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crypto-backtest

Convert natural-language trading ideas into Python backtests with HTML reports.

18|4|Updated Jan 22, 2026
Search Tags:#reporting#trading#indicators#backtest#crypto#ccxt#pandas-ta
One-click install
npx skills add https://github.com/0xrikt/crypto-skills --skill crypto-backtest
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: crypto-backtest
Source: https://github.com/0xrikt/crypto-skills/tree/main/crypto-backtest
Command: npx skills add https://github.com/0xrikt/crypto-skills --skill crypto-backtest

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps traders validate crypto trading ideas by turning natural-language ideas into structured backtests with indicators, risk controls, and professional reports.

Core Features & Use Cases

  • Multi-indicator backtesting: RSI, MACD, SMA/EMA, Bollinger Bands, MFI, ROC and drawdown analysis
  • Backtest reports and runnable code: HTML reports and Python scripts for further customization
  • CLI-driven workflow: describe an idea, translate to a complete strategy, and generate a shareable result
  • Works with spot trading concepts and risk parameters to evaluate feasibility and edge

Quick Start

Describe your trading idea to the Skill, and it will produce a backtest report and runnable Python code to validate it.

Frequently Asked Questions about crypto-backtest

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

FAQPage Schema
How do I backtest crypto trading strategies using natural language?▼

Backtesting crypto trading strategies from natural language involves translating your trading ideas into structured rules, fetching historical market data via CCXT, and calculating indicators with pandas-ta to generate a reproducible performance report.

Can I combine multiple indicators like RSI and MACD in a single crypto backtest?▼

Yes, you can combine multiple indicators like RSI, MACD, SMA, EMA, Bollinger Bands, MFI, and ROC in a single crypto backtest to create multi-indicator signals and evaluate risk-managed spot-trading strategies.

Does crypto backtesting with CCXT and pandas-ta support BTC/USDT spot trading?▼

Crypto backtesting with CCXT and pandas-ta supports spot-trading concepts and risk parameters for BTC/USDT and other pairs, allowing you to validate trading ideas across different cryptocurrency assets.

How do I generate an HTML report and runnable Python code for a trading strategy?▼

Generating an HTML report and runnable Python code for a trading strategy requires defining your indicators and risk controls, which the system then uses to calculate performance metrics and output shareable results for further customization.

What is the best way to validate a crypto trading idea with drawdown analysis?▼

The best way to validate a crypto trading idea with drawdown analysis is to translate the concept into a structured backtest, apply multi-indicator signals, and generate an HTML report detailing performance and risk metrics.

Are there limitations when using natural language to create crypto backtests?▼

Limitations when using natural language to create crypto backtests include focusing primarily on spot-trading concepts rather than complex derivatives, requiring clear specification of risk parameters to evaluate feasibility accurately.

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