Build a WallStreetBets Sentiment Dashboard in 50 Lines of Python

The do-it-yourself trap

Counting ticker mentions on r/wallstreetbets sounds like an afternoon project, and the first version usually is. Then reality arrives. You authenticate PRAW, pull the hot posts, and within a day you are managing Reddit's rate limits and pagination. You write a regex for tickers and discover that "ALL", "CEO", "YOLO" and "DD" are also valid symbols. You reach for a sentiment score and learn that off-the-shelf VADER reads "puts on $WEN, we're so back" as bearish and "this stock is going to zero, loading calls" as a coin flip.

None of that is hard, exactly. It is just a lot of small things that have to keep running, on a schedule, forever, before you see a single number you trust. The shortcut is to treat the scored data as a service and spend your time on the part you actually care about, the dashboard. That is what the rest of this does.

What you need

Three things, about two minutes of setup:

• A free Adanos API key. Register at adanos.org/register, no card required.
• Python 3.9 or newer.
• The libraries: pip install streamlit requests pandas

One call for the whole leaderboard

The trending endpoint returns the most-mentioned Reddit tickers for a window, each already scored. One request, no pagination:

Each row in the response carries everything the dashboard needs:

{
  "ticker": "AAPL",
  "company_name": "Apple Inc",
  "buzz_score": 79.3,           // 0-100 attention score
  "mentions": 257,              // posts and comments in the window
  "sentiment_score": -0.012,    // -1 bearish .. +1 bullish
  "bullish_pct": 20,
  "bearish_pct": 21,
  "trend": "rising",
  "trend_history": [77.6, 77.4, 80.5, 77.4, 80.3, 78.9, 79.3]  // 7-day buzz
}

The trend_history array is already a ready-made sparkline, and bullish_pct with bearish_pct is the real classification, not a mention count. That is the difference that makes the dashboard worth looking at.

The dashboard, in full

Save this as app.py. It fetches the leaderboard, renders it with inline buzz bars and sparklines, and adds a drill-down chart that pulls 30 days of history for whichever ticker you pick.

# app.py -- a live r/wallstreetbets sentiment dashboard
# pip install streamlit requests pandas   then:   streamlit run app.py
import requests
import pandas as pd
import streamlit as st

API_KEY = "YOUR_FREE_KEY"          # get one at adanos.org/register
BASE = "https://api.adanos.org/reddit/stocks/v1"
HEADERS = {"X-API-Key": API_KEY}

st.set_page_config(page_title="WSB Sentiment", layout="wide")
st.title("r/wallstreetbets sentiment dashboard")

@st.cache_data(ttl=900)            # data refreshes hourly upstream, so cache 15 min
def trending():
    r = requests.get(f"{BASE}/trending", params={"days": 1}, headers=HEADERS, timeout=20)
    r.raise_for_status()
    return pd.DataFrame(r.json())

@st.cache_data(ttl=900)
def history(ticker):
    r = requests.get(f"{BASE}/stock/{ticker}", params={"days": 30}, headers=HEADERS, timeout=20)
    r.raise_for_status()
    return pd.DataFrame(r.json()["daily_trend"])[::-1]   # API returns newest-first

df = trending()
st.caption(f"{len(df)} most-mentioned tickers on Reddit, refreshed hourly")
if df.empty:
    st.warning("No trending tickers right now, try again shortly.")
    st.stop()

top = df.iloc[0]
a, b, c = st.columns(3)
a.metric("Top ticker", top.ticker, f"{top.mentions} mentions")
b.metric("Buzz score", f"{top.buzz_score:.0f}/100", top.trend)
c.metric("Bull vs bear", f"{top.bullish_pct}% / {top.bearish_pct}%")

st.subheader("Trending leaderboard")
st.dataframe(
    df[["ticker", "mentions", "buzz_score", "sentiment_score",
        "bullish_pct", "bearish_pct", "trend_history"]],
    hide_index=True, use_container_width=True,
    column_config={
        "buzz_score": st.column_config.ProgressColumn("Buzz", min_value=0, max_value=100, format="%.0f"),
        "sentiment_score": st.column_config.NumberColumn("Sentiment", format="%.2f"),
        "trend_history": st.column_config.LineChartColumn("7-day buzz"),
    },
)

st.subheader("Drill into a ticker")
pick = st.selectbox("Ticker", df.ticker)
h = history(pick)
st.line_chart(h.set_index("date")[["buzz_score", "mentions"]])

The two @st.cache_data decorators matter more than they look. They hold each response for fifteen minutes, so flipping between tickers does not spend a request every click. On the free tier that keeps a personal dashboard comfortably inside the monthly quota.

Run it

One command, and the dashboard opens in your browser:

You get a three-number summary for the top ticker, a sortable leaderboard with a buzz bar and a seven-day sparkline on every row, and a drill-down that charts 30 days of buzz and mentions for whatever you select. The numbers move on their own, because the upstream data refreshes hourly and the cache lets go after fifteen minutes.

Going further

The same shape extends in a few directions:

• Alerts. Drop Streamlit and run the trending() call on a cron, then post to Slack or email when a ticker crosses a buzz or bullish threshold.
• More sources. The same key works on the X, news and Polymarket endpoints. Swap the base path and the response shape is identical, so the code barely changes.
• No code at all. If you just want the numbers in a terminal, the Finance Sentiment CLI wraps the same API.

If you are weighing this build against a free mention tracker, the ApeWisdom alternatives rundown covers when a scored API is worth it and when a free dashboard is plenty.

FAQ

Related