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Financial Charts / Graphs in Ruby or Python

What are my best options for creating a financial open-high-low-close (OHLC) chart in a high level language like Ruby or Python? While there seem to be a lot of options for graphing, I haven't seen any gems or eggs with this kind of chart.

http://en.wikipedia.org/wiki/Open-high-low-close_chart (but I don't need the moving average or Bollinger bands)

JFreeChart can do this in Java, but I'd like to make my codebase as small and simple as possible.

Thanks!

like image 678
Eric the Red Avatar asked Jul 06 '09 21:07

Eric the Red


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4 Answers

You can use matplotlib and the the optional bottom parameter of matplotlib.pyplot.bar. You can then use line plot to indicate the opening and closing prices:

For example:

#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import lines

import random


deltas = [4, 6, 13, 18, 15, 14, 10, 13, 9, 6, 15, 9, 6, 1, 1, 2, 4, 4, 4, 4, 10, 11, 16, 17, 12, 10, 12, 15, 17, 16, 11, 10, 9, 9, 7, 10, 7, 16, 8, 12, 10, 14, 10, 15, 15, 16, 12, 8, 15, 16]
bases = [46, 49, 45, 45, 44, 49, 51, 52, 56, 58, 53, 57, 62, 63, 68, 66, 65, 66, 63, 63, 62, 61, 61, 57, 61, 64, 63, 58, 56, 56, 56, 60, 59, 54, 57, 54, 54, 50, 53, 51, 48, 43, 42, 38, 37, 39, 44, 49, 47, 43]


def rand_pt(bases, deltas):
    return [random.randint(base, base + delta) for base, delta in zip(bases, deltas)]

# randomly assign opening and closing prices 
openings = rand_pt(bases, deltas)
closings = rand_pt(bases, deltas)

# First we draw the bars which show the high and low prices
# bottom holds the low price while deltas holds the difference 
# between high and low.
width = 0
ax = plt.axes()
rects1 = ax.bar(np.arange(50), deltas, width, color='r', bottom=bases)

# Now draw the ticks indicating the opening and closing price
for opening, closing, bar in zip(openings, closings, rects1):
    x, w = bar.get_x(), 0.2

    args = {
    }

    ax.plot((x - w, x), (opening, opening), **args)
    ax.plot((x, x + w), (closing, closing), **args)


plt.show()

creates a plot like this:

enter image description here

Obviously, you'd want to package this up in a function that drew the plot using (open, close, min, max) tuples (and you probably wouldn't want to randomly assign your opening and closing prices).

like image 128
Aaron Maenpaa Avatar answered Oct 21 '22 05:10

Aaron Maenpaa


You can use Pylab (matplotlib.finance) with Python. Here are some examples: http://matplotlib.sourceforge.net/examples/pylab_examples/plotfile_demo.html . There is some good material specifically on this problem in Beginning Python Visualization.

Update: I think you can use matplotlib.finance.candlestick for the Japanese candlestick effect.

like image 30
unmounted Avatar answered Oct 21 '22 05:10

unmounted


Have you considered using R and the quantmod package? It likely provides exactly what you need.

like image 26
David Locke Avatar answered Oct 21 '22 04:10

David Locke


Some examples about financial plots (OHLC) using matplotlib can be found here:

  • finance demo

    #!/usr/bin/env python
    from pylab import *
    from matplotlib.dates import  DateFormatter, WeekdayLocator, HourLocator, \
         DayLocator, MONDAY
    from matplotlib.finance import quotes_historical_yahoo, candlestick,\
         plot_day_summary, candlestick2
    
    # (Year, month, day) tuples suffice as args for quotes_historical_yahoo
    date1 = ( 2004, 2, 1)
    date2 = ( 2004, 4, 12 )
    
    
    mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays
    alldays    = DayLocator()              # minor ticks on the days
    weekFormatter = DateFormatter('%b %d')  # Eg, Jan 12
    dayFormatter = DateFormatter('%d')      # Eg, 12
    
    quotes = quotes_historical_yahoo('INTC', date1, date2)
    if len(quotes) == 0:
        raise SystemExit
    
    fig = figure()
    fig.subplots_adjust(bottom=0.2)
    ax = fig.add_subplot(111)
    ax.xaxis.set_major_locator(mondays)
    ax.xaxis.set_minor_locator(alldays)
    ax.xaxis.set_major_formatter(weekFormatter)
    #ax.xaxis.set_minor_formatter(dayFormatter)
    
    #plot_day_summary(ax, quotes, ticksize=3)
    candlestick(ax, quotes, width=0.6)
    
    ax.xaxis_date()
    ax.autoscale_view()
    setp( gca().get_xticklabels(), rotation=45, horizontalalignment='right')
    
    show()
    

enter image description here

  • finance work 2
like image 30
karlacio Avatar answered Oct 21 '22 06:10

karlacio