Zipline Daily Backtest

使用 Zipline 框架执行日频股票策略回测，支持多市场数据接入、因子研究、可视化绩效分析，默认本金千万级。。

Before installing or running this skill: - Clarify scope: ask the publisher whether this is a Zipline-only skill or a Zipline+ZVT blueprint. The SKILL.md mixes Zipline and ZVT concepts — that matters for runtime behavior. - Expect local side effects: the skill's runtime instructions will check for and may create ~/.zvt and run python import checks; run it in an isolated virtualenv or sandbox to avoid contaminating your global Python environment. - Credentials: if you plan to use paid data providers (joinquant, broker APIs), confirm where/how you must supply API keys — the skill metadata does not declare required env vars but the workflow references providers that need credentials. - Review seed.yaml and references: the package includes a large seed.yaml and many constraint/anti-pattern documents; read those to understand semantic locks (fatal constraints) that the skill enforces (e.g., no look-ahead, T+1 rules, MACD parameter lock). - Evidence quality & safety: the skill notes Evidence verify ratio = 48.1% and audit failures; treat generated code/results as needing independent verification. - If you need least privilege: run any backtests locally with your own controlled data and an explicit virtualenv; don't supply unrelated credentials or allow the agent to run system-wide installers. If you want, I can: (1) extract the exact precondition commands and show the file paths/actions it will perform; (2) produce a safe minimal wrapper (virtualenv + pip requirements) to test the skill in isolation; or (3) draft questions to ask the skill author to resolve the Zipline/ZVT mixing and missing install/credential declarations.

Type: OpenClaw Skill Name: zipline-daily-backtest Version: 0.3.3 The skill bundle is a highly structured framework for performing financial backtesting using the Zipline and ZVT libraries. It employs a sophisticated 'Doramagic' instruction set (crystals, blueprints, and semantic locks) designed to ensure the AI agent generates technically accurate and logically sound trading strategies. The bundle includes extensive documentation on avoiding common quant pitfalls (anti-patterns) and enforces strict 'fatal' constraints to prevent errors like look-ahead bias and survivorship bias. No indicators of malicious intent, data exfiltration, or unauthorized execution were found; instead, the bundle includes security-positive features such as shell operator restrictions and explicit warnings against hardcoding credentials.