Leverage cutting-edge Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment.
Python for Algorithmic Trading Cookbook is packed with practical Python code recipes to leverage freely available market data to design, backtest, and deploy algorithmic trading strategies using Python. It's an essential resource for traders and developers that want to use Python to build and deploy algorithmic trading strategies.
- Apply practical Python recipes for acquiring and storing free market data for market research
- Design, backtest, and evaluate the performance of trading strategies using professional techniques
- Deploy trading strategies built in Python to a live trading environment using API connectivity
- Learn how to acquire and process freely available market data with OpenBB
- Build a research environment and populate it with financial market data
- Use machine learning to identify alpha factors and engineer them into signals
- Use vectorbt to find strategy parameters using walk forward optimization
- Build production-ready backtests with Zipline and evaluate factor performance
- Set up the code framework to connect and send orders to Interactive Brokers
- Deploy your trading strategies to a live trading environment with the IB API
- Acquiring Free Financial Market Data with Cutting-Edge Python Libraries
- Analyzing and Transforming Financial Market Data with pandas
- Visualize Financial Market Data with Matplotlib, Plotly, and Streamlit
- Store Financial Market Data on Your Computer
- Build Alpha Factors for Stock Portfolios
- Vector-Based Backtesting with VectorBT
- Event-Based Backtesting Factor Portfolios with Zipline Reloaded
- Evaluate Factor Risk and Performance With AlphaLens
- Assess Backtest Risk and Performance Metrics with Pyfolio
- Set Up the Interactive Brokers Python API
- Manage Orders, Positions, and Portfolios with the IB API
- Deploy Strategies to a Live Environment
- Advanced Recipes for Market Data and Strategy Management
The Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. To learn from this book, you should have experience investing in the stock market, knowledge of Python data structures, and basic experience using Python libraries like pandas. This book is excellent for people with Python experience that are already active in the market or aspiring to be.
This book equips the reader with practical Python code to design, backtest, and automate effective algorithmic trading strategies. With example code using cutting-edge Python tools, readers can incorporate quantitative methods like alpha factor engineering, statistics, and machine learning to control risk more effectively and improve profitability. This book gives the reader the building blocks to add a systematic edge to their trading.
Jason Strimpel is the founder of PyQuant News and co-founder of Trade Blotter. He teaches popular courses on using Python for quant finance, algorithmic trading, and market data analysis.
Before his current pursuits, he traded professionally for a Chicago-based hedge fund, was a risk manager at JPMorgan, managed credit risk and market risk technology for an energy derivatives trading firm in London. In Singapore, he was the APAC Chief Information Officer for an agricultural trading firm and built the data science, analytics, and engineering team for a global metals trading firm.
Jason holds undergraduate degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career has spanned across America, Europe, and Asia, shares his experience through the PyQuant Newsletter, social media, and teaches the popular course Getting Started With Python for Quant Finance.
In algorithmic trading, acquiring and analyzing vast amounts of high-quality market data is crucial. This chapter provides recipes for using various Python libraries, including the advanced OpenBB Platform, to gather free financial market data. Key challenges, such as consolidating data from multiple sources, are addressed by the OpenBB Platform, which simplifies data collection for assets like stocks, options, futures, and Fama-French factors.
Readers will learn to handle discrepancies in data due to different sourcing methods and preprocessing options. While mainstream financial data is the focus, the chapter briefly touches on “alternative data” sources, highlighting their significance without delving into their acquisition or processing.
The pandas library, created by Wes McKinney at AQR Capital Management, has become essential for data analysis since its open-source release in 2009. Widely used in finance, academia, and business, pandas excels in handling tabular data, integrating seamlessly with other Python libraries.
This chapter focuses on using pandas for algorithmic trading. It begins with building DataFrames and Series, the core data structures, enabling operations like slicing, indexing, and subsetting large datasets. You'll learn to inspect and select data from DataFrames, followed by recipes for computing asset returns, measuring volatility, generating cumulative return series, and resampling data.
Additionally, the chapter addresses handling missing data and applying custom functions to time series. Integration with Matplotlib, NumPy, and Scikit-Learn enhances pandas' utility for comprehensive data analysis.
This chapter of Algorithmic Trading With Python Cookbook focuses on essential data visualization techniques for financial market data using pandas, Matplotlib, Seaborn, Plotly, and Plotly Dash. Each tool serves specific needs: pandas for quick plots, Matplotlib for advanced visualizations and animations, Seaborn for statistical data, Plotly for interactive charts, and Plotly Dash for interactive web apps.
In algorithmic trading, data is crucial. Storing data locally enhances access speed, reliability, and control. Local storage avoids disruptions from internet outages and allows for persistent updates to incorrect prices. Additionally, it offers cost-efficiency, avoiding the high recurring expenses of cloud storage. Local storage also facilitates easier data manipulation, integration with research workflows, and faster backtesting.
This chapter explores various methods to store financial market data locally. Starting with CSV files, easily handled by pandas, we then move to SQLite, a simple on-disk SQL database. We increase complexity by setting up a PostgreSQL database server for local storage, and finally, we use the ultra-fast HDF5 format for high-efficiency storage.
For SQLite and PostgreSQL, we'll develop a script that automates data acquisition post-market close using a task manager.
Professional traders leverage factor portfolios to exploit market inefficiencies and achieve superior risk-adjusted returns. By systematically selecting and weighting securities based on characteristics like value, size, or momentum, they create portfolios that capture desired exposures while minimizing unintended risks. Factors, which are fundamental drivers of asset returns, form the basis of a trading edge by consistently influencing prices.
Factor analysis involves identifying relevant factors, assessing a portfolio's sensitivity to these factors, and taking action—either hedging unwanted risks or enhancing factor exposure. This chapter delves into identifying factors, mitigating undesirable risks, and evaluating the predictive power of factors using Python libraries for statistical modeling.
We'll employ principal component analysis and linear regressions, and introduce the Zipline Pipeline API for advanced factor analysis.
In this chapter, we shift focus to backtesting, a crucial phase in the algorithmic trading workflow. Given the inherent unpredictability of most strategies and their potential short-lived profitability, rapid iteration through ideas is essential. We will explore vector-based backtesting for simulating and optimizing trading strategies.
VectorBT is a high-performance framework that processes entire time-series data arrays simultaneously, rather than sequentially, which greatly accelerates backtesting operations. This efficiency makes it ideal for quickly iterating and refining strategies. The framework's high customizability allows traders to fine-tune parameters and evaluate multiple strategies concurrently.
In this chapter, we focus on using Zipline Reloaded, an event-driven backtesting framework, to model realistic order execution and slippage by processing market events sequentially. Unlike vector-based frameworks, Zipline Reloaded is ideal for complex strategies involving conditional orders or asset interactions, providing a more accurate representation of trading conditions despite being slower.
Zipline Reloaded excels in backtesting large universes and complex portfolio construction techniques. Its Pipeline API efficiently computes factors across thousands of securities, making it suitable for backtesting portfolio factor strategies, with results easily analyzed using other tools in the ecosystem.
Factor investing focuses on selecting assets based on specific attributes or factors linked to higher returns, rather than traditional asset classes like stocks or bonds. This strategic approach aims to identify and exploit the underlying drivers of risk and return, potentially achieving returns above conventional benchmarks. While factor investing can enhance diversification and potential returns, it does not eliminate risk, as market conditions and economic changes can impact strategy effectiveness.
In Chapter 5, we built alpha factors for stock portfolios. This chapter delves into analyzing the risk and performance of these alpha factors using AlphaLens Reloaded. AlphaLens Reloaded is a specialized library for assessing predictive alpha factors, evaluating the quality of signals and their ability to predict future returns. It integrates with Zipline Reloaded, converting backtest outputs into insightful statistics and visualizations. The library offers utilities such as tear sheets that present performance metrics, cumulative returns, turnover analysis, and information coefficients.
In algorithmic trading, no single metric can fully capture how a backtest will perform live. Metrics like the Sharpe ratio focus on returns relative to volatility but overlook risks like drawdown or tail risk. Similarly, using maximum drawdown alone ignores risk-adjusted returns, potentially dismissing robust strategies. A comprehensive view using multiple metrics provides a nuanced understanding of strategy behavior under different market conditions. Visualizing these metrics over time can reveal performance dynamics across various market phases.
This chapter introduces Pyfolio Reloaded (Pyfolio), a risk and performance analysis library within the Zipline Reloaded ecosystem. Pyfolio processes Zipline backtest results to deliver a robust suite of metrics. We will explore how to use Pyfolio to generate and interpret these metrics, focusing on key performance indicators.
In Chapters 1 through 9, we covered the foundational tools and techniques of algorithmic trading. Now, we will apply these skills using the Interactive Brokers (IB) Trader Workstation (TWS). TWS is a versatile trading platform favored by both professional and retail traders for its comprehensive trading tools and robust risk management features. It offers unparalleled global market access, allowing traders to diversify across 135 markets in 33 countries.
TWS’s paper trading functionality provides a risk-free environment to test and refine trading strategies using real-time market conditions, essential for developing and honing algorithms without financial risk. Additionally, TWS's API integration is a significant advantage, exposing all TWS features through a Python-accessible API, enabling the automation of trading strategies.
Efficient management of orders, positions, and portfolio data is critical in algorithmic trading, and Python offers the tools to handle these tasks seamlessly. Managing orders involves executing new trades, canceling existing ones, and updating orders based on market conditions or strategy changes. Position management entails monitoring live data to track real-time profit and loss, enabling informed decisions about holding, selling, or adjusting positions. Real-time portfolio data provides crucial risk statistics, enhancing overall risk management. This is particularly important when trading on margin or with futures, where holding losing positions incurs financial and opportunity costs.
The IB API employs a consistent request-callback pattern, which is instrumental in various trading app functionalities, including order management, position management, and accessing portfolio details. This pattern initiates a request (e.g., placing or modifying an order, retrieving position data, gathering portfolio information) and follows up with a callback function to handle the response.
In Chapters 10 and 11, we laid the groundwork for deploying algorithmic trading strategies in a live or paper trading environment using the Interactive Brokers (IB) Python API. To fully prepare for live deployment, we need two more critical components: risk and performance metrics, and sophisticated order strategies for building and rebalancing asset portfolios.
For risk and performance metrics, we introduce the Empyrical Reloaded library, which provides performance and risk analytics similar to those in Pyfolio Reloaded. We'll use Empyrical Reloaded to calculate key performance indicators such as the Sharpe Ratio, Sortino Ratio, and Maximum Drawdown using real-time portfolio return data. To facilitate real-time data computation while executing trades or other code, we'll learn to run code asynchronously on a thread.
Additionally, we'll enhance our position management code with methods to submit orders based on target contracts, monetary value, or percentage allocation, enabling portfolio-based strategies. Finally, we introduce three algorithmic trading strategies: a monthly factor-based strategy using the Zipline Reloaded Pipeline API, an options combo strategy, and an intraday multi-asset mean-reversion strategy.
This final chapter covers advanced recipes for streaming and storing options data, generating risk alerts, and automating end-of-day reporting of key strategy information. We begin with a deep dive into real-time data handling using ThetaData, a service that specializes in providing comprehensive, unfiltered real-time options market data, including quotes, trades, volumes, and Greeks. ThetaData allows for the real-time pricing of complex options positions, making it ideal for algorithmic traders developing intricate strategies.
After covering data streaming, we introduce advanced data management and storage using ArcticDB, an open-source project by Man Group designed to store petabytes of data in DataFrame format. This storage solution is essential for handling large-scale data efficiently.
Building on the risk and performance metrics established in Chapter 12, we design an alerting system to send emails if predefined risk levels are breached—a common practice in professional trading to adhere to risk limits. Additionally, we provide recipes to store key strategy information in the SQL database created in Chapter 10, automating end-of-day strategy management.
By the end of this chapter, you will be equipped to handle and analyze real-time data, manage risk meticulously, and maintain detailed trade records—essential techniques for algorithmic traders.
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