fundamentals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b8d3324c",
   "metadata": {},
   "source": [
    "# Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc392b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U quandl numpy pandas requests pandas-datareader DateTime yfinance seaborn scipy numpy-financial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c55a782b-fe3a-46af-92da-209c30af7625",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import numpy_financial as npf\n",
    "import json\n",
    "import requests\n",
    "import yfinance as yf\n",
    "from statistics import NormalDist\n",
    "from scipy.optimize import minimize\n",
    "from scipy import stats\n",
    "from pandas_datareader import data, wb\n",
    "from datetime import datetime, timedelta\n",
    "from dateutil.relativedelta import relativedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5bcb118d-226f-4100-8471-d6a8f615b8de",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_return(annual_return, weights):\n",
    "    return sum(annual_return * weights.T)\n",
    "\n",
    "def calculate_volatility(cov_metrix, weights):\n",
    "    portfolio_variance = np.dot(weights.T, np.dot(cov_metrix, weights))\n",
    "    #standard deviation (risk of portfolio)\n",
    "    portfolio_risk = np.sqrt(portfolio_variance)\n",
    "    return portfolio_risk\n",
    "\n",
    "def calculate_sharpe_ratio(p_return, p_risk):\n",
    "    return p_return / p_risk\n",
    "\n",
    "def neg_sharpe(weights, cov_matrix, annual_returns):\n",
    "    p_risk = calculate_volatility(cov_matrix, weights)\n",
    "    p_return = calculate_return(annual_returns, weights)\n",
    "    return calculate_sharpe_ratio(p_return, p_risk) * -1\n",
    "\n",
    "def simulate_portfolio_returns(years, annual_payment, initial_investment, p_return, p_risk):\n",
    "    portfolio_distribution = NormalDist(mu=p_return, sigma=p_risk)\n",
    "    r_0 = portfolio_distribution.inv_cdf(random.random())\n",
    "    d = {\n",
    "        \"investment\": [initial_investment],\n",
    "        \"return\": [portfolio_distribution.inv_cdf(random.random())],\n",
    "        \"balance\": [initial_investment + initial_investment * r_0],\n",
    "    }\n",
    "    for year in range(years):\n",
    "        investement = d[\"balance\"][len(d[\"balance\"]) - 1] + annual_payment\n",
    "        return_this_year = portfolio_distribution.inv_cdf(random.random())\n",
    "        balance = investement + investement * return_this_year\n",
    "        d[\"investment\"].append(d[\"investment\"][len(d[\"investment\"]) - 1] + annual_payment)\n",
    "        d[\"return\"].append(return_this_year)\n",
    "        d[\"balance\"].append(balance)\n",
    "    return pd.DataFrame.from_dict(d)\n",
    "\n",
    "def simulate_ending_balances(n, years, annual_payment, initial_investment, p_return, p_risk):\n",
    "    result = []\n",
    "    for i in range(n):\n",
    "        returns = simulate_portfolio_returns(years, annual_payment, initial_investment, p_return, p_risk)\n",
    "        ending_balance = returns.iloc[-1:][\"balance\"]\n",
    "        result.append(float(ending_balance))\n",
    "    return np.array(result)\n",
    "\n",
    "def generate_portfolios(tickers, cov_metrix, annual_returns, num_portfolios):\n",
    "    num_assets = len(tickers)\n",
    "    # values = [weights..., volatility, return, sharp_ratio]\n",
    "    values = []\n",
    "    for single_portfolio in range(num_portfolios):\n",
    "        row = []\n",
    "        weights = np.random.random(num_assets)\n",
    "        weights /= np.sum(weights)\n",
    "        row.extend(weights)\n",
    "        \n",
    "        p_risk = calculate_volatility(cov_metrix, weights)\n",
    "        p_return = calculate_return(annual_returns, weights)\n",
    "        \n",
    "        row.append(p_risk)\n",
    "        row.append(p_return)\n",
    "        row.append(calculate_sharpe_ratio(p_return, p_risk))\n",
    "        values.append(row)\n",
    "        \n",
    "    headers = tickers.copy()\n",
    "    headers.extend(['risk', 'return', 'sharpe_ratio'])\n",
    "    return pd.DataFrame(values, columns=headers)\n",
    "\n",
    "def get_best_portfolio_optimized(tickers, cov_matrix, annual_returns, fn, method, constraints, bounds):\n",
    "    opt_results = minimize(\n",
    "        fn, \n",
    "        init_guess,\n",
    "        method=method,\n",
    "        bounds=bounds, \n",
    "        constraints=constraints,\n",
    "        args=(cov_matrix, annual_returns),\n",
    "    )\n",
    "    opt_weights = opt_results.x\n",
    "    p_risk = calculate_volatility(cov_matrix, opt_weights)\n",
    "    p_return = calculate_return(annual_returns, opt_weights)\n",
    "    sharpe_ratio = calculate_sharpe_ratio(p_return, p_risk)\n",
    "    \n",
    "    row = list(opt_weights) + [p_risk, p_return, sharpe_ratio]\n",
    "    headers = tickers.copy()\n",
    "    headers.extend(['risk', 'return', 'sharp_ratio'])\n",
    "    return pd.DataFrame([row], columns=headers)\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    if text.startswith(prefix):\n",
    "        return text[len(prefix):]\n",
    "    return text\n",
    "\n",
    "def get_us_treasuries_yield():\n",
    "    url = \"https://quote.cnbc.com/quote-html-webservice/quote.htm\"\n",
    "    params = {\n",
    "        \"noform\": \"1\",\n",
    "        \"partnerId\": \"2\",\n",
    "        \"fund\": \"1\",\n",
    "        \"exthrs\": \"0\",\n",
    "        \"output\": \"json\",\n",
    "        \"symbolType\": \"issue\",\n",
    "        \"symbols\": \"5093148|5093149|5093150|15183113|5093151|5093158|5093159|19892965|5093160|135058359|5093168\",\n",
    "        \"requestMethod\": \"extended\",\n",
    "    }\n",
    "    data = requests.get(url, params=params).json()\n",
    "    result = {\n",
    "        \"symbol\": [],\n",
    "        \"last\": [],\n",
    "    }\n",
    "    \n",
    "    for q in data[\"ExtendedQuoteResult\"][\"ExtendedQuote\"]:\n",
    "        result[\"symbol\"].append(q[\"QuickQuote\"][\"symbol\"])\n",
    "        result[\"last\"].append(q[\"QuickQuote\"][\"last\"])\n",
    "    return pd.DataFrame.from_dict(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec411a23-57e6-41c1-8a44-70215df69b46",
   "metadata": {},
   "source": [
    "# Inputs\n",
    "\n",
    "**tickers** - list of tickers in your portfolio\n",
    "\n",
    "**asset_type** - type of asset asociated with ticker\n",
    "\n",
    "**positions** - number of positions for each asset in your portfolio\n",
    "\n",
    "**historical_data_years** - number of years to fetch the historical data for\n",
    "\n",
    "**investment_years** - number of years to use for portfolio future value simulation\n",
    "\n",
    "**annual_payment** - annual payment that you're planning to invest during your **investment_years** period\n",
    "\n",
    "**initial_investment** - amount of money you're planning to start your investment with\n",
    "\n",
    "**simulations_number** - number of simulations to use in portfolio optimisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11ecfc69-1d97-4442-baa5-0c46417f4045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  5 of 5 completed\n",
      "US Treasuries Yield:\n",
      "\n",
      "   symbol   last\n",
      "0    US1M  0.041\n",
      "1    US3M  0.236\n",
      "2    US6M  0.557\n",
      "3    US1Y   0.88\n",
      "4    US2Y  1.316\n",
      "5    US3Y   1.54\n",
      "6    US5Y  1.773\n",
      "7    US7Y  1.892\n",
      "8   US10Y  1.916\n",
      "9   US20Y  2.277\n",
      "10  US30Y  2.213\n",
      "\n",
      "30 years US treasuries yield used as risk free rate:  0.02213\n"
     ]
    }
   ],
   "source": [
    "# Portfolio inputs:\n",
    "tickers = ['IVV', 'AGG', 'VGT', 'VWOB', 'MSFT']\n",
    "asset_class = ['stock', 'bond', 'stock', 'bond', 'stock']\n",
    "positions = [35, 29, 10, 20, 1]\n",
    "historical_data_years = 10\n",
    "\n",
    "# Project portfolio for 30 years from now\n",
    "investment_years = 30\n",
    "annual_payment = 1000\n",
    "initial_investment = 10000\n",
    "\n",
    "simulations_number = 100000\n",
    "np.random.seed(123)\n",
    "\n",
    "# Some dynamic input\n",
    "treasury_bills_yield = get_us_treasuries_yield()\n",
    "risk_free_rate = float(treasury_bills_yield.iloc[-1][\"last\"]) / 100\n",
    "live_prices = yf.download(tickers=tickers, period='1d', interval='1d')\n",
    "live_prices = [live_prices['Adj Close'][ticker][0] for ticker in tickers]\n",
    "\n",
    "print(\"US Treasuries Yield:\\n\")\n",
    "print(treasury_bills_yield)\n",
    "print(\"\\n30 years US treasuries yield used as risk free rate: \", risk_free_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7bf39b3-1320-4cd6-9f7c-3f19aeebb7be",
   "metadata": {},
   "source": [
    "# Basic info\n",
    "\n",
    "In this section we will see what are the weight of each asset and what balance between asset classes portfolio has"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e8c047ba-2d6c-49ea-ad7e-fec4ae9c5ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_23c47\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_23c47_level0_col0\" class=\"col_heading level0 col0\" >tickers</th>\n",
       "      <th id=\"T_23c47_level0_col1\" class=\"col_heading level0 col1\" >asset_class</th>\n",
       "      <th id=\"T_23c47_level0_col2\" class=\"col_heading level0 col2\" >live_prices</th>\n",
       "      <th id=\"T_23c47_level0_col3\" class=\"col_heading level0 col3\" >positions</th>\n",
       "      <th id=\"T_23c47_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
       "      <th id=\"T_23c47_level0_col5\" class=\"col_heading level0 col5\" >weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_23c47_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_23c47_row0_col0\" class=\"data row0 col0\" >IVV</td>\n",
       "      <td id=\"T_23c47_row0_col1\" class=\"data row0 col1\" >stock</td>\n",
       "      <td id=\"T_23c47_row0_col2\" class=\"data row0 col2\" >452.12</td>\n",
       "      <td id=\"T_23c47_row0_col3\" class=\"data row0 col3\" >35</td>\n",
       "      <td id=\"T_23c47_row0_col4\" class=\"data row0 col4\" >15,824.20</td>\n",
       "      <td id=\"T_23c47_row0_col5\" class=\"data row0 col5\" >63.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23c47_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_23c47_row1_col0\" class=\"data row1 col0\" >AGG</td>\n",
       "      <td id=\"T_23c47_row1_col1\" class=\"data row1 col1\" >bond</td>\n",
       "      <td id=\"T_23c47_row1_col2\" class=\"data row1 col2\" >111.62</td>\n",
       "      <td id=\"T_23c47_row1_col3\" class=\"data row1 col3\" >29</td>\n",
       "      <td id=\"T_23c47_row1_col4\" class=\"data row1 col4\" >3,237.12</td>\n",
       "      <td id=\"T_23c47_row1_col5\" class=\"data row1 col5\" >12.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23c47_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_23c47_row2_col0\" class=\"data row2 col0\" >VGT</td>\n",
       "      <td id=\"T_23c47_row2_col1\" class=\"data row2 col1\" >stock</td>\n",
       "      <td id=\"T_23c47_row2_col2\" class=\"data row2 col2\" >420.14</td>\n",
       "      <td id=\"T_23c47_row2_col3\" class=\"data row2 col3\" >10</td>\n",
       "      <td id=\"T_23c47_row2_col4\" class=\"data row2 col4\" >4,201.45</td>\n",
       "      <td id=\"T_23c47_row2_col5\" class=\"data row2 col5\" >16.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23c47_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_23c47_row3_col0\" class=\"data row3 col0\" >VWOB</td>\n",
       "      <td id=\"T_23c47_row3_col1\" class=\"data row3 col1\" >bond</td>\n",
       "      <td id=\"T_23c47_row3_col2\" class=\"data row3 col2\" >74.99</td>\n",
       "      <td id=\"T_23c47_row3_col3\" class=\"data row3 col3\" >20</td>\n",
       "      <td id=\"T_23c47_row3_col4\" class=\"data row3 col4\" >1,499.78</td>\n",
       "      <td id=\"T_23c47_row3_col5\" class=\"data row3 col5\" >5.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23c47_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_23c47_row4_col0\" class=\"data row4 col0\" >MSFT</td>\n",
       "      <td id=\"T_23c47_row4_col1\" class=\"data row4 col1\" >stock</td>\n",
       "      <td id=\"T_23c47_row4_col2\" class=\"data row4 col2\" >305.83</td>\n",
       "      <td id=\"T_23c47_row4_col3\" class=\"data row4 col3\" >1</td>\n",
       "      <td id=\"T_23c47_row4_col4\" class=\"data row4 col4\" >305.83</td>\n",
       "      <td id=\"T_23c47_row4_col5\" class=\"data row4 col5\" >1.22%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x128b7feb0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf = pd.DataFrame.from_dict({'tickers': tickers, 'asset_class': asset_class, 'live_prices': live_prices, 'positions': positions})\n",
    "pf['value'] = pf['live_prices'] * pf['positions']\n",
    "pf['weight'] = pf['value'] / pf['value'].sum()\n",
    "pf.style.format({'live_prices': '{:,.2f}'.format, 'value': '{:,.2f}'.format, 'weight': '{:,.2%}'.format})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e3403ea1-ee54-4b08-802d-3370d76dbe04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Allocation by class:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_29a0b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_29a0b_level0_col0\" class=\"col_heading level0 col0\" >weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >asset_class</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_29a0b_level0_row0\" class=\"row_heading level0 row0\" >bond</th>\n",
       "      <td id=\"T_29a0b_row0_col0\" class=\"data row0 col0\" >18.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29a0b_level0_row1\" class=\"row_heading level0 row1\" >stock</th>\n",
       "      <td id=\"T_29a0b_row1_col0\" class=\"data row1 col0\" >81.10%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1288e4700>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Allocation by class:')\n",
    "pf.groupby(['asset_class']) \\\n",
    "    .sum().drop(columns=['live_prices', 'value', 'positions']) \\\n",
    "    .style.format({'weight': '{:,.2%}'.format})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285d5698-a9af-494e-8875-db90255ad1c5",
   "metadata": {},
   "source": [
    "# Historical data\n",
    "\n",
    "In this section we retrieve historical data for each asset for the period of **historical_data_years** and calculate basic statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77ab7b9d-45a1-4982-8303-f90f01317e8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date: 04/02/2012 End date: 01/02/2022\n"
     ]
    }
   ],
   "source": [
    "start = datetime.now() - timedelta(days = 365 * historical_data_years)\n",
    "end = datetime.now()\n",
    "print('Start date:', start.strftime(\"%d/%m/%Y\"), 'End date:', end.strftime(\"%d/%m/%Y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f460c700-e304-4f66-bdc3-6263692e708c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_49ab9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_49ab9_level0_col0\" class=\"col_heading level0 col0\" >tickers</th>\n",
       "      <th id=\"T_49ab9_level0_col1\" class=\"col_heading level0 col1\" >starting_date</th>\n",
       "      <th id=\"T_49ab9_level0_col2\" class=\"col_heading level0 col2\" >observations(days)</th>\n",
       "      <th id=\"T_49ab9_level0_col3\" class=\"col_heading level0 col3\" >annual_return</th>\n",
       "      <th id=\"T_49ab9_level0_col4\" class=\"col_heading level0 col4\" >variance</th>\n",
       "      <th id=\"T_49ab9_level0_col5\" class=\"col_heading level0 col5\" >std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_49ab9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_49ab9_row0_col0\" class=\"data row0 col0\" >IVV</td>\n",
       "      <td id=\"T_49ab9_row0_col1\" class=\"data row0 col1\" >02/06/2012</td>\n",
       "      <td id=\"T_49ab9_row0_col2\" class=\"data row0 col2\" >2515</td>\n",
       "      <td id=\"T_49ab9_row0_col3\" class=\"data row0 col3\" >15.40%</td>\n",
       "      <td id=\"T_49ab9_row0_col4\" class=\"data row0 col4\" >2.69%</td>\n",
       "      <td id=\"T_49ab9_row0_col5\" class=\"data row0 col5\" >16.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49ab9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_49ab9_row1_col0\" class=\"data row1 col0\" >AGG</td>\n",
       "      <td id=\"T_49ab9_row1_col1\" class=\"data row1 col1\" >02/06/2012</td>\n",
       "      <td id=\"T_49ab9_row1_col2\" class=\"data row1 col2\" >2515</td>\n",
       "      <td id=\"T_49ab9_row1_col3\" class=\"data row1 col3\" >2.58%</td>\n",
       "      <td id=\"T_49ab9_row1_col4\" class=\"data row1 col4\" >0.16%</td>\n",
       "      <td id=\"T_49ab9_row1_col5\" class=\"data row1 col5\" >4.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49ab9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_49ab9_row2_col0\" class=\"data row2 col0\" >VGT</td>\n",
       "      <td id=\"T_49ab9_row2_col1\" class=\"data row2 col1\" >02/06/2012</td>\n",
       "      <td id=\"T_49ab9_row2_col2\" class=\"data row2 col2\" >2515</td>\n",
       "      <td id=\"T_49ab9_row2_col3\" class=\"data row2 col3\" >21.37%</td>\n",
       "      <td id=\"T_49ab9_row2_col4\" class=\"data row2 col4\" >4.20%</td>\n",
       "      <td id=\"T_49ab9_row2_col5\" class=\"data row2 col5\" >20.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49ab9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_49ab9_row3_col0\" class=\"data row3 col0\" >VWOB</td>\n",
       "      <td id=\"T_49ab9_row3_col1\" class=\"data row3 col1\" >06/04/2013</td>\n",
       "      <td id=\"T_49ab9_row3_col2\" class=\"data row3 col2\" >2183</td>\n",
       "      <td id=\"T_49ab9_row3_col3\" class=\"data row3 col3\" >3.94%</td>\n",
       "      <td id=\"T_49ab9_row3_col4\" class=\"data row3 col4\" >0.66%</td>\n",
       "      <td id=\"T_49ab9_row3_col5\" class=\"data row3 col5\" >8.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49ab9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_49ab9_row4_col0\" class=\"data row4 col0\" >MSFT</td>\n",
       "      <td id=\"T_49ab9_row4_col1\" class=\"data row4 col1\" >02/06/2012</td>\n",
       "      <td id=\"T_49ab9_row4_col2\" class=\"data row4 col2\" >2515</td>\n",
       "      <td id=\"T_49ab9_row4_col3\" class=\"data row4 col3\" >28.60%</td>\n",
       "      <td id=\"T_49ab9_row4_col4\" class=\"data row4 col4\" >6.53%</td>\n",
       "      <td id=\"T_49ab9_row4_col5\" class=\"data row4 col5\" >25.55%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12ace2520>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_info = {'tickers': tickers, 'starting_date': [], 'observations(days)': [], 'annual_return': [], 'variance': []}\n",
    "price_hist = {}\n",
    "\n",
    "sns.set(rc = {'figure.figsize': (16,8)})\n",
    "\n",
    "for ticker in tickers:\n",
    "    tmp = yf.download(ticker, start=start, end=end)\n",
    "    tmp['daily_change'] = tmp['Adj Close'].pct_change()\n",
    "    stock_info['starting_date'].append(tmp.index[0])\n",
    "    stock_info['annual_return'].append(tmp['daily_change'].mean() * 252)\n",
    "    stock_info['variance'].append(tmp['daily_change'].var() * 252)\n",
    "    stock_info['observations(days)'].append(len(tmp))\n",
    "    price_hist[ticker] = tmp['daily_change']\n",
    "    sns.lineplot(x=tmp.index, y='Adj Close', data=tmp, markers=True, label=ticker)\n",
    "    \n",
    "price_hist = pd.DataFrame.from_dict(price_hist)\n",
    "\n",
    "df = pd.DataFrame.from_dict(stock_info)\n",
    "df['std'] = df['variance'].apply(np.sqrt)\n",
    "df['starting_date'] = df['starting_date'].dt.strftime('%m/%d/%Y')\n",
    "df.style.format({'annual_return': '{:,.2%}'.format, 'variance': '{:,.2%}'.format, 'std': '{:,.2%}'.format})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9be3ec-e641-4466-b798-14e6b93797f1",
   "metadata": {},
   "source": [
    "# Correlation among portfolio assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8c3e72c3-7007-46e5-9dae-e6c487a8957f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Portfolio Pearson correlation')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize': (10, 8)})\n",
    "sns.heatmap(\n",
    "    price_hist.corr(method='pearson'), \n",
    "    annot=True, \n",
    "    square=True, \n",
    "    fmt=\".2%\", \n",
    "    cmap=sns.color_palette(\"coolwarm\", as_cmap=True),\n",
    "    vmin=-1, vmax=1\n",
    ").set_title('Portfolio Pearson correlation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690d3891-92f3-4c40-a0e3-ff5d8f0a2567",
   "metadata": {},
   "source": [
    "# Caculate risk and return of the portfolio based on historical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c7ca859c-de7e-4c20-8721-fc4e511aa5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portfolio_return: 14.22% \n",
      "Portfolio_risk: 14.10% \n",
      "Sharpe ratio: 1.01\n"
     ]
    }
   ],
   "source": [
    "p_return = calculate_return(df['annual_return'].to_numpy(), pf['weight'].to_numpy())\n",
    "p_risk = calculate_volatility((price_hist.cov() * 252).to_numpy(), pf['weight'].to_numpy())\n",
    "print(\n",
    "    \"Portfolio_return:\", \"{0:.2f}%\".format(p_return * 100), \n",
    "    \"\\nPortfolio_risk:\", \"{0:.2f}%\".format(p_risk * 100),\n",
    "    \"\\nSharpe ratio:\", \"{0:.2f}\".format(calculate_sharpe_ratio(p_return, p_risk)),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b99e9fb-d30c-46ab-af46-0d780ac522c7",
   "metadata": {},
   "source": [
    "# PV&FV + Confidence intervals\n",
    "\n",
    "Let's calculate the present and future value of portfolio based on **simulations_number** of portfolio return simulations.\n",
    "\n",
    "Additionally we will try to guess how much money we will end up with and how sure we are about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4662c878-a230-4028-97af-50482e44b5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your total investment over 30 years is 40000\n",
      "\n",
      "EndingBalance_Mean(FV): 785582.35 \n",
      "EndingBalance_Mean(PV): 433697.15\n",
      "\n",
      "With confidence of [col_name_here] you'll end up with: \n"
     ]
    },
    {
     "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>99%</th>\n",
       "      <th>95%</th>\n",
       "      <th>90%</th>\n",
       "      <th>80%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FV</th>\n",
       "      <td>166954.144941</td>\n",
       "      <td>249221.411941</td>\n",
       "      <td>308604.142639</td>\n",
       "      <td>401102.714141</td>\n",
       "      <td>660223.944059</td>\n",
       "      <td>1.085847e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PV</th>\n",
       "      <td>92170.523216</td>\n",
       "      <td>137587.886443</td>\n",
       "      <td>170371.363369</td>\n",
       "      <td>221437.131968</td>\n",
       "      <td>364490.419722</td>\n",
       "      <td>5.994647e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              99%            95%            90%            80%            50%  \\\n",
       "FV  166954.144941  249221.411941  308604.142639  401102.714141  660223.944059   \n",
       "PV   92170.523216  137587.886443  170371.363369  221437.131968  364490.419722   \n",
       "\n",
       "             20%  \n",
       "FV  1.085847e+06  \n",
       "PV  5.994647e+05  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ending_balances = simulate_ending_balances(\n",
    "    simulations_number, \n",
    "    investment_years, \n",
    "    annual_payment, \n",
    "    initial_investment, \n",
    "    p_return, \n",
    "    p_risk,\n",
    ")\n",
    "get_percentile_fv_and_pv = lambda balances, rate, years, percentile: [\n",
    "        np.percentile(balances, percentile), \n",
    "        npf.pv(rate, years, 0, fv=-np.percentile(balances, percentile))\n",
    "]\n",
    "\n",
    "print(\n",
    "    \"Your total investment over %d years is %d\" % \n",
    "    (investment_years, initial_investment + annual_payment * investment_years)\n",
    ")\n",
    "print(\n",
    "    \"\\nEndingBalance_Mean(FV):\", \"{0:.2f}\".format(ending_balances.mean()), \n",
    "    \"\\nEndingBalance_Mean(PV):\", \"{0:.2f}\".format(npf.pv(risk_free_rate, investment_years, 0, fv=-ending_balances.mean())),\n",
    ")\n",
    "print(\"\\nWith confidence of [col_name_here] you'll end up with: \")\n",
    "pd.DataFrame.from_dict({\n",
    "    \"99%\": get_percentile_fv_and_pv(ending_balances, risk_free_rate, investment_years, 1),\n",
    "    \"95%\": get_percentile_fv_and_pv(ending_balances, risk_free_rate, investment_years, 5),\n",
    "    \"90%\": get_percentile_fv_and_pv(ending_balances, risk_free_rate, investment_years, 10),\n",
    "    \"80%\": get_percentile_fv_and_pv(ending_balances, risk_free_rate, investment_years, 20),\n",
    "    \"50%\": get_percentile_fv_and_pv(ending_balances, risk_free_rate, investment_years, 50),\n",
    "    \"20%\": get_percentile_fv_and_pv(ending_balances, risk_free_rate, investment_years, 80),\n",
    "}).set_index([pd.Index(['FV', 'PV'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db1ab81-c83b-4da9-abbd-c252fa0722bd",
   "metadata": {},
   "source": [
    "# [Efficient Frontier](https://www.investopedia.com/terms/e/efficientfrontier.asp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37fc6c3e-aa52-4869-b734-4d936842116c",
   "metadata": {},
   "source": [
    "## Searching for best portfolio\n",
    "\n",
    "Lets find best portfolio generating different combinations of weights for assets. Number of simulated portfolios is **simulations_number**. The best portfolio has the biggest [Sharpe ratio](https://www.investopedia.com/terms/s/sharperatio.asp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "072bd1b6-00fa-432a-985c-b1942f693e1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best portfolio:\n",
      "\n"
     ]
    },
    {
     "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>IVV</th>\n",
       "      <th>AGG</th>\n",
       "      <th>VGT</th>\n",
       "      <th>VWOB</th>\n",
       "      <th>risk</th>\n",
       "      <th>return</th>\n",
       "      <th>sharpe_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75443</th>\n",
       "      <td>0.025</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.068</td>\n",
       "      <td>1.235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         IVV    AGG    VGT   VWOB   risk  return  sharpe_ratio\n",
       "75443  0.025  0.767  0.207  0.001  0.055   0.068         1.235"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_portfolios = generate_portfolios(\n",
    "    tickers, \n",
    "    (price_hist.cov() * 252).to_numpy(), \n",
    "    df['annual_return'].to_numpy(), \n",
    "    simulations_number,\n",
    ")\n",
    "best_portfolio = rand_portfolios.iloc[[rand_portfolios['sharpe_ratio'].argmax()]]\n",
    "print(\"Best portfolio:\\n\")\n",
    "pd.DataFrame(best_portfolio).round(decimals=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db354c8-b05a-4afd-9f6b-08c99e10b8e2",
   "metadata": {},
   "source": [
    "## Optimize by [Sharpe ratio](https://www.investopedia.com/terms/s/sharperatio.asp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61c82d87-d6eb-4e85-a063-ec029cd8d185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best portfolio optimized:\n",
      "\n"
     ]
    },
    {
     "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>IVV</th>\n",
       "      <th>AGG</th>\n",
       "      <th>VGT</th>\n",
       "      <th>VWOB</th>\n",
       "      <th>risk</th>\n",
       "      <th>return</th>\n",
       "      <th>sharp_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IVV    AGG    VGT  VWOB   risk  return  sharp_ratio\n",
       "0  0.0  0.763  0.237   0.0  0.057    0.07        1.237"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cons = ({'type': 'eq', 'fun': lambda weights: np.sum(weights)-1})\n",
    "bounds = [(0, 1) for ticker in tickers]\n",
    "init_guess = [0.25 for ticker in tickers]\n",
    "\n",
    "print(\"Best portfolio optimized:\\n\")\n",
    "best_portfolio_optimized = get_best_portfolio_optimized(\n",
    "    tickers, \n",
    "    (price_hist.cov() * 252).to_numpy(), \n",
    "    df['annual_return'].to_numpy(),\n",
    "    neg_sharpe,\n",
    "    'SLSQP',\n",
    "    cons,\n",
    "    bounds\n",
    ").round(decimals=3)\n",
    "best_portfolio_optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c692841d-d1a7-4629-bfbd-5666f40ffd99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig.suptitle('Efficient Frontier', fontsize=16)\n",
    "plt.scatter(rand_portfolios['risk'], rand_portfolios['return'], c=rand_portfolios['sharpe_ratio'], cmap='viridis')\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Risk')\n",
    "plt.ylabel('Return')\n",
    "plt.scatter(best_portfolio['risk'], best_portfolio['return'], c='red', s=50) # red dot\n",
    "plt.scatter(best_portfolio_optimized['risk'], best_portfolio_optimized['return'], c='red', s=100, marker='*') # red star\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b2eecc-9bc0-46c3-82fc-070ee5239a87",
   "metadata": {},
   "source": [
    "# References\n",
    "https://codingandfun.com/portfolio-risk-and-returns-python/\n",
    "\n",
    "https://www.investopedia.com/ask/answers/041515/what-difference-between-variance-and-covariance.asp\n",
    "\n",
    "https://www.investopedia.com/terms/c/correlationcoefficient.asp\n",
    "\n",
    "https://www.investopedia.com/ask/answers/041315/how-covariance-used-portfolio-theory.asp\n",
    "\n",
    "https://towardsdatascience.com/how-to-construct-an-efficient-portfolio-using-the-modern-portfolio-theory-in-python-5c5ba2b0cff4\n",
    "\n",
    "https://medium.com/@luo9137/how-to-generate-these-popular-stock-terms-using-python-4e69c6acc6b3\n",
    "\n",
    "https://amangupta16.medium.com/portfolio-optimization-using-python-part-1-2-9fd80097a606\n",
    "\n",
    "https://medium.com/python-data/effient-frontier-in-python-34b0c3043314\n",
    "\n",
    "https://towardsdatascience.com/python-markowitz-optimization-b5e1623060f5"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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