dynamic-investment-portfolio-optimization.ipynb

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   "source": [
    "# 1. Problem undestanding"
   ]
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    "First, it is important to understand that a portfolio is no more than just a collection of stock investments held by an investor. Optimizing a financial portfolio requires selecting the best possible portfolio out of a set of all portfolios being considered according to an objective.\n",
    "\n",
    "According to modern portfolio theory, one possible way to optimize a portfolio is to aim for an efficient frontier. The efficient frontier is a investment portfolio that lies on the most efficient(ideal) coordinates of the risk-return graph(spectrum). There is a formal definition that is used by many experts in this field, \"Efficient frontier is the set of portfolios which satisfy the condition that no other portfolio exists with a higher expected return but with the same standard deviation of return.\n",
    "\n",
    "Now we know the aim is to achieve one of the efficient frontier portfolios. However, we can even go one step further than the overall portfolio. We can identify the best possible proportion of the stocks to use in any given portfolio. This is possible by using the sharpe ratio.\n",
    "\n",
    "The sharpe ratio was developed by William Sharpe in 1966. The sharpe ratio describes how much excess return of money one recieves for the extra volatile time he/she holds onto the investment. Basically, it measures how much extra capital money one can recieve by waiting and holding onto a risky stock investment. The sharpe ratio can be calculated by taking the difference between the returns of the investment and the risk-free return, divided by the standard deviation of the investment.\n",
    "\n",
    "Usually experts define four categories for the result of the sharpe ratio:\n",
    "\n",
    "Sharpe Ratio\tCategory\n",
    "* ratio < 1.0\tsub-optimal\n",
    "* ratio > 1.0\tgood\n",
    "* ratio > 2.0\tvery good\n",
    "* ratio > 3.0\texcellent\n",
    "\n",
    "After considering the efficient frontier theory, as well as the sharpe ratio, it is time to start the code to optimize a sample financial portfolio.\n",
    "\n"
   ]
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   "source": [
    "# 2. Import dependencies"
   ]
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      "Successfully installed PyPortfolioOpt-1.5.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
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      "\u001b[0m"
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    "!pip install 'git+https://github.com/cvxgrp/cvxpy.git#egg=cvxpy[glpk]'\n",
    "\n",
    "!pip install PyPortfolioOpt\n",
    "\n",
    "!pip install plotly\n",
    "\n",
    "!pip install tqdm"
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   "source": [
    "from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices\n",
    "from pypfopt import EfficientFrontier\n",
    "from pypfopt import risk_models\n",
    "from pypfopt import expected_returns\n",
    "from pypfopt import plotting\n",
    "from pypfopt import discrete_allocation \n",
    "\n",
    "from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices\n",
    "import plotly.graph_objects as go\n",
    "from tqdm import tqdm\n",
    "import copy\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
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   "source": [
    "# 2. Transforming the data\n",
    "> Combining the multiple CSV files into a single one, so it gets easier to manipulate"
   ]
  },
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    "def create_df(location):\n",
    "    df = pd.DataFrame()\n",
    "\n",
    "    all_files = []\n",
    "    data = location\n",
    "    for path, subdirs, files in os.walk(data):\n",
    "        for name in files:\n",
    "            all_files.append(os.path.join(path, name))\n",
    "            \n",
    "    df.index = pd.read_csv(all_files[0] ,parse_dates = True, index_col = 'Date').index\n",
    "    \n",
    "    k = 0\n",
    "    for item in all_files:\n",
    "        df_item = pd.read_csv(item ,parse_dates = True, index_col = 'Date')\n",
    "        df_name = item[item.rfind('/')+1:item.rfind('.')]\n",
    "        df_item.rename(columns = {'Close': df_name }, inplace = True)\n",
    "        df = pd.concat([df, df_item[df_name]], axis=1, join='inner')\n",
    "        k+=1\n",
    "    return df\n",
    "\n",
    "df_crypto = create_df(\"../input/reduceddata/datahack_reduced/crypto\")"
   ]
  },
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   "source": [
    "## Plot Individual Stock Prices\n",
    "\n",
    "> The graph is plotted with plotly library. It is an interactive graph."
   ]
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   "source": [
    "fig_price = px.line(df_crypto, title='Price of Individual Stocks')\n",
    "fig_price.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73d0adf",
   "metadata": {
    "id": "A4pOj5dIHuOO",
    "papermill": {
     "duration": 0.02286,
     "end_time": "2022-06-26T19:54:04.745118",
     "exception": false,
     "start_time": "2022-06-26T19:54:04.722258",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As seen above, Bitcoin seems to dominate the scale of the graph as the absolute price of the stock is very high. The graphs of all other stocks are flattened out. A graph like this is not very useful to compare the relative performance of the stocks. To address this, let's see how we can better measure the performance of a stock by exploring the concepts of daily returns and volatility."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e2363c",
   "metadata": {
    "id": "05oqw2ZVHuOP",
    "papermill": {
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     "start_time": "2022-06-26T19:54:04.770497",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Daily Returns\n",
    "The daily returns of a stock is the fractional gain (or loss) on a given day relative to the previous day, it is given by (current day's close price - previous day's close price) / (previous day's close price). As it is a relative value, it provides a fairer comparison between stock returns regardless of absolute stock prices. The pct_change() method can be used to get the daily returns efficiently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aef50480",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:04.865062Z",
     "iopub.status.busy": "2022-06-26T19:54:04.864450Z",
     "iopub.status.idle": "2022-06-26T19:54:04.914234Z",
     "shell.execute_reply": "2022-06-26T19:54:04.913000Z"
    },
    "id": "j0rVz3tqHuOP",
    "outputId": "941711b4-f8a1-4263-b17c-5b48375ca055",
    "papermill": {
     "duration": 0.092305,
     "end_time": "2022-06-26T19:54:04.917559",
     "exception": false,
     "start_time": "2022-06-26T19:54:04.825254",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRON</th>\n",
       "      <th>USD Coin</th>\n",
       "      <th>Binance Coin</th>\n",
       "      <th>Dogecoin</th>\n",
       "      <th>Ethereum</th>\n",
       "      <th>XRP</th>\n",
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       "      <th>Litecoin</th>\n",
       "      <th>Aave</th>\n",
       "      <th>VeChain</th>\n",
       "      <th>THETA</th>\n",
       "      <th>Polkadot</th>\n",
       "      <th>Cardano</th>\n",
       "      <th>Chainlink</th>\n",
       "      <th>Bitcoin</th>\n",
       "      <th>Filecoin</th>\n",
       "      <th>Terra</th>\n",
       "      <th>Stellar</th>\n",
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       "      <th>Date</th>\n",
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       "      <th>2020-11-07</th>\n",
       "      <td>-0.043815</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>-0.044735</td>\n",
       "      <td>-0.031556</td>\n",
       "      <td>-0.044589</td>\n",
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       "      <td>-0.056081</td>\n",
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       "      <td>-0.051621</td>\n",
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       "      <th>2020-11-08</th>\n",
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       "      <td>0.044281</td>\n",
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       "      <td>0.036887</td>\n",
       "      <td>0.209637</td>\n",
       "      <td>0.071759</td>\n",
       "      <td>0.041373</td>\n",
       "      <td>0.031840</td>\n",
       "      <td>0.052320</td>\n",
       "      <td>0.082553</td>\n",
       "      <td>0.044192</td>\n",
       "      <td>0.026702</td>\n",
       "      <td>0.042582</td>\n",
       "      <td>0.024387</td>\n",
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       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>-0.017536</td>\n",
       "      <td>-0.000100</td>\n",
       "      <td>-0.010218</td>\n",
       "      <td>-0.012560</td>\n",
       "      <td>-0.022763</td>\n",
       "      <td>-0.013278</td>\n",
       "      <td>-0.000599</td>\n",
       "      <td>-0.024789</td>\n",
       "      <td>-0.066982</td>\n",
       "      <td>-0.029413</td>\n",
       "      <td>0.029784</td>\n",
       "      <td>-0.008628</td>\n",
       "      <td>-0.035666</td>\n",
       "      <td>-0.005943</td>\n",
       "      <td>-0.012195</td>\n",
       "      <td>-0.018082</td>\n",
       "      <td>-0.010107</td>\n",
       "      <td>0.029584</td>\n",
       "      <td>-0.019104</td>\n",
       "      <td>-0.023193</td>\n",
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       "      <th>2020-11-10</th>\n",
       "      <td>0.000682</td>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.003560</td>\n",
       "      <td>0.032548</td>\n",
       "      <td>0.013256</td>\n",
       "      <td>0.013217</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>-0.027011</td>\n",
       "      <td>0.147425</td>\n",
       "      <td>-0.023450</td>\n",
       "      <td>0.209508</td>\n",
       "      <td>0.050041</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>0.016096</td>\n",
       "      <td>0.006648</td>\n",
       "      <td>0.042434</td>\n",
       "      <td>-0.001540</td>\n",
       "      <td>-0.041996</td>\n",
       "      <td>0.036266</td>\n",
       "      <td>0.000251</td>\n",
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       "      <th>2020-11-11</th>\n",
       "      <td>0.009059</td>\n",
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       "      <td>0.025628</td>\n",
       "      <td>-0.010218</td>\n",
       "      <td>0.037589</td>\n",
       "      <td>0.009043</td>\n",
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       "                TRON  USD Coin  Binance Coin  Dogecoin  Ethereum       XRP  \\\n",
       "Date                                                                         \n",
       "2020-11-07 -0.043815  0.000200     -0.044735 -0.031556 -0.044589 -0.036792   \n",
       "2020-11-08  0.011117  0.000601      0.022334  0.013858  0.044281  0.019400   \n",
       "2020-11-09 -0.017536 -0.000100     -0.010218 -0.012560 -0.022763 -0.013278   \n",
       "2020-11-10  0.000682 -0.000300      0.003560  0.032548  0.013256  0.013217   \n",
       "2020-11-11  0.009059  0.000000     -0.007095 -0.001087  0.029119  0.007685   \n",
       "\n",
       "              Tether  Bitcoin Cash   Uniswap  Litecoin      Aave   VeChain  \\\n",
       "Date                                                                         \n",
       "2020-11-07  0.001600     -0.014716 -0.036590 -0.067702  0.147249 -0.071627   \n",
       "2020-11-08 -0.000599      0.066677  0.160566  0.036887  0.209637  0.071759   \n",
       "2020-11-09 -0.000599     -0.024789 -0.066982 -0.029413  0.029784 -0.008628   \n",
       "2020-11-10  0.000800     -0.027011  0.147425 -0.023450  0.209508  0.050041   \n",
       "2020-11-11 -0.000100     -0.001324 -0.045852  0.025535 -0.181438 -0.021065   \n",
       "\n",
       "               THETA  Polkadot   Cardano  Chainlink   Bitcoin  Filecoin  \\\n",
       "Date                                                                      \n",
       "2020-11-07 -0.059127 -0.075649 -0.070642  -0.034511 -0.048113 -0.056081   \n",
       "2020-11-08  0.041373  0.031840  0.052320   0.082553  0.044192  0.026702   \n",
       "2020-11-09 -0.035666 -0.005943 -0.012195  -0.018082 -0.010107  0.029584   \n",
       "2020-11-10 -0.000780  0.016096  0.006648   0.042434 -0.001540 -0.041996   \n",
       "2020-11-11 -0.046228  0.008147  0.000000  -0.018433  0.025628 -0.010218   \n",
       "\n",
       "               Terra   Stellar  \n",
       "Date                            \n",
       "2020-11-07 -0.049298 -0.051621  \n",
       "2020-11-08  0.042582  0.024387  \n",
       "2020-11-09 -0.019104 -0.023193  \n",
       "2020-11-10  0.036266  0.000251  \n",
       "2020-11-11  0.037589  0.009043  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_returns = df_crypto.pct_change().dropna()\n",
    "daily_returns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a637bf",
   "metadata": {
    "id": "mFqKZtWaHuOP",
    "papermill": {
     "duration": 0.027327,
     "end_time": "2022-06-26T19:54:04.980497",
     "exception": false,
     "start_time": "2022-06-26T19:54:04.953170",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's plot out the daily returns of 3 of the stocks, TRON, USD Coin and Bicoin, throughout the whole time period. We see that they tend to fluctuate gently around 0. Notably, the fluctuations are much greater during a period of high volatility (i.e. during the Covid crash in March 2020)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f36c93d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:05.035074Z",
     "iopub.status.busy": "2022-06-26T19:54:05.034660Z",
     "iopub.status.idle": "2022-06-26T19:54:05.191167Z",
     "shell.execute_reply": "2022-06-26T19:54:05.190251Z"
    },
    "id": "a4iJBiOgHuOQ",
    "outputId": "5ae3daaa-e4c1-4098-aba8-4fdfe71b87d3",
    "papermill": {
     "duration": 0.187011,
     "end_time": "2022-06-26T19:54:05.193874",
     "exception": false,
     "start_time": "2022-06-26T19:54:05.006863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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   ],
   "source": [
    "fig = px.line(daily_returns[['TRON', 'USD Coin','Bitcoin']], title='Daily Returns')\n",
    "fig.show()"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Volatility\n",
    "\n",
    "#### Daily Volatility\n",
    "\n",
    "Daily Volatility is the average difference between the return on a given day and the average return over the time period. Mathematically, it is just the standard deviation of the daily returns. Volatility is one of the measures of risk in that highly volatile investments can carry greater risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31159799",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:05.306344Z",
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     "iopub.status.idle": "2022-06-26T19:54:05.318033Z",
     "shell.execute_reply": "2022-06-26T19:54:05.316856Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRON            0.070508\n",
       "USD Coin        0.000547\n",
       "Binance Coin    0.081031\n",
       "Dogecoin        0.247940\n",
       "Ethereum        0.058878\n",
       "XRP             0.097018\n",
       "Tether          0.000630\n",
       "Bitcoin Cash    0.075933\n",
       "Uniswap         0.085497\n",
       "Litecoin        0.066459\n",
       "Aave            0.081461\n",
       "VeChain         0.086830\n",
       "THETA           0.081007\n",
       "Polkadot        0.081727\n",
       "Cardano         0.073899\n",
       "Chainlink       0.075961\n",
       "Bitcoin         0.043604\n",
       "Filecoin        0.081366\n",
       "Terra           0.112880\n",
       "Stellar         0.087391\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_returns.std()"
   ]
  },
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   "id": "4186cd9c",
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    "tags": []
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   "source": [
    "Here we see that Dogecoin has a slightly higher volatility, compared to that of Bitcoin. When we compare the density plots of their daily returns, we can see that Bitcoin has a narrower curve with a higher peak, while Dogecoin has a wider curve indicating higher standard deviation and hence volatility."
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     "shell.execute_reply": "2022-06-26T19:54:05.791118Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.1)"
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     "execution_count": 8,
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\n",
      "text/plain": [
       "<Figure size 983x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=daily_returns[['Dogecoin','Bitcoin']], kind = 'kde', aspect = 2.5)\n",
    "plt.xlim(-0.1, 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08e0f84",
   "metadata": {
    "id": "6Tnym1i0HuOR",
    "papermill": {
     "duration": 0.016818,
     "end_time": "2022-06-26T19:54:05.828271",
     "exception": false,
     "start_time": "2022-06-26T19:54:05.811453",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Annual Volatility\n",
    "\n",
    "For completeness, the Annual Volatility is a more common measure can be calculated simply by multiply the daily volatility by the square root of the number of trading days in a year i.e. 252. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "052cfe71",
   "metadata": {
    "id": "gkT4NmIzHuOS",
    "papermill": {
     "duration": 0.016684,
     "end_time": "2022-06-26T19:54:05.863026",
     "exception": false,
     "start_time": "2022-06-26T19:54:05.846342",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Plot Correlation Matrix between Stocks\n",
    "The correlation matrix gives us the correlation coefficients between every pair of stocks. Correlation coefficients are indicators of the strength of the linear relationship between two different variables. It is a value from 0 to 1, with 1 indicating the strongest relationship. If it is a negative value, then the two variables are inversely related. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db1fda39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:05.899174Z",
     "iopub.status.busy": "2022-06-26T19:54:05.898816Z",
     "iopub.status.idle": "2022-06-26T19:54:05.905172Z",
     "shell.execute_reply": "2022-06-26T19:54:05.904250Z"
    },
    "papermill": {
     "duration": 0.027635,
     "end_time": "2022-06-26T19:54:05.907583",
     "exception": false,
     "start_time": "2022-06-26T19:54:05.879948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def most_correlated(dataframe):\n",
    "    \"\"\"\n",
    "    Returns a dataframe that contains the most correlated features\n",
    "    \n",
    "    dataframe: dataframe that gives the column names and their correlation value\n",
    "    \"\"\"\n",
    "    corr_values = dataframe.abs().unstack()\n",
    "    sorted_values = pd.DataFrame(corr_values.sort_values(kind=\"quicksort\"), index= None)\n",
    "    sorted_values = sorted_values[(sorted_values[0] > 0.6) & (sorted_values[0] < 1)]\n",
    "    return sorted_values.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "909fd813",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:05.943608Z",
     "iopub.status.busy": "2022-06-26T19:54:05.942967Z",
     "iopub.status.idle": "2022-06-26T19:54:06.004770Z",
     "shell.execute_reply": "2022-06-26T19:54:06.003871Z"
    },
    "id": "FULDxDGgHuOS",
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       "    <tr>\n",
       "      <th>XRP</th>\n",
       "      <th>Dogecoin</th>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chainlink</th>\n",
       "      <th>VeChain</th>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Bitcoin Cash</th>\n",
       "      <th>Binance Coin</th>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRON</th>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dogecoin</th>\n",
       "      <th>Ethereum</th>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terra</th>\n",
       "      <th>Cardano</th>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aave</th>\n",
       "      <th>Stellar</th>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stellar</th>\n",
       "      <th>Polkadot</th>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Uniswap</th>\n",
       "      <th>Binance Coin</th>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chainlink</th>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chainlink</th>\n",
       "      <th>Bitcoin Cash</th>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XRP</th>\n",
       "      <th>TRON</th>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binance Coin</th>\n",
       "      <th>TRON</th>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Litecoin</th>\n",
       "      <th>Stellar</th>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chainlink</th>\n",
       "      <th>Litecoin</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "Terra        VeChain       0.61\n",
       "Chainlink    Dogecoin      0.62\n",
       "Bitcoin      Terra         0.63\n",
       "Ethereum     Stellar       0.64\n",
       "Polkadot     Terra         0.65\n",
       "Uniswap      Dogecoin      0.67\n",
       "Cardano      Aave          0.68\n",
       "Terra        TRON          0.69\n",
       "THETA        Ethereum      0.70\n",
       "Cardano      VeChain       0.71\n",
       "Filecoin     Bitcoin Cash  0.72\n",
       "XRP          Uniswap       0.73\n",
       "Aave         Binance Coin  0.74\n",
       "VeChain      Stellar       0.75\n",
       "Filecoin     Litecoin      0.76\n",
       "             XRP           0.77\n",
       "TRON         Polkadot      0.78\n",
       "Binance Coin Chainlink     0.79\n",
       "Bitcoin Cash Dogecoin      0.80\n",
       "XRP          Dogecoin      0.81\n",
       "Chainlink    VeChain       0.82\n",
       "Bitcoin Cash Binance Coin  0.83\n",
       "             TRON          0.84\n",
       "Dogecoin     Ethereum      0.85\n",
       "Terra        Cardano       0.86\n",
       "Aave         Stellar       0.87\n",
       "Stellar      Polkadot      0.88\n",
       "Uniswap      Binance Coin  0.89\n",
       "             Chainlink     0.90\n",
       "Chainlink    Bitcoin Cash  0.91\n",
       "XRP          TRON          0.92\n",
       "Binance Coin TRON          0.93\n",
       "Litecoin     Stellar       0.94\n",
       "Chainlink    Litecoin      0.95"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_correlated(corr_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fcbecd4",
   "metadata": {
    "id": "QGY4rlKUHuOS",
    "papermill": {
     "duration": 0.017465,
     "end_time": "2022-06-26T19:54:06.096489",
     "exception": false,
     "start_time": "2022-06-26T19:54:06.079024",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "In general (though not always), stock prices tend to move together (increase in bull market, decrease in bear market), hence the correlation would most likely be positive as shown above. \n",
    "To reduce the size of our given data I am going to delete one of the stocks that have a correlation >0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64307520",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:06.133496Z",
     "iopub.status.busy": "2022-06-26T19:54:06.133108Z",
     "iopub.status.idle": "2022-06-26T19:54:06.139174Z",
     "shell.execute_reply": "2022-06-26T19:54:06.138199Z"
    },
    "papermill": {
     "duration": 0.027074,
     "end_time": "2022-06-26T19:54:06.141087",
     "exception": false,
     "start_time": "2022-06-26T19:54:06.114013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_crypto = df_crypto.drop(columns=['Litecoin','Cardano','TRON','Stellar','Bitcoin Cash','Polkadot','VeChain','Terra'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e12439",
   "metadata": {
    "id": "xnkLhtyEHuOT",
    "papermill": {
     "duration": 0.018238,
     "end_time": "2022-06-26T19:54:06.177047",
     "exception": false,
     "start_time": "2022-06-26T19:54:06.158809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Optimize portfolio for max Sharpe ratio plot it out with efficient frontier curve\n",
    "The efficient frontier is the set of optimal portfolios that offer the highest expected return for a defined level of risk (volatility) or the lowest risk (volatility) for a given level of expected return. It is represented by a line on a Return vs Volatility graph. The max Sharpe ratio portfolio lies on the efficient frontier. \n",
    "\n",
    "To represent everything visually, the code below  generates 1000 portfolios of our stocks with random weights and plot out their returns and volatility. The efficient frontier and the max Sharpe ratio portfolio is also plotted on the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e87de0e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:06.213776Z",
     "iopub.status.busy": "2022-06-26T19:54:06.213038Z",
     "iopub.status.idle": "2022-06-26T19:54:22.805030Z",
     "shell.execute_reply": "2022-06-26T19:54:22.803978Z"
    },
    "papermill": {
     "duration": 16.612977,
     "end_time": "2022-06-26T19:54:22.807573",
     "exception": false,
     "start_time": "2022-06-26T19:54:06.194596",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:16<00:00, 60.32it/s]\n"
     ]
    }
   ],
   "source": [
    "from pypfopt.efficient_frontier import EfficientFrontier\n",
    "from pypfopt import risk_models\n",
    "from pypfopt import expected_returns\n",
    "\n",
    "#-- Store weights and tickers for labelling\n",
    "result = []\n",
    "max_sharpe = 0\n",
    "mu_op=0\n",
    "S_op =0\n",
    "for i in tqdm(range(1000)):\n",
    "    next_i = False\n",
    "    while True:\n",
    "        assets = []\n",
    "        #- Choose assets randomly without replacement\n",
    "        assets +=list(np.random.choice(df_crypto.columns, 5, replace=False))\n",
    "        mu = expected_returns.mean_historical_return(df_crypto[assets]) # returns.mean() * 252\n",
    "        S = risk_models.sample_cov(df_crypto[assets]) # Get the sample covariance matrix\n",
    "        ef = EfficientFrontier(mu, S)\n",
    "        try :\n",
    "            weights = ef.max_sharpe(risk_free_rate=0.05) #Maximize the Sharpe ratio, and get the raw weights\n",
    "        except:\n",
    "            continue\n",
    "        else: \n",
    "            cleaned_weights = ef.clean_weights() \n",
    "            expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance(risk_free_rate=0.05)\n",
    "            if sharpe_ratio > max_sharpe:\n",
    "                max_sharpe = sharpe_ratio\n",
    "                result = cleaned_weights\n",
    "                mu_op = mu\n",
    "                S_op = S\n",
    "        \n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84f6bac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:22.863392Z",
     "iopub.status.busy": "2022-06-26T19:54:22.861978Z",
     "iopub.status.idle": "2022-06-26T19:54:23.528174Z",
     "shell.execute_reply": "2022-06-26T19:54:23.527092Z"
    },
    "papermill": {
     "duration": 0.695777,
     "end_time": "2022-06-26T19:54:23.530812",
     "exception": false,
     "start_time": "2022-06-26T19:54:22.835035",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_efficient_frontier_and_max_sharpe(mu, S):  \n",
    "    # Optimize portfolio for maximal Sharpe ratio \n",
    "    ef = EfficientFrontier(mu, S)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8,6))\n",
    "    ef_max_sharpe = copy.deepcopy(ef)\n",
    "    plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)\n",
    "\n",
    "    # Find the max sharpe portfolio\n",
    "    ef_max_sharpe.max_sharpe(risk_free_rate=0.05)\n",
    "    ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance(risk_free_rate=0.05)\n",
    "    ax.scatter(std_tangent, ret_tangent, marker=\"*\", s=100, c=\"r\", label=\"Max Sharpe\")\n",
    "\n",
    "    # Generate random portfolios\n",
    "    n_samples = 1000\n",
    "    w = np.random.dirichlet(np.ones(ef.n_assets), n_samples)\n",
    "    rets = w.dot(ef.expected_returns)\n",
    "    stds = np.sqrt(np.diag(w @ ef.cov_matrix @ w.T))\n",
    "    sharpes = rets / stds\n",
    "    ax.scatter(stds, rets, marker=\".\", c=sharpes, cmap=\"viridis_r\")\n",
    "\n",
    "    # Output\n",
    "    ax.set_title(\"Efficient Frontier with Random Portfolios\")\n",
    "    ax.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "plot_efficient_frontier_and_max_sharpe(mu_op, S_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36202225",
   "metadata": {
    "id": "ZAqpdU-ZHuOT",
    "papermill": {
     "duration": 0.030689,
     "end_time": "2022-06-26T19:54:23.593529",
     "exception": false,
     "start_time": "2022-06-26T19:54:23.562840",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In the graph above, we see that the efficient frontier is like a boundary. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f5bdd8",
   "metadata": {
    "id": "olV9WbKwHuOU",
    "papermill": {
     "duration": 0.027187,
     "end_time": "2022-06-26T19:54:23.648163",
     "exception": false,
     "start_time": "2022-06-26T19:54:23.620976",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Get Weights for Optimized Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40bfad02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:23.704597Z",
     "iopub.status.busy": "2022-06-26T19:54:23.703917Z",
     "iopub.status.idle": "2022-06-26T19:54:23.709036Z",
     "shell.execute_reply": "2022-06-26T19:54:23.708103Z"
    },
    "id": "3VNiZAnaHuOU",
    "outputId": "55620c42-baef-46b4-9cee-9a971ccc5ec9",
    "papermill": {
     "duration": 0.036911,
     "end_time": "2022-06-26T19:54:23.711879",
     "exception": false,
     "start_time": "2022-06-26T19:54:23.674968",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('THETA', 0.00429), ('Dogecoin', 0.31941), ('Binance Coin', 0.4741), ('XRP', 0.0), ('Uniswap', 0.2022)])\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5248e473",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:23.769089Z",
     "iopub.status.busy": "2022-06-26T19:54:23.768756Z",
     "iopub.status.idle": "2022-06-26T19:54:23.780192Z",
     "shell.execute_reply": "2022-06-26T19:54:23.779146Z"
    },
    "id": "scp_zY2KHuOU",
    "outputId": "704abbd6-3b61-490a-9506-4ccc97e8a9a0",
    "papermill": {
     "duration": 0.042454,
     "end_time": "2022-06-26T19:54:23.782683",
     "exception": false,
     "start_time": "2022-06-26T19:54:23.740229",
     "status": "completed"
    },
    "tags": []
   },
   "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>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>THETA</th>\n",
       "      <td>0.00429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dogecoin</th>\n",
       "      <td>0.31941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binance Coin</th>\n",
       "      <td>0.47410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XRP</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniswap</th>\n",
       "      <td>0.20220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              weights\n",
       "THETA         0.00429\n",
       "Dogecoin      0.31941\n",
       "Binance Coin  0.47410\n",
       "XRP           0.00000\n",
       "Uniswap       0.20220"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights_df = pd.DataFrame.from_dict(result, orient = 'index')\n",
    "weights_df.columns = ['weights']\n",
    "weights_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c3ec9d",
   "metadata": {
    "id": "tqAlIfxpHuOU",
    "papermill": {
     "duration": 0.026699,
     "end_time": "2022-06-26T19:54:23.837282",
     "exception": false,
     "start_time": "2022-06-26T19:54:23.810583",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Notice how certain weights tend to dominate by high performing stocks (high returns with relatively ok volatility), while some weights tend to be 0. One of the weakness of the max Sharpe portfolio optimization approach is that the portfolio may not be as diversified (across types of stocks or industries) as we want it to be. Also, as cliche as it may sound, past performance is not indicative of future results. We are obtaining the expected returns and volatility based on the movement of stock prices in the past, the future returns/volatility may very well differ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c4c5ac",
   "metadata": {
    "id": "1EAt620rHuOU",
    "papermill": {
     "duration": 0.026764,
     "end_time": "2022-06-26T19:54:23.891474",
     "exception": false,
     "start_time": "2022-06-26T19:54:23.864710",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Expected Annual Return, Annual Volatility and Sharpe Ratio for Optimized Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4a9dc967",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:23.947939Z",
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     "iopub.status.idle": "2022-06-26T19:54:23.954501Z",
     "shell.execute_reply": "2022-06-26T19:54:23.953531Z"
    },
    "id": "ewfczBgUHuOU",
    "outputId": "67204a94-ed87-4145-c2c7-b59fa395a083",
    "papermill": {
     "duration": 0.0385,
     "end_time": "2022-06-26T19:54:23.957578",
     "exception": false,
     "start_time": "2022-06-26T19:54:23.919078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected annual return: 486.08%\n",
      "Annual volatility: 107.25%\n",
      "Sharpe ratio: 4.49\n"
     ]
    }
   ],
   "source": [
    "expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance(risk_free_rate=0.05)\n",
    "\n",
    "print('Expected annual return: {}%'.format((expected_annual_return*100).round(2)))\n",
    "print('Annual volatility: {}%'.format((annual_volatility*100).round(2)))\n",
    "print('Sharpe ratio: {}'.format(sharpe_ratio.round(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554d5d86",
   "metadata": {
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    "papermill": {
     "duration": 0.02688,
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     "exception": false,
     "start_time": "2022-06-26T19:54:23.985667",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Generate Portfolio with Optimized Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e245145f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:24.069101Z",
     "iopub.status.busy": "2022-06-26T19:54:24.068755Z",
     "iopub.status.idle": "2022-06-26T19:54:24.094009Z",
     "shell.execute_reply": "2022-06-26T19:54:24.093038Z"
    },
    "id": "tD5RW5y-HuOV",
    "outputId": "6f5b8398-48f8-4c93-81a1-348858c60ee8",
    "papermill": {
     "duration": 0.055761,
     "end_time": "2022-06-26T19:54:24.096018",
     "exception": false,
     "start_time": "2022-06-26T19:54:24.040257",
     "status": "completed"
    },
    "tags": []
   },
   "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>USD Coin</th>\n",
       "      <th>Binance Coin</th>\n",
       "      <th>Dogecoin</th>\n",
       "      <th>Ethereum</th>\n",
       "      <th>XRP</th>\n",
       "      <th>Tether</th>\n",
       "      <th>Uniswap</th>\n",
       "      <th>Aave</th>\n",
       "      <th>THETA</th>\n",
       "      <th>Chainlink</th>\n",
       "      <th>Bitcoin</th>\n",
       "      <th>Filecoin</th>\n",
       "      <th>Optimized Portfolio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2020-11-06</th>\n",
       "      <td>0.9986</td>\n",
       "      <td>29.06</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>455.72</td>\n",
       "      <td>0.25848</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.5690</td>\n",
       "      <td>38.71</td>\n",
       "      <td>0.6782</td>\n",
       "      <td>12.17</td>\n",
       "      <td>15577.9</td>\n",
       "      <td>31.74</td>\n",
       "      <td>169.518367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-07</th>\n",
       "      <td>0.9988</td>\n",
       "      <td>27.76</td>\n",
       "      <td>0.002670</td>\n",
       "      <td>435.40</td>\n",
       "      <td>0.24897</td>\n",
       "      <td>1.0016</td>\n",
       "      <td>2.4750</td>\n",
       "      <td>44.41</td>\n",
       "      <td>0.6381</td>\n",
       "      <td>11.75</td>\n",
       "      <td>14828.4</td>\n",
       "      <td>29.96</td>\n",
       "      <td>161.956899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-08</th>\n",
       "      <td>0.9994</td>\n",
       "      <td>28.38</td>\n",
       "      <td>0.002707</td>\n",
       "      <td>454.68</td>\n",
       "      <td>0.25380</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>2.8724</td>\n",
       "      <td>53.72</td>\n",
       "      <td>0.6645</td>\n",
       "      <td>12.72</td>\n",
       "      <td>15483.7</td>\n",
       "      <td>30.76</td>\n",
       "      <td>168.719854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>0.9993</td>\n",
       "      <td>28.09</td>\n",
       "      <td>0.002673</td>\n",
       "      <td>444.33</td>\n",
       "      <td>0.25043</td>\n",
       "      <td>1.0004</td>\n",
       "      <td>2.6800</td>\n",
       "      <td>55.32</td>\n",
       "      <td>0.6408</td>\n",
       "      <td>12.49</td>\n",
       "      <td>15327.2</td>\n",
       "      <td>31.67</td>\n",
       "      <td>165.118057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>0.9990</td>\n",
       "      <td>28.19</td>\n",
       "      <td>0.002760</td>\n",
       "      <td>450.22</td>\n",
       "      <td>0.25374</td>\n",
       "      <td>1.0012</td>\n",
       "      <td>3.0751</td>\n",
       "      <td>66.91</td>\n",
       "      <td>0.6403</td>\n",
       "      <td>13.02</td>\n",
       "      <td>15303.6</td>\n",
       "      <td>30.34</td>\n",
       "      <td>167.124151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            USD Coin  Binance Coin  Dogecoin  Ethereum      XRP  Tether  \\\n",
       "Date                                                                      \n",
       "2020-11-06    0.9986         29.06  0.002757    455.72  0.25848  1.0000   \n",
       "2020-11-07    0.9988         27.76  0.002670    435.40  0.24897  1.0016   \n",
       "2020-11-08    0.9994         28.38  0.002707    454.68  0.25380  1.0010   \n",
       "2020-11-09    0.9993         28.09  0.002673    444.33  0.25043  1.0004   \n",
       "2020-11-10    0.9990         28.19  0.002760    450.22  0.25374  1.0012   \n",
       "\n",
       "            Uniswap   Aave   THETA  Chainlink  Bitcoin  Filecoin  \\\n",
       "Date                                                               \n",
       "2020-11-06   2.5690  38.71  0.6782      12.17  15577.9     31.74   \n",
       "2020-11-07   2.4750  44.41  0.6381      11.75  14828.4     29.96   \n",
       "2020-11-08   2.8724  53.72  0.6645      12.72  15483.7     30.76   \n",
       "2020-11-09   2.6800  55.32  0.6408      12.49  15327.2     31.67   \n",
       "2020-11-10   3.0751  66.91  0.6403      13.02  15303.6     30.34   \n",
       "\n",
       "            Optimized Portfolio  \n",
       "Date                             \n",
       "2020-11-06           169.518367  \n",
       "2020-11-07           161.956899  \n",
       "2020-11-08           168.719854  \n",
       "2020-11-09           165.118057  \n",
       "2020-11-10           167.124151  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crypto['Optimized Portfolio'] = 0\n",
    "\n",
    "for ticker, weight in weights.items():\n",
    "    df_crypto['Optimized Portfolio'] += df_crypto[ticker]*weight\n",
    "\n",
    "df_crypto.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b8ade1",
   "metadata": {
    "id": "eqo85nmrHuOV",
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     "duration": 0.026976,
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     "start_time": "2022-06-26T19:54:24.124119",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Plot Cumulative Returns of Optimized Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43007a4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-26T19:54:24.208633Z",
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    },
    "id": "vL_dPxy-HuOV",
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    "papermill": {
     "duration": 0.05338,
     "end_time": "2022-06-26T19:54:24.232255",
     "exception": false,
     "start_time": "2022-06-26T19:54:24.178875",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'THETA': 9, 'Dogecoin': 15998, 'Binance Coin': 14, 'Uniswap': 91}\n",
      "Leftover Fund value in$ after building minimum volatility portfolio is $28.06\n"
     ]
    }
   ],
   "source": [
    "latest_prices = discrete_allocation.get_latest_prices(df_crypto[weights_df.index])\n",
    "# Allocate Portfolio Value in $ as required to show number of shares/stocks to buy, also bounds for shorting will affect allocation\n",
    "#Min Volatility Portfolio Allocation $10000\n",
    "allocation_minv, rem_minv = discrete_allocation.DiscreteAllocation(result, latest_prices, total_portfolio_value=10000).lp_portfolio()\n",
    "print(allocation_minv)\n",
    "print(\"Leftover Fund value in$ after building minimum volatility portfolio is ${:.2f}\".format(rem_minv))"
   ]
  }
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