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Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

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Bighead's Algorithm Notes

Apr 14, 2026 · Artificial Intelligence

How Self‑Supervised HINTS Extracts Human Insights from Time Series to Boost Forecast Accuracy

The paper introduces HINTS, a two‑stage self‑supervised framework that leverages Friedkin‑Johnsen opinion dynamics to mine latent human‑driven factors from time‑series residuals, integrates them via attention into state‑of‑the‑art predictors, and demonstrates consistent accuracy gains and interpretability across nine benchmark and real‑world datasets.

Friedkin-Johnsen modelattention mechanismbenchmark evaluation

0 likes · 17 min read

How Self‑Supervised HINTS Extracts Human Insights from Time Series to Boost Forecast Accuracy

Bighead's Algorithm Notes

Apr 13, 2026 · Artificial Intelligence

FactorMiner: Tsinghua’s Self‑Evolving Agent with Skill and Experience Memory for Alpha Factor Mining

FactorMiner is a lightweight, flexible self‑evolving agent framework that combines a modular skill architecture with structured experience memory, using a Ralph loop to guide search, reduce redundancy, and build a diverse, high‑quality alpha factor library that outperforms baselines across A‑share and cryptocurrency markets while leveraging GPU‑accelerated evaluation.

Alpha Factor MiningExperience MemoryFactorMiner

0 likes · 13 min read

FactorMiner: Tsinghua’s Self‑Evolving Agent with Skill and Experience Memory for Alpha Factor Mining

Bighead's Algorithm Notes

Apr 9, 2026 · Artificial Intelligence

WSDM2026 Quantitative Research Papers: Summaries and Insights

This article presents concise summaries of three recent AI‑driven finance papers—Diffolio’s diffusion‑based risk‑aware portfolio optimization, STORM’s dual‑vector‑quantized VAE factor model, and AutoHypo‑Fin’s autonomous web‑mined hypothesis generation—highlighting their motivations, methods, and experimental gains.

AI for financeVQ-VAEdiffusion models

0 likes · 9 min read

WSDM2026 Quantitative Research Papers: Summaries and Insights

Bighead's Algorithm Notes

Apr 7, 2026 · Artificial Intelligence

AutoHypo-Fin: Tsinghua's Web-Mining Method to Auto-Generate and Backtest Market Hypotheses

AutoHypo‑Fin is an end‑to‑end framework that harvests large‑scale web financial data, extracts entities via large language models, builds a temporal knowledge graph, uses retrieval‑augmented generation and statistical backtesting to automatically create, test, and iteratively optimize trading hypotheses, achieving superior risk‑adjusted returns compared with baseline strategies in experiments from 2019‑2024.

AutoHypo-FinKnowledge GraphLLM

0 likes · 11 min read

AutoHypo-Fin: Tsinghua's Web-Mining Method to Auto-Generate and Backtest Market Hypotheses

Bighead's Algorithm Notes

Apr 6, 2026 · Artificial Intelligence

STORM: A Bidirectional Spatiotemporal Factor Model Achieving Sharpe Ratio >1

STORM introduces a bidirectional VQ‑VAE‑based spatiotemporal factor model that extracts fine‑grained time‑series and cross‑sectional features, uses discrete codebooks for orthogonal, diverse factor embeddings, and outperforms nine baselines on portfolio management and algorithmic trading tasks, delivering Sharpe ratios exceeding 1.

Algorithmic TradingPortfolio ManagementQuantitative Finance

0 likes · 17 min read

STORM: A Bidirectional Spatiotemporal Factor Model Achieving Sharpe Ratio >1

Bighead's Algorithm Notes

Apr 2, 2026 · Artificial Intelligence

Diffolio: A Diffusion‑Model Framework for Risk‑Aware Portfolio Optimization

Diffolio introduces a diffusion‑model‑based approach that directly learns a pseudo‑optimal portfolio distribution conditioned on user risk preferences, generating diverse high‑quality portfolios and outperforming classic and recent baselines on six real‑world market datasets, with annualized returns improving up to 12.1 percentage points.

Financial AIGenerative ModelingQuantitative Finance

0 likes · 22 min read

Diffolio: A Diffusion‑Model Framework for Risk‑Aware Portfolio Optimization

Bighead's Algorithm Notes

Mar 31, 2026 · Artificial Intelligence

Top AI-Driven Quantitative Finance Papers from AAAI 2026

This article curates and summarizes recent AI research papers presented at AAAI 2026 that advance quantitative finance, covering controllable market generation, LLM‑powered alpha factor mining, risk‑aware multi‑agent portfolio management, foundation models for market data, and reinforcement‑learning trading policies.

AIFinancial Market SimulationMeta Learning

0 likes · 12 min read

Top AI-Driven Quantitative Finance Papers from AAAI 2026

Bighead's Algorithm Notes

Mar 29, 2026 · Artificial Intelligence

How MetaTrader Uses Reinforcement Learning to Boost Trading Strategy Generalization

The article reviews the MetaTrader method, which formulates sequential portfolio optimization as a partially offline reinforcement‑learning problem, introduces a double‑layer RL algorithm and a conservative TD objective to improve out‑of‑distribution generalization, and demonstrates superior performance on CSI‑300 and NASDAQ‑100 datasets compared with existing baselines.

Financial TradingMetaTraderOOD data augmentation

0 likes · 15 min read

How MetaTrader Uses Reinforcement Learning to Boost Trading Strategy Generalization

Bighead's Algorithm Notes

Mar 27, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)

This article presents concise English summaries of four recent AI‑driven quantitative finance papers, covering an agentic AI screening platform for portfolio investment, a wavelet‑based physics‑informed neural network for option pricing, the FinRL‑X modular trading infrastructure, and the S³G stock state‑space graph for enhanced trend prediction, each with authors, links, and key experimental results.

AIGraph Neural NetworksLLM

0 likes · 12 min read

Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)

Bighead's Algorithm Notes

Mar 26, 2026 · Artificial Intelligence

Paper Reading: ArchetypeTrader – A Reinforcement‑Learning Framework for Selecting and Optimizing Crypto Trading Strategies

The article reviews the ArchetypeTrader framework, which addresses market‑segmentation and demonstration‑data issues in crypto‑currency reinforcement learning by discovering discrete trading archetypes, selecting them via a hierarchical RL agent, and refining actions with a regret‑aware adapter, achieving superior profit and risk‑adjusted returns across multiple markets.

Reinforcement Learningcryptocurrency tradinghierarchical reinforcement learning

0 likes · 16 min read

Paper Reading: ArchetypeTrader – A Reinforcement‑Learning Framework for Selecting and Optimizing Crypto Trading Strategies