Know the most important libraries in Python
Know the most important libraries in Python
The Python Quant Stack is a collection of libraries that are widely used in quant finance. They are the foundational Python tools in your quant toolbelt.
They are used for scientific computing, data manipulation, statistical testing, portfolio and risk analysis, backtesting, and trading.
In today’s newsletter, you’ll understand these tools and have the resources to learn more.
Let’s go!
Statistical computing and statistics
Statistical computing refers to the use of computational techniques and algorithms to analyze, interpret, and visualize large datasets in order to uncover patterns, make inferences, and test hypotheses.
It combines the principles of statistics with the power of computing to efficiently process and model data in various fields such as finance, biology, and social sciences.
NumPy
Nearly everyone using Python in quantitative fields uses NumPy. It brings the computational power of languages like C and Fortran being easy to use.
It’s baked into pandas, statsmodels, SciPy, scikit-learn, NetworkX, TensorFlow, PyTorch, Dask, XGBoost, and hundreds of others.
NumPy’s main object is the “homogeneous multidimensional array.” In other words, a table of the same kind of numbers. NumPy lets you build N-dimensional arrays. Those dimensions are called axes.
NumPy has hundreds of mathematical functions that let you work on arrays. These range from simple (e.g. exponents) to more complex (e.g. eigenvectors).
Dive deeper:
- https://numpy.org
- https://numpy.org/doc/stable/user/absolute_beginners.html
- https://numpy.org/doc/stable/user/basics.html
- https://numpy.org/doc/stable/user/numpy-for-matlab-users.html
SciPy
SciPy is a collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data.
SciPy has algorithmic for clustering, integration, interpolation, linear algebra, optimization, root finding, statistics and distributions, fast Fourier transformations, and orthogonal regressions.
Dive deeper:
- https://scipy.org
- https://scipy-lectures.org/intro/scipy.html
- https://docs.scipy.org/doc/scipy/tutorial/general.html
Statsmodels
Statsmodels provides classes and functions for estimating different statistical models and conducting statistical tests and statistical data exploration.
An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. It complements SciPy's stats module.
Statsmodels is part of the Python scientific stack that is oriented toward data analysis, data science, and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, and integrates with Pandas for data handling. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries.
Dive deeper:
Data acquisition and manipulation
Data acquisition refers to the process of collecting and obtaining data from various sources, such as databases, sensors, or online platforms, for analysis.
Data manipulation involves transforming, cleaning, and organizing the acquired data into a format suitable for analysis, often using techniques like filtering, aggregating, or reshaping to prepare it for further processing.
pandas
In 2008, pandas development began at the hedge fund AQR Capital Management. It’s a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool.
pandas is the fundamental building block for doing practical, real-world data analysis in Python. It’s also one of the most powerful and flexible open-source data analysis and manipulation tools in any language.
pandas’ main object is a DataFrame. A DataFrame is very similar to a spreadsheet. It has structured rows and columns. It has tools for reading and writing data to and from CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format. It handles data alignment and handling of missing data for you and can reshape and pivot tables.
Dive deeper:
OpenBB Platform
The OpenBB Platform gives you programmatic access to the capabilities of the OpenBB Terminal. You have the building blocks to create your own financial tools and application—whether it be a visualization dashboard or a custom report on Jupyter Notebook.
In Getting Started With Python for Quant Finance, we use the OpenBB Platform for data. You get access to normalized financial data from dozens of data providers without having to develop your own integrations from scratch.
On top of financial data feeds, OpenBB Platform also provides you with a toolbox to perform financial analysis on a variety of asset classes, including stocks, crypto, ETFs, funds, and the economy as well as portfolio optimization and attribution.
Dive deeper:
Pricing and optimization
In quantitative finance, pricing involves calculating the fair value of financial instruments such as derivatives, bonds, or equities, often using models like the Black-Scholes or binomial model to account for factors like volatility, interest rates, and time to maturity.
Optimization in this context refers to the use of mathematical algorithms to maximize returns or minimize risk in portfolio management, asset allocation, or trading strategies, while adhering to constraints like capital limits or risk tolerance.
QuantLib
QuantLib is an open-source library designed for modeling, trading, and risk management in quantitative finance. It provides tools and frameworks for pricing derivatives, managing portfolios, simulating market conditions, and performing advanced financial calculations.
QuantLib supports a wide range of asset classes, such as bonds, options, interest rate products, and currencies, and includes models for interest rates, volatility, and credit risk. It is commonly used by financial institutions, researchers, and quants for developing pricing models, performing quantitative analysis, and conducting risk assessments.
Dive deeper:
Riskfolio-Lib
Riskfolio-Lib is a library for making quantitative strategic asset allocation or portfolio optimization in Python. It helps practitioners build investment portfolios based on mathematically complex models with low effort. It is built on top of cvxpy and closely integrated with pandas data structures.
Riskfolio-Lib helps with portfolio optimization using mean-variance, risk parity portfolio, clustering, worst case, Black Litterman, and dozens of others.
Dive deeper:
Backtesting and trading
Backtesting is the process of evaluating a trading strategy or model by applying it to historical market data to assess its performance and robustness before deploying it in live markets.
Trading refers to the actual execution of buy or sell orders in financial markets, often guided by quantitative models or algorithms designed to capitalize on market inefficiencies or specific patterns
Zipline Reloaded
Zipline is an open-source backtesting library in Python that is primarily used for developing and testing trading algorithms. It allows quants and researchers to simulate how trading strategies would have performed in the past using historical data, providing insight into potential profitability and risk.
Zipline integrates with various financial data sources and offers a range of features, including data handling, performance tracking, and event-driven execution, making it a powerful tool for algorithmic trading and quantitative research.
Dive deeper:
Interactive Brokers Python API (IB API)
The TWS API is a simple yet powerful interface through which IB clients can automate trading strategies, request market data, monitor account balances, and portfolios in real-time.
The API is designed to automate some of the operations you would normally perform manually within TWS such as placing orders, monitoring your account balance and positions, or viewing an instrument's live data.
There is no logic within the API other than to ensure the integrity of the exchanged messages. Most validations and checks occur in the backend of TWS and IB's servers. Because of this, it is highly convenient to familiarize yourself with the TWS itself to understand how the platform works.
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