OpenFinGym is a multi-task verifiable gym environment for quant-finance agents with automated task construction from publications, containerised runtime, paper trading engine, and support for SFT/RL training.
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Qlib: An ai-oriented quantitative investment platform
12 Pith papers cite this work. Polarity classification is still indexing.
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RAVEN proposes a regime-aware MoE architecture with cumulative importance thresholding and correlation-aware weighting to adaptively select temporal context for non-stationary financial forecasting.
PRISM-VQ integrates vector-quantized latent factors with financial priors and a structure-conditioned mixture-of-experts to deliver improved cross-sectional stock return predictions and portfolio performance on CSI 300 and S&P 500.
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.
An LLM-driven evolutionary framework that writes and evolves Python-coded trading factors reports large gains in predictive accuracy on CSI300, but with incomplete validation.
ADOWIP uses a decision-loss priority gate to update only when loss exceeds an empirical quantile under budget constraints, showing lower held-out decision loss than always-update or fixed-period baselines on ETT tasks.
AlphaMemo equips LLM alpha-mining agents with AST-diff motif memory, residual learning, and asymmetric veto control to improve out-of-sample factor discovery on CSI 500 and S&P 500.
Introduces a paired one-switch benchmark that quantifies protocol-induced inflation from decision-time leakage in financial ML backtests on equity panels from 2016-2024.
An empirical literature analysis reveals a bifurcation in RL environments into Semantic Prior (LLM-dominated) and Domain-Specific Generalization ecosystems with distinct cognitive fingerprints.
AlphaSAGE is a GFlowNet framework with an RGCN structure-aware encoder and dense multi-faceted rewards that mines diverse, novel, and predictive formulaic alphas for quantitative trading.
citing papers explorer
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OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
OpenFinGym is a multi-task verifiable gym environment for quant-finance agents with automated task construction from publications, containerised runtime, paper trading engine, and support for SFT/RL training.
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RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting
RAVEN proposes a regime-aware MoE architecture with cumulative importance thresholding and correlation-aware weighting to adaptively select temporal context for non-stationary financial forecasting.
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Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction
PRISM-VQ integrates vector-quantized latent factors with financial priors and a structure-conditioned mixture-of-experts to deliver improved cross-sectional stock return predictions and portfolio performance on CSI 300 and S&P 500.
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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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Cognitive Alpha Mining via LLM-Driven Code-Based Evolution
An LLM-driven evolutionary framework that writes and evolves Python-coded trading factors reports large gains in predictive accuracy on CSI300, but with incomplete validation.
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Adapt Only When It Pays: Budgeted Decision-Loss Priority for Delayed Online Time-Series Adaptation
ADOWIP uses a decision-loss priority gate to update only when loss exceeds an empirical quantile under budget constraints, showing lower held-out decision loss than always-update or fixed-period baselines on ETT tasks.
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AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents
AlphaMemo equips LLM alpha-mining agents with AST-diff motif memory, residual learning, and asymmetric veto control to improve out-of-sample factor discovery on CSI 500 and S&P 500.
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When Alpha Disappears: A One-Switch Benchmark for Decision-Time Leakage in Financial Backtests
Introduces a paired one-switch benchmark that quantifies protocol-induced inflation from decision-time leakage in financial ML backtests on equity panels from 2016-2024.
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From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
An empirical literature analysis reveals a bifurcation in RL environments into Semantic Prior (LLM-dominated) and Domain-Specific Generalization ecosystems with distinct cognitive fingerprints.
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AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration
AlphaSAGE is a GFlowNet framework with an RGCN structure-aware encoder and dense multi-faceted rewards that mines diverse, novel, and predictive formulaic alphas for quantitative trading.
- Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents
- FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting