Auto-Trader/agent/SKILL.md

name, version, description, dependencies, env, mcp

name	version	description	dependencies	env	mcp
vibe-trading	0.1.6	Professional finance research toolkit — backtesting (7 engines + benchmark comparison panel), factor analysis, options pricing, 72 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).	python pip >=3.11 vibe-trading-ai	name description required TUSHARE_TOKEN Tushare API token for China A-share data (optional — HK/US/crypto work without any key) false name description required OPENAI_API_KEY OpenAI-compatible API key — only needed for run_swarm (multi-agent teams). All other tools work without it. false name description required LANGCHAIN_MODEL_NAME LLM model name for run_swarm (e.g. deepseek/deepseek-v3.2). Only needed if using run_swarm. false	command args vibe-trading-mcp

Vibe-Trading

Professional finance research toolkit with AI-powered backtesting (7 engines), multi-agent teams, 72 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 72 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:

1. analyze_trade_journal — profile your behavior (holding period, win rate, disposition effect, chasing, overtrading, anchoring).
2. extract_shadow_strategy — distill 3-5 if-then rules that describe your profitable roundtrips.
3. run_shadow_backtest — backtest those rules across A/HK/US/crypto and compute delta-PnL vs your realized trades.
4. render_shadow_report — produce an HTML/PDF report (8 sections + charts) with today's matching signals.
5. 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:

1. Use list_skills() to discover strategy patterns
2. Use load_skill("strategy-generate") for the strategy creation guide
3. Use write_file() to create config.json and code/signal_engine.py
4. 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 (72)

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 72 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 72 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%