Page 7. Posts are ordered by date, with each page loading a bounded set of covers.
A practical record of turning idle RTX 4090 machines into local model servers with NVIDIA drivers, Ollama, OpenAI-compatible APIs, model downloads, benchmark scripts, and first-round model evaluation.
Article 10 connects market-data cleaning, factor signals, and A-share order rules to complete the first runnable signal-backtest loop, then reviews the stage from articles 6 to 10.
Article 9 implements factor calculation with the Python standard library: daily return, moving averages, momentum, annualized volatility, and three explainable signals: buy_watch, observe, and risk_watch.
Article 8 implements market-data cleaning: parse trade dates and numeric fields, validate OHLC, deduplicate rows, normalize volume units, and output coverage reports with rejection reasons.
Article 7 implements stock-universe construction: normalize symbols, filter ST and delisted names, deduplicate, cap size, and express the public A-share universe builder as testable pure functions.
Article 6 enters PostgreSQL and Alembic: why a quant platform cannot rely only on in-memory objects, how to inspect SQLAlchemy metadata, and how to turn schema and migrations into testable engineering boundaries.
Article 5 enters the data layer: why data-source abstraction, market and financial data, data-quality checks, provenance, and coverage must come before strategy work. It also reviews the first five articles.
Article 4 puts A-share board lots, T+1, limit-up/limit-down, suspensions, ST names, commission, and stamp duty into order-checking code, adding a testable trading_rules module in ZiQuant.
Article 3 translates terms such as stock universe, K-line, adjusted prices, factors, signals, positions, backtests, max drawdown, and Sharpe into ZiQuant models, fields, and testable objects.
Cursor Composer 2 can trigger image generation in real workflows, and Cursor's changelog points to Google Nano Banana Pro. This article records the workflow, cost intuition, and a same-aspect comparison with Codex-generated images.
Article 2 builds ZiQuant's Python engineering skeleton: directories, pyproject.toml, .env, configuration loading, health checks, and test commands. Make the project installable, runnable, and verifiable before the data layer.
Quant trading is not mysticism, and it is not passive income from one magic indicator. Article 1 reframes trading as data, rules, validation, risk control, and review, then starts ZiQuant as a runnable project.
A practical guide to combining Codex and Cursor: product boundaries, Codex IDE, Codex CLI, the Codex extension inside Cursor, Skills, Cursor Rules, session context, and a workable coexistence workflow.
Cursor's TypeScript SDK exposes the agent capability behind IDE, CLI, and Cloud Agents as a programmable interface. This article explains runtimes, hooks, MCP, examples, use cases, and a CI failure repair workflow.
Vector search is not finished when you concatenate every field and send it to an embedding model. This article uses a fictional API marketplace to explain noisy text, long descriptions, URLs, thresholds, hybrid retrieval, and evaluation sets.
A practical look at how display, CPU, network, and background work affect battery life in Android reading apps, plus notes on native versus cross-platform choices and open-source reader projects.
A practical note on bringing a Raspberry Pi 4B 4GB back to life with a 128GB card and Ubuntu 26.04 LTS: flashing, accessories, first boot, basic packages, SSH, and disabling the desktop for server use.
A practical internal package workflow using pypiserver and uv: authentication, PEP 440 versioning, setuptools-scm, Docker without git, uv project configuration, BuildKit secrets, and CI publishing pitfalls.