Reddit fetching & preprocessing algorithm 🧩

We fetch and prepare Reddit content using a predictable, low-noise pipeline that's part of the sentiment ingestion step above:

• Fetch posts from a subreddit by type (hot / new / top / rising / controversial).
• For "top" and "controversial" respect an optional time_filter (hour/day/week/month/year/all).
• Load only top-level comments and limit them per-post via comment_limit for predictable latency.
• Combine title, body, link and top comments into a single "full_text" blob for sentiment analysis and ticker extraction.
• Extract tickers using a simple regex for $TICKER tokens (e.g. $TSLA, $AAPL) and compute average sentiment per symbol.
• Normalize compound sentiment to improve ranking impact (small multiplier).

Quick view of the core behaviors (conceptual)

# Engine behaviors (conceptual)
# - fetch N posts per subreddit and up to comment_limit top-level comments
# - build full_text = title + body + top comments and run sentiment analysis
# - extract tickers via r"\$[A-Z]+" and aggregate counts + sentiment per ticker
# - enrich tickers with market data and run the 3-step ranking filter

Quickstart — run & call the API ⚡

Run the backend engine

# Backend engine lives in its own repo — follow the Engine README to run the service:
# https://github.com/HaiderMalikk/Reddish-Trends-Engine
# When running locally the engine exposes API endpoints on port 5000 by default

Call the API (curl)

curl -X POST http://localhost:5000/api/home \
  -H "Content-Type: application/json" \
  -d '{"request":{"type":"getgeneralanalysis"}}'

Playground (Python)

# example_playground.py (client)
import requests
payload = {
  "request": {"type": "getplaygroundgeneralanalysis",
    "parameters": {"subreddits": ["wallstreetbets","stocks"], "limit": 20, "comment_limit": 5, "sort": "hot", "time": None, "period": "1mo"}
  }
}
r = requests.post("http://localhost:5000/api/playground", json=payload)
print(r.json())

Internal usage (conceptual)

# Conceptual: use the engine's modules to run the pipeline and extract signals
# analysis = orchestration_module.run_general_analysis(["wallstreetbets","stocks"], limit=10)
# top = ranking_component.get_top_stock(analysis)
# rising = ranking_component.get_rising_stock(analysis, limit=3)

Sample Top_Stock shape (abbrev.)

{
  "symbol":"$SPY",
  "company_name":"SPDR S&P 500 ETF Trust",
  "count":3,
  "sentiment":8.71,
  "price":576.68,
  "percentage_change":-1.21,
  "rsi":28.53,
  "LLM_Analysis":{"overview":"...","prediction":"...","Confidence Score":78}
}

Firebase (NoSQL) — user document example

We use Firebase Firestore to store user preferences, cached requests, and logs. Example user document (abbreviated):

// example firebase user doc
{
  "uid": "mP7rW0bxvIV42ct9zMsqYMNJ7vE3",
  "createdAt": "2025-03-22T17:58:45.873Z",
  "email": "haidermalik662@gmail.com",
  "favorites": [
    { "companyName": "Identiv, Inc.", "symbol": "$INVE" }
  ],
  "requests": {
    "general_analysis": { "count": 37, "log": ["March 22, 2025 at 1:58:46..."] },
    "playground_analysis": {
      "count": 2,
      "log": [
        {
          "parameters": { "analysisType": "getplaygroundgeneralanalysis", "commentLimit": 10, "limit": 10, "period": "1mo", "sort": "hot", "subreddits": "wallstreetbets,stocks,stockmarket", "time": "none" },
          "timestamp": "2025-03-23T22:16:01-04:00"
        }
      ]
    }
  }
}

Play with it 🔬

• /api/home → cached Top/Worst/Rising summaries
• /api/playground → custom subreddit sets, time windows, symbol lists
• Cache stored in cached_analysis.json — scheduler refreshes daily

Design notes (short)

• Deterministic & explainable ranking > black-box for community products.
• Cache + scheduler = fewer rate-limit headaches with Reddit / market APIs.
• Modular design: swap sentiment, enrichment, or LLM independently.

Frontend / Website — full feature list & tech

The website surfaces ranked signals and explains them — designed for fast scanning and transparency.

Core user-facing features

• Trending discovery: Top, Worst and Rising stocks detected across financial communities.
• Post-level transparency: every signal links back to the Reddit post(s) that generated it so users can inspect context.
• Market enrichment: live price, daily high/low, percent change and RSI shown alongside sentiment scores.
• Confidence & explanations: optional LLM summaries explaining market sentiment, technical signals, fundamentals and a confidence score.
• Playgrounds & custom queries: run custom analyses across subreddits, periods, and symbol lists via the playground API.
• User personalization: favorites, request logs and account persistence using Firestore.
• Clean UI & visuals: responsive summaries, card-based layouts and compact visual cues for quick decision-making.

Frontend tech

• Typescript, Next.js, Tailwind CSS
• Firebase Firestore for user data and preferences
• Hosting: Vercel (frontend) + Vercel for backend (engine)

Screenshots & preview