advanced-algorithmic-trading

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alldays = DayLocator()
weekFormatter = DateFormatter("")
fig, ax = plt.subplots(figsize=(16,4))
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
# Sort the data by the cluster values and obtain
# a separate DataFrame listing the index values at
# which the cluster boundaries change
df = copy.deepcopy(data)
df.sort_values(by="Cluster", inplace=True)
df.reset_index(inplace=True)
df["clust_index"] = df.index
df["clust_change"] = df["Cluster"].diff()
change_indices = df[df["clust_change"] != 0]
# Plot the OHLC chart with cluster-ordered "candles"
csticks = candlestick_ohlc(
ax, df[
["clust_index", ’Open’, ’High’, ’Low’, ’Close’]
].values, width=0.6,
colorup=’#000000’, colordown=’#ff0000’
)
ax.set_axis_bgcolor((1,1,0.9))
# Add each of the cluster boundaries as a blue dotted line
for row in change_indices.iterrows():
plt.axvline(
row[1]["clust_index"],
linestyle="dashed", c="blue"
)
plt.xlim(0, len(df))
plt.setp(
plt.gca().get_xticklabels(),
rotation=45, horizontalalignment=’right’
)
plt.show()
The final function is create_follow_cluster_matrix. Its job is to produce a K×K matrix,
where K is the number of selected clusters in the K-Means Clustering process. Each element of
the matrix represents the percentage frequency of cluster j being the daily follow-on cluster to
cluster i. This is useful in a quantitative trading setting as it allows determination of the sample
distribution of cluster changes.
The matrix is constructed using the Pandas shift method, which allows a new column