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Machine Heart
Machine Heart
May 1, 2026 · Artificial Intelligence

LLMs Write and Evolve Code to Redefine Quantitative Factor Mining – The CogAlpha ACL Paper

The CogAlpha framework upgrades Alpha discovery from static formulas to executable Python code, organizes a 7‑layer, 21‑agent research hierarchy, iteratively evolves factor candidates, and on CSI300 10‑day prediction outperforms 21 baselines with a 16.39% annual excess return and an IR of 1.8999, demonstrating that large models can actively participate in the discovery process.

ACL 2026Alpha MiningCode Generation
0 likes · 9 min read
LLMs Write and Evolve Code to Redefine Quantitative Factor Mining – The CogAlpha ACL Paper
AI Explorer
AI Explorer
Apr 22, 2026 · Industry Insights

FinceptTerminal: Can This Open‑Source Tool Become the Swiss Army Knife for Investors and Analysts?

FinceptTerminal, an open‑source financial analysis terminal built with C++20, Qt6, and Python 3.11+, offers a free, modular platform that combines CFA‑level market tools, AI automation, and unlimited data connections, aiming to lower the barrier for independent investors, quant developers, and finance students.

AIC++20FinceptTerminal
0 likes · 6 min read
FinceptTerminal: Can This Open‑Source Tool Become the Swiss Army Knife for Investors and Analysts?
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 13, 2026 · Artificial Intelligence

FactorMiner: Tsinghua’s Self‑Evolving Agent with Skill and Experience Memory for Alpha Factor Mining

FactorMiner is a lightweight, flexible self‑evolving agent framework that combines a modular skill architecture with structured experience memory, using a Ralph loop to guide search, reduce redundancy, and build a diverse, high‑quality alpha factor library that outperforms baselines across A‑share and cryptocurrency markets while leveraging GPU‑accelerated evaluation.

Alpha Factor MiningExperience MemoryFactorMiner
0 likes · 13 min read
FactorMiner: Tsinghua’s Self‑Evolving Agent with Skill and Experience Memory for Alpha Factor Mining
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 7, 2026 · Artificial Intelligence

AutoHypo-Fin: Tsinghua's Web-Mining Method to Auto-Generate and Backtest Market Hypotheses

AutoHypo‑Fin is an end‑to‑end framework that harvests large‑scale web financial data, extracts entities via large language models, builds a temporal knowledge graph, uses retrieval‑augmented generation and statistical backtesting to automatically create, test, and iteratively optimize trading hypotheses, achieving superior risk‑adjusted returns compared with baseline strategies in experiments from 2019‑2024.

AutoHypo-FinKnowledge GraphLLM
0 likes · 11 min read
AutoHypo-Fin: Tsinghua's Web-Mining Method to Auto-Generate and Backtest Market Hypotheses
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 6, 2026 · Artificial Intelligence

STORM: A Bidirectional Spatiotemporal Factor Model Achieving Sharpe Ratio >1

STORM introduces a bidirectional VQ‑VAE‑based spatiotemporal factor model that extracts fine‑grained time‑series and cross‑sectional features, uses discrete codebooks for orthogonal, diverse factor embeddings, and outperforms nine baselines on portfolio management and algorithmic trading tasks, delivering Sharpe ratios exceeding 1.

Algorithmic TradingPortfolio ManagementQuantitative Finance
0 likes · 17 min read
STORM: A Bidirectional Spatiotemporal Factor Model Achieving Sharpe Ratio >1
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 2, 2026 · Artificial Intelligence

Diffolio: A Diffusion‑Model Framework for Risk‑Aware Portfolio Optimization

Diffolio introduces a diffusion‑model‑based approach that directly learns a pseudo‑optimal portfolio distribution conditioned on user risk preferences, generating diverse high‑quality portfolios and outperforming classic and recent baselines on six real‑world market datasets, with annualized returns improving up to 12.1 percentage points.

Financial AIGenerative ModelingQuantitative Finance
0 likes · 22 min read
Diffolio: A Diffusion‑Model Framework for Risk‑Aware Portfolio Optimization
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 31, 2026 · Artificial Intelligence

Top AI-Driven Quantitative Finance Papers from AAAI 2026

This article curates and summarizes recent AI research papers presented at AAAI 2026 that advance quantitative finance, covering controllable market generation, LLM‑powered alpha factor mining, risk‑aware multi‑agent portfolio management, foundation models for market data, and reinforcement‑learning trading policies.

AIFinancial Market SimulationMeta Learning
0 likes · 12 min read
Top AI-Driven Quantitative Finance Papers from AAAI 2026
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 27, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)

This article presents concise English summaries of four recent AI‑driven quantitative finance papers, covering an agentic AI screening platform for portfolio investment, a wavelet‑based physics‑informed neural network for option pricing, the FinRL‑X modular trading infrastructure, and the S³G stock state‑space graph for enhanced trend prediction, each with authors, links, and key experimental results.

AILLMModular Trading Infrastructure
0 likes · 12 min read
Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)
Bighead's Algorithm Notes
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.

AlphaBenchBenchmarkQuantitative Finance
0 likes · 11 min read
ICLR2026 Quantitative Finance Paper Summaries
Bighead's Algorithm Notes
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.

AlphaBenchBenchmarkFAFM
0 likes · 14 min read
Paper Review: AlphaBench – Benchmarking LLMs for Formalized Alpha‑Factor Mining
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 28, 2026 · Artificial Intelligence

Quantitative Finance Paper Digest: Key AI‑Driven Research Highlights (Feb 21‑27 2026)

This article curates six recent quantitative‑finance papers, covering Bayesian portfolio policies, signed‑network dimensionality reduction, fine‑grained multi‑agent LLM trading, sentiment‑driven momentum prediction for AAPL, event‑driven hierarchical‑gated reward trading, and a lightweight multi‑model anchoring framework for financial forecasting, summarizing each study’s methodology and empirical results.

Bayesian methodsQuantitative Financefinancial forecasting
0 likes · 14 min read
Quantitative Finance Paper Digest: Key AI‑Driven Research Highlights (Feb 21‑27 2026)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 23, 2026 · Artificial Intelligence

How AlphaPROBE Leverages DAGs for Efficient Alpha‑Factor Mining

AlphaPROBE reformulates alpha‑factor discovery as a strategy‑navigation problem on a directed acyclic graph, combining a Bayesian factor retriever with a DAG‑aware generator to achieve superior prediction accuracy, stable returns, and faster training across three major Chinese stock markets.

Alpha FactorAlphaPROBEBayesian Retrieval
0 likes · 22 min read
How AlphaPROBE Leverages DAGs for Efficient Alpha‑Factor Mining
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 20, 2026 · Industry Insights

Weekly Quantitative Paper Digest (Feb 14‑Feb 20, 2026)

This article presents concise summaries of three recent arXiv papers covering a high‑performance Python library for systematic financial factor computation, a self‑evolving agent for discovering explainable alpha factors, and an empirical study of the Shanghai‑Hong Kong Stock Connect's impact on A‑H share price premiums under varying market efficiency conditions.

Quantitative Financealpha discoveryarXiv
0 likes · 9 min read
Weekly Quantitative Paper Digest (Feb 14‑Feb 20, 2026)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 1, 2026 · Artificial Intelligence

Beyond Historical Data: Adaptive Synthesis for Financial Time Series

This article reviews a recent paper that proposes a drift‑aware data‑stream system integrating machine‑learning‑based adaptive control into financial data management, introducing a parametric data‑operation module, a gradient‑based bi‑level optimizer, and a curriculum planner to improve model robustness and risk‑adjusted returns in non‑stationary markets.

Quantitative Financeadaptive data synthesisconcept drift
0 likes · 18 min read
Beyond Historical Data: Adaptive Synthesis for Financial Time Series
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jan 30, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Jan 24‑Jan 30, 2026)

This article presents concise summaries of three recent quantitative finance papers—MarketGAN for high‑dimensional asset return generation, AlphaCFG for grammar‑guided Alpha factor discovery, and a hybrid AI‑driven trading system integrating technical analysis, machine learning, and sentiment—highlighting their methodologies, experimental results, and economic value, and provides links to additional related research.

Alpha Factor DiscoveryGenerative Adversarial NetworksHybrid AI Trading
0 likes · 9 min read
Weekly Quantitative Finance Paper Digest (Jan 24‑Jan 30, 2026)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jan 10, 2026 · Artificial Intelligence

Key Quantitative AI Papers Jan 3‑9 2026: Portfolio Optimization, Equity Correlation Forecasting, and Index Tracking Review

This article summarizes three recent quantitative finance papers—introducing a decision‑oriented SPO paradigm for portfolio optimization, a hybrid transformer‑graph neural network for forecasting S&P 500 equity correlations, and a comprehensive review of modeling approaches for financial index tracking—highlighting their methods, datasets, and empirical findings.

AIGraph Neural NetworkQuantitative Finance
0 likes · 9 min read
Key Quantitative AI Papers Jan 3‑9 2026: Portfolio Optimization, Equity Correlation Forecasting, and Index Tracking Review
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jan 3, 2026 · Artificial Intelligence

Quantitative Finance Paper Digest (Dec 27 2025 – Jan 2 2026)

This article curates recent quantitative finance research, summarizing five papers that explore generative‑AI‑enhanced portfolio construction, LLM‑driven alpha screening with reinforcement learning, statistical tests for look‑ahead bias in LLM forecasts, and a non‑stationarity‑complexity trade‑off framework for return prediction, each with links to the original arXiv PDFs and code.

Alpha ScreeningLLMsLookahead Bias
0 likes · 10 min read
Quantitative Finance Paper Digest (Dec 27 2025 – Jan 2 2026)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 19, 2025 · Artificial Intelligence

Quantitative Finance Paper Digest: Dec 13‑19 2025 Highlights

This digest presents recent arXiv papers (Dec 13‑19 2025) on AI‑driven quantitative finance, covering LLM‑based portfolio recommendation, reinforcement‑learning deep hedging, hybrid SV‑LSTM volatility forecasting, dynamic stacking ensembles, GA‑optimized SVR forecasting, and interpretable deep learning asset pricing, each with abstracts and key findings.

Deep LearningLLMQuantitative Finance
0 likes · 16 min read
Quantitative Finance Paper Digest: Dec 13‑19 2025 Highlights
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 22, 2025 · Artificial Intelligence

Quantitative Finance Paper Roundup (Nov 15‑21, 2025)

This roundup presents six recent arXiv papers covering crypto portfolio optimization, Sharpe‑driven stock selection with liquidity constraints, ensemble deep reinforcement learning for stock trading, dynamic machine‑learning‑based stock recommendation, a risk‑sensitive trading framework, and a generative AI model for limit order book messages, each with reported empirical results.

Quantitative Financecryptocurrencydeep reinforcement learning
0 likes · 12 min read
Quantitative Finance Paper Roundup (Nov 15‑21, 2025)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 4, 2025 · Artificial Intelligence

Key Quantitative Finance Papers from WWW2025 – Summaries & Insights

This article compiles concise English summaries of recent AI-driven quantitative finance papers presented at WWW2025, covering novel stock‑price forecasting frameworks such as CSPO, MERA, Ploutos, DINS, HedgeAgents, HRFT, and IDED, with links to the original PDFs, code repositories, authors, and abstracts.

Deep LearningFinancial AIQuantitative Finance
0 likes · 13 min read
Key Quantitative Finance Papers from WWW2025 – Summaries & Insights
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 20, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Sep 13‑19, 2025)

This digest summarizes seven recent arXiv papers that apply reinforcement learning, multi‑agent frameworks, dynamic factor models, high‑frequency trading LLMs, quantum GANs, multi‑LLM sentiment analysis, and context‑aware language models to advance quantitative finance and AI‑driven market prediction.

Quantitative FinanceQuantum Machine Learninglarge language models
0 likes · 12 min read
Weekly Quantitative Finance Paper Digest (Sep 13‑19, 2025)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 9, 2025 · Artificial Intelligence

How EFS Leverages Large Language Models for Sparse Portfolio Optimization

The paper introduces the Evolutionary Factor Search (EFS) framework, which uses large language models to automatically generate and evolve alpha factors, turning sparse portfolio selection into an LLM‑guided top‑m ranking task, and demonstrates superior performance on multiple Fama‑French benchmarks and real‑world market datasets.

Alpha FactorsEvolutionary AlgorithmsFactor Search
0 likes · 11 min read
How EFS Leverages Large Language Models for Sparse Portfolio Optimization
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 5, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Aug 30 – Sep 5, 2025)

This digest reviews four recent AI‑driven finance papers: a robust MCVaR portfolio optimizer with ellipsoidal support and RKHS uncertainty, a PPO‑based adaptive weighting system for LLM‑generated alphas, an empirical comparison of price‑based, GICS‑based, and LLM‑embedding stock clustering, and a diffusion‑model approach that generates future financial chart images from current charts and text prompts.

Quantitative Financediffusion modelslarge language models
0 likes · 9 min read
Weekly Quantitative Finance Paper Digest (Aug 30 – Sep 5, 2025)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Aug 31, 2025 · Artificial Intelligence

Paper Review: AlphaEval – A Comprehensive, Efficient Framework for Evaluating Alpha Mining

AlphaEval is a unified, parallelizable evaluation framework that assesses Alpha mining models across predictive ability, time stability, market‑perturbation robustness, financial logic, and diversity without backtesting, matching full backtest results while offering higher efficiency and open‑source reproducibility.

Alpha MiningEvaluation FrameworkLLM
0 likes · 10 min read
Paper Review: AlphaEval – A Comprehensive, Efficient Framework for Evaluating Alpha Mining
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Aug 29, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Aug 23‑29, 2025)

This digest summarizes nine recent arXiv papers covering quantum portfolio optimization, thematic investing with semantic stock representations, multi‑indicator reinforcement learning for trading, attention‑based asset pricing, ESG variable selection, deep neural networks for return distribution forecasting, a foundation model for financial time‑series, a multi‑agent trading system with self‑reflection, and dynamic weighting machine‑learning stock selection strategies.

Deep LearningESGQuantitative Finance
0 likes · 17 min read
Weekly Quantitative Finance Paper Digest (Aug 23‑29, 2025)
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Dec 4, 2024 · Artificial Intelligence

Implementation of a Cloud‑Native AI‑Powered Quantitative Research Platform Using Alibaba Cloud ACK

The article details how the Juqian intelligent investment research platform leverages Alibaba Cloud's ACK cloud‑native AI suite, Kubernetes, and various cloud services to build a high‑efficiency, scalable AI‑driven quantitative finance solution, improving resource utilization, reducing costs, and accelerating research workflows.

ACKAIAI Platform
0 likes · 5 min read
Implementation of a Cloud‑Native AI‑Powered Quantitative Research Platform Using Alibaba Cloud ACK
ITPUB
ITPUB
Apr 27, 2024 · Databases

How Vector Databases Enable High‑Dimensional Stock Quant Analysis

This interview‑style guide explores how vector databases handle massive, high‑dimensional time‑series data for quantitative stock trading, detailing data scaling challenges, selection criteria, and why the research team chose LanceDB over alternatives for efficient, scalable financial analysis.

AI InfrastructureLanceDBQuantitative Finance
0 likes · 7 min read
How Vector Databases Enable High‑Dimensional Stock Quant Analysis
Model Perspective
Model Perspective
Feb 12, 2023 · Artificial Intelligence

AI-Driven Adaptive Grid Model Beats Traditional Gold & Bitcoin Trading Strategies

This article reviews the award‑winning 2022 MCM/ICM C‑problem papers that develop and compare adaptive grid, ARIMA, LSTM, Prophet, and XGBoost‑based models for daily gold and Bitcoin trading, analyzing profitability, risk, transaction‑cost sensitivity, and providing evidence of superior strategy performance.

Quantitative Financeoptimizationtrading strategy
0 likes · 28 min read
AI-Driven Adaptive Grid Model Beats Traditional Gold & Bitcoin Trading Strategies
Python Crawling & Data Mining
Python Crawling & Data Mining
Jul 8, 2022 · Fundamentals

Master Intraday High‑Low Breakout Strategy with Python – Full Code & Explanation

This article explains the concept of intraday trading, details a popular high‑low breakout strategy for commodity futures, and provides a complete Python implementation with time‑based entry and exit rules, virtual position handling, and full source code for immediate testing.

Quantitative Financealgorithmic strategyhigh‑low breakout
0 likes · 13 min read
Master Intraday High‑Low Breakout Strategy with Python – Full Code & Explanation
Python Programming Learning Circle
Python Programming Learning Circle
Dec 29, 2021 · Fundamentals

Comprehensive List of Python Libraries for Quantitative Finance

This article compiles a categorized collection of Python packages for quantitative finance, covering scientific computation, pricing, technical indicators, backtesting, risk analysis, data sources, Excel integration, and visualization, with brief descriptions and reference links for each tool.

Quantitative Financebacktestingdata analysis
0 likes · 17 min read
Comprehensive List of Python Libraries for Quantitative Finance