portfolio-truth-import

name	portfolio-truth-import
title	Portfolio Truth — Import and Normalization
description	Robust workflow to ingest messy portfolio inputs (markdown/tables with line-break glitches) into portfolio_truth.json and portfolio_rules.json, with snapshots, symbol_map and needs_isin scaffolding.
version	1
created	"2026-04-14T00:00:00.000Z"
owner	Hermes
summary	Initialize and normalize portfolio data from semi-structured sources; handle HK broken rows, post-fill tickers, controlled CODE classes, rules writing, and snapshotting.

When to use

• You receive a portfolio in semi-structured markdown with mixed asset classes and currencies.
• HK tables are misformatted (tickers on their own lines, repeated), and some funds show Yahoo tickers on subsequent lines.
• You need to initialize or refresh ~/.hermes/portfolio/ files with snapshots and mapping scaffolds.

Prerequisites

• Paths: ~/.hermes/portfolio/portfolio_truth.json, portfolio_rules.json, snapshots/.
• Respect user conventions:
  • Cash/private/static classes must use controlled ISIN codes (CODE: prefix).
  • FX overrides are authoritative and dated (as_of).
  • 0700.HK two lots kept separate.
  • MMF USD: first $120k to safety obligation; remainder investable cash.
  • StructNote bucketed as US_EQUITY with static valuation.
  • JP Margin Loan is a liability deducted from net.
• Finnhub API key for ISIN mapping (optional initial step; mapping is "pending" if missing).

Workflow (numbered)

1. Sectioning
   • Split input by section headers beginning with "## ". Identify at least: FX, US Equities, HK Equities, Japan Equities, Japan Funds, Fixed Income, Singapore Funds, Cash & Private.
2. Utilities
   • Implement clean_number to parse comma-separated numerics.
3. FX overrides
   • Extract pairs like "- HKD/USD = 0.1285" into {"HKDUSD": 0.1285, ...}, and record as_of date from input.
4. US Equities (well-formed table)
   • Parse markdown table rows (skip header/divider). Capture: name, yahoo_ticker (or fallback to ticker), qty, cost_per_unit, cost_ccy=USD.
5. HK Equities (misformatted)
   • Scan lines for pattern NNNN.HK. The next line with a pipe-delimited row holds Name | Qty | Cost.
   • Create items with yahoo_ticker=ticker, cost_ccy=HKD. Keep duplicate tickers as separate lots (e.g., 0700.HK A/B remain two items).
6. Japan Equities (table)
   • Parse like US; cost_ccy=JPY.
7. JP Margin Loan
   • Extract e.g., "JP Margin Loan: ¥22,387,929" as liabilities entry.
8. Japan Funds
   • Parse table including ISIN; cost_ccy=JPY.
9. Fixed Income (broken ticker lines)
   • Parse table rows; if Yahoo Ticker cell is empty or "N/A (OTC)", scan the following non-table lines until the next row for a token like 0P000... or TICKER.SUFFIX and fill it.
   • Keep ISIN when present.
10. Singapore Funds (broken ticker lines)

• Parse table; collect ticker-like tokens in the block and post-fill missing Yahoo tickers in order.

11. Cash & Private (controlled codes)

• Map names to codes with tags:
  • Cash USD → CODE:CASH-USD (tags: [cash])
  • StructNote → CODE:STRUCTNOTE (tags: []; static valuation true; classification override US_EQUITY)
  • MMF USD → CODE:MMF-USD (tags: [mmf])
  • MMF2 → CODE:MMF2 (tags: [mmf])
  • PrivateFund → CODE:PRIVATEFUND (tags: [private, safety])
  • PrivateBiz → CODE:PRIVATEBIZ (tags: [private, safety])
• Represent these as items with qty=value and cost_per_unit=1.

12. Compose Truth

• Concatenate all items into an array with fields: isin, name, yahoo_ticker, qty, cost_per_unit, cost_ccy, meta.

13. Write files and snapshots

• Write portfolio_truth.json and portfolio_rules.json. Then snapshot to snapshots/_portfolio_truth.json and snapshots/_portfolio_rules.json.

14. Create mapping scaffolds

• From items with isin==null (exclude CODE:), build symbol_map.json keyed by yahoo_ticker with {isin:null, source:"pending", name, cost_ccy}.
• Persist needs_isin.json listing items needing mapping (counts should be ~items with null ISIN; duplicate tickers may reduce symbol_map key count).

15. ISIN mapping (when Finnhub key is available)

• For each pending symbol, query Finnhub by symbol/venue or by name+exchange; prefer exact ISIN. Record in symbol_map.json and backfill portfolio_truth.json if appropriate (after confirmation).

Verification

• Category counts match input: e.g., US 6, HK 7 (incl. 0700.HK A/B), JP equities 4, JP funds 1, Fixed Income 4, SG funds 2, Cash/Private 6; total ~30.
• needs_isin count reflects null-ISIN items (e.g., 17), while symbol_map may have fewer keys due to shared ticker across lots (e.g., 16 keys with 0700.HK shared).
• Snapshots exist with the current timestamp, and files are parseable JSON (no line-number prefixes).

Pitfalls and fixes

• read_file returns line-number-prefixed output (e.g., " 12| {") — strip the prefix before json.loads. Prefer direct Python file IO when inside execute_code to avoid prefixes.
• HK section often has repeated ticker lines and empty table scaffolding; anchor on ticker regex NNNN.HK and then find the next pipe-row.
• Fixed Income and Singapore funds often spill Yahoo tickers onto the next line — implement a lookahead to capture tokens like 0P000... or TICKER.SUFFIX.
• Ensure 0700.HK two lots are kept separate and both point to the same yahoo_ticker with distinct qty/cost.
• Never invent ISINs; if Finnhub lookup fails, leave as pending and report transparently.
• MMF safety allocation rule (first $120k) is applied at report time, not baked into Truth entries.

Example tool snippets

• Strip line-number prefixes: for line in raw.splitlines(): lines.append(line.split('|',1)[1] if '|' in line else line) text='\n'.join(lines) data=json.loads(text)

• Snapshot naming: use date +%Y%m%d-%H%M%S.

Outcome

• Clean portfolio_truth.json and portfolio_rules.json with snapshots.
• symbol_map.json and needs_isin.json ready for ISIN mapping and pricing workflows.