protfolio analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9498146-f3cd-45cd-8230-6dd56c2ca5a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "df1 = pd.read_csv('BCHAIN-MKPRU.csv',header=0,index_col=0,parse_dates = True)\n",
    "df2 = pd.read_csv('LBMA-GOLD.csv',header=0,index_col=0,parse_dates = True)\n",
    "df2.fillna(df2.mean(),axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "67205bc2-399d-4c3a-8c59-3d027ac59def",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Value</th>\n",
       "      <th>USD (PM)</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>2016-09-12</th>\n",
       "      <td>609.67</td>\n",
       "      <td>1324.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-13</th>\n",
       "      <td>610.92</td>\n",
       "      <td>1323.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-14</th>\n",
       "      <td>608.82</td>\n",
       "      <td>1321.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-15</th>\n",
       "      <td>610.38</td>\n",
       "      <td>1310.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-16</th>\n",
       "      <td>609.11</td>\n",
       "      <td>1308.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Value  USD (PM)\n",
       "Date                        \n",
       "2016-09-12  609.67   1324.60\n",
       "2016-09-13  610.92   1323.65\n",
       "2016-09-14  608.82   1321.75\n",
       "2016-09-15  610.38   1310.80\n",
       "2016-09-16  609.11   1308.35"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([df1,df2],join='inner',axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a0f72ef-6f37-4485-9154-13cbe4f22849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Value</th>\n",
       "      <th>USD (PM)</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>2021-09-06</th>\n",
       "      <td>51769.06</td>\n",
       "      <td>1821.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>52677.40</td>\n",
       "      <td>1802.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>46809.17</td>\n",
       "      <td>1786.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>46078.38</td>\n",
       "      <td>1788.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>46368.69</td>\n",
       "      <td>1794.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Value  USD (PM)\n",
       "Date                          \n",
       "2021-09-06  51769.06   1821.60\n",
       "2021-09-07  52677.40   1802.15\n",
       "2021-09-08  46809.17   1786.00\n",
       "2021-09-09  46078.38   1788.25\n",
       "2021-09-10  46368.69   1794.60"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "25b9e7dd-e18e-499d-9406-93989c1944ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Daily Returns\n",
    "returns = data.pct_change()\n",
    "returns = returns.dropna()\n",
    "returns.describe().to_csv(\"daily ret.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "77f6516b-a863-4d48-94c7-02a75807a3e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value       0.004617\n",
      "USD (PM)    0.000544\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean returns\n",
    "meanDailyReturns = returns.mean()\n",
    "print(meanDailyReturns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ba023dd2-2713-43fb-b448-88540ed94f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value       0.048424\n",
      "USD (PM)    0.025040\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate std returns\n",
    "stdDailyReturns = returns.std()\n",
    "print(stdDailyReturns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "be9fe1c7-d3f8-4842-8f6f-da3ac3f767d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define weights for the portfolio权重初始化\n",
    "weights = np.array([0.33,0.67])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bd5d7fcb-eb6f-431e-be72-991712238415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Value  USD (PM)\n",
      "Value     0.586226  0.016202\n",
      "USD (PM)  0.016202  0.156745\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the covariance matrix on daily returns\n",
    "import seaborn as sns\n",
    "cov_matrix = (returns.cov())*250\n",
    "print (cov_matrix)\n",
    "sns.heatmap(cov_matrix, cmap ='Blues', cbar=False,annot=True)\n",
    "plt.title('The covariance matrix on daily returns')\n",
    "plt.savefig('the covariance matrix on daily returns.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6dbb9e15-168d-4c1e-ab25-0f909fb46764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00188778562857391\n"
     ]
    }
   ],
   "source": [
    "# Calculate expected portfolio performance\n",
    "portReturn = np.sum(meanDailyReturns*weights)\n",
    "# Print the portfolio return\n",
    "print(portReturn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3229ef44-06e0-4c2a-b749-19e83f750b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create portfolio returns column\n",
    "returns['Portfolio'] = np.dot(weights,returns.T)\n",
    "returns.tail()\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.plot(returns['Portfolio'],color='#00338D',label = 'Performance')\n",
    "plt.legend()\n",
    "plt.title('Portfolio Performance')\n",
    "plt.savefig('perf.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3289dab9-f394-4f51-bc57-43b016d22c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Value  USD (PM)  Portfolio\n",
      "Date                                      \n",
      "2021-09-06  84.913248  1.375208   8.023938\n",
      "2021-09-07  86.403136  1.360524   8.012996\n",
      "2021-09-08  76.777880  1.348332   7.670312\n",
      "2021-09-09  75.579215  1.350030   7.637269\n",
      "2021-09-10  76.055391  1.354824   7.671317\n"
     ]
    }
   ],
   "source": [
    "# Calculate cumulative returns\n",
    "daily_cum_ret=(1+returns).cumprod()\n",
    "print(daily_cum_ret.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "962692f9-c393-4949-8bc8-13a2c5ec9d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns['Portfolio'].hist(legend=True,bins=3,color='#00338D',grid=False)\n",
    "plt.title('Portfolio Performance histplot')\n",
    "plt.savefig('hist.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8552a170-b354-4881-8b2d-9a99ab65a9e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates\n",
    "\n",
    "# Plot the portfolio cumulative returns only\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(daily_cum_ret.index, daily_cum_ret.Portfolio, color='#00A3A3', label=\"portfolio\")\n",
    "ax.xaxis.set_major_locator(matplotlib.dates.YearLocator())\n",
    "plt.title('Portfolio cumulative returns')\n",
    "plt.legend()\n",
    "plt.savefig('portfolio cumulative returns.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4633e65c-65c2-4b5e-afd8-b02fda459321",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean :  0.18877856285739084\n",
      "Std. dev:  2.3779626633392357\n",
      "Skew:  2.2319377295665452\n",
      "Kurt:  33.30553874211881\n",
      "Portfolio Sqrt Std. dev: 37.6%\n",
      "Portfolio Var: 14.14%\n",
      "Portfolio Annualized return in 1 year: 37.6%\n"
     ]
    }
   ],
   "source": [
    "# Print the mean\n",
    "print(\"Mean : \", returns['Portfolio'].mean()*100)\n",
    "\n",
    "# Print the standard deviation\n",
    "print(\"Std. dev: \", returns['Portfolio'].std()*100)\n",
    "\n",
    "# Print the skewness\n",
    "print(\"Skew: \", returns['Portfolio'].skew())\n",
    "\n",
    "# Print the kurtosis\n",
    "print(\"Kurt: \", returns['Portfolio'].kurtosis())\n",
    "# Calculate the standard deviation by taking the square root\n",
    "port_standard_dev = np.sqrt(np.dot(weights.T, np.dot(weights, cov_matrix)))\n",
    "\n",
    "# Print the results \n",
    "print('Portfolio Sqrt Std. dev:', str(np.round(port_standard_dev, 4) * 100) + '%')\n",
    "\n",
    "# Calculate the portfolio variance\n",
    "port_variance = np.dot(weights.T, np.dot(cov_matrix, weights))\n",
    "\n",
    "# Print the result\n",
    "print('Portfolio Var:',str(np.round(port_variance, 4) * 100) + '%')\n",
    "# Calculate total return and annualized return from price data \n",
    "total_return = (returns['Portfolio'][-1] - returns['Portfolio'][0]) / returns['Portfolio'][0]\n",
    "\n",
    "# Annualize the total return over 6 year \n",
    "annualized_return = ((total_return + 1)**(1/(365)))-1\n",
    "print('Portfolio Annualized return in 1 year:', str(np.round(port_standard_dev, 4) * 100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ec518427-5c90-43c6-bb9f-186c4a6f9c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.6%\n"
     ]
    }
   ],
   "source": [
    "# Calculate the standard deviation by taking the square root\n",
    "port_standard_dev = np.sqrt(np.dot(weights.T, np.dot(weights, cov_matrix)))\n",
    "\n",
    "# Print the results \n",
    "print(str(np.round(port_standard_dev, 4) * 100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c5e8e365-41c1-4672-b45d-306a41331a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.14%\n"
     ]
    }
   ],
   "source": [
    "# Calculate the portfolio variance\n",
    "port_variance = np.dot(weights.T, np.dot(cov_matrix, weights))\n",
    "\n",
    "# Print the result\n",
    "print(str(np.round(port_variance, 4) * 100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "17182d2f-a060-4491-90f6-7ab7472a2acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate total return and annualized return from price data \n",
    "total_return = (returns['Portfolio'][-1] - returns['Portfolio'][0]) / returns['Portfolio'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "393e00a4-be32-416d-a031-4436750734fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.737730857964838"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualize the total return over 6 year \n",
    "annualized_return = ((total_return + 1)**(1/(1)))-1\n",
    "annualized_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "afd61214-f243-4ba7-baf2-a5bc0c40c7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate annualized volatility from the standard deviation\n",
    "vol_port = returns['Portfolio'].std() * np.sqrt(250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b20ede63-008a-4d16-a685-28f556f2a768",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the Sharpe ratio \n",
    "rf = 0.01\n",
    "sharpe_ratio = ((annualized_return - rf) / vol_port)\n",
    "print (sharpe_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "3e62b377-f916-4be4-98cd-31d970168349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coming sharpe_ratio in 1 day: 57.79\n",
      "The coming sharpe_ratio in 2 day: 10.00\n",
      "The coming sharpe_ratio in 3 day: 4.85\n",
      "The coming sharpe_ratio in 4 day: 3.12\n",
      "The coming sharpe_ratio in 5 day: 2.28\n",
      "The coming sharpe_ratio in 6 day: 1.79\n",
      "The coming sharpe_ratio in 7 day: 1.47\n",
      "The coming sharpe_ratio in 8 day: 1.24\n",
      "The coming sharpe_ratio in 9 day: 1.08\n",
      "The coming sharpe_ratio in 10 day: 0.95\n",
      "The coming sharpe_ratio in 11 day: 0.85\n",
      "The coming sharpe_ratio in 12 day: 0.76\n",
      "The coming sharpe_ratio in 13 day: 0.70\n",
      "The coming sharpe_ratio in 14 day: 0.64\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,15):\n",
    "    # Annualize the total return over 6 year \n",
    "    annualized_return = ((total_return + 1)**(1/(i)))-1\n",
    "    sharpe_ratio = ((annualized_return - rf) / vol_port)\n",
    "    print ('The coming sharpe_ratio in {} day: {:.2f}'.format(i,sharpe_ratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "db2c5160-8da7-423f-b6fa-55dd33e69c56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected return:0.19 \n",
      "--------------------------------------------------\n",
      "Downside risk:\n",
      "Value        3.816442\n",
      "USD (PM)     2.181406\n",
      "Portfolio    1.693912\n",
      "dtype: float64\n",
      "--------------------------------------------------\n",
      "Sortino ratio:\n",
      "Value       -0.212560\n",
      "USD (PM)    -0.371880\n",
      "Portfolio   -0.478904\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a downside return column with the negative returns only\n",
    "target = 0\n",
    "downside_returns = returns.loc[returns['Portfolio'] < target]\n",
    "\n",
    "# Calculate expected return and std dev of downside\n",
    "expected_return = returns['Portfolio'].mean()\n",
    "down_stdev = downside_returns.std()\n",
    "\n",
    "# Calculate the sortino ratio\n",
    "rf = 0.01\n",
    "sortino_ratio = (expected_return - rf)/down_stdev\n",
    "\n",
    "# Print the results\n",
    "print(\"Expected return:{:.2f} \".format(expected_return*100))\n",
    "print('-' * 50)\n",
    "print(\"Downside risk:\")\n",
    "print(down_stdev*100)\n",
    "print('-' * 50)\n",
    "print(\"Sortino ratio:\")\n",
    "print(sortino_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "4a201a7e-7be0-4880-87fa-ed15d8697592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the max value \n",
    "roll_max = returns['Portfolio'].rolling(center=False,min_periods=1,window=120).max()\n",
    "\n",
    "# Calculate the daily draw-down relative to the max\n",
    "daily_draw_down = returns['Portfolio']/roll_max - 1.0\n",
    "\n",
    "# Calculate the minimum (negative) daily draw-down\n",
    "max_daily_draw_down = daily_draw_down.rolling(center=False,min_periods=1,window=120).min()\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(returns.index, daily_draw_down, label='Daily drawdown',color='#483699')\n",
    "plt.plot(returns.index, max_daily_draw_down, label='Maximum daily drawdown in time-window',color ='#00A3A3')\n",
    "plt.legend()\n",
    "plt.title('Daily drawdown & Maximum daily drawdown in time-window')\n",
    "plt.savefig('drawdown.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e26bab99-2d06-4ae5-afc6-6e555169ffdc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}