Stockbuddy

StockBuddy

Overview

StockBuddy is a stock analysis and portfolio execution support skill for A-share, Hong Kong, and US equities. It outputs quantified scores and clear action labels (Strong Buy / Buy / Hold / Sell / Strong Sell). By default, responses are decision-first: give the concise conclusion, score/confidence, event-adjusted second-pass suggestion, and practical order ideas before expanding into a long-form report.

Core rule: separate durable facts from derived values.

• Persist durable facts: share count, cost basis, account, available cash, market/currency, lot size, odd-lot support, and other user-confirmed trading constraints
• Compute in real time: latest price, market value, position weight, unrealized P&L, executable buy/sell size, and whether partial selling is actually possible
• Do not write latest price, position weight, or unrealized P&L back into durable storage

Five core scenarios:

1. Single-stock analysis — analyze one stock and produce an action recommendation
2. Batch portfolio analysis — analyze current positions and summarize stock-level and portfolio-level status
3. Position management — add, update, remove, and inspect positions
4. Account and allocation management — track account, cash, market/currency, and execution constraints
5. Watchlist management — add, remove, and inspect watched stocks while storing basic stock metadata and trading rules

Execution-Aware Advice Rules

Before giving execution-ready trading advice, confirm whether the durable constraints are sufficient.

Required durable facts for execution-aware advice:

• account context
• market / currency
• available cash for the account
• lot size
• odd-lot support when relevant

If these are incomplete:

• still give a directional view when possible
• label it as directional only or non-execution-ready
• ask only for the missing durable facts
• do not invent quantity, allocation, or partial-sell actions that may be impossible in the real market setup

Special rule for buy advice:

• If available cash is unknown, do not provide quantity, allocation, or order-size advice.
• Ask for the account plus available cash first.

Special rule for sell/trim advice:

• If lot size or odd-lot support is unknown, avoid suggesting partial sells as if they are definitely executable.
• If the user holds only one lot and odd-lot selling is not supported, suggest only executable actions such as hold or sell the full lot.

Environment Setup

Only install dependencies when they are actually missing, or when a script fails with a missing-package error:

bash {{SKILL_DIR}}/scripts/install_deps.sh

Required dependencies: numpy, pandas, built-in Python sqlite3. No yfinance dependency is required; the current implementation mainly uses Tencent Finance data.

Core Workflow

Scenario 1: Analyze a Single Stock

Trigger examples: "analyze Tencent", "can I buy this stock", "look at BYD", "analyze this ticker"

Steps:

1. Normalize the stock code

   • Hong Kong stocks: normalize to XXXX.HK
   • A-shares: normalize to SH600519 / SZ000001
   • US stocks: normalize to AAPL / TSLA
   • If the user provides only a company name, infer the market from context first; ask for confirmation only if the mapping is ambiguous

2. Run the analysis script

   python3 {{SKILL_DIR}}/scripts/analyze_stock.py <CODE> --period 6mo
   

   Optional period values: 1mo / 3mo / 6mo (default) / 1y / 2y / 5y

   Data and caching behavior:

   • Raw daily K-line data, watchlist data, and portfolio data are stored in ~/.stockbuddy/stockbuddy.db (SQLite)
   • Positions are linked through watchlist_id
   • Analysis results are cached separately in SQLite with a default TTL of 10 minutes
   • Cache cleanup runs automatically and total cached analysis rows are capped
   • If the user explicitly asks to "refresh data" or "reanalyze", add --no-cache
   • To clear analysis cache: --clear-cache

3. Interpret and present the result

   • The script returns JSON analysis data
   • For default single-stock requests, use the default query template in references/output_templates.md
   • The default response must include: stock basics, data-driven action recommendation (with score and confidence), important events, event-adjusted second-pass suggestion, and practical order ideas
   • Default order style = balanced. Only switch when the user explicitly asks for a conservative or aggressive version
   • Only produce the full report when explicitly requested with phrases like "full report", "detailed analysis", or "complete analysis"
   • The top natural-language summary is mandatory in both short and long versions: 2-4 sentences covering regime, main recommendation, confidence, support/risk points, and whether the stock is actionable today
   • Only expand into a more open-ended explanation when the user asks for detail such as "explain why", "show the reasoning", "how about short-term", or "what stop-loss/stop-profit should I use"
   • Final output must be normal Markdown, not wrapped in code fences; prefer short paragraphs, bullet points, and card-style formatting over wide tables unless the user explicitly wants a detailed report

Scenario 2: Batch Portfolio Analysis

Trigger examples: "analyze my portfolio", "look at my holdings", "how are my positions doing"

Default output should still be decision-first: for each position, give the action label, score/confidence, important events, event-adjusted second suggestion, and a compact practical order version. Do not expand every holding into a full long report unless the user explicitly wants a detailed version.

Steps:

1. Check portfolio data

   python3 {{SKILL_DIR}}/scripts/portfolio_manager.py list
   

   Portfolio data is stored in the positions table in ~/.stockbuddy/stockbuddy.db.

2. If the portfolio is empty → guide the user to add positions first (see Scenario 3)

3. Run batch analysis

   python3 {{SKILL_DIR}}/scripts/portfolio_manager.py analyze
   

4. Interpret and present the result

   • Format the result using the "Portfolio Batch Analysis Report" section in references/output_templates.md
   • Output normal Markdown, not code fences
   • It may use standard Markdown tables mixed with lists when helpful, but keep it readable on chat surfaces
   • Include stock-level recommendations and portfolio-level P&L summary
   • Prefer real-time computed fields in the output: latest price, market value, unrealized P&L, position weight, and executable action constraints such as whole-lot vs odd-lot behavior
   • Do not write latest price, position weight, or unrealized P&L back into durable storage; the database should only hold stable user-confirmed facts and trading rules

Scenario 3: Position Management

Trigger examples: "add a Tencent position", "I bought 100 shares of BYD", "remove Alibaba from my holdings"

When adding a position, ensure the stock exists in the watchlist and is linked through positions.watchlist_id -> watchlist.id. If the user does not provide a date, default to the current date. If the user provides natural-language trade info such as "I bought 100 shares of Tencent last week at 350", extract price, share count, date, and account info where possible, then execute the appropriate command.

After the first successful position record, proactively guide the user to fill the missing durable facts needed for execution-aware advice. Do not stop at only code / shares / cost if important constraints are still unknown.

Ask for or help the user confirm these fields, in this priority order:

1. Account context — which account this belongs to, and its market / currency
2. Cash — total cash or available cash in that account
3. Lot rule — lot size for the stock
4. Odd-lot support — whether the broker supports odd-lot selling / buying
5. Trade date — if omitted and relevant for later review
6. Notes — optional thesis, time horizon, or special constraints

If some fields are already known, only ask for the missing ones. Keep the follow-up compact: confirm what was captured, state what is still missing, and ask for the missing durable facts in one short message.

If the user gives only partial follow-up info, update what is available and continue asking only for the remaining missing fields. Once enough execution constraints are known, stop prompting and proceed normally.

Scenario 4: Account and Allocation Management

Trigger examples: "my HK account has 3000 HKD cash", "track available cash", "record this under my US account", "how concentrated is my portfolio"

Rules:

• Keep cash, account, market, and currency as durable facts
• Keep position weights, market value, and unrealized P&L as computed fields
• If the user has multiple markets or currencies, treat them as separate account contexts unless the user explicitly wants cross-account aggregation
• Use account information to improve practical trading advice: whether the user can afford a new lot, whether a rebalance is even possible, and whether the trade would increase concentration too much

Scenario 5: Watchlist Management

Trigger examples: "watch Tencent", "add Apple to my watchlist", "remove Moutai from watchlist"

Analysis Methodology

The scoring system combines technicals (roughly 60% weight) and basic valuation (roughly 40% weight). Final score range is approximately -10 to +10:

Only read references/technical_indicators.md when the user asks for detailed scoring logic, indicator interpretation, or when you need help calibrating a more detailed explanation.

When deciding the final output format, choosing between default query vs full report, or generating practical order suggestions, prefer references/output_templates.md. It defines the default query template, atomic templates, full-report composition rules, and the conservative / balanced / aggressive order-price generation rules (balanced is the default).

Important Notes

• All analysis is for reference only and is not investment advice
• The primary data source is Tencent Finance, which may have delays, gaps, or field limitations
• Hong Kong stocks do not have the same daily price-limit structure as A-shares and therefore carry higher intraday volatility risk
• Every final analysis output must include a risk disclaimer
• Technical analysis can fail during extreme market conditions
• Encourage the user to combine macro conditions, sector trends, and company fundamentals in final decision-making
• Only store user-confirmed durable facts in the database; latest price, market value, unrealized P&L, and position weight should be fetched or calculated at analysis time

Resource Files