trading-expert

Design, analyze, and backtest algorithmic trading systems with Python.

Updated Nov 25, 2025

Search Tags:#execution #Python #risk-management #backtesting #market-data #algorithmic-trading #quantitative-analysis

Ready to run

npx skills add https://github.com/Cambixx/bot-trading --skill trading-expert

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: trading-expert
Source: https://github.com/Cambixx/bot-trading/tree/main/.agents/skills/trading-expert
Command: npx skills add https://github.com/Cambixx/bot-trading --skill trading-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides expert guidance for building and evaluating algorithmic trading systems, including strategy development, backtesting, risk management, and execution architecture.

Core Features & Use Cases

Algorithmic strategy design: Develop and test quantitative trading strategies using Python libraries (pandas, numpy) and historical data.
Backtesting and performance analytics: Evaluate strategies with metrics like Sharpe ratio, drawdowns, and turnover.
Execution and risk management: Plan execution strategies, order routing, and risk controls for real-time trading.
Use Case: A quant researcher wants to prototype a mean-reversion strategy and benchmark it against historical market data.

Quick Start

Use the trading-expert skill to outline a scalable architecture for an automated trading system and generate a sample backtest workflow.

Frequently Asked Questions about trading-expert

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

FAQPage Schema

How do I design and backtest an algorithmic trading strategy in Python?▼

To backtest an algorithmic trading strategy, you process historical market data using Python libraries like pandas and numpy, then evaluate performance through metrics such as Sharpe ratio, drawdowns, and turnover to validate your quantitative models.

What is the best way to structure a backtesting workflow for quantitative analysis?▼

A scalable backtesting workflow for quantitative analysis separates market data processing, strategy development, and performance analytics, allowing you to systematically test trading rules against historical datasets and generate clear evaluation metrics.

How do I implement risk management controls for an automated trading system?▼

Risk management for automated trading systems involves defining execution strategies, configuring order routing rules, and setting exposure limits to control potential losses during real-time market execution.

Can I use this approach to prototype a mean-reversion strategy?▼

Yes, you can prototype a mean-reversion strategy by applying numerical methods to historical market data, leveraging Python-based examples to identify price deviations and benchmarking the results against standard performance metrics.

Does building an algorithmic trading architecture require specific Python dependencies?▼

Building algorithmic trading architecture relies on standard Python numerical libraries like pandas and numpy for data manipulation, requiring no external dependencies to structure your backtesting and execution optimization components.

What performance metrics should I calculate when evaluating a trading strategy?▼

When evaluating a trading strategy, calculate performance metrics including the Sharpe ratio for risk-adjusted returns, maximum drawdowns for downside risk, and turnover rates to measure trading frequency and execution costs.