by AlphaCeo | 2025-12-10 13:22:22+00:00
We’ve been building something unusual for the past year: a financial analyst that doesn’t hallucinate price levels, doesn’t give generic summaries, and doesn’t rely on a single monolithic model. It works the way an actual analyst desk works: multiple specialized agents, each with its own datasets, constraints, and reasoning stack, debating until the system reaches a conclusion.

We call it Alfred.

Most consumer “AI trading tools” are wrappers around a base model. Ours is a multi-agent financial analysis engine trained on real data feeds and designed to mimic an institutional research workflow.

How Alfred works When you ask something like “Where is US equities risk skew heading over the next month” or “Why is BTC diverging from NASDAQ today”, Alfred doesn’t produce a single-pass answer.

Instead, it triggers coordinated agents:

Macro Agent Parses high-frequency macro data, policy expectations, rate paths, global PMIs, fiscal signals.

Technical Agent Computes structure, volatility regimes, liquidity zones, and pattern validation using rule-based models.

Options Agent Runs options chain scans, infers probability distributions from IV surfaces, identifies where dealers and institutions are positioned.

News + Sentiment Agent Ingests real-time feeds, filters noise, extracts causal signals, and weighs them against historical reactions.

Risk Model Agent Evaluates cross-asset correlations, stress scenarios, and probabilistic outcomes.

Each agent produces its own thesis. Alfred uses arbitration logic to merge them, score them, and produce a final output that includes reasoning, not just a sentence.

The result feels more like reading a research note than talking to a chatbot.

Why we built it Retail traders and smaller funds have zero access to institutional-grade synthesis. Bloomberg is priced for banks. Research desks are inaccessible. But the data exists. The logic exists. The workflows exist.

So we rebuilt the process using AI agents, not monolithic prompts.

What’s new (launching Friday)

Faster chain-of-thought arbitration

Better regime detection

A rewritten risk engine

Sentiment agent upgrade

A more explicit “thesis explanation” mode

Performance tuned for daily market open prep

We’re making Alfred available to the public this Friday.

If you’re in markets, macro, quant, or just curious how multi-agent reasoning performs in the real world, we’d love feedback.

We are just scratching the surface of bridging the gap between retail and institutions by providing the same data quality, logic, and algorithms as institutions.

Why this might be interesting to HN

Multi-agent reasoning applied to real markets

Real data feeds instead of generic training sets

Clear, inspectable reasoning chains

A serious attempt at institutional-grade analysis for non-institutional users

An AI product where correctness and error-bounds matter more than style

If you want to try Alfred or stress-test any part of the architecture, the link is here:

https://alphatradeai.com/

We’re a tiny team, but we’ll be in the comments all day answering technical questions. Architectural critique is welcome. Break it. Challenge it. Tell us what assumptions are wrong.

We want Alfred to become a real analyst desk, not a toy.