Enterprise DNA
M MCP Servers Developer low

dolphinquant/echolon

by Various

LLM-agent-native backtest framework for futures research — MCP server, in-package skills, catalogued error codes, typed Pydantic configs. Production engine inside Qorka @ DolphinQu

D

MCP

dolphinquant/echolon

Added 1 June 2026

#agent-tools #algorithmic-trading #backtesting #claude #futures #llm-agents #mcp #mcp-server

Overview

Echolon is an LLM-agent-native backtest framework for futures research. It provides an MCP server, in-package skills, catalogued error codes, and typed Pydantic configurations. It serves as the production engine inside Qorka at DolphinQuant.

Best for

Best for
Developers building LLM-driven futures trading strategies

Use cases

  • Backtesting futures trading strategies with LLM agents
  • Integrating agent-based research via MCP server
  • Configuring reproducible experiments with typed Pydantic configs

Notes

Echolon is an LLM-agent-native backtest framework for futures research. It provides an MCP server, in-package skills, catalogued error codes, and typed Pydantic configurations. It serves as the production engine inside Qorka at DolphinQuant.

1 stars on GitHub. Last updated 2026-05-08. Licensed Apache-2.0.

Use cases

  • Backtesting futures trading strategies with LLM agents
  • Integrating agent-based research via MCP server
  • Configuring reproducible experiments with typed Pydantic configs

Pros

  • LLM-agent-native design enables flexible agent-driven backtesting
  • Typed Pydantic configs improve reliability and reproducibility
  • Catalogued error codes aid debugging and error handling

Cons

  • Very early stage with only 1 GitHub star, indicating limited adoption
  • Niche focus on futures research may not suit other asset classes
  • Small community and sparse documentation likely

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • LLM-agent-native design enables flexible agent-driven backtesting
  • Typed Pydantic configs improve reliability and reproducibility
  • Catalogued error codes aid debugging and error handling

Cons

  • Very early stage with only 1 GitHub star, indicating limited adoption
  • Niche focus on futures research may not suit other asset classes
  • Small community and sparse documentation likely

Pairs with

Other entries in the index that connect to this one. Click through to see the chain.

Free 27-page guide

Get the free Developer’s Field Guide

A 27-page field guide to the AI coding workflow with Claude. Claude Code, MCP servers, the prompt patterns that work, and what to delegate. Free.

Enter your work email. We send it straight over, plus a few short notes worth knowing. Unsubscribe any time.

No spam. Unsubscribe any time.

Running a business, not writing the code? See the MCP servers picked for operators, and get your first one wired up with us.

Operator picks