BestHub
Sign in
Bighead's Algorithm Notes
Author

Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

138
Articles
0
Likes
755
Views
0
Comments
Recent Articles

Latest from Bighead's Algorithm Notes

100 recent articles max
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.

Deep LearningLLMportfolio optimization
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.

Financial TradingLLM Agentsagent architecture
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.

AlphaQuanterLLM-agentfinancial AI
0 likes · 13 min read
AlphaQuanter: An End‑to‑End Tool‑Orchestrating Agent Using Reinforcement Learning for Stock Trading
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 5, 2025 · Artificial Intelligence

Quantitative Finance Paper Summaries (Nov 29–Dec 5 2025)

This article presents concise summaries of five recent AI‑driven finance papers, covering a stress‑testing framework for LLM trading agents, an orchestration framework for financial agents, an event‑reflection memory model for stock forecasting, a hybrid LLM‑Bayesian network architecture for options wheel strategies, and their experimental results.

LLMbenchmarkingfinancial AI
0 likes · 12 min read
Quantitative Finance Paper Summaries (Nov 29–Dec 5 2025)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 4, 2025 · Artificial Intelligence

Paper Review: RETuning Boosts Large‑Model Stock Trend Prediction Reasoning

This article analyzes the RETuning framework, which addresses LLMs' bias toward analyst opinions and lack of evidence weighting in stock movement prediction by introducing a two‑stage cold‑start fine‑tuning and reinforcement learning pipeline, evaluating it on the large Fin‑2024 dataset and demonstrating significant F1 gains, inference‑time scaling, and out‑of‑distribution robustness.

Fin-2024GRPOInference Scaling
0 likes · 12 min read
Paper Review: RETuning Boosts Large‑Model Stock Trend Prediction Reasoning