Follow our Telegram channel to get notified instantly whenever new books are published.
Hands-On Financial Trading with Python PDF – Jiri Pik
Total Downloads:
3
Hands-On Financial Trading with Python Book Summary & Review
Quick Summary
A practical coding guide focused on building quantitative financial models, executing algorithmic strategies, and backtesting trading pipelines using Python.
Book Topic and Premise
How do quantitative institutional investors transform raw market data into high-frequency, profitable automated trading execution? The exact technological engineering behind this capability is detailed in Hands-On Financial Trading with Python. Authored by quantitative strategist Jiri Pik, this hands-on coding guide bridges the gap between financial theory and software development.
The workbook approaches quantitative trading systematically, utilizing popular libraries like Pandas, NumPy, and specialized financial APIs. Pik walks readers through data gathering, cleaning historical market bars, and calculating complex mathematical indicators. Rather than presenting trading as speculative gambling, the text treats it as a disciplined data science challenge focused on risk management and statistical edge.
While reading this engineering text, developers engage with real programming architectures designed for backtesting strategies and deploying live orders safely. The PDF version allows programmers to study script layouts for analyzing order book dynamics, managing slippage, and automating stop-loss parameters. The text focuses heavily on source code implementations over vague market advice.
Additionally, the book details the technical plumbing required to connect scripts to brokerage execution venues. By building functional automated platforms throughout the text, Hands-On Financial Trading with Python ensures that readers can build, validate, and launch their own trading systems, making it an essential resource for software developers and finance professionals interested in quantitative market analysis.
Detailed Plot & Summary
Jiri Pik teaches data scientists and developers how to construct functional trading systems. The manual covers financial data ingestion via APIs, market microstructure analysis, technical indicators calculation, backtesting infrastructure design, risk management algorithms, and live execution via interactive broker interfaces.
✍️ Editor’s Note: A highly practical blueprint for financial engineers wishing to convert theoretical trading ideas into production-ready Python automation scripts.
Critical Review and Analysis
An exceptional, project-based programming manual for quantitative developers. Pik’s structural breakdown of API integrations and order execution pipelines is thoroughly detailed. Readers should note that Packt coding books can occasionally suffer from code-block syntax changes over time, requiring readers to update deprecated library versions manually.
Main Themes & Motifs
- Algorithmic Execution
- Quantitative Modeling
- Backtesting Infrastructure
- Risk Management Automation
- Financial Data Analytics
Who Should Read This Book?
Python developers, data scientists, quantitative analysts, financial engineers, and algorithmic hobbyist traders.
Why You Should Read It
It delivers clean, practical code blueprints for building automated trading systems from scratch, bypassing expensive proprietary software suites.
Key Takeaways & What You Will Learn
How to retrieve live market data via APIs, construct modular backtesting engines, code technical indicators, and automate risk parameters to prevent capital drawdown.
Technical & Bibliographic Details
| 📖 Title: | Hands-On Financial Trading with Python |
| 🔍 Original Title: | Hands-On Financial Trading with Python |
| ✍️ Author: | Jiri Pik |
| 🗣️ Translator: | N/A |
| 🏢 Publisher: | Packt Publishing |
| 📅 Publication Year: | 2021 |
| ⏳ First Published: | 2021 |
| 🔢 ISBN: | 9781838982881 |
| 📦 Amazon ASIN: | B08R4D7D2K |
| 📄 Total Pages: | 412 |
| 📁 Category: | Finance, Data Science, Quantitative Trading, English |
| 🌍 Language: | English |
| ⭐ Goodreads Rating: | 3.80 / 5.0 (10 votes) |
| ⏱️ Reading Time: | 11 hours |
| 📊 Difficulty Level: | Medium |
| 📚 Similar Books: | Python for Algorithmic Trading by Yves Hilpisch, Advances in Financial Machine Learning by Marcos López de Prado |
⚠️ Content Warnings: Financial risk warning regarding live algorithmic trading execution
Frequently Asked Questions (FAQ)
❓ Which specific Python libraries are emphasized in Jiri Pik’s book?
The book relies heavily on core data science libraries such as Pandas, NumPy, Matplotlib, along with specialized technical analysis toolkits and broker connection APIs.
❓ Do I need an advanced degree in finance to understand this text?
No, a basic understanding of market mechanics like stock orders and charts is sufficient, though intermediate knowledge of object-oriented Python programming is highly recommended.
❓ Does the text cover crypto assets alongside traditional stocks?
Yes, the algorithmic principles and API ingestion models outlined throughout the text can be adapted to equities, forex, and cryptocurrency markets seamlessly.
❓ How does the book handle historical backtesting validation?
It provides step-by-step guidance on constructing custom backtesting engines that account for realistic parameters like transaction fees, spread costs, and execution latency.
❓ Are machine learning trading models included in this volume?
The book focuses primarily on systematic quantitative models and technical strategies, leaving deep machine learning architectures to more advanced titles.
❓ Does the book provide support for connecting to a live broker?
Yes, it details execution layer workflows, showing how to connect automated scripts to live brokerage APIs for paper trading and active deployment.
📚 Recommended Category: Explore more in our Finance hub.