Mastering Financial Pattern Recognition: Finding and Back-Testing Candlestick Patterns with Python
暫譯: 掌握金融模式識別:使用 Python 尋找和回測蠟燭圖模式
Kaabar, Sofien
買這商品的人也買了...
-
簡報原力 ─ 邁向完美簡報的十堂必修課$320$272 -
Machine Learning for Algorithmic Trading, 2/e (Paperback)$2,100$1,995 -
$2,592Artificial Intelligence in Finance: A Python-Based Guide -
$1,980Training Data for Machine Learning: Human Supervision from Annotation to Data Science -
內網滲透實戰攻略$594$564 -
Power BI x Copilot x ChatGPT 商業報表設計入門:資料清理、資料模型、資料視覺化到報表共享建立全局觀念$630$497 -
Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python$2,195$2,079 -
AI 神助攻!程式設計新境界 – GitHub Copilot 開發 Python 如虎添翼 : 提示工程、問題分解、測試案例、除錯$560$442 -
生成式 AI:以 ChatGPT 與 OpenAI 模型實現高效創新 (Modern Generative AI with ChatGPT and OpenAI Models)$500$395 -
資料科學:困難部分 (Data Science: The Hard Parts: Techniques for Excelling at Data Science)$680$537 -
Python x AI 辦公室作業自動化 : Word、Excel、PowerPoint、PDF、CSV、Pandas -- 多執行緒、排程、藝術二維碼、短網址、電子郵件、爬蟲$880$695 -
Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines (Paperback)$2,565$2,430 -
$2,520AI Engineering : Building Applications with Foundation Models (Paperback) -
讓 AI 好好說話!從頭打造 LLM (大型語言模型) 實戰秘笈$680$537 -
生成式 AI 入門 – 揭開 LLM 潘朵拉的秘密 : 語言建模、訓練微調、隱私風險、合成媒體、認知作戰、社交工程、人機關係、AI Agent、OpenAI、DeepSeek (Introduction to Generative AI)$580$458 -
版本控制使用 Git, 3/e (Version Control with Git: Powerful Tools and Techniques for Collaborative Software Development, 3/e)$880$695 -
Microsoft Azure 學習手冊|雲端運算與雲端系統開發的關鍵知識 (Learning Microsoft Azure: Cloud Computing and Development Fundamentals)$880$695 -
Microsoft Azure AI Services 與 Azure OpenAI 開發基礎必修課 -- 使用 C#$550$435 -
AI 應用程式開發|活用 ChatGPT 與 LLM 技術開發實作, 2/e (Developing Apps with GPT-4 and ChatGPT: Build Intelligent Chatbots, Content Generators, and More, 2/e)$680$537
商品描述
Candlesticks have become a key component of platforms and charting programs for financial trading. With these charts, traders can learn underlying patterns for interpreting price action history and forecasts. This A-Z guide shows portfolio managers, quants, strategists, and analysts how to use Python to recognize, scan, trade, and backtest the profitability of candlestick patterns.
Financial author, trading consultant, and institutional market strategist Sofien Kaabar shows you how to create a candlestick scanner and indicator so you can compare the profitability of these patterns. With this hands-on guide, you'll also explore a new type of charting system similar to candlesticks, as well as new patterns that have never been presented before.
With this book, you will:
- Create and understand the conditions required for classic and modern candlestick patterns
- Learn the market psychology behind them
- Use a framework to learn how backtesting trading strategies are conducted
- Explore different charting systems and understand their limitations
- Import OHLC historical FX data in Python in different time frames
- Use algorithms to scan for and reproduce patterns
- Learn a pattern's potential by evaluating its profitability and predictability
商品描述(中文翻譯)
蠟燭圖已成為金融交易平台和圖表程式的關鍵組成部分。透過這些圖表,交易者可以學習解讀價格行為歷史和預測的潛在模式。本書提供了從A到Z的指南,向投資組合經理、量化分析師、策略師和分析師展示如何使用Python來識別、掃描、交易和回測蠟燭圖模式的獲利能力。
金融作家、交易顧問及機構市場策略師Sofien Kaabar將向您展示如何創建蠟燭圖掃描器和指標,以便您能夠比較這些模式的獲利能力。透過這本實用指南,您還將探索一種類似於蠟燭圖的新型圖表系統,以及從未呈現過的新模式。
透過本書,您將:
- 創建並理解經典和現代蠟燭圖模式所需的條件
- 學習其背後的市場心理
- 使用框架學習如何進行交易策略的回測
- 探索不同的圖表系統並理解其限制
- 在Python中以不同時間框架導入OHLC歷史外匯數據
- 使用算法掃描和重現模式
- 通過評估模式的獲利能力和可預測性來了解其潛力