vectorbt
CommunityVectorized backtesting for lightning-fast strategy simulation.
Finance & Accounting#vectorization#backtesting#quantitative finance#trading strategy#algorithmic trading#parameter optimization
Authoragiprolabs
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill dramatically accelerates the process of backtesting trading strategies by leveraging vectorized computation, enabling rapid parameter optimization and analysis.
Core Features & Use Cases
- High-Performance Backtesting: Run strategy simulations orders of magnitude faster than traditional loop-based methods.
- Parameter Optimization: Effortlessly test thousands of parameter combinations using built-in grid search.
- Comprehensive Metrics: Access over 50 performance metrics, including Sharpe ratio, max drawdown, and win rate.
- Use Case: Quickly backtest a new EMA crossover strategy across 100 different fast and slow period combinations on hourly crypto data to find the optimal parameters before deploying.
Quick Start
Use the vectorbt skill to backtest an EMA crossover strategy on the provided 'ohlcv.csv' file with fast EMA periods from 5 to 20 and slow EMA periods from 20 to 60.
Dependency Matrix
Required Modules
vectorbtpandasnumpypandas-ta
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: vectorbt Download link: https://github.com/agiprolabs/claude-trading-skills/archive/main.zip#vectorbt Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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