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quantitative trading

27 articles · Page 1 of 1
Bighead's Algorithm Notes
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.

EvoQuantSharpe ratioablation study
0 likes · 19 min read
EvoQuant: How Large Language Models Can Self‑Evolve as Quantitative Researchers
Bighead's Algorithm Notes
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.

adaptive executionfactor discoveryfinancial AI
0 likes · 19 min read
AlphaCrafter: A Full‑Stack Multi‑Agent Framework for Adaptive Cross‑Sectional Quant Trading
AI Architecture Path
AI Architecture Path
Jul 16, 2026 · Artificial Intelligence

How Vibe‑Trading’s AI Agents Cut Quant Research Barriers to Zero in Just 3 Months

Vibe‑Trading is an MIT‑licensed open‑source AI‑driven quant platform that bundles 74 financial skills and 29 pre‑configured multi‑agent teams, offers zero‑token data access, one‑click Pine Script export, and Docker deployment, while warning about hallucinations, data latency, and token costs.

AI AgentsMulti-agentPine Script
0 likes · 15 min read
How Vibe‑Trading’s AI Agents Cut Quant Research Barriers to Zero in Just 3 Months
Geek Labs
Geek Labs
Jun 15, 2026 · Artificial Intelligence

Open-Source AI Financial Assistants: Quant Trading Bot and A‑Share Research Skill Pack

The article reviews two open‑source AI finance projects—OpenAlice, an AI‑driven quantitative trading assistant that treats trading like a Git workflow, and Claude for Financial Services CN, a skill pack that equips Claude with A‑share research capabilities—detailing their designs, features, and use cases.

A-share researchAIClaude
0 likes · 9 min read
Open-Source AI Financial Assistants: Quant Trading Bot and A‑Share Research Skill Pack
AI Explorer
AI Explorer
May 1, 2026 · Artificial Intelligence

A New Multi‑Agent LLM Framework Redefines AI‑Driven Financial Trading

TradingAgents introduces a multi‑agent LLM framework that transforms AI from a single‑point price predictor into a collaborative trading team, offering roles such as analyst, researcher, trader, and risk manager, with open‑source code, Docker deployment, and over 59,000 GitHub stars.

AI financeDockerLLM
0 likes · 7 min read
A New Multi‑Agent LLM Framework Redefines AI‑Driven Financial Trading
AI Explorer
AI Explorer
Mar 26, 2026 · Artificial Intelligence

Reinventing Financial Trading with a Multi‑Agent LLM Framework

TradingAgents introduces a multi‑agent architecture that lets specialized LLM experts—researchers, analysts, traders and risk managers—collaborate to analyse markets, manage risk and execute trades, offering a new AI‑driven collaboration paradigm for quantitative finance while providing explainable decisions and enterprise‑grade stability.

AI collaborationLLMPython
0 likes · 6 min read
Reinventing Financial Trading with a Multi‑Agent LLM Framework
AI Explorer
AI Explorer
Mar 24, 2026 · Artificial Intelligence

Revolutionizing Financial Trading with a Multi‑Agent AI Framework

TradingAgents is an open‑source Python framework that uses multiple specialized LLM agents—Analyst, Researcher, Trader, and Risk Manager—to mimic a real investment bank’s workflow, offering a more robust and explainable approach to quantitative trading and financial research.

LLMPythonfinancial AI
0 likes · 6 min read
Revolutionizing Financial Trading with a Multi‑Agent AI Framework
Bighead's Algorithm Notes
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.

LLMTiMifinancial AI
0 likes · 17 min read
Paper Reading: TiMi – An Inference‑Driven Multi‑Agent System for Quantitative Trading
Old Meng AI Explorer
Old Meng AI Explorer
Feb 26, 2026 · Industry Insights

Three Must‑Try Open‑Source Tools of 2026: Quant Trading, Social Intelligence, and Chrome Power‑Ups

In 2026 the open‑source scene delivers three standout projects—a high‑performance Lean quant engine for replicating hedge‑fund research, the social‑analyzer for massive cross‑platform social data mining, and ChromeAppHeroes that curates 100 essential Chinese extensions—each offering free, powerful capabilities for developers, analysts, and everyday users.

Chrome extensionsGitHubopen-source
0 likes · 6 min read
Three Must‑Try Open‑Source Tools of 2026: Quant Trading, Social Intelligence, and Chrome Power‑Ups
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 18, 2026 · Artificial Intelligence

Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models

This paper evaluates point‑wise, pair‑wise, and list‑wise loss functions for Transformer‑based stock‑return prediction on 110 S&P 500 stocks, showing that Margin loss achieves the highest annual return (16.23%) and Sharpe ratio (0.75), ListNet delivers strong returns with low volatility, and BPR minimizes maximum drawdown, highlighting how loss design critically shapes ranking‑driven portfolio performance.

Loss FunctionsTransformerfinancial time series
0 likes · 15 min read
Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models
ShiZhen AI
ShiZhen AI
Feb 11, 2026 · Artificial Intelligence

Is the Chatbot Era Over? Deploy OpenClaw on Baidu Cloud and Let AI Trade Stocks

The author evaluates OpenClaw, a proactive AI agent framework deployed via Baidu Smart Cloud one‑click, and demonstrates its capabilities through 24/7 crypto trading, automated car‑price negotiation, and zero‑code quant strategy revival, highlighting strengths, limitations, and implications for AI‑driven personal OS.

AI NegotiationAI agentAgentSkill
0 likes · 18 min read
Is the Chatbot Era Over? Deploy OpenClaw on Baidu Cloud and Let AI Trade Stocks
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 21, 2025 · Artificial Intelligence

Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading

Logic-Q introduces a program‑sketch paradigm that injects lightweight, plug‑and‑play market‑trend logic into deep reinforcement learning agents, dramatically improving trend detection, reducing drawdowns, and outperforming state‑of‑the‑art DRL strategies on multiple quantitative‑trading benchmarks.

Bayesian optimizationLogic-QMarket Trend Detection
0 likes · 12 min read
Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading
DataFunTalk
DataFunTalk
Nov 4, 2025 · Artificial Intelligence

Can LLMs Trade Crypto Profitably? Inside the Alpha Arena Competition

Alpha Arena’s first season pitted six leading large language models against real crypto markets with $10,000 each, revealing stark differences in trading bias, risk management, and sensitivity to prompts, as Qwen3‑Max and DeepSeek outperformed GPT‑5, while detailed case studies expose model vulnerabilities and future research directions.

AI AgentsAlpha ArenaLLM
0 likes · 12 min read
Can LLMs Trade Crypto Profitably? Inside the Alpha Arena Competition
Bighead's Algorithm Notes
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 SynthesisLLMfinancial AI
0 likes · 14 min read
Paper Review: TradingGroup – A Multi‑Agent Quantitative Trading System with Self‑Reflection and Data Synthesis
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Jan 27, 2025 · Industry Insights

What Capital Currents Hide Behind DeepSeek’s R1 Model Surge?

The article analyzes how DeepSeek’s R1 model, touted as a low‑cost AI breakthrough, sparked Wall Street speculation, prompted a sharp Nvidia stock decline, and may be part of a broader quant‑driven strategy to manipulate market sentiment and capture short‑term capital gains.

AI hardwareDeepSeekNVIDIA
0 likes · 8 min read
What Capital Currents Hide Behind DeepSeek’s R1 Model Surge?
DataFunTalk
DataFunTalk
Jan 29, 2024 · Big Data

Case Study: Deploying RisingWave for Real-Time Stream Processing in a Large-Scale Quantitative Hedge Fund

An ultra‑large hedge fund with over $10 billion AUM replaced ksqlDB and Flink with RisingWave, leveraging its PostgreSQL‑compatible streaming SQL to achieve sub‑10 ms latency, lower learning and operational costs, rich connectors, advanced operators, and comprehensive observability for real‑time trade data processing.

Data IntegrationRisingWaveSQL
0 likes · 10 min read
Case Study: Deploying RisingWave for Real-Time Stream Processing in a Large-Scale Quantitative Hedge Fund
Python Programming Learning Circle
Python Programming Learning Circle
Jul 14, 2022 · Fundamentals

Python Libraries, Core Data Structures, and Algorithms for Quantitative Trading

This article introduces Python's extensive libraries such as Pandas and NumPy, explains their role in quantitative finance platforms, and reviews essential data structures and algorithmic techniques—including arrays, strings, trees, hash tables, DFS, recursion, divide‑and‑conquer, and greedy methods—providing a solid foundation for building trading strategies.

Algorithmsdata-structuresfinance
0 likes · 6 min read
Python Libraries, Core Data Structures, and Algorithms for Quantitative Trading
Python Crawling & Data Mining
Python Crawling & Data Mining
Jun 11, 2021 · Backend Development

Build a Java Stock Trading Monitoring System: From Design to Deployment

This article walks through designing and implementing a Java-based stock trading monitoring system, covering strategy overview, architecture with SpringBoot, data collection, notification services, code structure, deployment steps, and sample outputs, enabling readers to build low‑frequency grid and intraday T‑strategies themselves.

JavaSpringBootbackend development
0 likes · 8 min read
Build a Java Stock Trading Monitoring System: From Design to Deployment
Python Crawling & Data Mining
Python Crawling & Data Mining
Jun 7, 2021 · Artificial Intelligence

How to Build and Backtest Low‑Frequency Trading Strategies in Python

This article introduces two low‑frequency Python trading strategies—a grid‑based price‑difference approach and an intraday T‑strategy—explains their implementation on the RiceQuant platform, provides sample code, and presents back‑testing results that demonstrate their performance and practical considerations.

Grid StrategyIntraday T StrategyPython
0 likes · 10 min read
How to Build and Backtest Low‑Frequency Trading Strategies in Python
Programmer DD
Programmer DD
Sep 17, 2020 · Big Data

5 Open‑Source Quant Trading Tools Every Developer Should Explore

Discover five open‑source stock‑trading utilities—funds, ZVT, QUANTAXIS, StockAnalysisSystem, and match‑trade—each offering real‑time data, backtesting, multi‑asset support, and high‑performance matching to help programmers build powerful quantitative finance applications.

Pythonbig dataopen-source
0 likes · 5 min read
5 Open‑Source Quant Trading Tools Every Developer Should Explore