| compatibility | Python 3.8+, standard library only. No network. No third-party packages. |
| 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 |
| metadata | {"author":"stock-buddy","github-path":"skills/sentiment-news","github-pinned":"main","github-ref":"refs/heads/main","github-repo":"https://github.com/kuntal-r-d/my-skills","github-tree-sha":"76d6670a8127077f0155fd06361b920ccd43ceb0","mode":["momentum","investment"],"prd_refs":["PRD-002:REQ-019","PRD-001:REQ-023"],"version":"0.1.0"} |
| name | sentiment-news |
Sentiment & News
When to use
Activate when a user wants the news read on a DSE stock or sector: "what's the
sentiment on GP?", "is the news good or bad?", "is this just a rumour?", "score the
headlines". This is the Sentiment Agent (PRD-002 REQ-019). Its score is designed to
feed signal-synthesizer alongside the technical and fundamental legs.
What it does
Reads the shared contract's news array and produces an aggregate sentiment score in
[-1, +1]. Because the DSE is rumour-heavy (PRD-001 REQ-023), it explicitly
separates rumour-driven from fundamentals-driven items:
- Lexicon scoring — each headline is scored from positive vs negative keyword
counts (stdlib only; no NLP libraries).
- Rumour detection — an item is a
RUMOUR if its source is social/forum/
unconfirmed/rumour, its category is rumour-like, or its headline mentions
rumour/unconfirmed/speculation. Rumours are down-weighted to x0.3 and, when
they are the only material signal, the score is capped to [-0.4, +0.4] so a
rumour alone can never drive a strong signal.
- Fundamental classification — items in
earnings, regulatory,
corporate_action, or macro carry full weight.
Output is a Thinking Card (see suite README).
How to run
python3 scripts/sentiment.py --input data.json --pretty
cat data.json | python3 scripts/sentiment.py
Reads news (required) and optional mode/ticker/as_of. Returns score
(-1..+1, fundamental-weighted), confidence, rating (positive/neutral/negative),
key_metrics (item/fundamental/rumour counts, average fundamental sentiment),
per-item reasoning, and flags.
Try it now with the shared fixture:
python3 scripts/sentiment.py --input ../_fixtures/sample_input.json --pretty
Interpreting output
rating: positive (score >=0.2), negative (<=-0.2), else neutral.flags: rumour_dominated (no fundamental items, score capped) and
thin_coverage (<2 items) both lower confidence — never silent drops.- Compare
avg_fundamental_sentiment against the headline score to see how much of
the read is fundamentals vs noise.
Notes
The lexicon, rumour-vs-news separation rationale, and DSE rumour context are
documented in references/SENTIMENT.md. Output is
educational analysis only, never financial advice.
