diff --git "a/Notebooks/CapiPort.ipynb" "b/Notebooks/CapiPort.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f6ccd5b",
+ "metadata": {},
+ "source": [
+ "# CapiPort - PORTFOLIO OPTIMISATION"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1962897",
+ "metadata": {},
+ "source": [
+ " Two things to consider for Portfolio Optimisation:\n",
+ " \n",
+ " 1) Minimising Risk\n",
+ " 2) Maximising Return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d025126",
+ "metadata": {},
+ "source": [
+ " Basic process of Portfolio Optimisation:\n",
+ " \n",
+ " 1) Select the Asset class to work on.\n",
+ " 1.1) Asset Class choosen - Equity (Stocks)\n",
+ " 2) Select the Companies which you want to use to build a Portfolio.\n",
+ " 2.1) Companies choosen - \n",
+ " 2.1.1) Tata Power - TATAPOWER.NS\n",
+ " 2.1.2) Tata Motors - TATAMOTORS.NS\n",
+ " 2.1.3) Tata Steel - TATASTEEL.NS\n",
+ " 2.1.4) Zomato - ZOMATO.NS\n",
+ " 2.1.5) NHPC - NHPC.NS\n",
+ " 2.1.6) NCC - NCC.NS\n",
+ " 2.1.7) IREDA - IREDA.NS\n",
+ " 2.1.8) IRCON - IRCON.NS\n",
+ " 3) To try various Statistical Methods relating to Portfolio Optimisation.\n",
+ " 3.1) Method 1 - Result\n",
+ " 3.2) Method 2 - Result\n",
+ " 4) You will obtain Weigths or Percentages of Portfolio to invest.\n",
+ " 4.1) Method 1 - Weights\n",
+ " 4.2) Method 2 - Weights\n",
+ " 5) Testing the Portfolio for the future.\n",
+ " 5.1) Method 1 - Result\n",
+ " 5.2) Method 2 - Result\n",
+ " 6) Final Result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a80152f2",
+ "metadata": {},
+ "source": [
+ "# Steps of Implementation\n",
+ "\n",
+ " 1) Importing Libraries\n",
+ " 2) Select the Financial Instruments\n",
+ " 3) Get the Adjacent Close prices of Last 5 Years\n",
+ " 4) Calculating the Log-Return of Company Dataset\n",
+ " 5) Calculating the Sharpe Ratio\n",
+ " 6) Getting Started with Monte Carlo\n",
+ " 7) Let's look closer at the Simulations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42c5d329",
+ "metadata": {},
+ "source": [
+ "## Importing Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "945bbd48",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-03-07T20:09:35.347796Z",
+ "start_time": "2024-03-07T20:09:30.939936Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pathlib\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.optimize as sci_opt\n",
+ "\n",
+ "from pprint import pprint\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ "import yfinance as yf\n",
+ "\n",
+ "# Set some display options for Pandas.\n",
+ "pd.set_option('expand_frame_repr', False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8dbc2573",
+ "metadata": {},
+ "source": [
+ "## Select the Financial Instruments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "08345846",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-03-07T20:09:37.966003Z",
+ "start_time": "2024-03-07T20:09:37.963670Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "## Have Choosen Stocks\n",
+ "\n",
+ "## The Companies selected to build a Optimal Portfolio\n",
+ "com_sel = [\"TATAPOWER.NS\", \"TATAMOTORS.NS\", \"TATASTEEL.NS\", \"RELIANCE.NS\", \"ADANIENT.NS\", \"ADANIPORTS.NS\"]\n",
+ "\n",
+ "## We will need Number of Tickers for future\n",
+ "num_tick = len(com_sel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2376a747",
+ "metadata": {},
+ "source": [
+ "## Get the Adjacent Close prices of Last 5 Years"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "cb64a4c0",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-03-07T20:09:39.680530Z",
+ "start_time": "2024-03-07T20:09:38.995900Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[*********************100%%**********************] 6 of 6 completed\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Ticker | \n",
+ " ADANIENT.NS | \n",
+ " ADANIPORTS.NS | \n",
+ " RELIANCE.NS | \n",
+ " TATAMOTORS.NS | \n",
+ " TATAPOWER.NS | \n",
+ " TATASTEEL.NS | \n",
+ "
\n",
+ " \n",
+ " | Date | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2019-03-01 | \n",
+ " 132.264633 | \n",
+ " 322.673431 | \n",
+ " 1098.479736 | \n",
+ " 179.739807 | \n",
+ " 61.861309 | \n",
+ " 43.332832 | \n",
+ "
\n",
+ " \n",
+ " | 2019-03-05 | \n",
+ " 140.120483 | \n",
+ " 328.532288 | \n",
+ " 1108.872925 | \n",
+ " 193.447083 | \n",
+ " 63.798790 | \n",
+ " 44.540791 | \n",
+ "
\n",
+ " \n",
+ " | 2019-03-06 | \n",
+ " 137.600677 | \n",
+ " 326.401764 | \n",
+ " 1133.197998 | \n",
+ " 188.213394 | \n",
+ " 65.782417 | \n",
+ " 44.463959 | \n",
+ "
\n",
+ " \n",
+ " | 2019-03-07 | \n",
+ " 135.278519 | \n",
+ " 330.130127 | \n",
+ " 1138.080933 | \n",
+ " 188.711853 | \n",
+ " 65.090462 | \n",
+ " 44.489574 | \n",
+ "
\n",
+ " \n",
+ " | 2019-03-08 | \n",
+ " 136.563110 | \n",
+ " 331.340698 | \n",
+ " 1135.258667 | \n",
+ " 180.637009 | \n",
+ " 64.583023 | \n",
+ " 43.354172 | \n",
+ "
\n",
+ " \n",
+ "
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+ "