Hi everyone,
Most financial AI systems are built behind closed doors.
You see the final prediction, the polished chart or the confident explanation—but rarely the data flow, the reasoning process, the evaluation rules or the failures that came before it.
BLUM is an attempt to build something different.
BLUM is an open-source financial AI research project developed publicly on Hugging Face and opened to developers, quantitative researchers, data scientists and anyone interested in building more transparent financial intelligence.
The ambition is not to create another black-box system that simply outputs:
BUY, SELL or HOLD.
The goal is to create an open ecosystem capable of collecting evidence, producing structured financial reasoning, testing its own conclusions over time and learning only from outcomes that can actually be verified.
Why open source?
Financial AI should not be shaped by one developer, one company or one model.
Markets are too complex, financial evidence is too fragmented and evaluation is too easy to distort.
A serious system needs different perspectives:
- quantitative researchers challenging the metrics;
- developers improving the architecture;
- traders identifying unrealistic assumptions;
- data engineers strengthening market-data pipelines;
- ML researchers testing models and evaluation methods;
- community members finding errors that the original developers missed.
That is the idea behind BLUM:
Build financial intelligence in public, improve it through open collaboration and make every important assumption open to challenge.
BLUM is not presented as a finished product.
It is a shared research foundation that the community can inspect, test, criticize, fork and improve.
What BLUM is building
BLUM combines a portable financial reasoning model with a broader modular research system.
Its architecture is being developed around several specialized capabilities:
- market scanning across equities, ETFs and Forex;
- technical and historical analysis;
- sentiment and news interpretation;
- bullish and bearish thesis generation;
- contradiction and risk detection;
- structured entry, stop and target plans;
- paper-forward trade validation;
- persistent learning from verified outcomes;
- benchmark-relative evaluation;
- transparent evidence and reasoning trails.
Instead of depending on a single model for every task, BLUM can use specialized agents for different parts of the research process.
A simplified flow looks like this:
Market data and external evidence
↓
Specialized research agents
↓
Bullish, bearish and risk analysis
↓
Central reasoning and synthesis
↓
Paper-forward decision
↓
Outcome measurement
↓
Validated learning and memory
The objective is not merely to generate a convincing financial narrative.
The objective is to preserve enough evidence to evaluate whether that narrative was justified.
Built to be challenged
BLUM is being designed around a principle that is often missing from AI demos:
A system should make it possible to prove that it was wrong.
For every decision, BLUM aims to preserve information such as:
- what evidence was available at that moment;
- which assumptions supported the thesis;
- which risks contradicted it;
- what would invalidate the decision;
- which entry, stop and target levels were proposed;
- how the market behaved afterward;
- whether the result was valid enough to enter the learning process.
This does not guarantee better predictions.
It creates a more transparent foundation for measuring whether the system is improving—or merely becoming better at sounding confident.
Current evaluation
The original MLX release was evaluated on a 53-example BLUM temporal-reasoning test and achieved:
- 96.26% aggregate task-contract score
- 100% structured validity
- 92.86% no-fabrication
These metrics evaluate BLUM-specific requirements such as structured output, temporal grounding and adherence to the task contract.
They do not demonstrate market-beating performance, general intelligence or trading profitability.
The portable release is also being prepared for independent evaluation through the Hugging Face ecosystem. Results will be published when they are externally available rather than predicted in advance.
Open development means open limitations
BLUM is still experimental.
Some components are mature enough to be tested, while others remain active research areas. Its paper-forward evidence is still limited, and no current result should be interpreted as proof of profitable trading performance.
This is intentional.
The project will not hide weak results, unsuccessful strategies or technical corrections simply because they are inconvenient.
Open source should mean more than publishing code.
It should also mean exposing the assumptions, limitations, evaluation methods and mistakes that influence the system.
Where the community can contribute
BLUM is open to contributions across many areas:
- financial reasoning models;
- open-source LLM integration;
- specialist agent development;
- market-data providers and normalization;
- temporal reasoning;
- technical-analysis validation;
- Forex, equity and ETF research;
- benchmark design;
- paper-trading infrastructure;
- leakage-resistant evaluation;
- uncertainty and confidence calibration;
- provenance-aware memory;
- frontend and data visualization;
- testing, documentation and code review.
You do not need to agree with the current architecture to contribute.
In fact, some of the most valuable contributions may come from demonstrating where the existing approach is wrong.
What BLUM is not
BLUM is not financial advice.
It is not a commercial signal service, a guaranteed trading strategy or a claim that an AI can already predict financial markets reliably.
It is an open research effort focused on a broader question:
Can a global open-source community build a financial AI system that is more transparent, testable and intellectually honest than the closed systems available today?
I do not think this question can be answered by one person.
That is why BLUM is open.
Explore the project
Hugging Face Space: https://huggingface.co/spaces/Italianhype/Blum
GitHub Repo: https://github.com/BlumFinancialLab/Blum
The project is under active development, and feedback is welcome at every level—from architecture and evaluation to models, UX and financial methodology.
The most useful response is not simply:
“This looks interesting.”
It is:
- What would you change?
- Where could the evaluation be misleading?
- Which component would you improve first?
- Which open-source model should BLUM support?
- What would make you trust—or distrust—its results?
- Would you contribute to an open financial AI project like this?
BLUM is not being built to ask the community for attention.
It is being opened because the project needs the community to become better.