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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

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-FinLLMQuantitative Finance

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.

AILLMModular Trading Infrastructure

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.

cryptocurrency tradinghierarchical reinforcement learningregret-aware optimization

0 likes · 16 min read

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

Bighead's Algorithm Notes

Mar 24, 2026 · Artificial Intelligence

How an Interactive Imitation‑Learning Agent Framework Trains Robust Trading Strategies

The article analyzes the simulation‑reality gap in algorithmic trading and proposes an interactive market simulator that combines a pool of imitation‑learning agents, an action‑synthesis network, and a DDPG‑based reinforcement‑learning trader, showing superior robustness and downside protection on QQQ data.

Agent-Based ModelingDDPGFinancial AI

0 likes · 16 min read

How an Interactive Imitation‑Learning Agent Framework Trains Robust Trading Strategies

Bighead's Algorithm Notes

Mar 22, 2026 · Artificial Intelligence

DigMA: Controllable Generation of Financial Market Data – A Deep Dive

This article reviews the DigMA model, which uses a diffusion‑guided meta‑agent to generate high‑fidelity, controllable order‑flow data for financial markets, details its problem formulation, architecture, training on Chinese stock datasets, extensive experiments—including reinforcement‑learning‑based high‑frequency trading evaluation—and demonstrates its superior accuracy and ultra‑low latency generation.

Financial Market SimulationMeta‑Agentcontrollable generation

0 likes · 16 min read

DigMA: Controllable Generation of Financial Market Data – A Deep Dive