comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import yfinance as yf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index and Crypto Historical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pull index historical data using yfinance API and save to csv\n",
    "ticker = \"^GSPC\" \n",
    "start= \"1990-01-01\" \n",
    "end= \"2022-01-21\"\n",
    "\n",
    "history_df = yf.download(ticker, start=start, end=end, progress=False)\n",
    "history_df = history_df.asfreq(freq='D').ffill().dropna()\n",
    "history_df.to_csv('resources/sp_history.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S&amp;P_Close</th>\n",
       "      <th>S&amp;P_Daily_Ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-16</th>\n",
       "      <td>4662.850098</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-17</th>\n",
       "      <td>4662.850098</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-18</th>\n",
       "      <td>4577.109863</td>\n",
       "      <td>-0.018388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-19</th>\n",
       "      <td>4532.759766</td>\n",
       "      <td>-0.009690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-20</th>\n",
       "      <td>4482.729980</td>\n",
       "      <td>-0.011037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              S&P_Close  S&P_Daily_Ret\n",
       "Date                                  \n",
       "2022-01-16  4662.850098       0.000000\n",
       "2022-01-17  4662.850098       0.000000\n",
       "2022-01-18  4577.109863      -0.018388\n",
       "2022-01-19  4532.759766      -0.009690\n",
       "2022-01-20  4482.729980      -0.011037"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in \"Adj Close\" and \"Date\" columns and rename column\n",
    "sp_historical = pd.read_csv('resources/sp_history.csv', parse_dates=True, usecols=['Date','Adj Close'], index_col='Date')\n",
    "\n",
    "# Rename column\n",
    "sp_close = sp_historical.rename(columns={'Adj Close':'S&P_Close'})\n",
    "\n",
    "# Calculate daily return \n",
    "sp_close['S&P_Daily_Ret'] = sp_close['S&P_Close'].pct_change()\n",
    "\n",
    "# Select date range starting from 2015-08-08 to have the same starting date for all datasets\n",
    "sp_close = sp_close.loc['2015-08-08':,:]\n",
    "#sp_close = sp_close.reset_index()\n",
    "sp_close.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DJI_Close</th>\n",
       "      <th>DJI_Daily_Ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>36338.300781</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>36585.058594</td>\n",
       "      <td>0.006791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>36799.648438</td>\n",
       "      <td>0.005866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>36407.109375</td>\n",
       "      <td>-0.010667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>36236.468750</td>\n",
       "      <td>-0.004687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               DJI_Close  DJI_Daily_Ret\n",
       "Date                                   \n",
       "2022-01-02  36338.300781       0.000000\n",
       "2022-01-03  36585.058594       0.006791\n",
       "2022-01-04  36799.648438       0.005866\n",
       "2022-01-05  36407.109375      -0.010667\n",
       "2022-01-06  36236.468750      -0.004687"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dji_historical = pd.read_csv('resources/dji_history.csv', parse_dates=True, usecols=['Date','Adj Close'], index_col='Date')\n",
    "\n",
    "dji_close = dji_historical.rename(columns={'Adj Close':'DJI_Close'})\n",
    "dji_close['DJI_Daily_Ret'] = dji_close['DJI_Close'].pct_change()\n",
    "\n",
    "dji_close = dji_close.loc['2015-08-08':,:]\n",
    "#dji_close = dji_close.reset_index()\n",
    "dji_close.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NDX_Close</th>\n",
       "      <th>NDX_Daily_Ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>16320.080078</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>16501.769531</td>\n",
       "      <td>0.011133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>16279.730469</td>\n",
       "      <td>-0.013455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>15771.780273</td>\n",
       "      <td>-0.031201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>15765.360352</td>\n",
       "      <td>-0.000407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               NDX_Close  NDX_Daily_Ret\n",
       "Date                                   \n",
       "2022-01-02  16320.080078       0.000000\n",
       "2022-01-03  16501.769531       0.011133\n",
       "2022-01-04  16279.730469      -0.013455\n",
       "2022-01-05  15771.780273      -0.031201\n",
       "2022-01-06  15765.360352      -0.000407"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndx_historical = pd.read_csv('resources/ndx_history.csv', parse_dates=True, usecols=['Date','Adj Close'], index_col='Date')\n",
    "\n",
    "ndx_close = ndx_historical.rename(columns={'Adj Close':'NDX_Close'})\n",
    "ndx_close['NDX_Daily_Ret'] = ndx_close['NDX_Close'].pct_change()\n",
    "\n",
    "ndx_close = ndx_close.loc['2015-08-08':,:]\n",
    "#ndx_close = ndx_close.reset_index()\n",
    "ndx_close.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETH_Close</th>\n",
       "      <th>ETH_Daily_Ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>299.252991</td>\n",
       "      <td>-0.067411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-11</th>\n",
       "      <td>314.681000</td>\n",
       "      <td>0.051555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>307.907990</td>\n",
       "      <td>-0.021523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>316.716003</td>\n",
       "      <td>0.028606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>337.631012</td>\n",
       "      <td>0.066037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ETH_Close  ETH_Daily_Ret\n",
       "Date                                 \n",
       "2017-11-10  299.252991      -0.067411\n",
       "2017-11-11  314.681000       0.051555\n",
       "2017-11-12  307.907990      -0.021523\n",
       "2017-11-13  316.716003       0.028606\n",
       "2017-11-14  337.631012       0.066037"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth_historical = pd.read_csv('resources/ETH-USD.csv', parse_dates=True, usecols=['Date','Adj Close'], index_col='Date')\n",
    "\n",
    "eth_close = eth_historical.rename(columns={'Adj Close':'ETH_Close'})\n",
    "eth_close['ETH_Daily_Ret'] = eth_close['ETH_Close'].pct_change()\n",
    "eth_close = eth_close.dropna()\n",
    "\n",
    "eth_close = eth_close.loc['2015-08-08':,:]\n",
    "eth_close.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>BTC_Close</th>\n",
       "      <th>BTC_Daily_Ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-08</th>\n",
       "      <td>260.997009</td>\n",
       "      <td>-0.066484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-09</th>\n",
       "      <td>265.083008</td>\n",
       "      <td>0.015655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-10</th>\n",
       "      <td>264.470001</td>\n",
       "      <td>-0.002313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-11</th>\n",
       "      <td>270.385986</td>\n",
       "      <td>0.022369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12</th>\n",
       "      <td>266.376007</td>\n",
       "      <td>-0.014831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BTC_Close  BTC_Daily_Ret\n",
       "Date                                 \n",
       "2015-08-08  260.997009      -0.066484\n",
       "2015-08-09  265.083008       0.015655\n",
       "2015-08-10  264.470001      -0.002313\n",
       "2015-08-11  270.385986       0.022369\n",
       "2015-08-12  266.376007      -0.014831"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_historical = pd.read_csv('resources/BTC-USD.csv', parse_dates=True, usecols=['Date','Adj Close'], index_col='Date')\n",
    "\n",
    "btc_close = btc_historical.rename(columns={'Adj Close':'BTC_Close'})\n",
    "btc_close['BTC_Daily_Ret'] = btc_close['BTC_Close'].pct_change()\n",
    "\n",
    "btc_close = btc_close.loc['2015-08-08':,:]\n",
    "btc_close.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concat Close prices into one Dataframe\n",
    "combined_close = pd.concat([sp_close, dji_close, ndx_close, eth_close, btc_close],axis=1)\n",
    "# Set \"Date\" as index and drop NaN2\n",
    "combined_close = combined_close.astype('float').round(2)\n",
    "combined_close = combined_close.dropna()\n",
    "combined_close.to_csv('resources/combined_close.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Returns Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative Returns'}, xlabel='Date'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_return = combined_close[['S&P_Daily_Ret','DJI_Daily_Ret', 'NDX_Daily_Ret','ETH_Daily_Ret','BTC_Daily_Ret']]\n",
    "cumulative_ret = (1+combined_return).cumprod()\n",
    "cumulative_ret.plot(figsize=(12,7), title='Cumulative Returns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cumulative return graph shows that BTC and ETH provided an investor with genereous returns from 2016-2021."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x504 with 0 Axes>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = combined_return.corr()\n",
    "sns.heatmap(corr, annot=True)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.subplots_adjust(left=0.20, right=0.99, bottom=0.30, top=0.99)\n",
    "plt.figure(figsize=(12,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation matrix shows low correlation of cryptos and market indexes. This means they could be used as an effective diversifying asset class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S&P_Daily_Ret    0.011124\n",
       "DJI_Daily_Ret    0.011609\n",
       "NDX_Daily_Ret    0.012938\n",
       "ETH_Daily_Ret    0.051855\n",
       "BTC_Daily_Ret    0.041398\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate standard dev of daily returns\n",
    "combined_return_std = combined_return.std()\n",
    "combined_return_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Asset Risk'}>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_return.plot.box(title=\"Asset Risk\", rot=45, figsize=(12,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk plot shows significant ETH and BTC return outliers compared to market indexes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S&P_Daily_Ret    0.176585\n",
       "DJI_Daily_Ret    0.184294\n",
       "NDX_Daily_Ret    0.205380\n",
       "ETH_Daily_Ret    0.823174\n",
       "BTC_Daily_Ret    0.657172\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualized St Dev (cryptos are traded 365 days but here I assumed 252 days as with index data)\n",
    "combined_annualized_std = combined_return_std * np.sqrt(252)\n",
    "combined_annualized_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S&P_Daily_Ret    0.469742\n",
       "DJI_Daily_Ret    0.504104\n",
       "NDX_Daily_Ret    0.896620\n",
       "ETH_Daily_Ret    0.904893\n",
       "BTC_Daily_Ret    0.759854\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Sharpe Ratios (risk-adjusted returns)\n",
    "combined_sharpe_ratios = (combined_return.mean() * 252) / (combined_return.std() * np.sqrt(252))\n",
    "combined_sharpe_ratios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharpe Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sharpe Ratios'}, ylabel='Sharpe Ratio'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the sharpe ratios as a bar plot\n",
    "combined_sharpe_ratios.plot(kind=\"bar\", title=\"Sharpe Ratios\", figsize=(12,7), ylabel='Sharpe Ratio', rot=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ETH and BTC more than compensated the investor for the risk taken compared to investing into market index from 2017-2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S&P_Daily_Ret    0.391777\n",
       "DJI_Daily_Ret    0.406943\n",
       "NDX_Daily_Ret    0.886243\n",
       "ETH_Daily_Ret    1.229085\n",
       "BTC_Daily_Ret    1.025103\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Sortino Ratios (risk-adjusted returns)\n",
    "combined_sortino_ratios = (combined_return.mean() * 252) / (combined_return[combined_return<0].std() * np.sqrt(252))\n",
    "combined_sortino_ratios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sortino Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sortino Ratios'}, ylabel='Sortino Ratio'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the Sortino ratios as a bar plot\n",
    "combined_sortino_ratios.plot(kind=\"bar\", title=\"Sortino Ratios\", figsize=(12,7), ylabel='Sortino Ratio', rot=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at Sortino ratio that considers downside risk also shows that ETH and then BTC compensated sufficiently an investor for the risk taken from 2017-2022."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "1d1365bc7bea9ca53bbaaff2a498c5cc15b63a6fcc47bade387e2edfe7e7b068"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9 64-bit ('algotrading': conda)",
   "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": "3.7.9"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}