TradingQuant 01
Daily recap
Portfolio NAV, open risk and the decisions taken at the US close. Transparent numbers, no vanity metrics.
Public build · AI quant research
Quantitative research,
in public.
We run an AI consensus engine over US equities and publish what it finds: trade case studies, risk-management decisions and transparent daily recaps. No hype. No guarantees. Educational content only.
TradingQuant AI is an educational research project. Nothing here is investment advice, a personalized recommendation, or an offer to buy or sell any financial instrument. Markets involve risk of loss.
What we publish
Four formats, one principle: show the reasoning and the outcome, not just the win.
02
Trade case studies
Completed trades explained after the fact: thesis, entry, exit and what the engine got right or wrong.
03
Risk & HOLD decisions
Why not trading is often the edge. Patience, position sizing and capital preservation.
04
News analysis
Corporate events and market narratives parsed by the AI, with a clear separation between fact and interpretation.
How it works
A multi-agent pipeline evaluates, challenges and executes. We publish after the fact.
1. Evaluate
Five AI analysts score fundamentals, momentum, sentiment and risk. A judge agent synthesizes the consensus.
2. Execute
A deterministic risk layer sizes positions and places orders with the broker. No discretionary overrides.
3. Publish
We turn the decision into an educational thread: what we saw, what we did and what happened next.
Transparency first
We publish the process, not a promise.
No performance guarantees. Past results do not guarantee future results. Simulations and paper trading may not reflect real market conditions. Some content is AI-generated or AI-assisted and may contain errors. Always verify data with independent sources.
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