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Stock Copilot Pro

Global Multi-Source Stock Analysis with QVeris.

SEO Keywords

OpenClaw, stock analysis skill, AI stock copilot, China A-shares, Hong Kong stocks, US stocks, quantitative analysis, fundamental analysis, technical analysis, sentiment analysis, industry radar, morning evening brief, watchlist, portfolio monitoring, QVeris API, THS iFinD, Caidazi, Alpha Vantage, Finnhub, X sentiment, investment research assistant

Supported Capabilities

• Single-stock analysis (analyze): valuation, quality, technicals, sentiment, risk/timing
• Multi-stock comparison (compare): cross-symbol ranking and portfolio-level view
• Watchlist/holdings management (watch): list/add/remove for holdings and watchlist
• Morning/Evening brief (brief): holdings-focused daily actionable briefing
• Industry hot-topic radar (radar): multi-source topic aggregation for investable themes
• Multi-format output: markdown, json, chat
• OpenClaw LLM-ready flow: structured data in code + guided narrative in SKILL.md

Data Sources

• Core MCP/API gateway: qveris.ai (QVERIS_API_KEY)
• CN/HK quote and fundamentals:
  • ths_ifind.real_time_quotation
  • ths_ifind.financial_statements
  • ths_ifind.company_basics
  • ths_ifind.history_quotation
• CN/HK news and research:
  • caidazi.news.query
  • caidazi.report.query
  • caidazi.search.hybrid.list

What This Skill Does

Stock Copilot Pro performs end-to-end stock analysis with five data domains:

1. Market quote / trading context
2. Fundamental metrics
3. Technical signals (RSI/MACD/MA)
4. News and sentiment
5. X sentiment

It then generates a data-rich analyst report with:

• value-investing scorecard
• event-timing anti-chasing classification
• safety-margin estimate
• thesis-driven investment framework (drivers/risks/scenarios/KPIs)
• multi-style playbooks (value/balanced/growth/trading)
• event radar with candidate ideas from news and X
• scenario-based recommendations
• standard readable output (default) + optional full evidence trace (--evidence)

Key Advantages

• Deterministic tool routing via references/tool-chains.json
• Evolution v2 parameter-template memory to reduce recurring parameter errors
• Strong fallback strategy across providers and markets
• US/HK/CN market-aware symbol handling
• Structured outputs for both analyst reading and machine ingestion
• Safety-first handling of secrets and runtime state

Core Workflow

1. Resolve user input to symbol + market (supports company-name aliases, e.g. Chinese name -> 600089.SH).

2. Search tools by capability (quote, fundamentals, indicators, sentiment, X sentiment).

3. Route by hardcoded tool chains first (market-aware), then fallback generic capability search.

   • For CN/HK sentiment, prioritize caidazi channels (report/news/wechat).
   • For CN/HK fundamentals, prioritize THS financial statements (income/balance sheet/cash flow), then fallback to company basics.

4. Before execution, try evolution parameter templates; if unavailable, use default param builder.

5. Run quality checks:

   • Missing key fields
   • Data recency
   • Cross-source inconsistency

6. Produce analyst report with:

   • composite score
   • safety margin
   • event-driven vs pullback-risk timing classification
   • structured thesis (driver/risk/scenario/KPI)
   • event radar (timeline/theme) and candidate ideas
   • style-specific execution playbooks
   • market scenario suggestions
   • optional parsed/raw evidence sections when --evidence is enabled

Command Surface

Primary script: scripts/stock_copilot_pro.mjs

• Analyze one symbol:
  • node scripts/stock_copilot_pro.mjs analyze --symbol AAPL --market US --mode comprehensive
  • node scripts/stock_copilot_pro.mjs analyze --symbol "<company-name>" --mode comprehensive
• Compare multiple symbols:
  • node scripts/stock_copilot_pro.mjs compare --symbols AAPL,MSFT --market US --mode comprehensive
• Manage watchlist:
  • node scripts/stock_copilot_pro.mjs watch --action list
  • node scripts/stock_copilot_pro.mjs watch --action add --bucket holdings --symbol AAPL --market US
  • node scripts/stock_copilot_pro.mjs watch --action remove --bucket watchlist --symbol 0700.HK --market HK

OpenClaw scheduled tasks (morning/evening brief and radar)

To set up morning brief, evening brief, or daily radar in OpenClaw, use only the official OpenClaw cron format and create jobs via the CLI or Gateway cron tool. Do not edit ~/.openclaw/cron/jobs.json directly.

• Reference: the jobs array in config/openclaw-cron.example.json; each item is one cron.add payload (fields: name, schedule: { kind, expr, tz }, sessionTarget: "isolated", payload: { kind: "agentTurn", message: "..." }, delivery).
• Example (morning brief): openclaw cron add --name "Stock morning brief" --cron "0 9 * * 1-5" --tz Asia/Shanghai --session isolated --message "Use stock-copilot-pro to generate morning brief: run brief --type morning --max-items 8 --format chat" --announce. To deliver to Feishu, add .

CN/HK Coverage Details

• Company-name input is supported and auto-resolved to market + symbol for common names.
• Sentiment path prioritizes caidazi (research reports, news, wechat/public-account channels).
• Fundamentals path prioritizes THS financial statements endpoints, and always calls THS company basics for profile backfill:
  • revenue
  • netProfit
  • totalAssets
  • totalLiabilities
  • operatingCashflow
  • industry
  • mainBusiness
  • tags

Output Modes

• markdown (default): human-readable report
• json: machine-readable merged payload
• chat: segmented chat-friendly output for messaging apps
• summary-first: compact output style via --summary-only

Preference & Event Options

• Preference flags:

  • --horizon short|mid|long
  • --risk low|mid|high
  • --style value|balanced|growth|trading
  • --actionable (include execution-oriented rules)
  • --skip-questionnaire (force analysis without preference Q&A)

• Event radar flags:

  • --event-window-days 7|14|30
  • --event-universe global|same_market
  • --event-view timeline|theme

Dynamic Evolution

• Runtime learning state is stored in .evolution/tool-evolution.json.
• One successful execution can update tool parameter templates.
• Evolution stores param_templates and sample_successful_params for reuse.
• Evolution does not decide tool priority; tool priority is controlled by tool-chains.json.
• Use --no-evolution to disable loading/saving runtime learning state.

Safety and Disclosure

• Uses only QVERIS_API_KEY.
• Calls only QVeris APIs over HTTPS.
• full_content_file_url fetching is kept enabled for data completeness, but only HTTPS URLs under qveris.ai are allowed.
• Does not store API keys in logs, reports, or evolution state.
• Runtime persistence is limited to .evolution/tool-evolution.json (metadata + parameter templates only).
• Watchlist state is stored at config/watchlist.json (bootstrap from config/watchlist.example.json).
• OpenClaw scheduled tasks: see config/openclaw-cron.example.json. Create jobs with the official format (schedule.kind, payload.kind, , etc.) via or the Gateway cron tool; do not paste or merge the example JSON into (schema mismatch can cause Gateway parse failure or crash). Set and for your channel (e.g. feishu).

Single Stock Analysis Guide

When analyzing analyze output, act as a senior buy-side analyst and deliver a professional but not overlong report.

Required Output (7 Sections)

1. Data Snapshot (required)
   • Start with a compact metrics table built from data fields.
   • Include at least: price/change, marketCap, PE/PB, profitMargin, revenue, netProfit, RSI, 52W range.
   • Example format:

| Metric | Value |

|--------|-------|
| Price | $264.58 (+1.54%) |
| Market Cap | $3.89T |
| P/E | 33.45 |
| P/B | 57.97 |
| Profit Margin | 27% |
| Revenue (TTM) | $394B |
| Net Profit | $99.8B |
| RSI | 58.3 |
| 52W Range | $164 - $270 |

1. Key view (30 seconds)

   • One-line conclusion: buy/hold/avoid + key reason.

2. Investment thesis

   • Bull case: 2 points (growth driver, moat/catalyst)
   • Bear case: 2 points (valuation/risk/timing)
   • Final balance: what dominates now.

3. Valuation and key levels

   • PE/PB vs peer or history percentile (cheap/fair/expensive)
   • Key levels: current price, support, resistance, stop-loss reference

4. Recommendation (required)

   • Different advice by position status:
     • No position
     • Light position
     • Heavy position / underwater
   • Each suggestion must include concrete trigger/price/condition.

5. Risk monitor

   • Top 2-3 risks + invalidation condition (what proves thesis wrong).

6. Data Sources (required)

   • End with a source disclosure line showing QVeris attribution and data channels actually used.
   • Include generation timestamp and list of source/tool names from payload metadata such as dataSources, , or .

> Data powered by [QVeris](https://qveris.ai) | Sources: Alpha Vantage (quote/fundamentals), Finnhub (news sentiment), X/Twitter (social sentiment) | Generated at 2026-02-22T13:00:00Z

Quality Bar

• Avoid data dumping; each key number must include interpretation.
• Every numeric claim must be grounded in actual payload values; do not fabricate numbers.
• Keep concise but complete (target 250-500 characters for narrative).
• Must include actionable guidance and time window.
• Ticker and technical terms in English.

Daily Brief Analysis Guide

When analyzing brief output, generate an actionable morning/evening briefing for OpenClaw conversation.

Morning Brief

1. Market overview : risk-on/off + key overnight move + today's tone, plus an index snapshot table from marketOverview.indices (index name, price, % change, timestamp)
2. Holdings check : holdings that need action first, with per-holding price/% change/grade when available
3. Radar relevance : which radar themes impact holdings
4. Today's plan (required) : specific watch levels / event / execution plan
5. Data Sources (required) : one-line QVeris attribution and channels used in this brief

Evening Brief

1. Session recap : index + sector + portfolio one-line recap, with key index close/% change
2. Holdings change : biggest winners/losers and why, with quantized move (%) where available
3. Thesis check : whether thesis changed
4. Tomorrow's plan (required) : explicit conditions and actions
5. Data Sources (required) : one-line QVeris attribution and channels used in this brief

Quality Bar

• Prioritize user holdings, not generic market commentary.
• Quantify changes when possible (%, levels, counts).
• Keep concise and decision-oriented.
• Include a short source disclosure line at the end to improve traceability and credibility.

Hot Topic Analysis Guide

When analyzing radar output, cluster signals into investable themes and provide concise actionable conclusions.

Required Output (per theme)

• Theme : clear, investable label
• Driver : what changed and why now
• Impact : beneficiaries/losers + magnitude + duration
• Recommendation (required) : concrete trigger or level
• Risk note : key invalidation or monitoring signal
• Source tag (required) : include source label for each theme (for example: caidazi_report, alpha_news_sentiment, x_hot_topics)

Execution Rules

• Cluster into 3-5 themes max.
• Cross-verify sources; lower confidence for social-only signals.
• Distinguish short-term trade vs mid-term allocation.
• Keep each theme concise (<200 characters preferred).
• End with a QVeris source disclosure line listing channels that contributed to this radar run.

Weekly Installs

120

Repository

qverisai/open-q…s-skills

GitHub Stars

10

First Seen

Feb 27, 2026

Security Audits

Gen Agent Trust HubPassSocketPassSnykWarn

Installed on

gemini-cli120

github-copilot120

amp120

cline120

codex120

kimi-cli120