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Hummingbot Blog

Introducing the Hummingbot bug bounty program (discontinued)


Since Hummingbot is experimental, beta software that can be run in many different user configurations and markets, we are leveraging the power of our community to help us identify and properly handle all the edge cases which may arise.

As a small token of our appreciation for users who invest their time and effort to try out Hummingbot and report the issues they encounter, we are excited to announce a bounty program for reward users who help improve Hummingbot's stability and reliability!


The public, open source Hummingbot code base.


We will pay bug reporters 0.1 ETH for any bug reported that meets the following criteria: - It has a different root cause than any other bug reported by other users - Reporter follows the submission guidelines below (see Submission) - We decide to fix the bug

Deep dive: we answer 7 common questions about Hummingbot

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In this post, we answer some frequently asked questions about Hummingbot that we haven't addressed in other blog posts. This post is adapted from The BlockCrunch's recent podcast with Michael Feng, CEO and Co-Founder of Hummingbot.

Why is liquidity beneficial for exchanges and token projects?

One of the major benefits of liquidity is price discovery: knowing that the market price is fair. When you buy shares of a liquid asset like Apple shares from Robinhood or E*TRADE, you know that if you change your mind, you can sell it back without incurring a lot of losses. On the other hand, when the market is very illiquid such as in long-tail crypto markets, you often see situations where the buy price is 20% higher than the sell price, where the gap is called the bid-ask spread.

How I started crypto trading | Interview #2

Welcome to the 2nd interview of this series.

In this post, we will introduce Soham who is a business analyst by day and passionate crypto trader by night. He started crypto investing in late 2014 and crypto trading in early 2017. He is now managing a fund for his friends and family.

Let’s meet Soham!

Disclaimer: All views expressed in this interview are the interviewee’s and do not represent the opinions of

How did you become interested in crypto trading?

Crypto was something I wasn't necessarily looking out for. Trading wise, I’ve been trading stocks for a while. In early 2017, no one really knew what was happening [in crypto]. I was like, “oh, this is interesting”. And I kind of came back to bitcoin that I had in my life. I was like “oh, it's growing a lot like this is an interesting asset class”.

How I started crypto trading | Interview #3

Welcome to our new interview series How I Started Crypto Trading! This is a series of short interviews that feature crypto traders with different perspectives, one at a time. The goal of these short interviews is to help beginner traders overcome the feelings of intimidation and get started with crypto trading confidently, share knowledge, experience as well as resources, and build a vibrant and collaborative community.

In this post, we will introduce Tom aka Channaholic, an engineering student and passionate crypto trader from Germany. He has started trading since early 2017 and mainly trades altcoins these days. He is a believer of decentralization and decentralized exchanges. As an evangelist within his circle, he helped many of his friends get started with crypto trading. In his spare time, he likes to play with his pet snakehead fish.

Let’s meet Tom!

The Thin Crust of Liquidity: Why Crypto Needs More Market Makers

Eric Noll was getting frustrated. It was November 2011, and the senior Nasdaq executive was struggling to explain to a mostly disinterested House of Representatives panel why the changing stock exchange landscape was wreaking havoc for smaller public companies:

Today's US markets are increasingly fragmented and volatile. Liquidity in US stocks is dispersed across 13 exchanges and over 40 other execution venues. The declining cost of launching and operating electronic order crossing systems has led to a proliferation of decentralized pools of liquidity. However, the unintended consequences of that market fragmentation have been a lack of liquidity and price discovery in listed securities outside of the top 100 traded names. Such fragmentation of trading creates a thin crust of liquidity that is easily ruptured, as occurred on May 6th (i.e. the 2010 Flash Crash)

Stifled yawns from Congressional onlookers aside, Noll was describing an unintuitive but important phenomenon that would make Milton Friedman roll over in his grave: more competition from exchanges leads to less liquidity for small issuers and greater systemic risk.

Crypto trading guide for nontechnical users


In our previous post, we put together some helpful trading and finance-related resources for developers who want to dip their toes into crypto algo trading. On the flip side, this post aims to be a good resource for traders who have trading experience but little to no programming skills.

This post is also intended to be a "live" document, that we will update regularly with thoughtfully-selected readings and learning resources that we come across.

Manual trading vs. algo trading

What is algo trading?

As discussed in our previous post, algo trading is the process of using computer programs to automatically execute trades based on a predefined set of rules, called algorithms, with the goal to generate profits at high speed and high frequency.

Due to its low barrier to entry from easily accessible exchange APIs, sufficient supplies of free tutorials and documents, reasonable hardware requirements, and an around-the-clock market, algo trading in crypto is attracting more people than ever who are interested in and curious about how this all works.

A developer's guide to crypto algo trading

Algorithmic trading is the process of using computers programmed to follow a pre-defined set of rules for automatically placing trades in order to generate profits at high speed and high frequency. The pre-defined sets of rules, called an algorithm, can range from quantitative strategies to machine learning models that can reference any data or combination thereof, e.g. prices, volume, or tweets/news feeds for sentiment analysis. Algo trading makes markets more liquid by introducing a large volume of trades and orders; unlike human traders, computers don't need to rest or sleep, are much faster at calculating and sending instructions, and don't suffer from emotions (at least, not yet??).