Crypto-Analyzer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime,timedelta\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycoingecko import CoinGeckoAPI\n",
    "cg = CoinGeckoAPI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = cg.get_coin_market_chart_range_by_id(id = 'bitcoin', vs_currency='usd',from_timestamp= (datetime.now() - timedelta(days=720)).timestamp(),to_timestamp=datetime.now().timestamp())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2019, 12, 12, 5, 30)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.fromtimestamp(float(df['prices'][0][0])/1000.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prices</th>\n",
       "      <th>market_caps</th>\n",
       "      <th>total_volumes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1576108800000, 7197.426825880834]</td>\n",
       "      <td>[1576108800000, 130242932604.26935]</td>\n",
       "      <td>[1576108800000, 18016936696.22855]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1576195200000, 7200.7070180298015]</td>\n",
       "      <td>[1576195200000, 130489545691.80917]</td>\n",
       "      <td>[1576195200000, 19829801017.961727]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1576281600000, 7247.350277010915]</td>\n",
       "      <td>[1576281600000, 131188243085.0565]</td>\n",
       "      <td>[1576281600000, 18535665729.09757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1576368000000, 7081.740255265727]</td>\n",
       "      <td>[1576368000000, 128114177662.37697]</td>\n",
       "      <td>[1576368000000, 19497758114.150417]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1576454400000, 7108.563021778046]</td>\n",
       "      <td>[1576454400000, 128886696696.6067]</td>\n",
       "      <td>[1576454400000, 18896096612.075874]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                prices                          market_caps  \\\n",
       "0   [1576108800000, 7197.426825880834]  [1576108800000, 130242932604.26935]   \n",
       "1  [1576195200000, 7200.7070180298015]  [1576195200000, 130489545691.80917]   \n",
       "2   [1576281600000, 7247.350277010915]   [1576281600000, 131188243085.0565]   \n",
       "3   [1576368000000, 7081.740255265727]  [1576368000000, 128114177662.37697]   \n",
       "4   [1576454400000, 7108.563021778046]   [1576454400000, 128886696696.6067]   \n",
       "\n",
       "                         total_volumes  \n",
       "0   [1576108800000, 18016936696.22855]  \n",
       "1  [1576195200000, 19829801017.961727]  \n",
       "2   [1576281600000, 18535665729.09757]  \n",
       "3  [1576368000000, 19497758114.150417]  \n",
       "4  [1576454400000, 18896096612.075874]  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timestamp'] = df['prices'].apply(lambda x : datetime.fromtimestamp(float(x[0])/1000.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['prices'] = df['prices'].apply(lambda x : x[1])\n",
    "df['market_caps'] = df['market_caps'].apply(lambda x : x[1])\n",
    "df['total_volumes'] = df['total_volumes'].apply(lambda x : x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prices</th>\n",
       "      <th>market_caps</th>\n",
       "      <th>total_volumes</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7197.426826</td>\n",
       "      <td>1.302429e+11</td>\n",
       "      <td>1.801694e+10</td>\n",
       "      <td>2019-12-12 05:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7200.707018</td>\n",
       "      <td>1.304895e+11</td>\n",
       "      <td>1.982980e+10</td>\n",
       "      <td>2019-12-13 05:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7247.350277</td>\n",
       "      <td>1.311882e+11</td>\n",
       "      <td>1.853567e+10</td>\n",
       "      <td>2019-12-14 05:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7081.740255</td>\n",
       "      <td>1.281142e+11</td>\n",
       "      <td>1.949776e+10</td>\n",
       "      <td>2019-12-15 05:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7108.563022</td>\n",
       "      <td>1.288867e+11</td>\n",
       "      <td>1.889610e+10</td>\n",
       "      <td>2019-12-16 05:30:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prices   market_caps  total_volumes           timestamp\n",
       "0  7197.426826  1.302429e+11   1.801694e+10 2019-12-12 05:30:00\n",
       "1  7200.707018  1.304895e+11   1.982980e+10 2019-12-13 05:30:00\n",
       "2  7247.350277  1.311882e+11   1.853567e+10 2019-12-14 05:30:00\n",
       "3  7081.740255  1.281142e+11   1.949776e+10 2019-12-15 05:30:00\n",
       "4  7108.563022  1.288867e+11   1.889610e+10 2019-12-16 05:30:00"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prices</th>\n",
       "      <th>market_caps</th>\n",
       "      <th>total_volumes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prices</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999965</td>\n",
       "      <td>0.432738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market_caps</th>\n",
       "      <td>0.999965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_volumes</th>\n",
       "      <td>0.432738</td>\n",
       "      <td>0.428982</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 prices  market_caps  total_volumes\n",
       "prices         1.000000     0.999965       0.432738\n",
       "market_caps    0.999965     1.000000       0.428982\n",
       "total_volumes  0.432738     0.428982       1.000000"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27396ad9808>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['market_caps'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timestamp'] = df['timestamp'].apply(lambda x : x.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'datetime.date' object has no attribute 'date'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-136-0682e90c4210>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'timestamp'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\vedant\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[0;32m   4355\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4356\u001b[0m         \"\"\"\n\u001b[1;32m-> 4357\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mSeriesApply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4359\u001b[0m     def _reduce(\n",
      "\u001b[1;32mc:\\users\\vedant\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1041\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1043\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1044\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1045\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\vedant\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1099\u001b[0m                     \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                     \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# type: ignore[arg-type]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1101\u001b[1;33m                     \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1102\u001b[0m                 )\n\u001b[0;32m   1103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\vedant\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m<ipython-input-136-0682e90c4210>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'timestamp'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'datetime.date' object has no attribute 'date'"
     ]
    }
   ],
   "source": [
    "df['timestamp'].apply(lambda x : x.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2019-12-12\n",
       "1      2019-12-13\n",
       "2      2019-12-14\n",
       "3      2019-12-15\n",
       "4      2019-12-16\n",
       "          ...    \n",
       "715    2021-11-26\n",
       "716    2021-11-27\n",
       "717    2021-11-28\n",
       "718    2021-11-29\n",
       "719    2021-11-30\n",
       "Name: timestamp, Length: 720, dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timestamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27396ee2408>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df[['prices','timestamp']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27396fe1a08>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df[['total_volumes','timestamp']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['next_day_price'] = df['prices'].shift(periods=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>prices</th>\n",
       "      <th>total_volumes</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>next_day_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7197.426826</td>\n",
       "      <td>1.801694e+10</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>7200.707018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7200.707018</td>\n",
       "      <td>1.982980e+10</td>\n",
       "      <td>2019-12-13</td>\n",
       "      <td>7247.350277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7247.350277</td>\n",
       "      <td>1.853567e+10</td>\n",
       "      <td>2019-12-14</td>\n",
       "      <td>7081.740255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7081.740255</td>\n",
       "      <td>1.949776e+10</td>\n",
       "      <td>2019-12-15</td>\n",
       "      <td>7108.563022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7108.563022</td>\n",
       "      <td>1.889610e+10</td>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>6901.036915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prices  total_volumes   timestamp  next_day_price\n",
       "0  7197.426826   1.801694e+10  2019-12-12     7200.707018\n",
       "1  7200.707018   1.982980e+10  2019-12-13     7247.350277\n",
       "2  7247.350277   1.853567e+10  2019-12-14     7081.740255\n",
       "3  7081.740255   1.949776e+10  2019-12-15     7108.563022\n",
       "4  7108.563022   1.889610e+10  2019-12-16     6901.036915"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prices            0\n",
       "total_volumes     0\n",
       "timestamp         0\n",
       "next_day_price    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['timestamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range= (-1,1))\n",
    "scaler_2 = MinMaxScaler(feature_range= (-1,1))\n",
    "scaler_3 = MinMaxScaler(feature_range= (-1,1))\n",
    "\n",
    "df_scaled = df.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled['prices'] = scaler.fit_transform(df['prices'].values.reshape(-1,1))\n",
    "df_scaled['prices'] = np.reshape(df_scaled['prices'],len(df_scaled['prices']))\n",
    "df_scaled['total_volumes'] = scaler_2.fit_transform(df['total_volumes'].values.reshape(-1,1))\n",
    "\n",
    "df_scaled['total_volumes'] = np.reshape(df_scaled['total_volumes'],len(df_scaled['total_volumes']))\n",
    "df_scaled['next_day_price'] = scaler_3.fit_transform(df['next_day_price'].values.reshape(-1,1))\n",
    "\n",
    "df_scaled['next_day_price'] = np.reshape(df_scaled['next_day_price'],len(df_scaled['next_day_price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('timestamp',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(df_scaled[['prices']], df_scaled['next_day_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(539, 1)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense,LSTM\n",
    "from tensorflow.keras import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 539 samples\n",
      "Epoch 1/25\n",
      "539/539 [==============================] - 3s 6ms/sample - loss: 0.4492\n",
      "Epoch 2/25\n",
      "539/539 [==============================] - 0s 185us/sample - loss: 0.4133\n",
      "Epoch 3/25\n",
      "539/539 [==============================] - 0s 165us/sample - loss: 0.3677\n",
      "Epoch 4/25\n",
      "539/539 [==============================] - 0s 186us/sample - loss: 0.3057\n",
      "Epoch 5/25\n",
      "539/539 [==============================] - 0s 167us/sample - loss: 0.2232\n",
      "Epoch 6/25\n",
      "539/539 [==============================] - 0s 167us/sample - loss: 0.1360\n",
      "Epoch 7/25\n",
      "539/539 [==============================] - 0s 174us/sample - loss: 0.0769\n",
      "Epoch 8/25\n",
      "539/539 [==============================] - 0s 151us/sample - loss: 0.0498\n",
      "Epoch 9/25\n",
      "539/539 [==============================] - 0s 161us/sample - loss: 0.0294\n",
      "Epoch 10/25\n",
      "539/539 [==============================] - 0s 188us/sample - loss: 0.0179\n",
      "Epoch 11/25\n",
      "539/539 [==============================] - 0s 177us/sample - loss: 0.0119\n",
      "Epoch 12/25\n",
      "539/539 [==============================] - 0s 176us/sample - loss: 0.0077\n",
      "Epoch 13/25\n",
      "539/539 [==============================] - 0s 175us/sample - loss: 0.0047\n",
      "Epoch 14/25\n",
      "539/539 [==============================] - 0s 172us/sample - loss: 0.0037\n",
      "Epoch 15/25\n",
      "539/539 [==============================] - 0s 172us/sample - loss: 0.0033\n",
      "Epoch 16/25\n",
      "539/539 [==============================] - 0s 166us/sample - loss: 0.0031\n",
      "Epoch 17/25\n",
      "539/539 [==============================] - 0s 176us/sample - loss: 0.0030\n",
      "Epoch 18/25\n",
      "539/539 [==============================] - 0s 174us/sample - loss: 0.0028\n",
      "Epoch 19/25\n",
      "539/539 [==============================] - 0s 165us/sample - loss: 0.0027\n",
      "Epoch 20/25\n",
      "539/539 [==============================] - 0s 165us/sample - loss: 0.0026\n",
      "Epoch 21/25\n",
      "539/539 [==============================] - 0s 186us/sample - loss: 0.0025\n",
      "Epoch 22/25\n",
      "539/539 [==============================] - 0s 183us/sample - loss: 0.0025\n",
      "Epoch 23/25\n",
      "539/539 [==============================] - 0s 187us/sample - loss: 0.0024\n",
      "Epoch 24/25\n",
      "539/539 [==============================] - 0s 165us/sample - loss: 0.0024\n",
      "Epoch 25/25\n",
      "539/539 [==============================] - 0s 188us/sample - loss: 0.0023\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "model.add(LSTM(units=200, activation='relu', return_sequences=True, input_shape=(1,1)))\n",
    "model.add(LSTM(units=50, activation='relu'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(X_train.values.reshape(X_train.shape[0],X_train.shape[1],1),Y_train,epochs=25,batch_size=64,verbose=1)\n",
    "\n",
    "y_pred=model.predict(X_test.values.reshape(X_test.shape[0],X_test.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = scaler_3.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 1)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 33021.908695108345\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE\",np.sqrt(mean_squared_error(Y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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