Anomaly Detection for Market Irregularities: A Deep Learning Approach using Python

Python Anomaly Detection in Time-Series Data using an Autoencoder

This project explores anomaly detection in time-series data, focusing on identifying market irregularities. We’ll delve into building a Python-based system leveraging deep learning techniques with Keras to detect unusual patterns in stock market data. Using historical data and technical indicators, we’ll train an autoencoder model to recognize anomalies and potentially predict market shifts. This approach holds significant implications for traders and financial analysts seeking to understand and respond to market volatility effectively.

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Table of Contents

• Data Acquisition and Preprocessing: Downloading historical stock data from Yahoo Finance, handling missing values and normalizing the data.

• Feature Engineering: Constructing technical indicators like Moving Averages, RSI, Bollinger Bands and MACD.

• Deep Learning Model for Anomaly Detection: Building an autoencoder model using Keras for anomaly detection.

• Training the Anomaly Detection Model: Training the autoencoder on normal market data to learn the normal patterns.

• Anomaly Detection and Scoring: Using the trained model to score new data points for anomalies.

• Model Evaluation and Visualization: Evaluating the performance of the anomaly detection model and visualizing the results.

• Conclusion: Summary of the project and its potential implications.

This tutorial will guide you through each step, from acquiring and preprocessing financial data to building and training a robust anomaly detection model using Python. We’ll employ popular libraries like yfinance for data acquisition, pandas and numpy for data manipulation and Keras for constructing our deep learning model.