stockModelRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import datetime\n",
    "from tickers import getTickers\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([[4.3000e-03, 3.8000e-03, 4.7000e-03, 4.5000e-03, 3.9000e-03, 2.4600e-01,\n",
      "         0.0000e+00, 4.0000e+00, 1.0000e+00],\n",
      "        [4.6000e-03, 4.0000e-03, 4.9000e-03, 4.6000e-03, 3.9000e-03, 3.0480e-01,\n",
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      "        [3.7000e-03, 3.3000e-03, 4.0000e-03, 4.1000e-03, 3.5000e-03, 2.2100e-01,\n",
      "         4.0000e+00, 8.0000e+00, 1.0000e+00]]), tensor(0.0037)) (tensor([[ 0.9894,  0.9943,  0.9717,  0.9583,  0.9586,  0.0593,  3.0000, 16.0000,\n",
      "         12.0000],\n",
      "        [ 0.9358,  0.9506,  0.9654,  0.9519,  0.9521,  0.0839,  4.0000, 17.0000,\n",
      "         12.0000],\n",
      "        [ 0.9263,  0.9340,  0.9522,  0.9438,  0.9441,  0.0361,  0.0000, 20.0000,\n",
      "         12.0000],\n",
      "        [ 0.9451,  0.9490,  0.9620,  0.9626,  0.9628,  0.0273,  1.0000, 21.0000,\n",
      "         12.0000],\n",
      "        [ 0.9536,  0.9642,  0.9800,  0.9779,  0.9780,  0.0278,  2.0000, 22.0000,\n",
      "         12.0000]]), tensor(0.9816))\n"
     ]
    }
   ],
   "source": [
    "class StockDataset(Dataset):\n",
    "    def __init__(self, directory, ticker, context = 5) -> None:\n",
    "        super().__init__()\n",
    "        self.info = []\n",
    "        self.context = context\n",
    "\n",
    "        self.info = pd.read_csv(f\"{directory}/{ticker}.csv\")\n",
    "\n",
    "        self.info = np.array(self.info.values.tolist())\n",
    "        self.feature_peaks = {}\n",
    "                \n",
    "        if len(self.info) > 1:\n",
    "            for feature_col in range(1, len(self.info[0,:])):\n",
    "                col = self.info[:,feature_col].astype(np.double)\n",
    "                high, low, col = self.norm(col)\n",
    "                self.info[:,feature_col] = col\n",
    "                self.feature_peaks[feature_col] = (low, high)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.info) - self.context if len(self.info) > self.context else len(self.info)\n",
    "    def __getitem__(self, index):\n",
    "        index+=self.context\n",
    "        res = []\n",
    "\n",
    "        for day in self.info[index-self.context: index]:\n",
    "            time = self.transformDay(day[0])\n",
    "            res.append(np.concatenate((day[1:].astype(np.double) , time), axis = 0))\n",
    "            # res.append(day[1:])\n",
    "\n",
    "        \n",
    "        previousDays = torch.Tensor(res)\n",
    "        closingPrice = torch.tensor(float(self.info[index][4])).float()\n",
    "        return previousDays, closingPrice\n",
    "    def transformDay(self, date):\n",
    "\n",
    "        dt = datetime.datetime.strptime(date, \"%Y-%m-%d\")\n",
    "        res = [dt.weekday(), dt.day, dt.month]\n",
    "        return np.array(res)\n",
    "    def norm(self, feature):\n",
    "        high, low = max(feature), min(feature)\n",
    "        for i in range(len(feature)):\n",
    "            feature[i] = round(((feature[i]-low)/(high-low)), 4)\n",
    "        return high, low, feature\n",
    "    def reverse(self, output):\n",
    "        return ((output)*(self.feature_peaks[4][1]-self.feature_peaks[4][0])) + self.feature_peaks[4][0]\n",
    "    def getLatest(self):\n",
    "        res = []\n",
    "\n",
    "        for day in self.info[len(self.info)-self.context: len(self.info)]:\n",
    "            time = self.transformDay(day[0])\n",
    "            res.append(np.concatenate((day[1:].astype(np.double) , time), axis = 0))\n",
    "            # res.append(day[1:])\n",
    "\n",
    "        \n",
    "        previousDays = torch.Tensor(res)\n",
    "        return previousDays\n",
    "\n",
    "\n",
    "dkng = StockDataset(\"data\", \"AAPL\")\n",
    "print(dkng[0], dkng[3010])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "torch.Size([1, 5, 10]) torch.Size([1, 5, 10])\n",
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      "(tensor([[7.3850e+00, 7.4107e+00, 7.0414e+00, 7.0625e+00, 6.0551e+00, 8.8177e+08,\n",
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      "        [9.0964e+00, 9.1493e+00, 8.8468e+00, 9.0793e+00, 7.7843e+00, 7.6283e+08,\n",
      "         0.0000e+00, 1.7000e+01, 5.0000e+00]]), tensor(9.0129))\n",
      "(tensor([[9.2586e+00, 9.3975e+00, 9.2393e+00, 9.3604e+00, 8.0252e+00, 6.5438e+08,\n",
      "         2.0000e+00, 1.2000e+01, 5.0000e+00],\n",
      "        [9.4007e+00, 9.4643e+00, 9.1571e+00, 9.2271e+00, 7.9110e+00, 5.9971e+08,\n",
      "         3.0000e+00, 1.3000e+01, 5.0000e+00],\n",
      "        [9.1129e+00, 9.1600e+00, 8.9107e+00, 9.0650e+00, 7.7720e+00, 7.5936e+08,\n",
      "         4.0000e+00, 1.4000e+01, 5.0000e+00],\n",
      "        [9.0964e+00, 9.1493e+00, 8.8468e+00, 9.0793e+00, 7.7843e+00, 7.6283e+08,\n",
      "         0.0000e+00, 1.7000e+01, 5.0000e+00],\n",
      "        [9.1779e+00, 9.2339e+00, 8.9379e+00, 9.0129e+00, 7.7273e+00, 7.8268e+08,\n",
      "         1.0000e+00, 1.8000e+01, 5.0000e+00]]), tensor(8.8693))\n",
      "(tensor([[9.4007e+00, 9.4643e+00, 9.1571e+00, 9.2271e+00, 7.9110e+00, 5.9971e+08,\n",
      "         3.0000e+00, 1.3000e+01, 5.0000e+00],\n",
      "        [9.1129e+00, 9.1600e+00, 8.9107e+00, 9.0650e+00, 7.7720e+00, 7.5936e+08,\n",
      "         4.0000e+00, 1.4000e+01, 5.0000e+00],\n",
      "        [9.0964e+00, 9.1493e+00, 8.8468e+00, 9.0793e+00, 7.7843e+00, 7.6283e+08,\n",
      "         0.0000e+00, 1.7000e+01, 5.0000e+00],\n",
      "        [9.1779e+00, 9.2339e+00, 8.9379e+00, 9.0129e+00, 7.7273e+00, 7.8268e+08,\n",
      "         1.0000e+00, 1.8000e+01, 5.0000e+00],\n",
      "        [8.9107e+00, 9.0329e+00, 8.7446e+00, 8.8693e+00, 7.6042e+00, 1.0257e+09,\n",
      "         2.0000e+00, 1.9000e+01, 5.0000e+00]]), tensor(8.4914))\n",
      "(tensor([[9.1129e+00, 9.1600e+00, 8.9107e+00, 9.0650e+00, 7.7720e+00, 7.5936e+08,\n",
      "         4.0000e+00, 1.4000e+01, 5.0000e+00],\n",
      "        [9.0964e+00, 9.1493e+00, 8.8468e+00, 9.0793e+00, 7.7843e+00, 7.6283e+08,\n",
      "         0.0000e+00, 1.7000e+01, 5.0000e+00],\n",
      "        [9.1779e+00, 9.2339e+00, 8.9379e+00, 9.0129e+00, 7.7273e+00, 7.8268e+08,\n",
      "         1.0000e+00, 1.8000e+01, 5.0000e+00],\n",
      "        [8.9107e+00, 9.0329e+00, 8.7446e+00, 8.8693e+00, 7.6042e+00, 1.0257e+09,\n",
      "         2.0000e+00, 1.9000e+01, 5.0000e+00],\n",
      "        [8.6386e+00, 8.7089e+00, 8.4361e+00, 8.4914e+00, 7.2803e+00, 1.2829e+09,\n",
      "         3.0000e+00, 2.0000e+01, 5.0000e+00]]), tensor(8.6543))\n",
      "(tensor([[9.0964e+00, 9.1493e+00, 8.8468e+00, 9.0793e+00, 7.7843e+00, 7.6283e+08,\n",
      "         0.0000e+00, 1.7000e+01, 5.0000e+00],\n",
      "        [9.1779e+00, 9.2339e+00, 8.9379e+00, 9.0129e+00, 7.7273e+00, 7.8268e+08,\n",
      "         1.0000e+00, 1.8000e+01, 5.0000e+00],\n",
      "        [8.9107e+00, 9.0329e+00, 8.7446e+00, 8.8693e+00, 7.6042e+00, 1.0257e+09,\n",
      "         2.0000e+00, 1.9000e+01, 5.0000e+00],\n",
      "        [8.6386e+00, 8.7089e+00, 8.4361e+00, 8.4914e+00, 7.2803e+00, 1.2829e+09,\n",
      "         3.0000e+00, 2.0000e+01, 5.0000e+00],\n",
      "        [8.3150e+00, 8.7321e+00, 8.2625e+00, 8.6543e+00, 7.4199e+00, 1.2239e+09,\n",
      "         4.0000e+00, 2.1000e+01, 5.0000e+00]]), tensor(8.8129))\n",
      "(tensor([[9.1779e+00, 9.2339e+00, 8.9379e+00, 9.0129e+00, 7.7273e+00, 7.8268e+08,\n",
      "         1.0000e+00, 1.8000e+01, 5.0000e+00],\n",
      "        [8.9107e+00, 9.0329e+00, 8.7446e+00, 8.8693e+00, 7.6042e+00, 1.0257e+09,\n",
      "         2.0000e+00, 1.9000e+01, 5.0000e+00],\n",
      "        [8.6386e+00, 8.7089e+00, 8.4361e+00, 8.4914e+00, 7.2803e+00, 1.2829e+09,\n",
      "         3.0000e+00, 2.0000e+01, 5.0000e+00],\n",
      "        [8.3150e+00, 8.7321e+00, 8.2625e+00, 8.6543e+00, 7.4199e+00, 1.2239e+09,\n",
      "         4.0000e+00, 2.1000e+01, 5.0000e+00],\n",
      "        [8.8314e+00, 8.9607e+00, 8.7950e+00, 8.8129e+00, 7.5558e+00, 7.5424e+08,\n",
      "         0.0000e+00, 2.4000e+01, 5.0000e+00]]), tensor(8.7579))\n",
      "(tensor([[8.9107e+00, 9.0329e+00, 8.7446e+00, 8.8693e+00, 7.6042e+00, 1.0257e+09,\n",
      "         2.0000e+00, 1.9000e+01, 5.0000e+00],\n",
      "        [8.6386e+00, 8.7089e+00, 8.4361e+00, 8.4914e+00, 7.2803e+00, 1.2829e+09,\n",
      "         3.0000e+00, 2.0000e+01, 5.0000e+00],\n",
      "        [8.3150e+00, 8.7321e+00, 8.2625e+00, 8.6543e+00, 7.4199e+00, 1.2239e+09,\n",
      "         4.0000e+00, 2.1000e+01, 5.0000e+00],\n",
      "        [8.8314e+00, 8.9607e+00, 8.7950e+00, 8.8129e+00, 7.5558e+00, 7.5424e+08,\n",
      "         0.0000e+00, 2.4000e+01, 5.0000e+00],\n",
      "        [8.5482e+00, 8.8129e+00, 8.4700e+00, 8.7579e+00, 7.5087e+00, 1.0480e+09,\n",
      "         1.0000e+00, 2.5000e+01, 5.0000e+00]]), tensor(8.7182))\n",
      "(tensor([[8.6386e+00, 8.7089e+00, 8.4361e+00, 8.4914e+00, 7.2803e+00, 1.2829e+09,\n",
      "         3.0000e+00, 2.0000e+01, 5.0000e+00],\n",
      "        [8.3150e+00, 8.7321e+00, 8.2625e+00, 8.6543e+00, 7.4199e+00, 1.2239e+09,\n",
      "         4.0000e+00, 2.1000e+01, 5.0000e+00],\n",
      "        [8.8314e+00, 8.9607e+00, 8.7950e+00, 8.8129e+00, 7.5558e+00, 7.5424e+08,\n",
      "         0.0000e+00, 2.4000e+01, 5.0000e+00],\n",
      "        [8.5482e+00, 8.8129e+00, 8.4700e+00, 8.7579e+00, 7.5087e+00, 1.0480e+09,\n",
      "         1.0000e+00, 2.5000e+01, 5.0000e+00],\n",
      "        [8.9314e+00, 9.0046e+00, 8.7054e+00, 8.7182e+00, 7.4747e+00, 8.5065e+08,\n",
      "         2.0000e+00, 2.6000e+01, 5.0000e+00]]), tensor(9.0482))\n",
      "(tensor([[8.3150e+00, 8.7321e+00, 8.2625e+00, 8.6543e+00, 7.4199e+00, 1.2239e+09,\n",
      "         4.0000e+00, 2.1000e+01, 5.0000e+00],\n",
      "        [8.8314e+00, 8.9607e+00, 8.7950e+00, 8.8129e+00, 7.5558e+00, 7.5424e+08,\n",
      "         0.0000e+00, 2.4000e+01, 5.0000e+00],\n",
      "        [8.5482e+00, 8.8129e+00, 8.4700e+00, 8.7579e+00, 7.5087e+00, 1.0480e+09,\n",
      "         1.0000e+00, 2.5000e+01, 5.0000e+00],\n",
      "        [8.9314e+00, 9.0046e+00, 8.7054e+00, 8.7182e+00, 7.4747e+00, 8.5065e+08,\n",
      "         2.0000e+00, 2.6000e+01, 5.0000e+00],\n",
      "        [8.9500e+00, 9.0675e+00, 8.8968e+00, 9.0482e+00, 7.7576e+00, 6.6628e+08,\n",
      "         3.0000e+00, 2.7000e+01, 5.0000e+00]]), tensor(9.1743))\n",
      "(tensor([[8.8314e+00, 8.9607e+00, 8.7950e+00, 8.8129e+00, 7.5558e+00, 7.5424e+08,\n",
      "         0.0000e+00, 2.4000e+01, 5.0000e+00],\n",
      "        [8.5482e+00, 8.8129e+00, 8.4700e+00, 8.7579e+00, 7.5087e+00, 1.0480e+09,\n",
      "         1.0000e+00, 2.5000e+01, 5.0000e+00],\n",
      "        [8.9314e+00, 9.0046e+00, 8.7054e+00, 8.7182e+00, 7.4747e+00, 8.5065e+08,\n",
      "         2.0000e+00, 2.6000e+01, 5.0000e+00],\n",
      "        [8.9500e+00, 9.0675e+00, 8.8968e+00, 9.0482e+00, 7.7576e+00, 6.6628e+08,\n",
      "         3.0000e+00, 2.7000e+01, 5.0000e+00],\n",
      "        [9.2639e+00, 9.2643e+00, 9.0482e+00, 9.1743e+00, 7.8657e+00, 8.1561e+08,\n",
      "         4.0000e+00, 2.8000e+01, 5.0000e+00]]), tensor(9.3154))\n",
      "(tensor([[8.5482e+00, 8.8129e+00, 8.4700e+00, 8.7579e+00, 7.5087e+00, 1.0480e+09,\n",
      "         1.0000e+00, 2.5000e+01, 5.0000e+00],\n",
      "        [8.9314e+00, 9.0046e+00, 8.7054e+00, 8.7182e+00, 7.4747e+00, 8.5065e+08,\n",
      "         2.0000e+00, 2.6000e+01, 5.0000e+00],\n",
      "        [8.9500e+00, 9.0675e+00, 8.8968e+00, 9.0482e+00, 7.7576e+00, 6.6628e+08,\n",
      "         3.0000e+00, 2.7000e+01, 5.0000e+00],\n",
      "        [9.2639e+00, 9.2643e+00, 9.0482e+00, 9.1743e+00, 7.8657e+00, 8.1561e+08,\n",
      "         4.0000e+00, 2.8000e+01, 5.0000e+00],\n",
      "        [9.2746e+00, 9.4979e+00, 9.2486e+00, 9.3154e+00, 7.9867e+00, 8.7647e+08,\n",
      "         1.0000e+00, 1.0000e+00, 6.0000e+00]]), tensor(9.4268))\n",
      "(tensor([[8.9314e+00, 9.0046e+00, 8.7054e+00, 8.7182e+00, 7.4747e+00, 8.5065e+08,\n",
      "         2.0000e+00, 2.6000e+01, 5.0000e+00],\n",
      "        [8.9500e+00, 9.0675e+00, 8.8968e+00, 9.0482e+00, 7.7576e+00, 6.6628e+08,\n",
      "         3.0000e+00, 2.7000e+01, 5.0000e+00],\n",
      "        [9.2639e+00, 9.2643e+00, 9.0482e+00, 9.1743e+00, 7.8657e+00, 8.1561e+08,\n",
      "         4.0000e+00, 2.8000e+01, 5.0000e+00],\n",
      "        [9.2746e+00, 9.4979e+00, 9.2486e+00, 9.3154e+00, 7.9867e+00, 8.7647e+08,\n",
      "         1.0000e+00, 1.0000e+00, 6.0000e+00],\n",
      "        [9.4479e+00, 9.4571e+00, 9.2975e+00, 9.4268e+00, 8.0822e+00, 6.8855e+08,\n",
      "         2.0000e+00, 2.0000e+00, 6.0000e+00]]), tensor(9.3971))\n",
      "(tensor([[8.9500e+00, 9.0675e+00, 8.8968e+00, 9.0482e+00, 7.7576e+00, 6.6628e+08,\n",
      "         3.0000e+00, 2.7000e+01, 5.0000e+00],\n",
      "        [9.2639e+00, 9.2643e+00, 9.0482e+00, 9.1743e+00, 7.8657e+00, 8.1561e+08,\n",
      "         4.0000e+00, 2.8000e+01, 5.0000e+00],\n",
      "        [9.2746e+00, 9.4979e+00, 9.2486e+00, 9.3154e+00, 7.9867e+00, 8.7647e+08,\n",
      "         1.0000e+00, 1.0000e+00, 6.0000e+00],\n",
      "        [9.4479e+00, 9.4571e+00, 9.2975e+00, 9.4268e+00, 8.0822e+00, 6.8855e+08,\n",
      "         2.0000e+00, 2.0000e+00, 6.0000e+00],\n",
      "        [9.4707e+00, 9.4839e+00, 9.3004e+00, 9.3971e+00, 8.0568e+00, 6.5011e+08,\n",
      "         3.0000e+00, 3.0000e+00, 6.0000e+00]]), tensor(9.1414))\n"
     ]
    }
   ],
   "source": [
    "t = dkng[10][0]\n",
    "t = t.unsqueeze(0)\n",
    "print(t.shape)\n",
    "rnn = nn.GRU(9, hidden_size = 10, bias = False)\n",
    "o, h = rnn(t)\n",
    "\n",
    "print(o.shape, h.shape)\n",
    "\n",
    "for i, j in enumerate(dev):\n",
    "    if i > 100: break\n",
    "    print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, num_hidden = 128, num_layers = 1, context = 5, features = 9, dropout = 0.1) -> None:\n",
    "        super().__init__()\n",
    "        self.rnn = nn.GRU(features, num_hidden, num_layers, batch_first = True)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dropout(x)\n",
    "        output, state = self.rnn(x)\n",
    "        return output, state\n",
    "\n",
    "class PlainDecoder(nn.Module):\n",
    "    def __init__(self, num_hidden = 128, num_layers = 1, context = 5, features = 9, dropout = 0.1) -> None:\n",
    "        super().__init__()\n",
    "        self.rnn = nn.GRU(features, num_hidden, num_layers, batch_first = True)\n",
    "        self.dense = nn.Linear(num_hidden, 1)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "\n",
    "    def forward(self, x, state):\n",
    "\n",
    "        # x = self.dropout(x)\n",
    "        output, state = self.rnn(x, state)\n",
    "        output = self.dense(output)\n",
    "        return output\n",
    "\n",
    "class Seq2Seq(nn.Module):\n",
    "    def __init__(self, num_hidden = 128, num_layers = 1, context = 5, features = 9, dropout = 0.1) -> None:\n",
    "        super().__init__()\n",
    "        self.encoder = Encoder()\n",
    "        self.decoder = PlainDecoder()\n",
    "    def forward(self, x):\n",
    "        # print(x.shape)\n",
    "        encoder_output, encoder_hidden = self.encoder(x)# hidden --> layer, batch, hidden dim\n",
    "        most_recent_day = x[:,-1,:].unsqueeze(1) #batch, days, features\n",
    "        output = self.decoder(most_recent_day, encoder_hidden).squeeze(2)\n",
    "        return output\n",
    "\n",
    "\n",
    "class PlainModel(nn.Module):\n",
    "    def __init__(self, num_hidden = 128, num_layers = 1, context = 5, features = 9, dropout = 0.1) -> None:\n",
    "        super().__init__()\n",
    "        self.rnn = nn.GRU(features, num_hidden, num_layers, batch_first = True)\n",
    "        self.dense = nn.Linear(num_hidden, 1)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dropout(x)\n",
    "        output, state = self.rnn(x)\n",
    "        output = self.dense(output)\n",
    "        return output[:,-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Loss: 24.9930\n",
      "Epoch [2/10], Loss: 18.4269\n",
      "Epoch [3/10], Loss: 18.0211\n",
      "Epoch [4/10], Loss: 17.8454\n",
      "Epoch [5/10], Loss: 17.6108\n",
      "Epoch [6/10], Loss: 17.5327\n",
      "Epoch [7/10], Loss: 17.5647\n",
      "Epoch [8/10], Loss: 17.3212\n",
      "Epoch [9/10], Loss: 17.2589\n",
      "Epoch [10/10], Loss: 17.2402\n"
     ]
    }
   ],
   "source": [
    "model = Seq2Seq().to(device)\n",
    "# train_loader = DataLoader(dev, batch_size = 10, shuffle= True)\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.001)\n",
    "loss = nn.MSELoss()\n",
    "epochs = 10\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    total_mse = 0\n",
    "    for train_loader in train:\n",
    "        for x, y in train_loader:\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            output = model(x).squeeze()\n",
    "        \n",
    "            batch_loss = loss(output, y)\n",
    "            total_mse+=batch_loss\n",
    "            batch_loss.backward()\n",
    "            optimizer.step()\n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, total_mse))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "dev_tickers = ['AAPL','TSLA','COKE', 'AMAT']\n",
    "dev = [StockDataset(\"data\", ticker) for ticker in dev_tickers]\n",
    "dev = [DataLoader(ticker, batch_size = 64, shuffle= True) for ticker in dev]\n",
    "# dev = list(chain.from_iterable(dev))\n",
    "# print(len(dev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-8d4ef4ea189d>:42: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  feature[i] = round(((feature[i]-low)/(high-low)), 4)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 1502 tickers\n"
     ]
    }
   ],
   "source": [
    "train_tickers = getTickers()\n",
    "train = [StockDataset(\"data\", ticker) for ticker in train_tickers]\n",
    "train = [obj for obj in train if len(obj) == 3011]\n",
    "print(f\"Training set has {len(train)} tickers\")\n",
    "train = [DataLoader(ticker, batch_size = 64, shuffle= True) for ticker in train]\n",
    "\n",
    "test = train[:20]\n",
    "train = train[20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Loss: 37.5448\n",
      "Epoch [2/10], Loss: 20.4773\n",
      "Epoch [3/10], Loss: 19.4226\n",
      "Epoch [4/10], Loss: 18.8931\n",
      "Epoch [5/10], Loss: 18.6525\n",
      "Epoch [6/10], Loss: 18.3881\n",
      "Epoch [7/10], Loss: 18.3717\n",
      "Epoch [8/10], Loss: 18.1599\n",
      "Epoch [9/10], Loss: 18.2083\n",
      "Epoch [10/10], Loss: 18.1048\n"
     ]
    }
   ],
   "source": [
    "model2 = PlainModel(features=9).to(device)\n",
    "# train_loader = DataLoader(dev, batch_size = 10, shuffle= True)\n",
    "optimizer = optim.Adam(model2.parameters(), lr = 0.001)\n",
    "loss = nn.MSELoss()\n",
    "epochs = 10\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    total_mse = 0\n",
    "    for train_loader in train:\n",
    "        for x, y in train_loader:\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            output = model2(x).squeeze()\n",
    "        \n",
    "            batch_loss = loss(output, y)\n",
    "            total_mse+=batch_loss\n",
    "            batch_loss.backward()\n",
    "            optimizer.step()\n",
    "            # break\n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, total_mse))   \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.1900e-02, 1.1800e-02, 1.2500e-02, 1.2700e-02, 1.0800e-02, 3.3980e-01,\n",
      "        3.0000e+00, 2.7000e+01, 5.0000e+00], device='cuda:0') tensor(0.0132, device='cuda:0')\n",
      "tensor([[0.0186]], device='cuda:0', grad_fn=<SqueezeBackward1>)\n",
      "10.06372883224228\n",
      "9.137131085255325\n"
     ]
    }
   ],
   "source": [
    "s = dkng[100]\n",
    "x, y = s[0], s[1]\n",
    "x = x.to(device)\n",
    "y = y.to(device)\n",
    "print(x[0],y)\n",
    "x = x.unsqueeze(0)\n",
    "o = model(x)\n",
    "\n",
    "print(o)\n",
    "print(dkng.reverse(o.item()))\n",
    "print(dkng.reverse(y.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.7732]], device='cuda:0', grad_fn=<SqueezeBackward1>)\n",
      "[tensor(45.0009, device='cuda:0'), tensor(44.7806, device='cuda:0'), tensor(42.6087, device='cuda:0'), tensor(44.4611, device='cuda:0'), tensor(45.1218, device='cuda:0')]\n",
      "44.83547083447195\n",
      "45.8990049913444\n"
     ]
    }
   ],
   "source": [
    "test = 'ZUMZ'\n",
    "test = StockDataset(test)\n",
    "sample = test[len(test)-1]\n",
    "x, y =sample[0], sample[1]\n",
    "x = x.to(device)\n",
    "y = y.to(device)\n",
    "x = x.unsqueeze(0)\n",
    "o = model(x)\n",
    "\n",
    "print(o)\n",
    "x = x.squeeze(0)\n",
    "daysPrior = [test.reverse(day[4]) for day in x]\n",
    "print(daysPrior)\n",
    "print(test.reverse(o.item()))\n",
    "print(test.reverse(y.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day To Day Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.63799475025891\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import requests\n",
    "import os\n",
    "headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n",
    "if not os.path.exists('predict/'):\n",
    "    os.mkdir('predict/')\n",
    "checking = \"DKNG\"\n",
    "\n",
    "today = int(time.time())\n",
    "endPoint = 1638316800 #earlier date\n",
    "\n",
    "url =f'https://query1.finance.yahoo.com/v7/finance/download/\\\n",
    "{checking}?period1={endPoint}&period2={today}&interval=1d&events=history&includeAdjustedClose=true'\n",
    "\n",
    "try:\n",
    "    res = requests.get(url, headers=headers, allow_redirects=True)\n",
    "    open(f'predict/{checking}.csv', 'wb').write(res.content)\n",
    "except:\n",
    "    pass\n",
    "    # print(\"Error Grabbing {}\".format(ticker))\n",
    "    \n",
    "data = StockDataset(\"predict\", checking)\n",
    "\n",
    "predict = data.getLatest()\n",
    "x=predict\n",
    "#take off gpu when forreal\n",
    "x = x.to(device)\n",
    "\n",
    "\n",
    "x = x.unsqueeze(0)\n",
    "o = model(x)\n",
    "print(data.reverse(o.item()))\n",
    "# if os.path.exists(f\"predict/{checking}.csv\"):\n",
    "#     os.remove(f\"predict/{checking}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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