Many traders love Python for its simplicity and robust library ecosystem, harnessing its power and flexibility in deploying algo trading strategies. Python is a popular programming or coding language used by beginners and experts alike to create a wide variety of applications; from web development to data analysis and artificial intelligence. In this article, we will cover major Python libraries and frameworks that traders use to create, test, and run algo trading strategies. We’ll cover all areas: data analysis, technical analysis, backtesting, and machine learning, making it an all-inclusive resource for beginner and professional traders within the algo trading landscape.
Why is Python the Most Preferred Coding Language in Algo Trading?
There are many alternative coding languages like C++, Java, R, and MATLAB but have you wondered why Python is the most popular language used in algo trading? Several characteristics of Python make it the crowd-favourite. One of the main reasons is that Python is open-source, which means traders can modify and build their strategies.
Python is less complicated. It uses libraries that increase code readability and reduce the size of the code. So algo traders can save a lot of time while coding and strategising. The array of libraries that Python provides for algo trading also makes it one of the most highly efficient languages for backtesting and live trading.
Which Python Libraries are Useful for Algo Trading?
Before learning about Python libraries, you should know what a library is. Libraries are collections of pre-written code, usually in classes, functions, and modules, that programmers use without writing the code from scratch. Each coding language (like Python) has a wide range of libraries. Some of the popular Python libraries used on algo trading are:
1. NumPy
NumPy is one the most commonly used libraries for algo trading. It is the fundamental library used for computing in Python. Algo traders use this for numerical computations, data manipulation, preprocessing, and scientific computing. Remember, this library is more effective when it’s paired with other libraries like Pandas or Scikit-Learn. We will learn about them below. To install Numpy, you need to execute “pip install numpy” in a command-line interface or terminal (Command Prompt on Windows or Terminal app on macOS.
[Before running the command, make sure that you have pip installed. You can check by typing pip --version in your terminal. If it’s not installed, you may need to install it first.]
2. Pandas
Pandas helps in structured data manipulation and analysis. It is generally used in conjunction with NumPy. This library handles missing data, eliminates noisy data, and resamples data to different calculations. Its data structures like DataFrame and series allow traders to handle time-series data easily. Traders extensively use Pandas for data processing, feature engineering, and backtesting. You need to execute “pip install pandas” to install this.
3. LightGBM
LightGBM is a popular machine-learning library in Python developed by Microsoft. It manages large data sets and high dimensional data, making it one of the best choices for algo traders. LightBGM is a highly efficient and fast implementation of gradient boosting making the process optimised. Algo traders wishing to use this library can install it using “pip install lightgbm”.
4. Zipline
Zipline is an open-source library built in Python. Traders use this to develop, backtest and execute trading strategies. This is the best generalist trading strategy with more than 13,000 stars on GitHub. It provides an inclusive framework for backtesting and built-in support for various types of data. It has data bundles to access historical data and pipelined API for complex factor modelling. You can install this using “pip install zipline” or “pip install zipline-reloaded”.
5. Backtrader
Backtrader is an open-source library for strategy development. This provides a wide range of adat feeds, making it a versatile choice for both live trading and backtesting. It offers a user-friendly API that makes implementing the strategies easier and supports various data formats like CSV, Pandas DataFrame, and online data sources. Backtrader has an active community, making it easier for retail traders to start and get support. You need to use “pip install backtrader” to use this.
6. Ta-lib
Ta-lib is used extensively for technical analysis. It consists of over 150 technical indicators. Its candlestick pattern recognition can identify over 60 candlestick patterns. Ta-lib can easily integrate with libraries like Pandas, NumPy, and Backtrader for better performance.
7. Fast-trade
Fast-trade is a Python library developed for algo trading, focusing mainly on efficient backtesting and strategy development. It uses NumPy for performance and works with OHLCV (open, high, low, close, volume) data. It provides access to various technical indicators, including tools for creating, testing, and visualising trading strategies against historical data. As it is open source, it is open to customisation and contributions from the community. Fast-trade helps balance performance with flexibility and will support traders & developers working in the algo trading domain.
8. Tulip Indicators
Tulip Indicators is a well-known, open-source library used for technical analysis in algo trading. It hosts a collection of more than 100 technical indicators and claims high performance with low memory usage. This includes moving averages, oscillators, volatility measures, and other mathematical functions common in trading strategies. Tulip Indicators can be easily integrated into trading systems and backtesting frameworks.
Which Python Frameworks Do Traders Use in Algo Trading?
1. Backtrader
Backtrader is a Python framework for strategy development, testing, and execution. It has a user-friendly API to create trading systems, backtest them on historical data, or even live trade. Backtrader supports a wide array of data feeds, brokers, and analysers, hence it is versatile for various trading scenarios. The event-driven architecture allows easy implementation of complex strategies. It possesses plotting facilities with clear visualisation of backtest results. What differentiates Backtrader from other libraries is its flexibility, thorough documentation, and great community support.
2. QuantConnect (Lean)
QuantConnect is an integrated algo trading platform to be used with Lean (an open-source engine). It is a cloud-based environment where one can design, backtest, and go live to trade with quantitative trading algorithms on many asset classes like equities, forex, and cryptocurrencies. QuantConnect provides access to huge amounts of historical data and support for many programming languages like C#, Python, and F#. It differs from others because of its ability to smoothly transition from backtesting to live trading. This platform offers a marketplace for sharing and discovering trading algorithms.
3. Freqtrade
Freqtrade is an open-source cryptocurrency trading bot written in Python. The application targets the crypto market exclusively and supports various exchanges through the ccxt library. Freqtrade features backtesting, hyperopt, edge positioning, and risk management tools. It allows the user to build a strategy based on indicators and supports both spot and futures trading. Freqtrade is famous for the activeness of its development process, extensive documentation, and strong community support— thus very popular in the crypto algorithmic trader community.
4. Hummingbot
Hummingbot is a free source, community-driven framework aimed at creating and running crypto trading bots. It supports several exchanges and strategies, such as market making, arbitrage, and liquidity mining. Hummingbot is a decentralised and democratised algo trading platform in the cryptocurrency space. This has both a command-line interface and a very user-friendly Graphical User Interface, making it easy to use by both expert programmers and people who have never programmed before. One essential factor of this platform is transparency. It gives users the power to audit its code and contribute to its development. Hummingbot also provides educational resources and a supportive community for algo traders.
What are Other Coding Language Options for Algo Trading?
- C++: This is preferred for high-frequency trading systems where microsecond performance matters. Its low-level control and speed make it ideal for implementing complex algorithms and handling large volumes of market data efficiently.
- Java: It is widely used in large-scale trading systems due to its scalability. Java's extended libraries make it an ideal platform for building a reliable multi-threaded trading application that can handle advanced order management.
- R: Quantitative analysts widely use it for statistical modelling and backtesting trading strategies. Its powerhouse of data manipulation and visualisation features makes it excellent for exploratory data analysis and developing statistical trading models.
- MATLAB: Researchers and academics typically use it for developing and testing trading algorithms. Its presence of financial toolboxes enables a user to prototype quantitative strategies efficiently and analyse financial time series.
Conclusion
Python is the language of choice for algorithmic trading due to its simplicity, versatility, and strong support in libraries or frameworks. It’s open source and enjoys good support from various communities.
Although Python is the dominant language, C++, Java, R, and MATLAB still have their unique flavour, which only they can bring at certain points in the process of creating a trading system based on an algorithm. The choice of language usually rests on the specific requirements of the trader, the complexity of the strategies, and the trading infrastructure.
As the field of quantitative trading evolves, it is of utmost importance that traders stay updated on the latest tools and technologies. Whether a complete newbie or one wanting to improve their existing strategies, the resources and frameworks mentioned in this article could guarantee success in the world of algo trading.
