name: vibe-trading version: 0.1.9 description: Professional finance research toolkit — backtesting (7 engines + benchmark comparison panel), factor analysis, Alpha Zoo (452 pre-built alphas across qlib158/alpha101/gtja191/academic), options pricing, 77 finance skills, 29 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 7 data sources (tushare, yfinance, okx, akshare, mootdx, 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-v4-pro). 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, 77 specialized skills, the Alpha Zoo (452 pre-built quantitative alphas across qlib158 / alpha101 / gtja191 / academic with one-line CLI benchmarking), 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-tradingInteractive CLI / TUI vibe-trading serveLaunch FastAPI web server vibe-trading-mcpStart 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
Core research MCP tools work with zero API keys for HK/US/crypto. After pip install, backtesting, market data, factor analysis, options pricing, chart patterns, web search, document reading, trade journal analysis, shadow-account extraction/backtest/report, the Alpha Zoo (452 pre-built alphas), and all 77 skills are ready to use. IBKR tools require a local TWS / IB Gateway session; run_swarm requires an LLM key.
| 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 7 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 createconfig.jsonandcode/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.
Alpha Zoo (452 pre-built alphas)
One-line cross-sectional IC / IR / alive-reversed-dead categorisation across four bundled zoos:
- qlib158 (154 alphas) — Microsoft Qlib's
Alpha158feature handler, Apache-2.0 with pinned commit SHA. - alpha101 (101 alphas) — Kakushadze (2015) "101 Formulaic Alphas" (arXiv:1601.00991), written from the paper appendix.
- gtja191 (191 alphas) — Guotai Junan 2014 "191 Short-period Trading Alpha Factors" research report.
- academic (6 factors) — Fama-French 5 + Carhart momentum (honest price-based proxies).
Each alpha ships with __alpha_meta__ (formula LaTeX + theme + universe + warmup + columns required), guarded by an AST purity gate + 300-row lookahead sentinel test. Use the vibe-trading alpha {list,show,bench,compare,export-manifest} CLI, the /alpha/* REST routes (browser at /alpha-zoo), or compose multi-factor signals via ZooSignalEngine.from_zoo(...).
Finance Skills (77)
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 (35)
| Tool | Description | API Key |
|---|---|---|
list_skills |
List all 77 finance skills | None |
load_skill |
Load full skill documentation | None |
start_research_goal |
Create an auditable research goal | None |
get_research_goal |
Read the current research goal | None |
add_goal_evidence |
Attach evidence to a research goal | None |
update_research_goal_status |
Update goal lifecycle status | 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 |
reap_stale_runs |
Finalize stale swarm runs | None |
trading_connections |
List selectable connector profiles | None |
trading_select_connection |
Select the default connector profile | None |
trading_check |
Check connector readiness | Connector app/OAuth |
trading_account |
Read account summary from selected connector | Connector app/OAuth |
trading_positions |
Read positions from selected connector | Connector app/OAuth |
trading_orders |
Read open orders from selected connector | Connector app/OAuth |
trading_quote |
Read a quote snapshot from selected connector | Connector app/OAuth |
trading_history |
Read historical bars from selected connector | Connector app/OAuth |
*A-share symbols require TUSHARE_TOKEN. HK/US/crypto are free. Trading connector rows use the selected connector profile, e.g. IBKR local TWS/Gateway or Robinhood MCP OAuth.
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, the Alpha Zoo (vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025), and all 77 skills immediately.
Loading Tools from External MCP Servers
The built-in agent can load tools from your own external MCP servers in addition to its local toolset.
Note: This is the MCP client path — the opposite of the MCP plugin listed above. The plugin above makes Vibe-Trading's tools available to your agents. This section lets Vibe-Trading's own agent call tools from your servers.
Setup
Create ~/.vibe-trading/agent.json:
{
"mcpServers": {
"my-server": {
"command": "uvx",
"args": ["my-mcp-server"],
"toolTimeout": 30,
"enabledTools": ["*"]
}
}
}
Ordinary external MCP tools appear automatically in every vibe-trading run / vibe-trading chat call. They are injected after local tools under stable names: mcp_<server>_<tool>. Live-broker MCP servers are consumed through the connector-scoped trading_* tools instead of exposing raw mcp_<broker>_* tools to the agent.
Official IBKR MCP read-only probe
Add Interactive Brokers' official MCP endpoint as a read-only external server:
{
"mcpServers": {
"ibkr": {
"type": "streamableHttp",
"url": "https://api.ibkr.com/v1/api/mcp",
"auth": {
"type": "oauth",
"scopes": ["mcp.read"],
"clientName": "Vibe-Trading",
"cacheDir": "~/.vibe-trading/live/ibkr/oauth"
},
"enabledTools": ["*"]
}
}
}
Authorize it with vibe-trading connector authorize ibkr-live-official-mcp-readonly. The wildcard is accepted
only for this mcp.read probe. Generic trading_account and trading_positions
calls stay disabled until IBKR publishes stable read tool names that Vibe-Trading
can map safely; mcp.write requires an explicit tool allowlist and live
order-guard handling. If IBKR issues a pre-registered OAuth client, add
clientId and clientSecret inside auth.
Trading connector profiles
The public trading surface is connector-first. Choose a connector profile, then paper/live is just an attribute under that connector.
pip install "vibe-trading-ai[ibkr]"
vibe-trading connector list
vibe-trading connector use ibkr-paper-local
vibe-trading connector configure ibkr-paper-local --yes
vibe-trading connector check
vibe-trading connector account
vibe-trading connector positions
vibe-trading connector orders
vibe-trading connector quote AAPL
vibe-trading connector history AAPL --duration "30 D" --bar-size "1 day"
Default ports are TWS paper 7497, IB Gateway paper 4002, TWS live-readonly
7496, and IB Gateway live-readonly 4001.
Config fields
| Field | Required | Default | Description |
|---|---|---|---|
type |
stdio: no, HTTP: yes | inferred only for stdio | Transport type. Use sse or streamableHttp for URL-based servers. |
command |
stdio: yes | — | Executable to launch |
args |
no | [] |
Command arguments |
env |
no | {} |
Extra env vars for the subprocess |
url |
HTTP: yes | — | Remote SSE / streamable HTTP endpoint URL |
headers |
no | {} |
Extra HTTP headers for SSE / streamable HTTP servers |
toolTimeout |
no | 30 |
Seconds before a tool call is cancelled |
enabledTools |
no | ["*"] |
Allowlist of remote tool names. ["*"] enables all |
For URL-based transports, type is required. The agent no longer guesses between SSE and streamable HTTP from the URL suffix.
Per-session override (API)
Security — disabled by default.
mcpServersdefines subprocesscommand/args/envand is therefore restricted to operator-level trust. API callers cannot inject MCP server definitions throughPOST /sessionsunless the server operator explicitly opts in.
To enable session-level MCP injection, set the environment variable on the server before starting the agent:
export ALLOW_SESSION_MCP_SERVERS=1
With the opt-in active, pass mcpServers inside session.config to extend or replace the global config for that session only:
{
"config": {
"mcpServers": {
"research": {
"command": "uvx",
"args": ["research-mcp"],
"enabledTools": ["search"]
}
}
}
}
Without ALLOW_SESSION_MCP_SERVERS=1, any mcpServers key in session.config is silently stripped before config loading. The global operator config on disk (~/.vibe-trading/agent.json) is always respected regardless of this flag.
v1 limits
- Transport: stdio, SSE, and streamable HTTP.
- Execution: serial only. MCP tools never enter the parallel readonly path.
- Surfaces: tools only. Resources and prompts are not exposed.
- Swarm: MCP tools are excluded from Swarm worker registries in v1.
- Hot reload: not supported. Restart the process to pick up config changes.
Failure handling
| Case | Behavior |
|---|---|
| Missing config file | falls back to empty config — no MCP servers loaded |
| Invalid config file | logs a warning and falls back to empty config |
| Server fails to start | that server is skipped; local tools and other servers still load |
| Tool call times out | returns a normalized error payload instead of raising |
| Two server names collide after sanitization | deterministic hash suffix appended; operator warning emitted |
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%