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Alphahunter

Alphahunter is an open-source, asynchronous event-driven Python framework for cryptocurrency trading and market-making research. It provides market-data collection, data-matrix generation, strategy abstractions, backtesting and exchange-gateway interfaces, with a documented WebSocket interface for strategy development in other languages.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Alphahunter — OpenViST project profile in Exchanges & Trading.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Alphahunter when you need a code-based workflow that links cryptocurrency market-data collection and quantitative research with strategy backtesting and exchange-gateway integration.

Who it is for

  • Python developers building algorithmic cryptocurrency trading or market-making strategies.
  • Quantitative researchers preparing market data and analyzing strategy signals.
  • Developers who want to backtest strategies or connect them to documented exchange gateways.

Documented uses

  • Collect and store order-book, trade and candlestick data.
  • Generate data matrices and export research inputs as CSV for analysis in tools such as Jupyter Notebook.
  • Implement strategies using the Strategy base class and exchange-gateway callback interfaces.
  • Backtest strategy configurations on historical tick or candlestick data.
  • Develop strategies in other languages through the documented WebSocket interface.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. Its README specifies Python 3.5.3 or later; no account requirement or price is stated.

  • The framework uses Python’s asyncio and requires familiarity with asynchronous programming.
  • RabbitMQ is listed as a requirement for event publishing and subscription; MongoDB and Motor are optional.
  • The repository documents separate stages for data research, backtesting, online simulation and live trading; requirements differ by stage.
  • The README lists FTX, Huobi spot and futures, and OKEX spot examples; confirm the relevant gateway documentation for a specific exchange.
  • Data-matrix CSV output is intended for analysis in a separate research environment such as Jupyter Notebook.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Alphahunter and review its example workflows

Install the Python package, then locate the repository’s strategy, backtest and data-matrix examples.

Before you start

  • Python 3.5.3 or later
  • aiohttp 3.2.1 or later
  • aioamqp 0.13.0 or later
  • RabbitMQ server for event publishing and subscription

Steps

  1. Install the package with the command below.

    Documented command
    pip install alphahunter

  2. Review the example directory for strategy templates.

  3. Review backtest for backtesting examples and datamatrix for data-matrix examples.

Expected outcome

The package is installed and the documented examples for strategies, backtesting and data-matrix work are identified.

Guide limitations

  • The README does not provide a complete strategy execution procedure in this installation section.
  • Motor and MongoDB are optional dependencies for data storage; the guide does not configure them.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
phonegapX/alphahunter · Official homepage title · retained in its source language
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