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Predictive Stock Market Analysis Dashboard

A Python-based AI/ML dashboard that analyzes stock market trends, predicts future prices, and extracts sentiment insights from financial news to aid investors in making data-driven decisions.

PythonTensorFlowFlaskPandasNumPyMatplotlibYahoo Finance APINLTK / TextBlob
8 technologies
Updated 07/2023
Full-stack Application

Predictive Stock Market Analysis Dashboard

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Project Architecture

Comprehensive overview of the project architecture, features, and development process with modern design patterns

Project Features

Discover the comprehensive feature set that powers this application

Core Features

Stock price trend prediction using ML models
Real-time stock data fetching from Yahoo Finance API
User-friendly dashboard for portfolio monitoring
Historical data visualization and analysis
Alert system for significant market changes
Role-based access for Admin and Investors

Operational Features

Sentiment analysis from news articles and social media
Automated stock recommendation engine
Customizable watchlists and portfolios
Exportable reports (CSV, PDF)
Notification system for predictions and alerts

Analytics & Reporting

ML model performance metrics (accuracy, RMSE)
Prediction confidence intervals and trends
Portfolio performance and historical returns
Sentiment trend dashboards and word clouds

System Architecture

Explore the technical foundation and architectural decisions

Frontend Architecture

Framework

Flask with Jinja2 templates

State Management

Flask session & client-side JS

Charts Library

Matplotlib / Plotly for interactive graphs

AI Integration

TensorFlow and scikit-learn for predictions

UI Libraries

BootstrapChart.jsPlotly

Backend Architecture

Framework

Flask

Database

SQLite / PostgreSQL for storing user data and historical trends

Authentication

Flask-Login & JWT

Real-time Communication

WebSocket / Flask-SocketIO

AI Services

Stock Price Prediction

TensorFlow, LSTM / RNN models

Sentiment Analysis

NLTK / TextBlob / Vader for news sentiment scoring

Development Phases

Strategic roadmap for project implementation and delivery

1

Project Setup & Data Integration

2 weeks
Initialize Flask project structure
Integrate Yahoo Finance API for historical and live data
Set up database for storing users and stock data
Implement authentication and role-based access
2

ML Model Development

3 weeks
Develop LSTM/RNN model for stock prediction
Train and validate models on historical data
Implement sentiment analysis pipeline for news
Integrate prediction results into backend
3

Dashboard & Visualization

2 weeks
Develop interactive dashboards with Plotly / Chart.js
Add portfolio tracking and watchlist functionality
Visualize historical and predicted trends
Implement alerts for market anomalies
4

Testing & Optimization

1-2 weeks
Unit and integration testing for backend APIs
Validate ML model accuracy and sentiment scoring
Optimize performance and response times
Conduct end-to-end user acceptance testing

API Endpoints

RESTful API structure and endpoint organization

Authentication

Register User

POST /api/auth/register

Login

POST /api/auth/login

Get User Profile

GET /api/auth/me

Stocks

Get Stock Data

GET /api/stocks/:symbol

Get Prediction

GET /api/stocks/:symbol/predict

Add to Watchlist

POST /api/stocks/watchlist

Remove from Watchlist

DELETE /api/stocks/watchlist/:symbol

Sentiment Analysis

Analyze News Sentiment

POST /api/sentiment/analyze

Get Sentiment Report

GET /api/sentiment/report/:symbol

Notifications

Send Email Alert

POST /api/notifications/email

Send Browser Alert

POST /api/notifications/browser

Testing Strategy

Comprehensive quality assurance and testing methodologies

Unit Testing

pytest
unittest

Integration Testing

Flask-Testing
requests

End-to-End Testing

Selenium
Cypress

AI Testing

Prediction accuracy validation
Sentiment analysis performance metrics

Deployment Strategy

Production deployment and infrastructure management

Frontend

Heroku / Vercel

Backend

AWS EC2 / Heroku

Database

PostgreSQL / SQLite

CI/CD

GitHub Actions

Future Enhancements

Roadmap for upcoming features and improvements

Real-time stock alerts via mobile push notifications
Portfolio risk assessment using ML
Integration with multiple stock exchanges
Automated trading recommendations
Multi-language support for global investors