Budget: $150
Timeline: Flexible, but delivery within 3-4 weeks is preferred.
Objective:
Develop an AI-powered stock trading assistant to help predict stock trends and provide actionable insights. The tool will leverage machine learning models (e.g., LSTMs) to analyze stock market data and identify patterns for better decision-making. To save on budget and development time, you may use a pre-trained or existing AI model as the foundation, while focusing on implementing additional features and ensuring high performance.
Project Requirements:
Core Features:
Stock Prediction Model:
Use TensorFlow and Python to implement an LSTM-based predictive model.
Optionally adapt a pre-trained or existing model (open-source or from reputable libraries) to save time and resources.
Real-Time Data Integration:
Fetch live stock data using APIs such as Alpha Vantage, IEX Cloud, or Yahoo Finance.
Update predictions dynamically based on the latest market information.
Key Insights Dashboard:
Create a simple and intuitive user interface using libraries like Flask, Dash, or Tkinter.
Display stock predictions, historical trends, and confidence scores.
Basic Alerts System:
Provide notifications for key stock movements or when certain conditions (e.g., price thresholds) are met.
Additional Features (Stretch Goals):
Integrate a Naive Bayes model for sentiment analysis using news headlines or social media data.
Allow users to input specific stocks or portfolios to tailor predictions.
Implement downloadable reports summarizing trends and predictions for user-selected stocks.
Technical Requirements:
Programming Languages: Python (primary)
Libraries/Frameworks: TensorFlow, Pandas, NumPy, Matplotlib, Scikit-learn, Flask/Dash
APIs: Stock market data API (e.g., Alpha Vantage, IEX Cloud)
Developer Responsibilities:
Adapt an existing AI model or create a custom LSTM model for stock trend prediction.
Focus on enhancing the model's usability by integrating new features (e.g., alerts, sentiment analysis).
Build a user-friendly interface for data visualization and interaction.
Optimize the tool to run efficiently within a constrained budget and computational resources.
Document code, usage instructions, and provide support for basic troubleshooting. |
