Python. re
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This document discusses regular expressions (re) in Python. It provides examples of common regex patterns like \d, \s, ., *, +, ?, etc. It also demonstrates how to compile patterns, perform matches, searches, and find all occurrences using functions like re.compile(), re.match(), re.search(), and re.findall(). The document ends by asking if there are any questions.
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Participants got the chance to interact live with experts, ask questions, and gain practical, actionable skills in automated tradingmaking this webinar a useful resource for anyone serious about the future of trading technology.
In Session 1, renowned quant expert Dr. Ernest Chan explores the evolving role of Generative AI in finance, diving into advanced trading strategies that go beyond traditional language models (LLMs).
About the author:
Dr. Ernest P. Chan is a recognized expert in applying statistical models and machine learning to finance. He is the Founder and Chief Scientist at PredictNow.ai, where he helps investors make informed decisions using advanced data-driven insights. Additionally, he is the Founder and Non-executive Chairman of QTS Capital Management, LLC, which focuses on systematic trading strategies. Dr. Chan has worked at notable organizations like IBM Research, Morgan Stanley, and Credit Suisse, gaining experience in pattern recognition, data mining, and quantitative trading.
Dr. Chan obtained his PhD in Physics from Cornell University and his B.Sc. in Physics from the University of Toronto. He has also authored several influential books, including Quantitative Trading and Algorithmic Trading. He was an Adjunct Associate Professor of Finance at Nanyang Technological University in Singapore and an adjunct faculty at Northwestern Universitys Masters in Data Science program.
Dr. Chan combines extensive industry experience with deep technical knowledge, making him an excellent resource for understanding how to apply machine learning to trading effectively.
This webinar was conducted on: Thursday, April 10, 2025
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Python. re
- 2. Python. re import re Regular expression
- 3. Python. re d D s S w W b [] [amk] [0-5][0-9] '.' '^', '$' '*' '+' '?' {m} {m,n} (...) (?:...)
- 4. Python. compile compile(pattern, flags=0) re.I其 re.M其 re.U r = re.compile(b(ddsd)) m = r.match(text)
- 5. Python. match match(pattern, string, flags=0) m=re.match([a-z0-9_.-]+@[a-z0-9.-]+.w{2,4},text) m - match object m - None m.group(n)
- 6. Python. findall findall(pattern, string, flags=0) Return a list of all non-overlapping matches in the string Empty matches are included in the result
- 7. Python. findall text = hello, my friend! 1234 Hello, world 1616 ... It's time to understand 2323. Hiiiii! 4567 ooogh ... Good morning!!! 4345 heheheheh 5665 jfgjfjgjf 4545 ... That's all!!!! 7878 7678 0990 9900 7878''' results = re.findall('(d{2})1',text)
- 8. Python. search search(pattern, string, flags=0) match object None if no match was found m = re.search('(d{2})1',text)
- 9. Python. finditer finditer(pattern, string, flags=0) iterator over all non-overlapping matches iter = re.search('(d{2})1',text) for m in iter: ... print m.group(0)