| """Aurelius adapter — Finance (ingested mode): the financial knowledge graph. |
| |
| Not just companies any more: the ingested graph is heterogeneous — companies, |
| ETFs, sectors, executives, countries and macro indicators, wired together by |
| typed, weighted relationships (node kind rides in features.kind): |
| |
| sector_member / has_member company ↔ sector |
| holds / held_by ETF ↔ constituent company |
| led_by / leads company ↔ chief executive |
| based_in / headquarters_of company ↔ country |
| supplies / supplied_by supplier ↔ customer (curated seed set) |
| competes_with rivals (curated, symmetric) |
| owns_stake / stake_held_by investor ↔ holding |
| co_moves return-correlation kNN (computed) |
| macro_correlates macro indicator ↔ company (computed) |
| |
| The computed edges carry the actual correlation as weight, so every path |
| step through them is evidence ("co-moves, corr 0.72"), not assertion — |
| format_edge below is what the UI shows on paths and expansions. |
| |
| Ingestion (ingest/finance.py) pulls real daily closes from Yahoo's chart |
| endpoint for companies, ETFs and macro symbols, merges the curated |
| relationship seed set, and writes everything into core.store. Tickers are |
| almost pure structure, hence FUSION_ALPHA["finance"] = 0.8. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import random |
| from typing import Optional |
|
|
| from core.source import register |
| from core.store import StoreBackedSource |
|
|
| _PHRASES = { |
| "sector_member": "in sector", |
| "has_member": "sector includes", |
| "holds": "holds", |
| "held_by": "held by", |
| "led_by": "led by", |
| "leads": "leads", |
| "based_in": "headquartered in", |
| "headquarters_of": "home market of", |
| "supplies": "supplies", |
| "supplied_by": "supplied by", |
| "competes_with": "competes with", |
| "owns_stake": "owns a stake in", |
| "stake_held_by": "stake held by", |
| } |
|
|
| _DEMO_PAIRS = [ |
| ("NVDA", "Warren Buffett"), |
| ("Tim Cook", "XOM"), |
| ("TSLA", "Crude Oil"), |
| ("SPY", "Taiwan"), |
| ("JPM", "Jensen Huang"), |
| ("F", "US 10-Year Treasury Yield"), |
| ] |
|
|
|
|
| class FinanceSource(StoreBackedSource): |
| name = "finance" |
| description = ("Financial knowledge graph — companies, ETFs, sectors, " |
| "executives & macro indicators with typed relationships.") |
| edge_types = ("sector_member", "holds", "led_by", "based_in", "supplies", |
| "competes_with", "owns_stake", "co_moves", "macro_correlates") |
| supports_backlinks = True |
|
|
| def format_edge(self, typ: str, weight: float) -> str: |
| if typ in ("co_moves", "macro_correlates"): |
| label = "co-moves" if typ == "co_moves" else "macro-linked" |
| return f"{label} (corr {weight:.2f})" |
| return _PHRASES.get(typ, typ.replace("_", " ")) |
|
|
| async def resolve(self, query: str) -> Optional[object]: |
| |
| |
| |
| |
| return await super().resolve(query.strip().lstrip("$").upper() |
| if len(query.strip()) <= 6 else query) |
|
|
| async def sample_pair(self): |
| if not self.ingested(): |
| return None |
| return random.choice(_DEMO_PAIRS) |
|
|
|
|
| register(FinanceSource()) |
|
|
