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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 19, 2025 · Artificial Intelligence

QuantAgent Unveiled: A Multi‑Agent LLM Framework for High‑Frequency Trading (Code Open)

QuantAgent introduces a multi‑agent LLM framework that replaces text‑based inputs with raw OHLC price signals, decomposes trading decisions into Indicator, Pattern, Trend, Risk, and Decision agents, and achieves substantially higher direction accuracy and returns across ten financial assets in zero‑shot HFT experiments.

Financial AILLMRisk Management

0 likes · 10 min read

QuantAgent Unveiled: A Multi‑Agent LLM Framework for High‑Frequency Trading (Code Open)

Bighead's Algorithm Notes

Oct 18, 2025 · Artificial Intelligence

Time Series Paper Digest (Oct 11‑17 2025): FIRE, CauchyNet, EvoRate, CoRA

From Oct 11‑17 2025, this digest presents four recent AI papers on time‑series forecasting: FIRE introduces a frequency‑domain decomposition with independent amplitude‑phase modeling and adaptive weighting; CauchyNet leverages holomorphic activations for compact, data‑efficient learning; the EvoRate framework quantifies learnability via mutual information; and CoRA adds covariate‑aware adaptation to foundation models, all reporting significant accuracy gains and enhanced interpretability.

AI researchcovariate-aware adaptationdeep learning

0 likes · 10 min read

Time Series Paper Digest (Oct 11‑17 2025): FIRE, CauchyNet, EvoRate, CoRA

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 ResultsTime Series Analysis

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.

DatasetFinancial TradingLLM

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 DetectionTransformerdeep learning

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 Optimizationasset allocationcrypto trading

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.

Data SynthesisFinancial AILLM

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 comparisonQwen2.5-VL-3B

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