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INT3075 Programming and Problem Solving for Mathematics
Creation date：2024-04-19 15:17:36
Belonging category：数学mathematics
Tag INT3075
Programming and Problem Solving for Mathematics

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INT3075 Programming and Problem Solving for Mathematics


Project Description

I. Objective
This project aims to provide you with an opportunity to analyse the historical prices of a stock. You are
required to calculate the 3-month moving average prices of the stock over a given period of time and
determine the best and worst months for the stock in terms of its 3-month moving average prices. To
complete this project, you need to apply the knowledge and skills in relation to files, lists and
functions.
II. Specification
Data File
You will be given a CSV data file of the historical prices of a stock. This file could be opened by
WordPad and data fields in each row are delimited by comma. The data fields include:
• Date: the day of trading
• Open: the stock price at the beginning of the trading day
• High: the highest price the stock achieved on the trading day
• Low: the lowest price the stock achieved on the trading day
• Close: the stock price at the end of the trading day
• Adj Close: the adjusted closing price of the trading day (reflecting the stock’s value after
accounting for any corporate actions like dividends, stock splits and new stock offerings)
• Volume: the total number of shares were traded on the trading day
Functions
Given the file of stock prices, you are asked to develop a Python program to process the data by
designing appropriate functions. At minimum you need to implement and call the following three
functions:
• get_data_list(csv_file_name)
This function has one parameter, namely csv_file_name. When the function is called, you
need to pass along a CSV file name which is used inside the function to open and read the CSV
file. After reading each row, it will be split into a list. The list will then be appended into a main
list (a list of lists), namely data_list. The data_list will be returned at the end of the
function.
2
• get_monthly_averages(data_list)
This function has one parameter, namely data_list. You need to pass the data_list
generated by the get_data_list() function as the argument to this function and then
calculate the monthly average prices of the stock. The average monthly prices are calculated in
the following way. Suppose the volume and adjusted closing price of a trading day are V1 and C1,
respectively. The total sale of that day equals V1 x C1. Now, suppose the volume and adjusted
closing price of another trading day are V2 and C2, respectively. The average of these two trading
days is the sum of the total sales divided by the total volume:
Average price = (V1 x C1 + V2 x C2) / (V1 + V2)
To average a whole month, you need to add up the total sales (V1 x C1 + V2 x C2 + … +
Vn x Cn) for each day and divide it by the sum of all volumes (V1 + V2 + … + Vn) where n
is the number of trading days in the month.
A tuple with 2 items, including the date (year and month only) and the average for that month,
will be generated for each month. The tuple for each month will be appended to a main list,
namely monthly_averages_list. The monthly_averages_list will be returned at
the end of the function.
• get_moving_averages(monthly_averages_list)
This function has one parameter, namely monthly_averages_list. You need to pass the
monthly_averages_list generated by the get_monthly_averages() function as
the argument to this function and then calculate the 3-month moving average prices of the stock.
In general, a 3-month moving average is the average value of the 3 most recent monthly
observations taken from a time series.
For example, the following lists the monthly prices of a stock between October 2012 and July
2013:
Month Average Price
October 2020 100
November 2020 101
December 2020 103
January 2021 99
February 2021 97
March 2021 102

3
To construct a 3-month moving average, take the average of the first three observations, in this
case, October 2020, November 2020, and December 2020 prices:

Then find the average of the next three observations, starting with the second observation, so
you’re finding the average of the second, third, and fourth observations (or November 2020,
December 2020, and January 2021):

Continue the process for the entire sample. The resulting 3-month moving averages are shown
below:
Month Average Price 3-Month Moving Average
October 2020 100 -
November 2020 101 101.33
December 2020 103 101.00
January 2021 99 99.67
February 2021 97 99.33
March 2021 102 -
The first 3-month moving average is listed next to November 2020, even though it represents the
average of October 2020, November 2020, and December 2020. This shows that November 2020
is the “center” of the moving average. Similarly, the 3-month moving average constructed from
the November 2020, December 2020, and January 2021 prices is shown next to December 2020,
indicating that it’s the center of the second average.
A tuple with 2 items, including the date (year and month only) and the 3-month moving average
for that month, will be generated for each month except the first and the last ones. Each tuple
will be appended to a main list, namely moving_averages_list. The
moving_averages_list will be returned at the end of the function.
Program Input and Output
At the outset, your program needs to ask the user for a CSV file name:



4
Based on the CSV file name, a corresponding output text file (e.g. “Google_output.txt” for this
case) will be generated. In the output file, you are eventually required to print the best month (with the
highest moving average price) and the worst month (with the lowest moving average price) for the
stock. You need to first print a header line for the stock, and then print a date (MM-YYYY), a comma
followed by a moving average price (in 2 decimal places) on another line. You must follow the output
format as shown below:
The best month for Google:
12-2007, 669.23

The worst month for Google:
09-2004, 128.52
III. Deliverable
You have to include your student name and ID in your source code and name your project solution as
“XXXXXXXX_project.py” (where XXXXXXXX is your 8-digit student ID). Please remember to
upload your source code solution to Moodle by the submission deadline.
IV. Evaluation Criteria (40% of Overall Course Assessment)
The project will be graded using the following criteria:
• 15% - Correctness of program execution and output data
• 10% - Modularization (e.g. dividing the program functionality into different functions)
• 5% - Error handling
• 5% - Consistent style (e.g., capitalization, indenting, etc.)
• 5% - Appropriate comments
V. Plagiarism Policy
Your project should represent your own work. Do not include any code not written by you in your
project.
Case category
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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