VectorBT — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for VectorBT. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 25, 2026 • Sentiment updated: Apr 16, 2026

GitHub Statistics

Community Sentiment

Community Buzz: VectorBT is fast to get started with, as mentioned on Reddit. Another user on HackerNews said 'If you want to make something like this yourself, look into the Lerp function, and then look into the math behind easing in animation'

Pros & Cons

What People Love

Reddit users praise its speed and ease of use, HackerNews users appreciate its customizability

Common Complaints

Lack of 32-bit ints, Some users find it difficult to use

Biggest Positive: Fast Backtesting

Biggest Negative: Lack of 32-bit ints

Why VectorBT Stands Out

VectorBT is valuable because it provides a lightning-fast backtesting engine that allows users to test thousands of trading ideas in seconds, making it an ideal tool for both human researchers and AI agents. Its vectorized backtesting and strategy research capabilities, accelerated with Numba, enable rapid experimentation and optimization of trading strategies. Additionally, its pandas-native API and custom accessors provide a high-performance and flexible framework for data analysis and visualization.

Built With

Build a high-frequency trading bot — VectorBT's rapid experimentation capabilities and vectorized backtesting enable the testing of thousands of trading ideas in seconds, Build a cryptocurrency portfolio analyzer — VectorBT's portfolio backtesting and performance analysis features allow for the evaluation of portfolio performance across markets and timeframes, Build a trading strategy optimizer — VectorBT's walk-forward optimization and robustness testing enable the optimization of trading strategies for maximum returns, Build a risk management system — VectorBT's signal-based tooling and portfolio backtesting enable the analysis of risk and the optimization of trading strategies for minimum risk, Build a market data visualization dashboard — VectorBT's interactive visualization capabilities enable the creation of interactive dashboards for visualizing market data and trading performance

Getting Started

  1. Install VectorBT using pip: pip install -U vectorbt
  2. Download the required data using Yahoo Finance: data = vbt.YFData.download('BTC-USD')
  3. Create a portfolio from the data: pf = vbt.Portfolio.from_holding(data.get('Close'), init_cash=100)
  4. Configure the portfolio to include fees and risk management: pf = vbt.Portfolio.from_holding(data.get('Close'), init_cash=100, fees=0.001, risk_free=0.02)
  5. Try backtesting a simple moving average crossover strategy to verify it works: fast_ma = vbt.MA.run(data.get('Close'), 10); slow_ma = vbt.MA.run(data.get('Close'), 50); entries = fast_ma.ma_crossed_above(slow_ma); exits = fast_ma.ma_crossed_below(slow_ma); pf = vbt.Portfolio.from_signals(data.get('Close'), entries, exits, init_cash=100)

About

The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.

Official site: https://vectorbt.dev

Category & Tags

Category: trading

Tags: algorithmic-trading, algorithmic-traiding, backtesting, cryptocurrency, data-science, data-visualization, finance, machine-learning, portfolio-optimization, quantitative-analysis, quantitative-finance, time-series, trading, trading-strategies

Market Context

VectorBT is often compared to other backtesting frameworks like Backtrader and Zipline