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% UQO Working Paper — Economics / Finance
% VQuant: An Agentic LLM Architecture for Quantitative Financial Analysis
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% METADATA
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\newcommand{\WPnumber}{13}
\newcommand{\WPtitle}{VQuant: An Agentic Large Language Model Architecture for Automated Quantitative Financial Analysis and Document Generation}
\newcommand{\WPsubtitle}{Reasoning-Augmented Tool Orchestration, In-Loop Numerical Computation, and Multi-Format Reporting}
\newcommand{\WPdate}{June 2026}
\newcommand{\WPversion}{1.0}
\newcommand{\WPabstract}{%
  Large language models (LLMs) have shown remarkable fluency in financial discourse, yet
  conversational fluency alone does not guarantee numerically rigorous, reproducible, or
  citable analysis. This paper presents \textbf{VQuant}, a production-grade web platform that
  couples a reasoning-augmented LLM with a deterministic execution substrate to transform a
  natural-language request into a complete quantitative financial report. We document the
  system's methodology and its principal innovations: (i)~an \emph{agentic orchestration loop}
  that exposes more than two hundred financial and analytical tools---spanning over 265
  market-data endpoints, four web-research services, and an arbitrary-code Python sandbox of
  seventeen scientific libraries---to the model through a constrained function-calling protocol;
  (ii)~a \emph{context-management algorithm} that preserves conversational coherence over
  effectively unbounded histories via adaptive summarization under a token budget;
  (iii)~a \emph{single-call computation paradigm} that consolidates data acquisition, numerical
  estimation, and figure rendering into one sandboxed Python execution, collapsing what would
  otherwise be several model round trips into one; and (iv)~a \emph{unified document pipeline}
  that emits publication-ready PDF, DOCX, \LaTeX{}~Beamer slide decks, and spreadsheet workbooks
  with live formulas from a single Markdown artifact. We describe each mechanism as it is
  implemented---the streaming Server-Sent-Event protocol and its eight-way event dispatch, the
  recursive tool-calling loop and its tool-use/tool-result invariant, the batched-execution
  throttle, the two-stage context-management machinery, the token-accounting correction, and the
  inter-process sandbox that exec's model-authored code and captures its figures---so that the
  design can be reproduced. The classical quantitative methods the platform automates are
  conventional and relegated to an appendix; our contribution is the \emph{architecture} that
  makes them accessible through natural language while preserving reproducibility. We argue that
  VQuant illustrates a reusable design pattern in which the language model is positioned as a
  planner and synthesizer rather than as a calculator, with all numerical work delegated to
  auditable, deterministic code.
}
\newcommand{\WPkeywords}{Large language models, agentic systems, tool use, quantitative finance, automated reporting, retrieval-augmented generation, financial NLP}
\newcommand{\WPjel}{C45, C63, C88, G17}

% --- Author ---
\newcommand{\WPauthor}{Simon-Pierre Boucher}
\newcommand{\WPaffiliation}{%
  Département des sciences administratives\\
  Université du Québec en Outaouais%
}
\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
\newcommand{\WPaddress}{%
  Gatineau -- Pavillon Alexandre-Taché\\
  283, boulevard Alexandre-Taché\\
  Gatineau, Québec, Canada J9A 1L8%
}

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% DOCUMENT
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\begin{document}

% --- Title Page ---
\input{sections/titlepage}

% --- Main Body ---
\input{sections/introduction}
\input{sections/literature}
\input{sections/data}
\input{sections/methodology}
\input{sections/results}
\input{sections/robustness}
\input{sections/conclusion}

% --- References ---
\newpage
\bibliography{references}

% --- Appendix ---
\input{appendix/appendix}

\end{document}