Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you'll explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, and factor models, and modern machine learning and deep learning solutions.

You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, exploratory data analysis has been comprehensively covered to help you explore and visualize financial data. You'll also learn how to create elegant, interactive dashboards with Jupyter Notebook and Streamlit, to present the results of technical analyses. Finally, you'll become familiar with CNNs and RNNs, such as LSTM and GRU, as well as the most popular machine learning and deep learning models for analyzing and predicting financial trends and patterns.

Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.

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Spis treści

Python for Finance Cookbook. Over 80 powerful recipes for effective financial data analysis - Second Edition eBook -- spis treści

• 1. Acquiring Financial Data
• 2. Data Preprocessing
• 3. Visualizing Financial Time Series
• 4. Exploring Financial Time Series Data
• 5. Technical Analysis and Building Interactive Dashboards
• 6. Time Series Analysis and Forecasting
• 7. Machine Learning-Based Approaches to Time Series Forecasting
• 8. Multi-Factor Models
• 9. Modelling Volatility with GARCH Class Models
• 10. Monte Carlo Simulations in Finance
• 11. Asset Allocation
• 12. Backtesting Trading Strategies
• 13. Applied Machine Learning: Identifying Credit Default
• 14. Advanced Concepts for Machine Learning Projects
• 15. Deep Learning in Finance