backtester

Name: backtester
Availability: InStock
Author: Signal-Execution-Labs

Backtest RSI and EMA trading strategies against historical OHLCV data.

125|865|Updated Jun 5, 2026

Search Tags:#pandas #backtesting #historical-data #trading-strategies #ccxt #ta

Ready to run

npx skills add https://github.com/Signal-Execution-Labs/forex-trading-ai-agent --skill backtester-signal-execution-labs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: backtester
Source: https://github.com/Signal-Execution-Labs/forex-trading-ai-agent/tree/main/skills/backtester
Command: npx skills add https://github.com/Signal-Execution-Labs/forex-trading-ai-agent --skill backtester-signal-execution-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ccxt, ta, pandas, numpy, and includes scripts (resource) components.

What problem does it solve?

Automates the evaluation of trading ideas by backtesting against historical price data to quantify potential viability.

Core Features & Use Cases

Historical Data: Load OHLCV data from exchanges to feed backtests.
Strategy Testing: Run rule-based strategies like RSI or EMA crossovers to simulate trades.
Performance Metrics: Produce key metrics such as final capital, total return, and drawdown.
Report Generation: Generate actionable performance reports for decision making.

Quick Start

Run a simple RSI backtest on BTC/USDT using the included Python script.

Frequently Asked Questions about backtester

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

FAQPage Schema

How do I backtest trading strategies using historical OHLCV data?▼

To backtest trading strategies using historical OHLCV data, you load exchange price feeds into pandas dataframes and run rule-based logic to simulate trades. This process outputs final capital and performance metrics.

Can I backtest RSI and EMA crossover strategies for crypto assets?▼

Yes, you can backtest RSI and EMA crossover strategies for crypto assets. The tool loads historical OHLCV data via ccxt and applies ta indicators to simulate trades across multiple timeframes.

What performance metrics are generated when testing historical trading strategies?▼

Testing historical trading strategies generates key performance metrics such as final capital, total return, and drawdown. These reports quantify potential viability and support data-driven decision making.

Do I need Python and pandas to run historical data backtests?▼

Yes, you need Python3 along with pandas, numpy, ccxt, and ta to run historical data backtests. These dependencies handle OHLCV data loading, technical indicator calculations, and numerical analysis.

What's the best way to evaluate FX or crypto trading ideas without live capital?▼

The best way to evaluate FX or crypto trading ideas without live capital is running automated backtests against historical price data. This quantifies strategy viability by simulating indicator-driven trades and producing performance reports.

Are there limitations to using predefined trading strategies for historical data analysis?▼

Predefined trading strategies for historical data analysis are limited by the quality of loaded OHLCV data and inherent market simulation constraints. Viability metrics rely on historical performance, which does not guarantee future results.