Designing-a-Multi-Agent-Reinforcement-Learning-with-MCP-for-Portfolio-Optimisation MCP Server

Name: Prabudh28/Designing-a-Multi-Agent-Reinforcement-Learning-with-MCP-for-Portfolio-Optimisation
Rating: 2.1 (1 reviews)
Author: Prabudh28

Prabudh28/Designing-a-Multi-Agent-Reinforcement-Learning-with-MCP-for-Portfolio-Optimisation

Score: 28.0 Rank #12088 Agent Framework

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Developing a Multi-Agent Reinforcement Learning (MARL) framework employing the Model Context Protocol (MCP) for the purpose of dynamic asset allocation within algorithmic trading environments.

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Overview

Prabudh28/Designing-a-Multi-Agent-Reinforcement-Learning-with-MCP-for-Portfolio-Optimisation is a Jupyter Notebook MCP server licensed under GPL-3.0. Developing a Multi-Agent Reinforcement Learning (MARL) framework employing the Model Context Protocol (MCP) for the purpose of dynamic asset allocation within algorithmic trading environments.

Ranked #12088 out of 25022 indexed tools.

Ecosystem

Jupyter Notebook GPL-3.0

Score Breakdown

Stars 15% 2

2 stars → early stage

Freshness 25% 10mo ago

Last commit 10mo ago → stale

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 0

0 contributors → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 0

Description Detailed

License GPL-3.0

Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →

How to Improve

Freshness high impact

Last commit was 328 days ago — a recent commit would boost your freshness score

Contributors medium impact

Single-contributor projects carry bus-factor risk — welcoming contributors boosts confidence

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Details

Stars: 2
Forks: 0
Open Issues: 0
Closed Issues: 0
Contributors: 0
Dependents: —
Language: Jupyter Notebook
License: GPL-3.0
Last Commit: 10mo ago

Matched Queries

"model context protocol""model-context-protocol"

See alternatives to Prabudh28/Designing-a-Multi-Agent-Reinforcement-Learning-with-MCP-for-Portfolio-Optimisation → Browse agent-framework category → Compare Prabudh28/Designing-a-Multi-Agent-Reinforcement-Learning-with-MCP-for-Portfolio-Optimisation → A2A vs MCP: Agent Protocol Comparison →What is MCP? Model Context Protocol Explained →

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