We Keep Coding

how to do loop over the tickers and save result on the dictionary

I’m not so sure how to do the last part which is the dictionary part and ticker part, and also
On
“”file = open("/home/ubuntu/environment/hw5/" + tickers + “.txt”)””"
This line keep showing
TypeError: must be str, not list
Any suggestion on how to fix those or make the code works ?
Here’s my code
import json
def meanReversionStrategy(prices):
total_profit = 0
first_buy = None
buy = 0
for i in range(len(prices)):
if i >= 5:
current_price = prices[i]
moving_average = (prices[i-1] + prices[i-2] + prices[i-3] + prices[i-4] +prices[i-5]) / 5
if current_price < moving_average * 0.95 and buy == 0:
buy = current_price
print("buy at: ",round (current_price,2))
if first_buy is None:
first_buy = buy
elif current_price > moving_average * 1.05 and buy != 0:
print("sell at: ", round(current_price,2))
print("trade profit: ", round(current_price - buy,2))
total_profit = current_price - buy
buy = 0
final_profit_percentage = ( total_profit / first_buy ) * 100
print("First buy: " , round(first_buy,2))
print("Total profit: " , round(total_profit, 2))
print("Percentage return: ", round(final_profit_percentage, 2),"%")
def simpleMovingAverageStrategy(prices):
i = 0
buy = 0
total_profit = 0
first_buy = 0
for p in prices:
if i >= 5:
moving_average = (prices[i-1] + prices[i-2] + prices[i-3] + prices[i-4] +
prices[i-5]) / 5
#simple moving average logic, not mean
if p > moving_average and buy == 0: #buy
print("buying at: ", p)
buy = p
if first_buy == 0:
first_buy = p
elif p < moving_average and buy != 0: #sell
print("selling at: ", p)
print("trade profit: ", p - buy)
total_profit += p - buy
buy = 0
i += 1
final_percentage = (total_profit / first_buy) * 100
print("first buy: ", first_buy)
print("total profit: ", total_profit)
print("final percentage: ", final_percentage, "%")
return total_profit, final_percentage
tickers = ["AAPL1" , "ADBE" , "BA", "CMCSA", "CSCO", "CVS", "GOOG", "TLSYY","TM"]
file = open("/home/ubuntu/environment/hw5/" + tickers + ".txt")
lines = file.readlines()
# print(lines)
prices = []
for line in lines:
prices.append(float(line))
profit, returns = simpleMovingAverageStrategy(prices)
results = {}
results["AAPL1_profit"] =profit
results["AAPL1_returns"] = returns
json.dump(results, open("/home/ubuntu/environment/hw5/results.json", "w") )
Coding Requirements
-Create a function called meanReversionStrategy which takes a list called “prices” as an argument. The function runs a mean reversion strategy, and outputs to the console the buys and sells of the strategy (like you did in HW4). The function returns the profit and final returns percentage.
-Create a function called simpleMovingAverageStrategy which takes a list called “prices” as an argument. The function runs a Simple Moving Average strategy, and outputs to the console the buys and sells of the strategy. The function returns the profit and final returns percentage.
-Create a function called saveResults which takes a dictionary as an argument. Save the dictionary to a json file called “results.json”.
loop through the list of tickers
for ticker in tickers:
-load prices from a file <ticker>.txt, and store them in the results dictionary with the
key “<ticker>_prices”
-call meanReversionStrategy(prices) and store the profit and returns in the results
dictionary with the keys “<ticker>_mr_profit” and “<ticker>_mr_returns”
-call simpleMovingAverageStrategy(prices) and store the profit and returns in the
results dictionary with the keys “<ticker>_sma_profit” and “<ticker>_sma_profit”
with the keys “ticker_mr_profit” and “ticker_mr_returns”
call saveResults(results) and save the results dictionary to a file called results.json

Welcome!
As this is a homework question, I will not solve it for You, but here is my advice for You:
tickers is an array and as your homework description states, You must 'loop through the list of tickers' and open each of them. So how do You loop over tickers?
And reflect about the error. What do You think should be the result of "/home/ubuntu/environment/hw5/" + tickers + ".txt"?

Printing prompt from input statement?

I have an issue using dcoder. My input is:
20
50 8
30 10
Its outputting not 80 alone but the prompts from the input statements also. How do I get it to just print 80?
maxw = int(input("bag weight: "))
itemOne = input("item value & weight:") #item 1
itemTwo = input("item value & weight:") #item 2
lone, ltwo = itemOne[0:3], itemTwo[0:3] #rid of " "
iione, iitwo = int(lone), int(ltwo) #change to int
def total_value(x, y):
tval = x + y #add together 50 and 80
return tval
result = total_value(iione, iitwo)
print(result)
![enter image description here](https://i.stack.imgur.com/f8Jh4.png)![enter image description here](https://i.stack.imgur.com/zCAcC.png)

Python XML Parsing - need to correct while loop

Fairly new to Python. I'm parsing an XML file and the following code returns the undesired results. I can understand why I'm getting my results - there are two escalations in the XML for this deal and I'm getting results for each set. I'm need help updating my code to only return the monthly rent for each escalation in the XML:
<RentEscalations>
<RentEscalation ID="354781">
<BeginIn>7</BeginIn>
<Escalation>3.8</Escalation>
<RecurrenceInterval>12</RecurrenceInterval>
<EscalationType>bump</EscalationType>
</RentEscalation>
<RentEscalation ID="354782">
<BeginIn>61</BeginIn>
<Escalation>1.0</Escalation>
<RecurrenceInterval>12</RecurrenceInterval>
<EscalationType>bump</EscalationType>
</RentEscalation>
</RentEscalations>
The rent starts at $3.00/sqft for the first 6 months. This XML block shows that, for each 12 months (RecurrenceInterval), the rent will be $6.80/sqft ($3.00 base + $3.80 escalation). The following twelve months will be $10.60 ($6.80 + 3.80). Each year, the amount per square foot will increase by $3.80 until the 61st month in the term. At that point, the rent will increase by $1.00/sqft for the remainder of the term. The entire term of the lease is 120 months.
My results include 114 results based on the first escalation (3.80/sqft) followed by 114 rows showing as if the rent starts at $3.00/sqft incrementing by $1.00/sqft each year.
Any help is appreciated!
import xml.etree.ElementTree as ET
import pyodbc
import dateutil.relativedelta as rd
import datetime as dt
tree = ET.parse('C:\\FileLocation\\DealData.xml')
root = tree.getroot()
for deal in root.findall("Deals"):
for dl in deal.findall("Deal"):
dealid = dl.get("DealID")
for dts in dl.findall("DealTerms/DealTerm"):
dtid = dts.get("ID")
darea = float(dts.find("RentableArea").text)
dterm = int(dts.find("LeaseTerm").text)
for brrent in dts.findall("BaseRents/BaseRent"):
brid = brrent.get("ID")
rent = float(brrent.find("Rent").text)
darea = float(dts.find("RentableArea").text)
per = brrent.find("Period").text
dtstart = dts.find("CommencementDate").text
startyr = int(dtstart[0:4])
startmo = int(dtstart[5:7])
startday = int(dtstart[8:])
start = dt.date(startyr, startmo, startday)
end = start + rd.relativedelta(months=dterm)
if brrent.find("Duration").text is None:
duration = 0
else:
duration = int(brrent.find("Duration").text)
termbal = dterm - duration
for resc in dts.findall("RentEscalations/RentEscalation"):
rescid = resc.get("ID")
esctype = resc.find("EscalationType").text
begmo = int(resc.find("BeginIn").text)
esc = float(resc.find("Escalation").text)
intrvl = int(resc.find("RecurrenceInterval").text)
if intrvl != 0:
pers = termbal / intrvl
else:
pers = 0
escst = start + rd.relativedelta(months=begmo - 1)
i = 0
x = begmo
newrate = rent
while i < termbal:
billdt = escst + rd.relativedelta(months=i)
if per == "rsf/year":
monthlyamt = (newrate + esc) * darea / 12.0
if per == "month":
monthlyamt = newrate + esc
if per == "year":
monthlyamt = (newrate + esc) / 12.0
if per == "rsf/month":
monthlyamt = (newrate + esc) * darea
try:
if i % intrvl == 0:
level = x + 1
newrent = monthlyamt
x += 1
newrate += esc
else:
level = x
except ZeroDivisionError:
break
i += 1
if dealid == "1254278":
print(dealid, dtid, rescid, dterm, darea, escst, rent, intrvl, esctype, termbal, \
monthlyamt, billdt, pers, level, newrate, newrent)

Calculate the future value for only one category using the IRR (Python)

import xlrd
import numpy
fileWorkspace = 'C://Users/jod/Desktop/'
wb1 = xlrd.open_workbook(fileWorkspace + 'assign2.xls')
sh1 = wb1.sheet_by_index(0)
time,amount,category = [],[],[]
for a in range(2,sh1.nrows):
time.append(int(sh1.cell(a,0).value)) # Pulling time from excel (column A)
amount.append(float(sh1.cell(a,1).value)) # Pulling amount from excel (column B)
category.append(str(sh1.cell(a,2).value)) # Pulling category from excel (column C)
#print(time)
#print(amount)
#print(category)
print('\n')
p_p2 = str(sh1.cell(0,1))
p_p1 = p_p2.replace("text:'","")
pp = p_p1.replace("'","")
print(pp) # Printing the type of pay period (Row 1, col B)
c_p2 = str(sh1.cell(1,1))
c_p1 = c_p2.replace("text:'","")
cp = c_p1.replace("'","")
print(cp) # Printing the type of compound period (Row 2, col B)
netflow = 0
outflow = 0
inflow = 0
flow = 0
cat = ["Sales", "Salvage", "Subsidy", "Redeemable", "Utility", "Labor",
"Testing", "Marketing", "Materials", "Logistics"]
if pp == "Years" and cp == "Years": # if pay period and compound period are both in years
IRR = numpy.irr(amount) * 100 # Calculates the internal rate of return (IRR)
print ("IRR:", round(IRR, 2), '%', '\n') # prints (IRR)
for i in time: # for every value in time array
if cat[5] in category: # if "Labor" for cat array is in category array or not
# calculates the present values using all the amount values (col B) instead of
# just using the ones that has "Labor" category label beside them
# Need to make every other value 0, such as beside "Redeemable" and "Salvage"
flow = amount[i] / numpy.power((1 + (IRR/100)), time[i])
if flow>0:
inflow = inflow + flow
if flow<0:
outflow = outflow + flow
print ('Present Value (P) is:', round(flow,0), '\n')
netflow = outflow + inflow
print("In year 0 or current year")
print("-------")
print ('Outflow is: ', round(outflow,0))
print ('Inflow is: ', round(inflow,0))
print ('Netflow is: ', round(netflow,0), '\n')
outflow2 = (round(outflow,0))*(1+(IRR/100))**(9)
inflow2 = (round(inflow,0))*(1+(IRR/100))**(9)
netflow2 = outflow2 + inflow2
print("In year 9")
print("-------")
print ('Outflow is: ', round(outflow2,0))
print ('Inflow is: ', round(inflow2,0))
print ('Netflow is: ', round(netflow2,0), '\n')
I have commented important lines of code for clarification.
Here is the original question:
illustrate the breakdown of major project revenues and expenses by category as a percentage of that project’s future value in year 9. The illustration must also clearly indicate the total future value of the project in year 9 as well as the IRR.
There will be a total of 10 revenue and cost categories that a project may be composed of. The categories are: Sales, salvage, subsidy, redeemable, utility, labor, testing, marketing, materials and logistics. All revenues and expenses will fall in one of these ten categories. The project pay period and compound period will be identified at the top of the Excel sheet. Pay period and compound period may be designated as any of the following: years, quarters, months.
I am getting confused because I am not able to pull the only values from beside the "Labor", "Redeemable", or "Salvage". I just don't know where I am making a mistake, or there is something that is incomplete. Below is the excel file image:
Excel File Image 2
Excel File Image 3

After revising, all cashflows are discounted at the irr. What is done is the following:
i) determineAdjustments takes the pay period (column A) and adjusts if for the year ended (if it is a monthly amount it puts it in the proper year ended) and if its monthly puts in in the month ended (no adjustment necessary). This will divide the pay period by 12 if yearly cash flows are needed (yearly compounding)
ii) IRR is calculated, and the compounding period is used to adjust the monthly IRR for monthly pay periods
iii) all expenses are discounted at the IRR and input into a list for cat_contributions['category_name'] = [discounted period 1, discounted period 2 ... ]
iv) Then the net inflows and outflows are sums of these.
I can't type up data in the spreadsheets from the images as that would take a while, but maybe tinker with this and see if you can get it to work.
from __future__ import division
import xlrd
import numpy
import os
import math
def main(xls = 'xls_name.xlsx', sh = 0):
#save script in same folder as the xls file
os.chdir( os.getcwd() )
wb = xlrd.open_workbook(xls)
sh = wb.sheet_by_index(0)
pay_period = sh.cell_value(0,1)
compounding_period = sh.cell_value(1,1)
compounding_factor, pay_factor = determineAdjustments(
pay_period, compounding_period)
number_of_periods = max( sh.col_values(0, start_rowx = 2) )
flow_per_period = [ 0*i for i in range( int( math.ceil( number_of_periods/pay_factor ) ) + 1 ) ]#list of length number of pay_periods
for r in range(2,sh.nrows):
pay_period = int( math.ceil( sh.cell_value(r,0) / pay_factor ) )
flow_per_period[pay_period] += sh.cell_value(r,1) #unadjusted cash flows
irr = calculateIRR(flow_per_period, compounding_factor)
cat_contributions = sortExpenditures(sh, irr, pay_factor)
total_cat_contributions, netflow, total_outflow, total_inflow = calculateFlows(cat_contributions)
printStats(cat_contributions, irr, compounding_factor, pay_factor,
total_cat_contributions, netflow, total_outflow, total_inflow)
return
def determineAdjustments(pay_period, compounding_period):
if compounding_period == 'years':
compounding_factor = 1
if pay_period == 'months':
pay_factor = 12
if pay_period == 'years':
pay_factor = 1
#assume no days pay periods
if compounding_period == 'months':
compounding_factor = 12
#assume no yearly payouts and that the
#all payments are in months
pay_factor = 1
return compounding_factor, pay_factor
def calculateIRR(cashflow, compounding_factor):
irr = numpy.irr(cashflow)
irr_comp = (1 + irr)**compounding_factor - 1
#seems like in first example it uses rounded irr, can do something like:
#irr_comp = round(irr_comp,4)
return irr_comp
def sortExpenditures(sh, irr, pay_factor):
#percentages and discounting occurs at the IRR caculated in the main
#function
cat = ["Sales", "Salvage", "Subsidy", "Redeemable", "Utility", "Labor",
"Testing", "Marketing", "Materials", "Logistics"]
#python dictionary to sort contributions into categories
cat_contributions = {}
for c in cat:
cat_contributions[c] = []
# create list of contributions of each list item to FV in a dictionary
for r in range(2,sh.nrows):
try:
#discounted cash flow of each expenditure
#using formula FV = expenditure/(1+i)^n
cat_contributions[sh.cell_value(r,2)].append(
sh.cell_value(r,1) / ( (1 + irr) ** (sh.cell_value(r,0)/pay_factor) )
)
except KeyError:
print "No category for type: " + sh.cell_value(r,2) +'\n'
return cat_contributions
def calculateFlows(cat_contributions):
total_outflow = 0
total_inflow = 0
total_cat_contributions = {}
for cat in cat_contributions:
total_cat_contributions[cat] = sum( cat_contributions[cat] )
if total_cat_contributions[cat] < 0:
total_outflow += total_cat_contributions[cat]
else:
total_inflow += total_cat_contributions[cat]
netflow = total_inflow + total_outflow
return total_cat_contributions, netflow, total_outflow, total_inflow
def printStats(cat_contributions, irr, compounding_factor, pay_period,
total_cat_contributions, netflow, total_outflow, total_inflow):
print "IRR: "+str(irr*100) +' %'
if compounding_factor == 1: print "Compounding: Yearly"
if compounding_factor == 12: print "Compounding: Monthly"
if pay_period == 1: "Cashflows: Year Ended"
if pay_period == 12: "Cashflows: Month Ended"
print "Future Value (Net Adjusted Cashflow): " +str(netflow)
print "Adjusted Inflows: " + str(total_inflow)
print "Adjusted Outflows: " + str(total_outflow) +'\n'
for cat in total_cat_contributions:
if total_cat_contributions[cat] != 0:
print '-----------------------------------------------------'
print cat + '\n'
print "Total Contribution to FV " + str( total_cat_contributions[cat] )
if total_cat_contributions[cat] < 0:
print "Contribution to Expenses: " + str ( abs(100 * total_cat_contributions[cat]/total_outflow) )
else:
print "Contribution to Revenues: " + str ( abs(100 * total_cat_contributions[cat]/total_inflow) ) +'\n'
main(xls='Book1.xlsx')

Finding total and average salaries from 1000 lines of text

I need to write a python program that reads 1000 lines of text and take the total and average of the salaries of each professor category (assistant, associate, full, all faculty). Here is the problem:
A university posts its employee salary at
http://cs.armstrong.edu/liang/data/Salary.txt. Each line in the file
consists of faculty first name, last name, rank, and salary (see
Exercise 13.16). Write a program to display the total salary for
assistant professors, associate professors, full professors, and all
faculty, respectively, and display the average salary for assistant
professors, associate professors, full professors, and all faculty,
respectively.
This is the code I have so far:
import urllib.request
def main():
infile = urllib.request.urlopen('http://cs.armstrong.edu/liang/data/Salary.txt')
s = infile.read().decode().splitlines()
asstTotal = 0
asstCount = 0
asscTotal = 0
asscCount = 0
fullTotal = 0
fullCount = 0
for line in s:
if "assistant" in line:
asstCount += 1
asstTotal += int(float(s[3]))
if "associate" in line:
asscCount += 1
asscTotal += int(float(s[3]))
if "full" in line:
fullCount += 1
fullTotal += int(float(s[3]))
allFacTotal = asstTotal + asscTotal + fullTotal
avgAsst = int(asstTotal / asstCount)
avgAssc = int(asscTotal / asscCount)
avgFull = int(fullTotal / fullCount)
avgAll = int(allFacTotal / 1000)
print("Total assistant professor salary is", asstTotal)
print("Total associate professor salary is", asscTotal)
print("Total full professor salary is", fullTotal)
print("Total faculty salary is", allFacTotal)
print("Average assistant professor salary is", avgAsst)
print("Average associate professor salary is", avgAssc)
print("Average full professor salary is", avgFull)
print("Average faculty salary is", avgAll)
main()
I'm not sure how to get the salaries from the respective professor ranks and totaling and averaging them. Any input would be much appreciated!
Result:
Total assistant professor salary is 24306418
Total associate professor salary is 27235856
Total full professor salary is 27631726
Total faculty salary is 79174000
Average assistant professor salary is 79174
Average associate professor salary is 79174
Average full professor salary is 79174
Average faculty salary is 79174

This can be done to account for any type of rank, by building up a list of pay for each position in a dictionary, then simply dividing the sum by the length.
My example:
import urllib.request
def main():
infile = urllib.request.urlopen('http://cs.armstrong.edu/liang/data/Salary.txt')
s = infile.read().decode().splitlines()
pays = {}
for line in s:
*name, rank, pay = line.split()
pays[rank] = pays.get(rank, []) + [float(pay)]
for rank, sals in pays.items():
avg = sum(sals) / len(sals)
print("{:>10} ({:3}): ${:,.2f}".format(rank.title(), len(sals), avg))
if __name__ == '__main__':
main()
And the output:
Associate (344): $83,849.26
Full (349): $102,229.37
Assistant (307): $65,949.55

import urllib.request
def main():
infile = urllib.request.urlopen("http://cs.armstrong.edu/liang/data/Salary.txt")
asstSalTotal = 0
asstTotal = 0
asscSalTotal = 0
asscTotal = 0
fullSalTotal = 0
fullTotal = 0
for line in infile.readlines():
data = line.decode().split()
designation = data[2]
if designation == "assistant":
asstSalTotal += float(data[3])
asstTotal += 1
elif designation == "associate":
asscSalTotal += float(data[3])
asscTotal += 1
elif designation == "full":
fullSalTotal += float(data[3])
fullTotal += 1
allSalTotal = asstSalTotal + asscSalTotal + fullSalTotal
allTotal = asstTotal + asscTotal + fullTotal
print("Total Salary:\n\tAssistant:\t%d\n\tAssociate:\t%d\n\tFull:\t%d\n\tAll:\t%d\n\nAvg Salary:\n\tAssistant:\t%d\n\tAssociate:\t%d\n\tFull:\t%d\n\tAll:\t%d\n" % (asstSalTotal, asscSalTotal, fullSalTotal, allSalTotal, asstTotal, asscTotal, fullTotal, allTotal))
main()

In python2.7, use urllib2.urlopen and group salaries together by title with a table.
In python3, use urllib.request.urlopen and change print .. to print(..)
import urllib2
rs = urllib2.urlopen('http://cs.armstrong.edu/liang/data/Salary.txt')
title_salaries = {}
for line in rs.read().split("\n"):
_, _, title, salary = line.split(' ')
if not title_salaries.has_key(title):
title_salaries[title] = []
title_salaries[title].append(float(salary))
for title, salaries in title_salaries.items():
print title
print 'avg:', sum(salaries) / len(salaries)
print 'total:', sum(salaries)
print