test-trading-strategies

Name: test-trading-strategies
Availability: InStock
Author: RoboNet

Backtest crypto and Polymarket trading strategies on historical data.

1|Updated Feb 2, 2026

Search Tags:#metrics #historical data #backtest #crypto #strategy validation #Polymarket

Ready to run

npx skills add https://github.com/robonet-tech/skills --skill test-trading-strategies

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: test-trading-strategies
Source: https://github.com/robonet-tech/skills/tree/main/skills/test-trading-strategies
Command: npx skills add https://github.com/robonet-tech/skills --skill test-trading-strategies

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the rigorous backtesting of trading strategies to validate viability before live deployment, reducing the risk of costly mistakes.

Core Features & Use Cases

Crypto backtesting: evaluate strategies on historical crypto data across multiple symbols and timeframes.
Prediction market backtesting: test Polymarket-style strategies against historical market outcomes.
Result interpretation: obtain metrics such as Sharpe ratio, max drawdown, win rate, profit factor, and trade statistics to compare versions.

Quick Start

Load tools: Use MCPSearch to select: mcp__workbench__run_backtest, mcp__workbench__get_latest_backtest_results
Basic example: run_backtest( strategy_name="NewStrategy", start_date="2024-06-01", end_date="2024-12-31", symbol="BTC-USDT", timeframe="1h", config={"fee": 0.0005, "slippage": 0.0005, "leverage": 1} )
You can also run a Polymarket backtest: run_prediction_market_backtest( strategy_name="PolymarketStrategy", start_date="2024-06-01", end_date="2024-12-31", condition_id="12345", asset="BTC", interval="1h" )

Frequently Asked Questions about test-trading-strategies

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

FAQPage Schema

How do I backtest a crypto trading strategy using historical data?▼

You backtest a crypto trading strategy by running it against historical data across defined date ranges, symbols, and timeframes. This process returns performance metrics such as Sharpe ratio, max drawdown, and win rate to validate viability before live deployment.

What metrics do I get from a trading strategy backtest?▼

A trading strategy backtest returns metrics including Sharpe ratio, max drawdown, win rate, profit factor, and trade statistics. These metrics allow you to evaluate strategy viability and compare versions before live deployment.

Can I backtest Polymarket prediction market strategies?▼

Yes, you can backtest Polymarket prediction market strategies against historical market outcomes. By specifying a condition ID, asset, date range, and interval, you obtain performance metrics to validate the prediction market strategy.

How do I configure fees and slippage for a crypto perpetual backtest?▼

You configure fees and slippage for a crypto perpetual backtest by passing a config object with fee, slippage, and leverage parameters when running the backtest. This allows you to simulate realistic trading conditions and evaluate strategy viability.

Why validate a trading strategy with historical data before live deployment?▼

Validating a trading strategy with historical data before live deployment reduces the risk of costly mistakes. Backtesting automates rigorous evaluation to confirm viability using metrics like profit factor and max drawdown before risking capital.