| name | stock-screener |
| description | Screens a universe of DSE stocks by fundamental ratios, technical signals, pre-built templates, or a natural-language query, then ranks survivors. Use when the user wants to find/screen/filter stocks, asks "show me cheap stocks", "value stocks under P/E 15", "dividend champions", "momentum stocks breaking 52-week highs", "profitable banks", or wants a custom multi-criteria screen across many Dhaka Stock Exchange tickers. |
| license | Apache-2.0 |
| compatibility | Prompt-first Agent Skill. Usable with the script absent. Script is Python 3.8+ stdlib-only, no network. |
| metadata | {"author":"stock-buddy","version":"0.2.0","prd_refs":["PRD-001:REQ-027","PRD-001:REQ-040","PRD-001:REQ-114"],"mode":["momentum","investment"]} |
Stock Screener
Prompt-first skill. Filter and rank by the rules below. scripts/screen.py is OPTIONAL
(handles large universes deterministically). Output is a multi-ticker list, not a single card.
Role & objective
You screen a supplied universe of DSE stocks against filters (explicit, template, or parsed
from natural language), then rank the survivors by a quality+momentum score.
When to use
"Show me cheap stocks", "value stocks under P/E 15", "dividend champions", "momentum names
breaking 52-week highs", "profitable banks". Hand the top tickers to analyze_ticker.
Inputs you need
universe — array of {ticker, fundamentals{pe,pb,roe,debt_to_equity,dividend_yield, market_cap}, ohlcv[], sector}. Optional: filters, template, query, limit (25).
Method (follow in order)
- Compose filters: template seeds → NL query adds → explicit
filters override.
- Templates:
value (pe≤15, pb≤1.5, roe≥0.12), dividend_champions (div≥4%, roe≥0.12),
momentum_leaders (rsi≥55, breakout, pos_52w≥0.7), oversold_quality (rsi≤35, roe≥0.15,
de≤0.6), small_cap_growth (roe≥0.15).
- NL parse: "cheap/value"→value; "dividend/income"→dividend; "momentum/breakout"→momentum;
"oversold"→oversold; sector keywords; "p/e under N", "roe over N%", "52-week high",
"volume surge"→rel_volume≥1.5.
- Evaluate each stock against every active filter (fundamental + technical derived from
ohlcv: RSI, MA50/200 cross, 52-week position, breakout vs 40-bar high, rel-volume). A stock
passes only if it satisfies all active filters.
Scoring rubric (rank score)
rank = 0.5·(min(ROE,0.4)/0.4) + 0.3·pos_52w + 0.2·clamp((RSI−30)/50, 0, 1).
Sort survivors descending; return the top limit.
Output
{ "skill": "stock-screener", "as_of": "..",
"applied": { "template": null, "query": "..", "filters": {} },
"count": 0, "results": [ { "ticker": "..", "passes": true, "score": 0.0,
"matched_filters": {}, "key_metrics": { "pe": 0, "roe": 0, "rsi": 0, "pos_52w": 0 } } ],
"reasoning": ["filters applied; N of M passed; top K returned"],
"disclaimer": "Educational analysis only. Not financial advice." }
DSE pitfalls
- Stocks with missing fields fail a filter that needs them — don't pass unknowns; say which
filter excluded them if asked.
- Thin ohlcv means technical filters can't evaluate — those stocks drop out of technical screens.
- US-style P/E≤15 is strict for some DSE sectors — adjust thresholds via explicit
filters when relevant.
Optional precision helper
python3 scripts/screen.py --input universe.json --pretty
Returns the passing tickers ranked, with matched filters and key metrics.
Worked example
query "profitable banks with p/e under 15" → {sector:Bank, roe_min:0.10, pe_max:15}; 4 of 30
pass; ranked by ROE + 52-week position.
References
Templates & NL grammar: references/SCREENS.md.
Output is educational analysis only, never financial advice.
