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• ⚠ Solo or near-solo (1 contributor active in recent commits)
• ⚠ Non-standard license (Other) — review terms
• ⚠ No test directory detected

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

A Chinese-localized multi-agent LLM framework for financial trading analysis and A-share stock research built on FastAPI backend + Vue 3 frontend. It coordinates multiple AI agents (powered by OpenAI, Google AI, custom endpoints) to perform collaborative stock analysis, generating professional investment reports without providing real trading signals—designed as an educational platform for systematic learning of multi-agent AI in finance. Monorepo structure: /app contains FastAPI backend with modular organization (core/ for config/db/logging, models/ for data schemas, middleware/ for cross-cutting concerns); /frontend contains Vue3 SPA with Element Plus components; deployment via Dockerfile.backend and Dockerfile.frontend supporting multi-arch (amd64/arm64); configuration unified via /app/core/unified_config.py bridging legacy and new patterns.

Chinese finance students, retail investors, and AI enthusiasts who want to learn how multi-agent LLM systems work in stock analysis; also financial educators and researchers building localized AI trading education platforms with support for A-shares, Hong Kong, and US stocks.

Actively maintained (v1.0.1 stable), with recent architecture migration from Streamlit to FastAPI/Vue3 + MongoDB/Redis stack. Project has comprehensive CI/CD (GitHub Actions for Docker publishing), structured licensing, and security considerations (rate limiting, operation logs), but remains early-stage with limited public star count and no visible test suite in the file structure.

Critical risk: dual licensing model with proprietary /app and /frontend directories (Apache 2.0 only for other files) requires commercial licensing for production use—violating this triggers legal issues. Secondary risks: single maintainer (hsliuping), complex multi-provider LLM configuration surface area prone to integration bugs, v2.0 explicitly withheld from open source due to reported IP theft, and heavy dependency on external APIs (OpenAI, Google AI, AKShare) with no fallback mechanism guarantees.

v1.0.1 actively stabilizing post-migration: enhanced config UI with factory/model ordering, new AiHubMix aggregated LLM provider, fixed page refresh bugs in stock/report detail views, multi-level fallback for stock quotes (AKShare → fallback chains), and synced upstream abilities from original TradingAgents. v2.0 in closed beta (not open-sourced due to IP concerns).

git clone https://github.com/hsliuping/TradingAgents-CN.git
cd TradingAgents-CN
cp .env.example .env
# Edit .env with your OpenAI/Google API keys and MongoDB/Redis credentials
pip install -r requirements.txt  # Backend Python deps
cd frontend && npm install  # Frontend Vue3 deps
cd ..
python app/__main__.py  # Start FastAPI backend on http://localhost:8000
# In another terminal: cd frontend && npm run dev  # Start Vue dev server

Daily commands:

# Development (split terminals):
# Terminal 1 - Backend:
python -m app  # reads .env, starts FastAPI on 0.0.0.0:8000

# Terminal 2 - Frontend:
cd frontend && npm run dev  # Vite dev server on http://localhost:5173

# Production (Docker):
docker-compose up -d  # uses Dockerfile.backend + Dockerfile.frontend

• app/main.py — FastAPI application entry point—initializes all core services, middleware, and router configurations that every feature depends on.
• app/core/config.py — Central configuration management for LLM models, database connections, and multi-agent setup—all business logic flows through these settings.
• app/services/analysis_service.py — Core multi-agent LLM orchestration service for financial analysis—the primary business logic that processes trading signals and agent interactions.
• app/core/database.py — Database abstraction layer for stock data persistence—required for understanding data model, queries, and synchronization patterns.
• app/models/stock_models.py — Pydantic data models for stock, financial data, and trading entities—schema foundation for all API payloads and database operations.
• app/routers/stocks.py — Stock data API endpoint router—demonstrates the pattern for adding new endpoints and integrating services with HTTP handlers.
• app/core/unified_config.py — Multi-provider configuration bridge (tushare, akshare, baostock)—essential for understanding how to add or modify data source integrations.

Add a New API Endpoint for Stock Data

1. Define the data model and request/response schemas in app/models/stock_models.py (app/models/stock_models.py)
2. Create a handler function in an existing or new router (e.g., app/routers/stocks.py) that retrieves data from database or service (app/routers/stocks.py)
3. Add the new route using @router.get() or @router.post() with proper docstring and status codes (app/routers/stocks.py)
4. If new business logic is needed, implement it in app/services/ and inject via dependency injection (app/services/analysis_service.py)

Integrate a New Data Provider (Stock Source)

1. Add provider credentials and API settings to app/core/config.py under a new provider section (app/core/config.py)
2. Create provider initialization route in a new file like app/routers/newprovider_init.py (reference app/routers/tushare_init.py) (app/routers/tushare_init.py)
3. Add provider config abstraction in app/core/unified_config.py to normalize field mappings across providers (app/core/unified_config.py)
4. Implement or extend sync logic in app/services/basics_sync/processing.py to handle provider-specific data transformation (app/services/basics_sync/processing.py)

Add a New Multi-Agent Analysis Workflow

1. Define analysis request/response models in app/models/analysis.py (app/models/analysis.py)
2. Create a new analysis endpoint in app/routers/analysis.py or a specialized router that accepts analysis parameters (app/routers/analysis.py)
3. Extend or create agent orchestration logic in app/services/analysis_service.py to call LLM agents in sequence or parallel (app/services/analysis_service.py)
4. Connect status updates via app/services/analysis/status_update_utils.py to emit SSE events for real-time progress tracking (app/services/analysis/status_update_utils.py)

Add Request Validation & Error Handling

1. Define validation logic in Pydantic model validators within app/models/ files (app/models/stock_models.py)
2. Implement custom exception classes in app/middleware/error_handler.py or new file (app/middleware/error_handler.py)
3. Register error handlers in app/main.py using @app.exception_handler() decorator (app/main.py)
4. Test error responses and ensure consistent error formatting in app/core/response.py (app/core/response.py)

• FastAPI — High-performance async Python framework; native OpenAPI docs; excellent for LLM agent orchestration with concurrent request handling
• Multi-Agent LLM (GPT/Claude via API) — Core trading

Environment config mandatory: Must populate .env with real OPENAI_API_KEY, GOOGLE_API_KEY, MONGODB_URI, and REDIS_URL before startup; missing any causes silent failures. License enforcement: /app and /frontend are proprietary even though repo is public—running commercially without license violates terms stated in COPYRIGHT.md; v2.0 explicitly not open-sourced. Multi-provider LLM initialization: Config order matters—new providers/models added via UI must be serialized in unified_config.py exactly or cause parsing errors; check app/core/config_compat.py for migration quirks. Docker multi-arch complexity: Dockerfile.backend supports arm64 (critical for Apple Silicon) but requires Docker buildx; plain docker build may fail silently on non-native platforms. AKShare fallback chain: Stock quote fetching has hidden multi-level fallback (stock_bid_ask_em → stock_zh_a_spot → stock_zh_a_hist); if base fails but fallback succeeds, the error is not logged prominently, making debugging harder.

• Multi-Agent LLM Orchestration — Core design pattern of TradingAgents-CN: multiple AI agents with specialized roles coordinate via prompt chaining to analyze stocks; understanding agent decomposition and message passing is essential to extending analysis logic
• Token Bucket Rate Limiting — Implemented in /app/middleware/rate_limit.py to protect expensive LLM API calls (OpenAI/Google); understanding capacity-based throttling is critical for managing API costs in production
• Dual Licensing (Open + Proprietary) — Repo uses mixed licensing: Apache 2.0 for infrastructure, proprietary for core /app and /frontend; understanding the legal boundaries prevents accidental commercial IP violations
• Server-Sent Events (SSE) + WebSocket Real-Time Notifications — Mentioned as 'dual-channel push' for tracking analysis progress; SSE for one-way server→client streaming, WebSocket for bidirectional real-time updates—critical for long-running stock analysis workflows
• MongoDB + Redis Hybrid Caching — Two-tier persistence: Redis for fast hot cache (model configs, API responses), MongoDB for durable cold storage (analysis results, user data); understanding TTL/eviction policies is essential for cache coherency in multi-instance deployments
• Configuration as Code (Pydantic Validation) — All LLM provider configs, data sources, and system settings are Pydantic models in /app/core/config.py with validation; schema evolution and migration patterns (see config_compat.py) are critical to understand for adding new providers without breaking existing configs
• Middleware Chain Pattern (FastAPI) — Request lifecycle: RequestID → RateLimit → ErrorHandler → OperationLog; understanding middleware ordering and exception propagation is essential for debugging unexpected behavior in API responses

• TauricResearch/TradingAgents — Original upstream multi-agent trading framework (English); this repo is the Chinese-localized, architecture-enhanced derivative
• langchain-ai/langchain — LLM orchestration and agent framework; TradingAgents-CN could migrate to LangChain agents for multi-agent coordination if it hasn't already
• geekan/MetaGPT — Chinese-first multi-agent LLM framework with role-based agent patterns; architectural alternative for organizing AI agents in finance
• deepseek-ai/DeepSeek-LLM — Chinese-native LLM alternative to OpenAI/Google; relevant for users wanting on-premise or privacy-focused multi-agent setups
• akfamily/akshare — Chinese financial data API library used as fallback stock quote source in this project; understanding AKShare's A-share/HK/US data structure is essential for troubleshooting data pipeline

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add comprehensive unit tests for app/core/config.py and app/core/unified_config.py

The config system is critical to the application's startup and runtime behavior. Currently, there are no visible test files for the core configuration modules (config.py, config_bridge.py, config_compat.py, unified_config.py). With multiple config sources and a startup validator (app/core/startup_validator.py), these modules need test coverage for config merging logic, validation, and compatibility layer behavior.

• [ ] Create tests/core/test_config.py with tests for config.py initialization and parameter validation
• [ ] Create tests/core/test_unified_config.py to test config bridging and backward compatibility
• [ ] Add tests for edge cases: missing env vars, invalid types, conflicting configs from different sources
• [ ] Test the startup_validator.py integration with actual config objects
• [ ] Set up pytest fixtures for test config files (.env, config.yml)

Add GitHub Actions workflow for testing database migrations and model integrity

The repo has Docker workflows and sync checks (.github/workflows/docker-publish.yml, upstream-sync-check.yml), but is missing a Python-specific CI workflow. Given the presence of app/core/database.py and multiple SQLAlchemy models in app/models/ (user.py, analysis.py, screening.py, stock_models.py, operation_log.py), a workflow should verify that models are syntactically correct and migrations don't break on test runs.

• [ ] Create .github/workflows/python-tests.yml that runs on Python 3.10+ (as specified in README badge)
• [ ] Add linting steps using pylint or flake8 for app/ directory
• [ ] Add pytest command to test all models can be instantiated and serialize correctly
• [ ] Test database connection and table creation using SQLite in-memory database (from app/core/database.py)
• [ ] Include dependency checks (requirements.txt or pyproject.toml)

Create middleware integration tests and document error handling behavior in app/middleware/

The middleware stack includes error_handler.py, rate_limit.py, request_id.py, and operation_log_middleware.py, but there's no visible test coverage or integration documentation. These are security and reliability-critical components. Tests should verify middleware order, error propagation, and that operation logs are correctly created (app/models/operation_log.py integrates with this).

• [ ] Create tests/middleware/test_error_handler.py to verify error responses match app/core/response.py format
• [ ] Create tests/middleware/test_rate_limit.py using app/core/rate_limiter.py to validate rate limit enforcement
• [ ] Create tests/middleware/test_operation_log_middleware.py to verify operations are logged to database (app/models/operation_log.py)
• [ ] Add integration test that verifies middleware order doesn't cause request_id loss or log corruption
• [ ] Document in CONTRIBUTING.md or docs/ the middleware execution order and error handling contract

• Add comprehensive unit tests for /app/core/unified_config.py and /app/core/config_bridge.py—these are critical config-loading paths with no visible test coverage in the repo, making refactoring risky. Tests should cover schema migrations from legacy to new format.
• Document the exact fallback chain for stock data sources in /app (AKShare multi-level fallback logic) with examples in /docs—currently only mentioned in README but not detailed; users debugging quote failures need this clarity.
• Create an integration test harness in /frontend for model selection persistence across the config page, chat dialog, and analysis page—recent fix for 'model list ordering' suggests this was bug-prone; automated e2e tests would prevent regressions.

• Critical · Hardcoded Database Credentials in Environment Files — .env.example, docker-compose.yml. The .env.example file contains hardcoded default credentials for MongoDB (admin:tradingagents123) and Redis (:tradingagents123). These same credentials appear in the docker-compose.yml configuration. If developers copy these defaults to production without changing them, the database will be accessible with known credentials. Fix: 1) Remove all default credentials from .env.example and docker-compose.yml. 2) Document that users MUST generate strong random credentials. 3) Implement startup validation that prevents default credentials in production environments. 4) Use secrets management (Docker Secrets, AWS Secrets Manager, etc.) instead of environment variables for sensitive data.
• Critical · Weak Default JWT Secret — .env.example, line JWT_SECRET. The .env.example file contains a placeholder JWT secret 'your-super-secret-jwt-key-change-in-production'. If this default is not changed in production, authentication tokens can be forged by anyone with access to this known secret. Fix: 1) Generate a cryptographically secure random JWT secret using: python -c "import secrets; print(secrets.token_urlsafe(32))". 2) Enforce a minimum secret length (32+ characters) in startup validation. 3) Implement secret rotation mechanism. 4) Never commit actual secrets to version control.
• High · Exposed MongoDB Connection String in Docker Compose — docker-compose.yml, TRADINGAGENTS_MONGODB_URL environment variable. The docker-compose.yml explicitly defines MongoDB connection URI with credentials embedded in plaintext in the environment variable TRADINGAGENTS_MONGODB_URL. This exposes credentials in container logs and process inspection. Fix: 1) Use Docker Secrets or mounted secret files instead of environment variables. 2) Use .dockerignore to prevent .env files from being copied into Docker images. 3) Implement credential injection at runtime rather than build time. 4) Consider using Docker networking to avoid exposing credentials.
• High · Missing Rate Limiting Configuration Details — app/core/rate_limiter.py, .env.example. While rate_limiter.py and rate_limit.py middleware exist, the configuration in .env.example shows no rate limiting parameters. Without proper rate limiting configuration, the API is vulnerable to brute force attacks and DoS. Fix: 1) Define rate limiting parameters in .env (requests per minute, per IP, per user). 2) Ensure rate limiting is enabled by default. 3) Implement progressive backoff for repeated failures. 4) Log and alert on suspicious rate limit patterns.
• High · Insecure Database Scope Configuration — .env.example, MONGODB_DATABASE_SCOPE, ALLOW_SHARED_DB_IN_DEBUG. The MONGODB_DATABASE_SCOPE=auto setting in .env.example allows automatic database scope assignment, and ALLOW_SHARED_DB_IN_DEBUG=false suggests debug mode may allow shared database access. This could lead to data isolation issues. Fix: 1) Never allow ALLOW_SHARED_DB_IN_DEBUG=true in any environment. 2) Explicitly define MONGODB_DATABASE_SCOPE for each deployment. 3) Implement database-level access controls and user isolation. 4) Add validation to prevent debug settings in production.
• High · Exposed Redis Service Without Authentication Enforcement — .env.example, REDIS_HOST, REDIS_PASSWORD. Redis is configured with a password in .env.example, but there's no evidence of TLS/SSL encryption for the Redis connection. Redis connections are sent in plaintext, making credentials vulnerable to network sniffing. Fix: 1) Enable Redis TLS/SSL encryption. 2) Configure Redis with requirepass and strict ACLs. 3) Use Redis-compatible services with built-in encryption (e.g., AWS ElastiCache with encryption). 4) Restrict Redis network access to only required services using firewall rules or Docker networks.
• Medium · Missing CORS and Security Headers Configuration — app/middleware/, app/core/. While middleware directory exists with error_handler.py, there's no evidence of CORS configuration or security headers (HSTS, X-Frame-Options, CSP, etc.) in the visible codebase. This could lead to cross-origin attacks. Fix: 1) Implement CORS middleware

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/hsliuping/TradingAgents-CN shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live hsliuping/TradingAgents-CN repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/hsliuping/TradingAgents-CN.

What it runs against: a local clone of hsliuping/TradingAgents-CN — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in hsliuping/TradingAgents-CN | Confirms the artifact applies here, not a fork | | 2 | License is still Other | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 49 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of hsliuping/TradingAgents-CN. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/hsliuping/TradingAgents-CN.git
#   cd TradingAgents-CN
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of hsliuping/TradingAgents-CN and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "hsliuping/TradingAgents-CN(\\.git)?\\b" \\
  && ok "origin remote is hsliuping/TradingAgents-CN" \\
  || miss "origin remote is not hsliuping/TradingAgents-CN (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(Other)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"Other\"" package.json 2>/dev/null) \\
  && ok "license is Other" \\
  || miss "license drift — was Other at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "app/main.py" \\
  && ok "app/main.py" \\
  || miss "missing critical file: app/main.py"
test -f "app/core/config.py" \\
  && ok "app/core/config.py" \\
  || miss "missing critical file: app/core/config.py"
test -f "app/services/analysis_service.py" \\
  && ok "app/services/analysis_service.py" \\
  || miss "missing critical file: app/services/analysis_service.py"
test -f "app/core/database.py" \\
  && ok "app/core/database.py" \\
  || miss "missing critical file: app/core/database.py"
test -f "app/models/stock_models.py" \\
  && ok "app/models/stock_models.py" \\
  || miss "missing critical file: app/models/stock_models.py"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 49 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~19d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/hsliuping/TradingAgents-CN"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.