9.6. Supercharged resources/packages

A compendium of great python packages you can use, now that you have the skills needed to access the info below.

Please suggest others if you see something cool!

Package

Description

Where

streamlit, plotly, and shiny

Building interactive graphs, dashboards, and apps!

streamlit, plotly, shiny

OpenBloomberg

85% of a Bloomberg terminal for free, see the tweet below for a preview.

https://github.com/OpenBB-finance/OpenBBTerminal

pandas-datareader

Import a ton of data

https://pydata.github.io/pandas-datareader/remote_data.html

auto-sklearn

Automated Machine Learning with scikit-learn

https://github.com/automl/auto-sklearn

Open Source Asset Pricing

Data for asset pricing tests and code to replicate or use. If you are interested in asset pricing or trading, this is a MUST visit. It’s so cool that this is freely available, it blows my mind.

https://www.openassetpricing.com/data/

PyQL

QuantLib’s Python port.

https://github.com/enthought/pyql

QuantPy

A framework for quantitative finance In python.

https://github.com/jsmidt/QuantPy

vollib

Calculating option prices, implied volatility, and greeks

https://github.com/vollib/vollib

Finance-Python

Python tools for Finance

https://github.com/alpha-miner/Finance-Python

ffn

A financial function library for Python.

https://github.com/pmorissette/ffn

pynance

Lightweight Python library for assembling and analysing financial data.

https://github.com/GriffinAustin/pynance

pysabr

SABR model Python implementation.

https://github.com/ynouri/pysabr

FinancePy

Focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

https://github.com/domokane/FinancePy

gs-quant

Goldman-Sachs toolkit for quantitative finance

https://github.com/goldmansachs/gs-quant

willowtree

Willow tree lattice for derivatives pricing.

https://github.com/federicomariamassari/willowtree

financial-engineering

Applications of Monte Carlo methods to financial engineering projects

https://github.com/federicomariamassari/financial-engineering

optlib

A library for financial options pricing written in Python.

https://github.com/dbrojas/optlib

tf-quant-finance

High-performance TensorFlow library for quantitative finance.

https://github.com/google/tf-quant-finance

Q-Fin

A Python library for mathematical finance.

https://github.com/RomanMichaelPaolucci/Q-Fin

Quantsbin

Tools for pricing and plotting of vanilla option prices, greeks, and various other analysis around them.

https://github.com/quantsbin/Quantsbin

finoptions

Complete python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options.

https://github.com/bbcho/finoptions-dev

AssayingAnomolies

Quickly check if your signal generates alpha

Coming Soon

usaddress and probablepeople

Parsing messy names (people, address, firms)

https://github.com/datamade?q=&type=all&language=&sort=stargazers

SRAF

Textual analysis resources, designed for business text.

https://sraf.nd.edu/


Chat GPT and Generative AI

Chat GPT and other generative AI are at least, of course, fun novelties. Who doesn’t love this:

kingjames

Hilarious and silly!

But generative AI also have the potential to truly supercharge how work is done across many tasks and industries.

Developments in this area are happening so fast, what I write here will be outdated very soon, so I’ll keep it brief here and spend more time on the topic during the semester.

But as of November 2022, here are some thoughts on OpenAI’s ChatGPT:

  1. View it as having an infinite supply of interns with an 8th-grade IQ. Meaning:

    • Low-cost way to brute force some tasks (brainstorming, rough drafts, outlining, editing, starting code)

    • Its creativity is limited (bad at: “write a shocking surprise that was foreshadowed earlier”)

    • But it can be harnessed: “write a rap about monetary supply in the style of Common”, “draft a book proposal/sales copy/etc on topic X”, “write a corporate memo addressing X, Y, and Z”, “suggest 3 options for X”, “provide more detail on the second point”

    • Responses are often vague and lack specifics

  2. Do not assume its output is correct or the truth

    • Meaning: It will confidently lie and make up things (so-called “hallucinations” and “bullshiting”)

    • It’s not all wrong though - see the note in the margin

Those points imply that ChatGPT is only useful and accurate when paired with

  • Your guidance (via iterative conversation)

  • Your subject matter expertise (to correct errors)

Thus, ChatGPT does not look anything like a replacement for high-skill knowledge workers. It is a complement to our workflow - I always have it open as a tab on my computer now.

There are many implications for this technology - mostly exciting! But it certainly looks like a world-changing tool. Examples:

  • Makes new business formation/expansion easier and democratizes access

  • Writing workflow will change dramatically

    • Shift in emphasis to idea creation, editorial choices, truth checking

    • Useful in many domains: journalism, memos, marketing, consulting, etc.

    • Editing - overall writing quality should increase

  • The cost of BSing and cheating will fall

    • Typed essays decline in favor of oral evaluations, handwritten essays

    • Spam and fishing attempts will become much better


OpenBBTerminal

This Twitter thread has a nice preview of the OpenBBTerminal package, and you can find more in the official GitHub documentation.