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backtester

Backtest trading strategies on historical OHLCV data and compute performance metrics.

Updated Aug 27, 2026
Search Tags:#python#performance reporting#technical indicators#backtesting#trading strategy#historical data#risk metrics
One-click install
npx skills add https://github.com/datagridSolution/forex-trading-ai-agent --skill backtester-datagridsolution
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backtester
Source: https://github.com/datagridSolution/forex-trading-ai-agent/tree/main/skills/backtester
Command: npx skills add https://github.com/datagridSolution/forex-trading-ai-agent --skill backtester-datagridsolution

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Backtester helps you validate trading strategies using historical market data so you can estimate returns, volatility, and drawdowns before risking real capital.

Core Features & Use Cases

  • Historical data loading: Fetch OHLCV from exchanges (e.g., Binance) for a chosen symbol and timeframe.
  • Strategy simulation: Run rule-based strategies such as RSI thresholds or moving-average crossovers against the dataset.
  • Performance reporting: Compute key metrics like total return, Sharpe ratio, max drawdown, win rate, and basic trade statistics.
  • Use case: Evaluate whether an RSI “buy low / sell high” setup or an EMA crossover rule outperforms buy-and-hold over the last 365 days for BTC/USDT.

Quick Start

Ask it to backtest the RSI strategy for BTC/USDT using one-day candles over the last 365 days and print the resulting performance metrics and trade count.

Frequently Asked Questions about backtester

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

FAQPage Schema
How do I backtest a trading strategy using historical OHLCV data?▼

To backtest a trading strategy, you load historical OHLCV data, compute technical indicators, simulate entries and exits with position tracking, and generate performance metrics including total return and Sharpe ratio.

Can I compare an EMA crossover strategy against buy-and-hold for BTC/USDT?▼

Yes, you can simulate an EMA crossover system against a buy-and-hold benchmark for symbols like BTC/USDT across configurable timeframes to estimate relative profitability and risk characteristics.

What risk metrics are calculated when simulating RSI threshold strategies?▼

Simulating RSI threshold strategies calculates risk metrics including total return, Sharpe ratio, maximum drawdown, win rate, and basic trade statistics to evaluate overall strategy viability.

Does backtesting with Python require the ccxt library to fetch market data?▼

Fetching historical market data from exchanges like Binance relies on the ccxt library, while technical indicator computation utilizes ta, and data manipulation depends on pandas and numpy.

What is the best way to test trading strategies over the last 365 days?▼

The best way to test trading strategies is to fetch one-day candles over the last 365 days, apply rule-based simulations like RSI thresholds, and print the resulting performance metrics and trade counts.

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