advanced-algorithmic-trading

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clustered using K-Means and then plotted in a three-dimensional scatterplot to visualise cluster
membership.
These cluster labels will then be applied back to the original candle data and used to visualise
cluster membership (as well as boundaries) on a cluster label-ordered candlestick chart. Finally,
a "follow-on matrix" will be created, which describes the frequency of tomorrow’s cluster being
j, if today’s cluster is i. Such a matrix is useful for ascertaining whether there is any scope for
forming a predictive trading strategy based on today’s cluster membership.
This section of code requires many imports. The majority of these are due to necessary
formatting options for Matplotlib. The copy standard library is brought in to make deep copies
of DataFrames so that they are not overwritten by each subsequent plot function. In addition
NumPy and Pandas are imported for data manipulation. Finally the KMeans module is imported
from Scikit-Learn:
# 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
The first function takes a symbol string for an equities ticker, as well as starting and ending
dates, and uses these to create a three-dimensional time series. Each dimension represents the
High, Low and Close price normalised by the Open price, respectively. The remaining columns
are dropped and the DataFrame is returned:
def get_open_normalised_prices(symbol, start, end):
"""
Obtains a pandas DataFrame containing open normalised prices
for high, low and close for a particular equities symbol
from Yahoo Finance. That is, it creates High/Open, Low/Open
and Close/Open columns.
"""
df = web.DataReader(symbol, "yahoo", start, end)
df["H/O"] = df["High"]/df["Open"]
df["L/O"] = df["Low"]/df["Open"]
df["C/O"] = df["Close"]/df["Open"]
df.drop(