# vectorbt — High-Performance Quantitative Backtesting in Python

> A Python library for fast backtesting, analysis, and visualization of trading strategies using vectorized operations on NumPy and pandas.

## Install

Save as a script file and run:

# vectorbt — High-Performance Quantitative Backtesting in Python

## Quick Use
```bash
pip install vectorbt
```
```python
import vectorbt as vbt
price = vbt.YFData.download("AAPL").get("Close")
fast_ma = vbt.MA.run(price, 10)
slow_ma = vbt.MA.run(price, 50)
entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)
pf = vbt.Portfolio.from_signals(price, entries, exits)
print(pf.stats())
```

## Introduction
vectorbt is a Python library designed for quantitative traders and researchers who need to test thousands of strategy variations quickly. By leveraging NumPy broadcasting and vectorized operations instead of row-by-row iteration, it achieves orders-of-magnitude speedups over traditional event-driven backtesting frameworks, making exhaustive parameter sweeps practical.

## What vectorbt Does
- Runs vectorized backtests on trading strategies with minimal code
- Computes portfolio performance metrics (Sharpe, Sortino, max drawdown, etc.) across parameter grids
- Provides technical indicator wrappers (MA, RSI, BBANDS, etc.) with broadcasting support
- Generates interactive Plotly-based visualizations of equity curves, drawdowns, and trade analysis
- Supports multi-asset portfolio simulation with position sizing and fees

## Architecture Overview
vectorbt represents data as multi-dimensional NumPy arrays where extra dimensions encode parameter combinations. Indicators, signals, and portfolio logic operate on these arrays using broadcasting, so a single backtest call can evaluate thousands of parameter sets simultaneously. The `Portfolio` class simulates order execution, tracks positions, and computes statistics. Numba JIT compilation accelerates custom logic where pure NumPy is insufficient.

## Self-Hosting & Configuration
- Install via `pip install vectorbt` with optional extras for data downloads
- Use `vbt.settings` to configure global defaults for plotting, data caching, and computation
- Set `freq` parameter on time series to ensure correct annualization of metrics
- Enable Numba acceleration with `@njit` decorators for custom signal logic
- Configure data sources via `vbt.YFData`, `vbt.BinanceData`, or custom data feeds

## Key Features
- Vectorized execution enables testing millions of parameter combinations in seconds
- Built-in technical indicators that natively support parameter arrays
- Interactive Plotly visualizations for equity curves, trade scatter plots, and heatmaps
- Flexible portfolio simulation with configurable fees, slippage, and position sizing
- Numba-accelerated custom indicators and signals

## Comparison with Similar Tools
- **Backtrader** — Event-driven, easier to understand but much slower for parameter sweeps
- **Zipline** — Quantopian legacy framework; vectorbt is more flexible and actively maintained
- **bt** — Tree-based strategy composition; vectorbt excels at brute-force parameter optimization
- **QuantStats** — Analytics and reporting only; vectorbt includes the full backtest loop
- **pandas-ta** — Indicator library only; vectorbt adds backtesting and portfolio simulation

## FAQ
**Q: How fast is vectorbt compared to event-driven frameworks?**
A: Depending on the strategy, vectorbt can be 100x to 1000x faster than row-by-row backtesters because it processes entire arrays at once.

**Q: Can I use vectorbt for live trading?**
A: vectorbt is primarily a research and backtesting tool. For live trading, use its signal generation with a separate execution layer.

**Q: Does vectorbt support cryptocurrency data?**
A: Yes. It includes built-in data downloaders for Yahoo Finance and Binance, and accepts any pandas DataFrame.

**Q: What license does vectorbt use?**
A: vectorbt is released under a custom source-available license. Check the repository for details.

## Sources
- https://github.com/polakowo/vectorbt
- https://vectorbt.dev/

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Source: https://tokrepo.com/en/workflows/asset-d92390ad
Author: Script Depot