Python is a popular programming language for algo-trading due to its simplicity, extensive libraries, and strong community support. Here are some commonly used Python libraries for algo-trading:

1. NumPy: NumPy is a fundamental library for scientific computing in Python. It provides efficient numerical operations and multi-dimensional array manipulation, which are essential for data processing and analysis in algo-trading.

2. pandas: pandas is a powerful data manipulation and analysis library. It offers data structures and functions for handling time series data, such as OHLC (Open-High-Low-Close) prices, volume, and other financial data. pandas is widely used for data preprocessing, transformation, and manipulation in algo-trading.

3. matplotlib: matplotlib is a widely used plotting library in Python. It provides a variety of visualization tools for creating charts, graphs, and other visual representations of financial data. matplotlib is useful for analyzing price trends, plotting trading signals, and visualizing performance metrics.

4. scikit-learn: scikit-learn is a popular machine learning library in Python. It offers a wide range of algorithms and tools for classification, regression, clustering, and other machine learning tasks. scikit-learn can be used for developing and implementing machine learning-based trading strategies.

5. TensorFlow and Keras: TensorFlow is an open-source library for machine learning and deep learning, and Keras is a high-level neural networks API that runs on top of TensorFlow. These libraries are used for developing and training neural networks for complex trading strategies and predictive modeling.

6. statsmodels: statsmodels is a library for statistical modeling and analysis. It provides a range of statistical models, such as regression models, time series analysis, and econometric techniques. statsmodels is useful for statistical analysis and testing of trading strategies.

7. TA-Lib: TA-Lib is a popular technical analysis library that provides numerous indicators and functions for technical analysis of financial markets. It includes indicators like moving averages, Bollinger Bands, Relative Strength Index (RSI), and more. TA-Lib simplifies the implementation of common technical analysis indicators in trading strategies.

8. Backtrader: Backtrader is a versatile and feature-rich library for backtesting and live trading. It supports the development and testing of trading strategies using historical data, as well as real-time trading with various brokers. Backtrader offers flexibility in strategy development and has a broad range of built-in functionalities.

These are just a few examples of Python libraries commonly used in algo-trading. Depending on your specific requirements and strategies, there may be other libraries that can be relevant, such as pyfolio for portfolio analysis, Prophet for time series forecasting, or Optunity for hyperparameter optimization. It's important to explore and experiment with different libraries to find the ones that best suit your needs.