Multimodal Biotech Stock Trading Bot: When Large Language Models Meet Quantitative Analysis

Core Idea: This project is a Python trading pipeline that combines Large Language Models (LLM) with quantitative technical indicators, focusing on automated analysis and decision-making for biotech stocks, and exploring innovative applications of AI in financial investment. Maintained by seetarajpara, the project was released on GitHub on June 16, 2026, with the original title "multimodal-biotech-trading-bot" and the link: https://github.com/seetarajpara/multimodal-biotech-trading-bot. The core innovation is multimodal data fusion, integrating structured quantitative indicators and unstructured text information.

The biotech sector is highly volatile, and its value is closely tied to clinical trial results. Analysis faces four major challenges: 1. High specialization, requiring understanding of medical terminology, clinical trials, etc.; 2. Information scattered across multiple channels such as academic papers and FDA announcements; 3. Dominated by unstructured text, which is difficult to utilize by traditional quantitative models; 4. Time-sensitive—missing key timing affects investments. These factors drive the innovation of introducing LLM.

The core design concept is multimodal fusion, integrating two types of data sources:

Quantitative Technical Indicators: Including moving averages (SMA/EMA), RSI, Bollinger Bands, VWAP, MACD, etc., providing quantitative measures of market sentiment and price behavior; Text Information Sources: LLM processes financial reports, FDA announcements, medical papers, research reports, etc., to extract key information, identify sentiment, and discover correlations.

LLM as an intelligent analyst:

1. Entity Recognition and Relationship Extraction: Identify entities like drug names, targets, and their relationships;
2. Sentiment Analysis: Convert text attitudes into quantitative signals;
3. Event Detection: Identify major events such as the launch of clinical trials.

Signal Fusion: Multi-dimensional scoring (weighted technical/fundamental/news aspects), confidence calibration, balance between rules and learning (retain trading rules + LLM reasoning).

Individual Investors: Automatically monitor and push information, generate structured reports, provide reference signals; Institutional Investors: Quickly screen targets, assist in information organization and verification, improve risk control; Quantitative Developers: Demonstrate the path to incorporating unstructured data into quantitative frameworks, and practice combining LLM with traditional indicators.

Main Challenges:

1. Model Hallucination: Need information tracing, manual review, confidence thresholds;
2. Data Quality and Latency: Ensure text is authoritative and accurate, avoid look-ahead bias;
3. Regulatory Compliance: Comply with algorithmic trading regulations, keep logs;
4. Overfitting Risk: Strict out-of-sample testing and real-time verification.

Evolution direction of AI + Finance: More refined semantic understanding, deeper multimodal fusion (images/audio), real-time reasoning; Responsible AI Investment: Human-machine collaboration, interpretable decisions, strict risk control.

This project demonstrates the prospects of combining LLM with quantitative analysis. It is not a plug-and-play money-making tool but an exploration of a new human-machine collaboration model. Its value lies in providing cases for developers, pointing out tool directions for investors, and showing the path of LLM vertical implementation. In the future, AI will assist rather than replace humans, improving analysis efficiency.