advanced-algorithmic-trading

319
# Apply the K-Means Algorithm for k=3, which is
# equal to the number of true Gaussian clusters
km3 = KMeans(n_clusters=3)
km3.fit(X)
km3_labels = km3.labels_
# Apply the K-Means Algorithm for k=4, which is
# larger than the number of true Gaussian clusters
km4 = KMeans(n_clusters=4)
km4.fit(X)
km4_labels = km4.labels_
# Create a subplot comparing k=3 and k=4
# for the K-Means Algorithm
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14,6))
ax1.scatter(X[:, 0], X[:, 1], c=km3_labels.astype(np.float))
ax1.set_xlabel("$x_1$")
ax1.set_ylabel("$x_2$")
ax1.set_title("K-Means with $k=3$")
ax2.scatter(X[:, 0], X[:, 1], c=km4_labels.astype(np.float))
ax2.set_xlabel("$x_1$")
ax2.set_ylabel("$x_2$")
ax2.set_title("K-Means with $k=4$")
plt.show()
# ohlc_clustering.py
import copy
import datetime
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.finance import candlestick_ohlc
import matplotlib.dates as mdates
from matplotlib.dates import (
DateFormatter, WeekdayLocator, DayLocator, MONDAY
)
import numpy as np
import pandas as pd
import pandas_datareader.data as web
from sklearn.cluster import KMeans
def get_open_normalised_prices(symbol, start, end):
"""