{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cryptocurrency Analysis with Python - Buy and Hold\n",
    "\n",
    "In this part, I am going to analyze which coin (Bitcoin, Ethereum or Litecoin) was the most profitable in last two months if we would invest using buy and hold strategy. \n",
    "We'll go through the analysis of these 3 cryptocurrencies and try to give an objective answer.\n",
    "You can run this code by downloading [Jupyter notebook]({{site.url}}/assets/notebooks/2017-12-25-cryptocurrency-analysis-with-python-part2.ipynb).\n",
    "    \n",
    "Follow me on [twitter](https://twitter.com/romanorac) to get latest updates.\n",
    "\n",
    "![]({{site.url}}/assets/images/bitcoin-ethereum-litecoin.png \"Bitcoin, Ethereum, and Litecoin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disclaimer\n",
    "I am not a trader and this blog post is not a financial advice. This is purely introductory knowledge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements\n",
    "\n",
    "- [seaborn: statistical data visualization](https://seaborn.pydata.org/)\n",
    "\n",
    "For other requirements, see\n",
    "[previous blog post](https://romanorac.github.io/cryptocurrency/analysis/2017/12/17/cryptocurrency-analysis-with-python-part1.html)\n",
    "in this series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data\n",
    "\n",
    "To get the latest data, see [previous blog post]({{site.url}}/cryptocurrency/analysis/2017/12/17/cryptocurrency-analysis-with-python-part1.html),\n",
    "where I described how to download it using Cryptocompare API.\n",
    "You can also use the [data]({{site.url}}/assets/data/) I worked with in this example.\n",
    "\n",
    "First, we download hourly data for BTC, ETH and LTC from Coinbase exchange.\n",
    "This time we work with hourly time interval as it has higher granularity.\n",
    "Cryptocompare API limits response to 2000 samples, which is 2.7 months of data for each coin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def get_filename(from_symbol, to_symbol, exchange, datetime_interval, download_date):\n",
    "    return '%s_%s_%s_%s_%s.csv' % (from_symbol, to_symbol, exchange, datetime_interval, download_date)\n",
    "\n",
    "def read_dataset(filename):\n",
    "    print('Reading data from %s' % filename)\n",
    "    df = pd.read_csv(filename)\n",
    "    df.datetime = pd.to_datetime(df.datetime) # change type from object to datetime\n",
    "    df = df.set_index('datetime') \n",
    "    df = df.sort_index() # sort by datetime\n",
    "    print(df.shape)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading data from BTC_USD_Coinbase_hour_2017-12-24.csv\n",
      "(2001, 6)\n",
      "Reading data from ETH_USD_Coinbase_hour_2017-12-24.csv\n",
      "(2001, 6)\n",
      "Reading data from LTC_USD_Coinbase_hour_2017-12-24.csv\n",
      "(2001, 6)\n"
     ]
    }
   ],
   "source": [
    "df_btc = read_dataset(get_filename('BTC', 'USD', 'Coinbase', 'hour', '2017-12-24'))\n",
    "df_eth = read_dataset(get_filename('ETH', 'USD', 'Coinbase', 'hour', '2017-12-24'))\n",
    "df_ltc = read_dataset(get_filename('LTC', 'USD', 'Coinbase', 'hour', '2017-12-24'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volumefrom</th>\n",
       "      <th>volumeto</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-02 08:00:00</th>\n",
       "      <td>4435.00</td>\n",
       "      <td>4448.98</td>\n",
       "      <td>4435.01</td>\n",
       "      <td>4448.85</td>\n",
       "      <td>85.51</td>\n",
       "      <td>379813.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 09:00:00</th>\n",
       "      <td>4448.84</td>\n",
       "      <td>4470.00</td>\n",
       "      <td>4448.85</td>\n",
       "      <td>4464.49</td>\n",
       "      <td>165.17</td>\n",
       "      <td>736269.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 10:00:00</th>\n",
       "      <td>4450.27</td>\n",
       "      <td>4469.00</td>\n",
       "      <td>4464.49</td>\n",
       "      <td>4461.63</td>\n",
       "      <td>194.95</td>\n",
       "      <td>870013.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 11:00:00</th>\n",
       "      <td>4399.00</td>\n",
       "      <td>4461.63</td>\n",
       "      <td>4461.63</td>\n",
       "      <td>4399.51</td>\n",
       "      <td>326.71</td>\n",
       "      <td>1445572.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 12:00:00</th>\n",
       "      <td>4378.22</td>\n",
       "      <td>4417.91</td>\n",
       "      <td>4399.51</td>\n",
       "      <td>4383.00</td>\n",
       "      <td>549.29</td>\n",
       "      <td>2412712.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         low     high     open    close  volumefrom    volumeto\n",
       "datetime                                                                       \n",
       "2017-10-02 08:00:00  4435.00  4448.98  4435.01  4448.85       85.51   379813.67\n",
       "2017-10-02 09:00:00  4448.84  4470.00  4448.85  4464.49      165.17   736269.53\n",
       "2017-10-02 10:00:00  4450.27  4469.00  4464.49  4461.63      194.95   870013.62\n",
       "2017-10-02 11:00:00  4399.00  4461.63  4461.63  4399.51      326.71  1445572.02\n",
       "2017-10-02 12:00:00  4378.22  4417.91  4399.51  4383.00      549.29  2412712.73"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_btc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract closing prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to analyze closing prices, which are prices at which the hourly period closed. \n",
    "We merge BTC, ETH and LTC closing prices to a Dataframe to make analysis easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'BTC': df_btc.close,\n",
    "                   'ETH': df_eth.close,\n",
    "                   'LTC': df_ltc.close})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>ETH</th>\n",
       "      <th>LTC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-02 08:00:00</th>\n",
       "      <td>4448.85</td>\n",
       "      <td>301.37</td>\n",
       "      <td>54.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 09:00:00</th>\n",
       "      <td>4464.49</td>\n",
       "      <td>301.84</td>\n",
       "      <td>54.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 10:00:00</th>\n",
       "      <td>4461.63</td>\n",
       "      <td>301.95</td>\n",
       "      <td>54.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 11:00:00</th>\n",
       "      <td>4399.51</td>\n",
       "      <td>300.02</td>\n",
       "      <td>54.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 12:00:00</th>\n",
       "      <td>4383.00</td>\n",
       "      <td>297.51</td>\n",
       "      <td>53.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         BTC     ETH    LTC\n",
       "datetime                                   \n",
       "2017-10-02 08:00:00  4448.85  301.37  54.72\n",
       "2017-10-02 09:00:00  4464.49  301.84  54.79\n",
       "2017-10-02 10:00:00  4461.63  301.95  54.63\n",
       "2017-10-02 11:00:00  4399.51  300.02  54.01\n",
       "2017-10-02 12:00:00  4383.00  297.51  53.71"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "### Basic statistics\n",
    "\n",
    "In 2.7 months, all three cryptocurrencies fluctuated a lot as you can observe in the table below. \n",
    "\n",
    "For each coin, we count the number of events and calculate mean, standard deviation, minimum, quartiles and maximum closing price. Quartiles divide the data into four equal groups, each group comprising a quarter of the data.\n",
    "\n",
    "**Few interesting facts**\n",
    "- The difference between the highest and the lowest BTC price was more than \\$15000 in 2.7 months.\n",
    "- The LTC surged from \\$48.61 to \\$378.66 at a certain point, which is an increase of 678.98%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>ETH</th>\n",
       "      <th>LTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9060.256122</td>\n",
       "      <td>407.263793</td>\n",
       "      <td>106.790100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4404.269591</td>\n",
       "      <td>149.480416</td>\n",
       "      <td>89.142241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4150.020000</td>\n",
       "      <td>277.810000</td>\n",
       "      <td>48.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5751.020000</td>\n",
       "      <td>301.510000</td>\n",
       "      <td>55.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7319.950000</td>\n",
       "      <td>330.800000</td>\n",
       "      <td>63.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11305.000000</td>\n",
       "      <td>464.390000</td>\n",
       "      <td>100.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19847.110000</td>\n",
       "      <td>858.900000</td>\n",
       "      <td>378.660000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BTC          ETH          LTC\n",
       "count   2001.000000  2001.000000  2001.000000\n",
       "mean    9060.256122   407.263793   106.790100\n",
       "std     4404.269591   149.480416    89.142241\n",
       "min     4150.020000   277.810000    48.610000\n",
       "25%     5751.020000   301.510000    55.580000\n",
       "50%     7319.950000   330.800000    63.550000\n",
       "75%    11305.000000   464.390000   100.050000\n",
       "max    19847.110000   858.900000   378.660000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets dive deeper into LTC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the data in the table above with a box plot. \n",
    "A box plot shows the quartiles of the dataset with points that are determined to be outliers using a method of the [inter-quartile range](https://en.wikipedia.org/wiki/Interquartile_range) (IQR). \n",
    "`IQR = Q3 − Q1`. \n",
    "In other words, the IQR is the first quartile (25%) subtracted from the third quartile (75%).\n",
    "\n",
    "On the box plot below, \n",
    "we see that LTC closing hourly price was most of the time between \\$50 and \\$100 in the last 2.7 months. \n",
    "All values over \\$150 are outliers (using IQR) in our sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb680b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "ax = sns.boxplot(data=df['LTC'], orient=\"h\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram of LTC closing price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's estimate the frequency distribution of LTC closing prices. \n",
    "\n",
    "**Observations**\n",
    "- it shows the number of hours LTC had a certain value. For example, we can observe that LTC closing price was not over \\$100 for many hours.\n",
    "- it has right-skewed distribution because a natural limit prevents outcomes on one side.\n",
    "- blue dotted line (median) shows that half of the closing prices were under 63.50\\$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x10f69ee80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Um/hqkm1Vdd5JvA4AAADZfIBrSf5TVe2vqtXh2FJr7dFh/7EkS8P++UkeXve1jwzHAAAA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\nnRDgAAAAOvH/A7E5FZTbWzRcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111add470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['LTC'].hist(bins=30, figsize=(15,10)).axvline(df['LTC'].median(), color='b', linestyle='dashed', linewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize absolute closing prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart below shows absolute closing prices. \n",
    "It is not of much use as BTC closing prices are much higher \n",
    "then prices of ETH and LTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111c40470>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5AAAAMA7iiYGhL1Ck0Le3uy/sBXyezPXX7uyUMVLInx6Xu9rmNHYZieqgTx4j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\nHXYax3Yey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OYDmFkW+Djw9Rrm8VDyU3zwCczsaCADdNc0R9Msatm8HHgkhPClqunV3fMu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b46e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(grid=True, figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize relative changes of closing prices\n",
    "\n",
    "We are interested in a relative change of the price \n",
    "rather than in absolute price, so we use three different scales.\n",
    "\n",
    "We see that closing prices move in tandem. When one coin closing price increases so do the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11266cef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LC4UK7qV2ddV6uSJDTk+HFYiIiIiIyADI2iwGw5//DKefDo2NgHWVA53pTgBe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\nPT99D53pzuKdFXSlu2gIVb7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v+8Pec7z9xxtTuQxH4YCIiIiIjBidnd3D7LuFwxCLwUxv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\nbErayS/kX4hnFj9D43KNWdF3B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PsH6pepn25NgcuR3DkQWiyUMTePoVloM95f/T7R8dGX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122828d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(20, 10))\n",
    "ax2 = ax1.twinx()\n",
    "rspine = ax2.spines['right']\n",
    "rspine.set_position(('axes', 1.15))\n",
    "ax2.set_frame_on(True)\n",
    "ax2.patch.set_visible(False)\n",
    "fig.subplots_adjust(right=0.7)\n",
    "\n",
    "df['BTC'].plot(ax=ax1, style='b-')\n",
    "df['ETH'].plot(ax=ax1, style='r-', secondary_y=True)\n",
    "df['LTC'].plot(ax=ax2, style='g-')\n",
    "\n",
    "# legend\n",
    "ax2.legend([ax1.get_lines()[0],\n",
    "            ax1.right_ax.get_lines()[0],\n",
    "            ax2.get_lines()[0]],\n",
    "           ['BTC', 'ETH', 'LTC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measure correlation of closing prices \n",
    "\n",
    "We calculate [Pearson correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)\n",
    "between closing prices of BTC, ETH and LTC. \n",
    "Pearson correlation is a measure of the linear correlation between two variables X and Y. \n",
    "It has a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation.\n",
    "\n",
    "[Sifr Data](https://www.sifrdata.com/cryptocurrency-correlation-matrix/) daily updates Pearson correlations for many cryptocurrencies.\n",
    "\n",
    "**Observations**\n",
    "- Corelation matrix is symetric so we only show the lower half. \n",
    "- BTC, ETH and LTC were highly correlated in past 2 months. This means, when BTC closing price increased, ETH and LTC followed. \n",
    "- ETH and LTC were even more correlated with 0.9565 Pearson correlation coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11282cb38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P32NBfn/fnafsvbNhzJo5I+/7g+fnyE/8U0ZHR3P4s5+c3XbYOn/1tR9m9523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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f69e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = df.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, annot=True, fmt = '.4f', mask=mask, center=0, square=True, linewidths=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Buy and hold strategy\n",
    "\n",
    "[Buy and hold](https://www.investopedia.com/terms/b/buyandhold.asp) is a passive investment strategy in which an investor buys a cryptocurrency and holds it for a long period of time, regardless of fluctuations in the market. \n",
    "\n",
    "Let's analyze returns using buy and hold strategy for past 2.7 months. \n",
    "We calculate the return percentage, where $t$ represents a certain time period and $price_0$ is initial closing price:\n",
    "\n",
    "$$\n",
    "return_{t, 0} = \\frac{price_t}{price_0}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>ETH</th>\n",
       "      <th>LTC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-02 08:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 09:00:00</th>\n",
       "      <td>1.003516</td>\n",
       "      <td>1.001560</td>\n",
       "      <td>1.001279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 10:00:00</th>\n",
       "      <td>1.002873</td>\n",
       "      <td>1.001925</td>\n",
       "      <td>0.998355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 11:00:00</th>\n",
       "      <td>0.988909</td>\n",
       "      <td>0.995520</td>\n",
       "      <td>0.987025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02 12:00:00</th>\n",
       "      <td>0.985198</td>\n",
       "      <td>0.987192</td>\n",
       "      <td>0.981542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          BTC       ETH       LTC\n",
       "datetime                                         \n",
       "2017-10-02 08:00:00  1.000000  1.000000  1.000000\n",
       "2017-10-02 09:00:00  1.003516  1.001560  1.001279\n",
       "2017-10-02 10:00:00  1.002873  1.001925  0.998355\n",
       "2017-10-02 11:00:00  0.988909  0.995520  0.987025\n",
       "2017-10-02 12:00:00  0.985198  0.987192  0.981542"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_return = df.apply(lambda x: x / x[0])\n",
    "df_return.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize returns\n",
    "\n",
    "We show that LTC was the most profitable for time period between October 2, 2017 and December 24, 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x11234d5f8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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039PONbudzz0u3g4wM51DZr9+tgOiS8UdTH927s9S7rP3k9kh0/6e3BMk82F/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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1123349b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_return.plot(grid=True, figsize=(15, 10)).axhline(y = 1, color = \"black\", lw = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "The cryptocurrencies we analyzed fluctuated a lot but all of gained in given 2.7 months period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the percentage increase?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>ETH</th>\n",
       "      <th>LTC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-24 16:00:00</th>\n",
       "      <td>314.688065</td>\n",
       "      <td>226.900488</td>\n",
       "      <td>501.407164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            BTC         ETH         LTC\n",
       "datetime                                               \n",
       "2017-12-24 16:00:00  314.688065  226.900488  501.407164"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JEyYM1tj4f9XV1dq1a5feeustSdKJEyeUn58v6a9/9c+aNUtOp1MrVqzQggULtHv3bu3f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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142d3710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_perc = df_return.tail(1) * 100\n",
    "ax = sns.barplot(data=df_perc)\n",
    "df_perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many coins could we bought for $1000?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>ETH</th>\n",
       "      <th>LTC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-02 08:00:00</th>\n",
       "      <td>0.224777</td>\n",
       "      <td>3.31818</td>\n",
       "      <td>18.274854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          BTC      ETH        LTC\n",
       "datetime                                         \n",
       "2017-10-02 08:00:00  0.224777  3.31818  18.274854"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d94390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "budget = 1000 # USD\n",
    "df_coins = budget/df.head(1)\n",
    "\n",
    "ax = sns.barplot(data=df_coins)\n",
    "df_coins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How much money would we make?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>ETH</th>\n",
       "      <th>LTC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-24 16:00:00</th>\n",
       "      <td>3146.880655</td>\n",
       "      <td>2269.004878</td>\n",
       "      <td>5014.071637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             BTC          ETH          LTC\n",
       "datetime                                                  \n",
       "2017-12-24 16:00:00  3146.880655  2269.004878  5014.071637"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137668d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_profit = df_return.tail(1) * budget\n",
    "\n",
    "ax = sns.barplot(data=df_profit)\n",
    "df_profit"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}