可视化多智能体 LLM 交易研究平台 — 看见 Agent 怎么想、怎么辩、怎么拍板,而不只是最后那个 BUY/SELL。

A visual multi-agent LLM trading research workbench — watch the agents think, debate, and make the call, not just the final BUY/SELL.

English | 简体中文

⚠️ Research / educational tool only. Not investment advice. Full disclaimer ↓

Most LLM trading frameworks dump long Markdown reports and expect you to scroll. Studio parses the same reports into structured visualisations.

• Causal-chain cards — the event analyst's output becomes per-event cards: event → direct impact → supply chain → sector sentiment → individual stocks, rendered as a vertical chain with sentiment-tinted borders.

  

• Structured news report — the news analyst's output is parsed into a 核心发现速读 (key-takeaways) block plus a 关键事件清单 table that tags each item bullish / bearish with a weight and a one-line read, instead of a flat Markdown dump.

  

• Bull / Bear as a dialogue — no more two long blocks of text. Left/right chat bubbles split by round, role-tagged, with a live-pulse on the freshest turn. Risk debate (aggressive / conservative / neutral) gets the same treatment in three colours.

• Streamed live — the progress page subscribes to a debate_turn event over WebSocket and grows the dialogue in real time as the graph runs — not just "node X completed" timeline ticks.

Built for Chinese-market research from the data layer up, while keeping full US / HK / global coverage.

• AKShare (free, default) + Tushare Pro (optional paid fallback) — the vendor router auto-detects A-share tickers (6-digit, .SS/.SH/.SZ, sh/sz prefix) and routes them through the CN chain. No config flip.
• 4 A-share-native analysts:
  • cn_social — 东方财富股吧 retail discussion (HTTP-only, no credentials)
    • optional 微博/小红书/抖音 via MediaCrawler
  • event — LLM-reasoned causal chains, no keyword dictionary
  • capital_flow — 主力资金净流入 / 北向(沪深港通) / 融资融券 / 龙虎榜
  • macro — top-down regime read (CPI/PPI/M2/PMI/LPR/USDCNY/US 10Y) mapped to sector tilts
• Minute-level K-line for A-share (1/5/15/30/60-min via AKShare), live-refresh every 30–60s during trading hours.

Studio bundles the muscles a research workbench actually needs:

Everything inherited from upstream — the LangGraph workflow, multi-provider LLMs, decision log, checkpoint resume — still works as before.

This is an open-source community fork, not affiliated with Tauric Research. See Upstream credits for the original work and citation.

git clone <your-repo-url> TradingAgents-Studio
cd TradingAgents-Studio

# Recommended: virtual env
python -m venv .venv
.venv\Scripts\activate          # Windows
# source .venv/bin/activate     # Linux/macOS

# Install — pick the extras you need:
pip install -e ".[web,cn]"                    # Web UI + A-share (recommended)
# pip install -e ".[web]"                     # US-only, skip akshare/tushare
# pip install -e ".[web,cn,cn-pro,cn-social]" # + Tushare paid + 股吧/微博 sentiment
# pip install -e ".[all]"                     # everything except dev tooling
# pip install -e ".[web,cn,dev]"              # contributors (adds pytest)

cp .env.example .env
# Edit .env: set at least ONE LLM provider's key.
# Data-source keys (TUSHARE_TOKEN / ALPHA_VANTAGE_API_KEY) are all optional —
# the pipeline runs entirely on free sources by default.

You can also manage LLM API keys from the Web Studio's Settings page — values are written through to .env so the CLI sees the same keys.

Web Studio (recommended):

# Terminal 1 — backend (http://127.0.0.1:8000)
tradingagents-web

# Terminal 2 — frontend (http://localhost:3000)
cd web/frontend
npm install
npm run dev

For production single-process deployment, build the frontend once and let the backend serve the static bundle:

cd web/frontend && npx vite build   # use vite build, not npm run build: skips the vue-tsc type-check so type warnings can't block the bundle
cd ../..
tradingagents-web              # serves the built UI at http://127.0.0.1:8000/

CLI:

tradingagents

Docker (Web Studio, recommended):

cp .env.example .env      # set at least one LLM key
docker compose up -d --build
# open http://localhost:8000

The image installs the [all] extras (Web + A-share + sentiment stacks), serves the pre-built frontend, and publishes port 8000. The server binds 0.0.0.0 and runs without autoreload inside the container (set via TRADINGAGENTS_WEB_HOST / TRADINGAGENTS_WEB_RELOAD in compose). Analysis data persists in the tradingagents_data volume across restarts.

docker compose logs -f tradingagents   # follow logs
docker compose down                    # stop (keeps the data volume)

Ollama users: docker compose --profile ollama up -d --build also starts a local Ollama service alongside the app.

The whole Web Studio is a single process: the FastAPI backend listens on 8000 and serves the built frontend (web/frontend/dist) itself. The frontend calls the API via relative paths and opens WebSockets against location.host, so just run the backend and access it same-origin — do not split the frontend into a separate static site (splitting breaks the /ws/ live updates).

The server runtime usually has only Python (no Node), so build the frontend locally before uploading:

# 1) Build the frontend (vite build skips the type-check)
cd web/frontend && npx vite build && cd ../..

# 2) Make a clean deploy tarball (excludes venv / node_modules / caches / logs)
#    Works with Windows (bundled tar) / macOS / Linux:
tar -czf deploy.tar.gz \
  --exclude='*__pycache__*' --exclude='*.pyc' --exclude='*.log' \
  --exclude='web/frontend/node_modules' \
  tradingagents cli web pyproject.toml README.md .env uv.lock

The tarball must include web/frontend/dist (the built UI) and .env (your keys/config). After changing frontend src/ code, re-run npx vite build before repackaging, or the live site stays on the old UI.

Upload and extract deploy.tar.gz on the server, then fill the 1Panel Runtime → Python form:

🔒 Set an access password! A bare public IP gets scanned by bots that will hit the analysis endpoints and burn your LLM tokens. With TRADINGAGENTS_WEB_PASSWORD set, the browser shows a native login prompt on first visit (username defaults to admin, override with TRADINGAGENTS_WEB_USER); leave it unset only for localhost. Adding it under 1Panel's Env var tab is easiest — changing the password needs no repackaging. Note that over plain http://IP:8000 the password travels unencrypted, so pair it with a domain + HTTPS (below) when you can.

Start command (single line):

pip install ".[web,cn]" && python -m uvicorn web.backend.main:app --host 0.0.0.0 --port 8000

Then open http://<server-ip>:8000 for the full app. API keys and config are loaded automatically from .env in the project dir (via load_dotenv in tradingagents/__init__.py) — no need to fill them into the panel one by one.

To serve over a domain/443, add a 1Panel Website → reverse proxy pointing to 127.0.0.1:8000 and enable WebSocket support (forward the Upgrade / Connection headers) — otherwise live analysis progress and screener updates will drop.

1. Open http://localhost:3000/.
2. Settings → fill in your DEEPSEEK_API_KEY (or any LLM provider's key).
3. 新建分析 → type 研究茅台短期 in the smart-parse box → click 解析并填充 → ticker 600519, date today, all set.
4. Pick analyst team — check Event for the causal-chain output and CN Sentiment for 股吧 — start.
5. On the Analysis Progress page, the right side grows a live debate transcript between Bull and Bear as rounds complete.
6. On the Report Detail page open the 事件影响 tab — per-event cards with arrows showing event → impact → supply chain → sector → individual stocks (instead of a wall of Markdown).
7. Add the ticker to 持仓追踪 with shares + cost. The Holdings page shows real-time price, P&L, and links to the latest analysis signal.
8. From 模拟交易 open the K-line drawer for any held ticker — daily + 1/5/15/30/60-min bars with MA(5/10/20), volume, and entry/target/stop overlays from the decision card.

A single complete analysis (4 analysts + 1 debate round, ~5–10K input tokens

• ~3–5K output tokens) typically costs and takes:

Numbers are rough estimates from typical Studio runs; your usage will vary with analyst count, debate rounds, and report length. All Studio data sources are free — paid keys (Tushare, Alpha Vantage) are optional.

Analysts run in parallel (since 0.5.0): the analyst stage fans out instead of running one model at a time, so adding more analysts barely moves wall-clock time (it's bounded by the slowest analyst, not their sum). A process-wide LLM concurrency cap (TRADINGAGENTS_LLM_CONCURRENCY, default 16) with automatic 429 backoff keeps multi-ticker batches from tripping provider rate limits.

                 ┌────────────────────────────────────────────────────────┐
                 │             TradingAgentsGraph (LangGraph)             │
                 │                                                        │
   selected ───► │  Analysts ─► Researchers ─► Trader ─► Risk ─► Portfolio│
   analysts      │  market                    (debate)  (debate) Manager  │
                 │  social                                                │
                 │  news                                                  │
                 │  fundamentals                                          │
                 │  cn_social   ← Studio (A-share)                        │
                 │  event       ← Studio (LLM causal chain)               │
                 │  capital_flow← Studio (主力资金 / 北向 / 龙虎榜)        │
                 │  macro       ← Studio (CPI/PPI/M2/PMI/LPR)             │
                 └────────────────────────────────────────────────────────┘
                                          ▲
                                          │  WebSocket: agent_complete + debate_turn
                                          │
                 ┌────────────────────────────────────────────────────────┐
                 │   Web Studio                                            │
                 │   FastAPI ◄─► SQLite  │  Vue 3 + Naive UI frontend     │
                 │                                                        │
                 │   ▸ Natural-language analyze entry                     │
                 │   ▸ Causal-chain + debate-bubble visualisation         │
                 │   ▸ Factor screener → one-click analyze/paper/schedule │
                 │   ▸ Holdings tracking (real-time quote, latest signal) │
                 │   ▸ Scheduled analyses (interval / daily / weekly)     │
                 │   ▸ Paper trading (from-decision orders, NAV curve)    │
                 │   ▸ K-line panel (daily + 1/5/15/30/60-min, live)      │
                 │   ▸ Decision Replay backtest                           │
                 │   ▸ API-key + model-catalog management                 │
                 └────────────────────────────────────────────────────────┘
                                          ▲
                                          │  vendor router auto-routes A-share
                                          │  → akshare → tushare → yfinance
                 ┌────────────────────────────────────────────────────────┐
                 │   Data sources                                          │
                 │   AKShare  · Tushare · yfinance · Alpha Vantage         │
                 │   东方财富股吧 · Reddit · StockTwits · MediaCrawler    │
                 └────────────────────────────────────────────────────────┘

The Studio routes data requests through tradingagents/dataflows/interface.py:route_to_vendor, which auto-detects A-share tickers and falls back across vendors on failure.

You can run the entire pipeline with only free sources. Tushare and Alpha Vantage keys are optional; if not configured the vendor router simply skips them.

from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG

config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "deepseek"
config["deep_think_llm"] = "deepseek-v4-pro"
config["quick_think_llm"] = "deepseek-v4-flash"
config["max_debate_rounds"] = 2

ta = TradingAgentsGraph(
    selected_analysts=["market", "cn_social", "event", "news", "fundamentals"],
    config=config,
)
_, decision = ta.propagate("贵州茅台", "2026-01-15")  # or "600519"
print(decision)

A-share tickers are auto-routed through AKShare → Tushare → yfinance regardless of the global data_vendors setting. See tradingagents/default_config.py for the cn_data_vendors chain.

See examples/quickstart.py for a minimal runnable.

Always on. Each completed run appends its decision to ~/.tradingagents/memory/trading_memory.md. On the next run for the same ticker, the framework injects the most recent decisions and realised-return reflections into the Portfolio Manager prompt. Override the path with TRADINGAGENTS_MEMORY_LOG_PATH.

Opt-in via --checkpoint. LangGraph saves state after each node so a crashed run resumes from the last successful step. Per-ticker SQLite databases live at ~/.tradingagents/cache/checkpoints/<TICKER>.db.

The Web Studio's SQLite (runs, holdings, schedules, paper account, backtests) lives at ~/.tradingagents/web_state.db. Override with TRADINGAGENTS_WEB_DB. It is recreated automatically on startup if missing — deleting it just gives you a clean slate. API keys live in .env, not in this database.

Core (Python): Python 3.10+ · LangChain + LangGraph · Pydantic · AKShare / Tushare / yfinance / Alpha Vantage · beautifulsoup4 + lxml (股吧 HTML) · pymysql (MediaCrawler, optional) · stockstats · backtrader · Rich + Typer

Web backend: FastAPI + Uvicorn · WebSockets · SQLite

Web frontend: Vue 3 + TypeScript + Vite · Naive UI · Pinia · Vue Router · Chart.js + vue-chartjs · klinecharts · marked · axios

LLM providers: OpenAI · Google Gemini · Anthropic Claude · xAI Grok · DeepSeek · Qwen (DashScope intl + CN) · GLM (Z.AI + BigModel) · MiniMax (global + CN) · OpenRouter · Ollama · Azure OpenAI

• Phase 2 Backtest — live Agent inference replay (re-call the LLM per bar) for true forward-looking backtests
• Futures (期货) and HK market data adapters
• Multi-account paper trading
• Decision-card → real broker sandbox API bridge
• Responsive / mobile-friendly UI
• GitHub Actions CI (pytest -m unit on PR)
• Public Discussions / Issue templates

Have a suggestion? Open an issue or start a Discussion — feedback shapes the roadmap.

Issues / PRs welcome. Before submitting a PR:

1. Run unit tests: pytest -m unit
2. If you change LangGraph orchestration or schema, update CHANGELOG.md's [Unreleased] section
3. If you modify a file inherited from upstream, add a "Modified by" notice (Apache 2.0 §4(b) — see existing files for the format)

For end-to-end verification against a real LLM provider (new provider adapters, structured-output changes), use:

DEEPSEEK_API_KEY=... python scripts/smoke_structured_output.py deepseek

This project is shared and discussed on the following community:

• LINUX DO — a real tech community.

Feedback, issues, and suggestions are welcome there.

tradingagents/                  # core agent framework (inherited + extended)
├── agents/
│   ├── analysts/               # + cn_sentiment_analyst.py, event_analyst.py
│   │                           # + capital_flow_analyst.py, macro_analyst.py
│   ├── researchers/            # bull / bear (extended for CN reports)
│   ├── managers/               # research manager, portfolio manager
│   ├── trader/
│   └── risk_mgmt/
├── backtesting/                # Studio — event-driven backtest engine
│   ├── engine.py
│   ├── portfolio.py
│   ├── metrics.py
│   ├── slippage.py
│   └── signals/                # signal sources (memory_log, future: rule / live_agent)
├── screener/                   # Studio — factor screen + actionable signals
│   ├── universe.py             # candidate universe + metric fetch
│   ├── factors.py              # factor scoring / ranking
│   └── signals.py              # board-aware 操作建议 (no LLM, no I/O)
├── dataflows/                  # data-fetching layer
│   ├── _proxy.py               # Studio — NO_PROXY bootstrap for CN data domains
│   ├── akshare_stock.py        # Studio — A-share market data
│   ├── tushare_stock.py        # Studio — A-share paid-grade fallback
│   ├── cn_sentiment.py         # Studio — A-share sentiment aggregator
│   ├── eastmoney_guba.py       # Studio — 东方财富股吧 client
│   ├── mediacrawler_wrapper.py # Studio — MySQL wrapper for MediaCrawler
│   ├── event_intelligence.py   # Studio — event/causal-chain helpers
│   ├── capital_flow.py         # Studio — 主力资金 / 北向 / 龙虎榜 / 两融
│   ├── macro.py                # Studio — CPI/PPI/M2/PMI/LPR/USDCNY/10Y
│   └── ...
├── graph/                      # LangGraph orchestration
├── llm_clients/                # multi-provider LLM factory
└── utils/
    └── nl_query_parser.py      # Studio — "研究茅台短期" parser

web/                            # Web Studio
├── backend/                    # FastAPI + SQLite + WebSocket
│   ├── main.py
│   ├── database.py             # runs · events · reports · holdings ·
│   │                           # schedules · paper_accounts/positions/orders/nav
│   ├── graph_runner.py         # emits agent_complete + debate_turn events
│   ├── scheduler.py            # Studio — recurring-analysis asyncio loop
│   └── routers/
│       ├── analyze.py          # incl. POST /api/parse-query
│       ├── history.py
│       ├── dashboard.py
│       ├── holdings.py         # holdings CRUD + CSV import + quote
│       ├── schedule.py         # Studio — schedule CRUD + trigger + bulk-from-holdings
│       ├── paper.py            # Studio — paper trading + auto-trade
│       ├── screen.py           # Studio — screener run + to-analyze/paper/schedule
│       ├── quote.py            # Studio — K-line OHLC (daily + intraday)
│       ├── backtest.py         # Studio — backtest runs / curve / trades
│       └── settings.py         # incl. /api/api-keys + /api/model-catalog
└── frontend/                   # Vue 3 + Naive UI + Pinia + Vite
    └── src/
        ├── components/
        │   ├── EventReport.vue       # causal-chain visualisation
        │   ├── CausalChain.vue
        │   ├── DebateThread.vue      # bull/bear bubble dialogue
        │   ├── ModelPicker.vue       # per-provider model dropdown
        │   └── KLineChart.vue        # Studio — klinecharts panel
        └── pages/                    # Dashboard · NewAnalysis · Holdings ·
                                       # Schedule · Paper · Backtest ·
                                       # AnalysisProgress · History ·
                                       # ReportDetail · Settings

cli/                            # Typer-based CLI (inherited)
examples/                       # Minimal Python entry-point examples
scripts/                        # Real-LLM smoke tests (manual, costs $$$)
tests/                          # 22 files, 248 test cases

If Studio is useful to your research, star this repo — it helps others find it and motivates continued development.

Licensed under the Apache License 2.0, identical to the upstream project. See LICENSE.

Per Apache 2.0 §4(b), source files modified relative to upstream carry a "Modified by" notice. New files added by this fork carry their own copyright notice and remain under Apache 2.0.

This project is derivative work based on the open-source framework TradingAgents by Tauric Research. Please support and cite the original work:

• Upstream repository: github.com/TauricResearch/TradingAgents
• Paper: Xiao, Y., Sun, E., Luo, D., & Wang, W. (2025). TradingAgents: Multi-Agents LLM Financial Trading Framework. arXiv:2412.20138

@misc{xiao2025tradingagentsmultiagentsllmfinancial,
      title={TradingAgents: Multi-Agents LLM Financial Trading Framework},
      author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
      year={2025},
      eprint={2412.20138},
      archivePrefix={arXiv},
      primaryClass={q-fin.TR},
      url={https://arxiv.org/abs/2412.20138}
}

This project is not affiliated with, endorsed by, or sponsored by Tauric Research. "TradingAgents" is the upstream project name; this fork is published under a derivative name to avoid confusion. All trademark rights to the original project name belong to their respective owners.

Third-party libraries used by this fork retain their respective licenses. The MediaCrawler integration calls a separately-installed external project; please review and comply with the licensing and terms-of-service of any data source you crawl.

Changes from upstream are documented in CHANGELOG.md.

TradingAgents-Studio is intended for research, education, and personal experimentation only. It is NOT financial, investment, or trading advice.

• The project does not recommend any stock or security. The outputs of LLM-based agents — including any "Buy / Sell / Hold" signal, target price, stop-loss level, or confidence score — are the product of multi-agent algorithmic debate, not an investment opinion of the authors, contributors, or any institution.
• LLM outputs may be inaccurate, incomplete, biased, or otherwise misleading. Markets involve substantial risk of loss.
• You are solely responsible for any decisions made using this software, and for any resulting financial outcome.
• This project must not be used to provide investment advisory services, stock recommendations, or asset management to the public, whether for free or for a fee.

TradingAgents-Studio 仅供 研究、教育、个人学习与技术演示 使用, 不构成任何形式的投资、财务或交易建议。

• 本项目 不推荐任何股票或证券。Agent 输出的"买入 / 卖出 / 持有"信号、目标价、 止损价、置信度等字段,均为多智能体算法辩论的中间产物, 不代表作者、贡献者或任何机构的投资观点。
• LLM 输出可能存在错误、不完整、偏见或误导。证券市场有风险,投资需谨慎。
• 任何依据本项目输出做出的投资决策及其后果,均由使用者本人承担, 与作者、贡献者及任何关联方无关。
• 禁止将本项目用于面向公众的投资咨询、荐股、代客理财、私募/公募基金运作等行为, 无论是否收费。