Algorithmic trading frameworks often come with heavy dependencies, monolithic codebases, or tightly coupled modules that make reusing parts outside their ecosystem a pain. This repository breaks that mold by offering pure-Pandas technical indicators and object-oriented portfolio simulation components that can slot into any quantitative trading research pipeline with minimal fuss.
What the algorithmic-trading-with-python repository does
This project is the source code companion for Chris Conlan’s 2020 book “Algorithmic Trading with Python.” It provides a suite of modular, standalone utilities aimed at quantitative trading research and backtesting. The components are split into clear layers: data handling, technical indicators, signal generation, optimization, and portfolio simulation.
Most of the code is written in Python with a heavy reliance on Pandas for data manipulation. The key modules include:
indicators.py: Implements a variety of technical indicators purely with Pandas, avoiding external C extensions or specialized libraries.signals.py: Contains logic for ternary (buy, hold, sell) signal generation from indicator outputs.optimization.py: Supports numeric optimization via grid search methods to tune strategy parameters.portfolio.py: Object-oriented portfolio simulation, allowing users to assemble and test trading strategies in a flexible manner.
The repo also provides free simulated end-of-day (EOD) stock data and alternative data streams to experiment with. While the modules are designed to be useful independently of the accompanying text, full comprehension and effective use often require consulting the book.
The architecture favors separation of concerns, which not only aids clarity but also lets you pick and choose components without pulling in the entire codebase. This design is practical for practitioners who want to integrate specific utilities into their own projects without unnecessary bloat.
What distinguishes its code quality and design
The standout feature here is the pure-Pandas implementation of technical indicators in indicators.py. Many Python quant libraries rely on compiled extensions for performance or come with complex dependencies. This repo opts for clean, idiomatic Pandas code that is easy to read, modify, and integrate. While you might sacrifice some performance compared to highly optimized C++ or Rust libraries, the tradeoff is clear: better transparency and flexibility.
The portfolio simulation module (portfolio.py) uses an object-oriented approach, which is not universal in the quant Python ecosystem. This makes it easier to extend and customize portfolio behavior, simulate different trading scenarios, and manage state cleanly. The code is surprisingly clean for a research repo; the author pays attention to modularity and single responsibility principles.
The repo also includes support for multi-core repeated K-fold cross-validation, which is a thoughtful addition for robust strategy evaluation. This setup helps mitigate overfitting—a common pitfall in algorithmic trading research.
One limitation is the reliance on simulated data and the absence of integration with real-world market data APIs out of the box. This means the repo is more suited for research and learning than direct production deployment. Additionally, some modules assume familiarity with the book’s content, which could slow onboarding for newcomers.
Explore the project
The repository is organized around several key Python modules, each focusing on a different aspect of the quant research pipeline:
- Start with
indicators.pyto understand how technical indicators are calculated purely with Pandas. - Review
signals.pyto see how indicator outputs are transformed into actionable trading signals. - Check out
optimization.pyfor tuning parameters via grid search. - Dive into
portfolio.pyfor simulating trading strategies with an object-oriented design.
The README provides links to free simulated datasets under the data/ directory. This is useful for running experiments without worrying about external data sourcing.
The documentation is somewhat sparse but well-structured. The code itself is well-commented, making it straightforward to follow the data flow.
If you want to get a feel for the repo, start by importing the indicators module and experimenting with the provided sample data frames to calculate moving averages, RSI, or other indicators. Then, try generating signals and running simple portfolio simulations.
Verdict
This repository is a solid resource for developers and researchers focused on quantitative trading who want modular, dependency-light Python tools. The pure-Pandas implementation of indicators and OOP portfolio simulation are particularly handy for integrating into existing pipelines or building custom research frameworks.
However, the repo is best suited for those comfortable with Python and Pandas and who can supplement the code with the book or additional resources to fully grasp the concepts. Its reliance on simulated data and lack of real market data connectors mean it’s not a plug-and-play solution for live trading.
In sum, this project shines as a learning tool and as a set of reusable building blocks, not as a turnkey algorithmic trading platform. If you want transparent, tweakable quant research utilities with minimal external dependencies, this is worth a look.
→ GitHub Repo: chrisconlan/algorithmic-trading-with-python ⭐ 3,350 · Python