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Bighead's Algorithm Notes

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Bighead's Algorithm Notes

May 1, 2026 · Artificial Intelligence

Quantum‑Enhanced A3C² Leverages Time‑Series Dynamic Clustering for Adaptive ETF Stock Picking

Traditional ETF selection and plain A3C reinforcement learning struggle with high‑dimensional features and static clustering, so the authors propose Q‑A3C², which embeds variational quantum circuits and time‑series dynamic clustering into the A3C framework, achieving a 17.09% cumulative return versus a 7.09% benchmark on S&P 500 components.

A3CETF stock selectiondynamic clustering

0 likes · 16 min read

Quantum‑Enhanced A3C² Leverages Time‑Series Dynamic Clustering for Adaptive ETF Stock Picking

Bighead's Algorithm Notes

Apr 27, 2026 · Artificial Intelligence

STEAM: Wavelet‑Enhanced Attention Model for Stock Price Prediction

The STEAM model combines discrete wavelet transform, a wavelet‑enhanced attention mechanism, and a market‑index prefix within a Mamba‑2 encoder to capture multi‑frequency spatial and temporal dependencies in stock data, achieving state‑of‑the‑art performance across multiple international markets as measured by IC, PnL and Sharpe ratios.

Attention MechanismDeep LearningMamba-2

0 likes · 17 min read

STEAM: Wavelet‑Enhanced Attention Model for Stock Price Prediction

Bighead's Algorithm Notes

Apr 23, 2026 · Artificial Intelligence

Paper Review: TradeTrap – Evaluating the Reliability and Faithfulness of LLM‑Based Trading Agents

The article introduces TradeTrap, a unified framework that systematically stress‑tests large‑language‑model‑based autonomous trading agents by injecting component‑level perturbations—such as data falsification, prompt injection, and state tampering—into a historical US‑stock back‑test, revealing how small disturbances can cascade into extreme risk exposure, portfolio drawdown, and performance collapse.

LLMRobustnessTradeTrap

0 likes · 18 min read

Paper Review: TradeTrap – Evaluating the Reliability and Faithfulness of LLM‑Based Trading Agents

Bighead's Algorithm Notes

Apr 22, 2026 · Artificial Intelligence

How DeepAries’s Adaptive Rebalancing Timing Boosts Portfolio Returns

DeepAries is a novel deep reinforcement‑learning framework that jointly learns when to rebalance a portfolio and how to allocate assets by combining a Transformer‑based state encoder with PPO, and extensive experiments on four major markets show it significantly outperforms fixed‑frequency baselines in risk‑adjusted return, transaction cost, and drawdown.

DeepAriesPPOTransformer

0 likes · 15 min read

How DeepAries’s Adaptive Rebalancing Timing Boosts Portfolio Returns

Bighead's Algorithm Notes

Apr 20, 2026 · Artificial Intelligence

Exploring CSMD: A China‑Specific Multimodal Stock Dataset and the LightQuant Quantitative Framework

The article introduces CSMD, a high‑quality multimodal dataset built from Chinese financial news for the CSI‑300 and SSE‑50 stocks, describes LLM‑enhanced factor extraction and rigorous data validation, presents the modular LightQuant framework, and shows through extensive experiments that CSMD and LightQuant outperform existing resources such as CMIN‑CN in stock trend prediction and backtesting.

CSMDLLM factor extractionLightQuant

0 likes · 12 min read

Exploring CSMD: A China‑Specific Multimodal Stock Dataset and the LightQuant Quantitative Framework

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.

Attention MechanismBenchmark EvaluationFriedkin-Johnsen model

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

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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 TradingTransformerVQ-VAE

0 likes · 17 min read

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