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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 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.

FinRptLLMdataset

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 AILarge Language Modelalpha 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.

FinRSLLMfinancial markets

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.

LLMTime-seriesVTA

0 likes · 19 min read

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

Bighead's Algorithm Notes

Jan 3, 2026 · Artificial Intelligence

Quantitative Finance Paper Digest (Dec 27 2025 – Jan 2 2026)

This article curates recent quantitative finance research, summarizing five papers that explore generative‑AI‑enhanced portfolio construction, LLM‑driven alpha screening with reinforcement learning, statistical tests for look‑ahead bias in LLM forecasts, and a non‑stationarity‑complexity trade‑off framework for return prediction, each with links to the original arXiv PDFs and code.

Alpha ScreeningLLMsLookahead Bias

0 likes · 10 min read

Quantitative Finance Paper Digest (Dec 27 2025 – Jan 2 2026)

Bighead's Algorithm Notes

Dec 30, 2025 · Artificial Intelligence

MaGNet: Dual‑Hypergraph Mamba Network for Time‑Causal and Global Stock Trend Forecasting

MaGNet introduces a three‑component architecture—MAGE block with bidirectional Mamba, adaptive gating and sparse MoE, 2‑D spatio‑temporal attention, and a dual hypergraph framework (time‑causal and global probability hypergraphs)—that outperforms 17 baselines on six major stock indices in both prediction accuracy and risk‑adjusted returns.

Financial AIHypergraphMaGNet

0 likes · 14 min read

MaGNet: Dual‑Hypergraph Mamba Network for Time‑Causal and Global Stock Trend Forecasting