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Portfolio Management CLI Entry Point (
Tax-Optimized Selling Strategy (
Tax Loss Harvesting Opportunity Detection (
Fava Beancount Viewer
by
Tang Weigang
· GitHub ↗
· v0.3.3
· MIT-0
107
Downloads
0
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0
Active Installs
3
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Install in OpenClaw
/install fava-beancount-viewer
Description
提供基于Fava/Beancount的投资组合管理能力，支持税务亏损收割优化、资产配置分析与等价证券分组识别，辅助用户制定最优卖出策略。
README (SKILL.md)
Fava 账本查看 (fava-beancount-viewer)
提供基于Fava/Beancount的投资组合管理能力，支持税务亏损收割优化、资产配置分析与等价证券分组识别，辅助用户制定最优卖出策略。
Pipeline
data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization
Top Use Cases (5 total)
Portfolio Management CLI Entry Point (UC-101)
Provides a unified command-line interface for portfolio management operations including tax loss harvesting, asset allocation analysis, cash drag dete Triggers: portfolio management, CLI, command line
Tax-Optimized Selling Strategy (UC-103)
Determines optimal sell order for securities to minimize realized capital gains by analyzing cost basis and holding periods across multiple lots Triggers: minimize gains, tax-efficient selling, capital gains optimization
Tax Loss Harvesting Opportunity Detection (UC-105)
Identifies securities with unrealized losses that can be sold to harvest tax losses, typically looking back 30 days to find positions eligible for was Triggers: tax loss harvesting, loss identification, wash sale
For all 5 use cases, see references/USE_CASES.md.
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 (15 total)
AP-ACCOUNTING-001: Using floating-point arithmetic for monetary amountsAP-ACCOUNTING-002: Skipping initialization calls before VM/script executionAP-ACCOUNTING-003: Mixing different asset types in monetary operations
All 15 anti-patterns: references/ANTI_PATTERNS.md
Evidence Quality Notice
[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-078. Evidence verify ratio = 21.6% and audit fail total = 14. 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 | 15 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-078 blueprint at 2026-04-22T13:00:29.702985+00:00.
See human_summary.md for non-technical overview.
Usage Guidance
What to consider before installing/using this skill:
- The skill is instruction-only and will expect the agent/host to run Python (3.12+) and the ZVT ecosystem; the published metadata does NOT list these binaries or environment variables — verify the host requirements before running.
- The runtime steps include running python commands, creating/using ~/.zvt, installing packages (pip install zvt), and invoking data recorders which will access network data providers (eastmoney, joinquant, akshare, etc.). Run the skill in a sandbox or isolated environment first.
- The SKILL.md describes 'trading_execution' and tax-optimized sell strategies but does not declare or request broker API keys/credentials. Before connecting any real brokerage account, confirm exactly where and how trade orders would be executed (dry-run vs live) and whether the skill will prompt for credentials or call external trading endpoints.
- Evidence quality notice in SKILL.md indicates low verification (evidence verify ratio 21.6% and audit fail count). Treat automated recommendations as provisional; cross-check critical financial decisions yourself.
- Licensing is proprietary — review LICENSE.txt (not included) for usage/redistribution restrictions.
Actionable steps I recommend:
1) Ask the publisher for an explicit runtime requirements list (Python version, required packages, exact data providers/brokers and which credentials are needed). 2) If you decide to run it, do so in a sandboxed environment (VM/container) that has no access to real brokerage credentials or production funds. 3) Review seed.yaml and precondition commands before executing them; disable any automatic install steps until you confirm sources. 4) Use dry-run/backtest modes only until you validate outputs and semantic-lock compliance. 5) If you need higher assurance, request the skill owner to publish an install spec and declared env/credential requirements so the registry metadata matches actual runtime needs.
Capability Analysis
Type: OpenClaw Skill
Name: fava-beancount-viewer
Version: 0.3.3
The skill bundle provides a comprehensive framework for portfolio management and tax optimization using Beancount and the ZVT quant library. The instructions in SKILL.md and seed.yaml are focused on enforcing financial integrity, such as using Decimal types for monetary calculations (CW-ACCOUNTING-001) and adhering to IRS wash sale rules (finance-C-081). No evidence of data exfiltration, malicious persistence, or harmful prompt injection was found; the system's capabilities are strictly aligned with its stated purpose of financial analysis and reporting.
Capability Tags
Capability Assessment
Purpose & Capability
Name/description claim a Fava/Beancount viewer and portfolio management (tax-loss harvesting, allocation, sell-strategy). SKILL.md and seed.yaml however require Python/ZVT operations, a ZVT data directory (~/.zvt), ZVT recorders/data fetchers, and an end-to-end pipeline including 'trading_execution'. Yet the registry metadata lists no required binaries, no environment variables, and no install spec. That mismatch (required runtime dependencies undocumented) is disproportionate and unexplained. Also: trading-execution stage implies broker interaction, but no broker credentials or endpoints are declared.
Instruction Scope
The runtime instructions direct the agent to re-read seed.yaml, run precondition checks that invoke python3 commands (import zvt, run recorders, check/write ~/.zvt), and enforce numerous 'semantic locks' (fatal constraints) before trading/backtest. These actions involve filesystem access (~/.zvt), package installation guidance (pip install zvt), network data fetchers (eastmoney, joinquant, akshare, etc. referenced), and running recorders/recording scripts. The SKILL.md does not instruct arbitrary exfiltration, but it does grant broad discretion to run Python commands and write data locally; that broad scope should be expected for an end-to-end pipeline but is more privileged than a simple 'viewer'.
Install Mechanism
This is an instruction-only skill with no install spec and no code files to execute — lowest install risk. However, the instructions expect the agent/host to run Python and install packages like zvt if missing; those installs would be performed by the host (not prepackaged by the skill).
Credentials
The skill declares no required env vars or credentials, yet SKILL.md and seed.yaml expect/mention ZVT_HOME, python3 availability, and data providers (eastmoney, joinquant, broker/qmt). Trading-related capabilities imply broker credentials or API keys would be necessary, but none are declared. This under-declaration is suspicious because the runtime steps will likely prompt for or require sensitive provider/broker credentials that are not surfaced in metadata.
Persistence & Privilege
Skill does not request 'always:true' and does not claim the ability to modify other skills or global agent settings. It does expect to read/write host workspace paths (e.g., ZVT_HOME ~/.zvt, host_workspace/scripts, host_workspace/skills) as part of preconditions and data storage — typical for a data-processing/backtest tool, but this is a filesystem-modifying behavior to be aware of.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install fava-beancount-viewer - After installation, invoke the skill by name or use
/fava-beancount-viewer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.3
v0.3.3: bilingual metadata injected. H1 shows Fava 账本查看; 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
Metadata
Frequently Asked Questions
What is Fava Beancount Viewer?
提供基于Fava/Beancount的投资组合管理能力，支持税务亏损收割优化、资产配置分析与等价证券分组识别，辅助用户制定最优卖出策略。 It is an AI Agent Skill for Claude Code / OpenClaw, with 107 downloads so far.
How do I install Fava Beancount Viewer?
Run "/install fava-beancount-viewer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Fava Beancount Viewer free?
Yes, Fava Beancount Viewer is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Fava Beancount Viewer support?
Fava Beancount Viewer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Fava Beancount Viewer?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.
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