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Financial Time-Series Anomaly Detection

Project Overview

This project focuses on detecting anomalies in financial time-series data, specifically stock price trends. The primary goal is to identify unusual behavior or outliers in stock movement that could signal major events, fraud, or trading opportunities.

We use a combination of statistical techniques and machine learning algorithms to analyze historical stock price data and flag anomalies in trends, volatility, and price fluctuations.

Objectives

  • Load and preprocess historical stock market data
  • Calculate key financial indicators (moving averages, volatility, returns)
  • Apply anomaly detection algorithms (Isolation Forest, DBSCAN)
  • Forecast trends using LSTM or Prophet
  • Visualize anomalies and prediction uncertainty

Dataset

  • Source: Yahoo Finance API
  • Sample Ticker: AAPL, MSFT, TSLA, etc.
  • Time Period: Adjustable (e.g., last 5 years)
  • Features Used:
    • Open, High, Low, Close, Volume
    • Technical indicators (Moving Average, RSI, Volatility, etc.)

You can change the ticker and time range to analyze different stocks.

Technologies Used

  • Python
    • pandas, numpy – data manipulation
    • matplotlib, seaborn, plotly – visualization
    • scikit-learn – Isolation Forest, DBSCAN
    • tensorflow / keras – LSTM (forecasting)
    • prophet – Facebook Prophet time-series model
  • yfinance – fetch real-time and historical stock data

Project Workflow

  1. Data Collection

    • Fetch historical stock data using yfinance

  2. Preprocessing

    • Handle missing values, compute log returns
    • Calculate rolling averages and volatility

  3. Anomaly Detection

    • Apply Isolation Forest and/or DBSCAN on engineered features
    • Label points as anomalous or normal

  4. Forecasting

    • Use LSTM or Prophet to model and forecast future stock trends
    • Evaluate using RMSE, MAE

  5. Visualization

    • Plot stock prices with anomalies highlighted
    • Forecast with uncertainty intervals

Results & Insights

  • Successfully detected price spikes/drops using Isolation Forest
  • DBSCAN detected density-based outliers in low-volume periods
  • Prophet forecasted trends with seasonality and trend changepoints
  • LSTM showed potential for multi-step forecasting but required tuning

Anomalies can help in building alert systems for financial risk or trading signals.

How to Run

  1. Clone the repository: 
    git clone https://github.com/yourusername/financial-anomaly-detection.git
cd financial-anomaly-detection