Description

MlFinLab 提供金融机器学习高级实现，包括信息驱动 bars（tick/volume/dollar/imbalance bars）、分数阶差分和回测工具，支持多市场因子研究与策略验证。

README (SKILL.md)

金融机器学习 (advanced-financial-ml)

Name: Advanced Financial Ml
Author: tangweigang-jpg

MlFinLab 提供金融机器学习高级实现，包括信息驱动 bars（tick/volume/dollar/imbalance bars）、分数阶差分和回测工具，支持多市场因子研究与策略验证。

Pipeline

data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization

Top Use Cases (1 total)

Sphinx Documentation Configuration (`UC-101`)

How to configure and generate project documentation using Sphinx autodoc and extensions for API documentation coverage Triggers: documentation, sphinx, autodoc

Execute trigger: When user intent matches intent_router.uc_entries[].positive_terms AND user uses action verb (run/execute/跑/执行/backtest/fetch/collect)

What I'll Ask You

Target market: A-share (default), HK, or crypto? (US stocks in ZVT are half-baked — stockus_nasdaq_AAPL exists but coverage is thin)
Data source / provider: eastmoney (free, no account), joinquant (account+paid), baostock (free, good history), akshare, or qmt (broker)?
Strategy type: MACD golden-cross, MA crossover, volume breakout, fundamental screen, or custom factor?
Time range: start_timestamp and end_timestamp for backtest period
Target entity IDs: specific stocks (stock_sh_600000) or index components (SZ1000)?

Semantic Locks (Fatal)

ID	Rule	On Violation
`SL-01`	Execute sell orders before buy orders in every trading cycle	halt
`SL-02`	Trading signals MUST use next-bar execution (no look-ahead)	halt
`SL-03`	Entity IDs MUST follow format entity_type_exchange_code	halt
`SL-04`	DataFrame index MUST be MultiIndex (entity_id, timestamp)	halt
`SL-05`	TradingSignal MUST have EXACTLY ONE of: position_pct, order_money, order_amount	halt
`SL-06`	filter_result column semantics: True=BUY, False=SELL, None/NaN=NO ACTION	halt
`SL-07`	Transformer MUST run BEFORE Accumulator in factor pipeline	halt
`SL-08`	MACD parameters locked: fast=12, slow=26, signal=9	halt

Full lock definitions: references/LOCKS.md

Top Anti-Patterns (25 total)

AP-ZVT-183: 除权因子为 inf/NaN 时直接参与乘法导致复权静默失败
AP-ZVT-179: 第三方数据接口超限后异常被吞噬，数据静默缺失
AP-ZVT-183B: HFQ（后复权）与 QFQ（前复权）K 线表使用错误导致因子计算漂移

All 25 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-115. Evidence verify ratio = 43.7% and audit fail total = 34. Generated results may have uncaptured requirement gaps. Verify critical decisions against source files (LATEST.yaml / LATEST.jsonl).

Reference Files

File	Contents	When to Load
references/seed.yaml	V6+ 全量权威 (source-of-truth)	有行为/决策争议时必读
references/ANTI_PATTERNS.md	25 条跨项目反模式	开始实现前
references/WISDOM.md	跨项目精华借鉴	架构决策时
references/CONSTRAINTS.md	domain + fatal 约束	规则冲突时
references/USE_CASES.md	全量 KUC-* 业务场景	需要完整示例时
references/LOCKS.md	SL-* + preconditions + hints	生成回测/交易代码前
references/COMPONENTS.md	AST 组件地图（按 module 拆分）	查 API 时

Compiled by Doramagic crystal-compilation-v6.1 from finance-bp-115 blueprint at 2026-04-22T13:00:55.567727+00:00. See human_summary.md for non-technical overview.

Usage Guidance

This skill appears to be a genuine finance/backtesting guidance bundle, but it has small yet important inconsistencies you should address before running it. Watch for the following: (1) SKILL.md claims Python 3.12+ and a 'uv' package manager, yet the registry lists no required binaries — ensure you run it inside a controlled Python 3.12+ virtual environment (venv/conda) before letting the agent execute commands. (2) The instructions and seed.yaml expect to run python commands, possibly pip install zvt, and create/write under ~/.zvt (or ZVT_HOME). Do not allow the agent to run installs or filesystem writes without your explicit consent; review any pip install commands and the exact packages to be installed. (3) The seed.yaml execution_protocol tells the agent to re-read the seed file and run preconditions on behavioral decisions — be aware this skill enforces its own runtime policy which may change agent behavior. (4) If you intend to use real market data or live trading, audit the code and data sources carefully (this bundle focuses on backtesting and has many domain constraints you should follow to avoid look‑ahead bias and other pitfalls). What would increase confidence: explicit declared runtime requirements in the registry (python binary, pip/uv), an install spec pointing to vetted release sources (no arbitrary URLs), and an explicit list of pip packages the skill will install so you can review them beforehand.

Capability Analysis

Type: OpenClaw Skill Name: advanced-financial-ml Version: 0.3.3 The 'advanced-financial-ml' skill bundle is a highly structured framework designed for financial machine learning and backtesting using the ZVT and MlFinLab ecosystems. It contains extensive domain-specific logic, including 'semantic locks' (e.g., SL-01 to SL-12 in LOCKS.md) and 'fatal constraints' (e.g., finance-C-001 in CONSTRAINTS.md) aimed at preventing common quantitative finance errors like lookahead bias and data leakage. The bundle includes detailed anti-patterns from other financial libraries to guide the agent away from known bugs. The execution protocols and installation recipes (pip installs for numpy, pandas, zvt) are standard for the stated purpose, and no evidence of malicious intent, unauthorized data access, or harmful prompt injection was identified.

Capability Tags

cryptorequires-walletrequires-sensitive-credentials

Capability Assessment

ℹ Purpose & Capability

Name, description and the large set of reference docs consistently describe an advanced financial-ML/backtesting toolkit (ZVT / MlFinLab style). That high-level purpose justifies precondition checks and backtest rules. However SKILL.md explicitly says 'Requires Python 3.12+ with uv package manager' while the registry metadata lists no required binaries or env vars — an inconsistency between claimed runtime needs and the declared requirements.

ℹ Instruction Scope

The runtime instructions and seed.yaml expect the agent to run environment checks and preconditions (python commands that import zvt, assert kdata availability, create ~/.zvt, and suggest pip installs). Those actions are related to the skill's backtesting purpose but they instruct the agent to run commands, install packages, and write to disk. The instructions also demand the agent re-read seed.yaml and honor its execution_protocol; this gives the skill broad runtime control over agent behavior (within the agent's normal execution capabilities).

ℹ Install Mechanism

No install spec or code files are present (instruction-only), which is low-risk. Nonetheless seed.yaml's execution_protocol refers to install_recipes and an install_trigger flow (verify package imports, run host_adapter.install_recipes[]) even though no install recipes are provided in the bundle and the registry lists no install steps — an internal mismatch that may confuse or lead to runtime attempts to pip-install packages.

ℹ Credentials

The skill declares no required env vars in the registry, but SKILL.md and references/LOCKS.md refer to environment items (ZVT_HOME) and expect writable data dirs. Preconditions include commands that read ZVT_HOME and attempt to create/write files under it. The requested environment access is plausible for a backtesting toolkit, but it was not declared up front in the registry metadata — a proportionality/documentation gap.

✓ Persistence & Privilege

The skill does not request always: true and does not claim system-wide privileges. It does instruct the agent to run preconditions and to re-read seed.yaml on behavioral decisions, which is an unusual enforcement of internal policy but not an OS-level persistence or cross-skill configuration change. This is noteworthy but not itself privileged.

Version History

v0.3.3

v0.3.3: bilingual metadata injected. H1 shows 金融机器学习; tagline replaced with skill-specific Chinese hook; tags upgraded to Level 1-4.

v0.3.1

Remove install.sh — knowledge-only bundle. Host AI consumes directly from URL; no user-side installation needed. Fixes ClawHub suspicious flag.

v0.3.0

Doramagic crystal portfolio v0.3.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills

v0.2.0

Doramagic crystal portfolio v0.2.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills

v0.1.0

Doramagic crystal v0.2.0 portfolio. Compiled from finance blueprint. Source: github.com/tangweigang-jpg/doramagic-skills

Metadata

Slug advanced-financial-ml

Version 0.3.3

License MIT-0

All-time Installs 0

Active Installs 0

Total Versions 5

Frequently Asked Questions

What is Advanced Financial Ml?

MlFinLab 提供金融机器学习高级实现，包括信息驱动 bars（tick/volume/dollar/imbalance bars）、分数阶差分和回测工具，支持多市场因子研究与策略验证。 It is an AI Agent Skill for Claude Code / OpenClaw, with 129 downloads so far.

How do I install Advanced Financial Ml?

Run "/install advanced-financial-ml" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Advanced Financial Ml free?

Yes, Advanced Financial Ml is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Advanced Financial Ml support?

Advanced Financial Ml is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Advanced Financial Ml?

It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.

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