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Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

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Bighead's Algorithm Notes

Jun 22, 2026 · Artificial Intelligence

Paper Reading: TimeART – Tool‑Augmented Autonomous Time‑Series Reasoning

The article reviews the TimeART framework, which equips large language models with 21 ready‑to‑use time‑series analysis tools and a four‑stage training regime on the 100k‑trajectory TimeToolBench corpus, enabling an 8B Qwen‑3 model to act as a fully autonomous data scientist and achieve state‑of‑the‑art performance on multiple TSQA, prediction, and reasoning benchmarks.

LLMTSRMTimeART

0 likes · 15 min read

Paper Reading: TimeART – Tool‑Augmented Autonomous Time‑Series Reasoning

Bighead's Algorithm Notes

Jun 20, 2026 · Artificial Intelligence

Can Large Vision‑Language Models Really Understand Candlestick Charts?

This paper builds a multi‑scale candlestick‑chart dataset and a standardized evaluation framework to measure how well visual language models (VLMs) extract price information, using confusion‑matrix diagnostics and Information Coefficient (IC) metrics, and finds that VLMs excel only on monotonic trends and struggle with precise time‑based predictions.

Prompt engineeringStock Predictioncandlestick chart

0 likes · 13 min read

Can Large Vision‑Language Models Really Understand Candlestick Charts?

Bighead's Algorithm Notes

Jun 13, 2026 · Artificial Intelligence

Paper Reading: TimeGMM – An Adaptive GMM Framework for Probabilistic Time‑Series Forecasting

TimeGMM introduces an adaptive Gaussian‑mixture‑model framework with reversible instance normalization, a dual‑branch time encoder, and a conditional decoder, achieving up to 22.48 % improvement in CRPS and 21.23 % in NMAE over state‑of‑the‑art probabilistic forecasting methods across multiple benchmark datasets.

Adaptive NormalizationDeep LearningGaussian Mixture Model

0 likes · 15 min read

Paper Reading: TimeGMM – An Adaptive GMM Framework for Probabilistic Time‑Series Forecasting

Bighead's Algorithm Notes

Jun 6, 2026 · Artificial Intelligence

ProbFM: Deep Evidential Regression for Uncertainty Decomposition in Financial Forecasting

ProbFM introduces a Transformer‑based framework that leverages deep evidential regression to separate epistemic and aleatoric uncertainty in time‑series forecasting, and demonstrates on cryptocurrency returns that this decomposition retains competitive prediction accuracy while enabling risk‑aware trading strategies with superior risk‑adjusted returns.

Deep Evidential RegressionFinancial PredictionProbFM

0 likes · 13 min read

ProbFM: Deep Evidential Regression for Uncertainty Decomposition in Financial Forecasting

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%.

Variational Autoencoderensemble methodsfinancial RL

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 AgentsLLMbenchmark

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