Project Overview

AI Stock Tracker represents a cutting-edge fusion of financial analysis and artificial intelligence, delivering sophisticated stock prediction capabilities through advanced machine learning architectures. The platform combines multiple AI models including LSTM neural networks for time series forecasting, transformer-based sentiment analysis for news impact assessment, and comprehensive technical analysis engines. Built with a robust FastAPI backend and an intuitive React Native mobile interface, the system provides real-time predictions, sentiment-driven market insights, and advanced portfolio analytics. The ML pipeline features a custom feature store for consistent data processing, multi-horizon prediction capabilities, and production-ready model management with automated retraining workflows.

Key Features

Advanced LSTM neural networks with 60-day sequence learning for multi-horizon stock price prediction

Multi-model sentiment analysis using Hugging Face transformers, VADER, and TextBlob for financial news processing

Comprehensive technical indicator engine with 20+ advanced indicators (RSI, MACD, Bollinger Bands, ATR, ADX)

Custom feature store ensuring consistent data preprocessing between training and inference pipelines

Real-time WebSocket integration for live market data and prediction updates

Intelligent portfolio analytics with risk assessment and performance tracking

Advanced data validation and preprocessing pipeline with outlier detection and normalization

Production-ready model versioning and metadata management system

Asynchronous task processing with Celery for computationally intensive ML operations

Robust caching layer with Redis for optimized prediction retrieval and data management

Cross-platform mobile application with offline capability and push notifications

Comprehensive API documentation with interactive testing interface

Challenges Solved

Designing a scalable LSTM architecture capable of handling multi-dimensional time series data with varying prediction horizons while maintaining temporal consistency

Implementing production-grade feature engineering pipeline that ensures consistent data preprocessing between model training and real-time inference

Building a multi-model sentiment analysis system that effectively combines transformer models, lexicon-based approaches, and domain-specific financial sentiment scoring

Creating an efficient data validation framework that handles missing values, outliers, and market anomalies without compromising prediction accuracy

Developing a robust model management system with automated retraining, version control, and A/B testing capabilities for continuous model improvement

Optimizing memory usage and computational efficiency for real-time predictions while handling multiple concurrent users and large datasets

Implementing sophisticated technical indicator calculations that maintain mathematical accuracy while handling edge cases in financial data

Designing a scalable WebSocket architecture for real-time data streaming without overwhelming the ML inference pipeline

Technologies Used

TensorFlow & LSTM Networks

Implemented advanced LSTM architecture with custom layers for sequential pattern recognition in stock price movements. Features include dropout regularization, batch normalization, and adaptive learning rates with early stopping to prevent overfitting while maintaining predictive accuracy.

Hugging Face Transformers

Integrated pre-trained financial sentiment models including FinBERT and custom fine-tuned transformers for analyzing market news, social media sentiment, and earnings reports. Implements attention mechanisms to weight sentiment impact on price predictions.

Custom Feature Store

Built a production-ready feature engineering pipeline that calculates 18+ technical indicators, handles missing data imputation, and ensures consistency between training and inference. Includes automated feature validation and drift detection.

FastAPI & Async Architecture

Leveraged FastAPI for high-performance async API endpoints with automatic OpenAPI documentation. Implements connection pooling, request queuing, and efficient database operations for handling concurrent ML inference requests.

PyTorch Integration

Utilized PyTorch for experimental model architectures and research prototyping, enabling rapid iteration on new neural network designs and ensemble methods for improved prediction accuracy.

Redis & Caching Strategy

Implemented intelligent caching system for prediction results, model artifacts, and preprocessed features. Reduces API response times by 80% and enables efficient batch processing of multiple stock predictions.

Scikit-learn Pipeline

Integrated comprehensive preprocessing pipeline with RobustScaler, MinMaxScaler, and custom feature transformers. Includes automated hyperparameter tuning and cross-validation for optimal model performance.

React Native & Real-time Updates

Built cross-platform mobile application with Redux state management, real-time WebSocket connections, and optimized chart rendering using react-native-chart-kit for displaying prediction visualizations and portfolio analytics.

Development Process

Financial Data Research & ML Architecture Design

Conducted extensive research on financial time series analysis, studying market microstructure, technical analysis principles, and deep learning approaches for stock prediction. Designed a modular ML architecture supporting multiple prediction models and real-time inference.

Advanced Feature Engineering & Data Pipeline

Developed a comprehensive feature store calculating 18+ technical indicators, price-based features, and volume analysis metrics. Implemented robust data validation, outlier detection, and normalization strategies to ensure high-quality training data.

LSTM Neural Network Development

Built and optimized LSTM networks with custom architectures for multi-horizon predictions. Implemented advanced techniques including attention mechanisms, residual connections, and ensemble methods to improve forecasting accuracy and handle market volatility.

Sentiment Analysis Integration

Integrated multiple NLP models including transformer-based financial sentiment analysis, VADER sentiment scoring, and custom news processing pipelines. Developed correlation analysis between sentiment scores and price movements for enhanced prediction accuracy.

Production ML Infrastructure

Implemented production-ready ML infrastructure with model versioning, automated retraining workflows, A/B testing capabilities, and comprehensive monitoring. Built efficient caching and batch processing systems for scalable real-time predictions.

Mobile Application & Real-time Integration

Developed React Native mobile application with real-time WebSocket connections, interactive charting capabilities, and offline functionality. Implemented Redux for state management and optimized data synchronization between backend predictions and mobile interface.

Performance Optimization & Validation

Conducted extensive backtesting across multiple market conditions, optimized model performance using cross-validation techniques, and implemented comprehensive error analysis. Fine-tuned API performance and established monitoring systems for production deployment.

AI Stock Tracker