advanced-algorithmic-trading

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ClusterTomorrow to contain tomorrow’s cluster value. A ClusterMatrix column is then created
by forming a tuple of today’s cluster index and tomorrow’s cluster index. The Pandas
value_counts method is then used to create a frequency distribution of these pairs. Finally,
the K × K NumPy matrix is created and filled with the percentage frequency of occurance of
each cluster follow-on:
def create_follow_cluster_matrix(data):
"""
Creates a k x k matrix, where k is the number of clusters
that shows when cluster j follows cluster i.
"""
data["ClusterTomorrow"] = data["Cluster"].shift(-1)
data.dropna(inplace=True)
data["ClusterTomorrow"] = data["ClusterTomorrow"].apply(int)
sp500["ClusterMatrix"] = list(
zip(data["Cluster"], data["ClusterTomorrow"])
)
cmvc = data["ClusterMatrix"].value_counts()
clust_mat = np.zeros( (k, k) )
for row in cmvc.iteritems():
clust_mat[row[0]] = row[1]*100.0/len(data)
print("Cluster Follow-on Matrix:")
print(clust_mat)
The __main__ function ties all of the above functions together. It carries out the K-Means
algorithm and uses these cluster membership values in all subsequent functions:
if __name__ == "__main__":
# Obtain S&P500 pricing data from Yahoo Finance
start = datetime.datetime(2013, 1, 1)
end = datetime.datetime(2015, 12, 31)
sp500 = web.DataReader("^GSPC", "yahoo", start, end)
# Plot last year of price "candles"
plot_candlesticks(sp500, datetime.datetime(2015, 1, 1))
# Carry out K-Means clustering with five clusters on the
# three-dimensional data H/O, L/O and C/O
sp500_norm = get_open_normalised_prices(sp500, start, end)
k = 5
km = KMeans(n_clusters=k, random_state=42)
km.fit(sp500_norm)
labels = km.labels_
sp500["Cluster"] = labels
# Plot the 3D normalised candles using H/O, L/O, C/O
plot_3d_normalised_candles(sp500_norm)