# vectorbt

Runs vectorized backtests for trading strategies using NumPy and pandas array operations. It executes parameter grid searches and analyzes portfolio risk.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 492 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/vectorbt/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/vectorbt

## What it does

The skill teaches the coding agent how to evaluate trading strategies with the `vectorbt` Python library. Instead of processing historical bars in step-by-step loops, calculations rely on vector operations across pandas DataFrames. The agent generates boolean entry and exit arrays, then executes the simulation with `vbt.Portfolio.from_signals()`.

The workflow uses built-in indicators or formulas from `pandas-ta`. Your Python environment requires `uv pip install vectorbt pandas numpy` before running scripts. The simulation accounts for transaction fees and price slippage.

Vectorization gives raw speed. The agent can test dozens of parameter combinations simultaneously in seconds. However, strategies requiring complex path-dependent execution rules will demand custom array restructuring before vectorbt can process them.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/vectorbt/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/vectorbt/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/vectorbt/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/vectorbt/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills vectorbt
```

2. **Manually: put the files in place**
- File: `.github/skills/vectorbt/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/vectorbt
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/vectorbt/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/vectorbt/SKILL.md

## When to use it
- simulate trading strategies without bar-by-bar loops
- run parameter grid search across moving average windows
- calculate Sharpe ratio and maximum drawdown for a portfolio
- evaluate historical trades with slippage and trading fees

## Example prompts
- Write a vectorbt backtest for an EMA crossover strategy with 0.3% fees
- Run a grid search over RSI window parameters to maximize the Sharpe ratio
- Compute trade stats and drawdown plots using OHLCV data from ohlcv.csv

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE fb2a4c4384359b90
````text
---
name: vectorbt
description: High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics
---

# Vectorized Backtesting with vectorbt

## Overview

vectorbt is a Python library for **vectorized backtesting** — running strategy simulations using NumPy/pandas array operations instead of bar-by-bar loops. This makes it 100–1000x faster than event-driven frameworks (backtrader, zipline), enabling parameter optimization across thousands of combinations in seconds.

**Key strengths:**
- Blazing speed via NumPy vectorization
- Built-in parameter grid search and optimization
- 50+ built-in performance metrics (Sharpe, Sortino, Calmar, max drawdown, profit factor)
- Rich plotting (equity curves, drawdowns, trade markers, heatmaps)
- Native pandas integration — your data stays in DataFrames throughout

## Installation

```bash
uv pip install vectorbt pandas numpy
```

vectorbt pulls in pandas, NumPy, and Plotly automatically. For technical indicators, also install pandas-ta:

```bash
uv pip install vectorbt pandas-ta
```

## Core Concepts

### 1. Signals — Boolean Entry/Exit Arrays

Strategies in vectorbt are expressed as boolean pandas Series (or arrays) indicating where to enter and exit positions:

```python
import vectorbt as vbt
import pandas as pd

# Entry: buy when fast EMA crosses above slow EMA
entries = fast_ema > slow_ema
# Exit: sell when fast EMA crosses below slow EMA
exits = fast_ema < slow_ema
```

vectorbt resolves conflicting signals automatically (you can't enter while already in a position).

### 2. Portfolio — The Backtesting Engine

`vbt.Portfolio.from_signals()` is the primary backtesting function. It takes price data and entry/exit signals, simulates trades, and computes performance:

```python
pf = vbt.Portfolio.from_signals(
    close=close_prices,
    entries=entries,
    exits=exits,
    init_cash=10_000,
    fees=0.003,       # 0.3% per trade
    slippage=0.005,   # 0.5% slippage
    freq="1h",        # hourly data
)
```

### 3. Metrics — Built-in Performance Analysis

```python
# Full stats summary
print(pf.stats())

# Individual metrics
print(f"Total Return: {pf.total_return():.2%}")
print(f"Sharpe Ratio: {pf.sharpe_ratio():.3f}")
print(f"Max Drawdown: {pf.max_drawdown():.2%}")
print(f"Win Rate:     {pf.trades.win_rate():.2%}")
```

### 4. Parameter Optimization — Grid Search in Seconds

Pass arrays instead of scalars to test many parameter combos simultaneously:

```python
import numpy as np

fast_periods = np.arange(5, 25, 2)   # 10 values
slow_periods = np.arange(20, 60, 5)  # 8 values

fast_ma = vbt.MA.run(close, fast_periods, short_name="fast")
slow_ma = vbt.MA.run(close, slow_periods, short_name="slow")

# This creates 80 parameter combinations automatically
entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)
```

## Basic Workflow
````
END THIRD-PARTY FILE fb2a4c4384359b90

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