{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 51% attack\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "plt.style.use('seaborn-deep')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape_table():\n",
    "    \"\"\"\n",
    "    Scrapes crytpo51 table\n",
    "    :return: pandas dataframe\n",
    "    \"\"\"\n",
    "    r = requests.get(\"https://www.crypto51.app/\")\n",
    "    soup = BeautifulSoup(r.text, \"lxml\")\n",
    "    data = []\n",
    "    headers = []\n",
    "    table = soup.find('table')\n",
    "    column_names = table.find('thead').find_all('th')\n",
    "    headers = [ele.text.strip() for ele in column_names]\n",
    "    rows = table.find('tbody').find_all('tr')\n",
    "    for row in rows:\n",
    "        data.append([i.text.strip() for i in row.find_all('td') if i])\n",
    "    return pd.DataFrame(data, columns=headers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Market Cap</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Hash Rate</th>\n",
       "      <th>1h Attack Cost</th>\n",
       "      <th>NiceHash-able</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>$107.39 B</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>38,529 PH/s</td>\n",
       "      <td>$506,855</td>\n",
       "      <td>1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>$47.79 B</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>226 TH/s</td>\n",
       "      <td>$319,313</td>\n",
       "      <td>4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bitcoin Cash</td>\n",
       "      <td>BCH</td>\n",
       "      <td>$13.45 B</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>5,505 PH/s</td>\n",
       "      <td>$72,418</td>\n",
       "      <td>9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>$5.00 B</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>310 TH/s</td>\n",
       "      <td>$54,854</td>\n",
       "      <td>6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dash</td>\n",
       "      <td>DASH</td>\n",
       "      <td>$1.96 B</td>\n",
       "      <td>X11</td>\n",
       "      <td>3 PH/s</td>\n",
       "      <td>$18,950</td>\n",
       "      <td>22%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name Symbol Market Cap Algorithm    Hash Rate 1h Attack Cost  \\\n",
       "0       Bitcoin    BTC  $107.39 B   SHA-256  38,529 PH/s       $506,855   \n",
       "1      Ethereum    ETH   $47.79 B    Ethash     226 TH/s       $319,313   \n",
       "2  Bitcoin Cash    BCH   $13.45 B   SHA-256   5,505 PH/s        $72,418   \n",
       "3      Litecoin    LTC    $5.00 B    Scrypt     310 TH/s        $54,854   \n",
       "4          Dash   DASH    $1.96 B       X11       3 PH/s        $18,950   \n",
       "\n",
       "  NiceHash-able  \n",
       "0            1%  \n",
       "1            4%  \n",
       "2            9%  \n",
       "3            6%  \n",
       "4           22%  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df = scrape_table()\n",
    "# raw_df.to_pickle(\"data/raw_crypto51.p\")\n",
    "# raw_df = pd.read_pickle(\"data/raw_crypto51.p\")\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Market Cap</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Hash Rate</th>\n",
       "      <th>1h Attack Cost</th>\n",
       "      <th>NiceHash-able</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>SOILcoin</td>\n",
       "      <td>SOIL</td>\n",
       "      <td>$75,293</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>767 MH/s</td>\n",
       "      <td>$1</td>\n",
       "      <td>1,085,340%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Phoenixcoin</td>\n",
       "      <td>PXC</td>\n",
       "      <td>-</td>\n",
       "      <td>NeoScrypt</td>\n",
       "      <td>206 MH/s</td>\n",
       "      <td>$6</td>\n",
       "      <td>24,484%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Pixie Coin</td>\n",
       "      <td>PXC</td>\n",
       "      <td>-</td>\n",
       "      <td>NeoScrypt</td>\n",
       "      <td>206 MH/s</td>\n",
       "      <td>$6</td>\n",
       "      <td>24,484%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Monero Original</td>\n",
       "      <td>XMO</td>\n",
       "      <td>-</td>\n",
       "      <td>CryptoNight</td>\n",
       "      <td>956 MH/s</td>\n",
       "      <td>$8,250</td>\n",
       "      <td>64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Rupee</td>\n",
       "      <td>RUP</td>\n",
       "      <td>-</td>\n",
       "      <td>Lyra2REv2</td>\n",
       "      <td>55 GH/s</td>\n",
       "      <td>$50</td>\n",
       "      <td>13,142%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name Symbol Market Cap    Algorithm Hash Rate 1h Attack Cost  \\\n",
       "79         SOILcoin   SOIL    $75,293       Ethash  767 MH/s             $1   \n",
       "80      Phoenixcoin    PXC          -    NeoScrypt  206 MH/s             $6   \n",
       "81       Pixie Coin    PXC          -    NeoScrypt  206 MH/s             $6   \n",
       "82  Monero Original    XMO          -  CryptoNight  956 MH/s         $8,250   \n",
       "83            Rupee    RUP          -    Lyra2REv2   55 GH/s            $50   \n",
       "\n",
       "   NiceHash-able  \n",
       "79    1,085,340%  \n",
       "80       24,484%  \n",
       "81       24,484%  \n",
       "82           64%  \n",
       "83       13,142%  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_raw_market_cap(raw_mcap):\n",
    "    \"\"\"\n",
    "    Converts market cap string into integer, removing symbols.\n",
    "    \"\"\"\n",
    "    mcap = raw_mcap[1:].replace(\",\", \"\")\n",
    "    if len(mcap.split(\" \")) == 2:\n",
    "        num, power = mcap.split(\" \")\n",
    "        if power == \"M\":\n",
    "            return int(float(num) * 10**6)\n",
    "        elif power == \"B\":\n",
    "            return int(float(num) * 10**9)\n",
    "    else:\n",
    "        return int(mcap)\n",
    "    \n",
    "def process_raw_hashrate(raw_hashrate):\n",
    "    \"\"\"\n",
    "    Converts all hash rate strings to ints in MH/s\n",
    "    \"\"\"\n",
    "    num, power = raw_hashrate.replace(',', '').split(' ')\n",
    "    power = power.split('H/s')[0]\n",
    "    if power == \"M\":\n",
    "        return int(num)\n",
    "    elif power == \"G\":\n",
    "        return int(num) * 10**3\n",
    "    elif power == \"T\":\n",
    "        return int(num) * 10**6\n",
    "    elif power == \"P\":\n",
    "        return int(num) * 10**9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_df = raw_df.iloc[:-4,:].copy()\n",
    "processed_df[\"Hash Rate\"] = processed_df[\"Hash Rate\"].apply(process_raw_hashrate)\n",
    "processed_df[\"Market Cap\"] = processed_df[\"Market Cap\"].apply(process_raw_market_cap)\n",
    "processed_df[\"1h Attack Cost\"] = processed_df[\"1h Attack Cost\"].str.replace(\",\", \"\").str.replace(\"$\", \"\").astype(int)\n",
    "processed_df[\"NiceHash-able\"] = processed_df[\"NiceHash-able\"].str.replace(\",\", \"\").str.replace(\"%\", \"\").astype(float)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Market Cap</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Hash Rate</th>\n",
       "      <th>1h Attack Cost</th>\n",
       "      <th>NiceHash-able</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>107390000000</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>38529000000000</td>\n",
       "      <td>506855</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>47790000000</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>226000000</td>\n",
       "      <td>319313</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bitcoin Cash</td>\n",
       "      <td>BCH</td>\n",
       "      <td>13450000000</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>5505000000000</td>\n",
       "      <td>72418</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>310000000</td>\n",
       "      <td>54854</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dash</td>\n",
       "      <td>DASH</td>\n",
       "      <td>1960000000</td>\n",
       "      <td>X11</td>\n",
       "      <td>3000000000</td>\n",
       "      <td>18950</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name Symbol    Market Cap Algorithm       Hash Rate  \\\n",
       "0       Bitcoin    BTC  107390000000   SHA-256  38529000000000   \n",
       "1      Ethereum    ETH   47790000000    Ethash       226000000   \n",
       "2  Bitcoin Cash    BCH   13450000000   SHA-256   5505000000000   \n",
       "3      Litecoin    LTC    5000000000    Scrypt       310000000   \n",
       "4          Dash   DASH    1960000000       X11      3000000000   \n",
       "\n",
       "   1h Attack Cost  NiceHash-able  \n",
       "0          506855           0.01  \n",
       "1          319313           0.04  \n",
       "2           72418           0.09  \n",
       "3           54854           0.06  \n",
       "4           18950           0.22  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log transforms\n",
    "processed_df[\"Log Market Cap\"] = np.log(processed_df[\"Market Cap\"])\n",
    "processed_df[\"Log Attack Cost\"] = np.log(1 + processed_df[\"1h Attack Cost\"])\n",
    "processed_df[\"Log NiceHash\"] = np.log(1 + processed_df[\"NiceHash-able\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "=========\n",
      "R^2: 0.46055333511121543\n",
      "p value: 4.599451441925646e-12\n",
      "Power law\n",
      "=========\n",
      "R^2: 0.6508727899753779\n",
      "p value: 1.6643743376882498e-19\n",
      "\n",
      " cost = 8.26405175307903e-05 * mcap ^ 0.92\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import linregress\n",
    "\n",
    "# Linear fit\n",
    "print(\"Linear\\n=========\")\n",
    "_, _, r_value_1, p_value_1, _ = linregress(processed_df[\"Market Cap\"],\n",
    "                                           processed_df[\"1h Attack Cost\"])\n",
    "print(\"R^2:\", r_value_1**2)\n",
    "print(\"p value:\", p_value_1)\n",
    "\n",
    "# Log-log fit\n",
    "print(\"Power law\\n=========\")\n",
    "slope, intercept, r_value, p_value, _ = linregress(processed_df[\"Log Market Cap\"], \n",
    "                                                         processed_df[\"Log Attack Cost\"])\n",
    "print(\"R^2:\", r_value**2)\n",
    "print(\"p value:\", p_value)\n",
    "\n",
    "# Power law expression\n",
    "multiplier = np.exp(intercept)\n",
    "print(f\"\\ncost = {multiplier} * mcap ^ {slope:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f0a54a8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 372,
       "width": 565
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from adjustText import adjust_text\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "processed_df.plot.scatter(\"Log Market Cap\", \"Log Attack Cost\", c=\"Log NiceHash\", \n",
    "                          colormap=\"plasma\", ax=ax)\n",
    "\n",
    "# Labels\n",
    "texts = []\n",
    "def annotate_df(row):  \n",
    "    texts.append(ax.annotate(row.Symbol, (row[\"Log Market Cap\"], row[\"Log Attack Cost\"]),\n",
    "                xytext=(3,-5), \n",
    "                textcoords='offset points',\n",
    "                size=5, \n",
    "                color='darkslategrey'))\n",
    "ab = processed_df.apply(annotate_df, axis=1)    \n",
    "# adjust_text(texts)\n",
    "\n",
    "# Trendline\n",
    "x = np.linspace(11, 26)\n",
    "ax.plot(x, intercept + x * slope, \"--k\")\n",
    "\n",
    "# plt.savefig(\"attackcost-mcap.png\", dpi=500)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}