📡 RaniStocks — Social Sentiment Radar (Improved)

Real-time ticker intelligence scraped from Reddit, Stocktwits, Finviz & Yahoo Finance
with VADER NLP sentiment scoring.

This is an improved fork of Eliahur7/stocks_sentiment.
See the Changes section for a full diff summary.

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What it does

On every manual scan the app:

1. Scrapes 4 sources for the most-mentioned stock tickers
2. Scores sentiment on every post/headline using VADER NLP (bullish / bearish / neutral)
3. Enriches with price data from Yahoo Finance
4. Displays a ranked leaderboard with mention bars, sentiment badges, source breakdown, and sample posts

Sources

Source	Method	Coverage
Reddit	Public .json API (no auth needed)	Hot posts from r/wallstreetbets, r/stocks, r/investing, r/options, and more
Stocktwits	Public REST API	Trending symbols + message stream
Finviz	finvizfinance library	News & blog headlines
Yahoo Finance	Public HTML endpoint	Trending tickers page

No API keys required.

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Changes

1. Security fix — XSS via unsafe_allow_html

Original problem: Raw Reddit/Stocktwits post text was injected directly into HTML strings rendered with unsafe_allow_html=True. A post containing <script> tags or HTML markup could break the UI or inject arbitrary markup into the page.

Fix: All user-generated text (post previews, ticker symbols, sentiment labels) is now passed through Python's built-in html.escape() before being inserted into any HTML string.

# Before — unsafe
preview = txt[:300].replace("\n", " ").strip()

# After — safe
preview = html.escape(txt[:300].replace("\n", " ").strip())

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2. Ticker accuracy — real tickers incorrectly blacklisted

Original problem: The TICKER_BLACKLIST set contained several actively-traded tickers, causing them to silently vanish from all scan results regardless of mention volume:

Ticker	Company	Why it was wrongly blacklisted
AI	C3.ai	Matched the word "AI"
APP	AppLovin	Matched the word "APP"
RUN	Sunrun	Common English word
NOW	ServiceNow	Common English word
BUY	—	Useful trading signal word

Fix — two-part:

1. Removed incorrectly blacklisted tickers from the set.
2. Changed extract_tickers() to use a two-tier extraction approach:
   • $TICKER dollar-sign mentions → high confidence, bypass blacklist entirely
   • Plain ALL-CAPS words → lower confidence, blacklist still applied

# High-confidence: $TICKER — skip blacklist (preserves $AI, $APP, $NOW, $NET, $RUN)
dollar_tickers = set(re.findall(r'\$([A-Z]{1,5})\b', text_upper))

# Lower-confidence: plain CAPS — apply blacklist
caps_words = set(re.findall(r'\b([A-Z]{2,5})\b', text_upper))
caps_filtered = {t for t in caps_words if t not in TICKER_BLACKLIST}

return list(dollar_tickers | caps_filtered)

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3. Error visibility — silent source failures

Original problem: All four data-source fetchers used bare except Exception: pass or except Exception: continue. Network errors, HTTP 429 rate-limit responses, and API changes would silently produce empty results with no feedback to the user — the scan would just return fewer tickers with no indication why.

Fix: All exception handlers now call st.warning(f"Source X failed: {e}") so degraded results are visible rather than hidden.

# Before
except Exception:
    pass

# After
except Exception as e:
    st.warning(f"Stocktwits trending symbols failed: {e}")

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4. yfinance column compatibility

Original problem: data["Close"] raises a KeyError in some yfinance versions where the column is named differently (behaviour changed in 0.2.x depending on auto_adjust setting).

Fix: Safe column resolution with fallback:

close = (
    data["Close"] if "Close" in data.columns
    else data.get("Adj Close", pd.DataFrame())
)

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Local development

pip install -r requirements.txt
streamlit run app.py

Deploy on Streamlit Cloud

1. Push this folder to a GitHub repo
2. Go to share.streamlit.io → New app
3. Select repo → main file: app.py → Deploy

Live in ~60 seconds.

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Sentiment scoring

Each post title + body is scored with VADER (Valence Aware Dictionary and sEntiment Reasoner), a rule-based NLP model purpose-built for short social media text.

Score	Label
≥ 0.15	Bullish
0.05 – 0.15	Slightly Bullish
−0.05 – 0.05	Neutral
−0.15 – −0.05	Slightly Bearish
≤ −0.15	Bearish

Individual post scores are averaged per ticker to produce the final compound score.

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Notes

• No API keys required — all sources use public endpoints
• Not financial advice — this is a sentiment monitoring tool, not a trading signal
• Reddit rate limits — the app includes time.sleep(0.3) between subreddit requests
• Price data is delayed 15–20 min via Yahoo Finance
• Each scan takes 15–30 seconds depending on source selection and post count

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Built by RaniStocks · Educational use only, not a financal advice or a recommendation!