Stock Anomaly Detector

Overview

The Stock Anomaly Detector is a command-line tool designed to identify and analyze unusual patterns in stock price data. It processes real-time stock information and applies statistical methods to detect anomalies, providing investors and analysts with timely insights for decision-making.

Features

• Real-time processing of stock data
• Customizable anomaly detection parameters
• Support for multiple stock symbols
• Flexible data input options (file-based or direct symbol input)
• Configurable historical data range
• Optional test mode for simulation using historical data

Requirements

• Python 3.8+
• Required Python packages (see requirements.txt)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/stock-anomaly-detector.git
   cd stock-anomaly-detector
   

2. Install required packages:

   pip install -r requirements.txt
   

3. Set up environment variables:

   • APCA_API_KEY_ID: Your Alpaca API key
   • APCA_API_SECRET_KEY: Your Alpaca API secret key

Usage

The program is executed from the command line with various parameters:

python main.py [OPTIONS]

Options:

• --file FILE: File containing stock symbols (one per line)
• --symbols SYMBOLS: Comma-separated list of stock symbols
• --ndays NDAYS: Number of days of historical data to fetch (default: 2)
• --debug: Enable debug logging
• --test: Run in test mode using historical data
• --days_ago DAYS_AGO: Number of days ago to start simulation in test mode (default: 1)
• --stream_type {trades,bars}: Choose data to subscribe to (default: trades)
• --sigma_thresh SIGMA_THRESH: Sigma threshold for alerts
• --zscore_trend_thresh ZSCORE_TREND_THRESH: Z-score trend threshold for alerts

Examples:

1. Process data for symbols in a file with default settings:

   python main.py --file symbols.txt
   

2. Process specific symbols with custom thresholds:

   python main.py --symbols AAPL,GOOGL,MSFT --sigma_thresh 3.0 --zscore_trend_thresh 2.5
   

3. Run in test mode with historical data:

   python main.py --file symbols.txt --test --days_ago 5 --ndays 30
   

Output

The program outputs log messages to the console and a log file (app.log). Alerts for detected anomalies are displayed in the following format:

ALERT: SYMBOL | Price: PRICE | Z-Score: ZSCORE | Act: ACTION | Samples Ago: SAMPLES | Z-Trend: ZTREND | Lambda: LAMBDA | Ext. Price: EXT_PRICE

Configuration

Adjust the config/config.json file to modify default parameters:

• sigma_thresh: Default sigma threshold for alerts
• zscore_trend_thresh: Default Z-score trend threshold for alerts
• lambda_multiplier: Multipliers for different timeframes
• timeframe: Default timeframe for data analysis

Troubleshooting

• Ensure your Alpaca API credentials are correctly set in the environment variables.
• Check the app.log file for detailed error messages and debugging information.
• Verify that the input file (if used) contains valid stock symbols, one per line.

Notes

• The program uses the Alpaca API for real-time and historical stock data. Ensure you have a valid Alpaca account and API credentials.
• Performance may vary based on the number of symbols processed and the chosen parameters.
• Use the --debug option for verbose logging during troubleshooting.

For further assistance or to report issues, please contact Joe_O.