WISP

TECHNOLOGIES

FinTechCompleted

Naafa

Naafa is a comprehensive stock market analysis platform designed specifically for the Nepali market. It combines real-time data processing, machine learning algorithms, and intuitive user interfaces to help local investors make informed decisions. The platform features advanced charting capabilities, predictive analytics, and personalized recommendations based on market trends and individual investment patterns.

Naafa

2024
Year
8 months
Duration
5
Team Size
8
Technologies

Technologies Used

A comprehensive stack of modern technologies powering this project

ReactNode.jsPythonTensorFlowPostgreSQLChart.jsRedisDocker

Challenges

  • Real-time data processing from multiple sources

  • Building accurate prediction models for volatile markets

  • Creating intuitive UI for non-technical users

  • Ensuring data security and compliance

Solutions

  • Implemented microservices architecture for scalable data processing

  • Developed custom ML models trained on historical Nepali market data

  • Created responsive design with progressive web app features

  • Established comprehensive security protocols and data encryption

Results & Impact

Measurable outcomes and achievements from this project

40% improvement in user investment decisions

95% accuracy in short-term predictions

10,000+ active users within 6 months

Featured in major Nepali financial publications

Project Journey

A detailed timeline of the Naafa development process, showcasing challenges, solutions, and milestones.

Phase 1

Market Analysis & Requirements

Conducted extensive research on Nepali stock market patterns and user needs

2 months
2024-01-01

Challenges

  • Limited historical data
  • Understanding local market dynamics

Solutions

  • Partnered with local brokers
  • Created custom data collection methods

Technologies Used

PythonPandasJupyter
Phase 2

Core Platform Development

Built the main application with real-time data processing and ML integration

4 months
2024-03-01

Challenges

  • Real-time data synchronization
  • ML model optimization

Solutions

  • Implemented WebSocket connections
  • Used TensorFlow for model training

Technologies Used

ReactNode.jsTensorFlowPostgreSQL
Phase 3

Performance Tuning & User Testing

Optimized performance and conducted extensive user testing

2 months
2024-07-01

Challenges

  • Performance bottlenecks
  • User adoption

Solutions

  • Implemented caching strategies
  • Created comprehensive user guides

Technologies Used

RedisDockerJest

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