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Bighead's Algorithm Notes
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Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

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Bighead's Algorithm Notes
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 deploymentFinancial AISelf-Attention
0 likes · 16 min read
Paper Reading: Multi‑Cycle Learning Framework (MLF) for Financial Time‑Series Forecasting
Bighead's Algorithm Notes
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
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.

Financial AIHypergraphLLM
0 likes · 14 min read
How H3M‑SSMoEs Combines Hypergraph Multimodal Learning and LLM Reasoning to Predict Stock Direction
Bighead's Algorithm Notes
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.

Bayesian OptimizationLogic-QMarket Trend Detection
0 likes · 12 min read
Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading
Bighead's Algorithm Notes
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.

LLMQuantitative Financedeep learning
0 likes · 16 min read
Quantitative Finance Paper Digest: Dec 13‑19 2025 Highlights
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 11, 2025 · Artificial Intelligence

Paper Reading: CoRA – A Multimodal Covariate Adaptation Framework for Time‑Series Foundation Models

CoRA freezes pretrained time‑series foundation models, extracts multimodal covariate embeddings, evaluates their causal relevance with a trainable Granger‑Causal Embedding, and injects them via a zero‑initialized condition module, achieving up to 31.1% MSE reduction across single‑ and multi‑modal forecasting tasks.

Granger causal embeddingforecasting benchmarksfoundation models
0 likes · 12 min read
Paper Reading: CoRA – A Multimodal Covariate Adaptation Framework for Time‑Series Foundation Models
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 9, 2025 · Artificial Intelligence

How Do LLM Trading Agents Perform in a Competitive Market Arena?

The paper introduces Agent Market Arena (AMA), a lifelong, real‑time benchmark that evaluates diverse LLM‑based trading agents across crypto and equity markets, revealing that agent architecture, rather than the underlying LLM, drives performance differences and risk‑adjusted returns.

Agent ArchitectureFinancial TradingLLM agents
0 likes · 11 min read
How Do LLM Trading Agents Perform in a Competitive Market Arena?
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 7, 2025 · Artificial Intelligence

AlphaQuanter: An End‑to‑End Tool‑Orchestrating Agent Using Reinforcement Learning for Stock Trading

AlphaQuanter tackles the three major limitations of existing LLM trading agents by introducing a single‑agent framework that dynamically orchestrates market tools, learns transparent decision policies via reinforcement learning, and achieves state‑of‑the‑art performance on key financial metrics across extensive stock‑level experiments.

AlphaQuanterFinancial AILLM agent
0 likes · 13 min read
AlphaQuanter: An End‑to‑End Tool‑Orchestrating Agent Using Reinforcement Learning for Stock Trading