This project is a collection of Python codes that aim to replicate the Matlab codes for Dr. Ernest Chan's book Algorithmic Trading.
The file names for scripts reflect Dr. Chan's Matlab script names, for
example the code for Ratio.m is in Ratio.py.
All feedback, comments, pull requests are welcome.
Run pip install on
numpy
scipy
statsmodels
pandas
arch
There are three big data files that are hard to share through Github, they can be downloaded through the links below:
The code will assume these files are under [HOME]/Dropbox/Public/data folder.
I prefer to work with CSV files, the Pandas library makes it a breeze
to access them plus I can view the contents of CSV files easily,
manipulate them with Unix based tools if necessary, etc. For
converting any of mat files into csv, this is what I did. Find the
Matlab script from Dr. Chan's book that reads and prepares the data,
i.e. TU_mom.m, then find the point the data is all ready,
clear;
load('inputDataOHLCDaily_20120511', 'syms', 'tday', 'cl');
..
idx=strmatch('TU', syms, 'exact');
tday=tday(:, idx);
cl=cl(:, idx);
..
Now at this point you have tday,cl, all prepared, with the same
dimensions. We can create a data matrix out of these and write them to
disk, insert these in the script,
A = [tday cl];
save('/tmp/out','A');
exit;
and run it. Now from a seperate Python script,
from scipy import io as spio
import numpy as np
import pandas as pd
y = spio.loadmat('/tmp/out.mat')['A']
df = pd.DataFrame(y, columns=['Date','Close'])
df.to_csv('/tmp/out.csv',index=None)
This gives you a CSV file.
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Thhe code is licensed under GPL v3. See COPYING for details.