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Portfolio_Selection

Modern Portfolio Theory (MPT) studies the tradeoff between return and risk. The risk of a portfolio is determined by not only the variance but also the correlations among assets. For a given risk, a rational investor would prefer an allocation with a higher expected returns; On the other hand, for a given return, he or she would prefer the portfolio with a lower risk level.

Setup

1. Install Anaconda

Windows:

https://docs.anaconda.com/anaconda/install/windows/

Mac:

https://docs.anaconda.com/anaconda/install/mac-os/

Linux:

https://docs.anaconda.com/anaconda/install/linux/

2. Set Up the Virtual Environment via Terminal

Activate environtment and link your environment with Jupyter:

    conda create -n Portfolio_Selection python=3.6

    conda activate Portfolio_Selection

    pip install ipykernel
    
    python -m ipykernel install --user --name=Portfolio_Selection

3. Install Packages

Core Data Analytics

Matrix operations

    numpy

Scientific operations

    scipy

DataFrame operations

    pandas

Statistical models

    statsmodels

Data visualization

    matplotlib

Finance

Reading financial data from the web

    pandas_datareader

Web scraper for yahoo finance

    yfinance

Finance database for industry data

   FinanceDatabase

Volatility modelling

    arch

Core optimization

    cvxopt

Wrapper to nicely interface into cvxopt

    cvxpy

Facebook's timeseries forecasting tool

    prophet

Data Visualization

Colorful data visualization

    seaborn

Interactive data visualization

    bokeh

Create maps and geographical visualization

    geoplotlib

New framework for plotting graphics

    Altair

Similar to R's ggplot2 using grammer of graphics

    plotnine

Machine & Deep Learning

Core machine learning package

    scikit-learn

The first publicly available gradient boosting package. Released by Tianqi Chen (University of Washington, Seattle)

    xgboost

Gradient Boosted Decision Trees package (Microsoft)

    lightgbm

Gradient Boosting Decision Trees package (Yandex)

    catboost

High-level neural networks API

    keras

Deep learning package from Google

    Tensorflow

Natural language processing (NLP)

Loughran-McDonald sentiment word lists to perform sentiment analysis (csv_file)

    https://drive.google.com/file/d/12ECPJMxV2wSalXG8ykMmkpa1fq_ur0Rf/view

General NLP tasks

    nltk

Creating NLP prototypes quickly

    textblog

NLP applications for topic modelling, document similarity, etc.

    gensim

Web scraping

    scrapy

Production-level NLP library

    spacy

Install Packages from GitHub:

  • blitz

      git clone https://github.com/piEsposito/blitz-bayesian-deep-learning.git
  cd blitz-bayesian-deep-learning
  pip install .

  • pyfolio

      git clone https://github.com/quantopian/pyfolio
  cd pyfolio
  pip install .

Favourite JupyterLab extensions

nodejs

    conda install -c conda-forge nodejs

You can install plugins in Jupyter lab by clicking the jigsaw icon on the menu bar on the menu bar on the right of the editor.

Table of contents

    toc

Variable inspector in Jupyter Lab

    jupyterlab_variableInspector

Allows you to edit the notebook metadata

    jupyterlab_nbmetadata

Jump to definition of a variable or function in JupyterLab notebook and file editor

    jupyterlab_go_to_definition

Installing Jupyter notebook extensions

    conda install -c conda-forge jupyter_contrib_nbextensions