| name | vibe-trading |
| version | 0.1.7 |
| description | Professional finance research toolkit — backtesting (7 engines + benchmark comparison panel), factor analysis, options pricing, 74 finance skills, 29 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 6 data sources (tushare, yfinance, okx, akshare, ccxt, futu). |
| dependencies | {"python":">=3.11","pip":["vibe-trading-ai"]} |
| env | [{"name":"TUSHARE_TOKEN","description":"Tushare API token for China A-share data (optional — HK/US/crypto work without any key)","required":false},{"name":"OPENAI_API_KEY","description":"OpenAI-compatible API key — only needed for run_swarm (multi-agent teams). All other tools work without it.","required":false},{"name":"LANGCHAIN_MODEL_NAME","description":"LLM model name for run_swarm (e.g. deepseek/deepseek-v3.2). Only needed if using run_swarm.","required":false}] |
| mcp | {"command":"vibe-trading-mcp","args":[]} |
Vibe-Trading
Professional finance research toolkit with AI-powered backtesting (7 engines), multi-agent teams, 74 specialized skills, and the Shadow Account loop — extract your implicit trading rules from a journal, backtest them across A股/港股/美股/crypto, then see where they would have served you better.
Setup
pip install vibe-trading-ai
Package name vs commands: The PyPI package is vibe-trading-ai. Once installed, you get:
| Command | Purpose |
|---|
vibe-trading | Interactive CLI / TUI |
vibe-trading serve | Launch FastAPI web server |
vibe-trading-mcp | Start MCP server (for Claude Desktop, OpenClaw, Cursor, etc.) |
Add to your agent's MCP config:
{
"mcpServers": {
"vibe-trading": {
"command": "vibe-trading-mcp"
}
}
}
API Key Requirements
21 of 22 MCP tools work with zero API keys. After pip install, backtesting, market data, factor analysis, options pricing, chart patterns, web search, document reading, trade journal analysis, shadow-account extraction/backtest/report, and all 74 skills are ready to use for HK/US equities and crypto.
| Feature | Key needed | When |
|---|
| HK/US equities & crypto | None | Always free (yfinance + OKX) |
| China A-share data | TUSHARE_TOKEN | Only if you query A-share symbols |
Multi-agent swarm (run_swarm) | OPENAI_API_KEY + LANGCHAIN_MODEL_NAME | Swarm spawns internal LLM workers |
What You Can Do
Shadow Account — flagship loop
Feed a CSV broker export (同花顺 / 东财 / 富途 / generic), and the agent will:
analyze_trade_journal — profile your behavior (holding period, win rate, disposition effect, chasing, overtrading, anchoring).extract_shadow_strategy — distill 3-5 if-then rules that describe your profitable roundtrips.run_shadow_backtest — backtest those rules across A/HK/US/crypto and compute delta-PnL vs your realized trades.render_shadow_report — produce an HTML/PDF report (8 sections + charts) with today's matching signals.scan_shadow_signals — list today's symbols that match your shadow's entry cadence (research only).
Backtesting
Create and run quantitative strategies across 7 engines (ChinaA, GlobalEquity, Crypto, ChinaFutures, GlobalFutures, Forex + options) with 6 data sources:
- HK/US equities via yfinance (free, no API key)
- Cryptocurrency via OKX or CCXT/100+ exchanges (free, no API key)
- China A-shares via Tushare (token) or AKShare (free fallback)
- Futures, forex, macro via AKShare (free, no API key)
- HK & A-share equities via Futu (broker login required, optional)
Example workflow:
- Use
list_skills() to discover strategy patterns
- Use
load_skill("strategy-generate") for the strategy creation guide
- Use
write_file() to create config.json and code/signal_engine.py
- Use
backtest() to run and get metrics (Sharpe, return, drawdown, etc.)
Multi-Agent Swarm Teams
29 pre-built agent teams for complex research:
- Investment Committee: bull/bear debate → risk review → PM decision
- Global Equities Desk: A-share + HK/US + crypto → global strategist
- Crypto Trading Desk: funding/basis + liquidation + flow → risk manager
- Earnings Research Desk: fundamentals + revisions + options → earnings strategist
- Macro/Rates/FX Desk: rates + FX + commodities → macro PM
- Quant Strategy Desk: screening → factor research → backtest → risk audit
- Risk Committee: drawdown, tail risk, regime analysis
- And 22 more specialized teams
Use list_swarm_presets() to see all teams, then run_swarm() to execute.
Finance Skills (74)
Comprehensive knowledge base covering:
- Technical analysis (candlestick, Elliott wave, Ichimoku, SMC, harmonic, chanlun)
- Quantitative methods (factor research, ML strategy, pair trading, multi-factor)
- Risk management (VaR/CVaR, stress testing, hedging)
- Options (Black-Scholes, Greeks, multi-leg strategies, payoff diagrams)
- HK/US equities (SEC filings, earnings revisions, ETF flows, ADR/H-share arbitrage)
- Crypto trading desk (funding rates, liquidation heatmaps, stablecoin flows, token unlocks, DeFi yields)
- Behavioral finance, trade journal diagnostics, shadow account
- Macro analysis, credit research, sector rotation, and more
Use load_skill(name) to access full methodology docs with code templates.
Available MCP Tools (22)
| Tool | Description | API Key |
|---|
list_skills | List all 74 finance skills | None |
load_skill | Load full skill documentation | None |
backtest | Run vectorized backtest engine | None* |
factor_analysis | IC/IR analysis + layered backtest | None* |
analyze_options | Black-Scholes price + Greeks | None |
pattern_recognition | Detect chart patterns (H&S, double top, etc.) | None |
get_market_data | Fetch OHLCV data across 6 sources (auto-detect + fallback) | None* |
web_search | Search the web via DuckDuckGo | None |
read_url | Fetch web page as Markdown | None |
read_document | Extract text from PDF/DOCX/XLSX/PPTX/images | None |
write_file | Write files (config, strategy code) | None |
read_file | Read file contents | None |
analyze_trade_journal | Parse broker CSV → profile + behavior diagnostics | None |
extract_shadow_strategy | Distill 3-5 if-then rules from profitable roundtrips | None |
run_shadow_backtest | Multi-market backtest + delta-PnL attribution | None* |
render_shadow_report | HTML/PDF shadow report (8 sections + charts) | None |
scan_shadow_signals | Today's symbols matching the shadow's cadence | None |
list_swarm_presets | List multi-agent team presets | None |
run_swarm | Execute a multi-agent research team | LLM key |
get_swarm_status | Poll swarm run status without blocking | None |
get_run_result | Get final report and task summaries | None |
list_runs | List recent swarm runs with metadata | None |
*A-share symbols require TUSHARE_TOKEN. HK/US/crypto are free.
Quick Start
pip install vibe-trading-ai
That's it — no API keys needed for HK/US/crypto markets. Start using backtest, get_market_data, analyze_options, analyze_trade_journal, extract_shadow_strategy, web_search, and all 74 skills immediately.
Examples
Backtest a MACD strategy on Apple:
Backtest AAPL with MACD crossover strategy (fast=12, slow=26, signal=9) for 2024
Analyze my trade journal and build a Shadow Account:
Call analyze_trade_journal on ~/Downloads/tonghuashun.csv, then extract_shadow_strategy with min_support=3, then run_shadow_backtest for the last year, then render_shadow_report.
Run an investment committee review:
Use run_swarm with investment_committee preset to evaluate NVDA. Variables: target=NVDA.US, market=US
Factor analysis on CSI 300:
Run factor_analysis on CSI 300 stocks using pe_ttm factor from 2023 to 2024
Options analysis:
Use analyze_options: spot=100, strike=105, 90 days, vol=25%, rate=3%
