{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using VIP's Network Extractor on ETF Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we demonstrate how you can use VIP's Network Extractor to create a network dataset from a tabular spreadsheet of Exchange Traded Fund (ETF) data. This dataset has a mixture of numerical and categorical features, which works well when extracting a network. We can then use the network to understand which ETFs are similar to each other, which could help find patterns for stronger or weaker investments." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from virtualitics import api\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Ticker | \n", "URL | \n", "Volatility_1yr | \n", "Expense Ratio | \n", "AUM | \n", "Trading Volume | \n", "DivYield | \n", "Region | \n", "Issuer | \n", "Focus | \n", "Weighting Scheme | \n", "Return | \n", "Return_1yr | \n", "Return_3yr | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "FCG | \n", "http://finance.yahoo.com/q?s=FCG | \n", "57.95 | \n", "1.778151 | \n", "2.215347 | \n", "6.233085 | \n", "0.755112 | \n", "North America | \n", "Other | \n", "Energy | \n", "Equal | \n", "Below median | \n", "-65.54 | \n", "-37.70 | \n", "
1 | \n", "EWZS | \n", "http://finance.yahoo.com/q?s=EWZS | \n", "33.39 | \n", "1.792392 | \n", "1.347135 | \n", "4.694789 | \n", "0.627366 | \n", "Latin America | \n", "BlackRock | \n", "Small Cap | \n", "Market Cap | \n", "Below median | \n", "-42.87 | \n", "-36.89 | \n", "
2 | \n", "BRF | \n", "http://finance.yahoo.com/q?s=BRF | \n", "32.02 | \n", "1.778151 | \n", "1.828080 | \n", "4.759547 | \n", "0.575188 | \n", "Latin America | \n", "Other | \n", "Small Cap | \n", "Market Cap | \n", "Below median | \n", "-38.79 | \n", "-34.92 | \n", "
Category | Description |
---|---|
Community 2 | The strongest connections in Community 2 include:\n", "1. <i>Focus</i> = <b>Small Cap</b> for 49% of nodes.\n", "2. <i>Region</i> = <b>Asia-Pacific</b> for 42.2% of nodes. |
Community 1 | The strongest connections in Community 1 include:\n", "1. <i>Focus</i> = <b>Other</b> for 71.9% of nodes.\n", "2. <i>Region</i> = <b>Emerging Markets</b> for 29.2% of nodes.\n", "3. <i>Region</i> = <b>Other</b> for 42.7% of nodes.\n", "4. <i>Return</i> = <b>Below median</b> for 76% of nodes. |
Community 6 | The strongest connections in Community 6 include:\n", "1. <i>Focus</i> = <b>Large Cap</b> for 100% of nodes.\n", "2. <i>Return</i> = <b>Above median</b> for 81% of nodes. |
Community 9 | The strongest connections in Community 9 include:\n", "1. <i>Region</i> = <b>Europe</b> for 100% of nodes.\n", "2. <i>Focus</i> = <b>Total Market</b> for 74.4% of nodes.\n", "3. <i>Issuer</i> = <b>BlackRock</b> for 53.5% of nodes. |
Community 12 | The strongest connections in Community 12 include:\n", "1. <i>Focus</i> = <b>Mid Cap</b> for 84.6% of nodes. |
Community 10 | The strongest connections in Community 10 include:\n", "1. <i>Focus</i> = <b>Consumer Cyclicals</b> for 44.4% of nodes.\n", "2. <i>Focus</i> = <b>Consumer Non-cyclicals</b> for 27.8% of nodes.\n", "3. <i>Focus</i> = <b>Utilities</b> for 27.8% of nodes. |
Community 0 | The strongest connections in Community 0 include:\n", "1. <i>Focus</i> = <b>Energy</b> for 96.7% of nodes. |
Community 4 | The strongest connections in Community 4 include:\n", "1. <i>Focus</i> = <b>Technology</b> for 100% of nodes. |
Community 7 | The strongest connections in Community 7 include:\n", "1. <i>Focus</i> = <b>Financials</b> for 100% of nodes. |
Community 8 | The strongest connections in Community 8 include:\n", "1. <i>Focus</i> = <b>Health Care</b> for 100% of nodes. |
Community 3 | The features found in Community 3 were too infrequent to describe Community 3 using Relative Edge Density. |
Community 11 | The strongest connections in Community 11 include:\n", "1. <i>Focus</i> = <b>Real Estate</b> for 100% of nodes. |
Community 5 | The strongest connections in Community 5 include:\n", "1. <i>Focus</i> = <b>Industrials</b> for 100% of nodes. |
Topic | Insight |
---|---|
Community Leaders | There are 13 community leaders. These nodes are leaders because they have maximum connection coverage within their communities. Leaders include: PSCE, HAO, PXR, SPHQ, IEUS, IVOV, REMX, PSJ, PKB, PBS, PBE, PSCF, and TAO |
Largest Community | The largest community in the visible network is Community 2, with 102 member nodes. |
Most Influential | The most influential community in the visible network is Community 1. |
Most Tightly-Knit | The most tightly-knit community in the visible network is Community 0. |
Most Central | The most central community in the visible network is Community 1. |
Most Isolated | The most isolated community in the visible network is Community 3. |