Anomaly Detection in Financial Markets

This project focuses on detecting anomalies in financial market data using machine learning techniques. Identifying anomalies such as unexpected price movements, market manipulations, or unusual trading patterns can help in risk management and fraud detection.

Features

• Identifies unusual price spikes, drops, and trading volume changes in real-time market data.
• Employs advanced techniques like LSTM, Isolation Forest, and Autoencoders for detecting financial anomalies.
• Helps investors and analysts identify market manipulations and fraudulent activities.
• Scalable to handle large, high-frequency trading data from multiple financial instruments.

Installation

To set up the Anomaly Detection in Financial Markets system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/anomaly-detection-financial-markets.git
   cd anomaly-detection-financial-markets

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start detecting anomalies in financial market data, run the main script:

python anomaly_detection.py

The script will analyze real-time market data, detect anomalies, and provide alerts on unusual activities.

Results

The system successfully identifies anomalies in financial markets, providing actionable insights into market manipulations, frauds, and unusual trading patterns.