ChartDirector Ver 4.1 (Python Edition)

Finance Chart (2)




This example demonstrate creating a full-featured finance chart, with candlesticks, moving averages, Donchian channel, volume bars, MACD and Stochastic indicators.

This example employs the FinanceChart library add-on to allow complex financial charts to be composed easily. In this example, the key steps are:

For simplicity and to allow this example to run without connecting to a real database, a RanTable object is used to simulate the data. RanTable is a ChartDirector utility class used for creating tables with random numbers.

Source Code Listing

[Standalone Version] pythondemo\finance2.py
#!/usr/bin/python
from FinanceChart import *

# Create a finance chart demo containing 100 days of data
noOfDays = 100

# To compute moving averages starting from the first day, we need to get extra data
# points before the first day
extraDays = 30

# In this exammple, we use a random number generator utility to simulate the data. We
# set up the random table to create 6 cols x (noOfDays + extraDays) rows, using 9 as
# the seed.
rantable = RanTable(9, 6, noOfDays + extraDays)

# Set the 1st col to be the timeStamp, starting from Sep 4, 2002, with each row
# representing one day, and counting week days only (jump over Sat and Sun)
rantable.setDateCol(0, chartTime(2002, 9, 4), 86400, 1)

# Set the 2nd, 3rd, 4th and 5th columns to be high, low, open and close data. The
# open value starts from 100, and the daily change is random from -5 to 5.
rantable.setHLOCCols(1, 100, -5, 5)

# Set the 6th column as the vol data from 5 to 25 million
rantable.setCol(5, 50000000, 250000000)

# Now we read the data from the table into arrays
timeStamps = rantable.getCol(0)
highData = rantable.getCol(1)
lowData = rantable.getCol(2)
openData = rantable.getCol(3)
closeData = rantable.getCol(4)
volData = rantable.getCol(5)

# Create a FinanceChart object of width 600 pixels
c = FinanceChart(600)

# Add a title to the chart
c.addTitle("Finance Chart Demonstration")

# Set the data into the finance chart object
c.setData(timeStamps, highData, lowData, openData, closeData, volData, extraDays)

# Add a slow stochastic chart with %K = 14 and %D = 3
c.addSlowStochastic(70, 14, 3, 0x006060, 0x606000)

# Add the main chart with 210 pixels in height
c.addMainChart(210)

# Add a 10 period simple moving average to the main chart, using brown color
c.addSimpleMovingAvg(10, 0x663300)

# Add a 20 period simple moving average to the main chart, using purple color
c.addSimpleMovingAvg(20, 0x9900ff)

# Add an HLOC symbols to the main chart, using green/red for up/down days
c.addCandleStick(0x00ff00, 0xff0000)

# Add 20 days donchian channel to the main chart, using light blue (9999ff) as the
# border and semi-transparent blue (c06666ff) as the fill color
c.addDonchianChannel(20, 0x9999ff, 0xc06666ffL)

# Add a 70 pixels volume bars sub-chart to the bottom of the main chart, using
# green/red/grey for up/down/flat days
c.addVolBars(70, 0x99ff99, 0xff9999, 0x808080)

# Append a MACD(26, 12) indicator chart (70 pixels height) after the main chart,
# using 9 days for computing divergence.
c.addMACD(70, 26, 12, 9, 0x0000ff, 0xff00ff, 0x008000)

# output the chart
c.makeChart("finance2.png")

[CGI Version] pythondemo_cgi\finance2.py
#!/usr/bin/python
from FinanceChart import *

# Create a finance chart demo containing 100 days of data
noOfDays = 100

# To compute moving averages starting from the first day, we need to get extra data
# points before the first day
extraDays = 30

# In this exammple, we use a random number generator utility to simulate the data. We
# set up the random table to create 6 cols x (noOfDays + extraDays) rows, using 9 as
# the seed.
rantable = RanTable(9, 6, noOfDays + extraDays)

# Set the 1st col to be the timeStamp, starting from Sep 4, 2002, with each row
# representing one day, and counting week days only (jump over Sat and Sun)
rantable.setDateCol(0, chartTime(2002, 9, 4), 86400, 1)

# Set the 2nd, 3rd, 4th and 5th columns to be high, low, open and close data. The
# open value starts from 100, and the daily change is random from -5 to 5.
rantable.setHLOCCols(1, 100, -5, 5)

# Set the 6th column as the vol data from 5 to 25 million
rantable.setCol(5, 50000000, 250000000)

# Now we read the data from the table into arrays
timeStamps = rantable.getCol(0)
highData = rantable.getCol(1)
lowData = rantable.getCol(2)
openData = rantable.getCol(3)
closeData = rantable.getCol(4)
volData = rantable.getCol(5)

# Create a FinanceChart object of width 600 pixels
c = FinanceChart(600)

# Add a title to the chart
c.addTitle("Finance Chart Demonstration")

# Set the data into the finance chart object
c.setData(timeStamps, highData, lowData, openData, closeData, volData, extraDays)

# Add a slow stochastic chart with %K = 14 and %D = 3
c.addSlowStochastic(70, 14, 3, 0x006060, 0x606000)

# Add the main chart with 210 pixels in height
c.addMainChart(210)

# Add a 10 period simple moving average to the main chart, using brown color
c.addSimpleMovingAvg(10, 0x663300)

# Add a 20 period simple moving average to the main chart, using purple color
c.addSimpleMovingAvg(20, 0x9900ff)

# Add an HLOC symbols to the main chart, using green/red for up/down days
c.addCandleStick(0x00ff00, 0xff0000)

# Add 20 days donchian channel to the main chart, using light blue (9999ff) as the
# border and semi-transparent blue (c06666ff) as the fill color
c.addDonchianChannel(20, 0x9999ff, 0xc06666ffL)

# Add a 70 pixels volume bars sub-chart to the bottom of the main chart, using
# green/red/grey for up/down/flat days
c.addVolBars(70, 0x99ff99, 0xff9999, 0x808080)

# Append a MACD(26, 12) indicator chart (70 pixels height) after the main chart,
# using 9 days for computing divergence.
c.addMACD(70, 26, 12, 9, 0x0000ff, 0xff00ff, 0x008000)

# output the chart
print "Content-type: image/png\n"
binaryPrint(c.makeChart2(PNG))