Hubble is an LLM-driven framework that safely discovers diverse alpha factors via operator trees, RAG feedback, and out-of-sample validation on US equity data, with range and volatility factors showing persistence.
org/abs/2308.00016
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AlphaQuanter introduces a single-agent tool-augmented RL framework for stock trading that learns dynamic policies over a transparent decision workflow and reports state-of-the-art financial metrics.
Reproducibility audit of 30 LLM trading papers shows execution assumptions under-reported relative to agent architectures, illustrated by a 10-equity example where frictions compress returns.
QTMRL applies A2C reinforcement learning to a dataset of 23 years of S&P 500 OHLCV data enriched with trend, volatility, and momentum indicators, claiming better profitability and risk control than nine baselines including ARIMA and LSTM.
A survey categorizing LLM-powered agent systems into software-based, physical, and hybrid types, covering industrial applications and challenges such as latency and security.
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
citing papers explorer
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Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discovery
Hubble is an LLM-driven framework that safely discovers diverse alpha factors via operator trees, RAG feedback, and out-of-sample validation on US equity data, with range and volatility factors showing persistence.
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AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading
AlphaQuanter introduces a single-agent tool-augmented RL framework for stock trading that learns dynamic policies over a transparent decision workflow and reports state-of-the-art financial metrics.
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Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
Reproducibility audit of 30 LLM trading papers shows execution assumptions under-reported relative to agent architectures, illustrated by a 10-equity example where frictions compress returns.
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QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
QTMRL applies A2C reinforcement learning to a dataset of 23 years of S&P 500 OHLCV data enriched with trend, volatility, and momentum indicators, claiming better profitability and risk control than nine baselines including ARIMA and LSTM.
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LLM-Powered AI Agent Systems and Their Applications in Industry
A survey categorizing LLM-powered agent systems into software-based, physical, and hybrid types, covering industrial applications and challenges such as latency and security.
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Bridging Language Models and Financial Analysis
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.