StratEvo
Give your agent live quotes, backtests, screens, and genetic-algorithm strategy evolution while you prototype quant tools.
Overview
StratEvo is a Build-phase MCP server that exposes quantitative trading quotes, backtesting, screening, and genetic strategy evolution to coding agents.
What is this MCP server?
- Market quotes and screening exposed as MCP tools (StratEvo / stratevo on PyPI)
- Backtesting workflows callable from the agent without a separate quant UI
- Genetic algorithm strategy evolution for iterative rule and parameter search
- Stdio MCP transport; repository NeuZhou/stratevo at schema version 6.0.3
- MCP server version 6.0.3
- PyPI identifier stratevo
- Transport type stdio
Community signal: 19 GitHub stars.
What problem does it solve?
Building quant features in an agent session stalls when every backtest, screen, and evolution loop requires custom scripts and disconnected data tools.
Who is it for?
Indie quant builders and developers shipping trading experiments, scanners, or agent-driven research pipelines.
Skip if: Non-technical investors who only want buy-and-hold advice, or teams that need licensed execution and compliance without writing code.
What do I get? / Deliverables
After install, your agent can run StratEvo tools so strategy ideas move from chat to tested signals and evolved parameters in one workflow.
- MCP-registered StratEvo server
- Agent-invokable quote, screen, backtest, and evolution tools
- Repeatable quant experiment loop from the IDE
Recommended MCP Servers
Journey fit
Canonical shelf is Build because the MCP is for constructing and testing trading automation and research loops, not for running a live brokerage operation catalog. Integrations fits tool-backed market data, backtesting, and evolution APIs exposed to the agent during development.
How it compares
Quant research MCP toolkit, not a robo-advisor skill or a single static indicator script.
Common Questions / FAQ
Who is io.github.NeuZhou/stratevo for?
Developers and solo quants using MCP agents to prototype strategies, screens, and backtests programmatically.
When should I use io.github.NeuZhou/stratevo?
Use it in Build while integrating market data, backtests, or evolutionary strategy search into agents, CLIs, or APIs.
How do I add io.github.NeuZhou/stratevo to my agent?
Install the stratevo package from PyPI, add a stdio MCP server entry in your client config, and follow NeuZhou/stratevo repo setup for keys and data sources.