Trader Journal Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Trader Journal Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<p align="center">
An Open-Source Trading Intelligence Platform
Transform historical trading data into structured knowledge, behavioral insights, recurring patterns, and evidence-based coaching.
Version 1.0.0
</p>
Aureon AI Trading Coach is an open-source trading intelligence platform that helps traders understand why they win, why they lose, and how they can improve over time.
Unlike traditional trading journals that simply record historical trades, Aureon combines performance analytics, structured trade reviews, trader memory, pattern discovery, edge discovery, and AI-assisted coaching into a single intelligence platform.
Instead of focusing only on profit and loss, Aureon learns from the decisions behind every trade.
Its goal is simple:
Turn trading history into actionable knowledge.
Most trading software answers one question:
What happened?
Aureon is designed to answer a much more valuable question:
Why did it happen?
Every trade contains more information than its final result.
Aureon captures:
These observations become structured knowledge that allows Aureon to identify recurring strengths, weaknesses, and genuine trading edges.
Import historical MetaTrader 5 (MT5) trading history into Aureon's database.
Current supported formats:
Generate detailed trading statistics including:
Every completed trade can be reviewed using Aureon's structured review workflow.
Each review captures:
These observations become permanent trading knowledge.
Every reviewed trade becomes a structured decision record containing:
Decision records preserve far more information than a traditional trading journal.
Aureon continuously builds trader memory.
Examples include:
Instead of analyzing isolated trades, Aureon analyzes trading behaviour over time.
Identify recurring relationships between:
Patterns are discovered from historical evidence rather than assumptions.
Aureon identifies statistically supported trading strengths by evaluating:
Winning percentage alone is never treated as a trading edge.
The Aureon Coach combines every layer of intelligence to generate personalized coaching.
Examples include:
Expose Aureon's intelligence engine through REST APIs.
Aureon includes full Model Context Protocol (MCP) support, allowing compatible AI assistants to access trading intelligence using natural language.
Example questions:
MT5 History
│
▼
Trade Import Engine
│
▼
SQLite Database
│
▼
Decision Records
│
▼
Trader Memory
│
▼
Pattern Discovery
│
▼
Edge Discovery
│
▼
Aureon AI Coach
┌──────────┐
│ │
▼ ▼
FastAPI MCPCurrent Release
Version 1.0.0
Version 1 establishes Aureon's core intelligence architecture and includes:
git clone https://github.com/CepharsBonacci/aureon-ai-trading-coach.git
cd aureon-ai-trading-coachpython3 -m venv venv
source venv/bin/activatepython -m venv venv
venv\Scripts\activatepip install -r requirements.txtpython init_db.pyuvicorn server:app --reloadSwagger UI
http://127.0.0.1:8000/docsReDoc
http://127.0.0.1:8000/redocExport your trading history from MetaTrader 5 or Exness as a CSV file.
Then import it using Aureon's Import endpoint.
Once imported, Aureon automatically computes:
After importing your trading history, Aureon identifies trades that have not yet been reviewed.
The review workflow captures information that your broker cannot know, including:
These observations become permanent decision records.
As additional trades are reviewed, Aureon begins identifying recurring patterns in your decision making.
Instead of evaluating isolated trades, Aureon learns how you trade over time.
The AI Coach combines:
to identify statistically supported trading strengths and recurring weaknesses.
Aureon exposes its intelligence engine through FastAPI.
The API is organised into the following modules.
Aureon includes an MCP server allowing compatible AI assistants to access your trading knowledge using natural language.
Examples include:
What is my current expectancy?
Which market performs best?
Show my strongest trading edge.
Which emotional state hurts my performance?
Summarise my trader memory.
What recurring mistakes should I eliminate?
Rather than searching dashboards, traders can interact conversationally with their trading history.
aureon-ai-trading-coach/
│
├── app/
│ ├── analytics.py
│ ├── imports.py
│ ├── models.py
│ ├── database.py
│ │
│ ├── intelligence/
│ │ coach.py
│ │ review_engine.py
│ │ trader_memory.py
│ │ observations.py
│ │ decision_record.py
│ │ edge_engine.py
│ │ pattern_engine.py
│ │
│ ├── routes/
│ │ performance.py
│ │ behaviour.py
│ │ context.py
│ │ imports.py
│ │ observations.py
│ │ review.py
│ │ trader_memory.py
│ │ intelligence.py
│
├── db/
├── mcp_server/
├── requirements.txt
├── server.py
├── README.mdAureon AI Trading Coach is the first product in the Aureon ecosystem.
Future projects include:
Contributions are welcome.
Whether you are fixing bugs, improving documentation, proposing ideas, or building new features, your contributions help improve Aureon for the entire trading community.
Please open an issue before making significant architectural changes.
This project is licensed under the MIT License.
You are free to use, modify, distribute, and build upon this software in accordance with the terms of the license.
See the LICENSE file for details.
Cephars Bonacci
Founder — Aureon Capital AI
GitHub
https://github.com/CepharsBonacci
Most trading software tells you what happened.
Aureon is built to help you understand why it happened.
By combining structured trade reviews, trader memory, behavioural analysis, performance analytics, pattern discovery, edge discovery, and AI coaching, Aureon transforms historical trading activity into actionable intelligence that helps traders continuously improve their decision making.
Aureon AI Trading Coach was built with the belief that trading success comes not only from analysing markets, but from understanding the decisions behind every trade.
Historical data becomes valuable when it is transformed into knowledge.
Knowledge becomes valuable when it improves future decisions.
That is the mission of Aureon.
Aureon AI Trading Coach
Version 1.0.0
"Transforming Trading History into Trading Intelligence."
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.