{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hedgecraft Part 1: Building a Minimally Correlated Portfolio with Data Science\n", "\n", "What is the optimal way of constructing a portfolio? A portfolio that (1) consistently generates wealth while minimizing potential lossess and (2) is robust against large market fluctuations and economic downturns? I will explore these questions in some depth in a three-part ***Hedgecraft*** series. While the series is aimed at a technical audience, my intentions are to break the technical concepts down into digestible bite-sized pieces suitable for a general audience, institutional investors, and others alike. The approach documented in this notebook (to my knowledge) is novel. To this end, the penultimate goal of the project is an end-to-end FinTech/Portfolio Management product.\n", "\n", "## Summary\n", "\n", "Using insights from [Network Science](https://en.wikipedia.org/wiki/Network_science), we build a [centrality-based](https://en.wikipedia.org/wiki/Centrality) risk model for generating portfolio asset weights. The model is trained with the daily prices of 31 stocks from 2006-2014 and validated in years 2015, 2016, and 2017. As a benchmark, we compare the model with a portolfio constructed with [Modern Portfolio Theory (MPT)](https://en.wikipedia.org/wiki/Modern_portfolio_theory). Our proposed asset allocation algorithm significantly outperformed both the DIJIA and S&P500 indexes in every validation year with an average annual return rate of 38.7%, a 18.85% annual volatility, a 1.95 Sharpe ratio, a -12.22% maximum drawdown, a return over maximum drawdown of 9.75, and a growth-risk-ratio of 4.32. In comparison, the MPT portfolio had a 9.64% average annual return rate, a 16.4% annual standard deviation, a Sharpe ratio of 0.47, a maximum drawdown of -20.32%, a return over maximum drawdown of 1.5, and a growth-risk-ratio of 0.69.\n", "\n", "\n", "## Background\n", "\n", "In this series we play the part of an Investment Data Scientist at [Bridgewater Associates](https://www.bridgewater.com/) performing a go/no go analysis on a new idea for risk-weighted asset allocation. Our aim is to develop a network-based model for generating asset weights such that the probability of losing money in any given year is minimized. We've heard down the grapevine that all go-descisions will be presented to Dalio's inner circle at the end of the week and will likely be subject to intense scrutiny. As such, we work with a few highly correlated assets with strict go/no go criteria. We build the model using the daily prices of each stock (with a few replacements\\*) in the Dow Jones Industrial Average (DJIA). If our recommended portfolio either (1) loses money in **any** year, (2) does not outperform the market **every** year, or (3) does not outperform the MPT portfolio---the decision is no go.\n", "\n", "* We replaced Visa (V), DowDuPont (DWDP), and Walgreens (WBA) with three alpha generators: Google (GOOGL), Amazon (AMZN), and Altaba (AABA) and, for the sake of model building, one poor performing stock: General Electric (GE). The dataset is found on [Kaggle](https://www.kaggle.com/szrlee/stock-time-series-20050101-to-20171231/home).\n", "\n", "### Asset Diversification and Allocation\n", "\n", "The building blocks of a portfolio are assets (resources with economic value expected to increase over time). Each asset belongs to one of seven primary asset classes: cash, equitiy, fixed income, commodities, real-estate, alternative assets, and more recently, digital (such as cryptocurrency and blockchain). Within each class are different asset types. For example: stocks, index funds, and equity mutual funds all belong to the equity class while gold, oil, and corn belong to the commodities class. An emerging consensus in the financial sector is this: a portfolio containing assets of many classes and types hedges against potential losses by increasing the number of revenue streams. In general the more diverse the portfolio the less likely it is to lose money. Take stocks for example. A diversified stock portfolio contains positions in multiple sectors. We call this *asset diversification*, or more simply *diversification*. Below is a table summarizing the asset classes and some of their respective types.\n", "\n", "Cash | Equity | Fixed Income | Commodities | Real-Estate | Alternative Assets | Digital |\n", "-----|--------|--------------|-------------|-------------|--------------------|---------|\n", "US Dollar | US Stocks | US Bonds | Gold | REIT's | Structured Credit | Cryptocurrencies\n", "Japenese Yen | Foreign Stocks | Foreign Bonds | Oil | Commerical Properties | Liquidations | Security Tokens\n", "Chinese Yaun | Index Funds | Deposits | Wheat | Land | Aviation Assets | Online Stores\n", "UK Pound | Mutual Funds | Debentures | Corn | Industrial Properties | Collectables | Online Media\n", " •
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|\n", "\n", "**An investor solves the following (asset allocation) problem: given X dollars and N assets find the best possible way of breaking X into N pieces.** By \"best possible\" we mean maximizing our returns subject to minimizing the risk of our initial investment. In other words, we aim to consistently grow X irrespective of the overall state of the market. In what follows, we explore provocative insights by [Ray Dalio](https://en.wikipedia.org/wiki/Ray_Dalio) and others on portfolio construction.\n", "\n", "![The Holy Grail of Finance](https://i1.wp.com/macro-ops.com/wp-content/uploads/2017/09/The-Holy-Grail.jpg)\n", "Source: [Principles by Ray Dalio (Summary)](https://static1.squarespace.com/static/56f1d1777da24fd2594c0f51/t/5a35b71e652dea7bc2f59ef6/1513469734986/You+Exec+-+Principles+by+Ray+Dalio.pdf)\n", "\n", "The above chart depicts the behaviour of a portfolio with increasing diversification. Along the x-axis is the number of asset types. Along the y-axis is how \"spread out\" the annual returns are. A lower annual standard deviation indicates smaller fluctuations in each revenue stream, and in turn a diminished risk exposure. The \"Holy Grail\" so to speak, is to (1) find the largest number of assets that are the **least** correlated and (2) allocate X dollars to those assets such that the probability of losing money any given year is minimized. The underlying principle is this: the portfolio most robust against large market fluctuations and economic downturns is a portfolio with assets that are the **most independent** of eachother.\n", "\n", "# Exploratory Data Analysis and Cleaning\n", "\n", "Before we dive into the meat of our asset allocation model, we first explore, clean, and preprocess our historical price data for time-series analyses. In this section we complete the following.\n", "\n", "- Observe how many rows and columns are in our dataset and what they mean.\n", "- Observe the datatypes of the columns and update them if needed.\n", "- Take note of how the data is structured and what preprocessing will be necessary for time-series analyses.\n", "- Deal with any missing data accordingly.\n", "- Rename the stock tickers to the company names for readability." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", " | Date | \n", "Open | \n", "High | \n", "Low | \n", "Close | \n", "Volume | \n", "Name | \n", "
---|---|---|---|---|---|---|---|
0 | \n", "2006-01-03 | \n", "77.76 | \n", "79.35 | \n", "77.24 | \n", "79.11 | \n", "3117200 | \n", "MMM | \n", "
1 | \n", "2006-01-04 | \n", "79.49 | \n", "79.49 | \n", "78.25 | \n", "78.71 | \n", "2558000 | \n", "MMM | \n", "
2 | \n", "2006-01-05 | \n", "78.41 | \n", "78.65 | \n", "77.56 | \n", "77.99 | \n", "2529500 | \n", "MMM | \n", "
3 | \n", "2006-01-06 | \n", "78.64 | \n", "78.90 | \n", "77.64 | \n", "78.63 | \n", "2479500 | \n", "MMM | \n", "
4 | \n", "2006-01-09 | \n", "78.50 | \n", "79.83 | \n", "78.46 | \n", "79.02 | \n", "1845600 | \n", "MMM | \n", "
\n", " | Date | \n", "Open | \n", "High | \n", "Low | \n", "Close | \n", "Volume | \n", "Name | \n", "
---|---|---|---|---|---|---|---|
93607 | \n", "2017-12-22 | \n", "71.42 | \n", "71.87 | \n", "71.22 | \n", "71.58 | \n", "10979165 | \n", "AABA | \n", "
93608 | \n", "2017-12-26 | \n", "70.94 | \n", "71.39 | \n", "69.63 | \n", "69.86 | \n", "8542802 | \n", "AABA | \n", "
93609 | \n", "2017-12-27 | \n", "69.77 | \n", "70.49 | \n", "69.69 | \n", "70.06 | \n", "6345124 | \n", "AABA | \n", "
93610 | \n", "2017-12-28 | \n", "70.12 | \n", "70.32 | \n", "69.51 | \n", "69.82 | \n", "7556877 | \n", "AABA | \n", "
93611 | \n", "2017-12-29 | \n", "69.79 | \n", "70.13 | \n", "69.43 | \n", "69.85 | \n", "6613070 | \n", "AABA | \n", "
\n", " | Open | \n", "High | \n", "Low | \n", "Close | \n", "Volume | \n", "Name | \n", "
---|---|---|---|---|---|---|
Date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
2014-12-24 | \n", "50.19 | \n", "50.92 | \n", "50.19 | \n", "50.65 | \n", "5962870 | \n", "Altaba | \n", "
2014-12-26 | \n", "50.65 | \n", "51.06 | \n", "50.61 | \n", "50.86 | \n", "5170048 | \n", "Altaba | \n", "
2014-12-29 | \n", "50.67 | \n", "51.01 | \n", "50.51 | \n", "50.53 | \n", "6624489 | \n", "Altaba | \n", "
2014-12-30 | \n", "50.35 | \n", "51.27 | \n", "50.35 | \n", "51.22 | \n", "10703455 | \n", "Altaba | \n", "
2014-12-31 | \n", "51.54 | \n", "51.68 | \n", "50.46 | \n", "50.51 | \n", "9305013 | \n", "Altaba | \n", "
\n", " | Open | \n", "High | \n", "Low | \n", "Close | \n", "Volume | \n", "Name | \n", "
---|---|---|---|---|---|---|
Date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
2017-12-22 | \n", "71.42 | \n", "71.87 | \n", "71.22 | \n", "71.58 | \n", "10979165 | \n", "Altaba | \n", "
2017-12-26 | \n", "70.94 | \n", "71.39 | \n", "69.63 | \n", "69.86 | \n", "8542802 | \n", "Altaba | \n", "
2017-12-27 | \n", "69.77 | \n", "70.49 | \n", "69.69 | \n", "70.06 | \n", "6345124 | \n", "Altaba | \n", "
2017-12-28 | \n", "70.12 | \n", "70.32 | \n", "69.51 | \n", "69.82 | \n", "7556877 | \n", "Altaba | \n", "
2017-12-29 | \n", "69.79 | \n", "70.13 | \n", "69.43 | \n", "69.85 | \n", "6613070 | \n", "Altaba | \n", "
Name | \n", "3M | \n", "Altaba | \n", "Amazon | \n", "American Express | \n", "Apple | \n", "Boeing | \n", "Caterpillar | \n", "Chevron | \n", "Cisco Systems | \n", "Coca-Cola | \n", "... | \n", "Microsoft | \n", "Nike | \n", "Pfizer | \n", "Procter & Gamble | \n", "Travelers | \n", "United Technologies | \n", "UnitedHealth | \n", "Verizon | \n", "Walmart | \n", "Walt Disney | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
2015-01-02 | \n", "164.06 | \n", "50.17 | \n", "308.52 | \n", "93.02 | \n", "109.33 | \n", "129.95 | \n", "91.88 | \n", "112.58 | \n", "27.61 | \n", "42.14 | \n", "... | \n", "46.76 | \n", "47.52 | \n", "31.33 | \n", "90.44 | \n", "105.44 | \n", "115.04 | \n", "100.78 | \n", "46.96 | \n", "85.90 | \n", "93.75 | \n", "
2015-01-05 | \n", "160.36 | \n", "49.13 | \n", "302.19 | \n", "90.56 | \n", "106.25 | \n", "129.05 | \n", "87.03 | \n", "108.08 | \n", "27.06 | \n", "42.14 | \n", "... | \n", "46.32 | \n", "46.75 | \n", "31.16 | \n", "90.01 | \n", "104.17 | \n", "113.12 | \n", "99.12 | \n", "46.57 | \n", "85.65 | \n", "92.38 | \n", "
2015-01-06 | \n", "158.65 | \n", "49.21 | \n", "295.29 | \n", "88.63 | \n", "106.26 | \n", "127.53 | \n", "86.47 | \n", "108.03 | \n", "27.05 | \n", "42.46 | \n", "... | \n", "45.65 | \n", "46.48 | \n", "31.42 | \n", "89.60 | \n", "103.24 | \n", "111.52 | \n", "98.92 | \n", "47.04 | \n", "86.31 | \n", "91.89 | \n", "
2015-01-07 | \n", "159.80 | \n", "48.59 | \n", "298.42 | \n", "90.30 | \n", "107.75 | \n", "129.51 | \n", "87.81 | \n", "107.94 | \n", "27.30 | \n", "42.99 | \n", "... | \n", "46.23 | \n", "47.44 | \n", "31.85 | \n", "90.07 | \n", "105.00 | \n", "112.73 | \n", "99.93 | \n", "46.19 | \n", "88.60 | \n", "92.83 | \n", "
2015-01-08 | \n", "163.63 | \n", "50.23 | \n", "300.46 | \n", "91.58 | \n", "111.89 | \n", "131.80 | \n", "88.71 | \n", "110.41 | \n", "27.51 | \n", "43.51 | \n", "... | \n", "47.59 | \n", "48.53 | \n", "32.50 | \n", "91.10 | \n", "107.18 | \n", "114.65 | \n", "104.70 | \n", "47.18 | \n", "90.47 | \n", "93.79 | \n", "
5 rows × 31 columns
\n", "\n", " | Open | \n", "High | \n", "Low | \n", "Close | \n", "Volume | \n", "Name | \n", "Close_Diff | \n", "
---|---|---|---|---|---|---|---|
Date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
2006-01-03 | \n", "77.76 | \n", "79.35 | \n", "77.24 | \n", "79.11 | \n", "3117200 | \n", "3M | \n", "1.35 | \n", "
2006-01-04 | \n", "79.49 | \n", "79.49 | \n", "78.25 | \n", "78.71 | \n", "2558000 | \n", "3M | \n", "-0.78 | \n", "
2006-01-05 | \n", "78.41 | \n", "78.65 | \n", "77.56 | \n", "77.99 | \n", "2529500 | \n", "3M | \n", "-0.42 | \n", "
2006-01-06 | \n", "78.64 | \n", "78.90 | \n", "77.64 | \n", "78.63 | \n", "2479500 | \n", "3M | \n", "-0.01 | \n", "
2006-01-09 | \n", "78.50 | \n", "79.83 | \n", "78.46 | \n", "79.02 | \n", "1845600 | \n", "3M | \n", "0.52 | \n", "
Name | \n", "3M | \n", "Altaba | \n", "Amazon | \n", "American Express | \n", "Apple | \n", "Boeing | \n", "Caterpillar | \n", "Chevron | \n", "Cisco Systems | \n", "Coca-Cola | \n", "... | \n", "Microsoft | \n", "Nike | \n", "Pfizer | \n", "Procter & Gamble | \n", "Travelers | \n", "United Technologies | \n", "UnitedHealth | \n", "Verizon | \n", "Walmart | \n", "Walt Disney | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
2006-01-03 | \n", "79.11 | \n", "40.91 | \n", "47.58 | \n", "52.58 | \n", "10.68 | \n", "70.44 | \n", "57.80 | \n", "59.08 | \n", "17.45 | \n", "20.45 | \n", "... | \n", "26.84 | \n", "10.74 | \n", "23.78 | \n", "58.78 | \n", "45.99 | \n", "56.53 | \n", "61.73 | \n", "30.38 | \n", "46.23 | \n", "24.40 | \n", "
2006-01-04 | \n", "78.71 | \n", "40.97 | \n", "47.25 | \n", "51.95 | \n", "10.71 | \n", "71.17 | \n", "59.27 | \n", "58.91 | \n", "17.85 | \n", "20.41 | \n", "... | \n", "26.97 | \n", "10.69 | \n", "24.55 | \n", "58.89 | \n", "46.50 | \n", "56.19 | \n", "61.88 | \n", "31.27 | \n", "46.32 | \n", "23.99 | \n", "
2006-01-05 | \n", "77.99 | \n", "41.53 | \n", "47.65 | \n", "52.50 | \n", "10.63 | \n", "70.33 | \n", "59.27 | \n", "58.19 | \n", "18.35 | \n", "20.51 | \n", "... | \n", "26.99 | \n", "10.76 | \n", "24.58 | \n", "58.70 | \n", "46.95 | \n", "55.98 | \n", "61.69 | \n", "31.63 | \n", "45.69 | \n", "24.41 | \n", "
2006-01-06 | \n", "78.63 | \n", "43.21 | \n", "47.87 | \n", "52.68 | \n", "10.90 | \n", "69.35 | \n", "60.45 | \n", "59.25 | \n", "18.77 | \n", "20.70 | \n", "... | \n", "26.91 | \n", "10.72 | \n", "24.85 | \n", "58.64 | \n", "47.21 | \n", "56.16 | \n", "62.90 | \n", "31.35 | \n", "45.88 | \n", "24.74 | \n", "
2006-01-09 | \n", "79.02 | \n", "43.42 | \n", "47.08 | \n", "53.99 | \n", "10.86 | \n", "68.77 | \n", "61.55 | \n", "58.95 | \n", "19.06 | \n", "20.80 | \n", "... | \n", "26.86 | \n", "10.88 | \n", "24.85 | \n", "59.08 | \n", "47.23 | \n", "56.80 | \n", "61.40 | \n", "31.48 | \n", "45.71 | \n", "25.00 | \n", "
5 rows × 31 columns
\n", "Name | \n", "3M | \n", "Altaba | \n", "Amazon | \n", "American Express | \n", "Apple | \n", "Boeing | \n", "Caterpillar | \n", "Chevron | \n", "Cisco Systems | \n", "Coca-Cola | \n", "... | \n", "Microsoft | \n", "Nike | \n", "Pfizer | \n", "Procter & Gamble | \n", "Travelers | \n", "United Technologies | \n", "UnitedHealth | \n", "Verizon | \n", "Walmart | \n", "Walt Disney | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
2006-01-03 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "
2006-01-04 | \n", "-0.40 | \n", "0.06 | \n", "-0.33 | \n", "-0.63 | \n", "0.03 | \n", "0.73 | \n", "1.47 | \n", "-0.17 | \n", "0.40 | \n", "-0.04 | \n", "... | \n", "0.13 | \n", "-0.05 | \n", "0.77 | \n", "0.11 | \n", "0.51 | \n", "-0.34 | \n", "0.15 | \n", "0.89 | \n", "0.09 | \n", "-0.41 | \n", "
2006-01-05 | \n", "-0.72 | \n", "0.56 | \n", "0.40 | \n", "0.55 | \n", "-0.08 | \n", "-0.84 | \n", "0.00 | \n", "-0.72 | \n", "0.50 | \n", "0.10 | \n", "... | \n", "0.02 | \n", "0.07 | \n", "0.03 | \n", "-0.19 | \n", "0.45 | \n", "-0.21 | \n", "-0.19 | \n", "0.36 | \n", "-0.63 | \n", "0.42 | \n", "
2006-01-06 | \n", "0.64 | \n", "1.68 | \n", "0.22 | \n", "0.18 | \n", "0.27 | \n", "-0.98 | \n", "1.18 | \n", "1.06 | \n", "0.42 | \n", "0.19 | \n", "... | \n", "-0.08 | \n", "-0.04 | \n", "0.27 | \n", "-0.06 | \n", "0.26 | \n", "0.18 | \n", "1.21 | \n", "-0.28 | \n", "0.19 | \n", "0.33 | \n", "
2006-01-09 | \n", "0.39 | \n", "0.21 | \n", "-0.79 | \n", "1.31 | \n", "-0.04 | \n", "-0.58 | \n", "1.10 | \n", "-0.30 | \n", "0.29 | \n", "0.10 | \n", "... | \n", "-0.05 | \n", "0.16 | \n", "0.00 | \n", "0.44 | \n", "0.02 | \n", "0.64 | \n", "-1.50 | \n", "0.13 | \n", "-0.17 | \n", "0.26 | \n", "
5 rows × 31 columns
\n", "\n", " | 3M | \n", "Altaba | \n", "Amazon | \n", "American Express | \n", "Apple | \n", "Boeing | \n", "Caterpillar | \n", "Chevron | \n", "Cisco Systems | \n", "Coca-Cola | \n", "... | \n", "Microsoft | \n", "Nike | \n", "Pfizer | \n", "Procter & Gamble | \n", "Travelers | \n", "United Technologies | \n", "UnitedHealth | \n", "Verizon | \n", "Walmart | \n", "Walt Disney | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3M | \n", "1 | \n", "0.353645 | \n", "0.39234 | \n", "0.537989 | \n", "0.349158 | \n", "0.499624 | \n", "0.548548 | \n", "0.504802 | \n", "0.470029 | \n", "0.431732 | \n", "... | \n", "0.455552 | \n", "0.450839 | \n", "0.414834 | \n", "0.401242 | \n", "0.470891 | \n", "0.613992 | \n", "0.359024 | \n", "0.396697 | \n", "0.357485 | \n", "0.535781 | \n", "
Altaba | \n", "0.353645 | \n", "1 | \n", "0.351135 | \n", "0.341589 | \n", "0.290445 | \n", "0.312507 | \n", "0.315713 | \n", "0.267452 | \n", "0.343121 | \n", "0.246817 | \n", "... | \n", "0.302109 | \n", "0.31163 | \n", "0.24649 | \n", "0.218293 | \n", "0.269813 | \n", "0.337054 | \n", "0.211276 | \n", "0.218758 | \n", "0.209349 | \n", "0.37071 | \n", "
Amazon | \n", "0.39234 | \n", "0.351135 | \n", "1 | \n", "0.387674 | \n", "0.373537 | \n", "0.349593 | \n", "0.383719 | \n", "0.31787 | \n", "0.35512 | \n", "0.277101 | \n", "... | \n", "0.340249 | \n", "0.3993 | \n", "0.274551 | \n", "0.220758 | \n", "0.296325 | \n", "0.402777 | \n", "0.244168 | \n", "0.265955 | \n", "0.254739 | \n", "0.402439 | \n", "
American Express | \n", "0.537989 | \n", "0.341589 | \n", "0.387674 | \n", "1 | \n", "0.351312 | \n", "0.468953 | \n", "0.472919 | \n", "0.423415 | \n", "0.455908 | \n", "0.383586 | \n", "... | \n", "0.441841 | \n", "0.450744 | \n", "0.421407 | \n", "0.354739 | \n", "0.476044 | \n", "0.535638 | \n", "0.328788 | \n", "0.393664 | \n", "0.373938 | \n", "0.505921 | \n", "
Apple | \n", "0.349158 | \n", "0.290445 | \n", "0.373537 | \n", "0.351312 | \n", "1 | \n", "0.296182 | \n", "0.388631 | \n", "0.303327 | \n", "0.331026 | \n", "0.235683 | \n", "... | \n", "0.316923 | \n", "0.317469 | \n", "0.224457 | \n", "0.199562 | \n", "0.265335 | \n", "0.343254 | \n", "0.227484 | \n", "0.229273 | \n", "0.191001 | \n", "0.346844 | \n", "
5 rows × 31 columns
\n", "\n", " | Buy In: 2014-12-31 | \n", "
---|---|
Name | \n", "\n", " |
Amazon | \n", "310.35 | \n", "
UnitedHealth | \n", "101.09 | \n", "
Verizon | \n", "46.78 | \n", "
Mcdonald's | \n", "93.70 | \n", "
\n", " | Hedgecraft Returns | \n", "Hedgecraft MIS Returns | \n", "Efficient Frontier Returns | \n", "Dow Return Rates | \n", "S&P500 Return Rates | \n", "Hedgecraft Return Rates | \n", "Hedgecraft MIS Return Rates | \n", "Efficient Frontier Return Rates | \n", "
---|---|---|---|---|---|---|---|---|
2015 | \n", "9.8% | \n", "19.3% | \n", "-1.8% | \n", "0.1% | \n", "-0.73% | \n", "9.8% | \n", "19.3% | \n", "-1.8% | \n", "
2016 | \n", "35.5% | \n", "58.9% | \n", "5.5% | \n", "16.28% | \n", "9.54% | \n", "25.7% | \n", "39.6% | \n", "7.3% | \n", "
2017 | \n", "85.4% | \n", "119.1% | \n", "30.5% | \n", "27.97% | \n", "19.42% | \n", "49.9% | \n", "60.2% | \n", "25.0% | \n", "
\n", " | Hedgecraft | \n", "Hedgecraft MIS | \n", "Efficient Frontier | \n", "
---|---|---|---|
Avg Annual Rate of Returns | \n", "27.42% | \n", "38.7% | \n", "9.62% | \n", "
Annual Volatility | \n", "14.58% | \n", "18.85% | \n", "16.36% | \n", "
Maximum Drawdown | \n", "-12.29% | \n", "-12.22% | \n", "-20.32% | \n", "
Annualized Sharpe Ratio | \n", "1.74 | \n", "1.95 | \n", "0.47 | \n", "
Returns Over Maximum Drawdown | \n", "6.95 | \n", "9.75 | \n", "1.5 | \n", "
Growth-Risk Ratio | \n", "3.33 | \n", "4.32 | \n", "0.69 | \n", "
\n", " | Hedgecraft | \n", "Hedgecraft MIS | \n", "Efficient Frontier | \n", "
---|---|---|---|
probability of losing money from initial investement | \n", "1.59% | \n", "0.53% | \n", "33.77% | \n", "
maximum loss | \n", "-2.39% | \n", "-2.13% | \n", "-15.99% | \n", "
mean loss | \n", "-0.77% | \n", "-1.39% | \n", "-4.17% | \n", "
Probability of falling bellow 30 day rolling avg | \n", "21.32% | \n", "25.17% | \n", "38.41% | \n", "
Probability of falling bellow 50 day rolling avg | \n", "16.56% | \n", "19.07% | \n", "38.01% | \n", "
Probability of falling bellow 90 day rolling avg | \n", "11.79% | \n", "15.5% | \n", "35.1% | \n", "