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Quant Engine

Run quantitative trading algorithms and backtest strategies on historical market data using Python.

5|1|Updated Feb 9, 2026
Search Tags:#momentum#backtesting#quantitative finance#trading#cryptocurrency#arbitrage#mean reversion
One-click install
npx skills add https://github.com/kayzaa/k.i.t.-bot --skill quant-engine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Quant Engine
Source: https://github.com/kayzaa/k.i.t.-bot/tree/main/skills/quant-engine
Command: npx skills add https://github.com/kayzaa/k.i.t.-bot --skill quant-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, statsmodels, scikit-learn, asyncio, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides access to sophisticated quantitative trading algorithms and backtesting capabilities, democratizing Wall Street-level financial strategies for individual traders and developers.

Core Features & Use Cases

  • Statistical Arbitrage: Identifies and trades cointegrated asset pairs using techniques like pairs trading and mean reversion on spreads.
  • Momentum Strategies: Implements various momentum scans and ranking to identify trending assets.
  • Mean Reversion: Utilizes Bollinger Bands and RSI to detect and trade price reversals.
  • Backtesting: Offers a robust engine to test strategy performance on historical data.
  • Use Case: A user can ask the engine to find cointegrated pairs among a list of cryptocurrencies, scan for top momentum stocks, or backtest a mean reversion strategy on BTC/USDT.

Quick Start

Use the quant_engine skill to find cointegrated pairs among BTC, ETH, and SOL.

Frequently Asked Questions about Quant Engine

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

FAQPage Schema
How do I backtest a mean reversion strategy using Python?▼

To backtest a mean reversion strategy, this engine uses Bollinger Bands and RSI to detect price reversals on historical data and evaluates performance through its built-in backtesting framework. It processes market data directly via pandas and numpy.

Can I find cointegrated cryptocurrency pairs for statistical arbitrage?▼

Yes, the statistical arbitrage feature identifies cointegrated cryptocurrency pairs for pairs trading. It analyzes spreads using mean reversion techniques to generate actionable trading signals across assets like BTC and ETH.

What Python libraries do I need to run quantitative trading algorithms?▼

Running these quantitative trading algorithms requires Python libraries including numpy, pandas, scipy, statsmodels, and scikit-learn. These dependencies support market data analysis, statistical modeling, and signal generation.

How does momentum scanning work for identifying trending assets?▼

Momentum scanning works by implementing various momentum scans and ranking systems to identify trending assets in the market. It analyzes historical price data to generate trading signals based on asset momentum.

Is asyncio required for quantitative backtesting on historical data?▼

The asyncio dependency is included in the environment to support asynchronous processing within the quantitative trading framework. While the backtesting engine processes historical data, asyncio facilitates concurrent market data handling and signal generation.

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