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Strategy Guides

Strategy Guide: Grid Strike

Grid Strike (grid_strike) is a Controller in Hummingbot's StrategyV2 framework that automatically places a grid of buy or sell orders within a set price range.

Unlike basic grid strategies, Grid Strike utilizes Executors which dynamically manage each order level with configurable take profit and stop loss settings. This means every position opened by the strategy is automatically monitored and closed according to your risk parameters. It can be configured to move with the market trend —- either up or down -- letting you adapt the strategy to changing market conditions.

This guide explains how the Grid Strike strategy works and helps you decide when to use it. You'll learn how to configure and run the strategyt, as well as the key concepts and how to set it up for trending markets.

Strategy Code: https://github.com/hummingbot/hummingbot/blob/master/controllers/generic/grid_strike.py

Strategy Overview

  • Grid-based Order Placement: The strategy lets you place either a Long Grid or a Short Grid that places buy/sell orders across a defined price range
  • Automated Risk Management: Each side utilizes Grid Executors that automatically manages the risk of each position with configurable take profit, stop loss, and time limits
  • Exchange Flexibility: Works on both spot and perpetual exchange connectors, including support for one-way position mode on perp exchanges
  • Highly Configurable Can be tailored for various market scenarios (trending up, trending down, or consolidating) or trading objectives (volume, liquidity, P&L, etc) by adjusting price range, order spacing, and other parameters

Dashboard Tutorial: PMM Simple V2

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Welcome to the Dashboard Tutorial: PMM Simple V2 Strategy — a step-by-step guide designed to help you understand, configure, backtest, and deploy the PMM Simple V2 strategy using the Hummingbot Dashboard.

In this tutorial, you’ll learn how to set up the strategy’s parameters, test its performance through backtesting, fine-tune order spreads and risk controls, and finally save and deploy your configuration for live trading.

How to Configure a V2 Strategy Controller in Hummingbot

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What are Controllers?

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.

Coding a Liquidation Sniper V2 Strategy Controller

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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.

Liquidation Sniper 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.

Directional Trading with MACD and Bollinger Bands

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!

Introducing Strategy Experiments

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.

Hummingbot V1 Strategy Coding for Dummies

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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.

Technical Deep Dive into the Avellaneda & Stoikov Strategy

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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.

Original Model and Our Proposed Extensions

Let's start by revisiting the core equations from the Avellaneda-Stoikov paper:

Guide to the Avellaneda & Stoikov Strategy

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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.