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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

Mar 24, 2026 · Artificial Intelligence

How an Interactive Imitation‑Learning Agent Framework Trains Robust Trading Strategies

The article analyzes the simulation‑reality gap in algorithmic trading and proposes an interactive market simulator that combines a pool of imitation‑learning agents, an action‑synthesis network, and a DDPG‑based reinforcement‑learning trader, showing superior robustness and downside protection on QQQ data.

Agent-Based ModelingDDPGFinancial AI

0 likes · 16 min read

How an Interactive Imitation‑Learning Agent Framework Trains Robust Trading Strategies

Bighead's Algorithm Notes

Mar 22, 2026 · Artificial Intelligence

DigMA: Controllable Generation of Financial Market Data – A Deep Dive

This article reviews the DigMA model, which uses a diffusion‑guided meta‑agent to generate high‑fidelity, controllable order‑flow data for financial markets, details its problem formulation, architecture, training on Chinese stock datasets, extensive experiments—including reinforcement‑learning‑based high‑frequency trading evaluation—and demonstrates its superior accuracy and ultra‑low latency generation.

Financial Market SimulationMeta‑Agentcontrollable generation

0 likes · 16 min read

DigMA: Controllable Generation of Financial Market Data – A Deep Dive

Bighead's Algorithm Notes

Mar 20, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Summaries (Mar 14‑Mar 20, 2026)

This article compiles abstracts of four recent AI‑driven quantitative finance papers, covering an autonomous factor‑investing framework, a program‑level factor‑mining system, an adaptive regime‑aware stock‑price predictor with reinforcement learning, and a comprehensive analysis of AI agents in financial markets.

AI AgentsReinforcement Learningfactor investing

0 likes · 10 min read

Weekly Quantitative Finance Paper Summaries (Mar 14‑Mar 20, 2026)

Bighead's Algorithm Notes

Mar 19, 2026 · Artificial Intelligence

How ASDA Generates Structured Financial Reasoning Skills for LLMs Without Fine‑Tuning

The ASDA framework automatically creates modular, version‑controlled financial‑reasoning skill files by iteratively analyzing student model failures, clustering errors, and injecting structured guidance, achieving up to a 17.33‑point boost on arithmetic tasks and a 5.95‑point boost on non‑arithmetic tasks in the FAMMA benchmark, far surpassing prior zero‑training methods such as GEPA and ACE.

ASDALLM adaptationbenchmark FAMMA

0 likes · 22 min read

How ASDA Generates Structured Financial Reasoning Skills for LLMs Without Fine‑Tuning

Bighead's Algorithm Notes

Mar 17, 2026 · Artificial Intelligence

ICLR2026 Quantitative Finance Paper Summaries

This article compiles and summarizes recent ICLR2026 papers on quantitative finance, presenting their titles, authors, abstracts, code and paper links, and highlighting benchmarks such as AlphaBench, TiMi, STABLE, and AlphaSAGE that explore large language models and multi‑agent systems for factor mining and trading.

AlphaBenchQuantitative FinanceTiMi

0 likes · 11 min read

ICLR2026 Quantitative Finance Paper Summaries

Bighead's Algorithm Notes

Mar 15, 2026 · Artificial Intelligence

Paper Reading: TiMi – An Inference‑Driven Multi‑Agent System for Quantitative Trading

TiMi is a reasoning‑driven multi‑agent framework that decouples strategy development from minute‑level deployment, leverages LLMs for semantic analysis, code generation and mathematical reasoning, and achieves stable profits, high execution efficiency and strong risk control across more than 200 stock and crypto trading pairs.

Financial AILLMTiMi

0 likes · 17 min read

Paper Reading: TiMi – An Inference‑Driven Multi‑Agent System for Quantitative Trading

Bighead's Algorithm Notes

Mar 14, 2026 · Artificial Intelligence

Quantitative Finance Paper Digest: AI‑Driven Market Prediction Studies (Mar 7‑13 2026)

This digest summarizes four recent research papers that apply advanced AI techniques—node‑transformer graphs with BERT sentiment analysis, a quantum‑classical LSTM‑Born machine hybrid, large‑language‑model benchmarking for portfolio optimization, and a conditional diffusion model—to improve stock market prediction, volatility forecasting, and investment decision making, providing detailed experimental results and statistical validation.

BERTTransformerdiffusion model

0 likes · 10 min read

Quantitative Finance Paper Digest: AI‑Driven Market Prediction Studies (Mar 7‑13 2026)

Bighead's Algorithm Notes

Mar 13, 2026 · Artificial Intelligence

Paper Reading: STABLE – A Robust Portfolio Allocation Method Using Conditional Diffusion Estimates

The STABLE framework integrates a conditional diffusion generator with a Black‑Litterman mean‑variance optimizer to produce style‑aware return forecasts and risk‑aware portfolio weights, achieving up to a 122.9% Sharpe‑ratio boost, lower drawdowns, and a 15.7% MSE reduction across major equity markets.

Black-LittermanFinancial AIconditional diffusion

0 likes · 17 min read

Paper Reading: STABLE – A Robust Portfolio Allocation Method Using Conditional Diffusion Estimates

Bighead's Algorithm Notes

Mar 11, 2026 · Artificial Intelligence

Paper Review: AlphaBench – Benchmarking LLMs for Formalized Alpha‑Factor Mining

The article reviews AlphaBench, the first benchmark suite for assessing large language models in formalized alpha‑factor mining (FAFM), detailing its three core tasks—factor generation, evaluation, and search—along with experiments on various commercial and open‑source LLMs that reveal strong potential but challenges in robustness, efficiency, and practical usability.

AlphaBenchFAFMLLM

0 likes · 14 min read

Paper Review: AlphaBench – Benchmarking LLMs for Formalized Alpha‑Factor Mining

Bighead's Algorithm Notes

Mar 5, 2026 · Artificial Intelligence

AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management

The paper introduces AB‑SSM, an adaptive bidirectional state‑space model that incorporates a time‑varying linear structure and a bidirectional layer to capture market non‑stationarity and asset correlations, and demonstrates through extensive US, China, and crypto experiments that it outperforms traditional, deep‑learning, and DRL baselines in profit‑risk trade‑offs, efficiency, and scalability.

Financial AIadaptive linear time-varyingbidirectional SSM

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AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management