In one of our recent articles we’ve talked about most popular backtesting platforms for quantitative trading. Here we are sharing most widely used python trading platform and libraries for quantitative trading.
Python is a free open-source and cross-platform language which has a rich library for almost every task imaginable and specialized research environment. Python is an excellent choice for automated trading when the trading frequency is low/medium, i.e. for trades which do not last less than a few seconds. It has multiple APIs/Libraries that can be linked to make it optimal, cheaper and allow greater exploratory development of multiple trade ideas.
Below is a brief description of some of the important Algo Trading related libraries available for Python:
Event-driven library which focuses on backtesting and supports paper-trading and live-trading. PyAlgoTrade allows you to evaluate your trading ideas with historical data and see how it behaves with minimal effort. Supports event driven backtesting, access of data from Yahoo Finance, Google Finance, Ninja Trader CSVs and any type of time series data in CSV. The documentation is good and it supports TA-Lib integration (Technical Analysis Library). It outperforms other libraries in terms of speed and flexibility, however, it’s the biggest drawback is that it doesn’t support Pandas-object and pandas modules.
Zipline (Used by Quantopian)
It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production by Quantopian – a free, community-centred, hosted platform for building and executing trading strategies. Zipline is well documented, has a great community, supports Interactive Broker and Pandas integration. At the same time, since Quantopian is a web-based tool, cloud programming environment is really impressive. However, Zipline is slower compared to commercial platforms with backtesting functionality in a compiled application and isn’t very convenient for trading multiple products.
Vectorized backtesting framework in Python / pandas, designed to make your backtesting easier — compact, simple and fast. It allows the user to specify trading strategies using the full power of pandas while hiding all manual calculations for trades, equity, performance statistics and creating visualizations. Resulting strategy code is usable both in research and production environment. Currently, only supports single security backtesting, Multi-security testing could be implemented by running single-sec backtests and then combining equity. The developers are working to get include multi-asset backtest capabilities.
It is a vectorized system. A python project for real-time financial data collection, analyzing and backtesting trading strategies. Supports access to data from Yahoo Finance, Google Finance, HBade, and Excel.
TWP (Trading with Python)
TradingWithPython library is again a Vectorized system. It is a collection of functions and classes for Quantitative trading. It includes tools to get data from sources like Yahoo Finance, CBOE, and Interactive Brokers and often used P&L benchmarking functions. The documentation and course for this library, however, costs $395.
Interactive Brokers is an electronic broker which provides a trading platform for connecting to live markets using various programming languages including Python. It provides access to over 100 market destinations worldwide for a wide variety of electronically traded products including stocks, options, futures, forex, bonds, CFDs and funds. IB not only has a very competitive commission and margin rates but also has a very simple and user-friendly interface. Here we will discuss how we can connect to IB using Python.
Connecting with IB using Python
There are several Python libraries which can be used for connecting to live markets using IB, mentioned below are a top few. You need to first have an account with IB to be able to utilize these libraries to trade with real money.
It is an easy to use and flexible python library which can be used to trade with Interactive Brokers. It is a wrapper around IB’s API which provides a very simple to use solution while hiding IB’s complexities. To learn to utilize this library you can check out the following links
IBPy is another python library which can be used to trade using Interactive Brokers. Details about installing and using IBPy can be found here:
Quantopian – Python Trading Platform
Quantopian is a python trading platform which uses a library Zipline (as mentioned above) to develop trading strategies. You can take your strategies live and trade with real money by integrating with IB using your Quantopian account.
As mentioned above, each library has its own strengths and weaknesses. Based on the requirement of the strategy you can choose the most suitable Library after weighing the pros and cons. Watch the webinar on ‘Automated Trading in Python’ and learn how to create and execute a quant strategy in Python.