My CoinMarket Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position    Name              Market Cap       Price    Available Supply    Volume 24h     Change 7h\n",
      "----------  ----------------  ---------------  -------  ------------------  -------------  -----------\n",
      "1           Bitcoin           109208279124.00  6547.73  16678800.00         5292353370.29  3.74\n",
      "2           Ethereum          32289774280.20   337.35   4944974.11          1010982607.46  6.65\n",
      "3           Bitcoin Cash      21048510392.10   1252.73  3223434.88          3703533590.33  2.72\n",
      "4           Ripple            8009186014.26    0.21     1226558.08          145764910.49   3.04\n",
      "5           Litecoin          3320425581.46    61.69    508503.00           207069342.16   2.50\n",
      "6           Dash              3192189223.35    415.24   488864.51           186013806.80   -6.44\n",
      "7           Monero            1885297689.99    122.85   288721.22           74159589.98    -4.52\n",
      "8           NEO               1848730000.00    28.44    283121.15           36236840.23    0.13\n",
      "9           Ethereum Classic  1715014011.71    17.60    262643.73           495239522.87   13.20\n",
      "10          NEM               1699919999.81    0.19     260333.10           6476333.55     -1.74\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "import sys\n",
    "import json\n",
    "from tabulate import tabulate\n",
    "try: # For Python 3.0 and later\n",
    "    from urllib.request import urlopen\n",
    "except ImportError: # Fall back to Python 2's urllib2\n",
    "    from urllib2 import urlopen\n",
    "\n",
    "\n",
    "def get_position(json):\n",
    "    try:\n",
    "        return int(json['position'])\n",
    "    except KeyError:\n",
    "        return 1000\n",
    "\n",
    "count = 10\n",
    "currency = \"usd\"\n",
    "url = \"http://coinmarketcap.northpole.ro/api/v5/all.json\"\n",
    "headers = [\"Position\",\"Name\",\"Market Cap\",\"Price\",\"Available Supply\",\"Volume 24h\",\"Change 7h\"]\n",
    "\n",
    "if len(sys.argv) == 2:\n",
    "        count = int(sys.argv[1])\n",
    "\n",
    "\n",
    "data = urlopen(url).read()\n",
    "data = json.loads(data)['markets']\n",
    "data.sort(key=get_position)\n",
    "btc_price = float(data[0]['price'][currency])\n",
    "\n",
    "table = []\n",
    "for d in data[0:count]:\n",
    "        volume = float(d['volume24']['btc'])*btc_price\n",
    "        table.append([d['position'],d['name'],d['marketCap'][currency],d['price'][currency],d['marketCap']['btc'],volume,d['change7h'][currency]])\n",
    "\n",
    "print(tabulate(table,headers,numalign=\"left\",stralign=\"left\",floatfmt=\".2f\"))\n",
    "#print(data[:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p+VFpv9Ml3Vuqa7CkWyU9I+ncBp8PMzNronpCqX8Km6eBS4EflFdGxFvA6cCENKKaAPxf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\nLEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e34c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "names = tuple(map(lambda x: x['name'], data[:5]))\n",
    "MarketCap = tuple(map(lambda x: x['marketCap']['usd'], data[:5]))\n",
    "availableSupply = tuple(map(lambda x: x['availableSupply'], data[:5]))\n",
    "\n",
    "y_pos = np.arange(len(names))\n",
    "error = np.random.rand(len(names))\n",
    "\n",
    "ax.barh(y_pos, MarketCap, xerr=error, align='center',\n",
    "        color='lightblue', ecolor='black')\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(names)\n",
    "ax.invert_yaxis()  # labels read top-to-bottom\n",
    "ax.set_xlabel('MarketCap (10^11)')\n",
    "ax.set_title('How big is the market cap?')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "f = open(\"filename.txt\", \"w\")\n",
    "f.write(\"Writing into a file\")\n",
    "f.close() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'period': '+1DAY',\n",
      " 'published_at_end': datetime.datetime(2017, 11, 21, 0, 0, tzinfo=tzutc()),\n",
      " 'published_at_start': datetime.datetime(2017, 11, 14, 0, 0, tzinfo=tzutc()),\n",
      " 'time_series': [{'count': 167,\n",
      "                  'published_at': datetime.datetime(2017, 11, 14, 0, 0, tzinfo=tzutc())},\n",
      "                 {'count': 209,\n",
      "                  'published_at': datetime.datetime(2017, 11, 15, 0, 0, tzinfo=tzutc())},\n",
      "                 {'count': 220,\n",
      "                  'published_at': datetime.datetime(2017, 11, 16, 0, 0, tzinfo=tzutc())},\n",
      "                 {'count': 213,\n",
      "                  'published_at': datetime.datetime(2017, 11, 17, 0, 0, tzinfo=tzutc())},\n",
      "                 {'count': 82,\n",
      "                  'published_at': datetime.datetime(2017, 11, 18, 0, 0, tzinfo=tzutc())},\n",
      "                 {'count': 63,\n",
      "                  'published_at': datetime.datetime(2017, 11, 19, 0, 0, tzinfo=tzutc())},\n",
      "                 {'count': 234,\n",
      "                  'published_at': datetime.datetime(2017, 11, 20, 0, 0, tzinfo=tzutc())}]}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\ntype(api_response)\\nf = open(\"filename.txt\", \"w\")\\nf.write(api_response)\\nf.close() \\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import aylien_news_api\n",
    "from aylien_news_api.rest import ApiException\n",
    "from pprint import pprint\n",
    "\n",
    "# Configure API key authorization: app_id\n",
    "aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = '07769874'\n",
    "# Configure API key authorization: app_key\n",
    "aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = 'c95f777654e0656e80b409a2e796e05b'\n",
    "\n",
    "# create an instance of the API class\n",
    "api_instance = aylien_news_api.DefaultApi()\n",
    "\n",
    "\n",
    "opts = { \n",
    "    'text': 'bitcoin',\n",
    "    'not_sentiment_title_polarity': 'negative',\n",
    "    'not_sentiment_body_polarity': 'negative',\n",
    "    'published_at_start': 'NOW-7DAYS/DAY',\n",
    "    'published_at_end': 'NOW/DAY',\n",
    "    'period': '+1DAY'\n",
    "}\n",
    "\n",
    "def callback(res):\n",
    "    print('res type is ', type(res))\n",
    "    print('--pprint--')\n",
    "    pprint(res)\n",
    "    print('--to dict--')\n",
    "    pprint(res.to_dict())\n",
    "    print('res type is ', type(res))\n",
    "    print('--time_series--')\n",
    "    pprint(res.time_series)\n",
    "    print('res type is ', type(res))\n",
    "    \n",
    "try: \n",
    "    # List time series\n",
    "    '''\n",
    "    If the method is called asynchronously, \n",
    "    returns the request thread,\n",
    "    and the callback res is TimeSeriesList obj.\n",
    "        >>> def callback_function(response):\n",
    "        >>>     pprint(response)\n",
    "        >>>\n",
    "        >>> thread = api.list_time_series(callback=callback_function)\n",
    "    Else it return a thread with a list\n",
    "    '''\n",
    "    api_response = api_instance.list_time_series(**opts)#, callback=callback)\n",
    "    \n",
    "    pprint(api_response)\n",
    "except ApiException as e:\n",
    "    pprint(\"Exception when calling DefaultApi->list_time_series: %s\\n\" % e)\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# save file\n",
    "'''\n",
    "type(api_response)\n",
    "f = open(\"filename.txt\", \"w\")\n",
    "f.write(api_response)\n",
    "f.close() \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(type(api_response.time_series))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--NumberOfGoodNewsByDate--\n",
      "[{'count': 7, 'date': '2016-03-31'},\n",
      " {'count': 5, 'date': '2016-04-01'},\n",
      " {'count': 3, 'date': '2016-04-02'}]\n",
      "--NumberOfGoodNewsByDate--\n",
      "[{'count': 8, 'date': '2016-03-31'},\n",
      " {'count': 7, 'date': '2016-04-01'},\n",
      " {'count': 1, 'date': '2016-04-02'}]\n"
     ]
    }
   ],
   "source": [
    "import aylien_news_api\n",
    "from aylien_news_api.rest import ApiException\n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "\n",
    "# Configure API key authorization: app_id\n",
    "aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = '07769874'\n",
    "# Configure API key authorization: app_key\n",
    "aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = 'c95f777654e0656e80b409a2e796e05b'\n",
    "# create an instance of the API class\n",
    "api_instance = aylien_news_api.DefaultApi()\n",
    "# dataset\n",
    "NumberOfGoodNewsByDate = []\n",
    "\n",
    "optsGood = { \n",
    "    'text': 'bitcoin',\n",
    "    #'sentiment_title_polarity': 'positive',\n",
    "    'sentiment_body_polarity': 'positive',\n",
    "    'published_at_start': 'NOW-600DAYS/DAY',\n",
    "    'published_at_end': 'NOW/DAY',\n",
    "    'period': '+1DAY'\n",
    "}\n",
    "\n",
    "optsBad = dict(optsGood)\n",
    "optsBad.update({\n",
    "    #'sentiment_title_polarity': 'negative',\n",
    "    'sentiment_body_polarity': 'negative'\n",
    "})\n",
    "\n",
    "try: \n",
    "    # GET TimeSeriesList\n",
    "    resGoodNews = api_instance.list_time_series(**optsGood)\n",
    "    resBadNews = api_instance.list_time_series(**optsBad)\n",
    "    \n",
    "except ApiException as e:\n",
    "    pprint(\"Exception when calling DefaultApi->list_time_series: %s\\n\" % e)\n",
    "\n",
    "    \n",
    "NumberOfGoodNewsByDate = list(map(lambda s: {'count': s.count, 'date': s.published_at.strftime(\"%Y-%m-%d\")}, resGoodNews.time_series))\n",
    "print('--NumberOfGoodNewsByDate--')\n",
    "pprint(NumberOfGoodNewsByDate[:3])\n",
    "\n",
    "NumberOfBadNewsByDate = list(map(lambda s: {'count': s.count, 'date': s.published_at.strftime(\"%Y-%m-%d\")}, resBadNews.time_series))\n",
    "print('--NumberOfGoodNewsByDate--')\n",
    "pprint(NumberOfBadNewsByDate[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--News data frame--\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 600 entries, 2016-03-31 to 2017-11-20\n",
      "Data columns (total 3 columns):\n",
      "date        600 non-null object\n",
      "goodnews    600 non-null int64\n",
      "badnews     600 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 18.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>goodnews</th>\n",
       "      <th>badnews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-16</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>118</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17</th>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>105</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-18</th>\n",
       "      <td>2017-11-18</td>\n",
       "      <td>54</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-19</th>\n",
       "      <td>2017-11-19</td>\n",
       "      <td>43</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-20</th>\n",
       "      <td>2017-11-20</td>\n",
       "      <td>142</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  goodnews  badnews\n",
       "2017-11-16  2017-11-16       118      102\n",
       "2017-11-17  2017-11-17       105      119\n",
       "2017-11-18  2017-11-18        54       34\n",
       "2017-11-19  2017-11-19        43       36\n",
       "2017-11-20  2017-11-20       142      108"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To pandas\n",
    "newses = []\n",
    "for i in range(len(resGoodNews.time_series)):\n",
    "    row = [NumberOfBadNewsByDate[i]['date'], NumberOfGoodNewsByDate[i]['count'], NumberOfBadNewsByDate[i]['count']]\n",
    "    newses.append(row)\n",
    "\n",
    "newses = np.array(newses)\n",
    "df = pd.DataFrame(newses, index=newses[:, 0], columns=['date','goodnews','badnews'])\n",
    "df[['goodnews', 'badnews']] = df[['goodnews', 'badnews']].astype(int)\n",
    "print('--News data frame--')\n",
    "\n",
    "df.info()\n",
    "df.head()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['f', 'table']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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TVAIAAABgYpJKAAAAAExMUgkAAACAiUkqAQAAADAxSSUAAAAAJiapBAAAAMDEJJUAAAAA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\n2sQ2D083RtPLqup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Nuvy8y90O+6DcxSx3O+yDchez3O2wD8pdzHK3wz4odzHL3Q77oNzF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\n//rQqvq3qvpwVZ1XVYf10w+vqjOr6vy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x102328080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Yd60kD0/ylUkklQAAAAD2oQ1//a27/3DlL8nTklwjwyDapyS5+TbEBwAAAEzU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\nkqSpGSpJkiRJkiRpan8CMCSsiezoRngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110534a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (20, 6)\n",
    "\n",
    "# good news\n",
    "fig, ax = plt.subplots()\n",
    "dates = df['date'].tolist()\n",
    "numbers = df['goodnews'].tolist() \n",
    "\n",
    "y_pos = np.arange(len(dates))\n",
    "ax.bar(y_pos, numbers, align='center',\n",
    "        color='lightblue', ecolor='black')\n",
    "labels = [date if i%50==0 else '' for i, date in enumerate(dates)]\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "ax.set_xlabel('Date ('+str(len(dates))+ ' dayes)')\n",
    "ax.set_ylabel('Number of good news)')\n",
    "ax.set_title('How many good news published a day?')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# bad news\n",
    "fig, ax = plt.subplots()\n",
    "dates = df['date'].tolist()\n",
    "numbers = df['badnews'].tolist() \n",
    "\n",
    "y_pos = np.arange(len(dates))\n",
    "ax.bar(y_pos, numbers, align='center',\n",
    "        color='lightblue', ecolor='black')\n",
    "labels = [date if i%50==0 else '' for i, date in enumerate(dates)]\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "ax.set_xlabel('Date ('+str(len(dates))+ ' dayes)')\n",
    "ax.set_ylabel('Number of bad news)')\n",
    "ax.set_title('How many bad news published a day?')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"symbol\":\"BTC\",\"history\":[{\"position\":1,\"name\":\"Bitcoin\",\"symbol\":\"BTC\",\"category\":\"currency\",\"mark...\n",
      "{\"symbol\":\"BTC\",\"history\":[{\"position\":1,\"name\":\"Bitcoin\",\"symbol\":\"BTC\",\"category\":\"currency\",\"mark...\n",
      "BTCdata head\n",
      "{'price': 421.919, 'change24h': 0.66, 'marketCap': 6504292756.02, 'availableSupply': 15415975, 'volume24': 58959382.979, 'date': '2016-04-10'}\n",
      "BTCdata last\n",
      "{'price': 6505.33, 'change24h': 11.17, 'marketCap': 108496381640.0, 'availableSupply': 16678075, 'volume24': 6610260972.9, 'date': '2017-11-14'}\n"
     ]
    }
   ],
   "source": [
    "# get BTC dataset\n",
    "import requests\n",
    "import datetime\n",
    "import json\n",
    "Nyears = 2\n",
    "year = datetime.datetime.now().year\n",
    "\n",
    "# to YYYY-MM-DD\n",
    "def toYYYY_MM_DD(s):\n",
    "    return s[6:] +'-'+ s[3:5] +'-'+ s[:2]\n",
    "\n",
    "BTCdata = []\n",
    "for _ in range(Nyears):\n",
    "\n",
    "    headers = {\n",
    "        'accept': 'application/json',\n",
    "    }\n",
    "\n",
    "    params = (\n",
    "        ('coin', 'bitcoin'),\n",
    "        ('period', str(year)),\n",
    "        ('format', 'array'),\n",
    "    )\n",
    "\n",
    "    res = requests.get('https://coinmarketcap.northpole.ro/history.json', headers=headers, params=params)\n",
    "    print(res.text[:100] + '...')\n",
    "    dic = json.loads(res.text)\n",
    "    temp = list(  map(lambda x: {'price': x['price']['usd'], 'change24h': x['change24h'], 'marketCap': x['marketCap']['usd'], 'availableSupply': x['availableSupply'], 'volume24': x['volume24']['usd'], 'date': toYYYY_MM_DD(x['date'])}, dic['history']) )\n",
    "    BTCdata = temp + BTCdata\n",
    "    year -= 1\n",
    "    \n",
    "print('BTCdata head')\n",
    "print(BTCdata[0])\n",
    "print('BTCdata last')\n",
    "print(BTCdata[-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 574 entries, 2016-04-10 to 2017-11-14\n",
      "Data columns (total 6 columns):\n",
      "price              574 non-null float64\n",
      "change24h          574 non-null float64\n",
      "marketCap          574 non-null float64\n",
      "availableSupply    574 non-null float64\n",
      "volume24           574 non-null float64\n",
      "date               574 non-null object\n",
      "dtypes: float64(5), object(1)\n",
      "memory usage: 31.4+ KB\n",
      "              price  change24h     marketCap  availableSupply      volume24  \\\n",
      "2016-04-10  421.919       0.66  6.504293e+09       15415975.0  5.895938e+07   \n",
      "2016-04-11  421.900       0.67  6.504569e+09       15417325.0  7.157449e+07   \n",
      "2016-04-12  422.051       0.05  6.508533e+09       15421200.0  5.113823e+07   \n",
      "2016-04-13  424.834       0.63  6.553043e+09       15424950.0  6.939069e+07   \n",
      "2016-04-14  423.985      -0.21  6.541675e+09       15429025.0  6.935971e+07   \n",
      "\n",
      "                  date  \n",
      "2016-04-10  2016-04-10  \n",
      "2016-04-11  2016-04-11  \n",
      "2016-04-12  2016-04-12  \n",
      "2016-04-13  2016-04-13  \n",
      "2016-04-14  2016-04-14  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>change24h</th>\n",
       "      <th>marketCap</th>\n",
       "      <th>availableSupply</th>\n",
       "      <th>volume24</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>7146.46</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>1.191517e+11</td>\n",
       "      <td>16672825.0</td>\n",
       "      <td>3.275373e+09</td>\n",
       "      <td>2017-11-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-11</th>\n",
       "      <td>6743.98</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>1.124522e+11</td>\n",
       "      <td>16674450.0</td>\n",
       "      <td>5.240430e+09</td>\n",
       "      <td>2017-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>6388.54</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>1.065316e+11</td>\n",
       "      <td>16675425.0</td>\n",
       "      <td>5.069268e+09</td>\n",
       "      <td>2017-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>5756.22</td>\n",
       "      <td>-9.70</td>\n",
       "      <td>9.599245e+10</td>\n",
       "      <td>16676300.0</td>\n",
       "      <td>8.768910e+09</td>\n",
       "      <td>2017-11-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>6505.33</td>\n",
       "      <td>11.17</td>\n",
       "      <td>1.084964e+11</td>\n",
       "      <td>16678075.0</td>\n",
       "      <td>6.610261e+09</td>\n",
       "      <td>2017-11-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price  change24h     marketCap  availableSupply      volume24  \\\n",
       "2017-11-10  7146.46      -3.14  1.191517e+11       16672825.0  3.275373e+09   \n",
       "2017-11-11  6743.98      -5.70  1.124522e+11       16674450.0  5.240430e+09   \n",
       "2017-11-12  6388.54      -4.08  1.065316e+11       16675425.0  5.069268e+09   \n",
       "2017-11-13  5756.22      -9.70  9.599245e+10       16676300.0  8.768910e+09   \n",
       "2017-11-14  6505.33      11.17  1.084964e+11       16678075.0  6.610261e+09   \n",
       "\n",
       "                  date  \n",
       "2017-11-10  2017-11-10  \n",
       "2017-11-11  2017-11-11  \n",
       "2017-11-12  2017-11-12  \n",
       "2017-11-13  2017-11-13  \n",
       "2017-11-14  2017-11-14  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To pandas\n",
    "tempData = []\n",
    "keys = list(BTCdata[0].keys())\n",
    "idexOfDate = keys.index('date')\n",
    "for i in range(len(BTCdata)):\n",
    "    row = [BTCdata[i][key] for key in keys]\n",
    "    tempData.append(row)\n",
    "\n",
    "tempData = np.array(tempData)\n",
    "BTCdf = pd.DataFrame(tempData, index=tempData[:,idexOfDate], columns=keys)\n",
    "\n",
    "# to float\n",
    "keys.remove('date')\n",
    "BTCdf[keys] = BTCdf[keys].astype(float)\n",
    "\n",
    "# info\n",
    "BTCdf.info()\n",
    "pprint(BTCdf.head())\n",
    "BTCdf.tail()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JAAAAgFF5Sh0AAACwYYxk2pqMcAIAAADm0moWEmc+CZwAAAAAGJXACQAAAJgbRjRtDQIn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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c24390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UD8J7BDgoVqVOX5U4x61izMBqOqj1zUF936tsq1vk7jX87SSfT3K3sXmdRILp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\nAJjJ/w9l7eT13A5bnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f59550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kAQAAB23spVD79h9w2RTADiZIAgAAZkJoBLD7CJIAAIBNWykoEiAB7F6CJAAA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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110396d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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67z4Zbtf63Z0479wsSv9OvT7o7vtluB3k+hlGdyzfPrnmjbIgfTz5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\nPwHN5ZLHzeRv/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127c3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+G3kOt/q59tqBvU2QBAAAsOD2cjizlx8b7EWCJAAA4KAJA7bHbmnn1b5NbrfU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\nAAAAJhEkAQAAADDJ/wMutva6BCD5ygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113efe438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all attrs\n",
    "\n",
    "for attr in BTCdf.columns[:-1]:\n",
    "    # Plot BTC price\n",
    "    fig, ax = plt.subplots()\n",
    "    dates = BTCdf['date'].tolist() \n",
    "    numbers = BTCdf[attr].tolist() \n",
    "\n",
    "    y_pos = np.arange(len(dates))\n",
    "    ax.bar(y_pos, numbers, align='center', color='lightblue', ecolor='black')\n",
    "    labels = [date if i%50==0 else '' for i, date in enumerate(dates)]\n",
    "    ax.set_xticks(np.arange(len(labels)))\n",
    "    ax.set_xticklabels(labels)\n",
    "\n",
    "    ax.set_xlabel('Date ('+str(len(dates))+ ' dayes)')\n",
    "    ax.set_ylabel(attr)\n",
    "    ax.set_title('How many/much ' + attr + ' is?')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 574 entries, 2016-04-10 to 2017-11-14\n",
      "Data columns (total 9 columns):\n",
      "price              574 non-null float64\n",
      "change24h          574 non-null float64\n",
      "marketCap          574 non-null float64\n",
      "availableSupply    574 non-null float64\n",
      "volume24           574 non-null float64\n",
      "date               574 non-null object\n",
      "date               574 non-null object\n",
      "goodnews           574 non-null int64\n",
      "badnews            574 non-null int64\n",
      "dtypes: float64(5), int64(2), object(2)\n",
      "memory usage: 44.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>change24h</th>\n",
       "      <th>marketCap</th>\n",
       "      <th>availableSupply</th>\n",
       "      <th>volume24</th>\n",
       "      <th>date</th>\n",
       "      <th>date</th>\n",
       "      <th>goodnews</th>\n",
       "      <th>badnews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>7146.46</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>1.191517e+11</td>\n",
       "      <td>16672825.0</td>\n",
       "      <td>3.275373e+09</td>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>86</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-11</th>\n",
       "      <td>6743.98</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>1.124522e+11</td>\n",
       "      <td>16674450.0</td>\n",
       "      <td>5.240430e+09</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>6388.54</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>1.065316e+11</td>\n",
       "      <td>16675425.0</td>\n",
       "      <td>5.069268e+09</td>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>5756.22</td>\n",
       "      <td>-9.70</td>\n",
       "      <td>9.599245e+10</td>\n",
       "      <td>16676300.0</td>\n",
       "      <td>8.768910e+09</td>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>97</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>6505.33</td>\n",
       "      <td>11.17</td>\n",
       "      <td>1.084964e+11</td>\n",
       "      <td>16678075.0</td>\n",
       "      <td>6.610261e+09</td>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>102</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price  change24h     marketCap  availableSupply      volume24  \\\n",
       "2017-11-10  7146.46      -3.14  1.191517e+11       16672825.0  3.275373e+09   \n",
       "2017-11-11  6743.98      -5.70  1.124522e+11       16674450.0  5.240430e+09   \n",
       "2017-11-12  6388.54      -4.08  1.065316e+11       16675425.0  5.069268e+09   \n",
       "2017-11-13  5756.22      -9.70  9.599245e+10       16676300.0  8.768910e+09   \n",
       "2017-11-14  6505.33      11.17  1.084964e+11       16678075.0  6.610261e+09   \n",
       "\n",
       "                  date        date  goodnews  badnews  \n",
       "2017-11-10  2017-11-10  2017-11-10        86      107  \n",
       "2017-11-11  2017-11-11  2017-11-11        42       31  \n",
       "2017-11-12  2017-11-12  2017-11-12        29       26  \n",
       "2017-11-13  2017-11-13  2017-11-13        97      124  \n",
       "2017-11-14  2017-11-14  2017-11-14       102      111  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join by date\n",
    "BTCdfWithoutDate = BTCdf#.drop(columns=['date'])\n",
    "Alldf = pd.concat([BTCdfWithoutDate, df], axis=1, join='inner', copy=True)\n",
    "\n",
    "Alldf.info()\n",
    "Alldf.head()\n",
    "Alldf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 571 entries, 2016-04-12 to 2017-11-13\n",
      "Data columns (total 29 columns):\n",
      "price-0       571 non-null float64\n",
      "volume24-0    571 non-null float64\n",
      "goodnews-0    571 non-null float64\n",
      "badnews-0     571 non-null float64\n",
      "price-1       571 non-null float64\n",
      "volume24-1    571 non-null float64\n",
      "goodnews-1    571 non-null float64\n",
      "badnews-1     571 non-null float64\n",
      "price-2       571 non-null float64\n",
      "volume24-2    571 non-null float64\n",
      "goodnews-2    571 non-null float64\n",
      "badnews-2     571 non-null float64\n",
      "price-3       571 non-null float64\n",
      "volume24-3    571 non-null float64\n",
      "goodnews-3    571 non-null float64\n",
      "badnews-3     571 non-null float64\n",
      "price-4       571 non-null float64\n",
      "volume24-4    571 non-null float64\n",
      "goodnews-4    571 non-null float64\n",
      "badnews-4     571 non-null float64\n",
      "price-5       571 non-null float64\n",
      "volume24-5    571 non-null float64\n",
      "goodnews-5    571 non-null float64\n",
      "badnews-5     571 non-null float64\n",
      "price-6       571 non-null float64\n",
      "volume24-6    571 non-null float64\n",
      "goodnews-6    571 non-null float64\n",
      "badnews-6     571 non-null float64\n",
      "price+1       571 non-null float64\n",
      "dtypes: float64(29)\n",
      "memory usage: 133.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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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>price-0</th>\n",
       "      <th>volume24-0</th>\n",
       "      <th>goodnews-0</th>\n",
       "      <th>badnews-0</th>\n",
       "      <th>price-1</th>\n",
       "      <th>volume24-1</th>\n",
       "      <th>goodnews-1</th>\n",
       "      <th>badnews-1</th>\n",
       "      <th>price-2</th>\n",
       "      <th>volume24-2</th>\n",
       "      <th>...</th>\n",
       "      <th>badnews-4</th>\n",
       "      <th>price-5</th>\n",
       "      <th>volume24-5</th>\n",
       "      <th>goodnews-5</th>\n",
       "      <th>badnews-5</th>\n",
       "      <th>price-6</th>\n",
       "      <th>volume24-6</th>\n",
       "      <th>goodnews-6</th>\n",
       "      <th>badnews-6</th>\n",
       "      <th>price+1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-04-12</th>\n",
       "      <td>422.051</td>\n",
       "      <td>5.113823e+07</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>421.900</td>\n",
       "      <td>7.157449e+07</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>421.919</td>\n",
       "      <td>5.895938e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>124.0</td>\n",
       "      <td>6388.540</td>\n",
       "      <td>5.069268e+09</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6743.980</td>\n",
       "      <td>5.240430e+09</td>\n",
       "      <td>42.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>424.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-13</th>\n",
       "      <td>424.834</td>\n",
       "      <td>6.939069e+07</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>422.051</td>\n",
       "      <td>5.113823e+07</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>421.900</td>\n",
       "      <td>7.157449e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5756.220</td>\n",
       "      <td>8.768910e+09</td>\n",
       "      <td>97.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>6388.540</td>\n",
       "      <td>5.069268e+09</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>423.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-14</th>\n",
       "      <td>423.985</td>\n",
       "      <td>6.935971e+07</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>424.834</td>\n",
       "      <td>6.939069e+07</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>422.051</td>\n",
       "      <td>5.113823e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6505.330</td>\n",
       "      <td>6.610261e+09</td>\n",
       "      <td>102.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5756.220</td>\n",
       "      <td>8.768910e+09</td>\n",
       "      <td>97.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>424.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-15</th>\n",
       "      <td>424.224</td>\n",
       "      <td>4.537076e+07</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>423.985</td>\n",
       "      <td>6.935971e+07</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>424.834</td>\n",
       "      <td>6.939069e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>421.919</td>\n",
       "      <td>5.895938e+07</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6505.330</td>\n",
       "      <td>6.610261e+09</td>\n",
       "      <td>102.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>429.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-16</th>\n",
       "      <td>429.333</td>\n",
       "      <td>5.408007e+07</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>424.224</td>\n",
       "      <td>4.537076e+07</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>423.985</td>\n",
       "      <td>6.935971e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>421.900</td>\n",
       "      <td>7.157449e+07</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>421.919</td>\n",
       "      <td>5.895938e+07</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>430.841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            price-0    volume24-0  goodnews-0  badnews-0  price-1  \\\n",
       "2016-04-12  422.051  5.113823e+07        10.0       13.0  421.900   \n",
       "2016-04-13  424.834  6.939069e+07         6.0        4.0  422.051   \n",
       "2016-04-14  423.985  6.935971e+07         5.0        6.0  424.834   \n",
       "2016-04-15  424.224  4.537076e+07         5.0        5.0  423.985   \n",
       "2016-04-16  429.333  5.408007e+07         4.0        1.0  424.224   \n",
       "\n",
       "              volume24-1  goodnews-1  badnews-1  price-2    volume24-2  \\\n",
       "2016-04-12  7.157449e+07         6.0        7.0  421.919  5.895938e+07   \n",
       "2016-04-13  5.113823e+07        10.0       13.0  421.900  7.157449e+07   \n",
       "2016-04-14  6.939069e+07         6.0        4.0  422.051  5.113823e+07   \n",
       "2016-04-15  6.935971e+07         5.0        6.0  424.834  6.939069e+07   \n",
       "2016-04-16  4.537076e+07         5.0        5.0  423.985  6.935971e+07   \n",
       "\n",
       "             ...     badnews-4   price-5    volume24-5  goodnews-5  badnews-5  \\\n",
       "2016-04-12   ...         124.0  6388.540  5.069268e+09        29.0       26.0   \n",
       "2016-04-13   ...         111.0  5756.220  8.768910e+09        97.0      124.0   \n",
       "2016-04-14   ...           3.0  6505.330  6.610261e+09       102.0      111.0   \n",
       "2016-04-15   ...           7.0   421.919  5.895938e+07         3.0        3.0   \n",
       "2016-04-16   ...          13.0   421.900  7.157449e+07         6.0        7.0   \n",
       "\n",
       "             price-6    volume24-6  goodnews-6  badnews-6  price+1  \n",
       "2016-04-12  6743.980  5.240430e+09        42.0       31.0  424.834  \n",
       "2016-04-13  6388.540  5.069268e+09        29.0       26.0  423.985  \n",
       "2016-04-14  5756.220  8.768910e+09        97.0      124.0  424.224  \n",
       "2016-04-15  6505.330  6.610261e+09       102.0      111.0  429.333  \n",
       "2016-04-16   421.919  5.895938e+07         3.0        3.0  430.841  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat previous X days attrs in a row \n",
    "#tempData = Alldf[['price']].copy()\n",
    "#tempData.columns = ['price-1', 'date']\n",
    "#pprint(tempData.head())\n",
    "#tempData.tail()\n",
    "PERIOD = 7\n",
    "temp = Alldf[['date', 'price', 'volume24', 'change24h', 'goodnews', 'badnews']].values.tolist()\n",
    "\n",
    "withPrevDays = []\n",
    "for i in range(2, len(temp)-1):\n",
    "    prevX = []\n",
    "    date = [temp[i][0]]\n",
    "    for day in range(PERIOD):\n",
    "        prevX.extend(temp[i-day][1:]) # all attr except date at -1 day\n",
    "\n",
    "    future1dayPrice = [temp[i+1][1]] #target y price\n",
    "    withPrevDays.append(date + prevX + future1dayPrice)\n",
    "\n",
    "withPrevDays = np.array(withPrevDays)\n",
    "\n",
    "\n",
    "keys = ['price', 'volume24', 'change24h', 'goodnews', 'badnews']\n",
    "keys = ['date'] + [k+'-'+str(day) for day in range(PERIOD) for k in keys] + ['price+1']\n",
    "SUMdf = pd.DataFrame(withPrevDays, index=withPrevDays[:,0], columns=keys)\n",
    "SUMdf= SUMdf.drop(columns=['date'])\n",
    "SUMdf = SUMdf.astype(np.float)\n",
    "SUMdf.info()\n",
    "SUMdf.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test: 115 train: 456\n",
      "################ train ################\n",
      "trainX-------------------------\n",
      "            price-0    volume24-0  goodnews-0  badnews-0  price-1  \\\n",
      "2016-04-13  424.834  6.939069e+07         6.0        4.0  422.051   \n",
      "2016-04-14  423.985  6.935971e+07         5.0        6.0  424.834   \n",
      "2016-04-16  429.333  5.408007e+07         4.0        1.0  424.224   \n",
      "2016-04-17  430.841  3.736369e+07         1.0        4.0  429.333   \n",
      "2016-04-18  427.532  5.195711e+07         3.0        3.0  430.841   \n",
      "\n",
      "              volume24-1  goodnews-1  badnews-1  price-2    volume24-2  \\\n",
      "2016-04-13  5.113823e+07        10.0       13.0  421.900  7.157449e+07   \n",
      "2016-04-14  6.939069e+07         6.0        4.0  422.051  5.113823e+07   \n",
      "2016-04-16  4.537076e+07         5.0        5.0  423.985  6.935971e+07   \n",
      "2016-04-17  5.408007e+07         4.0        1.0  424.224  4.537076e+07   \n",
      "2016-04-18  3.736369e+07         1.0        4.0  429.333  5.408007e+07   \n",
      "\n",
      "              ...      goodnews-4  badnews-4   price-5    volume24-5  \\\n",
      "2016-04-13    ...           102.0      111.0  5756.220  8.768910e+09   \n",
      "2016-04-14    ...             3.0        3.0  6505.330  6.610261e+09   \n",
      "2016-04-16    ...            10.0       13.0   421.900  7.157449e+07   \n",
      "2016-04-17    ...             6.0        4.0   422.051  5.113823e+07   \n",
      "2016-04-18    ...             5.0        6.0   424.834  6.939069e+07   \n",
      "\n",
      "            goodnews-5  badnews-5   price-6    volume24-6  goodnews-6  \\\n",
      "2016-04-13        97.0      124.0  6388.540  5.069268e+09        29.0   \n",
      "2016-04-14       102.0      111.0  5756.220  8.768910e+09        97.0   \n",
      "2016-04-16         6.0        7.0   421.919  5.895938e+07         3.0   \n",
      "2016-04-17        10.0       13.0   421.900  7.157449e+07         6.0   \n",
      "2016-04-18         6.0        4.0   422.051  5.113823e+07        10.0   \n",
      "\n",
      "            badnews-6  \n",
      "2016-04-13       26.0  \n",
      "2016-04-14      124.0  \n",
      "2016-04-16        3.0  \n",
      "2016-04-17        7.0  \n",
      "2016-04-18       13.0  \n",
      "\n",
      "[5 rows x 28 columns]\n",
      "trainY-------------------------\n",
      "2016-04-13    423.985\n",
      "2016-04-14    424.224\n",
      "2016-04-16    430.841\n",
      "2016-04-17    427.532\n",
      "2016-04-18    428.004\n",
      "Name: price+1, dtype: float64\n",
      "trainY_clf-------------------------\n",
      "2016-04-13    0\n",
      "2016-04-14    1\n",
      "2016-04-16    1\n",
      "2016-04-17    0\n",
      "2016-04-18    1\n",
      "dtype: int64\n",
      "################ test ################\n",
      "testX-------------------------\n",
      "            price-0    volume24-0  goodnews-0  badnews-0  price-1  \\\n",
      "2016-04-12  422.051  5.113823e+07        10.0       13.0  421.900   \n",
      "2016-04-15  424.224  4.537076e+07         5.0        5.0  423.985   \n",
      "2016-04-21  441.407  7.672978e+07         9.0       11.0  431.419   \n",
      "2016-04-26  464.070  8.035743e+07        14.0       13.0  457.130   \n",
      "2016-05-08  459.015  4.187328e+07         4.0        3.0  459.542   \n",
      "\n",
      "              volume24-1  goodnews-1  badnews-1  price-2    volume24-2  \\\n",
      "2016-04-12  7.157449e+07         6.0        7.0  421.919  5.895938e+07   \n",
      "2016-04-15  6.935971e+07         5.0        6.0  424.834  6.939069e+07   \n",
      "2016-04-21  4.553110e+07        10.0        4.0  428.004  5.599277e+07   \n",
      "2016-04-26  6.960261e+07         7.0        7.0  447.625  4.593886e+07   \n",
      "2016-05-08  7.026030e+07        12.0        4.0  448.472  5.113298e+07   \n",
      "\n",
      "              ...      goodnews-4  badnews-4   price-5    volume24-5  \\\n",
      "2016-04-12    ...            97.0      124.0  6388.540  5.069268e+09   \n",
      "2016-04-15    ...             6.0        7.0   421.919  5.895938e+07   \n",
      "2016-04-21    ...             1.0        4.0   429.333  5.408007e+07   \n",
      "2016-04-26    ...             5.0        6.0   441.407  7.672978e+07   \n",
      "2016-05-08    ...            20.0        7.0   445.318  8.847444e+07   \n",
      "\n",
      "            goodnews-5  badnews-5   price-6    volume24-6  goodnews-6  \\\n",
      "2016-04-12        29.0       26.0  6743.980  5.240430e+09        42.0   \n",
      "2016-04-15         3.0        3.0  6505.330  6.610261e+09       102.0   \n",
      "2016-04-21         4.0        1.0   424.224  4.537076e+07         5.0   \n",
      "2016-04-26         9.0       11.0   431.419  4.553110e+07        10.0   \n",
      "2016-05-08        37.0       21.0   452.021  4.222532e+07       126.0   \n",
      "\n",
      "            badnews-6  \n",
      "2016-04-12       31.0  \n",
      "2016-04-15      111.0  \n",
      "2016-04-21        5.0  \n",
      "2016-04-26        4.0  \n",
      "2016-05-08       69.0  \n",
      "\n",
      "[5 rows x 28 columns]\n",
      "testY-------------------------\n",
      "2016-04-12    424.834\n",
      "2016-04-15    429.333\n",
      "2016-04-21    448.488\n",
      "2016-04-26    466.289\n",
      "2016-05-08    457.557\n",
      "Name: price+1, dtype: float64\n",
      "testY_clf-------------------------\n",
      "2016-04-12    1\n",
      "2016-04-15    1\n",
      "2016-04-21    1\n",
      "2016-04-26    1\n",
      "2016-05-08    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# split to training, testing data\n",
    "train, test = train_test_split(SUMdf, test_size=0.2)\n",
    "train.sort_index(inplace=True)\n",
    "test.sort_index(inplace=True)\n",
    "#pprint(test)\n",
    "\n",
    "\n",
    "# trainX, testX\n",
    "trainX = train.iloc[:,:-1] \n",
    "testX = test.iloc[:,:-1] \n",
    "\n",
    "# trainY, testY\n",
    "trainY = train['price+1'] \n",
    "testY = test['price+1'] \n",
    "\n",
    "# trainY_clf\n",
    "#trainY_clf = trainY.copy()\n",
    "#trainY_clf.map(lambda x: x.d)\n",
    "trainY_clf = np.array([ 1 if trainY.iloc[i] > trainX.iloc[i]['price-0'] else 0 for i in range(len(trainX)) ]) \n",
    "trainY_clf = pd.Series(trainY_clf, index=trainY.index) \n",
    "testY_clf = np.array([ 1 if testY.iloc[i] > testX.iloc[i]['price-0'] else 0 for i in range(len(testX)) ]) \n",
    "testY_clf = pd.Series(testY_clf, index=testY.index) \n",
    "'''\n",
    "# the y for regression, price value\n",
    "y = withoutDate[:,-1]\n",
    "\n",
    "# the y for classifiation, 1 goes up, 0 goes down\n",
    "y_clf = [ 1 if y[i] > X[i, 0] else 0 for i in range(len(X)) ] \n",
    "'''\n",
    "\n",
    "'''\n",
    "pprint(X[:3])\n",
    "pprint(y[:10])\n",
    "pprint(y_clf[:10])\n",
    "\n",
    "'''\n",
    "#print(y_clf[:100])\n",
    "print('test:',len(test), 'train:', len(train))\n",
    "print('################ train ################')\n",
    "print('trainX-------------------------')\n",
    "pprint(trainX.head())\n",
    "print('trainY-------------------------')\n",
    "pprint(trainY.head())\n",
    "print('trainY_clf-------------------------')\n",
    "pprint(trainY_clf.head())\n",
    "print('################ test ################')\n",
    "print('testX-------------------------')\n",
    "pprint(testX.head())\n",
    "print('testY-------------------------')\n",
    "pprint(testY.head())\n",
    "print('testY_clf-------------------------')\n",
    "pprint(testY_clf.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalizedTrainX [  3.512e-08   5.737e-03   4.960e-10   3.307e-10   3.489e-08   4.228e-03\n",
      "   8.267e-10   1.075e-09   3.488e-08   5.917e-03   4.960e-10   5.787e-10\n",
      "   3.488e-08   4.874e-03   2.480e-10   2.480e-10   5.378e-07   5.465e-01\n",
      "   8.433e-09   9.177e-09   4.759e-07   7.250e-01   8.019e-09   1.025e-08\n",
      "   5.282e-07   4.191e-01   2.398e-09   2.150e-09] ...\n",
      "########################################################################\n",
      "train data score 0.68201754386\n",
      "test data score 0.530434782609\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       rise       0.54      0.87      0.67        62\n",
      "       drop       0.47      0.13      0.21        53\n",
      "\n",
      "avg / total       0.51      0.53      0.45       115\n",
      "\n",
      "########################################################################\n",
      "sample of testY_predicct [1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1\n",
      " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1\n",
      " 1 1 1 1]\n",
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nclf.fit(X, y_clf)   \\ny_predicct = clf.predict(X)\\nprint(clf.score(X, y_clf))\\nprint(y_predicct)\\nprint(0 in y_predicct)\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# neural_network Classifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "scaler = Normalizer().fit(trainX)\n",
    "normalizedTrainX = scaler.transform(trainX)\n",
    "normalizedTestX = scaler.transform(testX)\n",
    "# summarize transformed data\n",
    "numpy.set_printoptions(precision=3) # show digit to .001\n",
    "print('normalizedTrainX', normalizedTrainX[0], '...')\n",
    "#print(normalizedTestX[:3], '...')\n",
    "\n",
    "\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(10, ), random_state=1)\n",
    "clf.fit(normalizedTrainX, trainY_clf)\n",
    "testY_predicct = clf.predict(normalizedTestX)\n",
    "print('########################################################################')\n",
    "print('train data score', clf.score(normalizedTrainX, trainY_clf))\n",
    "print('test data score', clf.score(normalizedTestX, testY_clf))\n",
    "print(classification_report(testY_clf, testY_predicct, labels=[1, 0], target_names=['rise', 'drop']))\n",
    "print('########################################################################')\n",
    " \n",
    "print('sample of testY_predicct', testY_predicct)\n",
    "print(0 in testY_predicct)\n",
    "'''\n",
    "clf.fit(X, y_clf)   \n",
    "y_predicct = clf.predict(X)\n",
    "print(clf.score(X, y_clf))\n",
    "print(y_predicct)\n",
    "print(0 in y_predicct)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################################################################\n",
      "train data score 0.982456140351\n",
      "test data score 0.486956521739\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       rise       0.52      0.68      0.59        62\n",
      "       drop       0.41      0.26      0.32        53\n",
      "\n",
      "avg / total       0.47      0.49      0.47       115\n",
      "\n",
      "########################################################################\n",
      "feature_importances:  [ 0.036  0.035  0.036  0.032  0.034  0.043  0.041  0.043  0.045  0.023\n",
      "  0.028  0.029  0.029  0.044  0.024  0.038  0.032  0.048  0.037  0.028\n",
      "  0.04   0.039  0.043  0.025  0.046  0.042  0.035  0.025]\n",
      "sample of testY_predicct:  [0 1 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1\n",
      " 0 0 1 0 0 0 0 1 0 0 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1\n",
      " 1 1 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1\n",
      " 0 1 1 0]\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "clf = RandomForestClassifier(max_depth=100, random_state=0)\n",
    "clf.fit(trainX, trainY_clf)\n",
    "testY_predicct = clf.predict(testX) \n",
    "\n",
    "print('########################################################################')\n",
    "print('train data score', clf.score(trainX, trainY_clf))\n",
    "print('test data score', clf.score(testX, testY_clf))\n",
    "print(classification_report(testY_clf, testY_predicct, labels=[1, 0], target_names=['rise', 'drop']))\n",
    "print('########################################################################')\n",
    "print('feature_importances: ', clf.feature_importances_)\n",
    "\n",
    "print('sample of testY_predicct: ', testY_predicct)\n",
    "print(0 in testY_predicct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.993090786285\n",
      "[ 428.157  435.916  449.961  452.971  458.541  452.395  447.649  538.111\n",
      "  578.211  637.574]\n"
     ]
    },
    {
     "data": {
      "image/png": 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SnFtVpye5Lskzhu4XJnlKkq1JvpTkeUnS3dur6jVJLh76vXrHAuIAAMu1nAW2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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113889f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sklearn.neural_network.MLPRegressor\n",
    "from sklearn.ensemble.forest import RandomForestRegressor\n",
    "\n",
    "reg = RandomForestRegressor(max_depth=10)\n",
    "                       \n",
    "reg.fit(trainX, trainY)   \n",
    "print(reg.score(testX, testY))\n",
    "y_predicct = reg.predict(testX)\n",
    "print(y_predicct[:10])\n",
    "\n",
    "\n",
    "\n",
    "# Plot BTC price\n",
    "fig, ax = plt.subplots()\n",
    "dates = testY.index\n",
    "numbers = y_predicct\n",
    "\n",
    "y_pos = np.arange(len(dates))\n",
    "ax.bar(y_pos, numbers, align='center', color='lightblue', ecolor='black')\n",
    "labels = [date if i%30==0 else '' for i, date in enumerate(dates)]\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "ax.set_xlabel('Date ('+str(len(dates))+ ' dayes)')\n",
    "ax.set_ylabel('Price')\n",
    "ax.set_title('BTC price predict')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.355141507905\n",
      "[ 0.075  0.039  0.036  0.034  0.039  0.04   0.038  0.041  0.032  0.034]\n"
     ]
    },
    {
     "data": {
      "image/png": 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qXj/0X1NVx031/dzQfnNVPWOedQIAAAAwztxCpqrakOSVSZ6ZZHOS51XV5gXD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138940f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sklearn.neural_network.MLPRegressor\n",
    "# neural_network does really bad, 500 dataset is too small!\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "normalizedTrainY = scaler.transform([trainY])[0]\n",
    "normalizedTestY = scaler.transform([testY])[0]\n",
    "\n",
    "reg = MLPRegressor(solver='lbfgs', hidden_layer_sizes=100, learning_rate='adaptive', random_state=1)\n",
    "reg.fit(normalizedTrainX, normalizedTrainY)   \n",
    "print(reg.score(normalizedTestX, normalizedTestY))\n",
    "y_predicct = reg.predict(normalizedTestX)\n",
    "print(y_predicct[:10])\n",
    "\n",
    "\n",
    "\n",
    "# Plot BTC price\n",
    "fig, ax = plt.subplots()\n",
    "dates = testY.index\n",
    "numbers = y_predicct\n",
    "\n",
    "y_pos = np.arange(len(dates))\n",
    "ax.bar(y_pos, numbers, align='center', color='lightblue', ecolor='black')\n",
    "labels = [date if i%30==0 else '' for i, date in enumerate(dates)]\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "ax.set_xlabel('Date ('+str(len(dates))+ ' dayes)')\n",
    "ax.set_ylabel('price')\n",
    "ax.set_title('BTC price predict')\n",
    "plt.show()\n"
   ]
  },
  {
   "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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