TrendTickr

Overview

TrendTickr was developed by Lunivere Creative House after identifying a gap in how retail investors accessed early market signals. The goal was to simplify complex financial data and highlight stocks gaining real momentum across Reddit, X, and other social platforms - transforming the chaotic world of penny stock speculation into a structured, data-driven opportunity discovery engine.

The platform combined social sentiment analysis, technical indicators, and company fundamentals into ranked, data-driven insights. At its core was a proprietary scoring algorithm - a sophisticated multi-factor system that weighed data points from across Reddit, X, trading forums, and Discord to rank penny stock opportunities in real-time.

After reaching a stable working prototype with live data feeds and sub-minute processing latency, the project was brought to a close as the AI landscape evolved rapidly and data access restrictions increased. Lunivere Creative House concluded the build with a detailed technical review and an End of an Era site, documenting the architecture, lessons learned, and insights gained throughout development.

Data Collection Layer

Built a Python-based scraping infrastructure operating 24/7, utilising multiple APIs and web scraping techniques to aggregate data from diverse sources:

• Reddit: Real-time scans of 15+ subreddits (r/pennystocks, r/stocks, r/investing, r/smallstreetbets, r/RobinHoodPennyStocks)
• X (Twitter): Tracked penny stock hashtags and key financial influencers live
• Trading Forums: Monitored 20+ platforms including Trade2Win, InvestorHub, Elite Trader
• Discord: Access to major trading servers via integrated bots
• Market Data: MarketStack API for live prices, volumes, and technical indicators
• Alternative Sources: StockTwits sentiment tracking, Yahoo Finance message boards, SEC filings aggregation

AI / ML Processing Engine

The heart of TrendTickr's intelligence - custom-built natural language processing models specifically trained on financial discussions:

• Proprietary NLP Models: Custom sentiment analysis trained on penny stock language patterns
• Multi-Platform Signal Correlation: Cross-referencing discussions across platforms to identify genuine momentum vs noise
• Quality-Weighted Analysis: Proprietary credibility scoring to distinguish informed analysis from speculation
• Risk Signal Detection: Filters for identifying potential pump-and-dump schemes and market manipulation
• Real-Time Processing: Sub-minute latency from data collection to insight generation

Technical Stack

• Backend: Python (BeautifulSoup, Selenium, custom NLP models, scikit-learn)
• Cloud Infrastructure: AWS/Railway hosting for 99.9% uptime target
• Database: PostgreSQL for structured data, Redis for real-time caching
• API Layer: RESTful APIs enabling mobile app integration
• Frontend: Native iOS application built in Swift/Xcode
• Security: End-to-end encryption, secure authentication, GDPR-compliant data handling

Scoring Algorithm

The proprietary multi-factor analysis framework evaluated opportunities across several key dimensions:

• Social Intelligence Factors: Discussion volume, sentiment velocity, community consensus
• Market Performance Indicators: Price movement, volume patterns, technical signals
• Catalyst Recognition: News events, earnings expectations, regulatory announcements
• Risk Assessment: Manipulation pattern detection, company stability metrics

iOS Application

Mobile-first design philosophy recognising that 78% of retail traders primarily use smartphones:

• Native Swift development for optimal performance
• Swipe-based navigation for rapid opportunity review
• Push notifications for breaking opportunities
• Real-time data visualisation with Recharts integration

Outcomes

• Functional prototype processing 100,000+ daily social posts
• Proprietary sentiment scoring algorithms with multi-platform correlation
• Complete iOS application with live data feeds
• Comprehensive 35-page technical whitepaper
• End-of-project retrospective documenting architecture and learnings