5 Python Data Science Problems

I need help with these 5 problems. Unfortunately there's no way I can post the data along with the code, but it's just publicly available Apple, Microsoft, and Google stock price data that includes dates. Any help would be appreciated.

#%% [markdown]

# Let us try to put together our idea of getting some useful stock market data for future data analysis.

#

# We will first take one stock, pull in the basic and free data from online, store it into a Stock class object.

# Next, derive some useful numbers out of it.

# We are focusing on the data collection and basic manipulation part here, storing the info in an convenient object form.

#

# When that is done, we will elevate the stock object into a basket of stocks (called electronic trading fund-ETF).

# Step by step, we will build it from the ground up.

#

# Fill in the steps below to make everything functional as instructed

#%%

# Step 0, try reading the data file to make sure it works.

# Run this cell as is. If you see some output in the interactive python window, then it is

# working. If not, you might need to fix the file path accordingly for your OS/platform.

# filepath = "/Users/edwinlo/GDrive_GWU/github_elo/GWU_classes_p/DATS_6103_DataMining/Class04_OOP/AAPL_20140912_20190912_daily_eod_vol.csv"

appl_date = []

appl_price_eod = []

filepath = os.path.join( os.getcwd(), "AAPL_20140912_20190912_daily_eod_vol.csv")

fh = open(filepath) # fh stands for file handle

# data pulled from https://old.nasdaq.com/symbol/aapl/historical (5 years, csv format, 9/12/2019) can also try www.quandl.com/EOD

for aline in fh.readlines(): # readlines creates a list of elements; each element is a line in the txt file, with an ending line return character.

# this file gives "23.57" as the string, including the quotes

tmp = aline.split(',')

appl_date.append(tmp[0].strip())

appl_price_eod.append(float(tmp[1]))

print(appl_date)

print(appl_price_eod)

#%%

# Step 1

# Create a class for a stock with daily end-of-day price recorded, along with the daily volume.

#

class Stock:

"""

Stock class of a publicly traded stock on a major market

"""

def __init__(self, symbol, name, firstdate, lastdate, init_filepath) :

"""

:param symbol: stock symbol

:param name: company name

:param firstdate: the first date (end of list) of the price list

:param lastdate: the last date (beginning of list) of the price list

:param init_filepath: locate the file for date, price (eod) and volume lists

"""

# note that the complete list of properties/attributes below has more than items than

# the numnber of arguments of the constructor. That's perfectly fine.

# Some property values are to be assigned later after instantiation.

self.symbol = symbol.upper()

self.name = name

self.firstdate = firstdate

self.lastdate = lastdate

# below can be started with empty lists, then read in data file and calculate the rest

self.price_eod = [] # record the end-of-day prices of the stock in a list. The 0-th position is the latest end-of-day price

self.volumes = [] # a list recording the daily trading volumn

self.dates = [] # starts from the latest/newest date,

self.delta1 = [] # daily change values, today's close price minus the previous close price. Example eod[0] - eod[1], eod[1] - eod[2],

self.delta2 = [] # daily change values, the previous close minus today's close, example eod[0] - eod[1], eod[1] - eod[2]

# change of the daily change values (second derivative, acceleration),

# given by, for the first entry, (delta1[0] - delta[1]),

# or if we want to, equals to (eod[0]-eod[1]) - (eod[1]-eod[2]) = eod[0] - 2*eod[1] + eod[2]

self.import_history(init_filepath)

self.compute_delta1_list() # Calculate the daily change values from stock price itself.

self.compute_delta2_list() # Calculate the daily values of whether the increase or decrease of the stock price is accelerating. A.k.a. the second derivative.

def import_history(self, filepath):

"""

import stock history from csv file, with colunms date, eod_price, volume, and save them to the lists

"""

with open(filepath,'r') as fh: # leaving the filehandle inside the "with" clause will close it properly when done. Otherwise, remember to close it when finished

for aline in fh.readlines(): # readlines creates a list of elements; each element is a line in the txt file, with an ending line return character.

# ###### QUESTION 1 ###### QUESTION 1 ###### QUESTION 1 ###### QUESTION 1 ######

# Fill in the codes here to put the right info in the lists self.dates, self.price_eod, self.volumes

# Should be similar to the codes in Step 0 above.

# ###### END QUESTION 1 ###### END QUESTION 1 ###### END QUESTION 1 ###### END QUESTION 1 ######

# fh.close() # close the file handle when done if it was not inside the "with" clause

# print('fh closed:',fh.closed) # will print out confirmation fh closed: True

return self

def compute_delta1_list(self):

"""

compute the daily change for the entire list of price_eod

"""

# goal: calculate the daily price change from the eod prices.

# idea:

# 1. duplicate the eod list

# 2. shift this new list by removing the 0-th element.

# 3. use the map function to find a list of delta's by subtracting the eod list from this new list.

# Okay, let's try

#

# eod_shift1 = self.price_eod # THIS WILL NOT WORK. Try. A shallow copy. We'll talk more about that next class.

eod_shift1 = self.price_eod.copy() # if you do not use the copy method here, you will get a shallow copy.

# The list here is a simple list of floats, not list of lists or list of dictionaries.

# So the copy() function will work. No need for other "deepcopy" variations

eod_shift1.pop(0) # remove the first element (shifting the day)

self.delta1 = list(map(lambda x,y: x-y, self.price_eod, eod_shift1))

print(self.name.upper(),": The latest 5 daily changes in delta1: ")

for i in range(0,5): print(self.delta1[i]) # checking the first five values

return self

def compute_delta2_list(self):

"""

compute the daily change for the entire list of delta1, essentially the second derivatives for price_eod

"""

# essentially the same function as compute_delta1_list. With some hindsight, or when the codes are re-factored, we can properly combine them

# ###### QUESTION 2 ###### QUESTION 2 ###### QUESTION 2 ###### QUESTION 2 ######

# Fill in the codes here

# Need to find the daily changes of the daily change, and save it to the list self.delta2

# It is the second derivative, the acceleration (or deceleration if negative) of the stock momentum.

# Essentially the same as compute_delta1_list, just on a different list

# Again you might want to print out the first few values of the delta2 list to inspect

# ###### END QUESTION 2 ###### END QUESTION 2 ###### END QUESTION 2 ###### END QUESTION 2 ######

return self

def add_newday(self, newdate, newprice, newvolume):

"""

add a new data point at the beginning of lists

"""

# ###### QUESTION 3 ###### QUESTION 3 ###### QUESTION 3 ###### QUESTION 3 ######

# After we have the batch of historical data to import, we

# most likely will need to do some daily updates (cron jobs, for example)

# going forward. There is no need to re-import the old data.

# This method is then used to insert just one row of new data point daily.

# We will need to insert the new date, the new eod value, the new delta1 value,

# the new delta2 value, as well as the new volume data.

#

# insert new price data to price_eod

# calculate and insert new data to delta1

# calculate and insert new data to delta2

# insert newdate to dates[]

#

# Fill in the codes here

#

# insert newdate to dates[]

self.dates.insert(?????????)

# insert newvolume to volumes[]

self.volumes.insert(?????????)

# insert new eod data value to price_eod

self.price_eod.insert(???????)

# calculate and insert new data to delta1

self.delta1.insert(????????)

# calculate and insert new data to delta2

self.delta2.insert(??????????)

#

# ###### END QUESTION 3 ###### END QUESTION 3 ###### END QUESTION 3 ###### END QUESTION 3 ######

return self

def nday_change_percent(self,n):

"""

calculate the percentage change in the last n days, returning a percentage between 0 and 100

"""

# ###### QUESTION 4 ###### QUESTION 4 ###### QUESTION 4 ###### QUESTION 4 ######

change = ?????? # calculate the change of price between newest price and n days ago

percent = ?????? # calculate the percent change (using the price n days ago as the base)

print(self.symbol,": Percent change in",n,"days is {0:.2f}".format(percent))

# ###### END QUESTION 4 ###### END QUESTION 4 ###### END QUESTION 4 ###### END QUESTION 4 ######

return percent

#%%

import os

# dirpath = os.getcwd() # print("current directory is : " + dirpath)

# filepath = dirpath+'/AAPL_20140912_20190912_daily_eod_vol.csv' # lastdate is 9/12/19, firstdate is 9/12/14,

# using os.path.join will take care of difference between

# mac/pc/platform issues how folder paths are used, backslash/forward-slash/etc

filepath = os.path.join( os.getcwd(), 'AAPL_20140912_20190912_daily_eod_vol.csv')

aapl = Stock('AAPL','Apple Inc','9/12/14','9/12/19',filepath)

#%%

# Great! Now we can get the competitors easily

filepath = os.path.join( os.getcwd(), 'MSFT_20140912_20190912_daily_eod_vol.csv')

msft = Stock('MSFT','Microsoft Inc','9/12/14','9/12/19',filepath)

filepath = os.path.join( os.getcwd(), 'GOOG_20140912_20190912_daily_eod_vol.csv')

goog = Stock('GOOG','Alphabet Inc','9/12/14','9/12/19',filepath)

#%%

# Next step, create a class for a basket of stocks, called electronic trading fund ETF

#

class ETF:

"""

ETF class of a collection of Stocks in a similar/related sector

"""

def __init__(self, name, sector, firstdate, lastdate) :

self.name = name

self.sector = sector

self.firstdate = firstdate

self.lastdate = lastdate

# below can be started with empty lists, then update and compute the rest later

self.stocks = {} # a dictionary in the format { 'AAPL': aaplStockObject, 'MSFT': msftStockObject, 'GOOG': googStockObject }

self.index_eod = [] # The EFT is an index fund, which has an eod price as well, calculated from the basket of stocks.

self.index_delta1 = [] # So we also need to calculate the daily changes delta1

self.index_delta2 = []

def add_stock(self, stock):

"""

add a stock (Stock class) to the dict/list self.stocks

:param stock: a Stock class instance

"""

# check if already exist stock list

if stock.symbol in self.stocks.keys():

print("new stock symbol already exist in stock list (dict): ", stock.symbol)

# return self # exit from the function

# continue below if not exist/duplicate, add it to the dictionary

self.stocks[stock.symbol] = stock

# to-be-implemented: some rules to overwrite firstdate and lastdate if the new stock has dates different from current records

# to-be-implemented: updates the daily_index_eod values

return self

def del_stock(self, stocksymbol):

"""

remove a stock (Stock class) from the list self.stocks

"""

# ###### QUESTION 5 ###### QUESTION 5 ###### QUESTION 5 ###### QUESTION 5 ######

# Fill in the codes here

#

# if the stocksymbol is in self.stocks, then remove it.

# print out some informative line in the process whether it is successful or not

#

# ###### END QUESTION 5 ###### END QUESTION 5 ###### END QUESTION 5 ###### END QUESTION 5 ######

return self

def compute_day_index(self):

"""

with daily price update from the stock stocks, need to update the etf index value as well

"""

# (nothing to do here. Just a placeholder for future projects)

# to-be-implemented:

# update index_eod

# update index_delta1

# update index_delta2

return self

def compute_day_index_list(self):

"""

with new stock added or removed, it will be needed to update the eod_index values and the derivatives.

"""

# (nothing to do here. Just a placeholder for future projects)

# to-be-implemented:

# update index_eod

# update index_delta1

# update index_delta2

return self