Menu

Individual skill

vibe-trading

0upvotes

Professional finance research toolkit — backtesting (12 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 89 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 28 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).

agent/SKILL.md

Read pinned source

Source commit a79afbdb048f873433fdb651a667e43dd0bb735d

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 9, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
37,968
Repository last updated
Nov 8, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Vibe-Trading

Professional finance research toolkit with AI-powered backtesting (12 engines), multi-agent teams, 89 specialized skills, the **Alpha Zoo** (478 pre-built quantitative alphas across qlib158 / alpha101 / gtja191 / academic / fundamental 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

```bash
pip install vibe-trading-ai
```

> **Package name vs commands:** The PyPI package is `vibe-trading-ai`. Once installed, you get:
>
> | Command | Purpose |
> |---------|---------|
Back to top