    "plt.plot(y, color = 'red', label = 'Real')\n",
    "plt.plot(y_pred, color = 'blue', label = 'Pred')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.9070],\n",
       "        [-0.9070],\n",
       "        [-0.9070],\n",
       "        ...,\n",
       "        [-0.6745],\n",
       "        [-0.6740],\n",
       "        [-0.6757]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}