AutoOption: RAG-Based Financial Intelligent Agent Workflow, a Market Decision Support System with Multi-Source Data Fusion

AutoOption is a financial intelligent workflow system combining RAG (Retrieval-Augmented Generation) technology and multi-agent architecture. It integrates options, financial reports, news, and macroeconomic data to provide signals and directional references for investment decisions. The system emphasizes informational support rather than direct trading execution advice; final decisions must rely on professional human judgment.

Financial markets have high information density and fast-changing dynamics. Investors need to process heterogeneous information such as market data, company financial reports, news, and macroeconomic data. Traditional manual collection and interpretation are inefficient and prone to missing key information. AutoOption uses RAG technology and Agentic Workflow to automate information collection and preliminary analysis, addressing these pain points.

RAG Technology Principles

In the retrieval phase, relevant document fragments are obtained from the knowledge base (historical financial reports, industry reports, news archives, macroeconomic databases); in the generation phase, retrieved information is combined with user queries to ensure answers are factually accurate, traceable, and highly timely.

Agentic Workflow Design

It includes:

• Data Collection Agent (multi-source data acquisition and cleaning)
• Options Analysis Agent (implied volatility/Greeks indicator analysis)
• Financial Report Analysis Agent (financial indicator extraction and trend comparison)
• News Sentiment Agent (sentiment tendency and event detection)
• Macro Analysis Agent (economic indicator monitoring)
• Signal Synthesis Agent (result integration)

Collaboration Mechanism

The Master Agent schedules specialized agents, shares intermediate results, summarizes outputs, handles conclusion conflicts, and provides confidence assessment.

Options Data

Analyze implied volatility (market expectation differences), option chain structure (support/resistance levels and large transactions), and Greeks indicators (risk exposure quantification).

Financial Report Data

Extract key metrics such as revenue/profit/EPS, conduct trend analysis, interpret management guidance, and perform industry horizontal comparisons.

News and Sentiment Data

Detect major events (mergers/acquisitions/regulatory changes), quantify sentiment tendencies, and identify hot topics.

Macroeconomic Data

Monitor monetary policies (interest rates/quantitative policies), economic indicators (GDP/inflation/unemployment rate), and geopolitical risks.

Signal Types

Includes directional signals (bullish/bearish/neutral), intensity ratings (strong/medium/weak), time frames (short/medium/long-term), and risk warnings.

Decision Support Positioning

The system's output serves as an informational reference and does not provide specific strike price/position advice. It emphasizes the final role of human judgment, reflecting a prudent attitude towards financial AI applications.

Data Pipeline Architecture

Data collection layer (multi-API calls and format conversion), storage layer (vector database for text / time-series database for prices), processing layer (cleaning/feature extraction/sentiment analysis), and interface layer (API query service).

Vector Retrieval Optimization

Text vectorization (pre-trained models), efficient indexing (HNSW), hybrid retrieval (keyword + semantic), and result reordering.

Prompt Engineering

Define the role of a financial analyst, provide context information, specify expected output formats, and clarify constraints such as prohibiting specific investment advice.

Investment Research

Automatically aggregate multi-source information, initially screen opportunities/risks, continuously monitor trends, and assist in report generation.

Risk Management

Early risk warning, portfolio exposure analysis, scenario simulation, and compliance monitoring.

Quantitative Strategy Development

Generate training features, validate the effectiveness of trading signals, support strategy backtesting, and explore alpha factors.

Limitations

Dependence on data quality, presence of model hallucinations, unsuitability for high-frequency trading (latency issues), and insufficient interpretability.

Usage Notes

Do not replace professional judgment, continuously validate output accuracy, comply with regulatory requirements, and implement independent risk management.

Industry Trends

AI evolves from rules to intelligence, single-source to multi-source, batch processing to real-time, and tools to assistants; RAG has great potential in research report analysis/compliance review/customer service/knowledge management; future directions include stronger reasoning ability, multi-modal fusion, personalized customization, and causal inference.