Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create, trade, and back-test trading algorithms based on machine learning and reinforcement learning.

Sofien Kaabar--financial author, trading consultant, and institutional market strategist--introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents out-of-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization.

• Create and understand machine learning and deep learning models
• Explore the details behind reinforcement learning and see how it's used in trading
• Understand how to interpret performance evaluation metrics
• Examine technical analysis and learn how it works in financial markets
• Create technical indicators in Python and combine them with ML models for optimization
• Evaluate the profitability and the predictability of the models to understand their limitations and potential

深度學習在金融和交易領域迅速崛起。但對於許多專業交易員來說，這個複雜的領域聲名狼藉，被視為困難重重。這本實用指南教你如何使用Python從頭開始開發深度學習交易模型，並幫助你基於機器學習和強化學習創建、交易和回測交易算法。

Sofien Kaabar是金融作家、交易顧問和機構市場策略師，他介紹了結合技術分析和量化分析的深度學習策略。通過將深度學習概念與技術分析相結合，這本獨特的書籍在金融交易領域提出了獨特的想法。這本A-Z指南還包括對技術分析的全面介紹、機器學習算法的評估和算法優化。

- 創建並了解機器學習和深度學習模型
- 探索強化學習的細節，並了解其在交易中的應用
- 理解如何解讀績效評估指標
- 研究技術分析，並了解其在金融市場中的運作方式
- 使用Python創建技術指標，並將其與機器學習模型結合進行優化
- 評估模型的盈利能力和可預測性，以了解其限制和潛力