We can now use Chromebooks for data science or quantitative research using Chromebooks with the introduction of Linux Debian distribution via the Crostini add-on in Chrome OS! This provides us with a hybrid solution of being able to perform intitial data explorations and training simple models locally before transitioning to Google CoLab for more heavy duty number crunching! See my post on Introduction to Google Colab for more details.
This article was written based on a request from UQBS marketing for the Momentum Magazine. Two additional peer-reviewed publications on cryptocurrency arose from my collaboration with Professor Emeritus Terry Marsh of UC Berkeley Haas School of Business on this topic. The links to these publications can be found on my publications page.
We briefly explore the mathematical and explanatory description of key asset pricing models (i.e., CAPM, Fama-French 3 Factor, Fama-French 5 factor), and how to run these models in Python.
We go through a quick tutorial on using pandas.read_csv and the pandas_datareader specifically for downloading data from Ken French's website. We will extract the following datasets
Import the necessary modules for file management and change the working directories accordingly
from pathlib import Path
import sys
import os
home = str(Path.home())
if sys.platform == 'linux':
inputDir = '/datasets/indices/'
elif sys.platform == 'win32':
inputDir = '\\datasets\indices\\'
fullDir = home+inputDir
os.chdir(fullDir)
import pandas as pd
As the dates from the Ken French website CSVs are in the format 201008, we need to write a function to parse this data. We show that parsing a input string like 192607 is parsed correctly.
dateparse = lambda x: pd.datetime.strptime(x,'%Y%m')
dateparse('192607')
Use read_csv from pandas
dataparse lambda function that was createdRead the full timeseries of industry and risk factor data.
readDir = fullDir
readFile = readDir + 'FF_10_Industry_Portfolios.CSV'
df_10indus_m = pd.read_csv(readFile,skiprows=11,nrows=1107,index_col=0,parse_dates=True,date_parser=dateparse)
readFile = readDir + 'FF_Research_Data_5_Factors_2x3.CSV'
df_5fac_m = pd.read_csv(readFile,skiprows=3,nrows=663,index_col=0,parse_dates=True,date_parser=dateparse)
Select the dates relevant for DeMiguel et al. (2009)
dfAsset = df_10indus_m.loc['1963-07-01':'2004-11-01']
dfFac = df_5fac_m.loc['1963-07-01':'2004-11-01']
df_10indus_m.head()
dfFac.head()
import pandas_datareader.data as web # module for reading datasets directly from the web
from pandas_datareader.famafrench import get_available_datasets
import pickleshare
We extract all the available datasets from Ken French's website and find that there are 286 of them. We can opt to see all the datasets available.
datasets = get_available_datasets()
print('No. of datasets:{0}'.format(len(datasets)))
#datasets # comment out if you want to see all the datasets
We are looking for a dataset of US 10 industries, thus use the keywords '10' and 'industry' to find out what the names of the relevant datasets.
df_10_industry = [dataset for dataset in datasets if '10' in dataset and 'Industry' in dataset]
print(df_10_industry)
We select 10_Industry_Portfolios from July 1963 to November 2004 (as per DeMiguel et al., 2009) If you do not have start or end dates, the default will extract portfolios from 2010 to the latest datapoints available
ds_industry = web.DataReader(df_10_industry[0],'famafrench',start='1963-07-01',end='2004-11-01') # Taking [0] as extracting '10_Industry_Portfolios'
Obtaining data from the datareader returns a dict. Thus we want to see what is inside the dict.
print(type(ds_industry))
ds_industry.keys()
We find that there are keys from 0-7, and also a DESCR. We read the contents of DESCR and find that it explains what dataset each key from 0-7 corresponds to.
print(ds_industry['DESCR'])
DeMiguel et al. (2009), use average value-weighted returns, thus we will use dataset 0.
ds_industry[0].head()
We perform the same process above Fama-French risk factor portfolio dataset
df_5_factor = [dataset for dataset in datasets if '5' in dataset and 'Factor' in dataset]
print(df_5_factor)
ds_factors = web.DataReader(df_5_factor[0],'famafrench',start='1963-07-01',end='2004-11-01') # Taking [0] as extracting 1F-F-Research_Data_Factors_2x3')
print('\nKEYS\n{0}'.format(ds_factors.keys()))
print('DATASET DESCRIPTION \n {0}'.format(ds_factors['DESCR']))
ds_factors[0].head()
We create copies of the industry and risk factor returns that we read from Ken French's website into dfAsset and dfFactor respectively.
dfAsset = ds_industry[0].copy()/100
dfFactor = ds_factors[0].copy()/100
We create excess returns by subtracting the risk-free rate from the asset returns
dfXsAsset = dfAsset.sub(dfFactor['RF'],axis=0)
dfXsAsset.head()
We pickle our files that now can be retrieved from other notebooks.
storeDir = fullDir+'/pickleshare'
db = pickleshare.PickleShareDB(storeDir)
db['dfXsAsset'] = dfXsAsset
db['dfFactor'] = dfFactor
This article was written based on a request from UQBS marketing by as a commentary on the findings of the Royal Comission into Misconduct in the Banking, Superannuation and Financial Services Industry. It was submitted to the Conversation, Australia. Below is the full copy of the article
This is a walk-through of how to set up a static blog with the Pelican module in Python that allows you to showcase your Jupyter notebooks. There are several websites that sort of show you how to do this, but not quite comprehensively in my view.
This article was written based on an expert opinion request from UQBS marketing by the Australian Financial Review.