Python Libraries Explained: Transforming Data for Effective Trading

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Ready to outsmart the market with Python? Dive into Trading Libraries & Algorithms! The financial world might seem complex, but for savvy Python coders, it's an algorithmic playground. Forget Wall Street suits and endless spreadsheets – with Python's treasure trove of trading libraries, you can automate your strategies, backtest them to perfection, and potentially beat the market from the comfort of your coding haven.

Python is one of the most popular programming languages for developing trading algorithms due to its simplicity, versatility, and extensive ecosystem of libraries tailored to finance and trading. Python trading algorithms leverage a combination of mathematical models, statistical analysis, and machine learning techniques to analyze market data, identify trading opportunities, and execute trades automatically.

The Global Algorithmic Trading Market is expected to reach a whopping $22.6 billion by 2026, with Python being the fuel that propels many of these sophisticated algorithms.
Source: GrandViewResearch

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, we cover the most popular and widely used Python libraries for quantitative trading beginning with a basic introduction. It is your one-stop shop to mastering this dynamic duo: Python trading libraries and algorithms. We'll crack open the hood on popular Python libraries and share pro tips to help you navigate the financial world.

P.S. We'll also sprinkle in some handy resources and keep it interesting – no financial jargon mumbo jumbo here! Ready to unleash your inner algorithmic trader? Let's get coding!

This blog covers:


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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.

A 2024 Stack Overflow Developer Survey revealed Python as the most popular language for quantitative finance, with 81.3% of respondents using it.
Source: Stackoverflow

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.

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.


Python libraries and 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.

Over 180,000 GitHub repositories are dedicated to Python trading libraries, showcasing the vibrant community actively developing and improving these tools.
Source: Github

Python trading algorithms can be deployed across various asset classes, including stocks, options, futures, and cryptocurrencies, allowing traders to diversify their trading strategies and portfolios across different markets.

Most of the libraries can be installed with the following syntax:

pip install [Python library name]

or

!pip install [Python library name]

Now, we will discuss different popular Python libraries belonging to each category. So, let us begin!


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Python libraries for fetching data

The following python libraries can be used in trading for fetching data.

yfinance

yfinance is a Python 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.

Alpha Vantage

Similar to 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.

Pandas-DataReader

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. 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

The following python libraries can be used in trading for collecting data.

Interactive Brokers

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

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. IBridgePy 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

The libraries under data manipulation are unique since they are used for mathematical functions. The following python libraries can be used in trading for manipulating data.

NumPy

NumPy or Numerical Python provides powerful implementations of large multi-dimensional arrays and matrices. This Python 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

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

SciPy, just as the name suggests, is an open-source Python library used for scientific computations such as for the signal processing as to whether to buy or sell etc.. 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.

A 2023 study by QuantConnect compared various trading languages. Python strategies achieved an average annualized return of 10.77%, outperforming C++ and Java.
Source: Medium article

Python library for technical analysis

The following python libraries can be used in trading for technical analysis.

TA-Lib

The technical analysis library or TA-Lib is meant for using technical indicators while trading. These indicators help the algorithmic trader to create a strategy on the basis of important findings. TA-Lib 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.

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.


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Python libraries for machine learning

The following python libraries can be used in trading for machine learning.

Scikit-learn

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.cluster,
  • sklearn.datasets,
  • sklearn.ensemble,
  • sklearn.mixture etc.

TensorFlow

TensorFlow 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. TensorFlow allows easy deployment of computation across various platforms like CPUs, GPUs, TPUs etc. due to its flexible architecture.

Here's a guide to installing tensorflow gpu for using in Python.

Keras

Keras is a deep learning library used to develop neural networks and other deep learning models. Furthermore, Keras can be installed on your system and 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

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.

These libraries are LightGBM, XGBoost, and CatBoost. All these libraries help in solving a common problem and can be utilised in almost a similar manner.

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.

LightGBM 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.

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.


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Python libraries for backtesting

Python trading algorithms are often backtested using historical market data to assess their performance and validate their effectiveness before deploying them in live trading environments. Backtesting helps traders optimize parameters, mitigate risks, and refine their trading strategies over time. The following Python libraries can be used in trading for backtesting.

Backtrader

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

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.

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

The following python libraries can be used in trading for plotting structures.

Matplotlib

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.

Matplotlib is the most commonly used plotting library. Matplotlib 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.

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

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. 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.

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.

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.

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Open source 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. Python trading algorithms can be integrated with trading platforms, broker APIs, and market data providers to access real-time market data, execute trades, and manage positions automatically. This automation reduces manual intervention, minimizes latency, and enhances efficiency in trade execution.

Blueshift

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.


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Benefits of using Python libraries for Trading

Python libraries offer several benefits for trading, making them a popular choice among traders and developers. Here are some key advantages of Python libraries:

  • Wide Range of Libraries: Python has a vast ecosystem of libraries specifically designed for trading and finance. These libraries cover various aspects of trading, including data analysis, algorithmic trading, machine learning, and backtesting. Traders can leverage these libraries to access powerful tools and functionalities without reinventing the wheel.
  • Pre-built functionality: Libraries like NumPy, Pandas, and Scikit-learn provide ready-made solutions for various tasks like data fetching, cleaning, analysis, and visualization, saving you time and effort compared to coding everything from scratch.
  • Rapid prototyping: Python's simple syntax and libraries enable quick development and testing of trading ideas, accelerating your iteration process.
  • Powerful data manipulation: Libraries like pandas and NumPy excel at cleaning, transforming, and managing large financial datasets efficiently.
  • Advanced analysis: Scikit-learn and other libraries offer tools for statistical analysis, feature engineering, and machine learning, allowing you to uncover deeper insights from your data.
  • Effective visualization: Matplotlib, Seaborn, and Bokeh create clear and informative visualizations to explore relationships, trends, and patterns in your data.
  • Integration with APIs: Many Python libraries provide seamless integration with financial data APIs, allowing traders to fetch real-time market data, historical prices, and economic indicators. This integration enables traders to access a wealth of financial information directly within their Python environment, facilitating data-driven decision-making and strategy development.
  • Backtesting frameworks: Libraries like Zipline and Backtrader allow you to simulate trading strategies on historical data to evaluate their performance before risking real capital.
  • Order management: Tools for handling orders, fees, and risk management help you design robust and automated trading strategies.
  • Performance analysis: Libraries like Pyfolio provide metrics and visualizations to assess the effectiveness of your trading strategies.
  • Cost-Effectiveness: Many Python libraries for trading are open-source and free to use, eliminating the need for expensive proprietary software or subscriptions. This cost-effectiveness allows traders to access advanced trading tools and technologies without incurring significant upfront costs, making Python an attractive option for traders of all levels. This promotes collaboration, transparency, and continuous improvement.
  • Integration with platforms: Numerous Python libraries integrate with brokerage APIs and trading platforms, allowing for seamless real-world implementation of your strategies.
  • Portability: Python code is largely portable across different operating systems, making your scripts adaptable to various environments.
  • Cost-effectiveness: Many Python libraries are free and open-source, reducing the cost barrier for starting with algorithmic trading.
  • Scalability: Python libraries can handle large datasets and complex calculations, making them suitable for both small-scale and large-scale trading operations.
  • Community support: Python has a large and active community of developers, traders, and finance professionals who contribute to the development and enhancement of trading libraries. This vibrant community provides valuable resources, documentation, tutorials, and support forums, making it easier for traders to learn, troubleshoot, and collaborate on trading projects.
  • Empowers risk management: Python trading algorithms enable traders to implement complex risk management techniques, such as portfolio optimization, position sizing, and stop-loss orders, to manage risk and maximize returns in volatile market conditions.
Skills about Python for Algorithmic Trading are highly sought-after by financial institutions. Indeed.com data from 2024 shows a 78% increase in job postings mentioning this specific skillset.
Source: Indeed.com

Overall, Python libraries offer a powerful and accessible platform for traders to analyze data, develop trading strategies, and automate trading processes, thereby enhancing efficiency, productivity in the financial markets.


Drawbacks of using Python libraries for Trading

Despite the numerous benefits, Python libraries in trading also come with certain drawbacks and limitations to consider:

  • Interpreted language: Python being an interpreted language can be slower than compiled languages like C++ or Java, especially for computationally intensive tasks. This might not be ideal for high-frequency trading requiring ultra-low latency.
  • Global Interpreter Lock (GIL): Python's GIL limits true multi-core parallelism, hindering performance gains on multi-core processors for specific tasks. However, libraries like NumPy offer workarounds for some operations.
  • Dynamic typing: Python's dynamic typing, while offering flexibility, can lead to unintended variable reassignment or type-related errors if not handled carefully. Robust testing and coding practices are crucial to ensure security and reliability.
  • Third-party library risks: While open-source libraries offer advantages, they also introduce potential vulnerabilities if not thoroughly reviewed and maintained.
  • Not all-encompassing: While vast, the Python library ecosystem might not cater to every specific trading need. You may need to combine libraries or write custom code for niche functionalities.
  • Learning curve: Mastering Python libraries requires time and effort, especially for those new to programming.
  • Community reliance: While Python libraries are helpful, relying solely on the community for support can have limitations, especially for advanced or niche issues.
  • Regulatory compliance: Ensure your trading strategies and chosen libraries comply with relevant regulations and financial laws.

Python trading algorithms are subject to regulatory scrutiny and compliance requirements, especially in regulated markets like equities and derivatives. Traders need to ensure that their algorithms comply with relevant regulations and market rules to avoid potential fines, penalties, or legal liabilities.

It's important to weigh the benefits and drawbacks of Python libraries based on your specific trading needs, risk tolerance, and technical expertise. Remember, these tools are powerful but require careful consideration and responsible usage.


Future of Python libraries in Trading

The future of Python libraries in trading looks promising, with several trends and developments shaping the landscape.

  • Faster libraries: Faster Python libraries like Cython which compiles Python to C-like code for significant performance boosts or Numba which offers just-in-time compilation for specific Python functions possess the potential to grow.
  • Hardware acceleration: Integration with hardware accelerators like GPUs unlocks parallel processing power for complex models and high-frequency trading.
  • Cloud integration: Seamless integration with platforms like Google Cloud AI Platform and Amazon SageMaker provides powerful infrastructure for large-scale data analysis and model training.
  • Emerging techniques: Libraries will incorporate cutting-edge AI like deep learning and reinforcement learning for more sophisticated strategies and market predictions.
  • Targeted functionalities: Specialized libraries tailored to specific trading domains like options or algorithmic portfolio management will emerge with focused functionalities.
  • AutoML for feature engineering: Libraries will incorporate AutoML (automated machine learning) features, simplifying feature selection and model building for non-experts.
  • Growing ecosystem: The vibrant open-source community like the likes of Github and others will continue to contribute and expand the vast library ecosystem, fostering innovation and adaptability.
  • Collaboration platforms: Platforms like Quantopian (after its return) will facilitate knowledge sharing, code development, and competition among traders and developers.
  • Standardization efforts: Initiatives to standardize library interfaces and functionalities will enhance compatibility and simplify integration across different projects.
  • Explainable AI (XAI): As AI models become more complex, libraries will integrate XAI techniques to understand model decisions and ensure responsible usage, thus enabling transparent decision-making.
  • Financial regulations: Libraries will increasingly incorporate features to comply with evolving financial regulations and ethical considerations.
  • Quantum computing: While still in its early stages, libraries might integrate with quantum computing platforms, opening up possibilities for advanced financial computations and risk analysis in the future.
The global AI in finance market will reach $1 trillion by 2030, with Python being a crucial enabler due to its compatibility with popular AI libraries like TensorFlow and PyTorch.
Source: McKinsey Global Institute

Continuous learning, staying updated on evolving libraries, and understanding the risks and limitations remain crucial for navigating the ever-changing landscape of Python libraries in trading. Responsible usage and ethical considerations are paramount as AI and automation play an increasingly prominent role in financial markets.


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Conclusion

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.

Python trading algorithms are continuously evolving with advancements in technology, data science, and quantitative finance. Traders need to stay updated with the latest developments, trends, and best practices in Python trading algorithms to remain competitive and adapt to changing market dynamics effectively.

If you wish to learn more about algorithmic trading with Python programming language, you can enroll in our learning track on Algorithmic Trading for Beginners consisting of several courses, each catering to your learning needs. 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. Happy learning!


Authors: Chainika Thakar (Previously updated by Shagufta Tahsildar & originally written by Apoorva Singh)


Note: The original post has been revamped on 8th February 2023 for accuracy, and recentness.

Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.

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