u/this_is_chetan

If you’re holding positions for days or weeks, your biggest enemy isn’t a 5-minute candle; it’s a tectonic macro shift you didn’t see coming. I built Alicanto to act as a 24/7 Macro Research Desk that specifically hunts for catalysts in the precious metals and global fiat space.

The Tech Stack:

• The 6-Agent Swarm: Alicanto routes global news through 4 Junior Analyst agents, a Senior vetting layer, and an Executive synthesis agent (70B model). It’s designed to extract structural data—like physical vault drains or central bank policy shifts—and ignore the daily market "chatter."
• The RAG Ledger: It uses a permanent vector memory core. If a new geopolitical variable enters the market (like a specific trade tariff logic), I "train" the agent via the Correction Ledger. It remembers that context for every future scan, building a massive institutional memory vault over time.

The Edge:

• Executive Briefs: High-level synthesis of how global interest rates or DXY moves are impacting physical supply.
• Contagion Maps: Visualizing how one event (e.g., a mining strike) ripples through the macro board.

The Ask: I need 10 swing traders to test the Executive Briefs and the Web Terminal. I want to know if the AI's "Trade Desk Verdicts" are sharp enough to help you hold a thesis or if the logic is too broad.

DM or comment for a free beta-access key to the terminal.

(Landing page link in the comments).

I’ve been trying to build an automated macro research desk for my own trading, specifically focused on precious metals and global fiat flows.

The core problem I hit immediately: standard "AI wrappers" or single-prompt LLMs are terrible at this. They hallucinate, get distracted by retail sentiment (e.g., Reddit pump-and-dumps), or mistake standard market volatility for structural shifts.

To solve the noise problem, I built Alicanto, a multi-agent reasoning engine that forces data through a strict hierarchy before it ever reaches a conclusion.

Here is the pipeline architecture. I’d love some feedback on where this logic might break down at scale.

1. Data Ingestion & The "Consent Wall" The system continuously sweeps Google News, institutional RSS feeds, and dark pool channels. I’m using a custom Jina + Trafilatura waterfall to handle extraction and bypass cloud-server consent blocks, standardizing the text payloads to ~800 characters to cut out journalistic fluff.

2. The 6-Analyst Swarm Pipeline Instead of dumping data into one massive prompt, the engine routes events through a strict chain of command:

• The 4 Junior Desks (GPT-4o-Mini): These are isolated agents programmed with specific personas (Finance, Physical Supply, Geopolitics, Alternative Data). Their only job is to extract hard numbers and structural events. If an article is just punditry or lacks hard metrics, they kill it immediately.
• The Senior Strategist (GPT-4o-Mini): This agent acts as a semantic shield. It reviews the Juniors' output against a strict ruleset to actively filter out retail/local noise (e.g., "Ignore a supply drain if it's just a local coin shop; focus only on COMEX/LBMA/SGE").
• The Executive (Groq 70B): If an event survives the first two tiers, it hits a high-speed Llama 3.3 70B model. This model checks for final "opinion traps" and synthesizes the data into a structured Executive Brief and Trade Desk Verdict.

3. The RAG "Correction Ledger" Traditional fine-tuning is too slow for evolving macro conditions. Instead, I built a vector-based feedback loop. If the Swarm makes a logic error (e.g., misinterpreting a tariff announcement), I issue a text correction. The system vectorizes that correction (text-embedding-3-small) and stores it in an SQLite ledger. Before the Junior desks process new data, they run a similarity search against the ledger to inject past corrections into their active prompt.

4. The Output The pipeline generates live macro matrices, calculates real-time arbitrage spreads (COMEX vs. Shanghai), and pushes "DEFCON" alerts for severe physical premiums.

The Ask: I am currently looking for 10 quants or developers to test the live Telegram bot and Web Terminal.

I don't need marketing advice; I need you to try and break the swarm logic. I want to know where the noise filter fails, if the RAG ledger is efficient enough, or if this architecture is just over-engineered for what it does.

If you are interested in stress-testing the architecture, drop a comment or DM me, and I will generate a free root-access key for the terminal.

(Link to the architecture dashboard in the comments so I don't trigger the auto-mod).