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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

Oct 17, 2025 · Artificial Intelligence

Exploring MLLM4TS: A Universal Multimodal Framework for Time‑Series Analysis

This article reviews the MLLM4TS framework, which fuses visual representations of multivariate time series with large language models to address complex temporal dependencies, cross‑channel interactions, and task generalization, and demonstrates superior performance on classification, anomaly detection, forecasting, and few‑shot scenarios across multiple benchmarks.

Ablation StudyBenchmark ResultsMultimodal LLM

0 likes · 11 min read

Exploring MLLM4TS: A Universal Multimodal Framework for Time‑Series Analysis

Bighead's Algorithm Notes

Oct 14, 2025 · Artificial Intelligence

How TS‑Agent Uses LLMs and Reflective Feedback to Automate Financial Time‑Series Modeling

TS‑Agent is a modular LLM‑driven framework that formalizes financial time‑series modeling as a three‑stage iterative decision process, leveraging structured knowledge bases, dynamic memory, and a feedback‑driven code‑editing loop to outperform AutoML baselines in accuracy, robustness, and auditability.

AutoMLKnowledge BaseLLM

0 likes · 12 min read

How TS‑Agent Uses LLMs and Reflective Feedback to Automate Financial Time‑Series Modeling

Bighead's Algorithm Notes

Oct 12, 2025 · Artificial Intelligence

Trading-R1: Open-Source LLM Framework for Explainable Financial Trading

This article reviews Trading‑R1, an open‑source LLM inference framework that integrates multimodal financial data, three‑stage supervised‑fine‑tuning and reinforcement learning to generate structured investment arguments and risk‑adjusted trade decisions, achieving superior Sharpe ratio and drawdown performance on real‑world stock and ETF tests.

Financial TradingLLMMultimodal

0 likes · 11 min read

Trading-R1: Open-Source LLM Framework for Explainable Financial Trading

Bighead's Algorithm Notes

Oct 11, 2025 · Artificial Intelligence

Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)

This article summarizes eight newly released AI papers on multivariate time‑series forecasting and anomaly detection, detailing each work's motivation, proposed methodology, key innovations such as CRIB, TS‑JEPA, DSAT‑HD, DIMIGNN, ASTGI, IndexNet, TsLLM, Moon, TimeSeriesScientist, MLG‑4TS, and Augur, and reports their experimental validation on real‑world datasets.

Anomaly DetectionLarge Language ModelTransformer

0 likes · 23 min read

Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)

Bighead's Algorithm Notes

Oct 10, 2025 · Artificial Intelligence

Quantitative Finance Paper Digest (Sep 27 – Oct 10 2025)

This digest summarizes recent arXiv papers that introduce new AI‑driven methods for portfolio similarity, Bayesian portfolio optimization, end‑to‑end deep‑learning portfolio construction, large‑language‑model‑based financial prediction, and multi‑agent crypto‑trading systems, highlighting their datasets, architectures, and empirical gains.

Bayesian OptimizationLarge Language Modelsasset allocation

0 likes · 18 min read

Quantitative Finance Paper Digest (Sep 27 – Oct 10 2025)

Bighead's Algorithm Notes

Oct 9, 2025 · Artificial Intelligence

Paper Review: TradingGroup – A Multi‑Agent Quantitative Trading System with Self‑Reflection and Data Synthesis

The paper introduces TradingGroup, a five‑agent LLM‑based quantitative trading framework that incorporates a self‑reflection mechanism, dynamic risk management, and an automated data‑synthesis pipeline, and demonstrates superior cumulative returns, Sharpe ratios, and lower drawdowns than rule‑based, ML, RL, and existing LLM strategies on five real‑world stock datasets.

Financial AILLMQuantitative Trading

0 likes · 14 min read

Paper Review: TradingGroup – A Multi‑Agent Quantitative Trading System with Self‑Reflection and Data Synthesis

Bighead's Algorithm Notes

Oct 2, 2025 · Artificial Intelligence

FinZero: Multimodal Large‑Model Reasoning for Financial Time‑Series Forecasting

FinZero is a multimodal large‑model that leverages a 30‑billion‑parameter Qwen2.5‑VL backbone fine‑tuned with the UARPO strategy on the FVLDB dataset, enabling accurate financial time‑series prediction, uncertainty quantification, and outperforming larger models such as GPT‑4o by about 13.5% in high‑confidence groups.

FinZeroGPT-4o comparisonMultimodal LLM

0 likes · 10 min read

FinZero: Multimodal Large‑Model Reasoning for Financial Time‑Series Forecasting

Bighead's Algorithm Notes

Sep 29, 2025 · Artificial Intelligence

AlphaAgents: BlackRock’s LLM‑Driven Multi‑Agent System for Stock Portfolio Management

AlphaAgents introduces a role‑based multi‑agent framework—Fundamental, Sentiment, and Valuation agents—leveraging LLMs to analyze 10‑K reports, news, and price data, with a debate mechanism via Microsoft AutoGen; experiments on 15 tech stocks show superior cumulative returns and Sharpe ratios under risk‑neutral and risk‑averse settings compared to single‑agent baselines.

AlphaAgentsFinancial AILLM

0 likes · 10 min read

AlphaAgents: BlackRock’s LLM‑Driven Multi‑Agent System for Stock Portfolio Management

Bighead's Algorithm Notes

Sep 27, 2025 · Artificial Intelligence

Weekly Time-Series Paper Digest (Sep 20‑26, 2025)

This digest summarizes three recent arXiv papers that propose novel diffusion‑based generation, a channel‑independent convolution for multivariate forecasting, and a style‑guided diffusion framework, each demonstrating improved realism, coherence, and diversity of synthetic time‑series data through extensive experiments.

DS-DiffusionIConvMMD loss

0 likes · 8 min read

Weekly Time-Series Paper Digest (Sep 20‑26, 2025)

Bighead's Algorithm Notes

Sep 26, 2025 · Artificial Intelligence

Paper Summaries: Recent AI-Driven Finance Research (Sep 20‑26, 2025)

This article presents concise English summaries of four recent arXiv papers that explore AI-driven trading frameworks, dual‑view risk‑relation identification from 10‑K filings, multimodal language models for financial forecasting, and credit‑spread prediction enhanced by non‑financial data, highlighting their methods, datasets, and performance results.

AICredit SpreadsFinance

0 likes · 9 min read

Paper Summaries: Recent AI-Driven Finance Research (Sep 20‑26, 2025)