zipline VS vectorbt

Compare zipline vs vectorbt and see what are their differences.

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zipline vectorbt
11 5
15,608 2,391
0.6% -
0.0 7.6
about 2 months ago 3 days ago
Python Python
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of zipline. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-09.


Posts with mentions or reviews of vectorbt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-14.
  • Is there any python libraries to backtest buy and sell signals with dates?
    2 projects | | 14 Jun 2022
    For exactly this I use this it’s really a powerful tool and you can tons of things with it. Recently the developer decided to maintain it but not adding new features, which from now on will be released on the pro version. However, the free version is still very valuable, incredibly fast and suitable for basic to intermediate tasks.
  • Why Building a Trading Algorithm is More Than Just the Algorithm - 3 Things
    4 projects | | 19 Feb 2022
    It’s super easy to get up and running with code. With the rise of data science as a field, datasets are far and wide. Accessible from just about any venue. Take a look at Kaggle, QuiverQuant, Yahoo Finance, or even directly from the brokerages and exchanges. Developers can easily download data directly as a .csv or .json and quickly get up and running by utilizing frameworks like or vectorbt. “Great, it seems like I can get up and running and I’ll have an awesome money making trading algorithm in no time”.... unfortunately, wrong. Why is this wrong? Well, simulation is NOT the real world. The real world is not a CSV file—the real world is a stream of events. Cause and effect. The real world works in a fashion where new data comes in, you make a decision, and then you figure it out, not “I have all of this data, let me run this all through time and figure it out”. Indeed, the data sources that you get in real-time are almost completely different from the data sources you use in simulation. Rather than .csv you use WebSockets; rather than QuiverQuant you use APIs; rather than backtesting frameworks you use more robust, event driven packages. Without it, you’re stuck duplicating code, rewriting it into an event-based system, and ultimately using that to go into production, and who knows if your code is going to change along the way.
  • Looking for active python backtesting framework
    3 projects | | 9 Feb 2021
    However, it's not the fastest framework. If you need speed, and are good with the data science tool chain in python and the concept of flattening loops into vectorized operations, check out vector-bt. I haven't gotten a chance to play with it yet, but I'm definitely going to as soon as I find some spare time. It seems like a great option with a nicely modernized approach.

What are some alternatives?

When comparing zipline and vectorbt you can also consider the following projects:

backtrader - Python Backtesting library for trading strategies - :mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.

pyfolio - Portfolio and risk analytics in Python

fast-trade - low code backtesting library utilizing pandas and technical analysis indicators

PyThalesians - Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)

jesse - An advanced crypto trading bot written in Python

qlib - Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.

quantstats - Portfolio analytics for quants, written in Python

OctoBot - Cryptocurrency trading bot using technical analysis based strategy with an advanced web interface

bcolz - A columnar data container that can be compressed.

Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python