advanced-algorithmic-trading

313
ax.set_axis_bgcolor((1,1,0.9))
ax.xaxis_date()
plt.setp(
plt.gca().get_xticklabels(),
rotation=45, horizontalalignment=’right’
)
plt.show()
The following function plot_3d_normalised_candles makes a scatter plot in three-dimensional
space of all of the candles, normalised by the open price. Each daily candle bar is coloured according
to cluster membership (which is determined in subsequent code snippets below):
def plot_3d_normalised_candles(data):
"""
Plot a 3D scatterchart of the open-normalised bars
highlighting the separate clusters by colour
"""
fig = plt.figure(figsize=(12, 9))
ax = Axes3D(fig, elev=21, azim=-136)
ax.scatter(
data["H/O"], data["L/O"], data["C/O"],
c=labels.astype(np.float)
)
ax.set_xlabel(’High/Open’)
ax.set_ylabel(’Low/Open’)
ax.set_zlabel(’Close/Open’)
plt.show()
The next function plot_cluster_ordered_candles is similar to the above candlestick plot,
except that it is now ordered by cluster membership, rather than date. In addition each cluster
boundary is visualised with a blue dotted line. The function is somewhat complex, but once
again this is mainly due to formatting issues with Matplotlib.
The latter section of the function involves creating a separate DataFrame called change_indices.
Its job is to determine the index at which a new cluster boundary is located. This is done by
sorting all elements by their cluster index and then using the diff method to obtain the change
points. This is then filtered by all values that do not equal zero, which returns a DataFrame
consisting of five rows, one for each boundary. This is then used by the Matplotlib axvline
method to plot the dotted blue line:
def plot_cluster_ordered_candles(data):
"""
Plot a candlestick chart ordered by cluster membership
with the dotted blue line representing each cluster
boundary.
"""
# Set the format for the axis to account for dates
# correctly, particularly Monday as a major tick
mondays = WeekdayLocator(MONDAY)