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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

By : Ivan Idris
2 (1)
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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

2 (1)
By: Ivan Idris

Overview of this book

This book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.
Table of Contents (16 chapters)
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14
C. NumPy Functions' References
15
Index

Time for action – detecting a trend in QQQ

Often we are more interested in the trend of a data sample than in detrending it. We can still get the trend back easily after detrending. Let's do that for one year of price data for QQQ.

  1. Write code that gets the close price and corresponding dates for QQQ:
    today = date.today()
    start = (today.year - 1, today.month, today.day)
    
    quotes = quotes_historical_yahoo("QQQ", start, today)
    quotes = np.array(quotes)
    
    dates = quotes.T[0]
    qqq = quotes.T[4]
  2. Detrend the signal:
    y = signal.detrend(qqq)
  3. Create month and day locators for the dates:
    alldays = DayLocator()
    months = MonthLocator()
  4. Create a date formatter that creates a string of month name and year:
    month_formatter = DateFormatter("%b %Y")
  5. Create a figure and subplot:
    fig = plt.figure()
    ax = fig.add_subplot(111)
  6. Plot the data and underlying trend by subtracting the detrended signal:
    plt.plot(dates, qqq, 'o', dates, qqq - y, '-')
  7. Set the locators and formatter...

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