There are several Python libraries and tools that are commonly used for stock forecasting, including:
- Pandas: A library for data analysis and manipulation, used for working with tabular data and time series. It is widely used in financial analysis for tasks such as data cleaning, exploration, and manipulation.
- NumPy: A library for scientific computing with Python, used for arrays, matrices, and mathematical operations. It is widely used in financial modeling and analysis for tasks such as data manipulation and mathematical computation.
- Scikit-learn: A machine learning library for Python, providing a range of tools for data mining and analysis. It includes a wide range of supervised and unsupervised learning algorithms, including linear regression, decision trees, and k-means clustering.
- TensorFlow: A library for machine learning and deep learning, used for building and deploying neural networks. It is an open-source library for dataflow and differentiable programming across a range of tasks, and it is widely used for training and deploying machine learning models.
- Matplotlib: A library for data visualization, used for creating static, animated, and interactive plots and charts. It allows creating various types of plots such as line plots, scatter plots, bar plots, and heatmaps.
- Seaborn: A library for data visualization, built on top of Matplotlib, it provides a high-level interface for creating attractive statistical graphics.
- Pyfolio: A library for performance and risk analysis of financial portfolios. It allows analyzing and visualize the performance of financial portfolios, and it provides tools for risk management and analysis.
- TA-Lib: A library for technical analysis, it provides a wide range of tools for financial modeling and analysis, such as trend analysis, moving averages, and oscillators.
