KUMAR_SHAURYA_Quant_02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IA2wZYDeBURQ"
   },
   "source": [
    "# **SHAURYA'S PORTFOLIO**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6rJunasTBr3W"
   },
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Pa0-WzrB4Zb"
   },
   "source": [
    "##Portfolio Constraints\n",
    "\n",
    "Investment constraints is one of the most integral part of any portfolio or an investment policy statement. Any asset class that is included in the portfolio has to be chosen only after a thorough understanding of the investment constraints.\n",
    "\n",
    "Investment constraints are the factors that restrict or limit the investment options available to an investor. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MgXNOPH4CmzI"
   },
   "source": [
    "So the constarints of my portfolio are -\n",
    "\n",
    "* **Time Horizon** :-\n",
    "  Investing in this portfolio requires the investor to hold his invested assets for a period of atleast 2 months or when the policy change is incorporated( whichever is earlier )\n",
    "\n",
    "* **Taxation** :-\n",
    "  The investor should note that once investing in this portfolio, whatever returns or dividends are provided will automatically be taxed as per the [SEBI ACT, 1992](https://www.sebi.gov.in/sebi_data/attachdocs/1456380272563.pdf) . It should also be noted that the dividends (specifically Long Term Capital Gains(LTCG)) which are tax-free, would still be liable to the service charges levied by the portfolio T&C.\n",
    "\n",
    "* **Legal & Regulatory** :- \n",
    "  Only the investor will be liable for any loss due to changes in Government Policies, Market Movement and Weight Adjustment of the assets in the portfolio. However, it should be noted that in case of weight adjustment the company will inform well in advance & the investor may withdraw the investments in a stipulated period of time. \n",
    "\n",
    "* **Liquidity** :- \n",
    "  The investor shall be aware that this portfolio consists only of stock investments, hence no liquidity of investment is enacted here. But, suppose if an emergency arises then the investor might take back the investment but on the terms of mortgage.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gTL25c0SEaph"
   },
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qHKan8_REfYi"
   },
   "source": [
    "##Portfolio Details \n",
    "\n",
    "**Stocks Traded** :-\n",
    "\n",
    "1. ASIAN PAINTS - FMCG (FAST MOVING CONSUMER GOODS) SECTOR\n",
    "2. HDFC BANK - BANKING SECTOR\n",
    "3. HINDUSTAN UNILEVER - FMCG (FAST MOVING CONSUMER GOODS) SECTOR\n",
    "4. EICHER MOTORS - AUTO SECTOR\n",
    "5. CIPLA PHARMA - PHARMA SECTOR\n",
    "6. RELIANCE - PRIVATE SECTOR ENTERPRISE\n",
    "7. TCS - IT SECTOR COMPANY\n",
    "\n",
    "**Stock Market** :- NSE\n",
    "\n",
    "**Time Considered for Analysis** :- 01/01/2010 to 31/12/2019\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jOnDSk3CGu1y"
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   "source": [
    "**Q1** How did you choose the stocks ?\n",
    "\n",
    "**Answer** :- My thought process for selecting the stocks is as follows :-\n",
    "\n",
    "1. Firstly, choose the stocks from different sector of companies( i.e. FMCG, Pharma, Bank, etc. ). The reason for this is - the concept of divesification ( as stated in MPT ) for risk minimization.\n",
    "\n",
    "2. Once you have selected the sectors, then pick up the companies performing well in long-run.\n",
    "\n",
    "3. Also try to analyse each stock stock individually to understand the risk-reward trade-off of each of them.\n",
    "\n",
    "4. The third and the least important step is to check the face value of the company and the dividend given over it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DFFtqAd-IYsz"
   },
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9oFy2MtsJp2p"
   },
   "source": [
    "Now after choosing the stocks, the most important task is assigning optimal weigths to the stocks, so that the portfolio lies in set of `Efficient Frontier` , such that for a given return the risk is minumum or for a given risk, return is maximum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "wQr-mLObBq85"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0myvd4C6NQB_"
   },
   "source": [
    "Let's initialize the data frame which would store the data for the stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pU2WiGmoNDtL",
    "outputId": "f89bb169-f6be-45b4-a4ac-ca3d4afa6ccb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_index = pd.date_range(start = \"2010-01-01\", end = \"2019-12-31\" , freq=\"D\")\n",
    "data = pd.DataFrame( index = date_index)\n",
    "\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "qI8cM54JKPBA"
   },
   "outputs": [],
   "source": [
    "#Function to read data for the different stocks\n",
    "\n",
    "def read_data(path, df, stock_name) :\n",
    "  data = pd.read_csv(path)\n",
    "  data.sort_index(ascending = False, axis=0, inplace=True)\n",
    "  \n",
    "  #converting all columns to loewr case & removing any leading or trailing spaces\n",
    "  data.columns = data.columns.str.lower()\n",
    "  data.columns = data.columns.str.strip()\n",
    "\n",
    "  #converting the index to the date\n",
    "  data = data.set_index(pd.to_datetime(data['date']))\n",
    "\n",
    "  #We need only adjusted price columns in our data frame\n",
    "  df[stock_name] = data['adj close']\n",
    "\n",
    "  return df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "G5tPTRgjQaHi",
    "outputId": "ab3c30b3-c3f3-4c4f-e3a4-70e753b8e6ec"
   },
   "outputs": [],
   "source": [
    "data = read_data('./content/sample_data/ASIANPAINT.NS.csv', data, 'ap')\n",
    "data = read_data('./content/sample_data/CIPLA.NS.csv', data, 'cipla')\n",
    "data = read_data('./content/sample_data/EICHERMOT.NS.csv', data, 'eicher')\n",
    "data = read_data('./content/sample_data/HDFCBANK.NS.csv', data, 'hdfc')\n",
    "data = read_data('./content/sample_data/HINDUNILVR.NS.csv', data, 'hu')\n",
    "data = read_data('./content/sample_data/RELIANCE.NS.csv', data, 'relaince')\n",
    "data = read_data('./content/sample_data/TCS.NS.csv', data, 'tcs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
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    "id": "neYBbyJFRh_5",
    "outputId": "63c6a444-4467-4279-f6b7-d928580c0704"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ap</th>\n",
       "      <th>cipla</th>\n",
       "      <th>eicher</th>\n",
       "      <th>hdfc</th>\n",
       "      <th>hu</th>\n",
       "      <th>relaince</th>\n",
       "      <th>tcs</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>162.925415</td>\n",
       "      <td>319.837677</td>\n",
       "      <td>57.927864</td>\n",
       "      <td>158.248413</td>\n",
       "      <td>216.147278</td>\n",
       "      <td>485.824005</td>\n",
       "      <td>302.451019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>163.440033</td>\n",
       "      <td>314.105194</td>\n",
       "      <td>55.994507</td>\n",
       "      <td>158.387589</td>\n",
       "      <td>215.453201</td>\n",
       "      <td>483.655701</td>\n",
       "      <td>302.531525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>162.948135</td>\n",
       "      <td>326.802032</td>\n",
       "      <td>55.527241</td>\n",
       "      <td>158.494308</td>\n",
       "      <td>216.678070</td>\n",
       "      <td>491.470398</td>\n",
       "      <td>295.690979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>161.285767</td>\n",
       "      <td>324.101532</td>\n",
       "      <td>54.751324</td>\n",
       "      <td>158.907150</td>\n",
       "      <td>216.228928</td>\n",
       "      <td>499.623901</td>\n",
       "      <td>287.462311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                    ap       cipla     eicher        hdfc          hu  \\\n",
       "2010-01-01         NaN         NaN        NaN         NaN         NaN   \n",
       "2010-01-02         NaN         NaN        NaN         NaN         NaN   \n",
       "2010-01-03         NaN         NaN        NaN         NaN         NaN   \n",
       "2010-01-04  162.925415  319.837677  57.927864  158.248413  216.147278   \n",
       "2010-01-05  163.440033  314.105194  55.994507  158.387589  215.453201   \n",
       "2010-01-06  162.948135  326.802032  55.527241  158.494308  216.678070   \n",
       "2010-01-07  161.285767  324.101532  54.751324  158.907150  216.228928   \n",
       "\n",
       "              relaince         tcs  \n",
       "2010-01-01         NaN         NaN  \n",
       "2010-01-02         NaN         NaN  \n",
       "2010-01-03         NaN         NaN  \n",
       "2010-01-04  485.824005  302.451019  \n",
       "2010-01-05  483.655701  302.531525  \n",
       "2010-01-06  491.470398  295.690979  \n",
       "2010-01-07  499.623901  287.462311  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mSNn0OUSR5wM"
   },
   "source": [
    "Now we would drop all the rows containing NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "v_HvSIe5RxcH"
   },
   "outputs": [],
   "source": [
    "data.dropna(axis = 0, how = 'any', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rpWkEu_RStka"
   },
   "source": [
    "Now, we would calculate the daily returns as follows:-  \n",
    "## **$\\frac{current \\ day \\ close \\ price - prev \\ day \\ close \\ price}{prev \\ day \\ close \\ price} $**\n",
    "\n",
    "\n",
    "For this we would use the pct_change() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "ewRdkUXcSJny",
    "outputId": "62e481e2-5a43-46a6-ea81-be0c528a14d2"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ap</th>\n",
       "      <th>cipla</th>\n",
       "      <th>eicher</th>\n",
       "      <th>hdfc</th>\n",
       "      <th>hu</th>\n",
       "      <th>relaince</th>\n",
       "      <th>tcs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2010-01-05</th>\n",
       "      <td>0.003159</td>\n",
       "      <td>-0.017923</td>\n",
       "      <td>-0.033375</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>-0.003211</td>\n",
       "      <td>-0.004463</td>\n",
       "      <td>0.000266</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>-0.003010</td>\n",
       "      <td>0.040422</td>\n",
       "      <td>-0.008345</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.005685</td>\n",
       "      <td>0.016158</td>\n",
       "      <td>-0.022611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>-0.010202</td>\n",
       "      <td>-0.008263</td>\n",
       "      <td>-0.013974</td>\n",
       "      <td>0.002605</td>\n",
       "      <td>-0.002073</td>\n",
       "      <td>0.016590</td>\n",
       "      <td>-0.027829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.016520</td>\n",
       "      <td>-0.002777</td>\n",
       "      <td>0.013467</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.004343</td>\n",
       "      <td>-0.002622</td>\n",
       "      <td>-0.020437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ap     cipla    eicher      hdfc        hu  relaince  \\\n",
       "2010-01-04       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-01-05  0.003159 -0.017923 -0.033375  0.000879 -0.003211 -0.004463   \n",
       "2010-01-06 -0.003010  0.040422 -0.008345  0.000674  0.005685  0.016158   \n",
       "2010-01-07 -0.010202 -0.008263 -0.013974  0.002605 -0.002073  0.016590   \n",
       "2010-01-08  0.016520 -0.002777  0.013467  0.001314  0.004343 -0.002622   \n",
       "\n",
       "                 tcs  \n",
       "2010-01-04       NaN  \n",
       "2010-01-05  0.000266  \n",
       "2010-01-06 -0.022611  \n",
       "2010-01-07 -0.027829  \n",
       "2010-01-08 -0.020437  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert daily stock prices in daily returns\n",
    "\n",
    "returns = data.pct_change()\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "KzCWdr4rUKyB"
   },
   "outputs": [],
   "source": [
    "returns.dropna(axis = 0, how = 'any', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z8Zyy0AmVKNp"
   },
   "source": [
    "We will need the mean daily returns for the calculation of portfolio return and covariance matrix in order to calculate the standad deviation for the whole porfolio, in which the diagonals represent variance of the returns and the other entries are pairwise-covariance of returns for different stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "-vkx1bEcYERN"
   },
   "outputs": [],
   "source": [
    "mean_daily_returns = returns.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "79L_C_tlUzMB"
   },
   "outputs": [],
   "source": [
    "cov_matrix = returns.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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       "      <th></th>\n",
       "      <th>ap</th>\n",
       "      <th>cipla</th>\n",
       "      <th>eicher</th>\n",
       "      <th>hdfc</th>\n",
       "      <th>hu</th>\n",
       "      <th>relaince</th>\n",
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       "      <th>ap</th>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cipla</th>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eicher</th>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfc</th>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hu</th>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relaince</th>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcs</th>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ap     cipla    eicher      hdfc        hu  relaince       tcs\n",
       "ap        0.000243  0.000043  0.000080  0.000062  0.000049  0.000064  0.000027\n",
       "cipla     0.000043  0.000245  0.000054  0.000046  0.000037  0.000053  0.000032\n",
       "eicher    0.000080  0.000054  0.000448  0.000068  0.000043  0.000078  0.000024\n",
       "hdfc      0.000062  0.000046  0.000068  0.000175  0.000048  0.000086  0.000042\n",
       "hu        0.000049  0.000037  0.000043  0.000048  0.000213  0.000045  0.000032\n",
       "relaince  0.000064  0.000053  0.000078  0.000086  0.000045  0.000272  0.000050\n",
       "tcs       0.000027  0.000032  0.000024  0.000042  0.000032  0.000050  0.000247"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tpCCXJX8WCJB"
   },
   "source": [
    "Now, it is not feasible to manually assign weights randomly and check for optimal values, instead a better way would be to code the weight assignments randomly and iterate over them to find the optimal values.\n",
    "\n",
    "The above technique is named as **Monte-Carlo** Simulation and we will implement it below.\n",
    "\n",
    "In the below code the three columns of *results* matrix represent, porfolio_returns , portfolio_standard_deviation & sharpe_ratio corresponding each weight point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "y1yk1rtlV2ja"
   },
   "outputs": [],
   "source": [
    "#set number of runs of random portfolio weights\n",
    "num_portfolios = 25000\n",
    "\n",
    "#assign the number of stocks in the portfolio\n",
    "num_stocks = len(data.columns)\n",
    "\n",
    "#set up array to hold results & the corresponding weights of the stocks over the iterations \n",
    "#which would help later to locate the optimal weights\n",
    "results = np.zeros((3 + num_stocks ,num_portfolios))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MmAc1khuZXmW"
   },
   "source": [
    "**Sharpe Ratio** :-\n",
    "\n",
    "##**$\\frac{return \\ on \\ risky \\ asset - return \\ on \\ risk \\ free \\ asset}  {risk \\ standard \\ deviation \\ of \\ portfolio}$**\n",
    "\n",
    "But in the below simulation the return on risk-free asset is assumed to be 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "yoSuHI1DW-Qo"
   },
   "outputs": [],
   "source": [
    "# iterating over the num_portfolos and assigning the results in results array \n",
    "\n",
    "for i in range(num_portfolios):\n",
    "\n",
    "    #select random weights for portfolio holdings\n",
    "    weights = np.random.random(num_stocks)\n",
    "\n",
    "    #rebalance weights to sum to 1, to ensure that all the money is used\n",
    "    weights /= np.sum(weights)\n",
    "    \n",
    "    #calculate portfolio return and volatility\n",
    "    #here 252 is taken as the number of avg working days in a year\n",
    "    portfolio_return = np.sum(mean_daily_returns * weights) * 252\n",
    "    portfolio_std_dev = np.sqrt(np.dot(weights.T,np.dot(cov_matrix, weights))) * np.sqrt(252)\n",
    "    \n",
    "    #store results in results array\n",
    "    results[0,i] = portfolio_return\n",
    "    results[1,i] = portfolio_std_dev\n",
    "    #store Sharpe Ratio (return / volatility) - risk free rate element excluded for simplicity\n",
    "    results[2,i] = results[0,i] / results[1,i]\n",
    "\n",
    "    #storing the corrsponding weights in the results matrix\n",
    "    for j in range(len(weights)):\n",
    "        results[j+3,i] = weights[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "JljrYna0YWuk",
    "outputId": "cbfedeb2-5e30-488c-ce6b-0332c2251462"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.23424563, 0.23399689, 0.23546825, ..., 0.25822045, 0.22567472,\n",
       "        0.26356098],\n",
       "       [0.15098492, 0.14715847, 0.14464988, ..., 0.16307529, 0.13912441,\n",
       "        0.15433153],\n",
       "       [1.55145053, 1.59010136, 1.62784957, ..., 1.58344311, 1.6221073 ,\n",
       "        1.70775849],\n",
       "       ...,\n",
       "       [0.20902774, 0.07117667, 0.16888645, ..., 0.1790004 , 0.13682856,\n",
       "        0.21617844],\n",
       "       [0.1167274 , 0.1377627 , 0.04706753, ..., 0.27738688, 0.08980322,\n",
       "        0.13785236],\n",
       "       [0.0167497 , 0.18184786, 0.12965486, ..., 0.02437729, 0.18888344,\n",
       "        0.12438828]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C1Z_WTJlZ0GX"
   },
   "source": [
    "Now we would plot the data to visualise the risk-return tradeoff related with the assignement of weights to different stocks in the portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "N6AiRH77ZKzf",
    "outputId": "de48a4d5-fbf4-4e14-c222-e2a2f807066f"
   },
   "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>ret</th>\n",
       "      <th>stdev</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>ap</th>\n",
       "      <th>cipla</th>\n",
       "      <th>eicher</th>\n",
       "      <th>hdfc</th>\n",
       "      <th>hu</th>\n",
       "      <th>relaince</th>\n",
       "      <th>tcs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.234246</td>\n",
       "      <td>0.150985</td>\n",
       "      <td>1.551451</td>\n",
       "      <td>0.274953</td>\n",
       "      <td>0.068605</td>\n",
       "      <td>0.027690</td>\n",
       "      <td>0.286247</td>\n",
       "      <td>0.209028</td>\n",
       "      <td>0.116727</td>\n",
       "      <td>0.016750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.233997</td>\n",
       "      <td>0.147158</td>\n",
       "      <td>1.590101</td>\n",
       "      <td>0.180115</td>\n",
       "      <td>0.185794</td>\n",
       "      <td>0.167199</td>\n",
       "      <td>0.076105</td>\n",
       "      <td>0.071177</td>\n",
       "      <td>0.137763</td>\n",
       "      <td>0.181848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.235468</td>\n",
       "      <td>0.144650</td>\n",
       "      <td>1.627850</td>\n",
       "      <td>0.059835</td>\n",
       "      <td>0.207108</td>\n",
       "      <td>0.169741</td>\n",
       "      <td>0.217707</td>\n",
       "      <td>0.168886</td>\n",
       "      <td>0.047068</td>\n",
       "      <td>0.129655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.245203</td>\n",
       "      <td>0.154163</td>\n",
       "      <td>1.590543</td>\n",
       "      <td>0.120159</td>\n",
       "      <td>0.067189</td>\n",
       "      <td>0.177598</td>\n",
       "      <td>0.173999</td>\n",
       "      <td>0.140349</td>\n",
       "      <td>0.247841</td>\n",
       "      <td>0.072864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.237141</td>\n",
       "      <td>0.152273</td>\n",
       "      <td>1.557334</td>\n",
       "      <td>0.167828</td>\n",
       "      <td>0.163709</td>\n",
       "      <td>0.183565</td>\n",
       "      <td>0.158926</td>\n",
       "      <td>0.075155</td>\n",
       "      <td>0.182318</td>\n",
       "      <td>0.068500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ret     stdev    sharpe        ap     cipla    eicher      hdfc  \\\n",
       "0  0.234246  0.150985  1.551451  0.274953  0.068605  0.027690  0.286247   \n",
       "1  0.233997  0.147158  1.590101  0.180115  0.185794  0.167199  0.076105   \n",
       "2  0.235468  0.144650  1.627850  0.059835  0.207108  0.169741  0.217707   \n",
       "3  0.245203  0.154163  1.590543  0.120159  0.067189  0.177598  0.173999   \n",
       "4  0.237141  0.152273  1.557334  0.167828  0.163709  0.183565  0.158926   \n",
       "\n",
       "         hu  relaince       tcs  \n",
       "0  0.209028  0.116727  0.016750  \n",
       "1  0.071177  0.137763  0.181848  \n",
       "2  0.168886  0.047068  0.129655  \n",
       "3  0.140349  0.247841  0.072864  \n",
       "4  0.075155  0.182318  0.068500  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert results array to Pandas DataFrame\n",
    "results_frame = pd.DataFrame(results.T, columns=['ret', 'stdev', 'sharpe' ] + list(data.columns))\n",
    "results_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fC6yextKbzq7"
   },
   "source": [
    "In the below code the colormap in the plot characterises Sharpe Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "9zQBj2SgaHVq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fd15b4d85d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create scatter plot coloured by Sharpe Ratio\n",
    "plt.figure(figsize = (15,8))\n",
    "plt.scatter(results_frame.stdev, results_frame.ret, c=results_frame.sharpe, cmap='RdYlBu')\n",
    "plt.title('Risk Return Plot')\n",
    "plt.xlabel('Risk')\n",
    "plt.ylabel('Returns')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "14zU2PNscCxA"
   },
   "source": [
    "Now from the abobe plot we can conclude that the blue boundary line corresponds to the **`Efficient Frontier`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b13uRUd5rP6R"
   },
   "source": [
    "Now, we would locate the optimal weights for :- \n",
    "\n",
    "1. The portfolio which has highest return( highest sharpe ratio)\n",
    "2. The portfolio with minimum risk( minimum standard deviation )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "OxRwPF8gcMWb"
   },
   "outputs": [],
   "source": [
    "#portfolio with highest Sharpe Ratio\n",
    "max_return_port = results_frame.iloc[results_frame['sharpe'].idxmax()]\n",
    "\n",
    "#locate positon of portfolio with minimum standard deviation\n",
    "min_risk_port = results_frame.iloc[results_frame['stdev'].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "Yl4gn2pCr1Dc",
    "outputId": "c8fe13df-4843-4907-a7a2-fc1e8939849a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ret         0.286571\n",
       "stdev       0.153021\n",
       "sharpe      1.872752\n",
       "ap          0.146123\n",
       "cipla       0.002014\n",
       "eicher      0.227960\n",
       "hdfc        0.172087\n",
       "hu          0.202649\n",
       "relaince    0.015682\n",
       "tcs         0.233485\n",
       "Name: 11409, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_return_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "DO2UVMeAr6wy",
    "outputId": "1aaa048f-d89c-4cdd-eddb-54a1a5475c7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ret         0.222575\n",
       "stdev       0.137899\n",
       "sharpe      1.614050\n",
       "ap          0.134075\n",
       "cipla       0.161854\n",
       "eicher      0.067551\n",
       "hdfc        0.158545\n",
       "hu          0.196107\n",
       "relaince    0.087026\n",
       "tcs         0.194842\n",
       "Name: 7996, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_risk_port"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GY4pwb9UsG4C"
   },
   "source": [
    "The following code locates the **max_return_port** & **min_risk_port** in the above plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "0YH0EsH-sBCi",
    "outputId": "051c86f8-7268-4261-d91f-d5dbf5513ef2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd15bd9e9d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create scatter plot coloured by Sharpe Ratio\n",
    "plt.figure(figsize = (15, 8))\n",
    "plt.scatter(results_frame.stdev,results_frame.ret,c=results_frame.sharpe,cmap='RdYlBu')\n",
    "plt.title('Location of Optimal Portfolio')\n",
    "plt.xlabel('Risk')\n",
    "plt.ylabel('Returns')\n",
    "plt.colorbar()\n",
    "#plot red star to highlight position of portfolio with highest Sharpe Ratio(i.e. max return)\n",
    "plt.scatter(max_return_port[1],max_return_port[0],marker=(5,1,0),color='r',s=1000)\n",
    "#plot green star to highlight position of minimum variance portfolio(i.e. minimum risk)\n",
    "plt.scatter(min_risk_port[1],min_risk_port[0],marker=(5,1,0),color='g',s=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YCBaIwdntIw_"
   },
   "source": [
    "----------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "esJtS27gtMAb"
   },
   "source": [
    "**Q2** What is the impact of increasing the number of stocks in the portfolio?\n",
    "\n",
    "**Answer** :- As the Capinski book gives the formula for standard deviation of portfolio returns as\n",
    "\n",
    "###$\\sigma_V^2 = Var(\\epsilon_V) \\ + \\ \\beta_V^2 \\sigma_M^2 $\n",
    "\n",
    "The two parts of the equation, i.e.  $Var(\\epsilon_V)$ signifies the unsystematic or diversifiable risk whereas $\\beta_V^2 \\sigma_M^2$ signifies systematic or undiversifiable risk. Now keeping that in mind, increasing the number of stocks in the portfolio assures of diversification due to which the diversifiable risk is reduced and as a result $\\sigma_V^2$(i.e. portfolio risk) is also reduced. One more thing to note is that systematic risk is inherent to the whole market and is not in our control, it is affected by Govt. Policies, Market Recession, etc.\n",
    "\n",
    "But even having a very large number of stocks would hurt the portfolio performance, say we have 100 stocks, in thar=t case even a good performing stock would contribute a little to the portfolio. Also, the rule regarding diversification is that it’s marginal benefit reduces as the number of stocks increases a certain value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O7WqXedTuCM9"
   },
   "source": [
    "------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVVfWon-uFF3"
   },
   "source": [
    "**Q3** Why are calculating the weights by optimization not giving equal weightage to every stock?\n",
    "\n",
    "**Answer** :- The very simple answer to this question is that, each stock has their respective returns as well as risk , so in order to maximise the return or minimize the risk of portfolio, we need a combination of optimal weights which may or may not be equal, subject to the constraint that $\\Sigma w_i = 1$\n",
    "\n",
    "A simple example is :-\n",
    "Suppose we want to maximise the below function\n",
    "a + 3b + 5c subject to the constraint a + b + c = 1, then for this we need not have a = b = c.\n"
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    "**Q4** Which optimization techniques are used by Mutual Funds currently?\n",
    "\n",
    "**Answer** :- Optimization Techniques are basically risk - management techniques for any portfolio. Some of the optimization techniques used by mutual funds currently are -\n",
    "\n",
    "* mean-semivariance portfolio optimization model \n",
    "* fuzzy optimization model\n",
    "* Stochastic Programming\n",
    "* CAPM\n",
    "\n",
    "Note :- This answer might be inaccurate, I would request you to please provide the right answer or reference to the right answer after the assignment is submitted.\n"
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