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HKUDS / Subagents

Vibe-Trading

"Vibe-Trading: Your Personal Trading Agent"

  • 36.4k estrellas
  • 5.9k forks
  • 12 issues abiertos
  • Lenguaje: Python
  • Licencia: MIT
  • actualizado

Instalación

Terminal
git clone https://github.com/HKUDS/Vibe-Trading && cp Vibe-Trading/*.md ~/.claude/agents/
  1. Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
  2. Start a new Claude Code session to load the agents.
  3. Delegate work to them with the Task/Agent tool or by name.

Nota editorial

Vibe-Trading is a multi-agent Python framework that gives Claude and other LLMs a full suite of quantitative trading tools through a single pip install (`vibe-trading-ai`). It connects to Claude via MCP, meaning Claude Code and Claude Desktop can invoke its tools directly, and it also exposes a FastAPI backend with a React 19 frontend for chat-based interaction. The agent system includes named swarm presets such as an investment committee, quant desk, and risk committee, each with real-time per-worker status cards streamed into the chat timeline. An Alpha Zoo module houses over 191 backtestable alphas that can be benchmarked head-to-head using IC mean, information ratio, and IC-positive ratio metrics. A Shadow Account feature lets users simulate trades without real capital. The project targets retail traders, quant researchers, and developers who want to wire LLM reasoning into algorithmic trading workflows without building execution, backtesting, or data infrastructure from scratch.

Items instalables

24 items en este repositorio

ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.

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akshareSkill

AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance.

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Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks "which alphas exist", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.

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A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报,预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示,并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股,不预测财务造假。

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Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.

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Diagnose failed or underperforming backtests, locate the root cause, and fix the issue

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Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.

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Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.

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Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.

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ccxtSkill

CCXT unified crypto exchange library (100+ exchanges). Free public market data. Fallback when OKX is unavailable.

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chanlunSkill

基于缠论(缠中说禅)的形态识别引擎,使用czsc库自动检测K线分型、笔、中枢,并生成一买/一卖/二买/二卖/三买/三卖等买卖点信号。支持多周期分析和形态分类(3/5/7/9/11笔形态)。

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Commodity analysis (oil supply-demand balance / gold pricing / copper as an economic predictor / inventory cycles / futures premium-discount structure / seasonality), generating directional commodity signals.

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A股可转债分析——转股/纯债/期权三维估值、下修/强赎/回售博弈、双低策略与转债轮动选债框架

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公司事件驱动分析:并购套利价差计算、大股东增减持信号、股权激励解读、定增配股影响评估、A股ST/退市预警

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Correlation and cointegration analysis — co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation

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Correlation-regime detection and crisis attribution — edge-density regime states with hysteresis, causal (no look-ahead) smoothing, regime-aware exposure context, first-mover crisis attribution with honest NAME / MACRO / AMBIGUOUS / ABSTAIN verdicts, and a correlation-rewiring leaderboard that catches slow bleed-outs

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固收与信用分析:信用债评级、利差分析、违约风险评估、城投债研究、可转债定价与策略。

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Write signal_engine.py for portfolios spanning multiple markets (A-shares + crypto, equity + forex, etc.)

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Crypto-derivatives strategies — perpetual funding-rate arbitrage, futures term-structure contango/backwardation trading, and option volatility-smile / Greeks analysis.

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The single ROUTER for every data need. Load this skill BEFORE any backtest, data-fetch, or research task to pick the best available source/tool, honour auth (env) requirements, and avoid ban-risk providers.

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Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Use when the user wants textbook-level depth on one company for public publishing (a single research report or earnings note is NOT this — use investment-research / earnings-review instead).

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DeFi yield analysis and optimization — lending rates, LP yields, staking returns, yield farming strategies, risk-adjusted yield comparison, and protocol-level sustainability assessment.

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Dividend stock analysis for income, dividend-growth, and shareholder-return strategies, including yield quality, payout sustainability, ex-dividend mechanics, and yield-trap checks.

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Read any common document/data file — PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx), images (OCR), CSV/TSV, plain text, JSON/YAML/TOML, HTML/XML, and most source-code files. Use the `read_document` tool.

Instalar
README

Resumen de Subagents

README completo en GitHub

English | 中文 | 日本語 | 한국어 | العربية | Español | Bahasa Indonesia

Vibe-Trading: Your Personal Trading Agent

One Command to Empower Your Agent with Comprehensive Trading Capabilities

Website  ·  Docs  ·  News  ·  Features  ·  Shadow Account  ·  Demo  ·  Quick Start  ·  Examples  ·  API / MCP  ·  Roadmap  ·  Contributing


📰 News

⚠️ Security warning: The X account VibeTrading_HKU, Virtuals project 101845, and token contract 0x640BDBF77b6447E8b7DB7894cED84BD1c40571f4 are not official Vibe-Trading assets. We have never launched or endorsed any token or memecoin. Do not buy, connect a wallet, or sign anything. Details.

  • 2026-10-23 🛠️ Model, portfolio and futures fixes (unreleased): Direct MiniMax connections default to M3.1 Flash and retain reasoning across tool calls (#1936). Longbridge exposes cash by currency and financing limits across Agent, CLI, API and Web (#1940). Correct global futures routing, contract multipliers and micro-contract fees, with explicit warnings when pricing defaults are used (#1965). Also fix portfolio error redaction, audit rotation, IC statistics, numeric parsing and Weixin attachment collisions. Details in CHANGELOG.

  • 2026-10-22 🚀 v0.1.18 released (Release notes, pip install -U vibe-trading-ai): a leaner agent runtime — tool schemas load on demand, oversized results stay readable in the run directory, Swarm keeps new observations, and usage accounting covers compaction and interrupted calls; automatic chat titles remain. On the 0.1.17 verification backtest it used 7 model calls instead of 12 and about a fifth of the input tokens. It also ships the post-0.1.17 security, grounding, paper-account and packaging fixes. Fixed on main today (next release): a retried provider error no longer halts live model decisions for the rest of the day, and a research goal's final answer is delivered even when it crosses the goal's token budget (CHANGELOG).

  • 2026-10-21 🚀 v0.1.17 released (Release notes, pip install -U vibe-trading-ai): 638 commits and 228 merged pull requests since 0.1.16, plus 41 pull requests whose work landed through maintainer commits, from 33 contributors. The headline is daily paper trading: a paper_trade_step tool for the agent and MCP, plus saved paper accounts through vibe-trading paper, the REST API and a Web page, match orders or a strategy against completed daily bars without ever reaching a broker; saved accounts move forward only (#1939). Most of the rest is correctness, and much of it has one shape: a value that cannot be computed now says so instead of turning into a plausible number. A profit factor with no losing trade, an undefined correlation and an unbounded ratio come back as null, and a factor with too few IC observations is skipped with the reason; a missing price no longer bridges a return; a misspelled or engine-irrelevant backtest setting is reported in the run card. New: Brazil (B3), 478 Alpha Zoo factors (from 462), Slack and Discord in the guided channel setup, Email reports as HTML or a password-protectable PDF, token usage in Run Detail, chat and swarm, a claude-cli provider, and Persian and Turkish for 11 UI languages. Fixed today: the fund-flow fallback names its actual source and keeps total flow apart from main-order flow, and the Telegram setup steps are clarified. Shipped the same evening in v0.1.18: mcp>=1.30 closes CVE-2026-59951 for --transport http; custom model endpoints are re-checked on every connection and work behind fake-IP proxies; grounding reads run_dir/… refs and −$ amounts; Beijing 920xxx codes resolve to .BJ in correlation (CHANGELOG). Thanks @rudycelekli, @Shizoqua, @he-yufeng, @shadowinlife, @zeus229, @alexwan02, @Jackzigen, @cgycorey, @MetaAviator, @lorenzozanee, @Mikesad, @remote-controlled-man, @Romzhou, @sambazhu, @wavebyrd, @BHhhh-123, @Bortlesboat, @bygordon130-hub, @davidalmeida90, @DrDLyuan, @Emad211, @Frank-zhu2000, @goatyyc, @hunters1011-ctrl, @incorpore, @mdb1230, @mminkus, @passionbull, @rwxyx518-prog, @ThreeTima, @tomaszkubiak-courses and @xaritoku!

Earlier news

  • 2026-10-20 🛠️ Backtest diagnostics and research correctness: Warn about unrecognized backtest config keys, show undefined correlations as unknown instead of zero, and reuse the saved annualisation factor when revalidating runs (#1935, #1933, #1915). Also improve factor sample checks, tool input validation, journal market classification, numeric formatting and WeCom streaming. All 14 fixes and remaining limits are in CHANGELOG.

  • 2026-10-19 🛠️ Nine financial correctness fixes: Correct recency weights in factor decay, A-share board limits and Beijing symbol routing, SEC Q4 flow derivation, and event-study estimation windows. Preserve Futu holdings identity and tiny MACD/Bollinger values; reject negative specific variances and apply pair-specific fees to Yahoo forex symbols (#1912, #1920, #1930). See CHANGELOG for all nine fixes and limits.

  • 2026-10-17 🛠️ Token usage and research reliability: View LLM usage in Run Detail, chat and swarm (#1890). Shared scheduling now powers research and optional background jobs (#1893); live scheduling is unchanged. Fix journal date/time pairing, evidence paths, factor ties, memory roots, numeric error handling and channel progress. See CHANGELOG.

Topics
ai-agentalgorithmic-tradingbacktestingfintechllmmcpmulti-agentpythonquantitative-financetrading
Preguntas frecuentes

Lo que la gente pregunta sobre Vibe-Trading

¿Qué es HKUDS/Vibe-Trading?

HKUDS/Vibe-Trading es subagents para el ecosistema de Claude AI. "Vibe-Trading: Your Personal Trading Agent" Tiene 36.4k estrellas en GitHub y su última actualización registrada es del 2026-10-23.

¿Cómo se instala Vibe-Trading?

Puedes instalar Vibe-Trading clonando el repositorio (https://github.com/HKUDS/Vibe-Trading) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar HKUDS/Vibe-Trading?

Nuestro agente de seguridad ha analizado HKUDS/Vibe-Trading y le ha asignado un Trust Score de 100/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.

¿Quién mantiene HKUDS/Vibe-Trading?

HKUDS/Vibe-Trading es mantenido por HKUDS. La última actividad registrada en GitHub es del 2026-10-23, con 12 issues abiertos.

¿Hay alternativas a Vibe-Trading?

Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.

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