Python libraries are the most useful part of the Python programming language. These libraries make the work of a programmer easy and quick. Each Python library is essential since each consists of a code that can be readily used for a particular purpose.
With this article on ‘Python Libraries, we would be covering the most popular and widely used Python libraries for quantitative trading beginning with a basic introduction.
This blog covers:
What are Python libraries?
Python is a free open-source and cross-platform language which has a rich library for almost every task imaginable and also has a specialised research environment. Python is an excellent choice for automated trading in case of low/medium trading frequency, i.e. for trades which last more than a few seconds. Python can also help obtain, visualise, and analyse stock market data.
It has multiple APIs/Libraries that can be linked to make it optimal and allow greater exploratory development of multiple trade ideas. For example, we can get the historical market data through the Python Stock API.
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A Python library is a collection of related modules. The libraries contain bundles of code that can be used repeatedly in different codes. The libraries make Python programming simpler and more convenient for the programmer as we don’t need to write the same code again and again for different programs. Python libraries play a very vital role in the fields of Machine Learning, Data Science, Data Visualization, etc.
Now that you understand Python Libraries, here is a comprehensive video on how to build, backtest, and go live with Algorithmic Trading using Python.
How are Python libraries useful for algorithmic trading?
A library, usually, is a collection of books or a room or place where many books are stored to be used later.
Similarly, in the programming world, a library is a collection of precompiled codes that can be used later on in a code for some specific well-defined operations. Other than pre-compiled codes, a library may contain documentation, configuration data, message templates, classes, values, etc.
When we trade algorithmically, Python libraries can be used while coding for different trade-related functions.
For example, Yahoo Finance allows data access from any time series data CSV.
Most of the libraries can be installed with the following syntax:
pip install Python library name
!pip install Python library name
Popular libraries with categories for different functions
Now, we will discuss different popular Python libraries belonging to each category. So, let us begin!
Python libraries for data fetching
yFinance is a library for fetching historical prices’ data of securities and their fundamental information from Yahoo Finance. In 2017, “Yahoo Finance” decommissioned its official data API. Ever since then, yFinance has become an alternative method to acquire financial data.
The yFinance usually fetches the OHLC data from Yahoo Finance and returns it in a data frame format.
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Similar to the yFinance, Alpha Vantage is another Python library that helps obtain the historical prices data as well as the fundamental data through the Alpha Vantage API.
One additional bonus of Alpha Vantage is that it also offers technical indicator data such as SMA, EMA, MACD, Bollinger Bands, etc.
In addition to the stock OLHC and fundamental data, the Pandas-DataReader allows to extract other alternative financial data such as the Federal Reserve Economic Data, Fama/French Data, World Bank Development Indicators, etc.
Summary: For fetching the historical OHLC data of stocks, yFinance, Alpha Vantage and Pandas-DataReader work the best. yFinance is simply a library for returning data, whereas, Alpha Vantage helps with the technical indicator data as well.
Going forward, Pandas-DataReader is usedful for the data needed from Federal Reserve Economic Data, Fama/French Data, World Bank Development Indicators, etc.
Python libraries for data collection
Trading on Interactive Brokers using Python
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 very competitive commission and margin rates but also has a very simple and user-friendly interface.
There are a couple of interesting Python libraries which can be used for connecting to live markets using IB. You need to first have an account with IB to be able to utilise these libraries to trade with real money.
IBridgePy library is an easy to use and flexible python library which can be used to trade with Interactive Brokers. It is a wrapper around IBridgePy’s API which provides a very simple to use solution while hiding IB’s complexities.
Summary: Interactive Brokers provides access to over 100 market destinations worldwide. It provides access to products such as stocks, options, futures, forex, bonds, CFDs and funds.
IBridgePy is used for trading with Interactive Brokers. It does not re-implement Interactive brokers’ API. Instead, it helps Python to call IB’s C++ API directly as it acts as a wrapper. Since IBridgePy calls on Interactive Broker’s C++ API directly, therefore, we can expect fewer errors and exceptions in the program.
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Python libraries for data manipulation
NumPy or Numerical Python provides powerful implementations of large multi-dimensional arrays and matrices. The library consists of functions for complex array processing and high-level computations on these arrays. Some of the mathematical functions of this library include:
- trigonometric functions (sin, cos, tan, radians),
- hyperbolic functions (sinh, cosh, tanh),
- logarithmic functions (log, logaddexp, log10, log2) etc.
Pandas is a vast Python library used for the purpose of data analysis and manipulation and also for working with numerical tables or data frames and time series, thus, being heavily used for algorithmic trading using Python. Pandas can be used for various functions including:
- importing CSV files,
- performing arithmetic operations in series,
- boolean indexing,
- collecting information about a data frame, etc.
SciPy, just as the name suggests, is an open-source Python library used for scientific computations. It is used along with the NumPy to perform complex functions like numerical integration, optimization, image processing etc. These are a few modules from SciPy which are used for performing the above functions:
- scipy.integrate (For numerical integration),
- scipy.signal (For signal processing),
- scipy.fftpack(For Fast Fourier Transform) etc.
Summary: The libraries under data manipulation are unique since they are used for mathematical functions. Numpy is used basically for complex array processing and high-level computations. Whereas, Pandas is mainly used for importing the data, for arithmetic operations, for using dataframe etc.
Coming to SciPy, the library is used for more scientific computations such as for the signal processing as to whether to buy or sell etc.
Python library for technical analysis
TA-Lib or Technical Analysis library is an open-source library and is extensively used to perform technical analysis on financial data using technical indicators such as RSI (Relative Strength Index), Bollinger bands, MACD etc. It not only works with Python but also with other programming languages such as C/C++, Java, Perl etc. Here are some of the functions ⁽¹⁾ available in TA-Lib:
- BBANDS - For Bollinger Bands
- AROONOSC - For Aroon Oscillator
- MACD - For Moving Average Convergence/Divergence
- RSI - For Relative Strength Index.
Summary: The technical analysis library is meant for using technical indicators while trading. These indicators help the algorithmic trader to create a strategy on the basis of important findings.
For example, RSI indicates the overbought and oversold conditions in the market for you to predict such a condition in the future. In the case of the prediction of overbought stocks, such stocks are good candidates for selling. Whereas, the prediction of an oversold condition implies that the stocks can be bought.
Python libraries for machine learning
Scikit-learn is a Machine Learning library built upon the SciPy library and consists of various algorithms including classification, clustering, and regression, and can be used along with other Python libraries like NumPy and SciPy for scientific and numerical computations. Some of its classes and functions are:
- sklearn.mixture etc.
TensorFlow ⁽²⁾ is an open-source software library for high-performance numerical computations and machine learning applications such as neural networks. Neural networks have various incredible applications, learn more about how neural network in trading can help enhance your skills.
Here's a guide to installing tensorflow gpu for using in Python.
TensorFlow allows easy deployment of computation across various platforms like CPUs, GPUs, TPUs etc. due to its flexible architecture.
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Keras ⁽³⁾ is a deep learning library used to develop neural networks and other deep learning models. Keras is a library that can be installed. Furthermore, Keras can be built on top of TensorFlow, Microsoft Cognitive Toolkit or Theano and focuses on being modular and extensible. It consists of the elements used to build neural networks such as layers, objectives, optimizers etc. This library can be used in trading for stock price prediction using Artificial Neural Networks.
Theano is a computational framework machine learning library in Python for computing multidimensional arrays. Theano works similarly to TensorFlow, but it is not as efficient as TensorFlow.
But, Theano can be used in distributed or parallel environments and is mostly used in deep learning projects.
LightGBM (Gradient Boost)
Gradient Boosting is one of the best and most popular machine learning libraries, which helps developers in building new algorithms by using redefined elementary models and namely decision trees. Therefore, there are special libraries which are available for fast and efficient implementation of this method.
Here are some unique points of LightGBM-
- Very fast computation ensures high production efficiency.
- Intuitive, hence making it user-friendly.
- Faster training than many other deep learning libraries.
- Will not produce errors when you consider NaN values and other canonical values.
This library provides highly scalable, optimised, and fast implementations of gradient boosting, which makes it popular among machine learning developers.
Summary: In the case of machine learning libraries, each library is used for a different training purpose. Sci-kit can be used for scientific and numerical computations.
Although TensorFlow and Theano are quite similar in their working, Theano is not as efficient as TensorFlow. But, Theano is usually preferred for deep learning projects since it allows us to evaluate mathematical operations including multi-dimensional arrays.
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Keras is used to build neural networks such as layers, objectives, optimizers etc. Coming to Eli5, it is efficient in supporting other libraries such as XGBoost, lightning, and scikit-learn so as to lead to accuracy in machine learning model predictions.
Last but not least, LightGBM is the most efficient for creating algorithms from scratch.
Python libraries for backtesting
Backtrader is an open-source Python library that you can use for backtesting, strategy visualisation, and live-trading.
Although it is quite possible to backtest your algorithmic trading strategy in Python without using any special library, Backtrader provides many features that facilitate this process. In general, every complex component of ordinary backtesting can be created with a single line of code by calling special functions.
BT is coded in Python and joins a vibrant and rich ecosystem for data analysis. Numerous libraries exist for machine learning, signal processing and statistics. This library can be used with other computer languages (such as C, C++, Java etc.) that don't have the same wealth of high-quality, open-source projects as Python.
Summary: Both Backtrader and BT are efficient for backtesting. Backtrader is generally used for backtesting while live trading but BT also included several libraries for backtesting while using machine learning.
Python library for plotting structures
Matplotlib is a Python library used for plotting 2D structures like graphs, charts, histograms, scatter plots etc. Along with the other libraries which are used for computations, it becomes necessary to use matplotlib to represent that data in a graphical format using charts and graphs. A few of the functions of matplotlib include-
- Scatter (for scatter plots)
- Pie (for pie charts)
- Stackplot (for stacked area plot)
- Colorbar (to add a colorbar to the plot) etc.
Now, let us also take a look at some popular Python trading platforms in which you can use the Python libraries for creating trading strategies, backtesting, research and doing live trading successfully.
Plotly is a Python library which helps in data visualisation in an interactive manner. But you might be wondering why do we need Plotly when we already have matplotlib which does the same thing. Plotly was created to make data more meaningful by having interactive charts and plots which could be created online as well. The fact that we could visualise data online removed a lot of hurdles which are associated with the offline usage of a library. However, Plotly can be used as both, an offline as well as online tool, thus giving us the best of both worlds.
Plotly has support for over 40 chart types and can even be used for 3 dimensional use cases. Considering the collaborative environment of Python, the company behind the library has kept the library open source and free so that it can be beneficial for everyone.
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The plotly python library consists of the following packages:
- plotly: This is the main package which contains all the functionality.
- graph_objs: This package contains objects, or templates of figures which are used for visualising.
- matplotllib: The interesting thing about plotly is its support matplotlib figures as well.
Summary: The matplotlib is the most commonly used plotting library. The matplotlib library is quite helpful to traders for plotting 2D structures like graphs, charts, histograms, scatter plots etc. as a part of their analysis for strategy creation.
To add on to the features of matplotlib, Plotly was created. Plotly was created to make data more meaningful by having interactive charts and plots which could be created online as well. Some still prefer matplotlib for its classic features and operations.
Python trading platforms
A Python trading platform offers multiple features like developing strategy codes, backtesting and providing market data, which is why quantitative and top algorithmic traders vastly use these Python trading platforms. Listed below are a couple of popular and free python trading platforms that can be used by Python enthusiasts for algorithmic trading.
Blueshift is a free and comprehensive trading and strategy development platform and enables backtesting too. It helps one to focus more on strategy development rather than coding and provides integrated high-quality minute-level data. Its cloud-based backtesting engine enables one to develop, test and analyse trading strategies in a Python programming environment.
In the field of algorithmic trading as well, Python is commonly used for trade related outputs and hence, the Python libraries help in quick and accurate coding.
We discussed the most popular Python programming libraries as well as some really helpful trading platforms in this blog.
If you wish to learn more about algorithmic trading with Python programming language, you can enrol in our learning track on Algorithmic Trading for Beginners. With this learning track, we have several courses, each catering to the learning needs of a beginner. With each course, you will learn to create and backtest trading strategies such as day trading, event-driven strategy, SARIMA, ARCH, GARCH, volatility and statistical arbitrage trading strategies.
Note: The original post has been revamped on 8th February 2023 for accuracy, and recentness.
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