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Nov 30 • 17 tweets • 5 min read
13 Python libraries for free market data everyone should know:
Theta Data

Real-time and historic, high-resolution, tick data for stocks and options. Theta Data is not free but there is a generous free tier and it's one of the cheapest sources of options data on the market.

thetadata.net
yfinance

Data for stocks (historic, intraday, fundamental), FX, crypto, and options. Uses Yahoo Finance so any data available through Yahoo is available through yfinance.

github.com/ranaroussi/yfi…
pandas-datareader

pandas-datareader used to be part of the pandas project. Now an independent project. Includes data for stocks, FX, economic indicators, Fama-French factors, and many others.

pandas-datareader.readthedocs.io/en/latest/
IBApi

The official API for Interactive Brokers provides access to all the data available through IB. Replaces IBPy.

interactivebrokers.github.io/tws-api/
Alpha Vantage

Alpha Vantage delivers a free API for real-time financial data and most used finance indicators in a simple JSON or CSV format.

github.com/RomelTorres/al…
Nasdaq Data Link (formerly Quandl)

Get millions of financial and economic datasets from hundreds of publishers directly into Python.

data.nasdaq.com/tools/python
Twelve Data

Access 100,000+ symbols for stock, forex, index, and fundamental data from global markets.

github.com/twelvedata/twe…
Polygon .io

Real-time and historical data for stocks, FX, and crypto.

github.com/polygon-io/cli…
Tradier

Python libraries to connect to the Tradier API.

documentation.tradier.com/brokerage-api/…
Alpaca-py

Do everything from streaming market data to creating your own investment apps.

github.com/alpacahq/alpac…
Finnhub

Real-time RESTful APIs and websockets for stocks, currencies, and crypto.

github.com/Finnhub-Stock-…
marketstack

Real-time intraday and historical market data with 30+ years of history and 170,000+ tickers.

marketstack.com/documentation
Tiingo

End-of-day stock price data API that emphasizes redundancy, transparency, and completeness

api.tiingo.com/documentation/…
Here's what we covered:

IBApi
Tiingo
Tradier
Alpaca
Finnhub
yfinance
Polygon .io
Theta Data
marketstack
Twelve Data
Alpha Vantage
Nasdaq Data Link
pandas-datareader
That's a lot of data!

What to keep all these handy?

Hop back to the top and retweet the top tweet so you can find it later - and so others can find it too!

Here's the link:
Learn the Python quant stack and go from beginner to up and running with Python for quant finance in 30 days.

• Community
• Frameworks
• Live sessions
• Special guests
• Jupyter Notebooks

January cohort is open - limited spots.

…gstartedwithpythonforquantfinance.com

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More from @pyquantnews

PyQuant News 🐍 Profile picture
Dec 1
Options models are wrong.

Implied volatility is not the same over the life of an option.

Prove it with volatility surfaces.

Here's how to do it in Python (step by step):
By reading this thread, you’ll be able to:

1. Get live options data
2. Analyze volatility skew
3. Analyze volatility structure
4. Build an implied volatility surface

But first, a primer on implied volatility…
If you’re not familiar with implied volatility:

• Market’s expectation of volatility
• Varies across strikes and expirations
• Where most quants and traders spend their time
• The input that sets model and market price equal

Let's build a volatility surface.
Read 14 tweets
PyQuant News 🐍 Profile picture
Nov 29
Quants use the volatility surface to price exotic options, calibrate models, and find mispricings.

You can build your own volatility surface with Python:
First, get the options chain data.
Then, inspect the volatility skew.
Read 10 tweets
PyQuant News 🐍 Profile picture
Nov 28
A Bloomberg Terminal costs $27,660 per year.

It's the portal to all the world's financial data.

Unaffordable for 99% of us.

Reserved for the Wall Street elite to outfox the normal investor.

Until now:
The OpenBB Terminal is a Python-based environment for investment research.

There are over 500 functions covering:

• ETFs
• Forex
• Stocks
• Crypto
• Options
• Portfolio
• Economy
• Mutual funds
• Econometrics

On 29 November, Terminal 2.0 launches.

What's new?
2 mind-blowing new frameworks:

• An SDK to access all the data directly in Python
• An artificial intelligence and machine learning toolkit

And you're invited.
Read 6 tweets
PyQuant News 🐍 Profile picture
Nov 27
The most-used analytics software of the last 37 years:

Excel

But Excel on your resume is no longer enough to get a quant job.

Because Python is the new Excel.

But with 533,000,000 results for "python tutorial", most people struggle to start.

The 6 steps get started in 1 day:
In case you’re unsure if you should learn Python:

• 40% of all hedge fund jobs require it on job descriptions
• 100,000s of powerful pre-built packages
• Base salaries topping US$200,000
• Easy to learn for beginners
• Open source (FREE)

So… where do you start?
First, 3 dead-simple tips after 10 years of using Python:

• Study other people’s code (learn from the best)
• Find a real problem to solve (puzzles get boring)
• Learn how to ask questions (expect to ask A LOT of questions)

With that out of the way, let's go.

Ready?
Read 18 tweets
PyQuant News 🐍 Profile picture
Nov 24
99% of beginners get backtesting wrong:

Build backtest, get poor results. Tweak backtest, get positive results.

Then they wonder why they lose money.

Here's the dead-simple framework the pros use to get backtesting right:
If you replicate this framework, you’ll:

• Setup a backtest with bt
• Run a backtest and analyze results
• Assess how random your results are

Plus, you’ll dramatically reduce the risk of your strategy performing poorly in the market after a great backtest.

But first…
In case you’re new to backtesting.

It's used to:

• Simulate how a strategy might perform live
• Test trading ideas against historic market data
• Optimize for performance parameters like Sharpe

It's easy to setup but hard to get right.

Here’s how to do it with Python.
Read 21 tweets
PyQuant News 🐍 Profile picture
Nov 23
Introducing Getting Started With Python for Quant Finance.

A cohort-based course and community that will take you from complete beginner to up and running with Python for quant finance in 30 days.

The next cohort starts 15 January with limited spots.

Here's everything you get:
🎥 10 live sessions where you'll install and configure Python, create a quant lab in Jupyter Notebook, install packages, understand the quant finance landscape, and build real quant code.
🥇 10-module curriculum you can review at your own pace (with lifetime access to these recordings).
Read 17 tweets

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