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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 24, 2025 · Artificial Intelligence

Weekly AI‑Finance Paper Digest (Oct 18‑24 2025)

This digest presents seven recent arXiv papers that explore large‑language‑model‑driven portfolio scoring, hybrid ResNet‑RMT covariance denoising for crypto, LLM‑enhanced financial causal analysis, multilingual news alignment for stock returns, three‑step bubble prediction with news and macro data, multimodal volatility forecasting, and news‑aware reinforcement trading, each with reported performance gains.

LLMMultimodal Learningcausal inference

0 likes · 15 min read

Weekly AI‑Finance Paper Digest (Oct 18‑24 2025)

Bighead's Algorithm Notes

Oct 23, 2025 · Artificial Intelligence

FinCast: A Foundation Model for Financial Time‑Series Forecasting

FinCast introduces a decoder‑only Transformer foundation model for financial time‑series forecasting that tackles non‑stationarity, multi‑domain diversity, and multi‑resolution challenges through input chunking with frequency embeddings, a sparse MoE decoder, and a PQ‑loss, achieving zero‑shot and supervised gains over state‑of‑the‑art baselines while running five times faster on consumer GPUs.

PQ lossTransformerfinancial time series

0 likes · 12 min read

FinCast: A Foundation Model for Financial Time‑Series Forecasting

Bighead's Algorithm Notes

Oct 21, 2025 · Artificial Intelligence

KANMixer: A New KAN‑Centric Paradigm for Long‑Term Time Series Forecasting

This article reviews the KANMixer model, which places Kolmogorov‑Arnold Networks at the core of a lightweight architecture for long‑term time series forecasting, detailing its design, extensive benchmark experiments on seven real‑world datasets, ablation analyses, and its computational trade‑offs versus MLP and Transformer baselines.

Ablation StudyKANLong-term Time Series Forecasting

0 likes · 8 min read

KANMixer: A New KAN‑Centric Paradigm for Long‑Term Time Series Forecasting

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.

LLMfinancial AIhigh-frequency trading

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 StudyFew‑Shot LearningTime 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.

AutoMLFinancial ModelingKnowledge Base

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.

ExplainabilityFinancial 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.

Large Language ModelTransformeranomaly detection

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.

asset allocationbayesian optimizationcrypto trading

0 likes · 18 min read

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