Author

Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

123

Articles

Likes

210

Views

Comments

Latest from Bighead's Algorithm Notes

100 recent articles max

Bighead's Algorithm Notes

Jun 3, 2026 · Artificial Intelligence

TF-CoDiT: A New Approach to Synthesizing Treasury Futures Data

TF-CoDiT introduces a diffusion‑Transformer framework that converts multi‑channel treasury futures time series into discrete wavelet coefficients, encodes cross‑channel dependencies with a U‑shaped VAE, conditions generation on a structured FinMAP prompt, and achieves state‑of‑the‑art MSE and MAE scores across multiple contracts and horizons.

FinMAPTF-CoDiTU-VAE

0 likes · 17 min read

TF-CoDiT: A New Approach to Synthesizing Treasury Futures Data

Bighead's Algorithm Notes

May 29, 2026 · Artificial Intelligence

AlphaCFG: Grammar‑Guided, Interpretable Alpha‑Factor Discovery Framework

AlphaCFG introduces a grammar‑based framework that defines a controllable search space for discovering syntactically valid, financially interpretable alpha factors, using syntax‑aware Monte‑Carlo tree search guided by value and policy networks, and demonstrates superior search efficiency and profitability on Chinese and US stock datasets.

Alpha FactorGrammar Guided SearchMonte Carlo Tree Search

0 likes · 17 min read

AlphaCFG: Grammar‑Guided, Interpretable Alpha‑Factor Discovery Framework

Bighead's Algorithm Notes

May 18, 2026 · Artificial Intelligence

FineFT: Efficient Risk-Aware Reinforcement Learning for Futures Trading

FineFT introduces a three‑stage ensemble reinforcement‑learning framework that tackles high‑leverage reward volatility and missing ability‑boundary awareness in crypto futures trading by using selective TD‑error updates, VAE‑based market‑state boundary detection, and a risk‑aware routing mechanism, ultimately outperforming twelve baselines on six financial metrics while cutting risk by over 40%.

Reinforcement LearningVariational Autoencoderensemble methods

0 likes · 12 min read

FineFT: Efficient Risk-Aware Reinforcement Learning for Futures Trading

Bighead's Algorithm Notes

May 11, 2026 · Artificial Intelligence

Analyzing CN‑Buzz2Portfolio: A Chinese Market Dataset for LLM‑Driven Macro and Sector Asset Allocation

This article reviews the CN‑Buzz2Portfolio benchmark, which maps daily Chinese hot‑news streams to macro‑ and industry‑level ETF allocations, introduces a three‑stage CPA pipeline for evaluating large language models as autonomous financial agents, and reports extensive experiments on nine state‑of‑the‑art LLMs across two rolling market periods.

CN-Buzz2PortfolioCPA frameworkLLM

0 likes · 18 min read

Analyzing CN‑Buzz2Portfolio: A Chinese Market Dataset for LLM‑Driven Macro and Sector Asset Allocation

Bighead's Algorithm Notes

May 6, 2026 · Artificial Intelligence

AI‑Trader: Real‑time Benchmark for Autonomous LLM Agents in Financial Markets

The AI‑Trader benchmark evaluates large language model agents in fully autonomous, real‑time US stock, Chinese A‑share, and cryptocurrency markets, revealing that general intelligence alone does not guarantee profitable trading, while robust risk‑control mechanisms drive cross‑market stability and excess returns.

Autonomous AgentsLLMRisk Management

0 likes · 17 min read

AI‑Trader: Real‑time Benchmark for Autonomous LLM Agents in Financial Markets

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.

Mamba-2attention mechanismdeep learning

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.

Financial AILLMRobustness

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.

DeepAriesPPOPortfolio Management

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