When it comes to backtesting trading strategies, Python offers one of the most versatile and efficient tools in the market. Its speed and ease of use make it a popular choice for data-centric financial models.
Python provides a huge set of machine learning, scraping, statistics, and other data science libraries. These are also popular in the algorithmic trading industry. Traders can utilize Python to design and implement order management, risk management, and other processes.
One of the most common backtesting frameworks in Python is Zipline. The software offers all the tools needed to create algorithmic trading strategies. Besides, it can be integrated with a number of brokers.
Another useful framework is Backtrader. This program supports live trading and provides a wide variety of data formats. Moreover, it is well-documented and has a community of users.
There are also several other open-source backtesting frameworks in Python. Some of these include SciKit-Learn, Numpy, and Pandas.
While Python can provide you with an impressive library of data science and machine learning libraries, it is also possible to backtest your strategies manually. A clean dataset is essential for backtesting.
One of the most important things to consider when backtesting is the transaction costs. For instance, if you want to backtest a momentum strategy, you must consider proportional transaction costs.
If you choose to write your own backtesting code, you will need to import some of the libraries and change some variables. You will then be able to review your results and evaluate how effective your strategy is.