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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "global_macro.ipynb",
"provenance": [],
"collapsed_sections": [],
"machine_shape": "hm",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/theo-dim/eb8d776dae017eea515fa1f377df02ff/global_macro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qcWfjXY7mpRu"
},
"source": [
"# **Long/Short Global Macro Strategies with Target β Using the 3-Factor Model**\r\n",
"Authors: Theo Dimitrasopoulos✝, Yuki Homma✝\r\n",
"\r\n",
"Advisor: Papa Momar Ndiaye✝\r\n",
"\r\n",
"✝ Department of Financial Engineering; Stevens Institute of Technology Babbio School of Business"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DwyUderPMUUv"
},
"source": [
"## **Authors**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Km9if5T4qehE"
},
"source": [
"**Final Project**\r\n",
"\r\n",
"FE630: Modern Portfolio Theory & Applications"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nbCbic1BPfWj"
},
"source": [
"### Yuki Homma\r\n",
"\r\n",
"Contribution: Presentation Preparation, Manuscript Generation\r\n",
"\r\n",
"Stevens MS in Financial Engineering '21"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZmOPVlLPPROw"
},
"source": [
"### Theo Dimitrasopoulos\r\n",
"\r\n",
"Contribution: Quant Analysis, Programming\r\n",
"\r\n",
"Stevens MS in Financial Engineering '21 | Princeton '17\r\n",
"\r\n",
"theonovak.com | +1 (609) 933-2990"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FLxzsanEfvZe"
},
"source": [
"## **Introduction**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HUQtZt-kgm-B"
},
"source": [
"### Investment Strategy"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YUcWIgu9fvZe"
},
"source": [
"We build a Long-Short Global Macro Strategy with a Beta target using a factor-based model and evaluate its sensitivity to variations of Beta.\r\n",
"\r\n",
"To optimize the portfolios, we deploy the following strategies:\r\n",
"1. Maximize the return of the portfolio subject to a constraint of target Beta, where Beta is the single-factor market risk measure. This allows us to evaluate the sensitivity of the portfolios to variations of Beta. The portfolio is re-optimized (weight recalibration) every week for the investment horizon between March 2007 to the end of October 2020.\r\n",
"2. Minimum variance with a target return."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZvP6QauOfvZf"
},
"source": [
"### Optimization Problem:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C86NP7BafvZf"
},
"source": [
"The strategy aims to maximize return with a certain Target Beta under constraints.\r\n",
"\r\n",
"It is defined as,\r\n",
"\r\n",
"\\begin{cases}\r\n",
"\\max\\limits_{{\\omega ∈ ℝ^{n}}}\\rho^{T}\\omega-\\lambda(\\omega-\\omega_{p})^{T}\\Sigma(\\omega-\\omega_{p})\\\\\r\n",
"\\sum_{i=1}^{n} \\beta_{i}^{m}\\omega_{i}=\\beta_{T}^{m}\\\\\r\n",
"\\sum_{i=1}^{n} \\omega_{i}=1, -2\\leq\\omega_{i}\\leq2\r\n",
"\\end{cases}\r\n",
"\r\n",
"$\\Sigma$ is the the covariance matrix between the securities returns (computed from\r\n",
"the Factor Model), $\\omega_{p}$ is the composition of a reference Portfolio (the previous Portfolio when rebalancing the portfolio and $\\omega_{p}$ has all its components equal to $1/n$ for the first allocation) and $\\lambda$ is a small regularization parameter to limit the turnover;\r\n",
"\r\n",
"$\\beta_{i}^{m}=\\frac{cov(r_{i},r_{M}}{\\sigma^{2}(r_{M})}$ is the Beta of security $S_{i}$ as defined in the CAPM Model so that $\\beta_{P}^{m}=\\sum_{i=1}^{n}\\beta_{i}^{m}\\omega_{i}$ is the Beta of the Portfolio;\r\n",
"\r\n",
"$\\beta_{T}^{m}$ is the Portfolio's Target Beta, for example $\\beta_{T}^{m}=-1$, $\\beta_{T}^{m}=-0.5$, $\\beta_{T}^{m}=0$, $\\beta_{T}^{m}=0.5$, $\\beta_{T}^{m}=1.5$."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z9akKhs1fvZf"
},
"source": [
"### Equivalent Optimization Problem:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hHrWs53WfvZg"
},
"source": [
"We can reformulate the optimization problem above to make the programming process more straightforward:\r\n",
"\r\n",
"$(\\omega-\\omega_{p})^{T}\\Sigma(\\omega-\\omega_{p})\\rightarrow$\r\n",
"\r\n",
"$=(\\omega-\\omega_{p})^{T}\\Sigma\\omega-(\\omega-\\omega_{p} )^{T}\\Sigma\\omega_{p}$\r\n",
"\r\n",
"$=\\omega^{T} \\Sigma\\omega-2(\\omega^{T} \\Sigma\\omega_{p})+\\omega_{p}^{T}\\Sigma \\omega_{p}$\r\n",
"\r\n",
"We simplify,\r\n",
"- $d=\\rho-2\\lambda\\Sigma\\omega_{p}$\r\n",
"- $P=\\lambda\\Sigma$\r\n",
"\r\n",
"Finally,\r\n",
"\r\n",
"$\\max\\limits_{{\\omega ∈ ℝ^{n}}}(\\rho-2\\lambda\\Sigma\\omega_{p} )^{T} \\omega-\\lambda\\omega^{T}\\Sigma\\omega+\\lambda\\omega_{p}^{T}\\Sigma\\omega_{p}=\\max\\limits_{{\\omega ∈ ℝ^{n}}}d^{T}\\omega-\\omega^{T}P\\omega$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h7qErEkefvZg"
},
"source": [
"\r\n",
"---\r\n",
"\r\n",
"The following formulation is equivalent,\r\n",
"\r\n",
"\\begin{cases}\r\n",
"\\max\\limits_{{\\omega ∈ ℝ^{n}}}d^{T}\\omega-\\omega^{T}P\\omega\\\\\r\n",
"\\sum_{i=1}^{n} \\beta_{i}^{m}\\omega_{i}=\\beta_{T}^{m}\\\\\r\n",
"\\sum_{i=1}^{n} \\omega_{i}=1, -2\\leq\\omega_{i}\\leq2\r\n",
"\\end{cases}\r\n",
"- $\\Sigma$ is the the covariance matrix between the returns of the portfolio assets;\r\n",
"- $\\omega_{p}$ is the composition of a reference Portfolio:\r\n",
" - When rebalancing the portfolio, $\\omega_{p}$ is the previous portfolio\r\n",
" - $\\omega_{p}$ has all its components equal to $1/n$ for the first allocation\r\n",
"- $\\lambda$ is a regularization parameter to limit the turnover\r\n",
"- $\\beta_{i}^{m}=\\frac{cov(r_{i},r_{M}}{\\sigma^{2}(r_{M})}$ is the Beta of security $S_{i}$ as defined in the CAPM Model s.t. $\\beta_{P}^{m}=\\sum_{i=1}^{n}\\beta_{i}^{m}\\omega_{i}$ is the portfolio Beta\r\n",
"- $\\beta_{T}^{m}$ is the Portfolio's Target Beta."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LRDq4Fk4f3RK"
},
"source": [
"### Fama French 3-Factor Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UNG-aMoDf3RL"
},
"source": [
"A three-factor model proposed by Fama and French(1993), includes not only market excess return, but a capitalization size and book to market ratio will also be added in as influencing factors."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F-veBX0vf3RL"
},
"source": [
"The random return of a given security is given by the formulas (equivalent),"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z_CIBJhCgHvF"
},
"source": [
"\\begin{equation}\r\n",
"\\boxed{r = r_{f}+\\beta_{1}(r_{m}-r_{f})+\\beta{2}(SMB)+\\beta_{3}(HML)+\\epsilon}\r\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7jEG6l_KgKxY"
},
"source": [
"\\begin{equation}\r\n",
"\\boxed{R_{i}-r_{f}=\\alpha_{i}+\\beta{i}^{MKT}(R_{M}-r_{f})+\\beta_{i}^{SMB}R_{SMB}+\\beta_{i}^{HML}R_{HML}}\r\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zrzUz6F5gNmY"
},
"source": [
"\r\n",
"- rSMB represents small size variables minus big one\r\n",
"- rHML represents high minus low in book value to equity to book value to the market."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gmtsvJVNf_Nn"
},
"source": [
"### ETF Data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TkpqUubLf_Nw"
},
"source": [
"The following ETFs represent the investment Universe of our portfolios. They range from the S&P 500 to ETFs representing all continents such as Europe, Asia and Africa and asset types such as bonds, stocks, and commodities.\r\n",
"\r\n",
"1. CurrencyShares Euro Trust (FXE)\r\n",
"2. iShares MSCI Japan Index (EWJ)\r\n",
"3. SPDR GOLD Trust (GLD)\r\n",
"4. Powershares NASDAQ-100 Trust (QQQ)\r\n",
"5. SPDR S&P 500 (SPY)$^*$\r\n",
"6. iShares Lehman Short Treasury Bond (SHV)\r\n",
"7. PowerShares DB Agriculture Fund (DBA)\r\n",
"8. United States Oil Fund LP (USO)\r\n",
"9. SPDR S&P Biotech (XBI)\r\n",
"10. iShares S&P Latin America 40 Index (ILF)\r\n",
"11. iShares MSCI Pacific ex-Japan Index Fund (EPP)\r\n",
"12. SPDR DJ Euro Stoxx 50 (FEZ)\r\n",
"\r\n",
"From this universe, we have created portfolios by utilizing the 3-factor Fama-French model. The investment portfolio that we created is compared to the following benchmark portfolios:\r\n",
"\r\n",
"1.\tThe Market Portfolio (S&P 500) \r\n",
"\r\n",
"The dataset includes daily price data between March 1st, 2007 to October 31th, 2020. We choose this investment horizon to match the Fama-French Factor data available.\r\n",
"\r\n",
"We have used three different look-back periods, which we have defined as: A. Short Term – 60 Days B. Medium Term – 120 Days C. Long Term – 200 Days To calculate the risk-return parameters of then portfolio we have used the target Beta as -1, -0.5, 0, 0.5, 1 and 1.5. The rebalance period is kept as one week as specified in the project.\r\n",
"\r\n",
"*$^*$The SPY market portfolio is the chosen benchmark*"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4kFm9MIi2aIQ"
},
"source": [
"## **Setup**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pKK47tV77YEB"
},
"source": [
"## Update System:\r\n",
"#!apt-get update\r\n",
"#!apt-get upgrade\r\n",
"#!apt-get autoremove"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "eFsrvFWO0sdr"
},
"source": [
"### System Time"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZpOl5Yat1jL7"
},
"source": [
"#!apt-get install tree"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gmnPTtSq0vBQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "143f8850-e75e-4baf-d85c-274e3ea98bbe"
},
"source": [
"print('System time_________________')\r\n",
"!date\r\n",
"!rm /etc/localtime\r\n",
"!ln -s /usr/share/zoneinfo/America/New_York /etc/localtime\r\n",
"print('\\nProcessing...\\nSystem time updated\\n\\nUpdated time________________')\r\n",
"!date"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"System time_________________\n",
"Wed Jan 27 10:52:47 EST 2021\n",
"\n",
"Processing...\n",
"System time updated\n",
"\n",
"Updated time________________\n",
"Wed Jan 27 10:52:47 EST 2021\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZmNZi28M2w6o"
},
"source": [
"### Environment"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-z1LUHSetAze",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2f05587a-39c1-4b8b-f9eb-d97c7e4b3ed4"
},
"source": [
"# -*- coding: utf-8 -*-\n",
"\n",
"# ENVIRONMENT CHECK:\n",
"import sys, os, inspect, site, pprint\n",
"# Check whether in Colab:\n",
"IN_COLAB = 'google.colab' in sys.modules\n",
"if IN_COLAB == True:\n",
" print('YES, this is a Google Colaboratory environment.')\n",
"else:\n",
" print('NO, this is not a Google Colaboratory environment.')\n",
"print(' ')\n",
"\n",
"# Python installation files:\n",
"stdlib = os.path.dirname(inspect.getfile(os))\n",
"python_version = !python --version\n",
"print('Python Standard Library is located in:\\n' + stdlib)\n",
"print(' ')\n",
"print('This environment is using {}'.format(str(python_version[0])))\n",
"print(' ')\n",
"print('Local system packages are located in:')\n",
"pprint.pprint(site.getsitepackages())\n",
"print(' ')\n",
"print('Local user packages are located in:\\n' + site.getusersitepackages())\n"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"YES, this is a Google Colaboratory environment.\n",
" \n",
"Python Standard Library is located in:\n",
"/usr/lib/python3.6\n",
" \n",
"This environment is using Python 3.6.9\n",
" \n",
"Local system packages are located in:\n",
"['/usr/local/lib/python3.6/dist-packages',\n",
" '/usr/lib/python3/dist-packages',\n",
" '/usr/lib/python3.6/dist-packages']\n",
" \n",
"Local user packages are located in:\n",
"/root/.local/lib/python3.6/site-packages\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WaEjpp0Dcgip"
},
"source": [
"### Default Packages"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wZ4_r3UackEj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0ea95253-744d-47f3-82db-5d2afef3c6a4"
},
"source": [
"# Installed packages:\r\n",
"!pip list -v\r\n",
"!pip list --user -v\r\n"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"Package Version Location Installer\n",
"----------------------------- --------------- -------------------------------------- ---------\n",
"absl-py 0.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"alabaster 0.7.12 /usr/local/lib/python3.6/dist-packages pip \n",
"albumentations 0.1.12 /usr/local/lib/python3.6/dist-packages pip \n",
"alembic 1.5.2 /usr/local/lib/python3.6/dist-packages pip \n",
"alphalens 0.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"altair 4.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"analytics-python 1.2.9 /usr/local/lib/python3.6/dist-packages pip \n",
"ansi2html 1.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"argcomplete 1.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"argon2-cffi 20.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"asgiref 3.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"astor 0.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"astropy 4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"astunparse 1.6.3 /usr/local/lib/python3.6/dist-packages pip \n",
"async-generator 1.10 /usr/local/lib/python3.6/dist-packages pip \n",
"atari-py 0.2.6 /usr/local/lib/python3.6/dist-packages pip \n",
"atomicwrites 1.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"attrs 20.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"audioread 2.1.9 /usr/local/lib/python3.6/dist-packages pip \n",
"autograd 1.3 /usr/local/lib/python3.6/dist-packages pip \n",
"Babel 2.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"backcall 0.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"bcolz 1.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"beautifulsoup4 4.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"bleach 3.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"blis 0.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"bokeh 2.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Bottleneck 1.3.2 /usr/local/lib/python3.6/dist-packages pip \n",
"branca 0.4.2 /usr/local/lib/python3.6/dist-packages pip \n",
"Brotli 1.0.9 /usr/local/lib/python3.6/dist-packages pip \n",
"bs4 0.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
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"certifi 2020.12.5 /usr/local/lib/python3.6/dist-packages pip \n",
"cffi 1.14.4 /usr/local/lib/python3.6/dist-packages pip \n",
"chainer 7.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"chardet 3.0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"click 7.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"cloudpickle 1.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"cmake 3.12.0 /usr/local/lib/python3.6/dist-packages pip \n",
"cmdstanpy 0.9.5 /usr/local/lib/python3.6/dist-packages pip \n",
"colorlover 0.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"community 1.0.0b1 /usr/local/lib/python3.6/dist-packages pip \n",
"contextlib2 0.5.5 /usr/local/lib/python3.6/dist-packages pip \n",
"convertdate 2.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"coverage 3.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"coveralls 0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"crcmod 1.7 /usr/local/lib/python3.6/dist-packages pip \n",
"cufflinks 0.17.3 /usr/local/lib/python3.6/dist-packages pip \n",
"cvxopt 1.2.5 /usr/local/lib/python3.6/dist-packages pip \n",
"cvxpy 1.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"cycler 0.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"cymem 2.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"Cython 0.29.21 /usr/local/lib/python3.6/dist-packages pip \n",
"daft 0.0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"dash 1.14.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-bootstrap-components 0.10.3 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-core-components 1.10.2 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-cytoscape 0.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-html-components 1.0.3 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-renderer 1.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-table 4.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dask 2.12.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dataclasses 0.8 /usr/local/lib/python3.6/dist-packages pip \n",
"datascience 0.10.6 /usr/local/lib/python3.6/dist-packages pip \n",
"debugpy 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"decorator 4.4.2 /usr/local/lib/python3.6/dist-packages pip \n",
"defusedxml 0.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"descartes 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dill 0.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"distributed 1.25.3 /usr/local/lib/python3.6/dist-packages pip \n",
"Django 3.1.5 /usr/local/lib/python3.6/dist-packages pip \n",
"dlib 19.18.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dm-tree 0.1.5 /usr/local/lib/python3.6/dist-packages pip \n",
"docopt 0.6.2 /usr/local/lib/python3.6/dist-packages pip \n",
"docutils 0.16 /usr/local/lib/python3.6/dist-packages pip \n",
"docx2txt 0.8 /usr/local/lib/python3.6/dist-packages pip \n",
"dopamine-rl 1.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"earthengine-api 0.1.238 /usr/local/lib/python3.6/dist-packages pip \n",
"easydict 1.9 /usr/local/lib/python3.6/dist-packages pip \n",
"EbookLib 0.17.1 /usr/local/lib/python3.6/dist-packages pip \n",
"ecos 2.0.7.post1 /usr/local/lib/python3.6/dist-packages pip \n",
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"extract-msg 0.23.1 /usr/local/lib/python3.6/dist-packages pip \n",
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"fastrlock 0.5 /usr/local/lib/python3.6/dist-packages pip \n",
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"filelock 3.0.12 /usr/local/lib/python3.6/dist-packages pip \n",
"firebase-admin 4.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"fix-yahoo-finance 0.0.22 /usr/local/lib/python3.6/dist-packages pip \n",
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"googledrivedownloader 0.4 /usr/local/lib/python3.6/dist-packages pip \n",
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"gspread-dataframe 3.0.8 /usr/local/lib/python3.6/dist-packages pip \n",
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"HeapDict 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
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"html5lib 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
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"ideep4py 2.0.0.post3 /usr/local/lib/python3.6/dist-packages pip \n",
"idna 2.10 /usr/local/lib/python3.6/dist-packages pip \n",
"image 1.5.33 /usr/local/lib/python3.6/dist-packages pip \n",
"imageio 2.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
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"imbalanced-learn 0.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"imblearn 0.0 /usr/local/lib/python3.6/dist-packages pip \n",
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"imutils 0.5.3 /usr/local/lib/python3.6/dist-packages pip \n",
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"inflection 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
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"iso4217 1.6.20180829 /usr/local/lib/python3.6/dist-packages pip \n",
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"joblib 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
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"setuptools 51.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"setuptools-git 1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"Shapely 1.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"simplegeneric 0.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"six 1.12.0 /usr/local/lib/python3.6/dist-packages pip \n",
"sklearn 0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"sklearn-pandas 1.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"smart-open 4.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"snowballstemmer 2.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"sortedcontainers 2.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"soupsieve 2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"spacy 2.2.4 /usr/local/lib/python3.6/dist-packages pip \n",
"SpeechRecognition 3.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Sphinx 1.8.5 /usr/local/lib/python3.6/dist-packages pip \n",
"sphinxcontrib-serializinghtml 1.1.4 /usr/local/lib/python3.6/dist-packages pip \n",
"sphinxcontrib-websupport 1.2.4 /usr/local/lib/python3.6/dist-packages pip \n",
"SQLAlchemy 1.3.22 /usr/local/lib/python3.6/dist-packages pip \n",
"sqlparse 0.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"srsly 1.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"statsmodels 0.11.1 /usr/local/lib/python3.6/dist-packages pip \n",
"sympy 1.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tables 3.4.4 /usr/local/lib/python3.6/dist-packages pip \n",
"tabulate 0.8.7 /usr/local/lib/python3.6/dist-packages pip \n",
"tblib 1.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorboard 2.2.2 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorboard-plugin-wit 1.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorboardcolab 0.0.22 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow 2.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-addons 0.8.3 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-datasets 4.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-estimator 2.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-gcs-config 2.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-hub 0.11.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-metadata 0.26.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-privacy 0.2.2 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-probability 0.12.1 /usr/local/lib/python3.6/dist-packages pip \n",
"termcolor 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"terminado 0.9.2 /usr/local/lib/python3.6/dist-packages pip \n",
"testpath 0.4.4 /usr/local/lib/python3.6/dist-packages pip \n",
"text-unidecode 1.3 /usr/local/lib/python3.6/dist-packages pip \n",
"textblob 0.15.3 /usr/local/lib/python3.6/dist-packages pip \n",
"textgenrnn 1.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"textract 1.6.3 /usr/local/lib/python3.6/dist-packages pip \n",
"Theano 1.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"thinc 7.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"threadpoolctl 2.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tifffile 2020.9.3 /usr/local/lib/python3.6/dist-packages pip \n",
"tokenizers 0.9.4 /usr/local/lib/python3.6/dist-packages pip \n",
"toml 0.10.2 /usr/local/lib/python3.6/dist-packages pip \n",
"toolz 0.11.1 /usr/local/lib/python3.6/dist-packages pip \n",
"torch 1.7.1+cu101 /usr/local/lib/python3.6/dist-packages pip \n",
"torchsummary 1.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"torchtext 0.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"torchvision 0.8.2+cu101 /usr/local/lib/python3.6/dist-packages pip \n",
"tornado 5.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tqdm 4.56.0 /usr/local/lib/python3.6/dist-packages pip \n",
"trading-calendars 2.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"traitlets 4.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"transformers 4.2.2 /usr/local/lib/python3.6/dist-packages pip \n",
"tweepy 3.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"typeguard 2.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"typing-extensions 3.7.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"tzlocal 1.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"umap-learn 0.4.6 /usr/local/lib/python3.6/dist-packages pip \n",
"uritemplate 3.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"urllib3 1.24.3 /usr/local/lib/python3.6/dist-packages pip \n",
"vega-datasets 0.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"wasabi 0.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"wcwidth 0.2.5 /usr/local/lib/python3.6/dist-packages pip \n",
"webencodings 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Werkzeug 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"wheel 0.36.2 /usr/local/lib/python3.6/dist-packages pip \n",
"widgetsnbextension 3.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"wordcloud 1.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"wrapt 1.12.1 /usr/local/lib/python3.6/dist-packages pip \n",
"xarray 0.15.1 /usr/local/lib/python3.6/dist-packages pip \n",
"xgboost 1.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"xkit 0.0.0 /usr/lib/python3/dist-packages \n",
"xlrd 1.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"XlsxWriter 1.3.7 /usr/local/lib/python3.6/dist-packages pip \n",
"xlwt 1.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"yellowbrick 0.9.1 /usr/local/lib/python3.6/dist-packages pip \n",
"zict 2.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"zipline 1.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"zipp 3.4.0 /usr/local/lib/python3.6/dist-packages pip \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1IHC8RsIfdqy"
},
"source": [
"### Mount Google Drive"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ek5xugZfrjRb",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "134e2b32-249d-439e-a3a1-c7fe1740ae0b"
},
"source": [
"# Mount Google Drive:\n",
"if IN_COLAB:\n",
" from google.colab import drive, output\n",
" drive.mount('/content/drive', force_remount=True)\n"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qEyedHZyfgtS"
},
"source": [
"### System Environment Variables"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uiY9mHXGv7Tr"
},
"source": [
"# Define Paths:\r\n",
"if IN_COLAB:\r\n",
" graphs_dir = '/content/drive/MyDrive/Colab Notebooks/global_macro/report/graphics/'\r\n",
" data_dir = '/content/drive/MyDrive/Colab Notebooks/global_macro/src/data/'\r\n",
" source_dir = '/content/drive/MyDrive/Colab Notebooks/global_macro/src/'\r\n",
"else:\r\n",
" graphs_dir = 'C:/Users/theon/GDrive/Colab Notebooks/global_macro/report/graphics/'\r\n",
" data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/global_macro/src/data/'\r\n",
" source_dir = '/content/drive/MyDrive/Colab Notebooks/global_macro/src/'\r\n"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zYf4wLMc2y8y"
},
"source": [
"### Packages"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gcfVcoi18HV9"
},
"source": [
"#### Uninstall/Install Packages:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "op-Hz0wwTYeZ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "4b83a1f5-1168-4999-8dca-87d2416de73e"
},
"source": [
"### Note: The kernel needs to be restarted after this procedure.\r\n",
"### os.kill(os.getpid(), 9) kills it automatically, but do \"Runtime > Restart Runtime\" regardless.\r\n",
"\r\n",
"#if IN_COLAB:\r\n",
"# # Uninstall Existing Libraries\r\n",
"# !pip uninstall bs4 -y\r\n",
"# !pip uninstall textract -y\r\n",
"# !pip uninstall numpy -y\r\n",
"# !pip uninstall pandas -y\r\n",
"# !pip uninstall cvxopt -y\r\n",
"# !pip uninstall matplotlib -y\r\n",
"# !pip uninstall pandas-datareader -y\r\n",
"# !pip uninstall zipline -y\r\n",
"# !pip uninstall pyfolio -y\r\n",
"# !pip uninstall alphalens -y\r\n",
"# !pip uninstall empyrical -y\r\n",
"# !pip uninstall mlfinlab -y\r\n",
"# !pip uninstall requests -y\r\n",
"# !pip uninstall tqdm -y\r\n",
"# !pip uninstall pytz -y\r\n",
"# !pip uninstall ipython-autotime -y\r\n",
"# !pip uninstall nltk -y\r\n",
"# !pip uninstall quandl -y\r\n",
"# !pip uninstall scikit-plot -y\r\n",
"# !pip uninstall seaborn -y\r\n",
"# !pip uninstall sklearn -y\r\n",
"# !pip uninstall torch -y\r\n",
"# !pip uninstall transformers -y\r\n",
"# !pip uninstall wordcloud -y\r\n",
"# !pip uninstall xgboost -y\r\n",
"#\r\n",
"# # Install Libraries\r\n",
"# !pip install bs4\r\n",
"# !pip install textract\r\n",
"# !pip install numpy\r\n",
"# !pip install pandas\r\n",
"# !pip install cvxopt\r\n",
"# !pip install matplotlib\r\n",
"# !pip install pandas-datareader\r\n",
"# !pip install zipline\r\n",
"# !pip install pyfolio\r\n",
"# !pip install alphalens\r\n",
"# !pip install empyrical\r\n",
"# !pip install mlfinlab\r\n",
"# !pip install requests\r\n",
"# !pip install tqdm\r\n",
"# !pip install pytz\r\n",
"# !pip install ipython-autotime\r\n",
"# !pip install nltk\r\n",
"# !pip install quandl\r\n",
"# !pip install scikit-plot\r\n",
"# !pip install seaborn\r\n",
"# !pip install sklearn\r\n",
"# !pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 -f https://download.pytorch.org/whl/torch_stable.html\r\n",
"# !pip install transformers\r\n",
"# !pip install wordcloud\r\n",
"# !pip install xgboost\r\n",
"# os.kill(os.getpid(), 9)\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Uninstalling bs4-0.0.1:\n",
" Successfully uninstalled bs4-0.0.1\n",
"\u001b[33mWARNING: Skipping textract as it is not installed.\u001b[0m\n",
"Uninstalling numpy-1.19.5:\n",
" Successfully uninstalled numpy-1.19.5\n",
"Uninstalling pandas-1.1.5:\n",
" Successfully uninstalled pandas-1.1.5\n",
"Uninstalling cvxopt-1.2.5:\n",
" Successfully uninstalled cvxopt-1.2.5\n",
"Uninstalling matplotlib-3.2.2:\n",
" Successfully uninstalled matplotlib-3.2.2\n",
"Uninstalling pandas-datareader-0.9.0:\n",
" Successfully uninstalled pandas-datareader-0.9.0\n",
"\u001b[33mWARNING: Skipping zipline as it is not installed.\u001b[0m\n",
"\u001b[33mWARNING: Skipping pyfolio as it is not installed.\u001b[0m\n",
"\u001b[33mWARNING: Skipping alphalens as it is not installed.\u001b[0m\n",
"\u001b[33mWARNING: Skipping empyrical as it is not installed.\u001b[0m\n",
"\u001b[33mWARNING: Skipping mlfinlab as it is not installed.\u001b[0m\n",
"Uninstalling requests-2.23.0:\n",
" Successfully uninstalled requests-2.23.0\n",
"Uninstalling tqdm-4.41.1:\n",
" Successfully uninstalled tqdm-4.41.1\n",
"Uninstalling pytz-2018.9:\n",
" Successfully uninstalled pytz-2018.9\n",
"\u001b[33mWARNING: Skipping ipython-autotime as it is not installed.\u001b[0m\n",
"Uninstalling nltk-3.2.5:\n",
" Successfully uninstalled nltk-3.2.5\n",
"\u001b[33mWARNING: Skipping quandl as it is not installed.\u001b[0m\n",
"\u001b[33mWARNING: Skipping scikit-plot as it is not installed.\u001b[0m\n",
"Uninstalling seaborn-0.11.1:\n",
" Successfully uninstalled seaborn-0.11.1\n",
"Uninstalling sklearn-0.0:\n",
" Successfully uninstalled sklearn-0.0\n",
"Uninstalling torch-1.7.0+cu101:\n",
" Successfully uninstalled torch-1.7.0+cu101\n",
"\u001b[33mWARNING: Skipping transformers as it is not installed.\u001b[0m\n",
"Uninstalling wordcloud-1.5.0:\n",
" Successfully uninstalled wordcloud-1.5.0\n",
"Uninstalling xgboost-0.90:\n",
" Successfully uninstalled xgboost-0.90\n",
"Collecting bs4\n",
" Downloading https://files.pythonhosted.org/packages/10/ed/7e8b97591f6f456174139ec089c769f89a94a1a4025fe967691de971f314/bs4-0.0.1.tar.gz\n",
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.6/dist-packages (from bs4) (4.6.3)\n",
"Building wheels for collected packages: bs4\n",
" Building wheel for bs4 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for bs4: filename=bs4-0.0.1-cp36-none-any.whl size=1273 sha256=fbd87d99fc60bb60ecf129952ba89c65a319dfb7e01b01729531c51eb480c5c0\n",
" Stored in directory: /root/.cache/pip/wheels/a0/b0/b2/4f80b9456b87abedbc0bf2d52235414c3467d8889be38dd472\n",
"Successfully built bs4\n",
"Installing collected packages: bs4\n",
"Successfully installed bs4-0.0.1\n",
"Collecting textract\n",
" Downloading https://files.pythonhosted.org/packages/32/31/ef9451e6e48a1a57e337c5f20d4ef58c1a13d91560d2574c738b1320bb8d/textract-1.6.3-py3-none-any.whl\n",
"Collecting beautifulsoup4==4.8.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/1a/b7/34eec2fe5a49718944e215fde81288eec1fa04638aa3fb57c1c6cd0f98c3/beautifulsoup4-4.8.0-py3-none-any.whl (97kB)\n",
"\u001b[K |████████████████████████████████| 102kB 4.0MB/s \n",
"\u001b[?25hCollecting docx2txt==0.8\n",
" Downloading https://files.pythonhosted.org/packages/7d/7d/60ee3f2b16d9bfdfa72e8599470a2c1a5b759cb113c6fe1006be28359327/docx2txt-0.8.tar.gz\n",
"Collecting EbookLib==0.17.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/00/38/7d6ab2e569a9165249619d73b7bc6be0e713a899a3bc2513814b6598a84c/EbookLib-0.17.1.tar.gz (111kB)\n",
"\u001b[K |████████████████████████████████| 112kB 9.8MB/s \n",
"\u001b[?25hCollecting python-pptx==0.6.18\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/bf/86/eb979f7b0333ec769041aae36df8b9f1bd8bea5bbad44620663890dce561/python-pptx-0.6.18.tar.gz (8.9MB)\n",
"\u001b[K |████████████████████████████████| 8.9MB 9.2MB/s \n",
"\u001b[?25hCollecting pdfminer.six==20181108\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/8a/fd/6e8746e6965d1a7ea8e97253e3d79e625da5547e8f376f88de5d024bacb9/pdfminer.six-20181108-py2.py3-none-any.whl (5.6MB)\n",
"\u001b[K |████████████████████████████████| 5.6MB 44.2MB/s \n",
"\u001b[?25hCollecting extract-msg==0.23.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a1/90/84485a914ed90adb5e87df17e626be04162fbba146dfecf34643659a4633/extract_msg-0.23.1-py2.py3-none-any.whl (45kB)\n",
"\u001b[K |████████████████████████████████| 51kB 6.1MB/s \n",
"\u001b[?25hCollecting SpeechRecognition==3.8.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/26/e1/7f5678cd94ec1234269d23756dbdaa4c8cfaed973412f88ae8adf7893a50/SpeechRecognition-3.8.1-py2.py3-none-any.whl (32.8MB)\n",
"\u001b[K |████████████████████████████████| 32.8MB 208kB/s \n",
"\u001b[?25hCollecting xlrd==1.2.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/b0/16/63576a1a001752e34bf8ea62e367997530dc553b689356b9879339cf45a4/xlrd-1.2.0-py2.py3-none-any.whl (103kB)\n",
"\u001b[K |████████████████████████████████| 112kB 61.3MB/s \n",
"\u001b[?25hCollecting argcomplete==1.10.0\n",
" Downloading https://files.pythonhosted.org/packages/4d/82/f44c9661e479207348a979b1f6f063625d11dc4ca6256af053719bbb0124/argcomplete-1.10.0-py2.py3-none-any.whl\n",
"Requirement already satisfied: chardet==3.0.4 in /usr/local/lib/python3.6/dist-packages (from textract) (3.0.4)\n",
"Collecting six==1.12.0\n",
" Downloading https://files.pythonhosted.org/packages/73/fb/00a976f728d0d1fecfe898238ce23f502a721c0ac0ecfedb80e0d88c64e9/six-1.12.0-py2.py3-none-any.whl\n",
"Collecting soupsieve>=1.2\n",
" Downloading https://files.pythonhosted.org/packages/02/fb/1c65691a9aeb7bd6ac2aa505b84cb8b49ac29c976411c6ab3659425e045f/soupsieve-2.1-py3-none-any.whl\n",
"Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from EbookLib==0.17.1->textract) (4.2.6)\n",
"Requirement already satisfied: Pillow>=3.3.2 in /usr/local/lib/python3.6/dist-packages (from python-pptx==0.6.18->textract) (7.0.0)\n",
"Collecting XlsxWriter>=0.5.7\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/6b/41/bf1aae04932d1eaffee1fc5f8b38ca47bbbf07d765129539bc4bcce1ce0c/XlsxWriter-1.3.7-py2.py3-none-any.whl (144kB)\n",
"\u001b[K |████████████████████████████████| 153kB 58.5MB/s \n",
"\u001b[?25hRequirement already satisfied: sortedcontainers in /usr/local/lib/python3.6/dist-packages (from pdfminer.six==20181108->textract) (2.3.0)\n",
"Collecting pycryptodome\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/2b/6f/7e38d7c97fbbc3987539c804282c33f56b6b07381bf2390deead696440c5/pycryptodome-3.9.9-cp36-cp36m-manylinux1_x86_64.whl (13.7MB)\n",
"\u001b[K |████████████████████████████████| 13.7MB 54.9MB/s \n",
"\u001b[?25hCollecting olefile==0.46\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/34/81/e1ac43c6b45b4c5f8d9352396a14144bba52c8fec72a80f425f6a4d653ad/olefile-0.46.zip (112kB)\n",
"\u001b[K |████████████████████████████████| 112kB 49.4MB/s \n",
"\u001b[?25hCollecting imapclient==2.1.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/dc/39/e1c2c2c6e2356ab6ea81fcfc0a74b044b311d6a91a45300811d9a6077ef7/IMAPClient-2.1.0-py2.py3-none-any.whl (73kB)\n",
"\u001b[K |████████████████████████████████| 81kB 11.0MB/s \n",
"\u001b[?25hRequirement already satisfied: tzlocal==1.5.1 in /usr/local/lib/python3.6/dist-packages (from extract-msg==0.23.1->textract) (1.5.1)\n",
"Collecting pytz\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/89/06/2c2d3034b4d6bf22f2a4ae546d16925898658a33b4400cfb7e2c1e2871a3/pytz-2020.5-py2.py3-none-any.whl (510kB)\n",
"\u001b[K |████████████████████████████████| 512kB 54.9MB/s \n",
"\u001b[?25hBuilding wheels for collected packages: docx2txt, EbookLib, python-pptx, olefile\n",
" Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for docx2txt: filename=docx2txt-0.8-cp36-none-any.whl size=3960 sha256=d5a3de92c7b9e862bd262f2af413d9df0ff9494664347f3dcd2f1a4493d4f968\n",
" Stored in directory: /root/.cache/pip/wheels/b2/1f/26/a051209bbb77fc6bcfae2bb7e01fa0ff941b82292ab084d596\n",
" Building wheel for EbookLib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for EbookLib: filename=EbookLib-0.17.1-cp36-none-any.whl size=38163 sha256=b9bd68c0a23dc3606a165aee7d1ec112213b8f45c135f2284c9cb042afc44372\n",
" Stored in directory: /root/.cache/pip/wheels/84/11/01/951369cbbf8f96878786a1f4da68bd7ac19a5d945b38e03d54\n",
" Building wheel for python-pptx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for python-pptx: filename=python_pptx-0.6.18-cp36-none-any.whl size=275707 sha256=cc10c04eefe5c385013a93064e7d7c7c44b666ac510df28797ccc867b5d90492\n",
" Stored in directory: /root/.cache/pip/wheels/1f/1f/2c/29acca422b420a0b5210bd2cd7e9669804520d602d2462f20b\n",
" Building wheel for olefile (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for olefile: filename=olefile-0.46-py2.py3-none-any.whl size=35416 sha256=4c3a2d3957803eba4123a10163f4a35b7f0f67115593e534776abe37b36f4da8\n",
" Stored in directory: /root/.cache/pip/wheels/4b/f4/11/bc4166107c27f07fd7bba707ffcb439619197638a1ac986df3\n",
"Successfully built docx2txt EbookLib python-pptx olefile\n",
"\u001b[31mERROR: tweepy 3.6.0 requires requests>=2.11.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: theano 1.0.5 requires numpy>=1.9.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow 2.4.0 requires numpy~=1.19.2, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-probability 0.12.1 requires numpy>=1.13.3, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-datasets 4.0.1 requires numpy, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-datasets 4.0.1 requires requests>=2.19.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-datasets 4.0.1 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorboard 2.4.0 requires numpy>=1.12.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorboard 2.4.0 requires requests<3,>=2.21.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tables 3.4.4 requires numpy>=1.8.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: sphinx 1.8.5 requires requests>=2.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: resampy 0.2.2 requires numpy>=1.10, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pymc3 3.7 requires numpy>=1.13.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pymc3 3.7 requires pandas>=0.18.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pymc3 3.7 requires tqdm>=4.8.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pyarrow 0.14.1 requires numpy>=1.14, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: patsy 0.5.1 requires numpy>=1.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pandas-profiling 1.4.1 requires matplotlib>=1.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pandas-profiling 1.4.1 requires pandas>=0.19, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: librosa 0.6.3 requires numpy>=1.8.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: knnimpute 0.1.0 requires numpy>=1.10, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: keras-vis 0.4.1 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: keras-preprocessing 1.1.2 requires numpy>=1.9.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: kapre 0.1.3.1 requires numpy>=1.8.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: kaggle 1.5.10 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: kaggle 1.5.10 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: imgaug 0.2.9 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: imgaug 0.2.9 requires numpy>=1.15.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: hyperopt 0.1.2 requires numpy, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: hyperopt 0.1.2 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: h5py 2.10.0 requires numpy>=1.7, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 requires pandas~=1.1.0; python_version >= \"3.0\", which is not installed.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 requires requests~=2.23.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: google-api-core 1.16.0 requires requests<3.0.0dev,>=2.18.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: gensim 3.6.0 requires numpy>=1.11.3, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: gdown 3.6.4 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: gdown 3.6.4 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: folium 0.8.3 requires numpy, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: folium 0.8.3 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires matplotlib>=2.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires numpy>=1.15.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires pandas>=1.0.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires tqdm>=4.36.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires numpy>=1.15, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires pandas, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires torch>=1.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fancyimpute 0.4.3 requires numpy>=1.10, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: cufflinks 0.17.3 requires numpy>=1.9.2, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: cufflinks 0.17.3 requires pandas>=0.19.2, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: chainer 7.4.0 requires numpy>=1.9.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: bokeh 2.1.1 requires numpy>=1.11.3, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: atari-py 0.2.6 requires numpy, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: albumentations 0.1.12 requires numpy>=1.11.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow 2.4.0 has requirement six~=1.15.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: nbclient 0.5.1 has requirement jupyter-client>=6.1.5, but you'll have jupyter-client 5.3.5 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement six~=1.15.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: convertdate 2.2.0 has requirement pytz<2020,>=2014.10, but you'll have pytz 2020.5 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Installing collected packages: soupsieve, beautifulsoup4, docx2txt, six, EbookLib, XlsxWriter, python-pptx, pycryptodome, pdfminer.six, olefile, imapclient, extract-msg, SpeechRecognition, xlrd, argcomplete, textract, pytz\n",
" Found existing installation: beautifulsoup4 4.6.3\n",
" Uninstalling beautifulsoup4-4.6.3:\n",
" Successfully uninstalled beautifulsoup4-4.6.3\n",
" Found existing installation: six 1.15.0\n",
" Uninstalling six-1.15.0:\n",
" Successfully uninstalled six-1.15.0\n",
" Found existing installation: xlrd 1.1.0\n",
" Uninstalling xlrd-1.1.0:\n",
" Successfully uninstalled xlrd-1.1.0\n",
"Successfully installed EbookLib-0.17.1 SpeechRecognition-3.8.1 XlsxWriter-1.3.7 argcomplete-1.10.0 beautifulsoup4-4.8.0 docx2txt-0.8 extract-msg-0.23.1 imapclient-2.1.0 olefile-0.46 pdfminer.six-20181108 pycryptodome-3.9.9 python-pptx-0.6.18 pytz-2020.5 six-1.12.0 soupsieve-2.1 textract-1.6.3 xlrd-1.2.0\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"pytz",
"six"
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{
"output_type": "stream",
"text": [
"Collecting numpy\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/14/32/d3fa649ad7ec0b82737b92fefd3c4dd376b0bb23730715124569f38f3a08/numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl (14.8MB)\n",
"\u001b[K |████████████████████████████████| 14.8MB 166kB/s \n",
"\u001b[31mERROR: yellowbrick 0.9.1 requires matplotlib!=3.0.0,>=1.5.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: xarray 0.15.1 requires pandas>=0.25, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: torchvision 0.8.1+cu101 requires torch==1.7.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: torchtext 0.3.1 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: torchtext 0.3.1 requires torch, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: torchtext 0.3.1 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: thinc 7.4.0 requires tqdm<5.0.0,>=4.10.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-datasets 4.0.1 requires requests>=2.19.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-datasets 4.0.1 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorboard 2.4.0 requires requests<3,>=2.21.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: statsmodels 0.10.2 requires pandas>=0.19, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: spacy 2.2.4 requires requests<3.0.0,>=2.13.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: spacy 2.2.4 requires tqdm<5.0.0,>=4.38.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: sklearn-pandas 1.8.0 requires pandas>=0.11.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: scikit-image 0.16.2 requires matplotlib!=3.0.0,>=2.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pymc3 3.7 requires pandas>=0.18.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pymc3 3.7 requires tqdm>=4.8.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: plotnine 0.6.0 requires matplotlib>=3.1.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: plotnine 0.6.0 requires pandas>=0.25.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: moviepy 0.2.3.5 requires tqdm<5.0,>=4.11.2, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: mlxtend 0.14.0 requires matplotlib>=1.5.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: mlxtend 0.14.0 requires pandas>=0.17.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: mizani 0.6.0 requires matplotlib>=3.1.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: mizani 0.6.0 requires pandas>=0.25.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: missingno 0.4.2 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: missingno 0.4.2 requires seaborn, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: matplotlib-venn 0.11.6 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: keras-vis 0.4.1 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: imgaug 0.2.9 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: hyperopt 0.1.2 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: holoviews 1.13.5 requires pandas, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: folium 0.8.3 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fix-yahoo-finance 0.0.22 requires pandas, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fix-yahoo-finance 0.0.22 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires matplotlib>=2.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires pandas>=1.0.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires tqdm>=4.36.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires pandas, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires torch>=1.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fa2 0.3.5 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: daft 0.0.4 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: cufflinks 0.17.3 requires pandas>=0.19.2, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: cmdstanpy 0.9.5 requires pandas, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: altair 4.1.0 requires pandas>=0.18, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: tensorflow 2.4.0 has requirement six~=1.15.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"\u001b[?25hInstalling collected packages: numpy\n",
"Successfully installed numpy-1.19.5\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"numpy"
]
}
}
},
"metadata": {
"tags": []
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{
"output_type": "stream",
"text": [
"Collecting pandas\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/c3/e2/00cacecafbab071c787019f00ad84ca3185952f6bb9bca9550ed83870d4d/pandas-1.1.5-cp36-cp36m-manylinux1_x86_64.whl (9.5MB)\n",
"\u001b[K |████████████████████████████████| 9.5MB 6.6MB/s \n",
"\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2020.5)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.1)\n",
"Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.6/dist-packages (from pandas) (1.19.5)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas) (1.12.0)\n",
"\u001b[31mERROR: pymc3 3.7 requires tqdm>=4.8.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: plotnine 0.6.0 requires matplotlib>=3.1.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pandas-profiling 1.4.1 requires matplotlib>=1.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: mlxtend 0.14.0 requires matplotlib>=1.5.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: mizani 0.6.0 requires matplotlib>=3.1.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 requires requests~=2.23.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fix-yahoo-finance 0.0.22 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires matplotlib>=2.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires tqdm>=4.36.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires matplotlib, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires requests, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires torch>=1.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement six~=1.15.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
"Installing collected packages: pandas\n",
"Successfully installed pandas-1.1.5\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"pandas"
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"metadata": {
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{
"output_type": "stream",
"text": [
"Collecting cvxopt\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/bd/da/385b85d3ef47e72a242abe304a03cea098cb9ba6cdb795b044e8c7806b18/cvxopt-1.2.5-cp36-cp36m-manylinux1_x86_64.whl (11.6MB)\n",
"\u001b[K |████████████████████████████████| 11.7MB 5.0MB/s \n",
"\u001b[?25hInstalling collected packages: cvxopt\n",
"Successfully installed cvxopt-1.2.5\n",
"Collecting matplotlib\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/d2/43/2bd63467490036697e7be71444fafc7b236923d614d4521979a200c6b559/matplotlib-3.3.3-cp36-cp36m-manylinux1_x86_64.whl (11.6MB)\n",
"\u001b[K |████████████████████████████████| 11.6MB 4.6MB/s \n",
"\u001b[?25hRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
"Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.19.5)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.8.1)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.7)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (7.0.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.3.1)\n",
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"\u001b[31mERROR: missingno 0.4.2 requires seaborn, which is not installed.\u001b[0m\n",
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],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
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{
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"text": [
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"\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement six~=1.15.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/42/9b/5a55c79ca66e1c3ed2d94155cb71eb033eaf96cea71b81eb0579610d489f/zipline-1.4.1.tar.gz (5.4MB)\n",
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" Downloading https://files.pythonhosted.org/packages/de/72/376dd5f3141d73adfa744314367b047b2860a5c30b7e69219b88cb93d928/python-interface-1.6.0.tar.gz\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/74/43/1b997c21411c6ab7c96dc034e160198272c7a785aeea7654c9bcf98bec83/empyrical-0.5.5.tar.gz (52kB)\n",
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"Collecting trading-calendars>=1.6.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/1e/6e/613df8268dea3aac81d3b9d9872d4e48526f8650e970ca1d14911f02dad0/trading_calendars-2.1.1.tar.gz (108kB)\n",
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"\u001b[?25hCollecting bcolz>=0.12.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/4e/23942de9d5c0fb16f10335fa83e52b431bcb8c0d4a8419c9ac206268c279/bcolz-1.2.1.tar.gz (1.5MB)\n",
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" Downloading https://files.pythonhosted.org/packages/00/a5/32ed6e10246cd341ca8cc205acea5d208e4053f48a4dced2b1b31d45ba3f/lru-dict-1.1.6.tar.gz\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/d3/2c/e473e54afc9fae58dfa97066ef6709a7e35a1dd1c28c5a3842989322be00/networkx-1.11-py2.py3-none-any.whl (1.3MB)\n",
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" Downloading https://files.pythonhosted.org/packages/68/f2/7ac00a52990623da868742697855ca2392ff11ee5934150fed0d94eb4d8c/iso4217-1.6.20180829-py2.py3-none-any.whl\n",
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"Collecting python-editor>=0.3\n",
" Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl\n",
"Collecting Mako\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/5c/db/2d2d88b924aa4674a080aae83b59ea19d593250bfe5ed789947c21736785/Mako-1.1.4.tar.gz (479kB)\n",
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"Building wheels for collected packages: zipline, Logbook, python-interface, empyrical, trading-calendars, bcolz, lru-dict, Mako\n",
" Building wheel for zipline (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for zipline: filename=zipline-1.4.1-cp36-cp36m-linux_x86_64.whl size=5285316 sha256=f9fed887665e273a910341b84821733985782752870afdd7ae2495f00f9fa076\n",
" Stored in directory: /root/.cache/pip/wheels/b8/01/d8/f22466b28d66ec4f732cf23057eacc4bd5a15ce37bfbd4660a\n",
" Building wheel for Logbook (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for Logbook: filename=Logbook-1.5.3-cp36-cp36m-linux_x86_64.whl size=66382 sha256=08d8989da77584c9ce6307fc554ba51946bdda49264de9da660e85be32fe54d2\n",
" Stored in directory: /root/.cache/pip/wheels/d2/70/07/68b99a8e05dcd1ab194a8e0ccb9e4d0ac5dd6d8d139c7149b4\n",
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" Stored in directory: /root/.cache/pip/wheels/3f/4c/d9/f3baecf7a1b94a697e147daa6573538050b87ec92031e2e099\n",
" Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for empyrical: filename=empyrical-0.5.5-cp36-none-any.whl size=39765 sha256=b9efbbc61d6dd61e350de2cda0a6bf6e6cfe7f7c03938b86021b9cb70d64e0ed\n",
" Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
" Building wheel for trading-calendars (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for trading-calendars: filename=trading_calendars-2.1.1-cp36-none-any.whl size=140920 sha256=65bcda431fa60f8078399b6d0d5c815b1c55b4877df8a56ef0a9baa156615865\n",
" Stored in directory: /root/.cache/pip/wheels/79/92/44/de8b4d9a7d86cd8f67ea3adfa91bdc7bd441c691b733418cca\n",
" Building wheel for bcolz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for bcolz: filename=bcolz-1.2.1-cp36-cp36m-linux_x86_64.whl size=2662185 sha256=fcd6b03c54f607d7423515b2ac3172699a41cf868a535a7b4dcf3ae1f2ba0911\n",
" Stored in directory: /root/.cache/pip/wheels/9f/78/26/fb8c0acb91a100dc8914bf236c4eaa4b207cb876893c40b745\n",
" Building wheel for lru-dict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for lru-dict: filename=lru_dict-1.1.6-cp36-cp36m-linux_x86_64.whl size=25863 sha256=0198214b31451ca0a547bbc94a7cacaafc6f708133cf359f9fb9755eb4ab34c7\n",
" Stored in directory: /root/.cache/pip/wheels/b7/ef/06/fbdd555907a7d438fb33e4c8675f771ff1cf41917284c51ebf\n",
" Building wheel for Mako (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for Mako: filename=Mako-1.1.4-py2.py3-none-any.whl size=75675 sha256=ad26edd2cd6fdfc32782181bf7584c714781dcfcdc6b8d16c856fb9acbe437ec\n",
" Stored in directory: /root/.cache/pip/wheels/ad/10/d3/aeb26e20d19045e2a68e5d3cbb57432e11b5d9c92c99f98d47\n",
"Successfully built zipline Logbook python-interface empyrical trading-calendars bcolz lru-dict Mako\n",
"\u001b[31mERROR: pymc3 3.7 requires tqdm>=4.8.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: hyperopt 0.1.2 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 requires tqdm>=4.36.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires torch>=1.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: xarray 0.15.1 has requirement pandas>=0.25, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: scikit-image 0.16.2 has requirement networkx>=2.0, but you'll have networkx 1.11 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: plotnine 0.6.0 has requirement pandas>=0.25.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: mizani 0.6.0 has requirement pandas>=0.25.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= \"3.0\", but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement six~=1.15.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: fbprophet 0.7.1 has requirement pandas>=1.0.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Installing collected packages: Logbook, python-interface, multipledispatch, pandas, pandas-datareader, empyrical, iso3166, trading-calendars, python-editor, Mako, alembic, bcolz, lru-dict, networkx, iso4217, zipline\n",
" Found existing installation: pandas 1.1.5\n",
" Uninstalling pandas-1.1.5:\n",
" Successfully uninstalled pandas-1.1.5\n",
" Found existing installation: pandas-datareader 0.9.0\n",
" Uninstalling pandas-datareader-0.9.0:\n",
" Successfully uninstalled pandas-datareader-0.9.0\n",
" Found existing installation: networkx 2.5\n",
" Uninstalling networkx-2.5:\n",
" Successfully uninstalled networkx-2.5\n",
"Successfully installed Logbook-1.5.3 Mako-1.1.4 alembic-1.5.2 bcolz-1.2.1 empyrical-0.5.5 iso3166-1.0.1 iso4217-1.6.20180829 lru-dict-1.1.6 multipledispatch-0.6.0 networkx-1.11 pandas-0.22.0 pandas-datareader-0.8.1 python-editor-1.0.4 python-interface-1.6.0 trading-calendars-2.1.1 zipline-1.4.1\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"pip_warning": {
"packages": [
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}
},
"metadata": {
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{
"output_type": "stream",
"text": [
"Collecting pyfolio\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/28/b4/99799b743c4619752f88b70354924132a2e9b82f4656fe7c55eaa9101392/pyfolio-0.9.2.tar.gz (91kB)\n",
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"Building wheels for collected packages: pyfolio\n",
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"Building wheels for collected packages: alphalens\n",
" Building wheel for alphalens (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for alphalens: filename=alphalens-0.4.0-cp36-none-any.whl size=24027560 sha256=380128f2e1af6597f1816c1d1a646c6040ddf02fb447802bdd156798fc083e7e\n",
" Stored in directory: /root/.cache/pip/wheels/b7/fb/58/653997175d0e91a6459d84eb52e0989cf5348f0a569bd06ef5\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/01/52/81c7ee29a4314bffc4f61694a987eca48b36bdf8571877156188e1148eae/mlfinlab-0.15.3-py3-none-any.whl (982kB)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/56/dc/0e3d3732fd62c73fbb3317fc7bba22574832ab7a8e075620557bd4311641/numba-0.49.1-cp36-cp36m-manylinux2014_x86_64.whl (3.6MB)\n",
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"\u001b[?25hCollecting dash-cytoscape==0.2.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a1/98/93b356b47aca71d4fb1065990137b6b75eb527e8d1cd0e87dc037cead113/dash_cytoscape-0.2.0.tar.gz (3.6MB)\n",
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"\u001b[?25hCollecting cvxpy==1.1.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/fa/89/6e4f99b36ce2d002f2792529b130fd8ed5d7004c92ce8ae7d56496f51426/cvxpy-1.1.1.tar.gz (990kB)\n",
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"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/70/e3/663eac537202dee730ad6e61769fc3ebce92a6085dbfd13ca902df5f1477/tensorflow-2.2.1-cp36-cp36m-manylinux2010_x86_64.whl (516.2MB)\n",
"\u001b[K |████████████████████████████████| 516.2MB 23kB/s \n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/cb/83/540fd83238a18abe6c2d280fa8e489ac5fcefa1f370f0ca1acd16ae1b860/statsmodels-0.11.1-cp36-cp36m-manylinux1_x86_64.whl (8.7MB)\n",
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" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/d9/3a/eb8d7bbe28f4787d140bb9df685b7d5bf6115c0e2a969def4027144e98b6/scikit_learn-0.23.1-cp36-cp36m-manylinux1_x86_64.whl (6.8MB)\n",
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" Building wheel for cvxpy (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for cvxpy: filename=cvxpy-1.1.1-cp36-cp36m-linux_x86_64.whl size=2654223 sha256=9c537019530704de9eb7be709027550cb00eb4bbf818f263819b798a99609037\n",
" Stored in directory: /root/.cache/pip/wheels/06/db/59/b5af93d86703e0903b9b94ccc300ac70daf9d273f13e6c0350\n",
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" Stored in directory: /root/.cache/pip/wheels/c0/96/96/1900eafa8fba572ad4f539891d37e37a480c0b045bf92df243\n",
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"Building wheels for collected packages: dash-cytoscape, dash, dash-renderer, dash-core-components, dash-html-components, dash-table\n",
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" Stored in directory: /root/.cache/pip/wheels/f6/99/e4/a3af0a6f2d07ace02dd25984c08294d2749818c38f44d55338\n",
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"\u001b[31mERROR: torchvision 0.8.1+cu101 requires torch==1.7.0, which is not installed.\u001b[0m\n",
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"\u001b[31mERROR: thinc 7.4.0 requires tqdm<5.0.0,>=4.10.0, which is not installed.\u001b[0m\n",
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"\u001b[31mERROR: spacy 2.2.4 requires tqdm<5.0.0,>=4.38.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: pymc3 3.7 requires tqdm>=4.8.4, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: moviepy 0.2.3.5 requires tqdm<5.0,>=4.11.2, which is not installed.\u001b[0m\n",
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"\u001b[31mERROR: fbprophet 0.7.1 requires tqdm>=4.36.1, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires torch>=1.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fa2 0.3.5 requires tqdm, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: zipline 1.4.1 has requirement networkx<2.0,>=1.9.1, but you'll have networkx 2.4 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: zipline 1.4.1 has requirement pandas<=0.22,>=0.18.1, but you'll have pandas 1.0.4 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= \"3.0\", but you'll have pandas 1.0.4 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement six~=1.15.0, but you'll have six 1.12.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Installing collected packages: ansi2html, brotli, flask-compress, dash-renderer, dash-core-components, dash-html-components, dash-table, dash, jupyter-dash, getmac, numpy, numba, matplotlib, dash-cytoscape, cvxpy, tensorboard, tensorflow-estimator, tensorflow, POT, analytics-python, pandas, statsmodels, networkx, dash-bootstrap-components, threadpoolctl, scikit-learn, mlfinlab\n",
" Found existing installation: numpy 1.19.5\n",
" Uninstalling numpy-1.19.5:\n",
" Successfully uninstalled numpy-1.19.5\n",
" Found existing installation: numba 0.48.0\n",
" Uninstalling numba-0.48.0:\n",
" Successfully uninstalled numba-0.48.0\n",
" Found existing installation: matplotlib 3.3.3\n",
" Uninstalling matplotlib-3.3.3:\n",
" Successfully uninstalled matplotlib-3.3.3\n",
" Found existing installation: cvxpy 1.0.31\n",
" Uninstalling cvxpy-1.0.31:\n",
" Successfully uninstalled cvxpy-1.0.31\n",
" Found existing installation: tensorboard 2.4.0\n",
" Uninstalling tensorboard-2.4.0:\n",
" Successfully uninstalled tensorboard-2.4.0\n",
" Found existing installation: tensorflow-estimator 2.4.0\n",
" Uninstalling tensorflow-estimator-2.4.0:\n",
" Successfully uninstalled tensorflow-estimator-2.4.0\n",
" Found existing installation: tensorflow 2.4.0\n",
" Uninstalling tensorflow-2.4.0:\n",
" Successfully uninstalled tensorflow-2.4.0\n",
" Found existing installation: pandas 0.22.0\n",
" Uninstalling pandas-0.22.0:\n",
" Successfully uninstalled pandas-0.22.0\n",
" Found existing installation: statsmodels 0.10.2\n",
" Uninstalling statsmodels-0.10.2:\n",
" Successfully uninstalled statsmodels-0.10.2\n",
" Found existing installation: networkx 1.11\n",
" Uninstalling networkx-1.11:\n",
" Successfully uninstalled networkx-1.11\n",
" Found existing installation: scikit-learn 0.22.2.post1\n",
" Uninstalling scikit-learn-0.22.2.post1:\n",
" Successfully uninstalled scikit-learn-0.22.2.post1\n",
"Successfully installed POT-0.7.0 analytics-python-1.2.9 ansi2html-1.6.0 brotli-1.0.9 cvxpy-1.1.1 dash-1.14.0 dash-bootstrap-components-0.10.3 dash-core-components-1.10.2 dash-cytoscape-0.2.0 dash-html-components-1.0.3 dash-renderer-1.6.0 dash-table-4.9.0 flask-compress-1.8.0 getmac-0.8.2 jupyter-dash-0.3.1 matplotlib-3.2.1 mlfinlab-0.15.3 networkx-2.4 numba-0.49.1 numpy-1.18.5 pandas-1.0.4 scikit-learn-0.23.1 statsmodels-0.11.1 tensorboard-2.2.2 tensorflow-2.2.1 tensorflow-estimator-2.2.0 threadpoolctl-2.1.0\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"matplotlib",
"mpl_toolkits",
"numpy",
"pandas"
]
}
}
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (2.25.1)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests) (2020.12.5)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests) (1.24.3)\n",
"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests) (3.0.4)\n",
"Collecting tqdm\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/80/02/8f8880a4fd6625461833abcf679d4c12a44c76f9925f92bf212bb6cefaad/tqdm-4.56.0-py2.py3-none-any.whl (72kB)\n",
"\u001b[K |████████████████████████████████| 81kB 3.7MB/s \n",
"\u001b[31mERROR: torchtext 0.3.1 requires torch, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: fastai 1.0.61 requires torch>=1.0.0, which is not installed.\u001b[0m\n",
"\u001b[?25hInstalling collected packages: tqdm\n",
"Successfully installed tqdm-4.56.0\n",
"Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (2020.5)\n",
"Collecting ipython-autotime\n",
" Downloading https://files.pythonhosted.org/packages/b4/c9/b413a24f759641bc27ef98c144b590023c8038dfb8a3f09e713e9dff12c1/ipython_autotime-0.3.1-py2.py3-none-any.whl\n",
"Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from ipython-autotime) (5.5.0)\n",
"Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (4.3.3)\n",
"Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (51.3.3)\n",
"Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (4.4.2)\n",
"Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (4.8.0)\n",
"Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (2.6.1)\n",
"Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (1.0.18)\n",
"Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (0.8.1)\n",
"Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->ipython-autotime) (0.7.5)\n",
"Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->ipython-autotime) (0.2.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->ipython-autotime) (1.12.0)\n",
"Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->ipython-autotime) (0.7.0)\n",
"Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (0.2.5)\n",
"Installing collected packages: ipython-autotime\n",
"Successfully installed ipython-autotime-0.3.1\n",
"Collecting nltk\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/92/75/ce35194d8e3022203cca0d2f896dbb88689f9b3fce8e9f9cff942913519d/nltk-3.5.zip (1.4MB)\n",
"\u001b[K |████████████████████████████████| 1.4MB 5.3MB/s \n",
"\u001b[?25hRequirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from nltk) (7.1.2)\n",
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"Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from nltk) (2019.12.20)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from nltk) (4.56.0)\n",
"Building wheels for collected packages: nltk\n",
" Building wheel for nltk (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for nltk: filename=nltk-3.5-cp36-none-any.whl size=1434675 sha256=c88cda0cad427eea1f9d789fc78c6940177d843c646b4c6e3d24cd06d11a5e16\n",
" Stored in directory: /root/.cache/pip/wheels/ae/8c/3f/b1fe0ba04555b08b57ab52ab7f86023639a526d8bc8d384306\n",
"Successfully built nltk\n",
"Installing collected packages: nltk\n",
"Successfully installed nltk-3.5\n",
"Collecting quandl\n",
" Downloading https://files.pythonhosted.org/packages/c2/58/9f0e69d836045e3865d263e9ed49f42b23a58526fdabb30f74c430baee3f/Quandl-3.6.0-py2.py3-none-any.whl\n",
"Requirement already satisfied: more-itertools in /usr/local/lib/python3.6/dist-packages (from quandl) (8.6.0)\n",
"Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from quandl) (2.8.1)\n",
"Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.18.5)\n",
"Collecting inflection>=0.3.1\n",
" Downloading https://files.pythonhosted.org/packages/59/91/aa6bde563e0085a02a435aa99b49ef75b0a4b062635e606dab23ce18d720/inflection-0.5.1-py2.py3-none-any.whl\n",
"Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.0.4)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from quandl) (1.12.0)\n",
"Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from quandl) (2.25.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.14->quandl) (2020.5)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2020.12.5)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (1.24.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2.10)\n",
"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (3.0.4)\n",
"Installing collected packages: inflection, quandl\n",
"Successfully installed inflection-0.5.1 quandl-3.6.0\n",
"Collecting scikit-plot\n",
" Downloading https://files.pythonhosted.org/packages/7c/47/32520e259340c140a4ad27c1b97050dd3254fdc517b1d59974d47037510e/scikit_plot-0.3.7-py3-none-any.whl\n",
"Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (1.4.1)\n",
"Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (3.2.1)\n",
"Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (0.23.1)\n",
"Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (1.0.0)\n",
"Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scipy>=0.9->scikit-plot) (1.18.5)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.3.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.10.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.4.7)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->scikit-plot) (2.1.0)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.0->scikit-plot) (1.12.0)\n",
"Installing collected packages: scikit-plot\n",
"Successfully installed scikit-plot-0.3.7\n",
"Requirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (0.11.1)\n",
"Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (3.2.1)\n",
"Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.4.1)\n",
"Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.18.5)\n",
"Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.0.4)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23->seaborn) (2020.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.12.0)\n",
"Collecting sklearn\n",
" Downloading https://files.pythonhosted.org/packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.23.1)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.0.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (2.1.0)\n",
"Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.18.5)\n",
"Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.4.1)\n",
"Building wheels for collected packages: sklearn\n",
" Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1316 sha256=10d23980c0718489eae7f2a28713f84ee5daf4ad106a8a94fe93fae31b668e32\n",
" Stored in directory: /root/.cache/pip/wheels/76/03/bb/589d421d27431bcd2c6da284d5f2286c8e3b2ea3cf1594c074\n",
"Successfully built sklearn\n",
"Installing collected packages: sklearn\n",
"Successfully installed sklearn-0.0\n",
"Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
"Collecting torch==1.7.1+cu101\n",
"\u001b[?25l Downloading https://download.pytorch.org/whl/cu101/torch-1.7.1%2Bcu101-cp36-cp36m-linux_x86_64.whl (735.4MB)\n",
"\u001b[K |████████████████████████████████| 735.4MB 24kB/s \n",
"\u001b[?25hCollecting torchvision==0.8.2+cu101\n",
"\u001b[?25l Downloading https://download.pytorch.org/whl/cu101/torchvision-0.8.2%2Bcu101-cp36-cp36m-linux_x86_64.whl (12.8MB)\n",
"\u001b[K |████████████████████████████████| 12.8MB 38.9MB/s \n",
"\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch==1.7.1+cu101) (3.7.4.3)\n",
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"Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.8.2+cu101) (7.0.0)\n",
"Installing collected packages: torch, torchvision\n",
" Found existing installation: torchvision 0.8.1+cu101\n",
" Uninstalling torchvision-0.8.1+cu101:\n",
" Successfully uninstalled torchvision-0.8.1+cu101\n",
"Successfully installed torch-1.7.1+cu101 torchvision-0.8.2+cu101\n",
"Collecting transformers\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/88/b1/41130a228dd656a1a31ba281598a968320283f48d42782845f6ba567f00b/transformers-4.2.2-py3-none-any.whl (1.8MB)\n",
"\u001b[K |████████████████████████████████| 1.8MB 5.2MB/s \n",
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"Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from transformers) (3.3.0)\n",
"Collecting tokenizers==0.9.4\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
"\u001b[K |████████████████████████████████| 2.9MB 22.6MB/s \n",
"\u001b[?25hCollecting sacremoses\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
"\u001b[K |████████████████████████████████| 890kB 39.2MB/s \n",
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"Building wheels for collected packages: sacremoses\n",
" Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=9b823f710018f037a2e4e07b310660fef7ffa5b0922c41527694503ef9029c33\n",
" Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
"Successfully built sacremoses\n",
"Installing collected packages: tokenizers, sacremoses, transformers\n",
"Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers-4.2.2\n",
"Collecting wordcloud\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/05/e7/52e4bef8e2e3499f6e96cc8ff7e0902a40b95014143b062acde4ff8b9fc8/wordcloud-1.8.1-cp36-cp36m-manylinux1_x86_64.whl (366kB)\n",
"\u001b[K |████████████████████████████████| 368kB 4.9MB/s \n",
"\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from wordcloud) (3.2.1)\n",
"Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from wordcloud) (1.18.5)\n",
"Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from wordcloud) (7.0.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->wordcloud) (1.3.1)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->wordcloud) (2.4.7)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->wordcloud) (0.10.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->wordcloud) (2.8.1)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->wordcloud) (1.12.0)\n",
"Installing collected packages: wordcloud\n",
"Successfully installed wordcloud-1.8.1\n",
"Collecting xgboost\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/2e/57/bf5026701c384decd2b995eb39d86587a103ba4eb26f8a9b1811db0896d3/xgboost-1.3.3-py3-none-manylinux2010_x86_64.whl (157.5MB)\n",
"\u001b[K |████████████████████████████████| 157.5MB 38kB/s \n",
"\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.4.1)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.18.5)\n",
"Installing collected packages: xgboost\n",
"Successfully installed xgboost-1.3.3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S2Z0H8zUPQE6"
},
"source": [
"#### Inspect Packages"
]
},
{
"cell_type": "code",
"metadata": {
"id": "urMcNI8vPUHB",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d86a867d-ad5f-4cf8-b6ba-33c5f2196b1b"
},
"source": [
"!pip list -v\r\n",
"!pip list --user -v\r\n"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Package Version Location Installer\n",
"----------------------------- --------------- -------------------------------------- ---------\n",
"absl-py 0.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"alabaster 0.7.12 /usr/local/lib/python3.6/dist-packages pip \n",
"albumentations 0.1.12 /usr/local/lib/python3.6/dist-packages pip \n",
"alembic 1.5.2 /usr/local/lib/python3.6/dist-packages pip \n",
"alphalens 0.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"altair 4.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"analytics-python 1.2.9 /usr/local/lib/python3.6/dist-packages pip \n",
"ansi2html 1.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"argcomplete 1.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"argon2-cffi 20.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"asgiref 3.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"astor 0.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"astropy 4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"astunparse 1.6.3 /usr/local/lib/python3.6/dist-packages pip \n",
"async-generator 1.10 /usr/local/lib/python3.6/dist-packages pip \n",
"atari-py 0.2.6 /usr/local/lib/python3.6/dist-packages pip \n",
"atomicwrites 1.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"attrs 20.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"audioread 2.1.9 /usr/local/lib/python3.6/dist-packages pip \n",
"autograd 1.3 /usr/local/lib/python3.6/dist-packages pip \n",
"Babel 2.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"backcall 0.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"bcolz 1.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"beautifulsoup4 4.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"bleach 3.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"blis 0.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"bokeh 2.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Bottleneck 1.3.2 /usr/local/lib/python3.6/dist-packages pip \n",
"branca 0.4.2 /usr/local/lib/python3.6/dist-packages pip \n",
"Brotli 1.0.9 /usr/local/lib/python3.6/dist-packages pip \n",
"bs4 0.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"CacheControl 0.12.6 /usr/local/lib/python3.6/dist-packages pip \n",
"cachetools 4.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"catalogue 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"certifi 2020.12.5 /usr/local/lib/python3.6/dist-packages pip \n",
"cffi 1.14.4 /usr/local/lib/python3.6/dist-packages pip \n",
"chainer 7.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"chardet 3.0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"click 7.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"cloudpickle 1.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"cmake 3.12.0 /usr/local/lib/python3.6/dist-packages pip \n",
"cmdstanpy 0.9.5 /usr/local/lib/python3.6/dist-packages pip \n",
"colorlover 0.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"community 1.0.0b1 /usr/local/lib/python3.6/dist-packages pip \n",
"contextlib2 0.5.5 /usr/local/lib/python3.6/dist-packages pip \n",
"convertdate 2.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"coverage 3.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"coveralls 0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"crcmod 1.7 /usr/local/lib/python3.6/dist-packages pip \n",
"cufflinks 0.17.3 /usr/local/lib/python3.6/dist-packages pip \n",
"cvxopt 1.2.5 /usr/local/lib/python3.6/dist-packages pip \n",
"cvxpy 1.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"cycler 0.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"cymem 2.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"Cython 0.29.21 /usr/local/lib/python3.6/dist-packages pip \n",
"daft 0.0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"dash 1.14.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-bootstrap-components 0.10.3 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-core-components 1.10.2 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-cytoscape 0.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-html-components 1.0.3 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-renderer 1.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dash-table 4.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dask 2.12.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dataclasses 0.8 /usr/local/lib/python3.6/dist-packages pip \n",
"datascience 0.10.6 /usr/local/lib/python3.6/dist-packages pip \n",
"debugpy 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"decorator 4.4.2 /usr/local/lib/python3.6/dist-packages pip \n",
"defusedxml 0.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"descartes 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dill 0.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"distributed 1.25.3 /usr/local/lib/python3.6/dist-packages pip \n",
"Django 3.1.5 /usr/local/lib/python3.6/dist-packages pip \n",
"dlib 19.18.0 /usr/local/lib/python3.6/dist-packages pip \n",
"dm-tree 0.1.5 /usr/local/lib/python3.6/dist-packages pip \n",
"docopt 0.6.2 /usr/local/lib/python3.6/dist-packages pip \n",
"docutils 0.16 /usr/local/lib/python3.6/dist-packages pip \n",
"docx2txt 0.8 /usr/local/lib/python3.6/dist-packages pip \n",
"dopamine-rl 1.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"earthengine-api 0.1.238 /usr/local/lib/python3.6/dist-packages pip \n",
"easydict 1.9 /usr/local/lib/python3.6/dist-packages pip \n",
"EbookLib 0.17.1 /usr/local/lib/python3.6/dist-packages pip \n",
"ecos 2.0.7.post1 /usr/local/lib/python3.6/dist-packages pip \n",
"editdistance 0.5.3 /usr/local/lib/python3.6/dist-packages pip \n",
"empyrical 0.5.5 /usr/local/lib/python3.6/dist-packages pip \n",
"en-core-web-sm 2.2.5 /usr/local/lib/python3.6/dist-packages pip \n",
"entrypoints 0.3 /usr/local/lib/python3.6/dist-packages pip \n",
"ephem 3.7.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"et-xmlfile 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"extract-msg 0.23.1 /usr/local/lib/python3.6/dist-packages pip \n",
"fa2 0.3.5 /usr/local/lib/python3.6/dist-packages pip \n",
"fancyimpute 0.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"fastai 1.0.61 /usr/local/lib/python3.6/dist-packages pip \n",
"fastdtw 0.3.4 /usr/local/lib/python3.6/dist-packages pip \n",
"fastprogress 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"fastrlock 0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"fbprophet 0.7.1 /usr/local/lib/python3.6/dist-packages \n",
"feather-format 0.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"filelock 3.0.12 /usr/local/lib/python3.6/dist-packages pip \n",
"firebase-admin 4.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"fix-yahoo-finance 0.0.22 /usr/local/lib/python3.6/dist-packages pip \n",
"Flask 1.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"Flask-Compress 1.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"flatbuffers 1.12 /usr/local/lib/python3.6/dist-packages pip \n",
"folium 0.8.3 /usr/local/lib/python3.6/dist-packages pip \n",
"future 0.16.0 /usr/local/lib/python3.6/dist-packages pip \n",
"gast 0.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"GDAL 2.2.2 /usr/lib/python3/dist-packages \n",
"gdown 3.6.4 /usr/local/lib/python3.6/dist-packages pip \n",
"gensim 3.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"geographiclib 1.50 /usr/local/lib/python3.6/dist-packages pip \n",
"geopy 1.17.0 /usr/local/lib/python3.6/dist-packages pip \n",
"getmac 0.8.2 /usr/local/lib/python3.6/dist-packages pip \n",
"gin-config 0.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"glob2 0.7 /usr/local/lib/python3.6/dist-packages pip \n",
"google 2.0.3 /usr/local/lib/python3.6/dist-packages pip \n",
"google-api-core 1.16.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-api-python-client 1.7.12 /usr/local/lib/python3.6/dist-packages pip \n",
"google-auth 1.17.2 /usr/local/lib/python3.6/dist-packages pip \n",
"google-auth-httplib2 0.0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"google-auth-oauthlib 0.4.2 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-bigquery 1.21.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-bigquery-storage 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-core 1.0.3 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-datastore 1.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-firestore 1.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-language 1.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-storage 1.18.1 /usr/local/lib/python3.6/dist-packages pip \n",
"google-cloud-translate 1.5.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-colab 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-pasta 0.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"google-resumable-media 0.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"googleapis-common-protos 1.52.0 /usr/local/lib/python3.6/dist-packages pip \n",
"googledrivedownloader 0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"graphviz 0.10.1 /usr/local/lib/python3.6/dist-packages pip \n",
"grpcio 1.32.0 /usr/local/lib/python3.6/dist-packages pip \n",
"gspread 3.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"gspread-dataframe 3.0.8 /usr/local/lib/python3.6/dist-packages pip \n",
"gym 0.17.3 /usr/local/lib/python3.6/dist-packages pip \n",
"h5py 2.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"HeapDict 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"holidays 0.10.4 /usr/local/lib/python3.6/dist-packages pip \n",
"holoviews 1.13.5 /usr/local/lib/python3.6/dist-packages pip \n",
"html5lib 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"httpimport 0.5.18 /usr/local/lib/python3.6/dist-packages pip \n",
"httplib2 0.17.4 /usr/local/lib/python3.6/dist-packages pip \n",
"httplib2shim 0.0.3 /usr/local/lib/python3.6/dist-packages pip \n",
"humanize 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"hyperopt 0.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"ideep4py 2.0.0.post3 /usr/local/lib/python3.6/dist-packages pip \n",
"idna 2.10 /usr/local/lib/python3.6/dist-packages pip \n",
"image 1.5.33 /usr/local/lib/python3.6/dist-packages pip \n",
"imageio 2.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"imagesize 1.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"IMAPClient 2.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"imbalanced-learn 0.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"imblearn 0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"imgaug 0.2.9 /usr/local/lib/python3.6/dist-packages pip \n",
"importlib-metadata 3.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"importlib-resources 4.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"imutils 0.5.3 /usr/local/lib/python3.6/dist-packages pip \n",
"inflect 2.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"inflection 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"iniconfig 1.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"intel-openmp 2021.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"intervaltree 2.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"ipykernel 4.10.1 /usr/local/lib/python3.6/dist-packages pip \n",
"ipython 5.5.0 /usr/local/lib/python3.6/dist-packages pip \n",
"ipython-autotime 0.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"ipython-genutils 0.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"ipython-sql 0.3.9 /usr/local/lib/python3.6/dist-packages pip \n",
"ipywidgets 7.6.3 /usr/local/lib/python3.6/dist-packages pip \n",
"iso3166 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"iso4217 1.6.20180829 /usr/local/lib/python3.6/dist-packages pip \n",
"itsdangerous 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"jax 0.2.7 /usr/local/lib/python3.6/dist-packages pip \n",
"jaxlib 0.1.57+cuda101 /usr/local/lib/python3.6/dist-packages pip \n",
"jdcal 1.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"jedi 0.18.0 /usr/local/lib/python3.6/dist-packages pip \n",
"jieba 0.42.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Jinja2 2.11.2 /usr/local/lib/python3.6/dist-packages pip \n",
"joblib 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"jpeg4py 0.1.4 /usr/local/lib/python3.6/dist-packages pip \n",
"jsonschema 2.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"jupyter 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"jupyter-client 5.3.5 /usr/local/lib/python3.6/dist-packages pip \n",
"jupyter-console 5.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"jupyter-core 4.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"jupyter-dash 0.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"jupyterlab-pygments 0.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"jupyterlab-widgets 1.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"kaggle 1.5.10 /usr/local/lib/python3.6/dist-packages pip \n",
"kapre 0.1.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Keras 2.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"Keras-Preprocessing 1.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"keras-vis 0.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"kiwisolver 1.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"knnimpute 0.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"korean-lunar-calendar 0.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"librosa 0.6.3 /usr/local/lib/python3.6/dist-packages pip \n",
"lightgbm 2.2.3 /usr/local/lib/python3.6/dist-packages pip \n",
"llvmlite 0.31.0 /usr/local/lib/python3.6/dist-packages pip \n",
"lmdb 0.99 /usr/local/lib/python3.6/dist-packages pip \n",
"Logbook 1.5.3 /usr/local/lib/python3.6/dist-packages pip \n",
"lru-dict 1.1.6 /usr/local/lib/python3.6/dist-packages pip \n",
"lucid 0.3.8 /usr/local/lib/python3.6/dist-packages pip \n",
"LunarCalendar 0.0.9 /usr/local/lib/python3.6/dist-packages pip \n",
"lxml 4.2.6 /usr/local/lib/python3.6/dist-packages pip \n",
"Mako 1.1.4 /usr/local/lib/python3.6/dist-packages pip \n",
"Markdown 3.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"MarkupSafe 1.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"matplotlib 3.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"matplotlib-venn 0.11.6 /usr/local/lib/python3.6/dist-packages pip \n",
"missingno 0.4.2 /usr/local/lib/python3.6/dist-packages pip \n",
"mistune 0.8.4 /usr/local/lib/python3.6/dist-packages pip \n",
"mizani 0.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"mkl 2019.0 /usr/local/lib/python3.6/dist-packages pip \n",
"mlfinlab 0.15.3 /usr/local/lib/python3.6/dist-packages pip \n",
"mlxtend 0.14.0 /usr/local/lib/python3.6/dist-packages pip \n",
"more-itertools 8.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"moviepy 0.2.3.5 /usr/local/lib/python3.6/dist-packages pip \n",
"mpmath 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"msgpack 1.0.2 /usr/local/lib/python3.6/dist-packages pip \n",
"multipledispatch 0.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"multiprocess 0.70.11.1 /usr/local/lib/python3.6/dist-packages pip \n",
"multitasking 0.0.9 /usr/local/lib/python3.6/dist-packages pip \n",
"murmurhash 1.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"music21 5.5.0 /usr/local/lib/python3.6/dist-packages pip \n",
"natsort 5.5.0 /usr/local/lib/python3.6/dist-packages pip \n",
"nbclient 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"nbconvert 5.6.1 /usr/local/lib/python3.6/dist-packages pip \n",
"nbformat 5.0.8 /usr/local/lib/python3.6/dist-packages pip \n",
"nest-asyncio 1.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"networkx 2.4 /usr/local/lib/python3.6/dist-packages pip \n",
"nibabel 3.0.2 /usr/local/lib/python3.6/dist-packages pip \n",
"nltk 3.5 /usr/local/lib/python3.6/dist-packages pip \n",
"notebook 5.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"np-utils 0.5.12.1 /usr/local/lib/python3.6/dist-packages pip \n",
"numba 0.49.1 /usr/local/lib/python3.6/dist-packages pip \n",
"numexpr 2.7.2 /usr/local/lib/python3.6/dist-packages pip \n",
"numpy 1.18.5 /usr/local/lib/python3.6/dist-packages pip \n",
"nvidia-ml-py3 7.352.0 /usr/local/lib/python3.6/dist-packages pip \n",
"oauth2client 4.1.3 /usr/local/lib/python3.6/dist-packages pip \n",
"oauthlib 3.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"okgrade 0.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"olefile 0.46 /usr/local/lib/python3.6/dist-packages pip \n",
"opencv-contrib-python 4.1.2.30 /usr/local/lib/python3.6/dist-packages pip \n",
"opencv-python 4.1.2.30 /usr/local/lib/python3.6/dist-packages pip \n",
"openpyxl 2.5.9 /usr/local/lib/python3.6/dist-packages pip \n",
"opt-einsum 3.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"osqp 0.6.2 /usr/local/lib/python3.6/dist-packages pip \n",
"packaging 20.8 /usr/local/lib/python3.6/dist-packages pip \n",
"palettable 3.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pandas 1.0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"pandas-datareader 0.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pandas-gbq 0.13.3 /usr/local/lib/python3.6/dist-packages pip \n",
"pandas-profiling 1.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pandocfilters 1.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"panel 0.9.7 /usr/local/lib/python3.6/dist-packages pip \n",
"param 1.10.1 /usr/local/lib/python3.6/dist-packages pip \n",
"parso 0.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pathlib 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"patsy 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pdfminer.six 20181108 /usr/local/lib/python3.6/dist-packages pip \n",
"pexpect 4.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pickleshare 0.7.5 /usr/local/lib/python3.6/dist-packages pip \n",
"Pillow 7.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pip 19.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pip-tools 4.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"plac 1.1.3 /usr/local/lib/python3.6/dist-packages pip \n",
"plotly 4.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"plotnine 0.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pluggy 0.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"portpicker 1.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"POT 0.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"prefetch-generator 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"preshed 3.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"prettytable 2.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"progressbar2 3.38.0 /usr/local/lib/python3.6/dist-packages pip \n",
"prometheus-client 0.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"promise 2.3 /usr/local/lib/python3.6/dist-packages pip \n",
"prompt-toolkit 1.0.18 /usr/local/lib/python3.6/dist-packages pip \n",
"protobuf 3.12.4 /usr/local/lib/python3.6/dist-packages pip \n",
"psutil 5.4.8 /usr/local/lib/python3.6/dist-packages pip \n",
"psycopg2 2.7.6.1 /usr/local/lib/python3.6/dist-packages pip \n",
"ptyprocess 0.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"py 1.10.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pyarrow 0.14.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pyasn1 0.4.8 /usr/local/lib/python3.6/dist-packages pip \n",
"pyasn1-modules 0.2.8 /usr/local/lib/python3.6/dist-packages pip \n",
"pycocotools 2.0.2 /usr/local/lib/python3.6/dist-packages pip \n",
"pycparser 2.20 /usr/local/lib/python3.6/dist-packages pip \n",
"pycryptodome 3.9.9 /usr/local/lib/python3.6/dist-packages pip \n",
"pyct 0.4.8 /usr/local/lib/python3.6/dist-packages pip \n",
"pydata-google-auth 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pydot 1.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pydot-ng 2.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pydotplus 2.0.2 /usr/local/lib/python3.6/dist-packages pip \n",
"PyDrive 1.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pyemd 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pyfolio 0.9.2 /usr/local/lib/python3.6/dist-packages pip \n",
"pyglet 1.5.0 /usr/local/lib/python3.6/dist-packages pip \n",
"Pygments 2.6.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pygobject 3.26.1 /usr/lib/python3/dist-packages \n",
"pymc3 3.7 /usr/local/lib/python3.6/dist-packages pip \n",
"PyMeeus 0.3.7 /usr/local/lib/python3.6/dist-packages pip \n",
"pymongo 3.11.2 /usr/local/lib/python3.6/dist-packages pip \n",
"pymystem3 0.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"PyOpenGL 3.1.5 /usr/local/lib/python3.6/dist-packages pip \n",
"pyparsing 2.4.7 /usr/local/lib/python3.6/dist-packages pip \n",
"pyrsistent 0.17.3 /usr/local/lib/python3.6/dist-packages pip \n",
"pysndfile 1.3.8 /usr/local/lib/python3.6/dist-packages pip \n",
"PySocks 1.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pystan 2.19.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"pytest 3.6.4 /usr/local/lib/python3.6/dist-packages pip \n",
"python-apt 1.6.5+ubuntu0.5 /usr/lib/python3/dist-packages \n",
"python-chess 0.23.11 /usr/local/lib/python3.6/dist-packages pip \n",
"python-dateutil 2.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"python-editor 1.0.4 /usr/local/lib/python3.6/dist-packages pip \n",
"python-interface 1.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"python-louvain 0.15 /usr/local/lib/python3.6/dist-packages pip \n",
"python-pptx 0.6.18 /usr/local/lib/python3.6/dist-packages pip \n",
"python-slugify 4.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"python-utils 2.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"pytz 2020.5 /usr/local/lib/python3.6/dist-packages pip \n",
"pyviz-comms 2.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"PyWavelets 1.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"PyYAML 3.13 /usr/local/lib/python3.6/dist-packages pip \n",
"pyzmq 20.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"qdldl 0.1.5.post0 /usr/local/lib/python3.6/dist-packages pip \n",
"qtconsole 5.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"QtPy 1.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"Quandl 3.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"regex 2019.12.20 /usr/local/lib/python3.6/dist-packages pip \n",
"requests 2.25.1 /usr/local/lib/python3.6/dist-packages pip \n",
"requests-oauthlib 1.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"resampy 0.2.2 /usr/local/lib/python3.6/dist-packages pip \n",
"retrying 1.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"rpy2 3.2.7 /usr/local/lib/python3.6/dist-packages pip \n",
"rsa 4.6 /usr/local/lib/python3.6/dist-packages pip \n",
"sacremoses 0.0.43 /usr/local/lib/python3.6/dist-packages pip \n",
"scikit-image 0.16.2 /usr/local/lib/python3.6/dist-packages pip \n",
"scikit-learn 0.23.1 /usr/local/lib/python3.6/dist-packages pip \n",
"scikit-plot 0.3.7 /usr/local/lib/python3.6/dist-packages pip \n",
"scipy 1.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"screen-resolution-extra 0.0.0 /usr/lib/python3/dist-packages \n",
"scs 2.1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"seaborn 0.11.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Send2Trash 1.5.0 /usr/local/lib/python3.6/dist-packages pip \n",
"setuptools 51.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"setuptools-git 1.2 /usr/local/lib/python3.6/dist-packages pip \n",
"Shapely 1.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"simplegeneric 0.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"six 1.12.0 /usr/local/lib/python3.6/dist-packages pip \n",
"sklearn 0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"sklearn-pandas 1.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"smart-open 4.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"snowballstemmer 2.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"sortedcontainers 2.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"soupsieve 2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"spacy 2.2.4 /usr/local/lib/python3.6/dist-packages pip \n",
"SpeechRecognition 3.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Sphinx 1.8.5 /usr/local/lib/python3.6/dist-packages pip \n",
"sphinxcontrib-serializinghtml 1.1.4 /usr/local/lib/python3.6/dist-packages pip \n",
"sphinxcontrib-websupport 1.2.4 /usr/local/lib/python3.6/dist-packages pip \n",
"SQLAlchemy 1.3.22 /usr/local/lib/python3.6/dist-packages pip \n",
"sqlparse 0.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"srsly 1.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"statsmodels 0.11.1 /usr/local/lib/python3.6/dist-packages pip \n",
"sympy 1.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tables 3.4.4 /usr/local/lib/python3.6/dist-packages pip \n",
"tabulate 0.8.7 /usr/local/lib/python3.6/dist-packages pip \n",
"tblib 1.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorboard 2.2.2 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorboard-plugin-wit 1.7.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorboardcolab 0.0.22 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow 2.2.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-addons 0.8.3 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-datasets 4.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-estimator 2.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-gcs-config 2.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-hub 0.11.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-metadata 0.26.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-privacy 0.2.2 /usr/local/lib/python3.6/dist-packages pip \n",
"tensorflow-probability 0.12.1 /usr/local/lib/python3.6/dist-packages pip \n",
"termcolor 1.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"terminado 0.9.2 /usr/local/lib/python3.6/dist-packages pip \n",
"testpath 0.4.4 /usr/local/lib/python3.6/dist-packages pip \n",
"text-unidecode 1.3 /usr/local/lib/python3.6/dist-packages pip \n",
"textblob 0.15.3 /usr/local/lib/python3.6/dist-packages pip \n",
"textgenrnn 1.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"textract 1.6.3 /usr/local/lib/python3.6/dist-packages pip \n",
"Theano 1.0.5 /usr/local/lib/python3.6/dist-packages pip \n",
"thinc 7.4.0 /usr/local/lib/python3.6/dist-packages pip \n",
"threadpoolctl 2.1.0 /usr/local/lib/python3.6/dist-packages pip \n",
"tifffile 2020.9.3 /usr/local/lib/python3.6/dist-packages pip \n",
"tokenizers 0.9.4 /usr/local/lib/python3.6/dist-packages pip \n",
"toml 0.10.2 /usr/local/lib/python3.6/dist-packages pip \n",
"toolz 0.11.1 /usr/local/lib/python3.6/dist-packages pip \n",
"torch 1.7.1+cu101 /usr/local/lib/python3.6/dist-packages pip \n",
"torchsummary 1.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"torchtext 0.3.1 /usr/local/lib/python3.6/dist-packages pip \n",
"torchvision 0.8.2+cu101 /usr/local/lib/python3.6/dist-packages pip \n",
"tornado 5.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"tqdm 4.56.0 /usr/local/lib/python3.6/dist-packages pip \n",
"trading-calendars 2.1.1 /usr/local/lib/python3.6/dist-packages pip \n",
"traitlets 4.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"transformers 4.2.2 /usr/local/lib/python3.6/dist-packages pip \n",
"tweepy 3.6.0 /usr/local/lib/python3.6/dist-packages pip \n",
"typeguard 2.7.1 /usr/local/lib/python3.6/dist-packages pip \n",
"typing-extensions 3.7.4.3 /usr/local/lib/python3.6/dist-packages pip \n",
"tzlocal 1.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"umap-learn 0.4.6 /usr/local/lib/python3.6/dist-packages pip \n",
"uritemplate 3.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"urllib3 1.24.3 /usr/local/lib/python3.6/dist-packages pip \n",
"vega-datasets 0.9.0 /usr/local/lib/python3.6/dist-packages pip \n",
"wasabi 0.8.0 /usr/local/lib/python3.6/dist-packages pip \n",
"wcwidth 0.2.5 /usr/local/lib/python3.6/dist-packages pip \n",
"webencodings 0.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"Werkzeug 1.0.1 /usr/local/lib/python3.6/dist-packages pip \n",
"wheel 0.36.2 /usr/local/lib/python3.6/dist-packages pip \n",
"widgetsnbextension 3.5.1 /usr/local/lib/python3.6/dist-packages pip \n",
"wordcloud 1.8.1 /usr/local/lib/python3.6/dist-packages pip \n",
"wrapt 1.12.1 /usr/local/lib/python3.6/dist-packages pip \n",
"xarray 0.15.1 /usr/local/lib/python3.6/dist-packages pip \n",
"xgboost 1.3.3 /usr/local/lib/python3.6/dist-packages pip \n",
"xkit 0.0.0 /usr/lib/python3/dist-packages \n",
"xlrd 1.2.0 /usr/local/lib/python3.6/dist-packages pip \n",
"XlsxWriter 1.3.7 /usr/local/lib/python3.6/dist-packages pip \n",
"xlwt 1.3.0 /usr/local/lib/python3.6/dist-packages pip \n",
"yellowbrick 0.9.1 /usr/local/lib/python3.6/dist-packages pip \n",
"zict 2.0.0 /usr/local/lib/python3.6/dist-packages pip \n",
"zipline 1.4.1 /usr/local/lib/python3.6/dist-packages pip \n",
"zipp 3.4.0 /usr/local/lib/python3.6/dist-packages pip \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DfgwQISU8C0u"
},
"source": [
"#### Import Packages:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FmsNFk1Pmkzm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ec90366e-a7ec-464c-b09e-0881e2ef6523"
},
"source": [
"# Python Libraries\r\n",
"import pprint\r\n",
"import os\r\n",
"import io\r\n",
"import re\r\n",
"import pickle\r\n",
"from tqdm.notebook import tqdm\r\n",
"import logging\r\n",
"import random\r\n",
"from collections import defaultdict, Counter\r\n",
"\r\n",
"# Import Time Libraries\r\n",
"import datetime as dt\r\n",
"from datetime import datetime, timedelta\r\n",
"from dateutil.relativedelta import *\r\n",
"from pytz import timezone\r\n",
"from pytz import all_timezones_set\r\n",
"import pytz\r\n",
"import time\r\n",
"\r\n",
"# Import Data Science Libraries\r\n",
"import numpy as np\r\n",
"import pandas as pd\r\n",
"import scipy\r\n",
"import scipy.stats as ss\r\n",
"from scipy.stats import kurtosis,skew,norm\r\n",
"from scipy.optimize import minimize, least_squares\r\n",
"import statsmodels.api as smf\r\n",
"\r\n",
"# Import Optimization Libraries\r\n",
"from cvxopt import matrix, solvers\r\n",
"\r\n",
"## Import Financial Libraries\r\n",
"#import zipline\r\n",
"#import pyfolio as pf\r\n",
"#import alphalens\r\n",
"#import empyrical\r\n",
"#import mlfinlab\r\n",
"\r\n",
"# Import Visualization Libraries\r\n",
"import seaborn as sns; sns.set()\r\n",
"import matplotlib.pyplot as plt\r\n",
"from matplotlib.ticker import FuncFormatter\r\n",
"from mpl_toolkits.mplot3d import Axes3D\r\n",
"import matplotlib.ticker as ticker\r\n",
"import matplotlib.pyplot as plt\r\n",
"import matplotlib.mlab as mlab\r\n",
"import seaborn as sns; sns.set()\r\n",
"plt.style.use('fivethirtyeight')\r\n",
"\r\n",
"# Import Scikit-Learn Libraries\r\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\r\n",
"from sklearn.metrics import accuracy_score, f1_score, plot_confusion_matrix\r\n",
"from sklearn.preprocessing import normalize\r\n",
"from sklearn.pipeline import Pipeline, FeatureUnion\r\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\r\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression, Perceptron, SGDClassifier\r\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\r\n",
"from sklearn.neighbors import KNeighborsClassifier, KernelDensity\r\n",
"from sklearn.naive_bayes import GaussianNB\r\n",
"from sklearn.tree import DecisionTreeClassifier\r\n",
"from sklearn.neural_network import MLPClassifier\r\n",
"from sklearn.svm import SVC, LinearSVC\r\n",
"from sklearn import model_selection\r\n",
"from sklearn.model_selection import GridSearchCV, cross_val_score, cross_validate, StratifiedKFold, learning_curve, RandomizedSearchCV, RepeatedStratifiedKFold\r\n",
"from sklearn.impute import SimpleImputer\r\n",
"import scikitplot as skplt\r\n",
"\r\n",
"# Import nltk Libraries\r\n",
"import nltk\r\n",
"from nltk.corpus import stopwords\r\n",
"from nltk.util import ngrams\r\n",
"from nltk.tokenize import word_tokenize, sent_tokenize\r\n",
"\r\n",
"# Import Pytorch Libraries\r\n",
"import torch\r\n",
"from torch import nn, optim\r\n",
"import torch.nn.functional as F\r\n",
"from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)\r\n",
"from torch.autograd import Variable\r\n",
"from torch.optim import Adam, AdamW\r\n",
"\r\n",
"# Import XGBoost Libraries\r\n",
"import xgboost as xgb\r\n",
"\r\n",
"# Import Utilities\r\n",
"import pandas_datareader.data as web\r\n",
"import pickle\r\n",
"import urllib.request\r\n",
"import zipfile\r\n"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" from pandas.util.testing import assert_frame_equal\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4YXab6-a7_Tx"
},
"source": [
"#### Settings"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9Suzx8xo77ui",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "36c86462-3278-48ed-9f92-f014cf328cff"
},
"source": [
"# General:\r\n",
"import warnings\r\n",
"warnings.filterwarnings('ignore')\r\n",
"%matplotlib inline\r\n",
"get_ipython().run_line_magic('matplotlib', 'inline')\r\n",
"\r\n",
"# Get Execution Time on Every Cell:\r\n",
"%load_ext autotime\r\n",
"\r\n",
"# Other:\r\n",
"#%load_ext zipline\r\n",
"#%reload_ext zipline\r\n",
"#!zipline ingest\r\n"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 89.6 µs (started: 2021-01-27 10:53:01 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NQElCguLaqnl",
"outputId": "c80508fe-ecfe-4493-e2d5-c20f10ce773f"
},
"source": [
"# Set display preference (Optional)\r\n",
"plt.rcParams['figure.figsize'] = (18,9)\r\n",
"plt.style.use('fivethirtyeight')\r\n",
"\r\n",
"# Set Seaborn Style\r\n",
"#sns.set(style='white', context='notebook', palette='deep')\r\n",
"\r\n",
"# Set Pandas output options\r\n",
"pd.options.display.max_rows = 30\r\n",
"pd.options.display.max_seq_items = 100\r\n",
"pd.set_option('display.max_colwidth', 100)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 3.02 ms (started: 2021-01-27 10:53:04 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 134
},
"id": "AcBaJb9d-WbN",
"outputId": "4d2acff4-8874-4b64-fbae-17ce3aebd1ff"
},
"source": [
"sys.path.append('/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/alphalens/')\r\n",
"sys.path.append('/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/empyrical/')\r\n",
"sys.path.append('/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/mlfinlab/')\r\n",
"sys.path.append('/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/pyfolio/')\r\n",
"sys.path.append('/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/zipline/')\r\n",
"path = pd.DataFrame(sys.path)\r\n",
"path.T\r\n"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <td>/usr/local/lib/python3.6/dist-packages/IPython/extensions</td>\n",
" <td>/root/.ipython</td>\n",
" <td>/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/alphalens/</td>\n",
" <td>/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/empyrical/</td>\n",
" <td>/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/mlfinlab/</td>\n",
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"</table>\n",
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" 0 ... 13\n",
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"\n",
"[1 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
},
{
"output_type": "stream",
"text": [
"time: 41.2 ms (started: 2021-01-27 10:53:05 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xUEvP2ONZHZA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b80a1152-3660-46c9-83ae-742d5160b8d3"
},
"source": [
"# Finalize nltk setup:\r\n",
"nltk.download('stopwords')\r\n",
"nltk.download('punkt')\r\n",
"nltk.download('wordnet')\r\n",
"\r\n",
"stop = set(stopwords.words('english'))\r\n",
"\r\n",
"# Test & Activate/Deactivate Pretty printing\r\n",
"pprint.pprint(sys.path)\r\n",
"%pprint\r\n"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"['',\n",
" '/env/python',\n",
" '/usr/lib/python36.zip',\n",
" '/usr/lib/python3.6',\n",
" '/usr/lib/python3.6/lib-dynload',\n",
" '/usr/local/lib/python3.6/dist-packages',\n",
" '/usr/lib/python3/dist-packages',\n",
" '/usr/local/lib/python3.6/dist-packages/IPython/extensions',\n",
" '/root/.ipython',\n",
" '/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/alphalens/',\n",
" '/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/empyrical/',\n",
" '/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/mlfinlab/',\n",
" '/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/pyfolio/',\n",
" '/content/drive/MyDrive/Colab Notebooks/global_macro/src/packages/zipline/']\n",
"Pretty printing has been turned OFF\n",
"time: 130 ms (started: 2021-01-27 10:53:09 -05:00)\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n",
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QojZiqKbhCbB",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d8ace112-e239-4ebe-f106-f82daabbfbb4"
},
"source": [
"## Use TPU\r\n",
"#if IN_COLAB:\r\n",
"# assert os.environ['COLAB_TPU_ADDR'], 'Select TPU: Runtime > Change runtime type > Hardware accelerator'\r\n",
"# VERSION = '20200220'\r\n",
"# !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\r\n",
"# !python pytorch-xla-env-setup.py --version $VERSION"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 930 µs (started: 2021-01-27 10:53:11 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9p61Q8h8hn8Q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e66cf562-cc1f-4785-bb30-4b9501bb7046"
},
"source": [
"## Use GPU Runtime:\n",
"#if IN_COLAB:\n",
"# if torch.cuda.is_available():\n",
"# torch.cuda.get_device_name(0)\n",
"# gpu_info = !nvidia-smi\n",
"# gpu_info = '\\n'.join(gpu_info)\n",
"# print(gpu_info)\n",
"# else:\n",
"# print('Select the Runtime > Change runtime type menu to enable a GPU accelerator, and then re-execute this cell.')\n",
"# #os.kill(os.getpid(), 9) \n"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"Select the Runtime > Change runtime type menu to enable a GPU accelerator, and then re-execute this cell.\n",
"time: 33.3 ms (started: 2021-01-27 10:53:18 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9v6gt8lI3DeM",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7a37d413-4301-4927-dbf3-567f4df7a922"
},
"source": [
"# Set logger\n",
"logger = logging.getLogger('mylogger')\n",
"logger.setLevel(logging.DEBUG)\n",
"timestamp = time.strftime('%Y.%m.%d_%H.%M.%S', time.localtime())\n",
"fh = logging.FileHandler('log_model.txt')\n",
"fh.setLevel(logging.DEBUG)\n",
"ch = logging.StreamHandler()\n",
"ch.setLevel(logging.DEBUG)\n",
"formatter = logging.Formatter('[%(asctime)s][%(levelname)s] ## %(message)s')\n",
"fh.setFormatter(formatter)\n",
"ch.setFormatter(formatter)\n",
"logger.addHandler(fh)\n",
"logger.addHandler(ch)"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 6.52 ms (started: 2021-01-27 10:53:32 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1cu02J7ydU8F",
"outputId": "bb4f94c9-4fba-4162-97b1-79d60197d0b6"
},
"source": [
"# Set Random Seed\r\n",
"random.seed(42)\r\n",
"np.random.seed(42)\r\n",
"torch.manual_seed(42)\r\n",
"torch.cuda.manual_seed(42)\r\n",
"rand_seed = 42"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 4.37 ms (started: 2021-01-27 10:53:37 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bwCg4etgkxWO"
},
"source": [
"## **Definitions**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JGwTc0EDpMim"
},
"source": [
"### General Utilities"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rLdY3Kk_kzdl",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "77bedcdd-6ce7-41c3-90cb-84aff9393baa"
},
"source": [
"# Generate random weights that sum up to 1:\r\n",
"def weights_randn(n):\r\n",
" k = np.random.rand(n)\r\n",
" return k / sum(k)\r\n",
"\r\n",
"def check_array(arr):\r\n",
" if len(np.array(arr).shape)==1:\r\n",
" days = len(np.array(arr))\r\n",
" cols = 1\r\n",
" elif len(np.array(arr).shape)==2:\r\n",
" days = np.array(arr).shape[0]\r\n",
" cols = np.array(arr).shape[1]\r\n",
" else:\r\n",
" raise TypeError('Input should be 1-D np.array or pd.Series or a 2-D np.array.')\r\n",
" return cols,days\r\n",
"\r\n",
"def var_w(rho, lamb, Q, wp, beta_im_ ,beta_T):\r\n",
" def constrain1(w):\r\n",
" return np.dot(beta_im_,w)-beta_T\r\n",
"\r\n",
" def constrain2(w):\r\n",
" return np.sum(w)-1\r\n",
"\r\n",
" cons = [{'type':'eq', 'fun': constrain1},\r\n",
" {'type':'eq', 'fun': constrain2}]\r\n",
" bnds = scipy.optimize.Bounds(-2.0, 2.0, keep_feasible = True)\r\n",
"\r\n",
" def f(w):\r\n",
" return -rho.dot(w) + lamb*(w-wp).dot(Q.dot(w-wp))\r\n",
"\r\n",
" w0 = np.array([1/12]*12)\r\n",
" res = minimize(f, w0, method='SLSQP', bounds=bnds, constraints=cons,\r\n",
" tol=1e-9)\r\n",
" return res.x\r\n",
" "
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 21.3 ms (started: 2021-01-27 10:53:39 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "crt11LRQpQai"
},
"source": [
"### Data Retrieval/Processing"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jEs6rGbhpTcS",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d7118729-80b1-48ae-9f2e-f25167c9c67f"
},
"source": [
"# Save data:\r\n",
"if IN_COLAB:\r\n",
" def save_data(df, file_name, dir_name=data_dir, index_csv=True):\r\n",
" if not os.path.exists(dir_name):\r\n",
" os.mkdir(dir_name)\r\n",
" # Save results to a picke file\r\n",
" file = open(dir_name + file_name + '.pickle', 'wb')\r\n",
" pickle.dump(df, file)\r\n",
" file.close()\r\n",
" print('Successfully saved {}.pickle. in {}'.format(file_name, dir_name + file_name + '.pickle'))\r\n",
" # Save results to a csv file\r\n",
" df.to_csv(dir_name + file_name + '.csv', index=index_csv)\r\n",
" print('Successfully saved {}.csv. in {}'.format(file_name, dir_name + file_name + '.csv'))\r\n",
"\r\n",
"else:\r\n",
" def save_data(df, file_name, dir_name=data_dir, index_csv=True):\r\n",
" # Save results to a .picke file\r\n",
" file = open(dir_name + file_name + '.pickle', 'wb')\r\n",
" pickle.dump(df, file)\r\n",
" file.close()\r\n",
" print('Successfully saved {}.pickle. in {}'.format(file_name, dir_name + file_name + '.pickle'))\r\n",
" # Save results to a .csv file\r\n",
" df.to_csv(dir_name + file_name + '.csv', index=index_csv)\r\n",
" print('Successfully saved {}.csv. in {}'.format(file_name, dir_name + file_name + '.csv'))\r\n",
"\r\n",
"# Download and prepare Fama French data:\r\n",
"def fama_french(frequency, no_factors):\r\n",
" if frequency == 'annual':\r\n",
" date_format = ' %Y'\r\n",
" if no_factors == 3:\r\n",
" ff_url = 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip'\r\n",
" filename = 'F-F_Research_Data_Factors'\r\n",
" elif no_factors == 5:\r\n",
" ff_url = 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_5_Factors_2x3_CSV.zip'\r\n",
" filename = 'F-F_Research_Data_5_Factors_2x3'\r\n",
" else:\r\n",
" print('Please choose 3 or 5 for the 3- and 5-Factor Model respectively.')\r\n",
" elif frequency == 'monthly':\r\n",
" date_format = '%Y%m'\r\n",
" if no_factors == 3:\r\n",
" ff_url = 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip'\r\n",
" filename = 'F-F_Research_Data_Factors'\r\n",
" elif no_factors == 5:\r\n",
" ff_url = 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_5_Factors_2x3_CSV.zip'\r\n",
" filename = 'F-F_Research_Data_5_Factors_2x3'\r\n",
" else:\r\n",
" print('Please choose 3 or 5 for the 3- and 5-Factor Model respectively.')\r\n",
" elif frequency == 'weekly':\r\n",
" date_format = '%Y%m%d'\r\n",
" if no_factors == 3:\r\n",
" ff_url = 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_weekly_CSV.zip'\r\n",
" filename = 'F-F_Research_Data_Factors_weekly'\r\n",
" elif no_factors == 5:\r\n",
" print ('No weekly data available for the 5-Factor Model.')\r\n",
" else:\r\n",
" print('Please choose 3 or 5 for the 3- and 5-Factor Model respectively.') \r\n",
" elif frequency == 'daily':\r\n",
" date_format = '%Y%m%d'\r\n",
" if no_factors == 3:\r\n",
" ff_url = 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_daily_CSV.zip'\r\n",
" filename = 'F-F_Research_Data_Factors_daily'\r\n",
" elif no_factors == 5:\r\n",
" ff_url = 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_5_Factors_2x3_daily_CSV.zip'\r\n",
" filename = 'F-F_Research_Data_5_Factors_2x3_daily'\r\n",
" else:\r\n",
" print('Please choose 3 or 5 for the 3- and 5-Factor Model respectively.')\r\n",
" else:\r\n",
" print('Please choose between annual, monthly, weekly or daily for the frequency.')\r\n",
" \r\n",
" urllib.request.urlretrieve(ff_url, data_dir + filename + '.zip')\r\n",
" zip = zipfile.ZipFile(data_dir + filename + '.zip', 'r')\r\n",
" with zipfile.ZipFile(data_dir + filename + '.zip', 'r') as zip_ref:\r\n",
" zip_ref.extractall(data_dir)\r\n",
" zip.close()\r\n",
"\r\n",
" try:\r\n",
" ff_factors = pd.read_csv(data_dir + filename + '.CSV', skiprows = 3, index_col = 0)\r\n",
" except ValueError:\r\n",
" ff_factors = pd.read_csv(data_dir + filename + '.CSV', skiprows = 3, index_col = 0)\r\n",
" ff_row = ff_factors.isnull().any(1).nonzero()[0][0]\r\n",
" try:\r\n",
" ff_factors = pd.read_csv(data_dir + filename + '.CSV', skiprows = 3, index_col = 0)\r\n",
" except ValueError:\r\n",
" ff_factors = pd.read_csv(data_dir + filename + '.csv', skiprows = 3, index_col = 0)\r\n",
" ff_factors = ff_factors.iloc[:-1]\r\n",
" if frequency == 'annual':\r\n",
" ff_factors = ff_factors.iloc[1134:,]\r\n",
" elif frequency == 'monthly':\r\n",
" ff_factors = ff_factors.iloc[0:1131,]\r\n",
" else:\r\n",
" pass\r\n",
" ff_factors = ff_factors.dropna()\r\n",
" ff_factors.index = pd.to_datetime(ff_factors.index, format=date_format)\r\n",
" ff_factors.index = ff_factors.index + pd.offsets.MonthEnd()\r\n",
" return ff_factors\r\n"
],
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 89.3 ms (started: 2021-01-27 10:53:39 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wxnEIi7EpXSu"
},
"source": [
"### Risk/Performance Metrics"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HGi4soS0pZYJ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fe531455-1e34-4be1-b4a6-d2cbd3a84515"
},
"source": [
"\r\n",
"def PnL(arr,P = 1000000):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" ret = []\r\n",
" s = (np.array([1.0 for _ in range(cols)]))*P\r\n",
" for i in range(days):\r\n",
" s += data[i,:]*s\r\n",
" ret.append(s.copy())\r\n",
" return np.array(ret)\r\n",
"\r\n",
"def geom_mean(arr):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" return np.power(np.prod(1+data,axis=0),1/days)-1\r\n",
"\r\n",
"def MaxDrawdown(arr, n=10):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" D_ = []\r\n",
" d_ = []\r\n",
" for day in range(n,days):\r\n",
" returns = pd.DataFrame(1+data[(day-n):day,:]).cumprod(axis = 0)\r\n",
" D = returns.cummax(axis=0)-returns\r\n",
" d = np.array(D)/(np.array(D+returns))\r\n",
" D_.append(np.max(np.array(D),axis=0))\r\n",
" d_.append(np.max(np.array(d),axis = 0))\r\n",
" return np.max(np.array(D_),axis=0),np.max(np.array(d_),axis=0)\r\n",
"\r\n",
"def Volatility(arr,yearly=False):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" if yearly:\r\n",
" return np.sqrt(np.var(data,axis=0))\r\n",
" else:\r\n",
" return np.sqrt((252/days)*np.sum((data-np.mean(data,axis=0))**2,axis=0))\r\n",
"\r\n",
"def Sharpe(arr,rf,yearly = False):\r\n",
" cols,days = check_array(arr)\r\n",
" c,row = check_array(rf)\r\n",
" if not days == row:\r\n",
" raise RuntimeError('length of columns of inputs do not match (%s, %s).'% (days,row))\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" r = np.array(rf).reshape(days,1)*250\r\n",
" ER = np.power(np.product(1+data,axis=0),250/days)-np.mean(r,axis=0)-1\r\n",
" return ER/Volatility(data)\r\n",
"\r\n",
"def Kurt(arr):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" return ss.kurtosis(data,axis=0)\r\n",
"\r\n",
"def Skew(arr):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" return ss.skew(data,axis=0)\r\n",
"\r\n",
"def VaR(arr,q):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" tmp = np.sort(data,axis=0)\r\n",
" n = int(np.around((1-q)*days))\r\n",
" return -tmp[max(0,n-1),:]\r\n",
"\r\n",
"def CVaR(arr,q):\r\n",
" cols,days = check_array(arr)\r\n",
" data = np.array(arr).reshape(days, cols)\r\n",
" tmp = np.sort(data,axis=0)\r\n",
" # print(tmp)\r\n",
" n = int(np.around((1 - q) * days))\r\n",
" return np.mean(-tmp[0:max(0, n - 1),:],axis=0)\r\n",
"\r\n",
"def Summary(arr,RF, q=0.99):\r\n",
" result = arr\r\n",
" cols,days = check_array(result)\r\n",
" print('Last PnL after %s: ' % days,PnL(result)[-1,:])\r\n",
" # Geometric mean\r\n",
" print('Geometric mean', geom_mean(result))\r\n",
" # min\r\n",
" print('Daily min', np.min(result, axis=0))\r\n",
" # max drawdown\r\n",
" print('max drawdown: ', MaxDrawdown(result))\r\n",
" # Vol\r\n",
" print('Volatility', Volatility(result))\r\n",
" print('Sharp ratio: ', Sharpe(result, RF))\r\n",
" print('Mean sharp: ', np.mean(Sharpe(result, RF), axis=0))\r\n",
" print('Kurt: ', Kurt(result))\r\n",
" print('Skewness: ', Skew(result))\r\n",
" print('%s VaR %s days: ' % (q,days), VaR(result,q))\r\n",
" print('%s CVaR %s days: ' % (q, days), CVaR(result, q))\r\n"
],
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 121 ms (started: 2021-01-27 10:53:39 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JxFQqPxipcV0"
},
"source": [
"### Backtesting"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QG8pd5QQpeLw",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1c3dcc78-8b9f-4ec1-fb9c-b96d5c741c7a"
},
"source": [
"def backtesting(ret_etf, ff_factors, return_period, variance_period, lamb, beta_tm):\r\n",
" port_returns = []\r\n",
" omegas = []\r\n",
" omega_p = np.array([1/12] *12)\r\n",
" look_back = max(return_period,\r\n",
" variance_period)\r\n",
" next_chang_date = look_back - 1\r\n",
" for i in range(len(ret_etf)):\r\n",
" omegas.append(omega_p)\r\n",
" today_return = np.asarray(ret_etf.iloc[i,:])\r\n",
" pr = np.dot(omega_p,today_return)\r\n",
" port_returns.append(pr)\r\n",
" if i == next_chang_date:\r\n",
" omega_p = omega(\r\n",
" ret_r = ret_etf.iloc[i+1-return_period:i+1], \r\n",
" factor_r =ff_factors.iloc[i+1-return_period:i+1],\r\n",
" return_v = ret_etf.iloc[i+1-variance_period:i+1],\r\n",
" factor_v = ff_factors.iloc[i+1-variance_period:i+1],\r\n",
" lamb_ = lamb,\r\n",
" beta_tm_ = beta_tm,\r\n",
" wp_ = omega_p)\r\n",
" next_chang_date += 5\r\n",
"\r\n",
" else:\r\n",
" continue\r\n",
"\r\n",
" return port_returns,omegas\r\n"
],
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 12.3 ms (started: 2021-01-27 10:53:40 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "08CxYOaKpfht"
},
"source": [
"### Analytics"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CRF_HfQdpjbh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "bb916647-d6fc-4402-b5f7-ab730b70be48"
},
"source": [
"def analytics(X,rf,confidenceLevel,position):\r\n",
" cum_ret_day=np.cumprod((X+1))\r\n",
" cum_ret_annual = (np.power(cum_ret_day.iloc[-1,0],1/len(X)))**250\r\n",
" arith_mean_ret_annual=np.mean(X)*250\r\n",
" geom_mean_ret_annual=(np.power(cum_ret_day.iloc[-1,0],1/len(X))-1)*250\r\n",
" min_ret_annual = np.min(X)*250\r\n",
" p_v =np.cumprod((X+1))*100\r\n",
" p_v_extend = pd.DataFrame(np.append([p_v.iloc[0,0]]*9,p_v))\r\n",
" rolling_window_max = p_v_extend.rolling(window=10).max()\r\n",
" ten_day_drawdown = float(np.min(p_v_extend/rolling_window_max-1)[0])\r\n",
" vol_annual=np.std(X)*np.sqrt(250)\r\n",
" ratio_annual=(arith_mean_ret_annual-rf)/vol_annual\r\n",
" kurt_annual=kurtosis(X*250)\r\n",
" skew_annual=skew(X*250)\r\n",
" kurt_day=kurtosis(X)\r\n",
" skew_day=skew(X)\r\n",
" z=norm.ppf(1-confidenceLevel)\r\n",
" t=z+((1/6)*(z**2-1)*skew_day)+((1/24)*(z**3-3*z))*kurt_day-((1/36)*(2*z**3-5*z)*(skew_day**2))\r\n",
" mVaR= position*(np.mean(X)+t*np.std(X))*np.sqrt(250)\r\n",
" alpha=norm.ppf(1-confidenceLevel, np.mean(X), np.std(X))\r\n",
" VaR= position*(alpha)\r\n",
" VaR_annual=VaR*np.sqrt(250)\r\n",
" CVaR = position*np.mean(X[X<=np.quantile(X,1-confidenceLevel)])[0]*np.sqrt(250)\r\n",
" df=pd.DataFrame([\r\n",
" cum_ret_annual,\r\n",
" arith_mean_ret_annual[0],\r\n",
" geom_mean_ret_annual,min_ret_annual[0],\r\n",
" ten_day_drawdown,vol_annual[0],\r\n",
" ratio_annual[0],\r\n",
" kurt_annual[0],\r\n",
" skew_annual[0],\r\n",
" mVaR[0],\r\n",
" VaR[0],\r\n",
" VaR_annual[0],\r\n",
" CVaR],\r\n",
" index=['Cumulative Returns (Annual)',\r\n",
" 'Arithmetic Mean Returns (Annual)',\r\n",
" 'Geometric Mean Returns (Annual)',\r\n",
" 'Minimum Return (Annual)',\r\n",
" 'Max 10-day Drawdown',\r\n",
" 'Volatility',\r\n",
" 'Sharpe Ratio (Annual)',\r\n",
" 'Kurtosis (Annual)',\r\n",
" 'Skew (Annual)',\r\n",
" 'mVaR (Annual)',\r\n",
" 'VaR (Daily)',\r\n",
" 'VaR (Annual)',\r\n",
" 'CVaR (Annual)'],\r\n",
" columns=['result'])\r\n",
" return df\r\n"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 24.7 ms (started: 2021-01-27 10:53:40 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kcLeAaAmpoyy",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "72d92bf4-db8e-4018-9923-41dd1ef12c2d"
},
"source": [
"def omega(ret_r, factor_r, return_v, factor_v, lamb_, beta_tm_, wp_):\r\n",
" rf = np.asarray(factor_r['RF'])\r\n",
" rM_rf = np.asarray(factor_r['Mkt-RF'])\r\n",
" rSMB = np.asarray(factor_r['SMB'])\r\n",
" rHML = np.asarray(factor_r['HML'])\r\n",
" SPY = np.asarray(ret_r['SPY'])\r\n",
" ri = np.asarray(ret_r)\r\n",
" var_market = np.var(SPY,ddof=1)\r\n",
" beta_im = np.array([0.0]*12)\r\n",
" for i in range (12):\r\n",
" temp = np.cov(ri[:,i],SPY,ddof=1)\r\n",
" beta_im[i] = temp[0,1] / var_market\r\n",
" Ri = ri - rf.reshape(-1,1)\r\n",
" f = np.array([rM_rf, rSMB, rHML])\r\n",
" F = f.T\r\n",
" lr = LinearRegression().fit(F, Ri)\r\n",
" alpha = lr.intercept_\r\n",
" B = lr.coef_\r\n",
" ft = f[:,-1]\r\n",
" rho_r = alpha + B.dot(ft) + rf[-1]\r\n",
"\r\n",
" rf_v = np.asarray(factor_v['RF'])\r\n",
" rM_rf_v = np.asarray(factor_v['Mkt-RF'])\r\n",
" rSMB_v = np.asarray(factor_v['SMB'])\r\n",
" rHML_v = np.asarray(factor_v['HML'])\r\n",
" SPY_v = np.asarray(return_v['SPY'])\r\n",
" ri_v = np.asarray(return_v)\r\n",
" var_market_v = np.var(SPY_v,ddof=1)\r\n",
" beta_im_v = np.array([0.0]*12)\r\n",
" for i in range (12):\r\n",
" temp_v = np.cov(ri_v[:,i],SPY_v,ddof=1)\r\n",
" beta_im_v[i] = temp_v[0,1] / var_market_v\r\n",
" Ri_v = ri_v - rf_v.reshape(-1,1)\r\n",
" f_v = np.array([rM_rf_v, rSMB_v, rHML_v])\r\n",
" F_v = f_v.T\r\n",
" lr_v = LinearRegression().fit(F_v, Ri_v)\r\n",
" alpha_v = lr_v.intercept_\r\n",
" B_v = lr_v.coef_\r\n",
" eph_v = Ri_v.T - (alpha_v.reshape(-1,1) + B_v.dot(f_v))\r\n",
" eph2_v = np.cov(eph_v,ddof=1)\r\n",
" eph2_diag_v = np.diag(eph2_v)\r\n",
" D_v = np.diag(eph2_diag_v)\r\n",
" omega_f_v = np.cov(f_v,ddof=1)\r\n",
" cov_Rt_v = B_v.dot(omega_f_v).dot(B_v.T) + D_v\r\n",
" result = var_w(rho_r, lamb_, cov_Rt_v, wp_, beta_im_v ,beta_tm_)\r\n",
" return result\r\n"
],
"execution_count": 54,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 41.7 ms (started: 2021-01-27 10:56:26 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "clPTJ9la2lvX"
},
"source": [
"## **Data Processing**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XBeIV-ds2iJz"
},
"source": [
"### Containers"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QsClziO_ou90",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2f118b7e-64f3-43bc-d2b4-48b34d735f24"
},
"source": [
"# Data containers:\r\n",
"p_u = pd.DataFrame()\r\n",
"p_aapl = pd.DataFrame()\r\n",
"p_spy = pd.DataFrame()\r\n",
"\r\n",
"# Ticker containers:\r\n",
"u_tix = ['FXE', 'EWJ', 'GLD', 'QQQ', 'SPY', 'SHV', 'GAF', 'DBA', 'USO', 'XBI', 'ILF', 'EPP', 'FEZ']\r\n",
"aapl_tix = ['AAPL']\r\n",
"spy_tix = ['SPY']\r\n"
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 6.16 ms (started: 2021-01-27 10:53:41 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HsAKHmHu784V"
},
"source": [
"### Load Data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BonOGTKC2TS2"
},
"source": [
"#### Fama French Factors"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1ywctNo1mDBl"
},
"source": [
"A three-factor model proposed by Fama and French(1993), includes not only market excess return, but a capitalization size and book to market ratio will also be added in as influencing factors."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sPIQrBXvta23"
},
"source": [
"The random return of a given security is given by the formulas (equivalent),\r\n",
"\r\n",
"\\begin{equation}\r\n",
"\\boxed{r = r_{f}+\\beta_{1}(r_{m}-r_{f})+\\beta{2}(SMB)+\\beta_{3}(HML)+\\epsilon}\r\n",
"\\end{equation}\r\n",
"\r\n",
"\r\n",
"\\begin{equation}\r\n",
"\\boxed{R_{i}-r_{f}=\\alpha_{i}+\\beta{i}^{MKT}(R_{M}-r_{f})+\\beta_{i}^{SMB}R_{SMB}+\\beta_{i}^{HML}R_{HML}}\r\n",
"\\end{equation}\r\n",
"\r\n",
"\r\n",
"- rSMB represents small size variables minus big one\r\n",
"- rHML represents high minus low in book value to equity to book value to the market."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 103
},
"id": "A62PKJbN2IXk",
"outputId": "40d18ee8-2af6-47be-bc2c-6f51358d4b00"
},
"source": [
"# Using definition above:\r\n",
"# Fama/French 3-Factor Model:\r\n",
"'''\r\n",
"ff_3_daily = fama_french('daily', 3)\r\n",
"print('Fama/French 3-Factor Model Daily Data\\n' + str(ff_3_daily.tail(10)))\r\n",
"\r\n",
"ff_3_weekly = fama_french('weekly', 3)\r\n",
"print('Fama/French 3-Factor Model Weekly Data\\n' + str(ff_3_weekly.tail(10)))\r\n",
"\r\n",
"ff_3_monthly = fama_french('monthly',3)\r\n",
"print('Fama/French 3-Factor Model Monthly Data\\n' + str(ff_3_monthly.tail(10)))\r\n",
"\r\n",
"ff_3_annual = fama_french('annual', 3)\r\n",
"print('Fama/French 3-Factor Model Annual Data\\n' + str(ff_3_annual.tail(10)))\r\n",
"'''\r\n"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"\"\\nff_3_daily = fama_french('daily', 3)\\nprint('Fama/French 3-Factor Model Daily Data\\n' + str(ff_3_daily.tail(10)))\\n\\nff_3_weekly = fama_french('weekly', 3)\\nprint('Fama/French 3-Factor Model Weekly Data\\n' + str(ff_3_weekly.tail(10)))\\n\\nff_3_monthly = fama_french('monthly',3)\\nprint('Fama/French 3-Factor Model Monthly Data\\n' + str(ff_3_monthly.tail(10)))\\n\\nff_3_annual = fama_french('annual', 3)\\nprint('Fama/French 3-Factor Model Annual Data\\n' + str(ff_3_annual.tail(10)))\\n\""
]
},
"metadata": {
"tags": []
},
"execution_count": 27
},
{
"output_type": "stream",
"text": [
"time: 3.98 ms (started: 2021-01-27 10:53:45 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cKsJzrax7_HD",
"outputId": "132fd2a0-bce6-4d2d-e50f-1cba12ac8088"
},
"source": [
"# Manual loading:\r\n",
"# Fama/French 3-Factor Model:\r\n",
"ff_3_annual = pd.read_csv(data_dir + 'F-F_Research_Data_Factors.CSV', skiprows = 3, index_col = 0)\r\n",
"ff_3_annual = ff_3_annual.iloc[:-1]\r\n",
"ff_3_annual = ff_3_annual.iloc[1134:,]\r\n",
"ff_3_annual.index = ff_3_annual.index.map(lambda h: ' '.join(h).replace(' ', ''))\r\n",
"ff_3_annual.index = pd.to_datetime(ff_3_annual.index, format='%Y')\r\n",
"ff_3_annual = ff_3_annual.dropna()\r\n",
"#ff_3_annual = ff_3_annual/100\r\n",
"print('Fama/French 3-Factor Model Annual Data\\n' + str(ff_3_annual.head(10)))\r\n",
"\r\n",
"ff_3_monthly = pd.read_csv(data_dir + 'F-F_Research_Data_Factors.CSV', skiprows = 3, index_col = 0)\r\n",
"ff_3_monthly = ff_3_monthly.iloc[0:1131,]\r\n",
"ff_3_monthly = ff_3_monthly.dropna()\r\n",
"ff_3_monthly.index = pd.to_datetime(ff_3_monthly.index, format= '%Y%m')\r\n",
"#ff_3_monthly = ff_3_monthly/100\r\n",
"print('Fama/French 3-Factor Model Monthly Data\\n' + str(ff_3_monthly.head(10)))\r\n",
"\r\n",
"ff_3_weekly = pd.read_csv(data_dir + 'F-F_Research_Data_Factors_weekly.csv', skiprows = 3, index_col = 0)\r\n",
"ff_3_weekly = ff_3_weekly.dropna()\r\n",
"ff_3_weekly.index = pd.to_datetime(ff_3_weekly.index, format= '%Y%m%d')\r\n",
"ff_3_weekly = ff_3_weekly/100\r\n",
"print('Fama/French 3-Factor Model Weekly Data\\n' + str(ff_3_weekly.head(10)) + '\\n')\r\n",
"\r\n",
"ff_3_daily = pd.read_csv(data_dir + 'F-F_Research_Data_Factors_daily.csv', skiprows = 3, index_col = 0)\r\n",
"ff_3_daily = ff_3_daily.dropna()\r\n",
"ff_3_daily.index = pd.to_datetime(ff_3_daily.index, format= '%Y%m%d')\r\n",
"ff_3_daily = ff_3_daily/100\r\n",
"print('Fama/French 3-Factor Model Daily Data\\n' + str(ff_3_daily.head(10)) + '\\n')\r\n",
"\r\n",
"# Fama/French 5-Factor Model:\r\n",
"ff_5_annual = pd.read_csv(data_dir + 'F-F_Research_Data_5_Factors_2x3.csv', skiprows = 3, index_col = 0)\r\n",
"ff_5_annual = ff_5_annual.iloc[690:,]\r\n",
"ff_5_annual.index = ff_5_annual.index.map(lambda h: ' '.join(h).replace(' ', ''))\r\n",
"ff_5_annual.index = pd.to_datetime(ff_5_annual.index, format='%Y')\r\n",
"ff_5_annual = ff_5_annual.dropna()\r\n",
"#ff_5_annual = ff_5_annual/100\r\n",
"print('Fama/French 5-Factor Model Annual Data\\n' + str(ff_5_annual.head(10)) + '\\n')\r\n",
"\r\n",
"ff_5_monthly = pd.read_csv(data_dir + 'F-F_Research_Data_5_Factors_2x3.csv', skiprows = 3, index_col = 0)\r\n",
"ff_5_monthly = ff_5_monthly.iloc[:688,]\r\n",
"ff_5_monthly = ff_5_monthly.dropna()\r\n",
"ff_5_monthly.index = pd.to_datetime(ff_5_monthly.index, format='%Y%m')\r\n",
"#ff_5_monthly = ff_5_monthly/100\r\n",
"print('Fama/French 5-Factor Model Monthly Data\\n' + str(ff_5_monthly.tail(10)) + '\\n')\r\n",
"\r\n",
"ff_5_daily = pd.read_csv(data_dir + 'F-F_Research_Data_5_Factors_2x3_daily.csv', skiprows = 3, index_col = 0)\r\n",
"ff_5_daily = ff_5_daily.dropna()\r\n",
"ff_5_daily.index = pd.to_datetime(ff_5_daily.index, format='%Y%m%d')\r\n",
"ff_5_daily = ff_5_daily/100\r\n",
"print('Fama/French 5-Factor Model Daily Data\\n' + str(ff_5_daily.head(10)) + '\\n')\r\n"
],
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": [
"Fama/French 3-Factor Model Annual Data\n",
" Mkt-RF SMB HML RF\n",
"1927-01-01 29.47 -2.46 -3.75 3.12\n",
"1928-01-01 35.39 4.41 -5.83 3.56\n",
"1929-01-01 -19.54 -30.78 11.96 4.75\n",
"1930-01-01 -31.23 -5.19 -12.29 2.41\n",
"1931-01-01 -45.11 3.51 -14.32 1.07\n",
"1932-01-01 -9.39 4.91 10.49 0.96\n",
"1933-01-01 57.05 48.86 28.15 0.30\n",
"1934-01-01 3.02 25.43 -27.38 0.16\n",
"1935-01-01 44.96 9.99 9.78 0.17\n",
"1936-01-01 32.07 17.89 35.86 0.18\n",
"Fama/French 3-Factor Model Monthly Data\n",
" Mkt-RF SMB HML RF\n",
"1926-07-01 2.96 -2.30 -2.87 0.22\n",
"1926-08-01 2.64 -1.40 4.19 0.25\n",
"1926-09-01 0.36 -1.32 0.01 0.23\n",
"1926-10-01 -3.24 0.04 0.51 0.32\n",
"1926-11-01 2.53 -0.20 -0.35 0.31\n",
"1926-12-01 2.62 -0.04 -0.02 0.28\n",
"1927-01-01 -0.06 -0.56 4.83 0.25\n",
"1927-02-01 4.18 -0.10 3.17 0.26\n",
"1927-03-01 0.13 -1.60 -2.67 0.30\n",
"1927-04-01 0.46 0.43 0.60 0.25\n",
"Fama/French 3-Factor Model Weekly Data\n",
" Mkt-RF SMB HML RF\n",
"1926-07-02 0.0160 -0.0057 -0.0090 0.00056\n",
"1926-07-10 0.0036 -0.0086 0.0027 0.00056\n",
"1926-07-17 0.0101 0.0083 -0.0184 0.00056\n",
"1926-07-24 -0.0205 0.0015 -0.0025 0.00056\n",
"1926-07-31 0.0304 -0.0186 -0.0085 0.00056\n",
"1926-08-07 0.0201 0.0008 0.0053 0.00063\n",
"1926-08-14 0.0033 -0.0066 0.0076 0.00063\n",
"1926-08-21 -0.0111 0.0026 0.0195 0.00063\n",
"1926-08-28 0.0053 0.0007 0.0084 0.00063\n",
"1926-09-03 0.0187 -0.0041 0.0055 0.00057\n",
"\n",
"Fama/French 3-Factor Model Daily Data\n",
" Mkt-RF SMB HML RF\n",
"1926-07-01 0.0010 -0.0024 -0.0028 0.00009\n",
"1926-07-02 0.0045 -0.0032 -0.0008 0.00009\n",
"1926-07-06 0.0017 0.0027 -0.0035 0.00009\n",
"1926-07-07 0.0009 -0.0059 0.0003 0.00009\n",
"1926-07-08 0.0021 -0.0036 0.0015 0.00009\n",
"1926-07-09 -0.0071 0.0044 0.0056 0.00009\n",
"1926-07-10 0.0062 -0.0050 -0.0015 0.00009\n",
"1926-07-12 0.0004 0.0003 0.0054 0.00009\n",
"1926-07-13 0.0048 -0.0026 -0.0023 0.00009\n",
"1926-07-14 0.0004 0.0009 -0.0048 0.00009\n",
"\n",
"Fama/French 5-Factor Model Annual Data\n",
" Mkt-RF SMB HML RMW CMA RF\n",
"1964-01-01 12.54 0.33 9.86 -2.99 6.80 3.54\n",
"1965-01-01 10.52 24.41 7.36 -0.79 -3.17 3.93\n",
"1966-01-01 -13.51 2.15 -0.68 -0.12 -0.34 4.76\n",
"1967-01-01 24.49 50.40 -8.58 7.53 -15.04 4.21\n",
"1968-01-01 8.79 26.32 18.49 -12.84 16.25 5.21\n",
"1969-01-01 -17.54 -14.06 -9.81 11.77 -4.14 6.58\n",
"1970-01-01 -6.49 -12.36 22.34 -2.65 24.45 6.52\n",
"1971-01-01 11.78 5.58 -11.29 10.16 -5.86 4.39\n",
"1972-01-01 13.05 -11.43 1.75 7.99 -3.05 3.84\n",
"1973-01-01 -26.19 -20.00 18.08 -9.03 6.66 6.93\n",
"\n",
"Fama/French 5-Factor Model Monthly Data\n",
" Mkt-RF SMB HML RMW CMA RF\n",
"2020-01-01 -0.11 -4.41 -6.30 -1.36 -2.34 0.13\n",
"2020-02-01 -8.13 -0.04 -3.96 -1.61 -2.49 0.12\n",
"2020-03-01 -13.38 -8.40 -14.11 -1.38 1.21 0.12\n",
"2020-04-01 13.65 2.79 -1.35 2.51 -1.03 0.00\n",
"2020-05-01 5.58 1.92 -4.95 0.71 -3.28 0.01\n",
"2020-06-01 2.46 1.94 -2.22 0.04 0.34 0.01\n",
"2020-07-01 5.77 -3.03 -1.31 0.55 1.06 0.01\n",
"2020-08-01 7.63 -0.94 -2.95 4.27 -1.44 0.01\n",
"2020-09-01 -3.63 0.07 -2.56 -1.15 -1.77 0.01\n",
"2020-10-01 -2.10 4.76 3.88 -0.60 -0.53 0.01\n",
"\n",
"Fama/French 5-Factor Model Daily Data\n",
" Mkt-RF SMB HML RMW CMA RF\n",
"1963-07-01 -0.0067 0.0000 -0.0032 -0.0001 0.0015 0.00012\n",
"1963-07-02 0.0079 -0.0027 0.0027 -0.0007 -0.0019 0.00012\n",
"1963-07-03 0.0063 -0.0017 -0.0009 0.0017 -0.0033 0.00012\n",
"1963-07-05 0.0040 0.0008 -0.0028 0.0008 -0.0033 0.00012\n",
"1963-07-08 -0.0063 0.0004 -0.0018 -0.0029 0.0013 0.00012\n",
"1963-07-09 0.0045 0.0000 0.0010 0.0014 -0.0004 0.00012\n",
"1963-07-10 -0.0018 0.0021 0.0001 0.0006 -0.0007 0.00012\n",
"1963-07-11 -0.0016 0.0014 -0.0030 -0.0006 0.0005 0.00012\n",
"1963-07-12 -0.0012 0.0002 -0.0011 0.0012 0.0004 0.00012\n",
"1963-07-15 -0.0062 0.0007 -0.0003 0.0017 -0.0006 0.00012\n",
"\n",
"time: 4.01 s (started: 2021-01-27 10:53:46 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mZrqXZeMRAFy",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "57e577da-1a60-4a2d-9932-f728da9d1f43"
},
"source": [
"# Last date of time series data must match that of the Fama/French data:\r\n",
"last_datapoint = str(ff_3_daily.index[-1].strftime('%m/%d/%Y'))\r\n",
"print('Last Date for Fama/French data: ' + last_datapoint)\r\n"
],
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"text": [
"Last Date for Fama/French data: 10/30/2020\n",
"time: 2.36 ms (started: 2021-01-27 10:54:05 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2bfHGD2g2en7"
},
"source": [
"#### Historical Time Series"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Uv5LLcsKbzpJ"
},
"source": [
"The following ETFs represent the investment Universe of our portfolios. They range from the S&P 500 to ETFs representing all continents such as Europe, Asia and Africa and asset types such as bonds, stocks, and commodities.\r\n",
"\r\n",
"1. CurrencyShares Euro Trust (FXE)\r\n",
"2. iShares MSCI Japan Index (EWJ)\r\n",
"3. SPDR GOLD Trust (GLD)\r\n",
"4. Powershares NASDAQ-100 Trust (QQQ)\r\n",
"5. SPDR S&P 500 (SPY) **(THE MARKET PORTFOLIO S&P 500 IS THE BENCHMARK)**\r\n",
"6. iShares Lehman Short Treasury Bond (SHV)\r\n",
"7. PowerShares DB Agriculture Fund (DBA)\r\n",
"8. United States Oil Fund LP (USO)\r\n",
"9. SPDR S&P Biotech (XBI)\r\n",
"10. iShares S&P Latin America 40 Index (ILF)\r\n",
"11. iShares MSCI Pacific ex-Japan Index Fund (EPP)\r\n",
"12. SPDR DJ Euro Stoxx 50 (FEZ)\r\n",
"\r\n",
"From this universe, we have created portfolios by utilizing the 3-factor Fama-French model. The investment portfolio that we created is compared to the following benchmark portfolios:\r\n",
"\r\n",
"1.\tThe Market Portfolio (S&P 500) \r\n",
"\r\n",
"The dataset includes daily price data between March 1st, 2007 to October 31th, 2020. We choose this investment horizon to match the Fama-French Factor data available.\r\n",
"\r\n",
"We have used three different look-back periods, which we have defined as: A. Short Term – 60 Days B. Medium Term – 120 Days C. Long Term – 200 Days To calculate the risk-return parameters of then portfolio we have used the target Beta as -1, -0.5, 0, 0.5, 1 and 1.5. The rebalance period is kept as one week as specified in the project."
]
},
{
"cell_type": "code",
"metadata": {
"id": "pvCpi0_F0aF4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b34cfa67-57fc-4114-a9d6-df841936bf9e"
},
"source": [
"# Retrieve ETF Data:\r\n",
"start_date = '07/24/2007'\r\n",
"end_date = '10/30/2020'\r\n",
"\r\n",
"for i in u_tix:\r\n",
" tmp = web.DataReader(i, 'yahoo', start_date, end_date)\r\n",
" p_u[i] = tmp['Adj Close']\r\n",
"for i in aapl_tix:\r\n",
" tmp = web.DataReader(i, 'yahoo', start_date, end_date)\r\n",
" p_aapl[i] = tmp['Adj Close']\r\n",
"for i in spy_tix:\r\n",
" tmp = web.DataReader(i, 'yahoo', start_date, end_date)\r\n",
" p_spy[i] = tmp['Adj Close']\r\n"
],
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 14.8 s (started: 2021-01-27 10:54:08 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6I74H5cYnMlq"
},
"source": [
"### Preprocess Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tWvBZOWE0fB-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6c576d63-9511-4456-fbb6-5055f3c49096"
},
"source": [
"# Clean data:\r\n",
"p_u.isnull().sum().sum()\r\n",
"p_aapl.isnull().sum().sum()\r\n",
"p_spy.isnull().sum().sum()\r\n",
"\r\n",
"p_u = p_u.dropna()\r\n",
"p_aapl = p_aapl.dropna()\r\n",
"p_spy = p_spy.dropna()\r\n"
],
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 12.3 ms (started: 2021-01-27 10:54:23 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5ltpJwVC3CeR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "24bd074b-7633-4692-80d3-6422fe70a852"
},
"source": [
"# Sample data:\r\n",
"sys.stdout.write('\\nInvestment Universe U Time Series ({} - {}):\\n'.format(start_date, end_date) + str(p_u.tail(10)))\r\n",
"print('\\n' + str(p_u.shape))\r\n",
"sys.stdout.write('\\nAAPL Time Series ({} - {}):\\n'.format(start_date, end_date) + str(p_aapl.head(10)))\r\n",
"print('\\n' + str(p_aapl.shape))\r\n",
"sys.stdout.write('\\nSPY Time Series ({} - {}):\\n'.format(start_date, end_date) + str(p_spy.head(10)))\r\n",
"print('\\n' + str(p_spy.shape))\r\n"
],
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"text": [
"\n",
"Investment Universe U Time Series (07/24/2007 - 10/30/2020):\n",
" FXE EWJ GLD ... ILF EPP FEZ\n",
"Date ... \n",
"2017-07-11 110.889999 50.363228 115.620003 ... 28.470179 38.662926 35.557228\n",
"2017-07-12 110.440002 50.713497 116.029999 ... 29.125084 38.846035 35.813622\n",
"2017-07-13 110.300003 50.647224 115.820000 ... 29.225140 39.247135 35.932671\n",
"2017-07-14 110.940002 50.770298 116.769997 ... 29.579882 39.744156 36.134121\n",
"2017-07-17 111.050003 50.704025 117.290001 ... 29.516207 39.595921 35.978451\n",
"2017-07-18 111.779999 50.969101 118.110001 ... 29.670839 39.726723 35.914360\n",
"2017-07-19 111.370003 51.177368 118.080002 ... 29.689030 40.241177 36.024239\n",
"2017-07-20 112.470001 51.366703 118.239998 ... 29.743605 40.215019 36.271492\n",
"2017-07-21 112.849998 51.461372 119.260002 ... 29.588974 39.953434 36.015087\n",
"2017-07-24 112.559998 51.357235 119.309998 ... 29.652645 40.058067 35.978451\n",
"\n",
"[10 rows x 13 columns]\n",
"(2519, 13)\n",
"\n",
"AAPL Time Series (07/24/2007 - 10/30/2020):\n",
" AAPL\n",
"Date \n",
"2007-07-24 4.155818\n",
"2007-07-25 4.228837\n",
"2007-07-26 4.498106\n",
"2007-07-27 4.431867\n",
"2007-07-30 4.357309\n",
"2007-07-31 4.059388\n",
"2007-08-01 4.159208\n",
"2007-08-02 4.205113\n",
"2007-08-03 4.062162\n",
"2007-08-06 4.166911\n",
"(3344, 1)\n",
"\n",
"SPY Time Series (07/24/2007 - 10/30/2020):\n",
" SPY\n",
"Date \n",
"2007-07-24 114.902466\n",
"2007-07-25 115.137909\n",
"2007-07-26 112.411560\n",
"2007-07-27 110.201576\n",
"2007-07-30 111.925461\n",
"2007-07-31 110.664864\n",
"2007-08-01 111.204033\n",
"2007-08-02 112.092567\n",
"2007-08-03 109.206757\n",
"2007-08-06 111.036964\n",
"(3344, 1)\n",
"time: 36.8 ms (started: 2021-01-27 10:54:23 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kZ9i-e13s___"
},
"source": [
"### Transform Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Y7v8kL0iqjTM",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "aff74ede-f3cd-4607-ec9a-2c29d805ba9c"
},
"source": [
"# Useful variables:\r\n",
"w = weights_randn(len(u_tix))\r\n",
"print('Random weights:\\n' + str(w))\r\n",
"days = 252\r\n"
],
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": [
"Random weights:\n",
"[0.0533206 0.1353464 0.10420875 0.08522673 0.02221126 0.02220782\n",
" 0.00826895 0.12331131 0.08557645 0.10080323 0.00293047 0.13807913\n",
" 0.11850891]\n",
"time: 2.53 ms (started: 2021-01-27 10:54:23 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "AhGcOzkVqJcp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6125fdac-6634-47d3-bc52-dd0ab571f6a7"
},
"source": [
"# ETF Returns:\r\n",
"R_u = p_u.pct_change()\r\n",
"R_u = R_u.dropna()\r\n",
"print('\\nETF Portfolio Prices:\\n' + str(R_u.head(10)))\r\n",
"rho_u = np.mean(R_u, axis=0)\r\n",
"print('\\nETF Portfolio Mean Returns:\\n' + str(rho_u))\r\n"
],
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"text": [
"\n",
"ETF Portfolio Prices:\n",
" FXE EWJ GLD ... ILF EPP FEZ\n",
"Date ... \n",
"2007-07-25 -0.007149 0.000683 -0.008448 ... -0.006571 0.004929 -0.005135\n",
"2007-07-26 0.001819 -0.017735 -0.018685 ... -0.051744 -0.049493 -0.032801\n",
"2007-07-27 -0.007260 -0.009723 -0.003656 ... -0.002373 -0.026816 -0.013599\n",
"2007-07-30 0.004461 0.016830 0.005504 ... 0.026730 0.025322 0.020768\n",
"2007-07-31 -0.000801 -0.004827 0.000304 ... -0.005420 -0.006667 -0.004274\n",
"2007-08-01 -0.001016 -0.007623 0.002128 ... -0.008850 -0.013835 0.005494\n",
"2007-08-02 0.002487 -0.004888 -0.000607 ... 0.012688 0.006876 0.003757\n",
"2007-08-03 0.006349 -0.014035 0.012141 ... -0.046079 -0.032214 -0.013610\n",
"2007-08-06 -0.000218 0.014946 -0.002549 ... 0.013038 0.020670 0.014315\n",
"2007-08-07 -0.002466 -0.002104 -0.000601 ... 0.013925 0.001048 0.010032\n",
"\n",
"[10 rows x 13 columns]\n",
"\n",
"ETF Portfolio Mean Returns:\n",
"FXE -0.000040\n",
"EWJ 0.000135\n",
"GLD 0.000302\n",
"QQQ 0.000557\n",
"SPY 0.000362\n",
"SHV 0.000025\n",
"GAF 0.000151\n",
"DBA -0.000022\n",
"USO -0.000447\n",
"XBI 0.000820\n",
"ILF 0.000233\n",
"EPP 0.000286\n",
"FEZ 0.000160\n",
"dtype: float64\n",
"time: 32.7 ms (started: 2021-01-27 10:54:23 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "I1F5w0CEqK7q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "794d3fe8-1f86-411f-ea0f-4ccde6c33567"
},
"source": [
"# SPY Returns:\r\n",
"R_spy = p_spy.pct_change()\r\n",
"R_spy = R_spy.dropna()\r\n",
"print('\\nSPY Prices:\\n' + str(R_spy.head(10)))\r\n",
"rho_spy = np.mean(R_spy, axis=0)\r\n",
"print('\\nSPY Mean Return:\\n' + str(rho_spy))\r\n"
],
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"text": [
"\n",
"SPY Prices:\n",
" SPY\n",
"Date \n",
"2007-07-25 0.002049\n",
"2007-07-26 -0.023679\n",
"2007-07-27 -0.019660\n",
"2007-07-30 0.015643\n",
"2007-07-31 -0.011263\n",
"2007-08-01 0.004872\n",
"2007-08-02 0.007990\n",
"2007-08-03 -0.025745\n",
"2007-08-06 0.016759\n",
"2007-08-07 0.010669\n",
"\n",
"SPY Mean Return:\n",
"SPY 0.000399\n",
"dtype: float64\n",
"time: 14 ms (started: 2021-01-27 10:54:23 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hus3mYWTuwQ0"
},
"source": [
"### Save Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YzfYaQuI3pmL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5b4a0814-8bc6-4822-e442-4f06992e4cc2"
},
"source": [
"# Securities:\r\n",
"save_data(p_u, 'p_u')\r\n",
"save_data(p_aapl, 'p_aapl')\r\n",
"save_data(p_spy, 'p_spy')\r\n",
"save_data(R_spy,'R_spy')\r\n",
"save_data(R_u,'R_u')\r\n",
"\r\n",
"# Fama-French Processed Datasets (Archive):\r\n",
"save_data(ff_3_annual, 'ff_3_annual')\r\n",
"save_data(ff_5_annual, 'ff_5_annual')\r\n",
"save_data(ff_3_monthly, 'ff_3_monthly')\r\n",
"save_data(ff_5_monthly, 'ff_5_monthly')\r\n",
"save_data(ff_3_weekly, 'ff_3_weekly')\r\n",
"save_data(ff_3_daily, 'ff_3_daily')\r\n",
"save_data(ff_5_daily, 'ff_5_daily')\r\n"
],
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"text": [
"Successfully saved p_u.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/p_u.pickle\n",
"Successfully saved p_u.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/p_u.csv\n",
"Successfully saved p_aapl.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/p_aapl.pickle\n",
"Successfully saved p_aapl.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/p_aapl.csv\n",
"Successfully saved p_spy.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/p_spy.pickle\n",
"Successfully saved p_spy.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/p_spy.csv\n",
"Successfully saved R_spy.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_spy.pickle\n",
"Successfully saved R_spy.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_spy.csv\n",
"Successfully saved R_u.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_u.pickle\n",
"Successfully saved R_u.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_u.csv\n",
"Successfully saved ff_3_annual.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_annual.pickle\n",
"Successfully saved ff_3_annual.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_annual.csv\n",
"Successfully saved ff_5_annual.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_5_annual.pickle\n",
"Successfully saved ff_5_annual.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_5_annual.csv\n",
"Successfully saved ff_3_monthly.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_monthly.pickle\n",
"Successfully saved ff_3_monthly.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_monthly.csv\n",
"Successfully saved ff_5_monthly.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_5_monthly.pickle\n",
"Successfully saved ff_5_monthly.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_5_monthly.csv\n",
"Successfully saved ff_3_weekly.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_weekly.pickle\n",
"Successfully saved ff_3_weekly.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_weekly.csv\n",
"Successfully saved ff_3_daily.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_daily.pickle\n",
"Successfully saved ff_3_daily.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_3_daily.csv\n",
"Successfully saved ff_5_daily.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_5_daily.pickle\n",
"Successfully saved ff_5_daily.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/ff_5_daily.csv\n",
"time: 5 s (started: 2021-01-27 10:54:23 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "icXUqsDTtzQf"
},
"source": [
"### Visualize Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "93vfjkROqlbz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 508
},
"outputId": "d26403c3-97d1-4b98-b763-92e67dc68dd6"
},
"source": [
"# Visualize ETF Price Time Series:\r\n",
"fig = plt.figure(figsize=(15, 7.5))\r\n",
"ts_u = fig.add_subplot(111)\r\n",
"ts_u.plot(R_u['FXE'], linewidth=0.5, alpha=0.9, label='FXE')\r\n",
"ts_u.plot(R_u['EWJ'], linewidth=0.5, alpha=0.9, label='EWJ')\r\n",
"ts_u.plot(R_u['GLD'], linewidth=0.5, alpha=0.9, label='GLD')\r\n",
"ts_u.plot(R_u['QQQ'], linewidth=0.5, alpha=0.9, label='QQQ')\r\n",
"ts_u.plot(R_u['SPY'], linewidth=0.5, alpha=0.9, label='SPY')\r\n",
"ts_u.plot(R_u['SHV'], linewidth=0.5, alpha=0.9, label='SHV')\r\n",
"ts_u.plot(R_u['DBA'], linewidth=0.5, alpha=0.9, label='DBA')\r\n",
"ts_u.plot(R_u['USO'], linewidth=0.5, alpha=0.9, label='USO')\r\n",
"ts_u.plot(R_u['XBI'], linewidth=0.5, alpha=0.9, label='XBI')\r\n",
"ts_u.plot(R_u['ILF'], linewidth=0.5, alpha=0.9, label='ILF')\r\n",
"ts_u.plot(R_u['EPP'], linewidth=0.5, alpha=0.9, label='EPP')\r\n",
"ts_u.plot(R_u['FEZ'], linewidth=0.5, alpha=0.9, label='FEZ')\r\n",
"ts_u.set_xlabel('Year', fontweight='bold', fontsize=12)\r\n",
"ts_u.set_ylabel('Price', fontweight='bold', fontsize=12)\r\n",
"ts_u.set_title('Historical Time Series of Portfolio Securities', fontweight='bold', fontsize=15)\r\n",
"ts_u.legend(loc='upper right', fontsize=10)\r\n",
"plt.savefig(graphs_dir + 'rho_u.png', bbox_inches='tight')\r\n"
],
"execution_count": 38,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x540 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 3.53 s (started: 2021-01-27 10:54:28 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ivmAazblqXFj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 509
},
"outputId": "e6eba721-0606-4ae1-ad28-4cf9db3e7fce"
},
"source": [
"# Visualize ETF Price Time Series:\r\n",
"R_u = p_u\r\n",
"returns_u, axs = plt.subplots(4,3,figsize=(15, 7.5))\r\n",
"returns_u.suptitle('Historical Time Series of Portfolio Securities', fontweight='bold', fontsize=15)\r\n",
"axs[0,0].plot(R_u['FXE'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,0].set_title('FXE')\r\n",
"axs[0,1].plot(R_u['EWJ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,1].set_title('EWJ')\r\n",
"axs[0,2].plot(R_u['GLD'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,2].set_title('GLD')\r\n",
"axs[1,0].plot(R_u['QQQ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,0].set_title('QQQ')\r\n",
"axs[1,1].plot(R_u['SPY'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,1].set_title('SPY')\r\n",
"axs[1,2].plot(R_u['SHV'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,2].set_title('SHV')\r\n",
"axs[2,0].plot(R_u['DBA'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,0].set_title('DBA')\r\n",
"axs[2,1].plot(R_u['USO'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,1].set_title('USO')\r\n",
"axs[2,2].plot(R_u['XBI'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,2].set_title('XBI')\r\n",
"axs[3,0].plot(R_u['ILF'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,0].set_title('ILF')\r\n",
"axs[3,1].plot(R_u['EPP'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,1].set_title('EPP')\r\n",
"axs[3,2].plot(R_u['FEZ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,2].set_title('FEZ')\r\n",
"plt.tight_layout()\r\n",
"returns_u.subplots_adjust(top=0.9)\r\n",
"plt.savefig(graphs_dir + 'prices_u_raw.png', bbox_inches='tight')\r\n"
],
"execution_count": 39,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x540 with 12 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 4.89 s (started: 2021-01-27 10:54:32 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FjJh5cJcqPS_",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 509
},
"outputId": "d04cc848-64ae-4d50-8543-c9c52171fac2"
},
"source": [
"# Visualize ETF Log-Returns:\r\n",
"R_spy = np.log(p_spy / p_spy.shift(1))\r\n",
"returns_u, axs = plt.subplots(4,3,figsize=(15, 7.5))\r\n",
"returns_u.suptitle('Log-Returns of Portfolio Securities', fontweight='bold', fontsize=15)\r\n",
"axs[0,0].plot(R_u['FXE'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,0].set_title('FXE')\r\n",
"axs[0,1].plot(R_u['EWJ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,1].set_title('EWJ')\r\n",
"axs[0,2].plot(R_u['GLD'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,2].set_title('GLD')\r\n",
"axs[1,0].plot(R_u['QQQ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,0].set_title('QQQ')\r\n",
"axs[1,1].plot(R_u['SPY'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,1].set_title('SPY')\r\n",
"axs[1,2].plot(R_u['SHV'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,2].set_title('SHV')\r\n",
"axs[2,0].plot(R_u['DBA'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,0].set_title('DBA')\r\n",
"axs[2,1].plot(R_u['USO'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,1].set_title('USO')\r\n",
"axs[2,2].plot(R_u['XBI'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,2].set_title('XBI')\r\n",
"axs[3,0].plot(R_u['ILF'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,0].set_title('ILF')\r\n",
"axs[3,1].plot(R_u['EPP'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,1].set_title('EPP')\r\n",
"axs[3,2].plot(R_u['FEZ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,2].set_title('FEZ')\r\n",
"plt.tight_layout()\r\n",
"returns_u.subplots_adjust(top=0.9)\r\n",
"plt.savefig(graphs_dir + 'returns_u_log.png', bbox_inches='tight')\r\n"
],
"execution_count": 40,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x540 with 12 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 4.67 s (started: 2021-01-27 10:54:37 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z5QnHmTFGPDV"
},
"source": [
"## **Analysis**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nC787CjQosrO"
},
"source": [
"### Theory"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "covS6dTGa3p7"
},
"source": [
"#### Optimization Problem:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "10i6ikkMoCcu"
},
"source": [
"The strategy aims to maximize return with a certain Target Beta under constraints.\r\n",
"\r\n",
"It is defined as,\r\n",
"\r\n",
"\\begin{cases}\r\n",
"\\max\\limits_{{\\omega ∈ ℝ^{n}}}\\rho^{T}\\omega-\\lambda(\\omega-\\omega_{p})^{T}\\Sigma(\\omega-\\omega_{p})\\\\\r\n",
"\\sum_{i=1}^{n} \\beta_{i}^{m}\\omega_{i}=\\beta_{T}^{m}\\\\\r\n",
"\\sum_{i=1}^{n} \\omega_{i}=1, -2\\leq\\omega_{i}\\leq2\r\n",
"\\end{cases}\r\n",
"\r\n",
"$\\Sigma$ is the the covariance matrix between the securities returns (computed from\r\n",
"the Factor Model), $\\omega_{p}$ is the composition of a reference Portfolio (the previous Portfolio when rebalancing the portfolio and $\\omega_{p}$ has all its components equal to $1/n$ for the first allocation) and $\\lambda$ is a small regularization parameter to limit the turnover;\r\n",
"\r\n",
"$\\beta_{i}^{m}=\\frac{cov(r_{i},r_{M}}{\\sigma^{2}(r_{M})}$ is the Beta of security $S_{i}$ as defined in the CAPM Model so that $\\beta_{P}^{m}=\\sum_{i=1}^{n}\\beta_{i}^{m}\\omega_{i}$ is the Beta of the Portfolio;\r\n",
"\r\n",
"$\\beta_{T}^{m}$ is the Portfolio's Target Beta, for example $\\beta_{T}^{m}=-1$, $\\beta_{T}^{m}=-0.5$, $\\beta_{T}^{m}=0$, $\\beta_{T}^{m}=0.5$, $\\beta_{T}^{m}=1.5$."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z5A2cOZpbAf8"
},
"source": [
"#### Equivalent Optimization Problem:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZgMtDN9MbLFI"
},
"source": [
"We can reformulate the optimization problem above to make the programming process more straightforward:\r\n",
"\r\n",
"$(\\omega-\\omega_{p})^{T}\\Sigma(\\omega-\\omega_{p})\\rightarrow$\r\n",
"\r\n",
"$=(\\omega-\\omega_{p})^{T}\\Sigma\\omega-(\\omega-\\omega_{p} )^{T}\\Sigma\\omega_{p}$\r\n",
"\r\n",
"$=\\omega^{T} \\Sigma\\omega-2(\\omega^{T} \\Sigma\\omega_{p})+\\omega_{p}^{T}\\Sigma \\omega_{p}$\r\n",
"\r\n",
"We simplify,\r\n",
"- $d=\\rho-2\\lambda\\Sigma\\omega_{p}$\r\n",
"- $P=\\lambda\\Sigma$\r\n",
"\r\n",
"Finally,\r\n",
"\r\n",
"$\\max\\limits_{{\\omega ∈ ℝ^{n}}}(\\rho-2\\lambda\\Sigma\\omega_{p} )^{T} \\omega-\\lambda\\omega^{T}\\Sigma\\omega+\\lambda\\omega_{p}^{T}\\Sigma\\omega_{p}=\\max\\limits_{{\\omega ∈ ℝ^{n}}}d^{T}\\omega-\\omega^{T}P\\omega$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YW_hSq95csMx"
},
"source": [
"\r\n",
"---\r\n",
"\r\n",
"The following formulation is equivalent,\r\n",
"\r\n",
"\\begin{cases}\r\n",
"\\max\\limits_{{\\omega ∈ ℝ^{n}}}d^{T}\\omega-\\omega^{T}P\\omega\\\\\r\n",
"\\sum_{i=1}^{n} \\beta_{i}^{m}\\omega_{i}=\\beta_{T}^{m}\\\\\r\n",
"\\sum_{i=1}^{n} \\omega_{i}=1, -2\\leq\\omega_{i}\\leq2\r\n",
"\\end{cases}\r\n",
"- $\\Sigma$ is the the covariance matrix between the returns of the portfolio assets;\r\n",
"- $\\omega_{p}$ is the composition of a reference Portfolio:\r\n",
" - When rebalancing the portfolio, $\\omega_{p}$ is the previous portfolio\r\n",
" - $\\omega_{p}$ has all its components equal to $1/n$ for the first allocation\r\n",
"- $\\lambda$ is a regularization parameter to limit the turnover\r\n",
"- $\\beta_{i}^{m}=\\frac{cov(r_{i},r_{M}}{\\sigma^{2}(r_{M})}$ is the Beta of security $S_{i}$ as defined in the CAPM Model s.t. $\\beta_{P}^{m}=\\sum_{i=1}^{n}\\beta_{i}^{m}\\omega_{i}$ is the portfolio Beta\r\n",
"- $\\beta_{T}^{m}$ is the Portfolio's Target Beta."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1z2_Vb2koLL1"
},
"source": [
"### Algebra"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3qo7TTG3GXzn",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3ab8572d-4585-47e2-8f99-54bf2fa42c69"
},
"source": [
"# Create hybrid dataset:\r\n",
"R_u_ff = pd.merge(R_u,ff_3_daily,how='inner',left_index=True,right_index=True)\r\n",
"R_spy_ff = pd.merge(R_spy,ff_3_daily,how='inner',left_index=True,right_index=True)\r\n",
"\r\n",
"# Rename Market Excess Column Index:\r\n",
"R_u_ff.rename(columns={'Mkt-RF':'Mkt_RF'}, inplace=True)\r\n",
"R_u_ff['Portfolio_Excess'] = R_u_ff.sum(axis=1) - R_u_ff['RF']\r\n",
"print(R_u_ff.head(10))\r\n",
"\r\n",
"# Quick save:\r\n",
"save_data(R_u_ff, 'R_u_ff')\r\n",
"save_data(R_spy_ff, 'R_spy_ff')\r\n"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
" FXE EWJ GLD ... HML RF Portfolio_Excess\n",
"2007-07-24 131.245972 47.484745 67.470001 ... -0.0029 0.00019 1152.384270\n",
"2007-07-25 130.307693 47.517162 66.900002 ... 0.0012 0.00019 1169.430999\n",
"2007-07-26 130.544678 46.674431 65.650002 ... -0.0017 0.00019 1147.853936\n",
"2007-07-27 129.596893 46.220638 65.410004 ... 0.0013 0.00019 1152.447516\n",
"2007-07-30 130.175034 46.998547 65.769997 ... -0.0007 0.00019 1157.401457\n",
"2007-07-31 130.070740 46.771664 65.790001 ... -0.0013 0.00019 1161.776019\n",
"2007-08-01 129.938644 46.415127 65.930000 ... -0.0026 0.00018 1154.524460\n",
"2007-08-02 130.261795 46.188232 65.889999 ... -0.0030 0.00018 1157.955990\n",
"2007-08-03 131.088791 45.539982 66.690002 ... -0.0075 0.00018 1140.587490\n",
"2007-08-06 131.060257 46.220638 66.519997 ... -0.0039 0.00018 1127.743439\n",
"\n",
"[10 rows x 18 columns]\n",
"Successfully saved R_u_ff.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_u_ff.pickle\n",
"Successfully saved R_u_ff.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_u_ff.csv\n",
"Successfully saved R_spy_ff.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_spy_ff.pickle\n",
"Successfully saved R_spy_ff.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/R_spy_ff.csv\n",
"time: 1 s (started: 2021-01-27 10:54:41 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7BauvKVD-jMb",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "988a158b-f2bb-41e1-8612-c5c3cc297d2e"
},
"source": [
"# Estimate Security Betas:\r\n",
"betas = []\r\n",
"for i in range(0,len(u_tix)):\r\n",
" reg_mult = smf.formula.ols(formula = 'R_u_ff.iloc[:, i] - RF ~ Mkt_RF - RF + SMB + HML', data = R_u_ff).fit()\r\n",
" betas.append(list(reg_mult.params))\r\n",
"\r\n",
"betas = pd.DataFrame(betas, index=u_tix)\r\n",
"betas.columns = ['Intercept', 'Mkt_RF', 'SMB', 'HML']\r\n",
"print(betas)\r\n",
"\r\n",
"# Quick save:\r\n",
"save_data(betas, 'betas')\r\n"
],
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"text": [
" Intercept Mkt_RF SMB HML\n",
"FXE 128.040536 -8.855711 38.186598 38.746207\n",
"EWJ 38.835661 15.083004 -11.500107 -8.352894\n",
"GLD 120.290106 61.425513 -17.519682 -16.238490\n",
"QQQ 68.340521 69.369413 -62.544144 -61.023450\n",
"SPY 134.467452 104.068450 -102.853360 -72.616111\n",
"SHV 103.810315 2.400149 1.680502 0.124881\n",
"GAF 63.520729 42.016199 -8.741440 -5.499749\n",
"DBA 25.765034 -7.108904 5.185835 13.559184\n",
"USO 284.471768 -591.630907 280.431142 738.790124\n",
"XBI 36.437137 33.185993 -18.656989 -47.728247\n",
"ILF 30.811243 23.072755 7.093945 2.496551\n",
"EPP 30.464827 26.879494 -15.191168 -10.528097\n",
"FEZ 29.166296 11.863733 -8.498105 -3.551429\n",
"Successfully saved betas.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/betas.pickle\n",
"Successfully saved betas.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/betas.csv\n",
"time: 542 ms (started: 2021-01-27 10:54:42 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PFDtwmJ-RsHt",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "98bd5cf5-c547-484d-f522-20a636e2afa7"
},
"source": [
"# Calculate Annualized Average Expected Returns under FF 3-Factor Model:\r\n",
"rho_daily = []\r\n",
"\r\n",
"for i in range(0,len(u_tix)):\r\n",
" step_0 = (R_spy_ff.sum(axis=1) - R_spy_ff['RF']).mul((betas.iloc[i,0] + betas.iloc[i,1]))\r\n",
" step_1 = R_spy_ff['SMB'].mul(betas.iloc[i,2])\r\n",
" step_2 = R_spy_ff['HML'].mul(betas.iloc[i,3])\r\n",
" step_4 = step_0 + step_1 + step_2\r\n",
" rho_daily.append(step_4)\r\n",
"rho_daily = pd.DataFrame(rho_daily)\r\n",
"rho_daily = rho_daily.T\r\n",
"rho_daily.columns = u_tix\r\n",
"print('Daily Average Expected Returns:\\n' + str(rho_daily.head(10)))\r\n",
"\r\n",
"rho_annual = rho_daily * 252\r\n",
"print('Annualized Average Expected Returns:\\n' + str(rho_annual.head(10)))\r\n",
"\r\n",
"# Quick Save:\r\n",
"save_data(rho_daily, 'rho_daily')\r\n",
"save_data(rho_annual, 'rho_annual')\r\n"
],
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"text": [
"Daily Average Expected Returns:\n",
" FXE EWJ GLD ... ILF EPP FEZ\n",
"2007-07-24 -3.660490 -1.439932 -4.991442 ... -1.561529 -1.512142 -1.105107\n",
"2007-07-25 0.082868 0.135921 0.376805 ... 0.065207 0.155906 0.105658\n",
"2007-07-26 -5.968845 -2.650524 -8.957243 ... -2.670972 -2.815534 -2.021766\n",
"2007-07-27 -4.055287 -1.850974 -6.235372 ... -1.846838 -1.968935 -1.405025\n",
"2007-07-30 2.485890 1.208906 4.017046 ... 1.157789 1.293673 0.917386\n",
"2007-07-31 -2.422785 -1.163125 -3.861095 ... -1.111421 -1.245246 -0.887854\n",
"2007-08-01 -0.026103 0.202238 0.542295 ... 0.079122 0.234461 0.145313\n",
"2007-08-02 1.366298 0.652588 2.195460 ... 0.647516 0.694543 0.488560\n",
"2007-08-03 -8.454828 -3.449528 -11.848671 ... -3.645737 -3.637705 -2.647588\n",
"2007-08-06 2.006565 1.342495 4.231634 ... 1.083735 1.468543 1.007713\n",
"\n",
"[10 rows x 13 columns]\n",
"Annualized Average Expected Returns:\n",
" FXE EWJ ... EPP FEZ\n",
"2007-07-24 -922.443581 -362.862976 ... -381.059690 -278.486952\n",
"2007-07-25 20.882838 34.252206 ... 39.288188 26.625842\n",
"2007-07-26 -1504.148889 -667.932100 ... -709.514615 -509.484948\n",
"2007-07-27 -1021.932365 -466.445406 ... -496.171558 -354.066405\n",
"2007-07-30 626.444369 304.644348 ... 326.005494 231.181197\n",
"2007-07-31 -610.541719 -293.107513 ... -313.802076 -223.739212\n",
"2007-08-01 -6.577950 50.964058 ... 59.084207 36.618782\n",
"2007-08-02 344.307176 164.452235 ... 175.024716 123.117038\n",
"2007-08-03 -2130.616756 -869.281102 ... -916.701704 -667.192249\n",
"2007-08-06 505.654328 338.308706 ... 370.072841 253.943677\n",
"\n",
"[10 rows x 13 columns]\n",
"Successfully saved rho_daily.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/rho_daily.pickle\n",
"Successfully saved rho_daily.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/rho_daily.csv\n",
"Successfully saved rho_annual.pickle. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/rho_annual.pickle\n",
"Successfully saved rho_annual.csv. in /content/drive/MyDrive/Colab Notebooks/global_macro/src/data/rho_annual.csv\n",
"time: 1.85 s (started: 2021-01-27 10:54:43 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7VoV25ScUw66",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "49f6c4dd-a7b0-4318-9886-7bbd423b294f"
},
"source": [
"# Calculate other variables:\r\n",
"ones = np.ones(len(u_tix))\r\n",
"mu_u = np.mean(rho_annual, axis=0)\r\n",
"print('Mean Average Expected Returns (Annual): \\n' + str(mu_u))\r\n",
"mu_u_daily = np.mean(rho_daily, axis=0)\r\n",
"print('\\nMean Average Expected Returns (Daily): \\n' + str(mu_u_daily))\r\n",
"Sigma_u = np.cov(rho_annual, rowvar=False)\r\n",
"print('\\nCovariance Matrix: \\n' + str(Sigma_u))\r\n",
"\r\n",
"P = 2 * (Sigma_u + 0.01 * np.identity(len(mu_u)))\r\n",
"#print('\\nP Matrix: \\n' + str(P))\r\n",
"omega_u = np.repeat(1/len(mu_u), len(mu_u))\r\n",
"A_eq = np.repeat(1,len(mu_u))\r\n",
"A_mat = pd.DataFrame(np.identity(len(mu_u))).merge(pd.DataFrame(-np.identity(len(mu_u))))\r\n"
],
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"text": [
"Mean Average Expected Returns (Annual): \n",
"FXE 14.227031\n",
"EWJ 7.523143\n",
"GLD 24.879569\n",
"QQQ 20.916048\n",
"SPY 34.740728\n",
"SHV 14.141880\n",
"GAF 14.275117\n",
"DBA 1.883168\n",
"USO -73.649170\n",
"XBI 11.384549\n",
"ILF 7.083722\n",
"EPP 8.066569\n",
"FEZ 5.597900\n",
"dtype: float64\n",
"\n",
"Mean Average Expected Returns (Daily): \n",
"FXE 0.056456\n",
"EWJ 0.029854\n",
"GLD 0.098728\n",
"QQQ 0.083000\n",
"SPY 0.137860\n",
"SHV 0.056119\n",
"GAF 0.056647\n",
"DBA 0.007473\n",
"USO -0.292259\n",
"XBI 0.045177\n",
"ILF 0.028110\n",
"EPP 0.032010\n",
"FEZ 0.022214\n",
"dtype: float64\n",
"\n",
"Covariance Matrix: \n",
"[[ 992318.61756132 405812.86465583 1391312.46270538 975572.94698988\n",
" 1730040.87370016 829806.43354953 813195.53783065 163462.9744872\n",
" -1424349.55937012 480239.29842193 426725.36605788 428317.16860261\n",
" 312114.08170803]\n",
" [ 405812.86465583 168566.43962524 576210.55702766 409129.38338456\n",
" 723192.00049143 342552.9686569 336493.63637308 66505.09394701\n",
" -642034.0909168 201805.06832183 175730.12687909 178152.04964093\n",
" 129471.1983102 ]\n",
" [ 1391312.46270538 576210.55702766 1970852.99907193 1396124.94041715\n",
" 2469001.21515873 1172390.14702222 1151089.16482265 228137.99331754\n",
" -2168014.11295245 688727.01488807 601752.79377157 608811.18025991\n",
" 442653.56346031]\n",
" [ 975572.94698988 409129.38338456 1396124.94041715 998894.51439229\n",
" 1761805.93225201 828364.52624024 814832.69008793 159230.47622345\n",
" -1640536.94232966 493768.04442366 424371.95196235 432727.88900457\n",
" 313941.90953554]\n",
" [ 1730040.87370016 723192.00049143 2469001.21515873 1761805.93225201\n",
" 3110756.15940434 1465863.22822545 1441386.13208859 283069.57677364\n",
" -2832251.62366401 869185.87438446 751191.45810878 764745.45865191\n",
" 555209.74356425]\n",
" [ 829806.43354953 342552.9686569 1172390.14702222 828364.52624024\n",
" 1465863.22822545 697882.94284954 684859.75498769 136198.58089527\n",
" -1268531.48584003 408523.51601888 358388.12919166 361832.06679597\n",
" 263223.35050963]\n",
" [ 813195.53783065 336493.63637308 1151089.16482265 814832.69008793\n",
" 1441386.13208859 684859.75498769 672344.69289041 133416.84892324\n",
" -1258331.11862827 401797.19866341 351551.28292627 355509.98907444\n",
" 258530.02489572]\n",
" [ 163462.9744872 66505.09394701 228137.99331754 159230.47622345\n",
" 283069.57677364 136198.58089527 133416.84892324 27083.828644\n",
" -220014.28718039 77940.34783989 70053.22033019 70175.61519406\n",
" 51207.67938313]\n",
" [-1424349.55937012 -642034.0909168 -2168014.11295245 -1640536.94232966\n",
" -2832251.62366401 -1268531.48584003 -1258331.11862827 -220014.28718039\n",
" 3814570.97685865 -840474.26135497 -645188.29443434 -682227.79772207\n",
" -487680.3837452 ]\n",
" [ 480239.29842193 201805.06832183 688727.01488807 493768.04442366\n",
" 869185.87438446 408523.51601888 401797.19866341 77940.34783989\n",
" -840474.26135497 245496.60243339 209371.36574515 213432.87793212\n",
" 154704.57939678]\n",
" [ 426725.36605788 175730.12687909 601752.79377157 424371.95196235\n",
" 751191.45810878 358388.12919166 351551.28292627 70053.22033019\n",
" -645188.29443434 209371.36574515 184131.09456281 185576.47416416\n",
" 135048.79413693]\n",
" [ 428317.16860261 178152.04964093 608811.18025991 432727.88900457\n",
" 764745.45865191 361832.06679597 355509.98907444 70175.61519406\n",
" -682227.79772207 213432.87793212 185576.47416416 188305.81381772\n",
" 136822.30374653]\n",
" [ 312114.08170803 129471.1983102 442653.56346031 313941.90953554\n",
" 555209.74356425 263223.35050963 258530.02489572 51207.67938313\n",
" -487680.3837452 154704.57939678 135048.79413693 136822.30374653\n",
" 99465.74880346]]\n",
"time: 19 ms (started: 2021-01-27 10:54:45 -05:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-4f-go5EBqNK"
},
"source": [
"### Visualizations"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sjstBq2DwPfA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 511
},
"outputId": "bb1facc1-5d69-4fef-fd90-f67787f778f9"
},
"source": [
"# Visualize Daily Average Expected Returns:\r\n",
"r = np.transpose(np.linspace(0, 1, len(rho_annual)))\r\n",
"exp_returns_day, axs = plt.subplots(4,3,figsize=(15, 7.5))\r\n",
"exp_returns_day.suptitle('Daily Average Expected Returns of Portfolio Securities', fontweight='bold', fontsize=15)\r\n",
"axs[0,0].plot(rho_daily['FXE'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,0].set_title('FXE')\r\n",
"axs[0,1].plot(rho_daily['EWJ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,1].set_title('EWJ')\r\n",
"axs[0,2].plot(rho_daily['GLD'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[0,2].set_title('GLD')\r\n",
"axs[1,0].plot(rho_daily['QQQ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,0].set_title('QQQ')\r\n",
"axs[1,1].plot(rho_daily['SPY'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,1].set_title('SPY')\r\n",
"axs[1,2].plot(rho_daily['SHV'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[1,2].set_title('SHV')\r\n",
"axs[2,0].plot(rho_daily['DBA'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,0].set_title('DBA')\r\n",
"axs[2,1].plot(rho_daily['USO'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,1].set_title('USO')\r\n",
"axs[2,2].plot(rho_daily['XBI'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[2,2].set_title('XBI')\r\n",
"axs[3,0].plot(rho_daily['ILF'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,0].set_title('ILF')\r\n",
"axs[3,1].plot(rho_daily['EPP'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,1].set_title('EPP')\r\n",
"axs[3,2].plot(rho_daily['FEZ'], 'black', linewidth=0.5, alpha=0.9)\r\n",
"axs[3,2].set_title('FEZ')\r\n",
"plt.tight_layout()\r\n",
"exp_returns_day.subplots_adjust(top=0.9)\r\n",
"plt.savefig(graphs_dir + 'exp_returns_daily.png', bbox_inches='tight')\r\n"
],
"execution_count": 45,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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