Financial Time Series RAG

FinSeer

FinSeer is a specialized retrieval model developed at The Fin AI to improve financial time-series forecasting. It leverages LLM feedback and optimized query-sequence alignment to retrieve historically significant patterns with minimal noise.

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FinSeer is a cutting-edge retrieval model designed to revolutionize financial time-series forecasting by uncovering historically significant patterns with precision. By integrating LLM feedback and optimized query-sequence alignment, FinSeer refines financial data retrieval, enhancing predictive accuracy and minimizing noise in complex market analyses.

Developed within The Fin AI community, FinSeer addresses the limitations of traditional retrieval methods, which rely on simplistic numeric similarity or text-based training. Unlike generic approaches, FinSeer leverages financial indicators and historical trends to retrieve the most impactful sequences, setting a new standard for retrieval-augmented forecasting. By bridging the gap between retrieval quality and predictive performance, FinSeer empowers financial researchers and institutions to develop more robust, data-driven market strategies.

What is FinSeer?

Built on a novel retrieval-augmented generation framework, FinSeer is designed to enhance financial time-series forecasting by retrieving historically significant patterns with precision. It introduces three core innovations:

StockLLM: A fine-tuned 1B parameter financial LLM serving as the backbone for market prediction and analysis.
LLM-Guided Candidate Selection: A novel retrieval mechanism leveraging large language model feedback to identify the most relevant historical sequences.
FinSeer Retriever: A specialized retrieval model trained to maximize query-sequence alignment, outperforming traditional methods and achieving 8% higher accuracy on BIGDATA22.

Best in Forecasting

FinSeer’s superior results emphasize the need for domain-specific retrieval models tailored to financial forecasting. Unlike traditional methods, FinSeer integrates LLM-driven candidate selection and optimized query-sequence alignment, allowing it to retrieve the most relevant stock movement patterns and significantly improve prediction accuracy.

Broad Market Coverage

FinSeer captures a wider range of financial indicators than other retrieval methods. While models like Instructor and LLM Embedder focus on closing prices (close, adj_close), FinSeer integrates high, low, VWAP, and movement, providing a more complete view of market trends by considering both price range and liquidity factors.

Volatility & Trend Detection

FinSeer emphasizes volatility-based indicators like MACD crossover, Bollinger Bands, and exceeding_upper/lower levels, which are key for identifying trend reversals and breakout signals. Compared to UAE and E5, which use similar signals with less emphasis, FinSeer offers stronger market sentiment analysis, benefiting risk management and tactical trading.

Alpha Factor Utilization

Unlike traditional retrieval models, FinSeer effectively integrates diverse quantitative alpha factors (e.g., alpha021, alpha032, alpha101). This is crucial for systematic trading strategies, allowing it to capture hidden market inefficiencies and improve forecasting accuracy in financial applications.

The embedding visualization provides an overall representation of how different financial indicators are structured within the learned embedding space. By mapping financial features into a shared representation, it reveals clusters and relationships between key indicators, such as price movements, volume trends, volatility signals, and alpha factors. A well-structured embedding space enhances retrieval effectiveness, allowing models to identify historically relevant sequences for improved stock movement prediction. FinSeer’s embedding space is optimized to capture meaningful financial patterns, leading to more accurate and data-driven market forecasts.

FinSeer Contributors

The University of Manchester
Columbia University
Yale Univeristy

The National Center of Text Mining, UK
Archimedes RC, GR

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