Time Series Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crypto Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers import Bidirectional\n",
    "from keras.models import Sequential\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(X):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1574274"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('bitcoin-historical-data/coinbaseUSD_1-min_data_2014-12-01_to_2018-01-08.csv')\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Timestamp</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>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1414735920</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>0.0951</td>\n",
       "      <td>3507.288</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1414735980</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>0.0951</td>\n",
       "      <td>3507.288</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1414736040</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>0.0951</td>\n",
       "      <td>3507.288</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1414736100</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>0.0951</td>\n",
       "      <td>3507.288</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1414736160</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>36880</td>\n",
       "      <td>0.0951</td>\n",
       "      <td>3507.288</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Timestamp   Open   High    Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1414735920  36880  36880  36880  36880        0.0951           3507.288   \n",
       "1  1414735980  36880  36880  36880  36880        0.0951           3507.288   \n",
       "2  1414736040  36880  36880  36880  36880        0.0951           3507.288   \n",
       "3  1414736100  36880  36880  36880  36880        0.0951           3507.288   \n",
       "4  1414736160  36880  36880  36880  36880        0.0951           3507.288   \n",
       "\n",
       "   Weighted_Price  \n",
       "0         36880.0  \n",
       "1         36880.0  \n",
       "2         36880.0  \n",
       "3         36880.0  \n",
       "4         36880.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coincheck_df = pd.read_csv('bitcoin-historical-data/coincheckJPY_1-min_data_2014-10-31_to_2018-01-08.csv')\n",
    "coincheck_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</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>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1325317920</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1325317980</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1325318040</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1325318100</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1325318160</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Timestamp  Open  High   Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1325317920  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "1  1325317980  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "2  1325318040  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "3  1325318100  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "4  1325318160  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "\n",
       "   Weighted_Price  \n",
       "0            4.39  \n",
       "1            4.39  \n",
       "2            4.39  \n",
       "3            4.39  \n",
       "4            4.39  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp_df = pd.read_csv('bitcoin-historical-data/bitstampUSD_1-min_data_2012-01-01_to_2018-01-08.csv')\n",
    "bitstamp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>btc_market_price</th>\n",
       "      <th>btc_total_bitcoins</th>\n",
       "      <th>btc_market_cap</th>\n",
       "      <th>btc_trade_volume</th>\n",
       "      <th>btc_blocks_size</th>\n",
       "      <th>btc_avg_block_size</th>\n",
       "      <th>btc_n_orphaned_blocks</th>\n",
       "      <th>btc_n_transactions_per_block</th>\n",
       "      <th>btc_median_confirmation_time</th>\n",
       "      <th>...</th>\n",
       "      <th>btc_cost_per_transaction_percent</th>\n",
       "      <th>btc_cost_per_transaction</th>\n",
       "      <th>btc_n_unique_addresses</th>\n",
       "      <th>btc_n_transactions</th>\n",
       "      <th>btc_n_transactions_total</th>\n",
       "      <th>btc_n_transactions_excluding_popular</th>\n",
       "      <th>btc_n_transactions_excluding_chains_longer_than_100</th>\n",
       "      <th>btc_output_volume</th>\n",
       "      <th>btc_estimated_transaction_volume</th>\n",
       "      <th>btc_estimated_transaction_volume_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-11-10 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1339450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>26958.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-11-11 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1342900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>27036.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>93450.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-11-12 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1346400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-11-13 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1349900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>673.076923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>27179.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-11-14 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1354050.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>27262.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  btc_market_price  btc_total_bitcoins  btc_market_cap  \\\n",
       "0  2009-11-10 00:00:00               0.0           1339450.0             0.0   \n",
       "1  2009-11-11 00:00:00               0.0           1342900.0             0.0   \n",
       "2  2009-11-12 00:00:00               0.0           1346400.0             0.0   \n",
       "3  2009-11-13 00:00:00               0.0           1349900.0             0.0   \n",
       "4  2009-11-14 00:00:00               0.0           1354050.0             0.0   \n",
       "\n",
       "   btc_trade_volume  btc_blocks_size  btc_avg_block_size  \\\n",
       "0               0.0              0.0            0.000215   \n",
       "1               0.0              0.0            0.000323   \n",
       "2               0.0              0.0            0.000215   \n",
       "3               0.0              0.0            0.000242   \n",
       "4               0.0              0.0            0.000216   \n",
       "\n",
       "   btc_n_orphaned_blocks  btc_n_transactions_per_block  \\\n",
       "0                    0.0                           1.0   \n",
       "1                    0.0                           1.0   \n",
       "2                    0.0                           1.0   \n",
       "3                    0.0                           1.0   \n",
       "4                    0.0                           1.0   \n",
       "\n",
       "   btc_median_confirmation_time                  ...                   \\\n",
       "0                           0.0                  ...                    \n",
       "1                           0.0                  ...                    \n",
       "2                           0.0                  ...                    \n",
       "3                           0.0                  ...                    \n",
       "4                           0.0                  ...                    \n",
       "\n",
       "   btc_cost_per_transaction_percent  btc_cost_per_transaction  \\\n",
       "0                          0.000000                       0.0   \n",
       "1                         19.166667                       0.0   \n",
       "2                          0.000000                       0.0   \n",
       "3                        673.076923                       0.0   \n",
       "4                          0.000000                       0.0   \n",
       "\n",
       "   btc_n_unique_addresses  btc_n_transactions  btc_n_transactions_total  \\\n",
       "0                    71.0                71.0                   26958.0   \n",
       "1                    71.0                78.0                   27036.0   \n",
       "2                    70.0                70.0                   27106.0   \n",
       "3                    73.0                73.0                   27179.0   \n",
       "4                    83.0                83.0                   27262.0   \n",
       "\n",
       "   btc_n_transactions_excluding_popular  \\\n",
       "0                                  71.0   \n",
       "1                                  78.0   \n",
       "2                                  70.0   \n",
       "3                                  73.0   \n",
       "4                                  83.0   \n",
       "\n",
       "   btc_n_transactions_excluding_chains_longer_than_100  btc_output_volume  \\\n",
       "0                                               71.0               3550.0   \n",
       "1                                               78.0              93450.0   \n",
       "2                                               70.0               3500.0   \n",
       "3                                               73.0               4100.0   \n",
       "4                                               83.0               4150.0   \n",
       "\n",
       "   btc_estimated_transaction_volume  btc_estimated_transaction_volume_usd  \n",
       "0                               0.0                                   0.0  \n",
       "1                           18000.0                                   0.0  \n",
       "2                               0.0                                   0.0  \n",
       "3                             520.0                                   0.0  \n",
       "4                               0.0                                   0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bit_df = pd.read_csv('bitcoin-historical-data/bitcoin_dataset.csv')\n",
    "bit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d534b94cf8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d534b55828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bit_df['btc_blocks_size'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}