QuantsLab

QuantsLab is a Python project designed for quantitative research with Hummingbot. It provides functionalities for fetching historical data, calculating metrics, backtesting, and generating trading configurations.

Installation

Prerequisites

• Anaconda (or Miniconda) must be installed on your system. You can download it from https://www.anaconda.com/products/distribution

Steps

1. Clone the repository:

   git clone https://github.com/hummingbot/quants-lab.git
   cd quants-lab
   

2. Create and activate the Conda environment:

   make install
   

   This command will create a new Conda environment and install all the necessary dependencies.

3. Activate the environment:

   conda activate quants-lab
   

You're now ready to use QuantsLab!

Usage

1. Research Notebooks

• Under the research notebooks folder you will find a folder for each strategy. The idea is that you can use them as an inspiration to do research on your own strategies.
• The main steps are:
  • Exploratory Data Analysis
  • Design the controller
  • Backtest a simple controller
  • Optimize and find the best parameters

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2. Task Orchestration

Prerequisites

1. Ensure Docker and Docker Compose are installed. If not, you can install them with the following commands:

   curl -fsSL https://get.docker.com -o get-docker.sh
   sh get-docker.sh

2. Verify the Docker installation by running:

   docker --version
   docker compose version

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Configuration

1. Modify Task Settings:

   • Navigate to the config folder and locate the tasks.yml file.
   • Update the file to include the specific tasks you want to execute. By default, it is configured to run the pool-fetching task.

2. Customize Database Credentials (Optional):

   • The default credentials for MongoDB and PostgreSQL are specified in the docker-compose-db.yml file.
   • Update these credentials if necessary, especially for production environments.

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Steps to Run Tasks

1. Build the Docker Image: Build the local Quants-Lab Docker image by running:

   make build

2. Start Databases: Start the necessary databases (MongoDB and PostgreSQL) using:

   make run-db

3. Run the Task Runner: Execute tasks specified in tasks.yml with the following command:

   make run-task config=tasks.yml

4. Monitor Database Activity: Use Mongo Compass UI to inspect the database data:

   • Open your web browser and visit:

     http://localhost:28081/
     

   • Default credentials:
     • Username: admin
     • Password: changeme
   • Update these credentials in docker-compose-db.yml if needed.

   Replace localhost with your machine's IP address if accessing remotely.

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Stopping Services

1. Stop the Task Runner:

   make stop-task

2. Stop the Databases:

   make stop-db

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Notes:

• Ensure all required ports are open and accessible.
• Regularly check the logs for errors using docker logs <container_name>.
• Make sure to re-build the local Docker image using make build after any changes are made to tasks.yml.

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Data Source

• CLOB (Central Limit Order Book)

  • Last Traded Price
  • Current Order Book
  • Historical Candles
  • Historical Trades
  • Trading Rules
  • Funding Info

• AMM (Automated Market Maker)

  • Last Traded Price
  • Current Liquidity
  • Pool Stats
    • Fees Collected
    • Volume (24h)
  • Historical Trades

• GeckoTerminal

  • Networks
  • Dexes by Network
  • Top Pools by Network
  • Top Pools by Network Dex
  • Top Pools by Network Token
  • New Pools by Network
  • New Pools (All Networks)
  • OHLCV

• CoinGecko

  • Top Tokens Stats
  • Top Exchange Stats
  • Market Stats by Token
  • Market Stats by Exchange

• Spice (DuneAnalytics)

  • Queries

Modules

• Labeling

  • Triple Barrier Method

• Backtesting

• Optimization

• Visualization

  • OHLC
  • Order Book
  • Backtesting Report

• Features

  • Signals