| compatibility | Python 3.8+, standard library only. No network. No third-party packages. |
| description | Explains financial and DSE-trading terms bilingually (English + Bangla) with dual-strategy impact for long-term investors versus momentum traders, good/bad thresholds, a Bengali-context analogy, and what-to-do notes. Use when the user asks "what is ROE/PE/RSI/MACD", "explain this metric in Bangla", "is a PEG of 0.8 good or bad", wants a glossary, or wants their stock's fundamentals/indicators annotated with plain-language meaning for a Dhaka Stock Exchange context. |
| license | Apache-2.0 |
| metadata | {"author":"stock-buddy","github-path":"skills/financial-terms-educator","github-pinned":"main","github-ref":"refs/heads/main","github-repo":"https://github.com/kuntal-r-d/my-skills","github-tree-sha":"1e1ca13126bb9dd7ef993da8170402cf5a4b0251","mode":["education"],"prd_refs":["PRD-001:REQ-040","PRD-001:REQ-045","PRD-001:REQ-047","PRD-002:REQ-038","PRD-002:REQ-050"],"version":"0.1.0"} |
| name | financial-terms-educator |
Financial Terms Educator
When to use
Activate when a user wants a term explained or a metric interpreted: "what does ROE mean?",
"explain P/E in Bangla", "is debt-to-equity of 1.4 bad?", "annotate these fundamentals",
"give me the glossary". This is the bilingual education layer (PRD-001 REQ-040/045/047;
PRD-002 REQ-038/050) — it teaches the why behind the numbers other skills produce.
What it does
Looks up a curated, bilingual glossary (assets/glossary.json). Each entry carries:
term / term_bn, one_liner / one_liner_bn, an analogy in a Bangladesh/DSE context
(GP, Square, BRAC Bank, BATBC), a dual-strategy split (for_investor vs for_trader),
good_vs_bad thresholds, and what_to_do. The dual split is the core philosophy: the same
metric means different things to a long-term holder and a momentum trader (PRD-001
REQ-045/047). See references/GLOSSARY.md.
The core set is ~38 key terms (ROE, ROA, PE, PB, PEG, EPS, debt_to_equity, current_ratio,
free_cash_flow, dividend_yield, margin_of_safety, economic_moat, RSI, MACD, ADX, ATR,
moving_average, OBV, MFI, ROC, bollinger_bands, support_resistance, breakout, momentum,
volatility, market_cap, NAV, NCAV, intrinsic_value, beta, circuit_breaker, floor_price,
DSEX, book_value, earnings_growth, operating_margin, interest_coverage, position_sizing) —
extensible toward the 120+ target documented in the references.
How to run
echo '{"term":"ROE"}' | python3 scripts/lookup.py --pretty
echo '{"terms":["PE","PEG"]}' | python3 scripts/lookup.py
echo '{"metrics":{"roe":0.23,"pe":14.2}}' | python3 scripts/lookup.py --pretty
echo '{"list":true}' | python3 scripts/lookup.py
{"term": "..."} — one entry (case-insensitive; metric aliases like rsi_14 resolve).{"terms": [...]} — several entries.{"metrics": {...}} — annotate each value with its entry and a good/bad verdict.{"list": true} — all term keys.
Interpreting output
Returns results[], count, language: "en+bn", and disclaimer. In metrics mode each
result adds verdict (good/fair/weak/n-a) and an assessment string. Educational analysis
only, never financial advice.
