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Bighead's Algorithm Notes

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Bighead's Algorithm Notes

Sep 3, 2026 · Artificial Intelligence

LLM Compression of Financial Texts Alters Decisions Despite Factual Accuracy

A new arXiv paper introduces Information Fidelity to measure how LLM compression of 10-Q MD&A sections and earnings calls changes downstream investment decisions, identifying decontextualization and model dependency as key failure modes and proposing Agentic Context Compression (ACC) to audit and reduce decision flips.

10-Q MD&ALLM compressionagentic context compression

0 likes · 22 min read

LLM Compression of Financial Texts Alters Decisions Despite Factual Accuracy

Bighead's Algorithm Notes

Sep 1, 2026 · Artificial Intelligence

XAlpha: AI Quant Researcher with Memory & Reflection for Alpha Discovery

XAlpha introduces a memory-driven AI quantitative researcher that automates the full hypothesis-to-code alpha discovery loop via a multi-brain architecture, integrating report knowledge, hypothesis planning, factor evolution, empirical validation, and feedback integration, achieving superior performance on CSI300.

AI quantitative researcherCSI300LLM Agents

0 likes · 35 min read

XAlpha: AI Quant Researcher with Memory & Reflection for Alpha Discovery

Bighead's Algorithm Notes

Aug 30, 2026 · Artificial Intelligence

EvoQuant: How Large Language Models Can Self‑Evolve as Quantitative Researchers

EvoQuant introduces a self‑evolving, validator‑guided framework that lets large language models diagnose, propose, and verify improvements to existing quantitative trading strategies, achieving Sharpe ratio gains of up to 199% across A‑share and cryptocurrency benchmarks while demonstrating robustness through ablation and walk‑forward tests.

EvoQuantLarge Language ModelSharpe ratio

0 likes · 19 min read

EvoQuant: How Large Language Models Can Self‑Evolve as Quantitative Researchers

Bighead's Algorithm Notes

Aug 27, 2026 · Artificial Intelligence

MetaPS: Adaptive Strategy Selection for Financial Markets Using Simulated Supervision

The article analyzes MetaPS, a simulation‑guided framework that adaptively selects executable trading programs from a strategy library, showing that supervised meta‑strategy learning improves returns across 0.8B‑9B parameter models and outperforms fixed‑strategy baselines, direct decision agents, and prompt‑based LLM agents in both stock and sandbox environments.

MetaPSadaptive strategy selectionfinancial markets

0 likes · 12 min read

MetaPS: Adaptive Strategy Selection for Financial Markets Using Simulated Supervision

Bighead's Algorithm Notes

Aug 26, 2026 · Artificial Intelligence

How AlphaMemo Enables Self‑Evolving Alpha Factor Mining with Structured Search Memory

AlphaMemo introduces a structured‑search‑process memory for LLM agents that records effective and failed edit patterns in specific parent‑factor contexts, uses AST‑difference extraction, confidence‑gated residual learning, and asymmetric veto to tackle combinatorial search, noisy feedback, redundancy, and over‑fitting, achieving superior out‑of‑sample performance and discovery efficiency on CSI 500 and S&P 500 benchmarks.

AST diffAlpha factor miningAlphaMemo

0 likes · 18 min read

How AlphaMemo Enables Self‑Evolving Alpha Factor Mining with Structured Search Memory

Bighead's Algorithm Notes

Aug 24, 2026 · Artificial Intelligence

Paper Review: PandaAI – An Intelligent Factor‑Mining Agent

This article reviews the PandaAI framework, a closed‑loop neural‑symbolic LLM agent that models market regimes, applies constrained Monte‑Carlo Tree Search for factor generation, and continuously adapts via back‑test feedback, achieving significantly higher Rank IC and lower drawdown on CSI‑300 data.

LLMMonte Carlo Tree Searchclosed-loop agent

0 likes · 17 min read

Paper Review: PandaAI – An Intelligent Factor‑Mining Agent

Bighead's Algorithm Notes

Aug 21, 2026 · Artificial Intelligence

RankGLU: Residual Gated Component Boosts Cross‑Sectional Stock Ranking Performance by 11%

The article analyzes cross‑sectional stock prediction as a ranking task, identifies the prediction head as a bottleneck, introduces the RankGLU residual bottleneck GLU module that preserves a linear scoring path while adding bounded multiplicative interaction, and demonstrates an 11% IC improvement on CSI300 with extensive experiments and ablations.

CSI300GLURankGLU

0 likes · 14 min read

RankGLU: Residual Gated Component Boosts Cross‑Sectional Stock Ranking Performance by 11%

Bighead's Algorithm Notes

Aug 19, 2026 · Artificial Intelligence

Can LLMs Uncover Real Economic Links to Boost Cross‑Stock Prediction?

The paper proposes a two‑stage Retrieve‑then‑Reason framework that first builds a sparse candidate graph from 10‑K text embeddings and then uses a large language model to filter edges for true economic relationships, resulting in a higher‑Sharpe, lower‑drawdown cross‑stock trading signal on S&P 500 constituents.

LLMcross‑stock predictionfinancial networks

0 likes · 17 min read

Can LLMs Uncover Real Economic Links to Boost Cross‑Stock Prediction?

Bighead's Algorithm Notes

Aug 16, 2026 · Artificial Intelligence

AlphaCrafter: A Full‑Stack Multi‑Agent Framework for Adaptive Cross‑Sectional Quant Trading

AlphaCrafter tackles the non‑stationary nature of financial markets by integrating LLM‑driven factor mining, market‑aware factor screening, and risk‑constrained execution into a closed‑loop multi‑agent system, and experiments on CSI 300 and S&P 500 demonstrate consistently higher risk‑adjusted returns, lower variance, and robust performance compared with five baseline methods.

Large Language Modeladaptive executionfactor discovery

0 likes · 19 min read

AlphaCrafter: A Full‑Stack Multi‑Agent Framework for Adaptive Cross‑Sectional Quant Trading

Bighead's Algorithm Notes

Jul 30, 2026 · Artificial Intelligence

How MSH‑LLM Fuses Multi‑Scale Hypergraphs with Large Language Models for Time‑Series Analysis

The paper introduces MSH‑LLM, a multi‑scale hypergraph framework that aligns natural language and time‑series modalities via a cross‑modal alignment module and mixed prompts, achieving state‑of‑the‑art performance on 27 real‑world datasets across forecasting, classification, few‑shot and zero‑shot tasks.

MSH-LLMcross-modal alignmentlarge language models

0 likes · 18 min read

How MSH‑LLM Fuses Multi‑Scale Hypergraphs with Large Language Models for Time‑Series Analysis