vibe-trading

Automate finance research, backtesting, and analysis using Python libraries.

Updated May 25, 2026

Search Tags:#analysis #machine learning #data analysis #finance #backtesting #options pricing #swarm teams

One-click install

npx skills add https://github.com/NigarumOvum/AutoTrading --skill vibe-trading-nigarumovum

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: vibe-trading
Source: https://github.com/NigarumOvum/AutoTrading/tree/main/Vibe-Trading/agent
Command: npx skills add https://github.com/NigarumOvum/AutoTrading --skill vibe-trading-nigarumovum

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, duckdb, tushare, requests, yfinance, akshare, ccxt, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive suite of tools for professional finance research, backtesting, and analysis, powered by AI and machine learning.

Core Features & Use Cases

Backtesting: Backtest trading strategies across multiple markets and time frames with various engines.
Factor Analysis: Analyze factor predictive power and perform layered backtesting.
Options Pricing: Calculate option prices and Greeks using the Black-Scholes model.
Pattern Recognition: Detect technical chart patterns in OHLCV data.
Web & Document Reading: Fetch web pages, extract text from documents, and search the web.
Swarm Teams: Run multi-agent research teams for complex tasks.
Alpha Zoo: Access a library of pre-built quantitative alphas for analysis.
Finance Skills: Use a knowledge base of 75 specialized finance skills.
Shadow Account: Extract and backtest trading rules from a journal.
Use Case: Imagine you are a quantitative analyst looking to backtest a new trading strategy. Use this Skill to build and test your strategy across multiple markets and time frames.

Quick Start

Use the vibe-trading skill to backtest a strategy on AAPL with the MACD crossover strategy (fast=12, slow=26, signal=9) for 2024.

Frequently Asked Questions about vibe-trading

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I backtest a trading strategy across multiple markets using Python?▼

Yes, you can calculate option prices and Greeks using the Black-Scholes model within this analysis toolkit. It leverages scipy and numpy to perform the mathematical computations required for accurate options pricing.

Can I fetch market data from yfinance and ccxt for factor analysis?▼

Swarm teams allow you to run multi-agent research teams for complex finance tasks. This feature coordinates multiple agents to automate comprehensive market analysis, risk management, and investment research workflows.

Do I need to install duckdb and pandas before using this finance research toolkit?▼

Yes, you need dependencies like pandas, numpy, scipy, and duckdb installed in your Python environment. These libraries are required for the data manipulation, database operations, and machine learning tasks the toolkit performs.

What is the best way to detect technical chart patterns in OHLCV data?▼

The best way to detect technical chart patterns in OHLCV data is by using the pattern recognition features in this toolkit. It applies machine learning and data analysis techniques to identify patterns within the fetched market data.