This project implements an anomaly detection system using Python, real-time financial data, and a Z-score-based algorithm to identify anomalies. The anomalies are detected and plotted dynamically in real-time with continuous stock price streams from Yahoo Finance.
The objective of this project is to detect unusual patterns in a continuous data stream that represents stock prices. The script flags anomalies by identifying deviations from the norm in real-time, allowing for early detection of unusual behavior.
- Algorithm Used: Z-score Anomaly Detection
- Real-Time Data: Stock price data stream from Yahoo Finance
- Visualization: Real-time plotting using
matplotlib - Programming Language: Python
- Real-Time Data Stream: Continuously fetches and processes stock data.
- Anomaly Detection: Uses the Z-score method to detect anomalies when the data points exceed a defined threshold.
- Data Visualization: Displays a real-time plot of the data stream and highlights anomalies with red dots.
- Optimization: Efficiently handles API rate limits and batch processing for smooth real-time detection.
The project is divided into four main components:
- Data Stream: Uses Alpha Vantage API to fetch real-time stock data.
- Anomaly Detection: Applies Z-score-based logic to flag anomalies.
- Visualization: Generates real-time visualizations with anomalies marked.
- Error Handling: Includes error handling for API limits and other issues.
![Graph Example]
Terminal showing detected anomalies:
Anomaly detected: 223.94000244140625 (Z-score: 3.185542874274288)
Anomaly detected: 223.1385040283203 (Z-score: -4.367154440057992)
Anomaly detected: 222.87860107421875 (Z-score: -3.431356428497915)