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Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

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Latest from Bighead's Algorithm Notes

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Bighead's Algorithm Notes

Jan 30, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Jan 24‑Jan 30, 2026)

This article presents concise summaries of three recent quantitative finance papers—MarketGAN for high‑dimensional asset return generation, AlphaCFG for grammar‑guided Alpha factor discovery, and a hybrid AI‑driven trading system integrating technical analysis, machine learning, and sentiment—highlighting their methodologies, experimental results, and economic value, and provides links to additional related research.

Alpha Factor DiscoveryGenerative Adversarial NetworksHybrid AI Trading

0 likes · 9 min read

Weekly Quantitative Finance Paper Digest (Jan 24‑Jan 30, 2026)

Bighead's Algorithm Notes

Jan 28, 2026 · Artificial Intelligence

How HiveMind Optimizes LLM Multi‑Agent Trading Systems via Contribution‑Guided Online Prompts

The HiveMind framework introduces a contribution‑guided online prompt optimization (CG‑OPO) that quantifies each LLM‑driven agent’s impact with Shapley values and uses a DAG‑Shapley algorithm to efficiently attribute credit, enabling real‑time adaptive optimization of multi‑agent stock‑trading systems and achieving superior returns with far fewer LLM calls.

DAG-ShapleyFinancial TradingLLM

0 likes · 15 min read

How HiveMind Optimizes LLM Multi‑Agent Trading Systems via Contribution‑Guided Online Prompts

Bighead's Algorithm Notes

Jan 25, 2026 · Artificial Intelligence

FinAgent Orchestration Framework: Shifting from Algorithmic to Agent‑Based Trading

The article presents FinAgent, a multi‑agent orchestration framework that maps traditional algorithmic trading components to autonomous agents, validates it on hourly stock and minute‑level Bitcoin back‑tests, and reports superior risk control, auditability, and scalability compared with standard benchmarks.

Algorithmic TradingFinAgentFinancial AI

0 likes · 15 min read

FinAgent Orchestration Framework: Shifting from Algorithmic to Agent‑Based Trading

Bighead's Algorithm Notes

Jan 21, 2026 · Artificial Intelligence

Lead–LagNet: Modeling Cross‑Series Lead‑Lag Dependencies for Time‑Series Forecasting

Lead–LagNet addresses three key limitations of existing graph neural networks for multivariate time‑series forecasting—loss of fine‑grained temporal detail, shared weight assumptions, and reduced interpretability—by introducing a sequence preprocessor with a global influence separator and subsequence detector, a subsequence dependency encoder, and a decoupled message‑passing mechanism, achieving superior performance on synthetic benchmarks and S&P 500 market data.

Financial Market PredictionLead‑Lag DependencyLead–LagNet

0 likes · 13 min read

Lead–LagNet: Modeling Cross‑Series Lead‑Lag Dependencies for Time‑Series Forecasting

Bighead's Algorithm Notes

Jan 13, 2026 · Artificial Intelligence

Do Complex Multi‑Agent Mechanisms Really Boost Investment Returns? A CMU Validation

A five‑agent GPT‑4o‑mini trading system was evaluated over 21 months across technology, general, and financial markets, revealing that while communication among agents can boost returns, the optimal dialogue style depends on market volatility, and higher dialogue quality does not guarantee better performance.

Financial AILLM tradingalpha generation

0 likes · 12 min read

Do Complex Multi‑Agent Mechanisms Really Boost Investment Returns? A CMU Validation

Bighead's Algorithm Notes

Jan 11, 2026 · Artificial Intelligence

FinRpt: A Multi‑Agent Framework for Automatic Generation and Evaluation of Stock Research Reports

FinRpt introduces a novel multi‑agent pipeline that builds a high‑quality stock research report (ERR) dataset from six financial data sources, defines a comprehensive 11‑metric evaluation suite, and demonstrates that supervised‑fine‑tuned and reinforcement‑learned LLM agents significantly outperform single LLM baselines in both accuracy and efficiency.

DatasetFinRptFinancial NLP

0 likes · 14 min read

FinRpt: A Multi‑Agent Framework for Automatic Generation and Evaluation of Stock Research Reports

Bighead's Algorithm Notes

Jan 10, 2026 · Artificial Intelligence

Key Quantitative AI Papers Jan 3‑9 2026: Portfolio Optimization, Equity Correlation Forecasting, and Index Tracking Review

This article summarizes three recent quantitative finance papers—introducing a decision‑oriented SPO paradigm for portfolio optimization, a hybrid transformer‑graph neural network for forecasting S&P 500 equity correlations, and a comprehensive review of modeling approaches for financial index tracking—highlighting their methods, datasets, and empirical findings.

AIGraph Neural NetworkQuantitative Finance

0 likes · 9 min read

Key Quantitative AI Papers Jan 3‑9 2026: Portfolio Optimization, Equity Correlation Forecasting, and Index Tracking Review

Bighead's Algorithm Notes

Jan 8, 2026 · Artificial Intelligence

Alpha‑R1: Reinforcement‑Learning‑Driven Large‑Model Alpha Factor Selection

Alpha‑R1 integrates reinforcement learning with an 8‑billion‑parameter LLM to jointly process price and news data, creating context‑aware factor embeddings that outperform traditional quantitative and generic LLM baselines on CSI 300 and CSI 1000 portfolios, demonstrating robust alpha‑decay resistance and zero‑sample generalization.

Financial AIReinforcement Learningalpha factor selection

0 likes · 16 min read

Alpha‑R1: Reinforcement‑Learning‑Driven Large‑Model Alpha Factor Selection

Bighead's Algorithm Notes

Jan 6, 2026 · Artificial Intelligence

FinRS: A Risk‑Sensitive Trading Framework for Real‑World Financial Markets

FinRS integrates hierarchical market analysis, dual decision agents, and multi‑time‑scale reward feedback to enable risk‑aware multi‑stage trading, achieving higher cumulative returns, better Sharpe ratios, and lower maximum drawdowns than existing LLM‑based and reinforcement‑learning baselines across diverse stocks.

FinRSLLMReinforcement Learning

0 likes · 14 min read

FinRS: A Risk‑Sensitive Trading Framework for Real‑World Financial Markets

Bighead's Algorithm Notes

Jan 4, 2026 · Artificial Intelligence

How VTA Combines Large‑Model Reasoning for Precise and Explainable Stock Time‑Series Forecasting

The VTA framework integrates large language model reasoning with textual annotation of technical indicators, employs a Time‑GRPO reinforcement‑learning objective and multi‑stage joint conditional training, and achieves state‑of‑the‑art accuracy and expert‑rated interpretability on US, Chinese and European stock datasets.

LLMReinforcement LearningVTA

0 likes · 19 min read

How VTA Combines Large‑Model Reasoning for Precise and Explainable Stock Time‑Series Forecasting