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A builder shares a concept for a deterministic, node-based graph system that LLMs can both understand and construct for complex time-series financial calculations, arguing that black-box LLM math output is untrustworthy for finance agents. The project is still a proof of concept, with longer-form deep dives planned for community feedback.
Quantitative Finance with OCaml is a comprehensive technical book/guide covering derivatives pricing, risk management, credit modeling, algorithmic trading, and HFT infrastructure using OCaml, with production-quality code, honest mathematical derivations, and coverage of modern OCaml 5 features (domains, effects, OxCaml extensions).
Business Plan Writer is an AI agent that assists in building investor-ready business plans by testing strategies, sizing markets, and modeling financials for various funding needs.
The article advocates for using a strict pass metric instead of partial credit to evaluate finance AI agents, referencing the Ling-3.0-flash-Fin release to improve reliability in complex, step-dependent tasks.
A framework using distribution-valued firm characteristics and language-model embeddings provides portfolio risk bounds without cross-asset covariance estimates, demonstrating low-variance allocations with Qwen3-Embedding-8B representations.
The paper introduces EventTime, a multi-resolution framework using multiscale contrastive learning to estimate post-event financial losses from cybersecurity incidents, and presents the SECURE dataset for this purpose.
GPT-5.5 brings a 19 percentage point improvement in multi-step reasoning and financial modeling, significantly reducing the burden of knowledge work, which excites the Box team.