The accessible, beneficial guide to developing algorithmic trading solutions. The rise of commission free trading APIs along with cloud computing has made it possible for the average person to run their own algorithmic trading strategies. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. 8 min read. My post got hundreds of upvotes and several redditors contributed links to more books and lists, thus confirming the interest in such a resource.

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By Eric Matthes (No Starch Press, 2019) Matthes is a high school teacher who adopts a patient yet expert tone throughout the book. Python is the de facto language for data scientists, statisticians, machine learning experts, and web enthusiasts.

He is the author of the books • Python for Finance (2nd ed., O’Reilly, 2018), • Derivatives Analytics with Python (Wiley, 2015) and • Listed Volatility and Variance Derivatives (Wiley, 2017). python machine-learning trading feature-selection model-selection quant trading-strategies investment market-maker feature-engineering algorithmic-trading backtesting-trading-strategies limit-order-book quantitative-trading orderbook market-microstructure high-frequency-trading market-making orderbook-tick-data

Yves Hilpisch, CEO of The Python Quants and The AI Machine, has authored three books on the use of Python for Quantitative Finance.

Therefore I later put together a list of all the free Python books I had found and posted it to r/Python. The second is Derivatives Analytics with Python (Wiley Finance, 2015).