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sarahwang94712

broker-monitor

作者 sarahwang94712 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install broker-monitor
功能描述
Generate a weekly monitoring report for US retail brokerage stocks (IBKR, SCHW, HOOD, FUTU) AND the broader trading ecosystem including US equity/options mar...
使用说明 (SKILL.md)

US Retail Broker & Trading Ecosystem Weekly Monitoring Report

Overview

This skill generates a structured weekly monitoring report covering five pillars:

  1. US equity & options market-wide volume — Consolidated tape equity volume, OCC options volume, 0DTE share, Put/Call ratio, VIX (Cboe, OCC, NYSE, NASDAQ, FINRA ATS)
  2. Four US-listed retail brokerages — IBKR, SCHW, HOOD, FUTU monthly/quarterly operating data
  3. Crypto market — Total market cap, trading volume, BTC/ETH prices, stablecoin supply, Fear & Greed (CoinGecko/CoinMarketCap)
  4. Exchange market share — CEX/DEX spot & derivatives volume, per-exchange share (The Block, CCData, CoinDesk)
  5. Prediction markets — Polymarket, Kalshi, HOOD Events volume & growth (Dune Analytics)

Published weekly (weekend or Monday). Each metric includes a 牛熊周期定位 (bull/bear spectrum) comparing current levels to historical extremes.

When to Use

  • User asks to "update the broker report", "weekly update", or "generate the weekly monitor"
  • User asks about retail broker metrics (DARTs, client assets, trading volumes, margin loans)
  • User wants to compare IBKR/SCHW/HOOD/FUTU on key metrics
  • User asks about EPS revisions, valuation, or analyst target price changes
  • User asks about crypto market cap, CEX volume, exchange market share
  • User asks about prediction market volume, Polymarket, Kalshi, Dune Analytics
  • User asks about US equity trading volume, options volume, 0DTE, put/call ratio
  • User mentions "券商监控", "周度监控", "零售券商", "全市场交易生态", "weekly monitor"

Workflow

Step 1: Determine Data Freshness

Source Cadence Where to Find
Cboe equity volume Daily (aggregate weekly) cboe.com/us/equities/market_statistics
OCC options volume Daily/Monthly optionsclearing.com/data/volume
Cboe options/VIX Daily cboe.com/us/options/market_statistics
IBKR Monthly (~1st biz day) Business Wire / interactivebrokers.com/ir
SCHW Monthly (~15th) Business Wire / aboutschwab.com
HOOD Monthly (~12th) GlobeNewsWire / investors.robinhood.com
FUTU Quarterly (March/June/Sept/Dec) PR Newswire / futuhk.com/newsroom
CoinGecko/CMC Real-time (snapshot weekly) coingecko.com / coinmarketcap.com
The Block Monthly dashboards theblock.co/data
Dune Analytics Real-time dashboards dune.com (@polymarket, @rchen8)
Kalshi Monthly blog/PR kalshi.com/blog

Step 2: Smart Data Freshness Check (Avoid Redundant Searches)

CRITICAL: Before searching, check dates to avoid wasting tokens.

DATE_CHECK_LOGIC:
1. Determine today's date and the report week (Mon–Fri).
2. For each data source, check if new data should be available:
   - IBKR: New data ~1st biz day of month. If today \x3C 2nd AND last report already has prior month → SKIP, carry forward with ⏭️.
   - SCHW: New data ~15th. If today \x3C 16th AND last report has prior month → SKIP.
   - HOOD: New data ~12th. If today \x3C 13th AND last report has prior month → SKIP.
   - FUTU: Quarterly only (Mar/Jun/Sep/Dec earnings). If not earnings month → SKIP.
   - The Block CEX: Monthly. If same month as last report → SKIP.
   - Crypto / VIX / equity volume / prediction markets: ALWAYS refresh (weekly).
3. In report header "📋 本期数据覆盖范围" table, mark each module:
   - ✅ = searched and updated this week
   - ⏭️ = carried forward (not yet due)
   - 🆕 = first time this month/quarter's data appears

Weekly-refresh data (ALWAYS search):

"US equity market volume week [DATE]"
"Cboe daily equity volume [MONTH] [YEAR]"
"OCC options volume [MONTH] [YEAR]"
"0DTE options share [MONTH] [YEAR]"
"Cboe put call ratio"
"VIX close [DATE]"
"crypto total market cap [MONTH] [YEAR]"
"bitcoin dominance stablecoin market cap [MONTH] [YEAR]"
"crypto fear greed index"
"Polymarket volume [MONTH] [YEAR]"
"Polymarket trading volume Dune Analytics"
"Kalshi trading volume [MONTH] [YEAR]"

Monthly/quarterly data (search ONLY when due):

"Interactive Brokers [MONTH] [YEAR] monthly metrics DARTs accounts"
"Charles Schwab [MONTH] [YEAR] monthly activity report client assets"
"Robinhood [MONTH] [YEAR] operating data funded customers"
"Futu Holdings [QUARTER] [YEAR] earnings paying clients AUM"
"crypto exchange market share [MONTH] [YEAR] The Block"
"CEX spot volume [MONTH] [YEAR]"
"Binance Coinbase OKX market share [MONTH] [YEAR]"
"SCHW IBKR HOOD FUTU EPS estimate revision [YEAR]"
"[TICKER] analyst price target [MONTH] [YEAR]"

Step 3: Generate the Report

Read references/report-template.md and fill in data. The report has nine mandatory sections:

Step 4: Update the Excel Database

The Excel file 券商监控数据库.xlsx has 16 sheets. See references/excel-structure.md for full schema.

  1. Check if user uploaded the file in /mnt/user-data/uploads/.
  2. Read with openpyxl (NOT pandas, to preserve formatting).
  3. Append new rows to relevant sheets (NEVER overwrite existing rows).
  4. If new sheets don't exist (upgrading from old template), CREATE them with headers.
  5. Save to /mnt/user-data/outputs/券商监控数据库.xlsx.

Step 5: Output

  • Save Excel to /mnt/user-data/outputs/券商监控数据库.xlsx
  • Save report to /mnt/user-data/outputs/美股零售券商_[YYYY]W[WW]_周度监控报告.md
  • Present BOTH files; remind user to re-upload Excel next time.

Report Structure (9 Sections) — Complete Specification

Section 0: 全市场交易生态总览

Four sub-sections, each with a data table AND a 📊 牛熊周期定位 spectrum table:

0.1 美股全市场交易量

  • Equity: daily avg volume (B shares), daily avg notional ($B), NYSE volume, NASDAQ volume, dark pool share
  • Options: daily avg contracts (M), equity/index/ETF options breakdown, 0DTE share (%), Put/Call Ratio, VIX
  • Each metric has a 4-tier spectrum: 🟢 熊底 → 🔵 常态 → 🟡 牛市活跃 → 🔴 极端亢奋, with historical reference points
  • Paragraph linking to broker relevance (SCHW DAT vs market, IBKR DARTs vs market)

0.2 加密市场快照

  • Total market cap ($T), 24h volume ($B), BTC price, BTC dominance (%), ETH price, stablecoin market cap ($B), Fear & Greed index
  • 7-metric 牛熊定位 spectrum: market cap, BTC price, BTC dominance, 24h volume, stablecoin supply, Fear & Greed, CEX monthly spot volume
  • Historical anchors: 2022.11 FTX crash (bear bottom) → 2023 recovery → 2024-25 bull → 2021.11/2024.10 ATH (extreme)
  • Paragraph linking to HOOD crypto revenue, FUTU crypto, Schwab BTC/ETH entry

0.3 交易所市占率 (CEX)

  • Per-exchange spot market share: Binance, Coinbase, OKX, Bybit, Upbit (with MoM change)
  • Aggregate: CEX spot total ($B), CEX derivatives total ($T), DEX spot total ($B), DEX/CEX ratio (%)
  • Monthly data from The Block; carry forward with ⏭️ when no new month available

0.4 预测市场

  • Per-platform weekly volume: Polymarket ($M), Kalshi ($M), HOOD Events (亿张)
  • Active markets count, open interest ($M)
  • Total volume with WoW and YoY
  • Trend judgment paragraph

Section 1: 🚩 核心发现与预警信号

  • 3–5 positive signals (⬆️) with specific numbers and sources
  • 3–5 warning flags (⚠️🚩) with specific numbers and sources
  • Must cover ALL five pillars (equity volume, brokers, crypto, CEX, prediction markets)
  • Each signal cites a data source with date
  • Flag any metric with >10% MoM decline, any EPS downgrade, margin at historical highs

Section 2: 跨公司横向对比

Four comparison tables across all 4 brokers, latest 3 months:

  1. 客户数/账户数 — SCHW active accounts, HOOD funded customers, IBKR accounts, FUTU funded accounts
  2. 客户资产/AUM — SCHW total client assets ($T), IBKR client equity ($B), HOOD platform assets ($B), FUTU client assets ($B)
  3. 交易活跃度 — SCHW DAT, IBKR DARTs, HOOD equity trading volume, FUTU quarterly trading volume
  4. 净资金流入 — SCHW Core NNA, HOOD Net Deposits, IBKR credit balance changes, FUTU quarterly NNA

Each row must be marked with ✅/⏭️/🆕 to show data freshness.

Section 2.5: 📐 衍生指标(二次计算)

Six sub-tables, each with formula, raw inputs, and source footnote:

  1. AUC/Account ($K) — Total Client Assets ÷ Total Accounts
  2. 年化ARPU ($) — (Latest Quarter Revenue × 4) ÷ End-of-Period Accounts
  3. 杠杆率 (%) — Margin Loans ÷ Client Assets (IBKR ~11%, track vs 2022 bear 11.8%)
  4. 收入结构 (%) — Commission / NII / Other as % of Total Revenue (from latest 8-K)
  5. 平台特色指标 — Gold Adoption (HOOD), DARTs/Account (IBKR), Cash/Assets (SCHW), Gross Margin (FUTU), crypto/total revenue (HOOD), event contracts/total (HOOD)
  6. 加密/预测市场衍生指标 — HOOD crypto % of global CEX, Coinbase share trend, prediction market penetration, HOOD Events market share, crypto cap / US equity cap

Section 3: 个股三个月滚动仪表盘

One table per broker with 3-month trailing data. Each row includes:

  • Metric name, 3 months of data, MoM%, YoY%, 历史牛熊定位 (where vs 2020 COVID low / 2021 Meme peak / 2022 bear / current)

IBKR metrics: DARTs (M), client accounts (万), client equity ($B), margin loans ($B), credit balances ($B), avg commission/order ($), options contracts (M), futures contracts (M)

SCHW metrics: DAT (M), total client assets ($T), Core NNA ($B), new accounts (万), margin ($B), sweep cash ($B), active accounts (万)

HOOD metrics: Funded customers (万), platform assets ($B), net deposits ($B), equity trading vol ($B), options contracts, crypto trading vol ($B), event contracts (亿张), margin ($B)

FUTU metrics: Paying accounts (万), client assets ($B), trading volume ($B), revenue ($M), net income ($M), WM AUM ($B), net new accounts (万)

Section 4: 关键趋势分析(牛熊周期视角)

Six sub-sections:

  1. 交易活跃度 — Current SCHW DAT / IBKR DARTs vs historical cycle table (2018→present). Is growth accelerating or decelerating? Structural vs cyclical?
  2. 客户资产 — SCHW asset milestones table ($4T 2019 → $12T+ 2026). Market-driven vs organic?
  3. 保证金贷款 — Margin/equity ratio analysis. IBKR杠杆率 vs 2022 bear (11.8%). SCHW $120B+ record.
  4. FUTU的特殊位置 — Growth vs valuation disconnect. China risk premium.
  5. 加密交易生态演变 — Crypto cycle positioning table (2022 FTX bear → 2024 ETF ATH → current). CEX volume trends, DEX share growth, Coinbase vs Binance dynamics, Schwab entry impact.
  6. 预测市场发展轨迹 — Volume evolution table (pre-2024 niche → 2024 election peak → post-election). Polymarket/Kalshi/HOOD dynamics. CFTC regulatory posture.

Section 5: 估值深度分析

Four sub-sections:

  1. 四家券商估值横向对比 — Stock price, market cap, TTM PE, Forward PE, TTM EPS, FY+1 EPS estimate, FY+1 EPS growth %, analyst consensus rating. All with source citations.
  2. 历史牛熊市PE区间对比 — Bear bottom PE, cycle average PE, bull peak PE, current PE, positioning for each broker. SCHW (13x bear → 31.5x bull), IBKR (~15x bear → ~40x bull), HOOD (N/A → 134x peak), FUTU (~8x bear → 45x+ bull).
  3. 估值判断摘要 — One paragraph per broker with specific judgment.
  4. Forward EPS 下修追踪 — Table: 公司, 此前共识, 最新共识, 变化幅度, 时间, 方向, 驱动因素, 来源. Flag any broker with >5% EPS downgrade.

Section 6: 宏观环境与利率敏感性

Table of 8 macro variables with current state and impact:

  1. Fed Funds Rate — status, impact on NII (SCHW/IBKR)
  2. VIX波动率 — level with 牛熊定位, impact on trading volumes
  3. S&P 500走势 — level, weekly range, impact on client assets
  4. 关税/贸易战 — status, impact on sentiment
  5. Forward EPS增速 — market-wide, direction impact
  6. 加密市场(BTC/总市值) — BTC price + cap, impact on HOOD/FUTU crypto revenue
  7. 预测市场监管(CFTC) — regulatory posture, impact on HOOD Events growth ceiling
  8. CEX监管(SEC) — enforcement actions, impact on Coinbase/Binance compliance costs, HOOD crypto boundary

Section 7: 下周/下月关注重点

Table of upcoming events: earnings dates (SCHW, IBKR, HOOD, FUTU), data releases (monthly operating data), FOMC meetings, policy events, crypto events (ETF decisions, protocol upgrades), prediction market milestones.

Section 8: 风险预警检查

10-item dashboard with status indicators (❌ not triggered / ⚠️ warning / 🔴 triggered):

  1. 交易量持续萎缩
  2. 客户资产大规模外流
  3. 保证金贷款过高
  4. Cash Sorting恶化
  5. 监管冲击
  6. 利率急变
  7. 中概风险(FUTU)
  8. 加密市场崩盘(>30%回撤)
  9. 预测市场监管收紧
  10. CEX流动性危机

Data Source Citation Rules

  • Every number must have a footnote with source name and date
  • Acceptable sources:
    • Equity/Options: Cboe Global Markets, OCC, NYSE, NASDAQ, FINRA ATS
    • Brokers: SEC filings, Business Wire, GlobeNewsWire, PR Newswire, company IR
    • Estimates: Seeking Alpha, Zacks, TipRanks, WallStreetZen, StockAnalysis
    • Crypto: CoinGecko, CoinMarketCap, DefiLlama
    • Exchange share: The Block Data Dashboard, CCData, Kaiko, CoinDesk
    • Prediction markets: Dune Analytics (@polymarket, @rchen8), Polymarket blog, Kalshi blog
    • Macro: FRED, CME FedWatch
  • Footnotes numbered sequentially, listed at end of report

Key Reference Files

  • references/report-template.md — Full report template with placeholder markers (9 sections)
  • references/metrics-by-broker.md — Complete list of metrics tracked per broker
  • references/excel-structure.md — Data schema for the 16-sheet Excel database
  • references/dashboard-schema.md — Data schema for the React dashboard
安全使用建议
This skill is internally consistent with its reporting purpose, but consider these points before installing: - The agent will read an Excel file from /mnt/user-data/uploads/ if present and will create/save updated workbooks (e.g., '券商监控数据库_updated.xlsx'). Do not upload sensitive files to that directory unless you trust the skill. - The instructions expect Python + openpyxl; your runtime may not have that library installed. If it isn't available, report generation or Excel updates may fail. - The SKILL.md also mentions browser-facing dashboard actions (window.storage, regenerating JSX). Those steps require access to your dashboard code or browser environment and are not automatically performed by a normal agent — treat those as manual developer tasks rather than autonomous actions the skill will take. - The skill performs web searches against public data sources (Cboe, OCC, CoinGecko, Dune, The Block). It does not request API keys, but some dashboards (Dune/theblock premium endpoints) may require credentials; the skill does not provide a mechanism for handling authorized API access. - If you proceed, verify output filenames and review any files the agent writes. If you prefer limiting file exposure, avoid placing unrelated documents in the upload directory and restrict directory permissions. If you want, I can list the exact places in SKILL.md where the agent will read/write files or require openpyxl, or suggest a minimal checklist to safely run this skill in your environment.
功能分析
Type: OpenClaw Skill Name: broker-monitor Version: 1.0.0 The skill automates the generation of financial reports by performing extensive web searches for market data and updating a local Excel database using Python (openpyxl). It also involves generating JSX dashboard components that utilize browser storage (window.storage). While these capabilities (network access, file manipulation, and code generation) are directly aligned with the stated purpose of monitoring brokerage and crypto metrics, they constitute risky behaviors that require a 'suspicious' classification according to the provided guidelines. No evidence of malicious intent, data exfiltration, or harmful prompt injection was found in SKILL.md, excel-structure.md, or dashboard-schema.md.
能力标签
crypto
能力评估
Purpose & Capability
Name/description match the instructions: the SKILL.md describes gathering market and broker metrics, updating a multi-sheet Excel database, and generating a weekly report. All declared resources (report template, excel schema, metrics lists) are consistent with that purpose; no unrelated credentials, binaries, or install steps are requested.
Instruction Scope
Instructions explicitly direct the agent to search public market data sources and to read/write an Excel file (path: /mnt/user-data/uploads/ and local workbook names). This is coherent for a reporting skill, but you should note the agent is instructed to access user-uploaded files and to create/save updated workbooks (e.g., '券商监控数据库_updated.xlsx'). The SKILL.md also references updating a React dashboard (window.storage API, regenerating JSX constants) which is out-of-band for a pure agent and represents scope creep: it assumes ability to modify web app code or browser storage that may not be available in the runtime.
Install Mechanism
No install spec is provided (instruction-only), which minimizes risk. The runtime examples use openpyxl and Python; that is reasonable for Excel manipulation but the skill does not declare or install openpyxl—if the agent environment lacks it the code paths will fail. No remote downloads, obscure URLs, or package installs are present.
Credentials
The skill requests no environment variables, credentials, or config paths. All external access is to public web sources (Cboe, OCC, CoinGecko, The Block, Dune, etc.). There are no requests for unrelated secrets or elevated access tokens.
Persistence & Privilege
The skill writes persistent artifacts (Excel files) and expects to read user uploads from a specific path. always:false (normal). This persistent read/write behavior is expected for a database-backed reporting workflow, but users should be aware the agent will read any file placed in the stated upload directory and will write updated workbooks to disk.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install broker-monitor
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /broker-monitor 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
broker-weekly-monitor 1.0.0 initial release - Introduces an automated weekly report for US retail brokers (IBKR, SCHW, HOOD, FUTU) and the broader trading ecosystem (equities, options, crypto, exchange market share, prediction markets). - Smart data freshness detection—skips unnecessary re-searches for monthly/quarterly sources and always refreshes weekly-moving metrics. - Provides detailed data coverage table marking real-time, updated, or carried-forward metrics (✅, ⏭️, 🆕) for full transparency. - Implements structured 9-section Markdown reporting and preserves/updates a 16-sheet Excel database. - Triggers on a wide range of keywords, metrics, and both English/Chinese user intent related to market/broker monitoring.
元数据
Slug broker-monitor
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

broker-monitor 是什么?

Generate a weekly monitoring report for US retail brokerage stocks (IBKR, SCHW, HOOD, FUTU) AND the broader trading ecosystem including US equity/options mar... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 89 次。

如何安装 broker-monitor?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install broker-monitor」即可一键安装,无需额外配置。

broker-monitor 是免费的吗?

是的,broker-monitor 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

broker-monitor 支持哪些平台?

broker-monitor 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 broker-monitor?

由 sarahwang94712(@sarahwang94712)开发并维护,当前版本 v1.0.0。

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