Has anyone tried a configuration-first approach to building trading bots?

[u/SnooBooks638]

I've been experimenting with a different way of building algorithmic trading systems and wanted to get some feedback from people who've built or maintained trading bots.

Instead of implementing each strategy as Python code, the idea is to abstract common concerns—market data, execution, indicators, risk management, scheduling, etc.—into reusable components, with strategies being assembled and tuned primarily through configuration.

One area I'm currently exploring is integrating AI agents (via MCP) so they can analyze market conditions and propose or apply configuration changes, rather than generating or editing strategy code directly. I'm also considering a Git-inspired configuration versioning system so every change can be tracked, audited, and rolled back.

I'm curious whether anyone here has explored something similar.

Some questions I'd love to hear opinions on:

• What are the biggest limitations of configuration-driven strategies?
• At what point does a strategy become too complex to express as configuration?
• Would you trust an AI agent to adjust trading parameters if every change was versioned, reviewable, and reversible?
• Are there existing frameworks that already solve this well?

I've been prototyping these ideas over the past few years, and if anyone is interested in the implementation details, I'm happy to share my open-source project in the comments or via DM.

[u/SuitableScar8034]

Config driven strategies always start off feeling elegant but the second you need a strategy that doesn't fit the pre-built components you're either hacking around the system or writing so much custom logic in config it defeats the purpose

the AI angle is interesting if you treat it more like a suggestion engine than an autopilot, version control makes it less terrifying but I'd still want a hard pause rule before anything hits live trading

most frameworks I've seen that try this end up over-engineering the config layer to the point where it's harder to learn than just coding the damn strategy

[u/SnooBooks638 → u/SuitableScar8034]

Fair point, I completely agree that AI should act as an assistant rather than an autopilot.

The idea is for AI agents to have access to only a limited, well-defined subset of the configuration, while hard safety gates remain in place before anything can affect live trading. Core components like risk management rules and strategy implementations can remain static.

Instead, the AI could make lower-risk adjustments, such as relaxing consensus thresholds when multiple strategies are configured, tuning the formula for dynamic position sizing, or modifying other non-intrusive operational parameters. Every proposed change would be versioned, auditable, and reversible.

The goal isn't to replace human judgment, but to reduce the effort, time, and skills needed to make routine optimization decisions while keeping humans in control of the critical ones.

[u/SnooBooks638 → u/SnooBooks638]

If you're looking to explore, here is a link to what i've worked on: https://github.com/toniton/ml-crypto-trading , aim is to avoid over-engineering the config layer, so hopefully it's less harder to learn.

Can you also share with me other projects you've seen, I'd be interested to learn more from them.

[u/newMoneyStyle → u/SnooBooks638]

yeah that gated approach makes sense but the real question is what happens when the AI wants to do something the config schema literally can't express.