goldbees-pipeline

Name: goldbees-pipeline
Availability: InStock
Author: Mosaic-agent

Generate ML-driven trading signals with Kelly-weighted positions and risk governance.

36|7|Updated Feb 22, 2026

Search Tags:#pipeline #trading #risk #ml #signal #kelly #goldbees

Ready to run

npx skills add https://github.com/Mosaic-agent/Mosaic-fund-agent --skill goldbees-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: goldbees-pipeline
Source: https://github.com/Mosaic-agent/Mosaic-fund-agent/tree/main/.gravity/skills/goldbees-pipeline
Command: npx skills add https://github.com/Mosaic-agent/Mosaic-fund-agent --skill goldbees-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The GOLDBEES pipeline automates end-to-end generation of ML-driven trading signals. It computes a Kelly-weighted position and blends it with a risk-governed output to inform actionable investment decisions.

Core Features & Use Cases

Executes the GOLDBEES ML pipeline to produce a probability of price increase (prob_up) and a corresponding position weight.
Blends the Kelly-optimal weight with risk governance (inverse-volatility and regime-based adjustments) to produce a final recommendation.
Supports flags for dry runs, past-call evaluation, and retrieving the latest stored signal; ideal for daily investment workflows and backtesting.

Quick Start

Invoke /goldbees-pipeline (with or without flags) to generate and view the latest GOLDBEES signal

Frequently Asked Questions about goldbees-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate ML-driven trading signals for daily investment workflows?▼

The pipeline blends a Kelly-optimal weight with risk governance adjustments using inverse-volatility and regime-based logic. This combination produces a probabilistic signal and final position recommendation that balances return optimization with downside protection.

Can I evaluate past ML signal calls without overwriting my database?▼

Yes, you can evaluate past signal calls without overwriting by using the --no-save and --evaluate flags. These flags execute the pipeline in a dry-run mode against historical data while preventing new checkpoints from being written to your database.

How do I retrieve the latest stored trading signal from the database?▼

What is the best way to apply Kelly criterion position sizing with risk governance?▼

The best way to apply Kelly criterion position sizing with risk governance is using a pipeline that computes the Kelly-optimal weight and blends it with inverse-volatility and regime-based adjustments. This produces a risk-governed final recommendation.

Does the GOLDBEES ML pipeline require any external dependencies to run?▼

No, the GOLDBEES ML pipeline does not require external dependencies to run. It operates independently using Python scripts and built-in flags to manage signal generation, evaluation, and database checkpoint retrieval.