List of Most Extensive Backtesting Frameworks Available in Python

List of Most Extensive Backtesting Frameworks Available in Python
Shreyans Jain's photo
Shreyans Jain

Published on Sep 8, 2021

5 min read

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Aakash is new to algorithmic trading, and as expected, he is very excited to run his first strategy in the real market. He invested all his pocket money. Boom, all his pocket money is gone. Now, can you name a few reasons why Aakash failed? Well, he ignored the step of backtesting his winning strategy, and now he regrets it.

In this article, we will look into one of the significant components of Algo Trading, which is Backtesting, and the various backtesting frameworks available to budding algo traders. And of course, it goes without saying, don't be like Aakash, be wise and backtest!

Just in case you don't know what backtesting means, here is a good definition from Investopedia.

Backtesting is the general method for seeing how well a strategy or model would have done ex-post. Backtesting assesses the viability of a trading strategy by discovering how it would play out using historical data. If backtesting works, traders and analysts may have the confidence to employ it going forward.

Now, we will look at some common backtesting platforms and their pros and cons.

1. Blueshift by QuantInsti

blueshift.png

Blueshift's platform is highly similar to Quantopian's, and the fundamentals of strategy backtesting are the same. It does backtest with the Zipline package, gives comparable data, and is simple to use; just to be a little biased, this platform is based out of India and developed by QuantInsti (the most famous place on the internet to love algo-trading)

Pros

  • Easy to use.
  • Seamless transition between backtesting and live trading by integrating Indian brokers.
  • Based on the popular and extensively tested Zipline package.
  • Reports are generated in the same way as powerful zipline generates.

Cons

  • On Fridays, all active algorithms are halted after the market closes.
  • The platform is not navigation-friendly.
  • Relatively newer tool and hence requires more stability.
  • The Zipline package is not actively maintained anymore, and Blueshift is at the mercy of its own developers to enhance their tech without depending on Zipline over time.

-> Click Here to visit Blueshift Documentation.

2. backtrader

Backtrader is a Python framework with a plethora of features for backtesting and trading. backtrader is designed to be simple, allowing you to focus on creating reusable trading strategies, indicators, and analyzers rather than spending time creating infrastructure from scratch. It is the most widely used backtesting platform in the industry.

Pros

  • Clean code and easy to use for beginners in Python with tons of examples.
  • Supports backtesting as well as Live trading.
  • In backtrader, we can easily create custom indicators.
  • Various Analyzers like TimeReturn, Sharpe Ratio, SQN are already available.
  • Documentation is very crisp and clear.

Cons

  • Handling enormous datasets have issues
  • Even for some basic computation, we need to use analyzers in the code.

-> Click Here to visit Backtrader Documentation.

3. Lean(Quantconnect)

LEAN.png

QuantConnect's LEAN is an open-source algorithmic trading engine built for easy strategy research, backtesting, and live trading. Lean integrates with the standard data providers, and brokerages deploy algorithmic trading strategies is quick.

Pros

  • Supports strategies developed in various languages and not just Python.
  • It is relatively faster than other platforms.
  • Supports backtesting and Live trading.
  • It has a fantastic community.

Cons

  • Strategy analysis needs improvement.
  • Difficult to use for non-native C# users as the core of the LEAN engine is written in C#.

-> Github Link

4. PyAlgoTrade

PyAlgoTrade is a Python Algorithmic Trading Library that was started to focus just on backtesting, but with the response they got, they have now allowed paper and live trading in Bitcoins via Bitstamp.

Pros

  • Supports event-driven backtesting.
  • Excellent documentation
  • Supports TA-lib integration
  • Comparatively flexible than other platforms.

Cons

  • Does not support Pandas object and modules.
  • Fewer strategy analyses are available.

-> Github Link

5. Bt

Bt is a Python backtesting framework for testing quantitative trading methods. This framework makes it simple to develop strategies that combine various Algos. It seeks to promote the construction of readily tested, reusable, and adaptable pieces of strategy logic to aid in the rapid development of complicated trading strategies. bt is still in the alpha stage.

Pros

  • The tree form makes it easier to build and compose complicated algorithmic trading systems.
  • Charts and reports in pdf can be easily generated in bt.
  • It has detailed statistics, which helps in comparison between strategies.
  • Coded in Python, hence supports various machine learning and statistical operations.

Cons

  • Slow in comparison to other platforms.
  • Does not support strategies in languages other than Python.

-> Github Link

6. finmarketpy

finmarketpy.png

finmarketpy is a Python-based library that allows you to study market data and backtest trading strategies using a simple API that includes prebuilt templates for you to define backtest.

Pros

  • Prebuilt templates for backtesting trading strategies.
  • A built-in calculator for risk weighting based on volatility targeting is included.
  • Written in an object-oriented way to make code more reusable.
  • Conducts market event research in the context of data events.
  • Can examine the seasonality of trading techniques.

Cons

  • Very little support for other languages.
  • Less usable in High-frequency trading.

-> Github Link

7. Fastquant

Fastquant makes it simple to backtest investing strategies with as few as three lines of Python code. Its objective is to encourage data-driven investing by making quantitative finance analysis accessible to everyone.

Pros

  • Easy to use.
  • Minimal code is required.
  • Simple integration with machine learning and statistical models.
  • Custom strategies can be made with a minimum amount of code.

Cons

  • Relatively slower than other platforms.
  • Does not support languages other than Python.

-> Github Link

That's it. I hope you like the content. Constructive criticism is appreciated.

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Any information present on this blog does not constitute any form of investment advice or recommendation by Trade With Python.