In Hummingbot’s new Strategy V2 framework, Controllers are tools that manage your trading strategies. They gather market data (such as price information and trading volumes) and use it to make trading decisions automatically.
Controllers allow you to handle multiple trading strategies or trading pairs without needing separate bots. This helps simplify management and saves resources.
by Patrick Meier
In this article, we'll explore how to create a custom V2 Controller for Hummingbot to snipe future liquidations on Binance. We'll use a generic controller to demonstrate the correct usage and implementation of this strategy.
This strategy shall be able to exploit liquidations that happen regularly in Crypto Futures Markets. When they happen there is a tendency to have a quick rebound of the market that this strategy aims to catch with different order levels used (DCA).
The goal of this trading strategy is to capitalize on the quick rebounds that often follow liquidation events in the cryptocurrency futures market. These liquidations, caused by traders being forcibly closed out of their leveraged positions, create sharp price movements that can temporarily disrupt market equilibrium.
Welcome to the inaugural post in our Strategy Experiments series, where we publish hypotheses, code, and performance analyses of live bot strategies.
We're thrilled to return to the Hummingbot blog with an exciting experiment: developing a crypto trading bot using the open-source Hummingbot platform. This bot employs an indicator-based directional strategy on the Binance Futures exchange, leveraging MACD and Bollinger Bands.
Sign up for Binance Futures using our Hummingbot referral code to receive a 10% fee rebate!
As the first entry in our Strategy Experiments series, we aim to foster a collaborative environment where users can share their strategic insights with the community, enhancing growth and knowledge exchange among quantitative traders.
We invite everyone to contribute their Strategy Experiments, regardless of outcome, as there is valuable learning in each experience. Through sharing insights and discussing novel concepts, we aspire to cultivate a vibrant ecosystem for continual improvement and refinement of trading strategies.
Below is a detailed account of our first experiment. You can watch the accompanying video or read the following description.
by Ben Smeaton
Hi! I've been using Hummingbot for about a year on and off and wanted to give an overview on how to actually go about defining your own strategies. I got into market making because of a hobbyist interest in python coding and I was at that time tinkering with buying and selling shares automatically on the stock market which was incredibly unsuccessful.
Hummingbot is very accessible for hobby coders due to its mainly python-based code base and the fact that its all open source (Meaning you can play around with the code). It's pretty easy to scrape together your own strategies although it�s taken quite a lot of trial and error to get there myself! I wanted to give a basic idea of how you can go about doing this in the simplest way possible.
In our previous blog post, we introduced the new avellaneda_market_making strategy. This time, we delve deeper into the mathematical aspects of this strategy. We aim to explain how we adapted the original Avellaneda-Stoikov model for the cryptocurrency market and simplified the calculation of key parameters, known as greeks.
This article mathematically substantiates the assumptions and calculations that made the authors' model more suitable for Hummingbot traders.
Let's start by revisiting the core equations from the Avellaneda-Stoikov paper:
Welcome back to the Hummingbot Academy!
The latest Hummingbot release (0.38) introduces an exciting strategy based on classical academic market-making models. This article will delve into the Avellaneda & Stoikov paper from 2008 and its implementation in Hummingbot.
For those who enjoy in-depth scientific papers, the original publication is readily accessible online or directly here.