btc-predict-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-22 09:41:39.395074: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-02-22 09:41:39.511305: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-02-22 09:41:39.511327: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-02-22 09:41:40.141417: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-02-22 09:41:40.141547: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-02-22 09:41:40.141558: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,LSTM,Dropout\n",
    "from plotly.offline import iplot\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unix</th>\n",
       "      <th>Date</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Volume Currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1676588400000</td>\n",
       "      <td>2023-02-16 23:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24002.33</td>\n",
       "      <td>24098.56</td>\n",
       "      <td>23515.96</td>\n",
       "      <td>23520.73</td>\n",
       "      <td>48.328991</td>\n",
       "      <td>1.136733e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1676584800000</td>\n",
       "      <td>2023-02-16 22:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24521.00</td>\n",
       "      <td>24555.40</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>24002.33</td>\n",
       "      <td>41.758527</td>\n",
       "      <td>1.002302e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1676581200000</td>\n",
       "      <td>2023-02-16 21:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24562.44</td>\n",
       "      <td>24575.06</td>\n",
       "      <td>24364.72</td>\n",
       "      <td>24521.00</td>\n",
       "      <td>44.653526</td>\n",
       "      <td>1.094949e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1676577600000</td>\n",
       "      <td>2023-02-16 20:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24876.29</td>\n",
       "      <td>24886.09</td>\n",
       "      <td>24454.89</td>\n",
       "      <td>24562.44</td>\n",
       "      <td>108.987269</td>\n",
       "      <td>2.676993e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1676574000000</td>\n",
       "      <td>2023-02-16 19:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24979.87</td>\n",
       "      <td>24999.77</td>\n",
       "      <td>24846.43</td>\n",
       "      <td>24876.29</td>\n",
       "      <td>50.910118</td>\n",
       "      <td>1.266455e+06</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>64515</th>\n",
       "      <td>1444323600</td>\n",
       "      <td>2015-10-08 17:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>244.25</td>\n",
       "      <td>244.99</td>\n",
       "      <td>244.02</td>\n",
       "      <td>244.99</td>\n",
       "      <td>3.920632</td>\n",
       "      <td>9.605156e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64516</th>\n",
       "      <td>1444320000</td>\n",
       "      <td>2015-10-08 16:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.25</td>\n",
       "      <td>244.25</td>\n",
       "      <td>3.895252</td>\n",
       "      <td>9.514154e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64517</th>\n",
       "      <td>1444316400</td>\n",
       "      <td>2015-10-08 15:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>245.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.92</td>\n",
       "      <td>3.016926</td>\n",
       "      <td>7.389055e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64518</th>\n",
       "      <td>1444312800</td>\n",
       "      <td>2015-10-08 14:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>245.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>244.50</td>\n",
       "      <td>245.00</td>\n",
       "      <td>4.453649</td>\n",
       "      <td>1.091144e+03</td>\n",
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       "    <tr>\n",
       "      <th>64519</th>\n",
       "      <td>1444309200</td>\n",
       "      <td>2015-10-08 13:00:00</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>0.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>0.606654</td>\n",
       "      <td>1.486303e+02</td>\n",
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       "<p>64520 rows × 9 columns</p>\n",
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      "text/plain": [
       "                Unix                 Date   Symbol      Open      High  \\\n",
       "0      1676588400000  2023-02-16 23:00:00  BTC/USD  24002.33  24098.56   \n",
       "1      1676584800000  2023-02-16 22:00:00  BTC/USD  24521.00  24555.40   \n",
       "2      1676581200000  2023-02-16 21:00:00  BTC/USD  24562.44  24575.06   \n",
       "3      1676577600000  2023-02-16 20:00:00  BTC/USD  24876.29  24886.09   \n",
       "4      1676574000000  2023-02-16 19:00:00  BTC/USD  24979.87  24999.77   \n",
       "...              ...                  ...      ...       ...       ...   \n",
       "64515     1444323600  2015-10-08 17:00:00  BTC/USD    244.25    244.99   \n",
       "64516     1444320000  2015-10-08 16:00:00  BTC/USD    244.92    244.92   \n",
       "64517     1444316400  2015-10-08 15:00:00  BTC/USD    245.00    245.00   \n",
       "64518     1444312800  2015-10-08 14:00:00  BTC/USD    245.00    245.00   \n",
       "64519     1444309200  2015-10-08 13:00:00  BTC/USD      0.00    245.00   \n",
       "\n",
       "            Low     Close  Volume BTC  Volume Currency  \n",
       "0      23515.96  23520.73   48.328991     1.136733e+06  \n",
       "1      24000.00  24002.33   41.758527     1.002302e+06  \n",
       "2      24364.72  24521.00   44.653526     1.094949e+06  \n",
       "3      24454.89  24562.44  108.987269     2.676993e+06  \n",
       "4      24846.43  24876.29   50.910118     1.266455e+06  \n",
       "...         ...       ...         ...              ...  \n",
       "64515    244.02    244.99    3.920632     9.605156e+02  \n",
       "64516    244.25    244.25    3.895252     9.514154e+02  \n",
       "64517    244.92    244.92    3.016926     7.389055e+02  \n",
       "64518    244.50    245.00    4.453649     1.091144e+03  \n",
       "64519      0.00    245.00    0.606654     1.486303e+02  \n",
       "\n",
       "[64520 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from datetime import datetime\n",
    "# df['Date'] = pd.to_datetime(df['Timestamp'], unit = 's')\n",
    "# df = df.sort_values('Date')\n",
    "# groups = pd.factorize(df['Date'].dt.day.astype(str) + '_' + df['Date'].dt.month.astype(str) + '_' + df['Date'].dt.year.astype(str))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unix</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Volume Currency</th>\n",
       "      <th>Vwap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2015-10-08 13:00:00</th>\n",
       "      <td>1444309200</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>0.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>0.606654</td>\n",
       "      <td>1.486303e+02</td>\n",
       "      <td>11329.893700</td>\n",
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       "    <tr>\n",
       "      <th>2015-10-08 14:00:00</th>\n",
       "      <td>1444312800</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>245.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>244.50</td>\n",
       "      <td>245.00</td>\n",
       "      <td>4.453649</td>\n",
       "      <td>1.091144e+03</td>\n",
       "      <td>11329.894537</td>\n",
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       "    <tr>\n",
       "      <th>2015-10-08 15:00:00</th>\n",
       "      <td>1444316400</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>245.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.92</td>\n",
       "      <td>3.016926</td>\n",
       "      <td>7.389055e+02</td>\n",
       "      <td>11329.900619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08 16:00:00</th>\n",
       "      <td>1444320000</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.25</td>\n",
       "      <td>244.25</td>\n",
       "      <td>3.895252</td>\n",
       "      <td>9.514154e+02</td>\n",
       "      <td>11329.904739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08 17:00:00</th>\n",
       "      <td>1444323600</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>244.25</td>\n",
       "      <td>244.99</td>\n",
       "      <td>244.02</td>\n",
       "      <td>244.99</td>\n",
       "      <td>3.920632</td>\n",
       "      <td>9.605156e+02</td>\n",
       "      <td>11329.910059</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 19:00:00</th>\n",
       "      <td>1676574000000</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24979.87</td>\n",
       "      <td>24999.77</td>\n",
       "      <td>24846.43</td>\n",
       "      <td>24876.29</td>\n",
       "      <td>50.910118</td>\n",
       "      <td>1.266455e+06</td>\n",
       "      <td>24486.473072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 20:00:00</th>\n",
       "      <td>1676577600000</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24876.29</td>\n",
       "      <td>24886.09</td>\n",
       "      <td>24454.89</td>\n",
       "      <td>24562.44</td>\n",
       "      <td>108.987269</td>\n",
       "      <td>2.676993e+06</td>\n",
       "      <td>24395.270174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 21:00:00</th>\n",
       "      <td>1676581200000</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24562.44</td>\n",
       "      <td>24575.06</td>\n",
       "      <td>24364.72</td>\n",
       "      <td>24521.00</td>\n",
       "      <td>44.653526</td>\n",
       "      <td>1.094949e+06</td>\n",
       "      <td>24172.654582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 22:00:00</th>\n",
       "      <td>1676584800000</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24521.00</td>\n",
       "      <td>24555.40</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>24002.33</td>\n",
       "      <td>41.758527</td>\n",
       "      <td>1.002302e+06</td>\n",
       "      <td>24025.324410</td>\n",
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       "    <tr>\n",
       "      <th>2023-02-16 23:00:00</th>\n",
       "      <td>1676588400000</td>\n",
       "      <td>BTC/USD</td>\n",
       "      <td>24002.33</td>\n",
       "      <td>24098.56</td>\n",
       "      <td>23515.96</td>\n",
       "      <td>23520.73</td>\n",
       "      <td>48.328991</td>\n",
       "      <td>1.136733e+06</td>\n",
       "      <td>23807.260000</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>64520 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Unix   Symbol      Open      High       Low  \\\n",
       "Date                                                                        \n",
       "2015-10-08 13:00:00     1444309200  BTC/USD      0.00    245.00      0.00   \n",
       "2015-10-08 14:00:00     1444312800  BTC/USD    245.00    245.00    244.50   \n",
       "2015-10-08 15:00:00     1444316400  BTC/USD    245.00    245.00    244.92   \n",
       "2015-10-08 16:00:00     1444320000  BTC/USD    244.92    244.92    244.25   \n",
       "2015-10-08 17:00:00     1444323600  BTC/USD    244.25    244.99    244.02   \n",
       "...                            ...      ...       ...       ...       ...   \n",
       "2023-02-16 19:00:00  1676574000000  BTC/USD  24979.87  24999.77  24846.43   \n",
       "2023-02-16 20:00:00  1676577600000  BTC/USD  24876.29  24886.09  24454.89   \n",
       "2023-02-16 21:00:00  1676581200000  BTC/USD  24562.44  24575.06  24364.72   \n",
       "2023-02-16 22:00:00  1676584800000  BTC/USD  24521.00  24555.40  24000.00   \n",
       "2023-02-16 23:00:00  1676588400000  BTC/USD  24002.33  24098.56  23515.96   \n",
       "\n",
       "                        Close  Volume BTC  Volume Currency          Vwap  \n",
       "Date                                                                      \n",
       "2015-10-08 13:00:00    245.00    0.606654     1.486303e+02  11329.893700  \n",
       "2015-10-08 14:00:00    245.00    4.453649     1.091144e+03  11329.894537  \n",
       "2015-10-08 15:00:00    244.92    3.016926     7.389055e+02  11329.900619  \n",
       "2015-10-08 16:00:00    244.25    3.895252     9.514154e+02  11329.904739  \n",
       "2015-10-08 17:00:00    244.99    3.920632     9.605156e+02  11329.910059  \n",
       "...                       ...         ...              ...           ...  \n",
       "2023-02-16 19:00:00  24876.29   50.910118     1.266455e+06  24486.473072  \n",
       "2023-02-16 20:00:00  24562.44  108.987269     2.676993e+06  24395.270174  \n",
       "2023-02-16 21:00:00  24521.00   44.653526     1.094949e+06  24172.654582  \n",
       "2023-02-16 22:00:00  24002.33   41.758527     1.002302e+06  24025.324410  \n",
       "2023-02-16 23:00:00  23520.73   48.328991     1.136733e+06  23807.260000  \n",
       "\n",
       "[64520 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Calculate vwap\n",
    "df['Vwap'] = (df['Volume BTC']*(df['High']+df['Low'])/2).cumsum() / df['Volume BTC'].cumsum()\n",
    "### Sort time\n",
    "df = df.sort_values(\"Date\")\n",
    "### Set index\n",
    "btc = df.set_index(\"Date\")\n",
    "btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unix               0\n",
       "Symbol             0\n",
       "Open               0\n",
       "High               0\n",
       "Low                0\n",
       "Close              0\n",
       "Volume BTC         0\n",
       "Volume Currency    0\n",
       "Vwap               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Vwap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-08 13:00:00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>0.606654</td>\n",
       "      <td>11329.893700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08 14:00:00</th>\n",
       "      <td>245.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>244.50</td>\n",
       "      <td>245.00</td>\n",
       "      <td>4.453649</td>\n",
       "      <td>11329.894537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08 15:00:00</th>\n",
       "      <td>245.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.92</td>\n",
       "      <td>3.016926</td>\n",
       "      <td>11329.900619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08 16:00:00</th>\n",
       "      <td>244.92</td>\n",
       "      <td>244.92</td>\n",
       "      <td>244.25</td>\n",
       "      <td>244.25</td>\n",
       "      <td>3.895252</td>\n",
       "      <td>11329.904739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08 17:00:00</th>\n",
       "      <td>244.25</td>\n",
       "      <td>244.99</td>\n",
       "      <td>244.02</td>\n",
       "      <td>244.99</td>\n",
       "      <td>3.920632</td>\n",
       "      <td>11329.910059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 19:00:00</th>\n",
       "      <td>24979.87</td>\n",
       "      <td>24999.77</td>\n",
       "      <td>24846.43</td>\n",
       "      <td>24876.29</td>\n",
       "      <td>50.910118</td>\n",
       "      <td>24486.473072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 20:00:00</th>\n",
       "      <td>24876.29</td>\n",
       "      <td>24886.09</td>\n",
       "      <td>24454.89</td>\n",
       "      <td>24562.44</td>\n",
       "      <td>108.987269</td>\n",
       "      <td>24395.270174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 21:00:00</th>\n",
       "      <td>24562.44</td>\n",
       "      <td>24575.06</td>\n",
       "      <td>24364.72</td>\n",
       "      <td>24521.00</td>\n",
       "      <td>44.653526</td>\n",
       "      <td>24172.654582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 22:00:00</th>\n",
       "      <td>24521.00</td>\n",
       "      <td>24555.40</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>24002.33</td>\n",
       "      <td>41.758527</td>\n",
       "      <td>24025.324410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 23:00:00</th>\n",
       "      <td>24002.33</td>\n",
       "      <td>24098.56</td>\n",
       "      <td>23515.96</td>\n",
       "      <td>23520.73</td>\n",
       "      <td>48.328991</td>\n",
       "      <td>23807.260000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64520 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Open      High       Low     Close  Volume BTC  \\\n",
       "Date                                                                      \n",
       "2015-10-08 13:00:00      0.00    245.00      0.00    245.00    0.606654   \n",
       "2015-10-08 14:00:00    245.00    245.00    244.50    245.00    4.453649   \n",
       "2015-10-08 15:00:00    245.00    245.00    244.92    244.92    3.016926   \n",
       "2015-10-08 16:00:00    244.92    244.92    244.25    244.25    3.895252   \n",
       "2015-10-08 17:00:00    244.25    244.99    244.02    244.99    3.920632   \n",
       "...                       ...       ...       ...       ...         ...   \n",
       "2023-02-16 19:00:00  24979.87  24999.77  24846.43  24876.29   50.910118   \n",
       "2023-02-16 20:00:00  24876.29  24886.09  24454.89  24562.44  108.987269   \n",
       "2023-02-16 21:00:00  24562.44  24575.06  24364.72  24521.00   44.653526   \n",
       "2023-02-16 22:00:00  24521.00  24555.40  24000.00  24002.33   41.758527   \n",
       "2023-02-16 23:00:00  24002.33  24098.56  23515.96  23520.73   48.328991   \n",
       "\n",
       "                             Vwap  \n",
       "Date                               \n",
       "2015-10-08 13:00:00  11329.893700  \n",
       "2015-10-08 14:00:00  11329.894537  \n",
       "2015-10-08 15:00:00  11329.900619  \n",
       "2015-10-08 16:00:00  11329.904739  \n",
       "2015-10-08 17:00:00  11329.910059  \n",
       "...                           ...  \n",
       "2023-02-16 19:00:00  24486.473072  \n",
       "2023-02-16 20:00:00  24395.270174  \n",
       "2023-02-16 21:00:00  24172.654582  \n",
       "2023-02-16 22:00:00  24025.324410  \n",
       "2023-02-16 23:00:00  23807.260000  \n",
       "\n",
       "[64520 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc.dropna(axis=0,inplace=True)\n",
    "btc.drop(columns=['Unix', 'Symbol', 'Volume Currency'],axis=1,inplace=True)\n",
    "btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Open', 'High', 'Low', 'Close', 'Volume BTC', 'Vwap']\n"
     ]
    }
   ],
   "source": [
    "print(list(btc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# btc[['Open', 'High', 'Low', 'Close', 'Volume BTC', 'Vwap']]=scaler.fit_transform(btc[['Open', 'High', 'Low', 'Close', 'Volume BTC', 'Vwap']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.04698234e-05],\n",
       "       [2.04698234e-05],\n",
       "       [1.93001192e-05],\n",
       "       ...,\n",
       "       [3.54967207e-01],\n",
       "       [3.47383576e-01],\n",
       "       [3.40341957e-01]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scalerY = MinMaxScaler(feature_range=(0,1))\n",
    "y = btc[['Close']]\n",
    "y = y.values\n",
    "y = scalerY.fit_transform(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 2.03617409e-05, 0.00000000e+00, 7.11471904e-05,\n",
       "        0.00000000e+00],\n",
       "       [3.56950541e-03, 2.03617409e-05, 3.57049292e-03, 5.22314878e-04,\n",
       "        3.12002439e-08],\n",
       "       [3.56950541e-03, 2.03617409e-05, 3.57662628e-03, 3.53818918e-04,\n",
       "        2.57753131e-07],\n",
       "       ...,\n",
       "       [3.57860255e-01, 3.53879203e-01, 3.55803928e-01, 5.23687457e-03,\n",
       "        4.78383214e-01],\n",
       "       [3.57256499e-01, 3.53593265e-01, 3.50477833e-01, 4.89735500e-03,\n",
       "        4.72895276e-01],\n",
       "       [3.49699783e-01, 3.46948939e-01, 3.43409279e-01, 5.66792561e-03,\n",
       "        4.64772540e-01]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scalerX = MinMaxScaler(feature_range=(0,1))\n",
    "X = btc.drop(columns=['Close'],axis=1)\n",
    "X = scalerX.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-22 09:41:41.286631: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2023-02-22 09:41:41.286671: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-02-22 09:41:41.286693: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (BakanPC): /proc/driver/nvidia/version does not exist\n",
      "2023-02-22 09:41:41.287170: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 5, 50)             10400     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 5, 50)             20200     \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 50)                20200     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,shuffle=False)\n",
    "X_train=np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "\n",
    "X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))\n",
    "X_test = np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))\n",
    "\n",
    "y_train=np.array(y_train)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(50,return_sequences=True,input_shape=(X_train.shape[1],1)))\n",
    "model.add(LSTM(50,return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "404/404 [==============================] - 9s 8ms/step - loss: 0.0017\n",
      "Epoch 2/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 3.2859e-05\n",
      "Epoch 3/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 2.2252e-05\n",
      "Epoch 4/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 1.5813e-05\n",
      "Epoch 5/15\n",
      "404/404 [==============================] - 4s 10ms/step - loss: 1.4529e-05\n",
      "Epoch 6/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 1.2087e-05\n",
      "Epoch 7/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 1.1294e-05\n",
      "Epoch 8/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 1.1291e-05\n",
      "Epoch 9/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 9.7837e-06\n",
      "Epoch 10/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 1.1141e-05\n",
      "Epoch 11/15\n",
      "404/404 [==============================] - 4s 10ms/step - loss: 1.3249e-05\n",
      "Epoch 12/15\n",
      "404/404 [==============================] - 4s 9ms/step - loss: 9.9986e-06\n",
      "Epoch 13/15\n",
      "404/404 [==============================] - 3s 8ms/step - loss: 1.1451e-05\n",
      "Epoch 14/15\n",
      "404/404 [==============================] - 4s 9ms/step - loss: 1.0610e-05\n",
      "Epoch 15/15\n",
      "404/404 [==============================] - 4s 9ms/step - loss: 1.0081e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7ea7b1f070>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,verbose=1,epochs=15,batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses=pd.DataFrame(model.history.history)\n",
    "losses.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1613/1613 [==============================] - 4s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.00429956],\n",
       "       [-0.00183692],\n",
       "       [-0.00184355],\n",
       "       ...,\n",
       "       [ 0.72004616],\n",
       "       [ 0.7192109 ],\n",
       "       [ 0.7202242 ]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainPredict = model.predict(X_train)\n",
    "trainPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "404/404 [==============================] - 2s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.7202054 ],\n",
       "       [0.7200892 ],\n",
       "       [0.7194981 ],\n",
       "       ...,\n",
       "       [0.35785502],\n",
       "       [0.35664007],\n",
       "       [0.34929603]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict = model.predict(X_test)\n",
    "testPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "testPredict = scalerY.inverse_transform(testPredict)\n",
    "testY = scalerY.inverse_transform(y_test)\n",
    "trainPredict = scalerY.inverse_transform(trainPredict)\n",
    "trainY = scalerY.inverse_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -50.4612 ],\n",
       "       [  117.96654],\n",
       "       [  117.51353],\n",
       "       ...,\n",
       "       [49489.977  ],\n",
       "       [49432.85   ],\n",
       "       [49502.152  ]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[49500.867],\n",
       "       [49492.918],\n",
       "       [49452.492],\n",
       "       ...,\n",
       "       [24718.506],\n",
       "       [24635.412],\n",
       "       [24133.129]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE train: 45860.14691176113, MAE train: 121.1599351838801, RMSE train: 214.1498235155965\n"
     ]
    }
   ],
   "source": [
    "mse_train = mean_squared_error(trainPredict, trainY)\n",
    "mae_train = mean_absolute_error(trainPredict, trainY)\n",
    "rmse_train = math.sqrt(mse_train)\n",
    "print('MSE train: {0}, MAE train: {1}, RMSE train: {2}'.format(mse_train, mae_train, rmse_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE test: 255069.09901999068, MAE test: 392.7467627255841, RMSE test: 505.043660508664\n"
     ]
    }
   ],
   "source": [
    "mse_test = mean_squared_error(testPredict, testY)\n",
    "mae_test = mean_absolute_error(testPredict, testY)\n",
    "rmse_test = math.sqrt(mse_test)\n",
    "print('MSE test: {0}, MAE test: {1}, RMSE test: {2}'.format(mse_test, mae_test, rmse_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 87701.93733340706, MAE: 175.4773006922209, RMSE: 296.14512883619585\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(np.concatenate((trainPredict, testPredict)), np.concatenate((trainY, testY)))\n",
    "mae = mean_absolute_error(np.concatenate((trainPredict, testPredict)), np.concatenate((trainY, testY)))\n",
    "rmse = math.sqrt(mse)\n",
    "print('MSE: {0}, MAE: {1}, RMSE: {2}'.format(mse, mae, rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_array = np.array(df['Date'])\n",
    "plt.plot(date_array, np.concatenate((trainY, testY)), label='Actuation')\n",
    "plt.plot(date_array, np.concatenate((trainPredict, testPredict)), label='Predict')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"BTC Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_array = np.array(df['Date'])\n",
    "plt.plot(date_array[30000:31000], trainY[30000:31000], label='Actuation')\n",
    "plt.plot(date_array[30000:31000], trainPredict[30000:31000], label='Predict')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"BTC Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_array = np.array(df['Date'])\n",
    "plt.plot(date_array[30000:31000], trainY[30000:31000], label='Actuation')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"BTC Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_array = np.array(df['Date'])\n",
    "plt.plot(date_array[30000:31000], trainPredict[30000:31000], label='Predict')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"BTC Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.models import save_model\n",
    "# save_model(model, \"btc-predict-model.h5\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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