Mastering Financial Pattern Recognition is popular PDF and ePub book, written by Sofien Kaabar in 2022-10-18, it is a fantastic choice for those who relish reading online the Business & Economics genre. Let's immerse ourselves in this engaging Business & Economics book by exploring the summary and details provided below. Remember, Mastering Financial Pattern Recognition can be Read Online from any device for your convenience.

Mastering Financial Pattern Recognition Book PDF Summary

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 back-test 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 book, 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 back-testing 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

Detail Book of Mastering Financial Pattern Recognition PDF

Mastering Financial Pattern Recognition
  • Author : Sofien Kaabar
  • Release : 18 October 2022
  • Publisher : "O'Reilly Media, Inc."
  • ISBN : 9781098120443
  • Genre : Business & Economics
  • Total Page : 291 pages
  • Language : English
  • PDF File Size : 7,5 Mb

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