stocktwits-sentiment

name	stocktwits-sentiment
description	This skill should be used when the user asks about StockTwits sentiment, trader bullish/bearish ratio, StockTwits stream, real-time trader sentiment, what traders are saying on StockTwits, or the StockTwits signal for a stock.

StockTwits Sentiment Analysis

Purpose

Fetch StockTwits sentiment for a ticker by scraping the symbol page, tally bullish vs bearish self-labeled messages, and produce a channel score from -10 to +10.

Step 1 — Fetch Symbol Page

WebFetch the StockTwits symbol page with this prompt:

"Extract: (1) bullish message count or percentage, (2) bearish message count or percentage, (3) total labeled messages, (4) any overall sentiment label (e.g. Bullish, Bearish, Extremely Bullish, Extremely Bearish, Neutral), (5) up to 2 recent message bodies labeled Bullish and up to 2 labeled Bearish."

https://stocktwits.com/symbol/{ticker}

If that page returns no useful data, retry with:

https://stocktwits.com/symbol/{ticker}/sentiment

Step 2 — Extract Counts or Label

If raw counts are available (bullish_count and bearish_count):

• Set score_method = "ratio"
• total_labeled = bullish_count + bearish_count
• Proceed to Step 3a.

If only a percentage is available (e.g. "73% Bullish"):

• Derive counts: bullish_count = round(pct × total_labeled), bearish_count = total_labeled - bullish_count
• Set score_method = "pct-derived"
• Proceed to Step 3a.

If only a qualitative label is available (no counts or percentages):

• Set score_method = "label-derived"
• Map label to score and proceed to Step 3b:

StockTwits Label	stocktwits_score
Extremely Bullish	+8.5
Bullish	+5.0
Neutral	0.0
Bearish	-5.0
Extremely Bearish	-8.5

If the page fails to load, returns an error, or yields no sentiment data: Set stocktwits_available = false. Note: "StockTwits: Page unavailable or no sentiment data found"

Step 3a — Compute Score from Ratio

stocktwits_score = (bullish_count - bearish_count) / total_labeled × 10

Step 3b — Score from Label (skip if 3a used)

Use the mapped score from the label table in Step 2. Set total_labeled = N/A.

Step 4 — Sample Messages

Extract the most recent Bullish-labeled message body and most recent Bearish-labeled message body from the page content. If unavailable, note "Sample unavailable".

Step 5 — Output

STOCKTWITS SENTIMENT RESULT
Ticker: {ticker}
Channel Score: {stocktwits_score} / 10
Signal: {label per signal bands below}
Score Method: {ratio | pct-derived | label-derived}
Labeled Messages: {total_labeled} ({bullish_count} bullish / {bearish_count} bearish)

Sample Bullish: "{message_body}"
Sample Bearish: "{message_body}"

If unavailable:

STOCKTWITS SENTIMENT RESULT
Ticker: {ticker}
Channel Score: N/A
Signal: N/A
Note: {reason}

Signal bands (applied to stocktwits_score):

• score >= +7.0 → Very Bullish
• score >= +3.0 and < +7.0 → Bullish
• score > -3.0 and < +3.0 → Neutral
• score <= -3.0 and > -7.0 → Bearish
• score <= -7.0 → Very Bearish