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if you personalize losses, you can't trade

Developing algo-trading skills is a major endeavor, and this page lists some resources that can help with it. These resources are not limited to roboquant and could benefit any algo-trading development.


When looking for books to learn more about (algo-)trading, it is recommended to look for books that focus on the foundation. So books that explain how to develop and test a strategy, what are good metrics to monitor and what are possible pitfalls. Books (or people for that matter) that promise strategies that make a lot of profit, should be best avoided (IMHO).


Machine Learning for Algorithmic Trading - Stefan Jansen

This book explains how different machine learning techniques can be applied to algo-trading. It doesn’t focus on only a single technique like deep learning, but touches many techniques instead. For example, it has some good content for Bayesian machine learning.

Because of the all the different techniques described, this book is over 800 pages long. If you want to start using machine learning in your strategies, this book is a must-have in my honest opinion. One thing to keep in mind is that because machine learning is progressing so fast, some libraries and examples are outdated.


Advances in Financial Machine Learning - Marcos Lopez de Prado

This book has several innovative ideas about different approaches to algo-trading. And although the code-snippets that are included are written in Python, the underlying theory is applicable to any implementation. Also, this book contains good information about how easy it is for your approach to developing look-ahead bias without realizing it.

Marcos Lopez de Prado also has some good YouTube videos where he explains much of the same theory. So check those out first if you are not sure yet about buying this book.


Trading Systems - Emilio Tomasini

A good book for a beginner that has plenty of pragmatic tips how to build, test, optimise and implement robustness trading systems. There is some AmiBroker code included, but even without understanding this, you can still follow along.

If you are new to statistics, this book also has some good introduction on how to deal with forecasts in the context of uncertainty.


Automated Stock Trading Systems - Laurens Bensdorp

Easy to understand and follow with extra attention on how to diversify your portfolio using different strategies. The book also describes 7 concrete strategies and the rules that need to be implemented for each of these strategies. The strategies are not hard to understand and are a mix of trend-following and mean-reversion.

This book doesn’t contain any code snippets. But I would argue implementing some strategies yourself from scratch, is a great learning experience.

Like many other "Amazon printed" books, the images are not great. But in this case fortunately they are not that important to understand the content.


Systematic Trading - Robert Carver

Robert Carver is a regular guest at the podcast Top Traders Unplugged and has written several good books, of which this is one.

He develops his own algo-trading software (available under open-source) and that shows. There are enough details in the book to actual implement the strategies in your own code. If anything, due to the high density of information, some chapters require multiple reads before grasping everything.


Algorithmic Trading - Ernest P. Chan

This book does not try to sell you trading strategies, but teaches you the methods and underlying discipline to achieve good strategies.

The code snippets included are written in MATLAB. But don’t let that put you off, they are easy to understand, especially if you have some background in math or another computer language. The chapters are divided between mean-reversion and momentum strategies and the last chapter has useful tips on how to approach risk management.

This book is more theoretical than some others books on this list. But that is also a major bonus; it means it will stay relevant longer, and not be outdated as quick as books that focus more on programming using third-party libraries.


Applied Time Series Econometrics - Helmut L├╝tkepohl

This book provides a great foundation for understanding much of the theory behind time series analysis. It clearly explains commonly used models and important concepts like stationary processes. However, it requires a good knowledge of math at least at an undergraduate level. The examples used in this book cover both macro-economical and investment oriented use-cases.

Besides the great content, there is also a Java GUI project provided at that implements various models explained in this book, like an ARIMA model. This Java project is however a bit outdated since the code-base pre-dates 2010.

Market Data

The following table provides an overview of popular market data providers. The list includes free as well as paid providers. Many of them have out-of-the-box support in roboquant.

Provider Asset Types API Historic Realtime Delayed[1] Realtime Pricing model Remarks


US Stocks, Options & crypto

Fee per month

Good performing API

Yahoo Finance

US Stocks

Free, no API key required

Can have some quality issues

Alpha Vantage

Stocks, fundamental, forex, crypto and economic indicators

Fee based on API calls per minute

Also covers exchanges outside US


US stocks, UK stocks, World stocks, macroeconomic indicators


Website is in Polish


US Stocks & Crypto

Fee per month

Subscription prices fluctuated a lot in recent years


US/China Stocks & ETF & Crypto

Low fee per month

30+ year of end-of-day data and IEX Live feed with very small delay


US Stocks L3

Fee per download


Forex, Commodities, CFD





IEX Exchange

IEX traded US stocks L3


Large daily files that require additional processing


Various datasets


Some datasets have major quality issues

1. typically 15 minutes delay due to distributions restrictions imposed by US exchanges


This section provides an overview of some popular podcasts about (algo-)trading. The provided links are pointing to Apple Podcast shows, but typically the same podcasts can also be found on most other podcasting platforms.

  • Better System Trader - Great podcast about systematic trading. Since often there is supporting visual content, you might want to watch the episodes on their YouTube channel.

  • Chat with Traders - several episodes and interviews with algo traders. Especially the discussed pitfalls of going live with fully automated trading systems I found very are helpful.

    Click this link to find out which episodes might be of most interest to algo-traders: algo-trading episodes

  • Top Traders Unplugged - rule based investment in the context of trend following strategies. There are some very experienced traders on this show, and they share a lot of useful tips and best practices.

    An additional benefit for beginning algo-traders is that their trend following rules they are not that hard to translate into executable code.

  • Patrick Boyle on Finance - good background information on trending financial events. Besides providing excellent insights into these hot topics, I really enjoy the dry humor in these podcasts.

  • Flirting with Models - explores systematic investment strategies, ranging from value to momentum and merger arbitrage to managed futures. Nice thing about this podcast is that there are many types of guests interviewed, ranging from traditional fund managers to retail crypto traders.

  • Roots Report - discuss recent developments in the financial market on a weekly basis. If you don’t have the time to stay up to date with the latest financial news, this is an easy way to catch-up.

  • Quant/Financial Engineering - discuss the latest trends in Data analytics, Asset Management, Blockchain and Risk Management. If you are interested in the application of machine learning, definitely listen to this podcast.

  • Confessions of a Market Maker - educational trading experience, coupled with some entertaining stories. Not that much focus on trading automation, but good content on trading in different asset classes with knowledgeable guests.