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
MCP
dolphinquant/echolon
Added 1 June 2026
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
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