Introducing QuantCoderFS R&D
LLM-Based Financial Analysis, Strategy Development, and Equity Research - Last updated: Wednesday, April 16, 2025
Introduction
This publication, QuantCoderFS R&D, serves as the research and development log for the QuantCoderFS platform—a full-stack application for language model–driven workflows in quantitative finance.
The platform applies large language models and agent-based frameworks to tasks such as summarizing financial literature, generating algorithmic strategies, and analyzing structured financial data. Its workflows are modular, reproducible, and designed for serialization and distribution.
QuantCoderFS R&D documents both the development of the platform and the research outputs it enables. These include code representations of trading strategies, equity-focused analytical notes, and structured summaries of financial and academic material. The focus is on methodological rigor, traceability, and clarity.
What is QuantCoderFS?
QuantCoderFS is a modular AI-powered platform spanning the full research-to-code pipeline. It enables:
Sourcing and summarizing academic and financial research
AI-assisted generation of trading strategies for QuantConnect
Retrieval and interpretation of financial data through API integration
A dedicated data pipeline powered by EODHD supports the Chat with Fundamentals module, which allows real-time interaction with market data, financial statements, and company news. The project is affiliated with EODHD, ensuring direct access to high-quality data sources.
The software architecture is composed of:
Backend: FastAPI
Frontend: Next.js
Orchestration Layer: LangChain and CrewAI
In contrast to the typical CrewAI design—often reliant on multi-file, agent-specific configuration—this implementation consolidates orchestration logic into a single dynamic module. This design decision prioritizes development velocity, reproducibility, and simplified distribution.
Rationale for Hosting on Substack
As an independent developer, maintaining clear, rigorous documentation is essential. Substack provides an appropriate platform for:
Archiving technical progress
Logging implementation details and change histories
Publishing applied research in an accessible, low-friction format
This publication supersedes prior updates hosted on Medium and consolidates all development and research content in one location. It also enables minimal, opt-in monetization without embedding commercial mechanisms into the software itself.
Scope of the Publication
This Substack will serve as a central repository for:
Technical documentation of each functional module
Detailed development notes and version histories
Research on AI workflow structures and performance
Records of experimental modules and implementations
Active Modules (As of Q2 2025)
Summary Generation v1.0
Domain-agnostic module comprising a three-agent structure:
(1) Insight Extraction, (2) Summarization, and (3) Refinement, supported by a proprietary PDF parsing tool. Planned extensions include integration with Notion for structured knowledge storage.
Chat with Fundamentals v1.0
Operates via parallel API calls to three EODHD endpoints, producing structured executive summaries from historical financials, fundamental metrics, and market news. Future extensions aim to incorporate real-time data streaming capabilities.
Code Generation v0.4
Implements a three-agent architecture: (1) Strategy Parsing, (2) Code Writing, and (3) Syntax Validation. The current version integrates validation tools and is being extended toward full compatibility with the QuantConnect LEAN engine, enabling runtime feedback and reinforcement learning loops. A previous version (v0.3) remains publicly available on GitHub as a CLI/GUI prototype based on an NLP–LangChain hybrid model.
Search Workflow v1.0
Supports academic content retrieval via CrossRef and arXiv. A dedicated interpreter agent semantically expands initial user queries for more relevant search results.
Forecasting Module (Planned)
Targeted for later development as a data analysis extension. This module will leverage EODHD time-series datasets and incorporate predictive models based on CNN and XGBoost pipelines.
Roadmap
User Account Management and CI/CD Deployment
This is the next major engineering milestone, scheduled for completion in Q3 2025. It will support user authentication, workflow persistence, and automated deployment processes.
Subscription Structure
Subscribing provides access to:
Ongoing documentation of workflow design and evolution
Research notes derived from equity and strategy analysis
The ability to propose new modules or suggest modifications to existing workflows
Founding Members will receive early access to selected code modules and implementation updates. Feedback and collaboration are welcomed, although responses may be limited in order to preserve focused development cycles.
Concluding Remarks
This publication serves as both a formal development record and a space for applied research in the use of LLM-based systems within quantitative finance. The emphasis is on precision, reproducibility, and practical utility—rather than content volume or visibility metrics.
There will be no commercial advertising, algorithmic promotion, or cross-platform syndication. The objective is to develop, document, and iterate in a manner consistent with academic rigor and technical transparency.
Whether you are following the project or actively contributing, your interest and support are sincerely appreciated.
S. M. Laignel
quantcoder-fs.com