Choosing Backtesting Engines and Historical Futures Data Sources

# Alternatives to Zipline backtester / Alternatives to futures data from Quandl


# Alternatives to Zipline backtester / Alternatives to futures data from Quandl












I intend to set up a fully automated system for trading equities and futures. As preparation for this project, I worked through a couple of books on the topic, e.g., "Trading Evolved" by Andreas F. Clenow. In said book, Andreas uses the Python library Zipline for backtesting trading strategies whereas data for both equities and futures is sourced from Quandl. While working through the book, I made a couple of observations which might influence my choice of the backtesting engine as well as the source of financial data and I am hoping to obtain some hints here.

First of all, I found out that the algorithmic trading library Zipline is not maintained anymore (though it worked perfectly for me to run all the sample code regarding equities). Therefore, I would like to know if there exist alternatives to Zipline, which are advisable and have the same (or even superior) functionality as compared to Zipline?

Moreover, when working through the sample code of said book, I was not able to run trading strategies regarding futures. The reason is that I could not source historic futures data from Quandl (which, however, worked perfectly fine for historic equities data). Therefore, I am wondering if someone has hints how to source historic futures data from Quandl (since it should work according to the book) and/or which alternative data sources for historic futures data exist and are recommendable.

Thanks a lot in advance.

## Answer by Brian from QuantRocket (score 2)

https://quant.stackexchange.com/a/63210

QuantRocket supports backtesting and live trading with Zipline:

https://www.quantrocket.com/zipline/

QuantRocket maintains its own fork of Zipline and thus is unaffected by the shutdown of Quantopian, Zipline's original maintainer. End-of-day and 1-minute historical equities data are included, and you can backtest and trade futures strategies by connecting to an Interactive Brokers account for futures data.

Disclaimer: I'm affiliated with QuantRocket.

## Answer by vonjd (score 2)

https://quant.stackexchange.com/a/63214

Concerning the backtesting engine I would recommend R, especially with the packages `quantmod` and `PerformanceAnalytics`.

I wrote a blog post which gets you started by providing a simple step-by-step template:

- Load libraries and data

- Create your indicator

- Use indicator to create equity curve

- Evaluate strategy performance

You can find the post here: Backtest Trading Strategies Like a Real Quant

## Answer by mac13k (score 1)

https://quant.stackexchange.com/a/64105

There are two Zipline forks worth checking:

- zipline-reloaded

- zipline-trader

I have no affiliation with neither of the two, but personally I like zipline-reloaded the most, because it runs on Python 3.9 while zipline-trader only recently added support for Python 3.6. However zipline-trader supports live trading with Alpaca or IB, so you should take a look at these brokers to see what they offer in terms of the future trading and how much does it cost.

## Answer by Soheel Haque-Everding (score 1)

https://quant.stackexchange.com/a/72201

Andreas Clenow himself suggests Zipline-Reloaded Documentation can be found here. It is maintained by Stefan Jansen (thank you Stefan), the author of Machine Learning for Algorithmic Trading

## Answer by Brett Elliot (score 0)

https://quant.stackexchange.com/a/63215

For backtesters, id recommend quantconnect or backtrader

https://www.quantconnect.com/ https://www.backtrader.com/

For data sources...

https://iexcloud.io/ https://polygon.io/

All the best, Brett