5 most popular algorithmic trading libraries.
Tons of tools and libraries out there help you whip up a strategy and backtest it in matter of minutes. Some even only require you to push a few buttons and boom … a couple of moments later it spits out a nice little report.
But nothing feels as good as having the power of the python ecosystem (or any programming language of your choice) at your side. It gives you the freedom to do what you want, not what the tools allows/wants you to do.
If you’re not familiar with python yet, these libraries can be a great introduction to python and will give you a purpose to get familiar. Lets get right into it, here are the 5 most popular algorithmic trading libraries:
1. Zipline
With over 11.2k stars on GitHub, zipline is by far the most popular lib in this list. It has great documentation and was, in my opinion, the most intuitive to use. If you want to get started with python or algo trading and get a feel of the powers of pandas, zipline is your tool!
2. Backtrader
Live trading doesn’t come out of the box with zipline, it takes a little more to get that all up and running. With backtrader however, you get a ton of functionality you would find in zipline but with the ability to easily switch to live trading.
3. pyalgotrade
PyAlgoTrade is an event driven algorithmic trading Python library. Although the initial focus was on backtesting, paper and live trading are possible for bitstamp crypto trading. It’s a good choice for people who want to get into crypto algo trading. Another interesting feature is that it can handle twitter events in realtime. Something I’ll definitely want to give a try in the near future.
4. Catalyst
Catalyst is create by enigma, a company that brings privacy to smart contracts and public blockchains. It looks to be the best choice for people interested in trading crypto currencies and supports multiple exchanges, including Bitfinex, Bittrex, Poloniex and Binance.
5. Lean
If you want to avoid using Python, Lean might be a good choice. It’s a great library developed by QuantConnect with support for various brokerages and great support for live trading. It supports algorithms written in python, C# and F#.
Other libraries worth mentioning (ordered by GitHub stars):
- rqalpha — 3.5k stars
- crypto-signal — 2k stars
- qtpylib — 862 stars
- pysystemtrade — 733 stars
Let me know if you’ve used any of these libraries and what your experience was. What did you struggle with, what was easy right out of the box, what would you like to see improved? We’d love to hear your thoughts!
I’m on a journey to learn more about algorithmic trading and quickly realized that good quality data is hard to come by and can be expensive. That is why I started icebergh, an easy way for you to get high quality historical market data at an affordable price.
With icebergh we want to help create a community of learners and individual (aspiring) traders. As we learn, grow and backtest, we too should have to the high quality data that large organizations and funds can access. Join icebergh today and help me spread the word!
