Author

Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

116

Articles

Likes

Views

Comments

Latest from Bighead's Algorithm Notes

100 recent articles max

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.

Large Language Modelalpha factor selectionfinancial AI

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.

ExplainabilityLLMVTA

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 ScreeningGenerative AILLMs

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.

HypergraphMaGNetMamba

0 likes · 14 min read

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

Bighead's Algorithm Notes

Dec 28, 2025 · Artificial Intelligence

Paper Reading: Multi‑Cycle Learning Framework (MLF) for Financial Time‑Series Forecasting

The paper introduces MLF, a multi‑cycle learning framework that integrates three novel modules—inter‑cycle redundancy filtering (IRF), learnable weighted integration (LWI), and multi‑cycle adaptive patch (MAP)—plus a patch‑squeeze component, achieving higher accuracy and efficiency on financial time‑series tasks such as fund‑sales prediction and outperforming strong single‑ and multi‑cycle baselines, with successful deployment in Alipay’s fund inventory system.

Alipay deploymentSelf-attentionfinancial AI

0 likes · 16 min read

Paper Reading: Multi‑Cycle Learning Framework (MLF) for Financial Time‑Series Forecasting

Bighead's Algorithm Notes

Dec 25, 2025 · Artificial Intelligence

Paper Review: DeltaLag – An End‑to‑End Deep Learning Framework for Dynamically Learning Lead‑Lag Patterns in Financial Markets

DeltaLag introduces a sparse cross‑attention mechanism that dynamically discovers pair‑specific, time‑varying lead‑lag relationships in US equity markets and uses them to construct interpretable trading signals, achieving significantly higher annualized returns, Sharpe ratios, and information coefficients than fixed‑lag, statistical, and other spatio‑temporal deep learning baselines.

DeltaLagdeep learningfinancial time series

0 likes · 13 min read

Paper Review: DeltaLag – An End‑to‑End Deep Learning Framework for Dynamically Learning Lead‑Lag Patterns in Financial Markets

Bighead's Algorithm Notes

Dec 23, 2025 · Artificial Intelligence

How H3M‑SSMoEs Combines Hypergraph Multimodal Learning and LLM Reasoning to Predict Stock Direction

The paper introduces H3M‑SSMoEs, a framework that integrates a multi‑context hypergraph for fine‑grained spatio‑temporal dynamics with a frozen Llama‑3.2‑1B LLM adapter, and a style‑structured expert mixture to jointly model stock relationships, multimodal semantics, and market regimes, achieving superior accuracy and investment returns on DJIA, NASDAQ‑100, and S&P‑100 benchmarks.

HypergraphLLMMultimodal Learning

0 likes · 14 min read

How H3M‑SSMoEs Combines Hypergraph Multimodal Learning and LLM Reasoning to Predict Stock Direction

Bighead's Algorithm Notes

Dec 21, 2025 · Artificial Intelligence

Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading

Logic-Q introduces a program‑sketch paradigm that injects lightweight, plug‑and‑play market‑trend logic into deep reinforcement learning agents, dramatically improving trend detection, reducing drawdowns, and outperforming state‑of‑the‑art DRL strategies on multiple quantitative‑trading benchmarks.

Deep Reinforcement LearningLogic-QMarket Trend Detection

0 likes · 12 min read

Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading

Bighead's Algorithm Notes

Dec 19, 2025 · Artificial Intelligence

Quantitative Finance Paper Digest: Dec 13‑19 2025 Highlights

This digest presents recent arXiv papers (Dec 13‑19 2025) on AI‑driven quantitative finance, covering LLM‑based portfolio recommendation, reinforcement‑learning deep hedging, hybrid SV‑LSTM volatility forecasting, dynamic stacking ensembles, GA‑optimized SVR forecasting, and interpretable deep learning asset pricing, each with abstracts and key findings.

LLMdeep learningportfolio optimization

0 likes · 16 min read

Quantitative Finance Paper Digest: Dec 13‑19 2025 Highlights