Tag
Python
English articles and guides tagged Python.
Quantitative trading system development record (5): Python performance tuning practice
Transform performance optimization from empirical guesswork into a verifiable investigation process: start from the 3-second chart delay, locate the real bottleneck, compare optimization solutions, and establish benchmarks and rollback strategies.
Record of Quantitative Trading System Development (Part 3): Python Pitfalls Practical Pitfalls Avoidance Guide (Part 2)
Continuing to reorganize Python risks into a reference piece: how GUI lifecycles, asynchronous network failures, security boundaries, and deployment infrastructure affect the long-term stability of quantitative trading systems.
Quantitative trading system development record (2): Python Pitfalls practical pitfall avoidance guide (1)
Reorganize Python traps from a long list into an engineering risk reference for quantitative trading systems: how to amplify the three types of risks, syntax and scope, type and state, concurrency and state, into real trading system problems.
Quantitative trading system development record (1): five key decisions in project startup and architecture design
Taking Micang Trader as an example, this article starts from system boundaries, data flow, trading-session ownership, unified backtesting/live-trading interfaces, and AI collaboration boundaries to establish the architecture thread for the quantitative trading system series.
Original Analysis: Why FastAPI Rises in the AI Era—The Engineering Value of Type Hints and Async I/O
Analyzing Python type hints, async I/O, and FastAPI's rise logic; establishing a feature-capability matching framework for LLM API service development
Original Analysis: Why Python Monopolizes LLM Development—Ecosystem Flywheel and Data Evidence
Synthesizing multi-source data from Stack Overflow 2025, PEP 703 industry testimonies, and LangChain ecosystem to analyze the causes and flywheel effects of Python's dominance in AI
Original Analysis: Capability Building for Python Developers in the AI Tools Era—A Practical Guide for Frontline Engineers
Based on Stack Overflow 2025 data, establishing a capability building roadmap from beginner to expert, providing stage assessment, priority ranking, and minimum executable solutions
Python Memory Model Deep Dive Series Overview (7 Parts)
This page serves as the navigation hub for the Python Memory Model Deep Dive Series, providing complete entry points in reading order to establish a comprehensive cognitive framework from underlying mechanisms to engineering practice to career development.
Original Interpretation: The Three-Layer World of Python Memory Architecture
Why doesn't memory drop after deleting large lists? Understanding the engineering trade-offs and design logic of Python's Arena-Pool-Block three-layer memory architecture
Original Interpretation: Python Garbage Collection - The Three Most Common Misconceptions
Deconstructing the three major misconceptions about reference counting, gc.collect(), and del statements, establishing a complete cognitive framework for Python GC mechanisms (reference counting + generational GC + cycle detection)
Original Analysis: 72 Processes vs 1 Process—How GIL Becomes a Bottleneck for AI Training and PEP 703's Breakthrough
Reviewing real production challenges at Meta AI and DeepMind, analyzing PEP 703's Biased Reference Counting (BRC) technology, and exploring the implications of Python 3.13+ nogil builds for large-scale model concurrency
Original Analysis: Python as a Glue Language—How Bindings Connect Performance and Ease of Use
A comparative analysis of ctypes, CFFI, PyBind11, Cython, and PyO3/Rust, exploring the technical nature and engineering choices of Python as a glue language for large models