Successful Algorithmic Trading(原书加代码),包含全部章节的内容。
代码片段和文件信息
#!/usr/bin/python
# -*- coding: utf-8 -*-
# cadf.py
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
import pandas.io.data as web
import pprint
import statsmodels.tsa.stattools as ts
from pandas.stats.api import ols
def plot_price_series(df ts1 ts2):
months = mdates.MonthLocator() # every month
fig ax = plt.subplots()
ax.plot(df.index df[ts1] label=ts1)
ax.plot(df.index df[ts2] label=ts2)
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(mdates.DateFormatter(‘%b %Y‘))
ax.set_xlim(datetime.datetime(2012 1 1) datetime.datetime(2013 1 1))
ax.grid(True)
fig.autofmt_xdate()
plt.xlabel(‘Month/Year‘)
plt.ylabel(‘Price ($)‘)
plt.title(‘%s and %s Daily Prices‘ % (ts1 ts2))
plt.legend()
plt.show()
def plot_scatter_series(df ts1 ts2):
plt.xlabel(‘%s Price ($)‘ % ts1)
plt.ylabel(‘%s Price ($)‘ % ts2)
plt.title(‘%s and %s Price Scatterplot‘ % (ts1 ts2))
plt.scatter(df[ts1] df[ts2])
plt.show()
def plot_residuals(df):
months = mdates.MonthLocator() # every month
fig ax = plt.subplots()
ax.plot(df.index df[“res“] label=“Residuals“)
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(mdates.DateFormatter(‘%b %Y‘))
ax.set_xlim(datetime.datetime(2012 1 1) datetime.datetime(2013 1 1))
ax.grid(True)
fig.autofmt_xdate()
plt.xlabel(‘Month/Year‘)
plt.ylabel(‘Price ($)‘)
plt.title(‘Residual Plot‘)
plt.legend()
plt.plot(df[“res“])
plt.show()
if __name__ == “__main__“:
start = datetime.datetime(2012 1 1)
end = datetime.datetime(2013 1 1)
arex = web.DataReader(“AREX“ “yahoo“ start end)
wll = web.DataReader(“WLL“ “yahoo“ start end)
df = pd.Dataframe(index=arex.index)
df[“AREX“] = arex[“Adj Close“]
df[“WLL“] = wll[“Adj Close“]
# Plot the two time series
plot_price_series(df “AREX“ “WLL“)
# Display a scatter plot of the two time series
plot_scatter_series(df “AREX“ “WLL“)
# Calculate optimal hedge ratio “beta“
res = ols(y=df[‘WLL‘] x=df[“AREX“])
beta_hr = res.beta.x
# Calculate the residuals of the linear combination
df[“res“] = df[“WLL“] - beta_hr*df[“AREX“]
# Plot the residuals
plot_residuals(df)
# Calculate and output the CADF test on the residuals
cadf = ts.adfuller(df[“res“])
pprint.pprint(cadf)
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