advanced-algorithmic-trading

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Note: You will likely need to run pip install pykalman to install the PyKalman library as
it is not bundled with Anaconda.
The next step is write the function draw_date_coloured_scatterplot to produce a scatterplot
of the asset adjusted closing prices (such a scatterplot is inspired by that produced by
Aidan O’Mahony[74]). The scatterplot will be coloured using a Matplotlib colour map, specifically
"Yellow To Red", where yellow represents price pairs closer to 2010, while red represents
price pairs closer to 2016:
def draw_date_coloured_scatterplot(etfs, prices):
"""
Create a scatterplot of the two ETF prices, which is
coloured by the date of the price to indicate the
changing relationship between the sets of prices
"""
# Create a yellow-to-red colourmap where yellow indicates
# early dates and red indicates later dates
plen = len(prices)
colour_map = plt.cm.get_cmap(’YlOrRd’)
colours = np.linspace(0.1, 1, plen)
# Create the scatterplot object
scatterplot = plt.scatter(
prices[etfs[0]], prices[etfs[1]],
s=30, c=colours, cmap=colour_map,
edgecolor=’k’, alpha=0.8
)
# Add a colour bar for the date colouring and set the
# corresponding axis tick labels to equal string-formatted dates
colourbar = plt.colorbar(scatterplot)
colourbar.ax.set_yticklabels(
[str(p.date()) for p in prices[::plen//9].index]
)
plt.xlabel(prices.columns[0])
plt.ylabel(prices.columns[1])
plt.show()
I have commented the code to make it fairly straightforward to see what all of the commands
are doing. The main work is being done within the colour_map, colours and scatterplot
variables. It is given in Figure 13.1.
13.3.5 Time-Varying Slope and Intercept
The next step is to actually use PyKalman to dynamically adjust the intercept and slope between
TFT and IEI. This function is more complex and requires some explanation.
Firstly we define a variable called delta, which is used to control the transition covariance
for the system noise. This controls the value of W t . We simply multiply such a value by the