Watchlist import

Watchlist Import

Import a watchlist for: $ARGUMENTS

Overview

• Implementation status: code-backed
• Local entry script: <bundle-root>/watchlist-import/run.py
• Primary purpose: normalize raw universe input into resolved and unresolved items without silently guessing ambiguous names
• Research layer: universe formation (Stage 1: Strategy Design & Hypothesis Formation - Universe definition)
• Workflow stage: stage 1 Strategy Design & Universe Formation
• Local executor guarantee: parse raw text or supported files, normalize symbol-like items, preserve unresolved entries, and return a structured import summary

Use When

• The user starts with pasted symbols, symbol-like text, or a watchlist file.
• The caller needs symbol normalization before screening, dashboards, or repeat coverage.
• The user wants unresolved names separated from recognized symbols instead of guessed mapping.
• The user wants to build a universe from external sources (spreadsheets, text files, web scraping results).
• The user wants to validate and clean a symbol list before analysis.

Do Not Use When

• The user already has a clean symbol list and wants ranking. Use market-screen or decision-dashboard.
• The user wants post-import grouping, review cadence, or repeated tracking. Use watchlist.
• The user wants one-symbol analysis rather than universe construction.
• The user wants portfolio construction with position sizing. Use decision-support or strategy-design.

Inputs

• Direct positional items interpreted as raw symbol-like text.
• Optional --text TEXT: explicit text payload as the watchlist source.
• Optional --file PATH: load from a local text or .xlsx file.
• Current implementation scope: text and .xlsx parsing only.
• Universe-construction note:
  • import does not validate liquidity, sector fit, tradeability, or strategy fit
  • it only resolves symbol identity as far as the local parser can do so safely

Execution

Step 1: Define import source and objectives

Before importing, clarify the source and purpose:

Import source types:

• Direct text input (pasted symbols, comma-separated, space-separated)
• Text file (one symbol per line, or delimited)
• Spreadsheet file (.xlsx, .csv with symbol column)
• Mixed format (symbols + company names + other text)
• Web scraping result (HTML table, JSON, etc.)

Import objectives:

• Build initial universe for screening
• Clean and validate existing watchlist
• Merge multiple watchlists
• Convert company names to symbols
• Validate symbol format and existence

Expected output:

• Clean symbol list (resolved symbols only)
• Unresolved items list (for manual review)
• Import statistics (total, resolved, unresolved, confidence)
• Ready for downstream screening or analysis

Step 2: Parse and normalize input

Apply systematic parsing and normalization:

Text parsing:

• Delimiter detection: Identify separators (comma, space, newline, tab, semicolon)
• Symbol extraction: Extract symbol-like patterns (6-digit codes, alphanumeric with dots)
• Company name detection: Identify Chinese company names (contains 公司, 股份, 集团, etc.)
• Noise removal: Remove common non-symbol text (headers, labels, punctuation)

Symbol normalization:

• A-share format: 6-digit code (e.g., 600519, 000001, 300750)
• Board prefix: Add board prefix if missing (SH for 600xxx/601xxx/603xxx/688xxx, SZ for 000xxx/001xxx/002xxx/300xxx)
• Case normalization: Uppercase for consistency
• Padding: Pad short codes to 6 digits if needed (e.g., 1 → 000001)

Company name resolution:

• Exact match: Look up company name in symbol database
• Fuzzy match: Try partial matching for common abbreviations
• Ambiguity handling: If multiple matches, mark as unresolved
• No match: Keep in unresolved list for manual review

Spreadsheet parsing:

• Column detection: Identify symbol column (by header or content pattern)
• Multi-column support: Extract symbols from multiple columns if present
• Row filtering: Skip header rows, empty rows, total rows
• Data type handling: Convert numeric codes to strings with leading zeros

Step 3: Validate and categorize symbols

Validate each resolved symbol:

Symbol format validation:

• 6-digit numeric code
• Valid board prefix (SH or SZ)
• Matches A-share symbol pattern
• No invalid characters

Symbol existence validation:

• Symbol exists in A-share market
• Symbol is not delisted
• Symbol has recent trading data
• Symbol board matches prefix

Symbol categorization:

• Main board: 600xxx, 601xxx, 603xxx (Shanghai), 000xxx, 001xxx (Shenzhen)
• ChiNext: 300xxx (Shenzhen)
• STAR Market: 688xxx (Shanghai)
• Beijing Stock Exchange: 8xxxxx, 4xxxxx
• ST/*ST: Special treatment stocks (higher risk)

Confidence scoring (0-100):

• High confidence (90-100): Valid 6-digit code, exists in market, has recent data
• Medium confidence (70-89): Valid format, exists in market, but stale data or suspended
• Low confidence (50-69): Valid format, but cannot verify existence
• Unresolved (0-49): Invalid format, ambiguous, or no match found

Step 4: Handle unresolved items

Process items that cannot be resolved:

Unresolved categories:

• Invalid format: Not a valid A-share symbol format
• Ambiguous: Multiple possible matches (e.g., company name matches multiple symbols)
• Not found: Valid format but symbol does not exist
• Delisted: Symbol was valid but is now delisted
• Foreign: Non A-share symbol (H-share, US stock, etc.)

For each unresolved item:

• Original text (as provided by user)
• Reason for non-resolution (invalid format, ambiguous, not found, etc.)
• Suggested actions (manual lookup, format correction, removal)
• Possible matches (if ambiguous, list candidates)

Unresolved handling strategies:

• Preserve for manual review: Keep in separate list for user to resolve
• Suggest corrections: Provide likely corrections based on fuzzy matching
• Flag for removal: Clearly invalid items that should be removed
• Request clarification: Ambiguous items that need user input

Step 5: Generate import summary

Organize results into structured report:

Part 1: Import Summary

• Import source (text, file, spreadsheet)
• Import timestamp
• Total items processed
• Resolved symbols (count and %)
• Unresolved items (count and %)
• Average confidence score

Part 2: Resolved Symbols For each resolved symbol:

• Symbol (with board prefix)
• Company name (if available)
• Board (Main/ChiNext/STAR/Beijing)
• Confidence score (0-100)
• Status (active/suspended/ST/*ST)
• Latest price and date (if available)

Part 3: Resolved Symbol Statistics

• Board distribution (% Main, ChiNext, STAR, Beijing)
• Status distribution (% active, suspended, ST/*ST)
• Confidence distribution (% high, medium, low)
• Sector distribution (if available)
• Market cap distribution (if available)

Part 4: Unresolved Items For each unresolved item:

• Original text
• Reason for non-resolution
• Suggested action
• Possible matches (if any)

Part 5: Unresolved Item Statistics

• Unresolved by category (invalid format, ambiguous, not found, delisted, foreign)
• Unresolved by source (which part of input had most issues)
• Resolvability assessment (% that could be resolved with manual effort)

Part 6: Import Quality Assessment

• Import quality score (0-100)
  • Based on resolution rate, confidence scores, and data completeness
• Data completeness (% with company name, price, status)
• Source quality (clean vs. noisy input)
• Screening readiness (ready/needs cleanup/major issues)

Part 7: Recommended Next Steps

• If high quality (>90% resolved, high confidence):
  • Ready for market-screen or decision-dashboard
• If medium quality (70-90% resolved):
  • Review unresolved items, then proceed to screening
• If low quality (<70% resolved):
  • Manual cleanup required before screening
  • Suggest using watchlist for ongoing management

Step 6: Run the local executor

python3 <bundle-root>/watchlist-import/run.py [--text TEXT] [--file PATH] [items...]

Step 7: Review result as universe-intake artifact

When delivering results, maintain proper framing:

Import interpretation:

• This is symbol normalization and validation, not investment recommendation
• Resolved symbols are format-valid and exist in market, not necessarily good investments
• Confidence scores reflect data quality, not investment quality
• Unresolved items require manual review, not automatic exclusion

Source transparency:

• State import source (text, file, spreadsheet, mixed)
• State source quality (clean, noisy, mixed format)
• State whether source looked like pure symbols or mixed content
• State any parsing assumptions made

Quality assessment:

• State import quality score and what it means
• State resolution rate and confidence distribution
• State whether result is screening-ready or needs cleanup
• State specific issues found (invalid formats, ambiguous names, etc.)

Ambiguity preservation:

• Do not silently coerce ambiguous names into symbols
• Keep unresolved items explicit with reasons
• Provide suggested actions for each unresolved item
• Do not guess or fabricate symbol mappings

Validation limitations:

• Import validates format and existence, not tradeability
• Import does not validate liquidity, listing status, or strategy fit
• Import does not check for duplicates across different formats (e.g., 600519 vs. SH600519)
• Import does not validate sector fit, market cap range, or other strategy criteria

Step 8: Route to appropriate next skill

Based on import quality, route to next step:

High quality import (>90% resolved):

• market-screen: Rank symbols by technical strength
• decision-dashboard: Generate batch decision summary
• watchlist: Save for ongoing monitoring

Medium quality import (70-90% resolved):

• Manual review of unresolved items first
• Then proceed to market-screen or decision-dashboard
• Consider using watchlist for ongoing cleanup

Low quality import (<70% resolved):

• Manual cleanup required
• Use watchlist for iterative refinement
• Consider re-importing with cleaner source

For individual symbol analysis:

• market-brief: Quick overview of specific symbols
• analysis: Full thesis development for selected symbols

Output Contract

• Minimum local executor output: human-readable text beginning with 导入结果.
• Core fields: total item count, resolved symbol count, unresolved count, resolved entries, unresolved entries.
• Side effects: stores a session-history entry for the import operation.
• Caller-facing delivery standard:
  • Seven-part structure: Import summary, resolved symbols, resolved statistics, unresolved items, unresolved statistics, quality assessment, recommended next steps
  • Source transparency: State source form (text, file, spreadsheet, mixed) and quality (clean, noisy)
  • Resolution accounting: Distinguish resolved symbols from unresolved items with specific reasons
  • Confidence scoring: Provide confidence scores (0-100) for each resolved symbol
  • Ambiguity preservation: Keep unresolved entries explicit, do not silently coerce or guess
  • Quality assessment: Import quality score, resolution rate, confidence distribution, screening readiness
  • Validation scope: State what was validated (format, existence) and what was NOT (liquidity, tradeability, strategy fit)
  • Next steps guidance: Recommend appropriate downstream skills based on import quality
  • No investment claims: Resolved symbols are format-valid, not investment recommendations
• Non-guarantees:
  • No liquidity, listing-status, survivorship, or strategy-fit validation is guaranteed here
  • No durable local watchlist store is created by this skill alone (use watchlist for persistence)
  • No duplicate detection across different symbol formats
  • No sector, market cap, or other strategy criteria validation

Failure Handling

• Parse and argument errors: non-zero exit with a readable 命令错误 message.
• File read or parse failures: readable failure text beginning with 执行失败:.
• Unrecognized names: stay in the unresolved list instead of being guessed.
• If the source format is unsupported, say so directly instead of partial silent import.
• Ambiguous company names: list all possible matches in unresolved items, do not pick one arbitrarily.
• Invalid symbol formats: keep in unresolved list with specific format error.
• Delisted symbols: flag as delisted in unresolved list, do not silently include.

Key Rules

• Treat import as preprocessing, not as recommendation.
• Preserve ambiguity and unresolved rows explicitly.
• Do not silently coerce names into symbols.
• Use the import result as the intake gate before ranking, dashboarding, or repeated coverage.
• Confidence scoring is mandatory. Every resolved symbol must have confidence score.
• Unresolved categorization is mandatory. Every unresolved item must have reason.
• Quality assessment is mandatory. Provide import quality score and screening readiness.
• Source transparency is mandatory. State source type and quality.
• Validation scope must be explicit. State what was validated and what was NOT.
• No silent guessing. If ambiguous, keep unresolved with suggestions.
• No fabricated mappings. Only resolve when confident.
• Routing guidance must be specific. Recommend next skill based on import quality.

Composition

• Natural upstream step for market-screen, decision-dashboard, and watchlist.
• Can also prepare symbol lists for analysis and repeated one-symbol workflows.
• Should be followed by watchlist for persistent storage and ongoing management.
• High quality imports can proceed directly to market-screen or decision-dashboard.
• Low quality imports should route to watchlist for iterative cleanup.
• Resolved symbols can feed into any single-symbol skill (market-brief, analysis, etc.).