| name | sentiment-news |
| description | Computes ticker/sector news sentiment for DSE stocks and separates rumour-driven items from fundamentals-driven news, since the Dhaka Stock Exchange is rumour-heavy. Use when the user asks about news sentiment, "what's the news saying about GP", headline tone, rumour vs real news, or wants a sentiment score to feed signal-synthesizer. |
| 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"],"mode":["momentum","investment"]} |
Sentiment & News
Prompt-first skill. Score the headlines by the rules below. scripts/sentiment.py
is OPTIONAL (lexicon scorer). The key discipline: separate rumour from fundamentals —
DSE is rumour-heavy, so rumours can never drive a strong score on their own.
Role & objective
You produce an aggregate news sentiment score (−1..+1) for a ticker, explicitly down-weighting
rumours and surfacing fundamentals-driven items.
When to use
"What's the news saying about GP?", "headline tone", "rumour vs real news", "sentiment score".
Feed the score into signal-synthesizer.
Inputs you need
news — array of {headline, source, category}. Categories that count as
fundamental: earnings, regulatory, corporate_action, macro. Sources/terms that
mark rumour: social/forum/unconfirmed/rumour/speculation/alleged. If news missing →
error; if empty → error.
Method (follow in order)
- Per-headline tone — count positive vs negative keywords; tone = (pos−neg)/(pos+neg),
clamp −1..+1 (0 if no keywords).
- Classify & weight each item: rumour → ×0.3; fundamental → ×1.0; general → ×0.7.
- Aggregate = weighted average of tones.
- Rumour discipline: if there are rumours but zero fundamental items
(
rumour_dominated), clamp the score to [−0.4, +0.4] and flag it.
- Flag
thin_coverage if fewer than 2 items.
Scoring rubric
Rating: score ≥ 0.2 → positive · ≤ −0.2 → negative · else neutral.
Confidence = clamp(0.4 + 0.1·min(fundamental_count,4) + 0.05·min(item_count,4)
− 0.2 (if rumour_dominated) − 0.15 (if thin_coverage), 0.15, 0.9).
Output (emit this Thinking Card)
{ "skill": "sentiment-news", "ticker": "..", "mode": "both", "as_of": "..",
"score": 0.0, "confidence": 0.0, "rating": "positive|negative|neutral",
"key_metrics": { "item_count": 0, "fundamental_count": 0, "rumour_count": 0,
"avg_fundamental_sentiment": 0.0 },
"reasoning": ["[FUNDAMENTAL|RUMOUR|general] headline -> tone"], "flags": ["..."],
"disclaimer": "Educational analysis only. Not financial advice." }
DSE pitfalls
- Rumour-heavy market: a wave of social/forum buzz must never produce a strong signal —
enforce the ±0.4 clamp when no fundamental item is present.
- Thin coverage: one headline is not sentiment — flag and lower confidence.
- Prefer disclosure/earnings/regulatory items; treat anonymous tips as noise.
Optional precision helper
python3 scripts/sentiment.py --input data.json --pretty
Returns the per-item classification and the weighted aggregate.
Worked example
3 items: earnings "record profit, dividend" (+1.0, fundamental ×1.0), regulatory "approval"
(+1.0, ×1.0), forum "rumour of merger" (+0.5, ×0.3) → aggregate ≈ +0.93 → positive,
confidence ≈ 0.75 (2 fundamental items).
References
Lexicon & rules: references/SENTIMENT.md.
Output is educational analysis only, never financial advice.
