update,

1st_copy/NOTES.md

@@ -0,0 +1,114 @@
+### objective
+
+env: Windows 
+deadline 23/12
+
+
+### CAUTION
+Do not include any code not written by you in your
+project. You are NOT allowed to import any Python libraries in your solution except the
+modules namely os (https://docs.python.org/3/library/os.html), sys
+(https://docs.python.org/3/library/sys.html) and csv (https://docs.python.org/3/library/csv.html). If
+cheating is found or the import requirement is violated, you will receive a zero mark.
+
+
+### 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.
+
+
+### drill down
+
+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 get_monthly_averages() as the argument
+to this function and then calculate the 5-month exponential moving average (EMA) stock prices.
+In general, the EMA for a particular month can be calculated by the following formula:
+EMA = (Monthly average price – previous month’s EMA) x smoothing constant
++ previous month’s EMA
+where
+smoothing constant = 2 / (number of time periods in months + 1)
+3
+For example, the following table shows the stock prices between Oct 2020 and Apr 2021:
+Month Monthly Average Price
+Oct 2020 14
+Nov 2020 13
+Dec 2020 14
+Jan 2021 12
+Feb 2021 13
+Mar 2021 12
+Apr 2021 11
+The initial 5-month EMA for Feb 2021 can be calculated by the simple average formula, as
+shown below:
+5-month EMA for Feb 2021 = (14 + 13 + 14 + 12 + 13) / 5 = 13.2
+The 5-month EMA for Mar 2021 can be calculated by the EMA formula, as shown below:
+5-month EMA for Mar 2021 = (Monthly average price – previous month’s EMA) x
+smoothing constant + previous month’s EMA
+= (12 – 13.2) x (2 / 6) + 13.2
+= 12.8
+The 5-month EMA for Apr 2021 can be calculated by the EMA formula, as shown below:
+5-month EMA for Apr 2021 = (Monthly average price – previous month’s EMA) x
+smoothing constant + previous month’s EMA
+= (11 – 12.8) x (2 / 6) + 12.8
+= 12.2
+The resulting 5-month EMA stock prices are shown below:
+Month Average Price 5-month EMA Price
+Oct 2020 14 -
+Nov 2020 13 -
+Dec 2020 14 -
+Jan 2021 12 -
+Feb 2021 13 13.2
+Mar 2021 12 12.8
+Apr 2021 11 12.2
+4
+A tuple with 2 items, including the date (year and month only) and the 5-month EMA price for
+that month, will be generated for each month except the first 4 months. 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:
+Based on the entered 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 EMA price) and the worst month (with the lowest EMA 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 (please note the values are not true, which are for reference only)
+
+
+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

1st_copy/_ref/test.csv

@@ -0,0 +1,15 @@
+Date,Open,High,Low,Close,Adj Close,Volume
+2008-07-04,460,463.24,449.4,450.26,450.26,4848500
+2008-07-03,468.73,474.29,459.58,464.41,464.41,4314600
+2008-06-02,476.77,482.18,461.42,465.25,465.25,6111500
+2008-06-29,469.75,471.01,462.33,463.29,463.29,3848200
+2008-05-08,452.02,452.94,417.55,419.95,419.95,9017900
+2008-05-05,445.49,452.46,440.08,444.25,444.25,4534300
+2008-04-04,460,463.24,449.4,450.26,450.26,4848500
+2008-04-03,468.73,474.29,459.58,464.41,464.41,4314600
+2008-03-02,476.77,482.18,461.42,465.25,465.25,6111500
+2008-03-29,469.75,471.01,462.33,463.29,463.29,3848200
+2008-02-28,472.49,476.45,470.33,473.78,473.78,3029700
+2008-02-27,473.73,474.83,464.84,468.58,468.58,4387100
+2008-01-28,472.49,476.45,470.33,473.78,473.78,3029700
+2008-01-27,473.73,474.83,464.84,468.58,468.58,4387100

1st_copy/build.sh

@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+rm -rf _temp/*
+rm -rf delivery.zip
+
+mkdir -p _temp
+
+set -ex
+
+cp src/main.py _temp/XXXXXXXX_project.py
+
+pushd _temp
+  7za a -tzip ../delivery1.zip *
+popd
+
+rm -rf _temp

1st_copy/src/Pipfile

@@ -0,0 +1,11 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+
+[dev-packages]
+
+[requires]
+python_version = "3.11"

1st_copy/src/main.py

@@ -0,0 +1,243 @@
+#!/usr/bin/env python3
+
+# Do not include any code not written by you in your project. 
+# You are NOT allowed to import any Python libraries in your solution except the modules namely 
+  # - os (https://docs.python.org/3/library/os.html), 
+  # - sys (https://docs.python.org/3/library/sys.html) and 
+  # - csv (https://docs.python.org/3/library/csv.html). 
+# 
+# If cheating is found or the import requirement is violated, you will receive a zero mark.
+
+import os,sys, csv
+
+# column from csv file
+# COL_DATE: the day of trading
+# COL_OPEN: the stock price at the beginning of the trading day
+# COL_HIGH: the highest price the stock achieved on the trading day
+# COL_LOW: the lowest price the stock achieved on the trading day
+# COL_CLOSE: the stock price at the end of the trading day
+# COL_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)
+# COL_VOLUME: the total number of shares were traded on the trading day
+COL_DATE=0
+COL_OPEN=1
+COL_HIGH=2
+COL_LOW=3
+COL_CLOSE=4
+COL_ADJ_CLOSE=5
+COL_VOLUME=6
+
+# append at middle stage
+COL_TOTAL_SALE_OF_DAY=7
+COL_MONTH_ONLY=8
+COL_EMA=9
+
+# monthly_averages_list
+COL_MONTHLY_AVERAGE_PRICE=1
+COL_EMA=2
+
+
+# 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.
+def get_data_list(csv_file_name):
+  '''read data list from csv file'''
+  data_list = []
+  try:
+    with open(csv_file_name, newline='') as csvfile:
+      temp = []
+      temp = csv.reader(csvfile, delimiter=',', quotechar='"')
+      data_list = list(temp)
+    
+    return data_list
+  except Exception as e:
+    print('error during reading csv file ')
+    print('exitting...')
+    sys.exit()
+
+
+# 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. 
+# 
+# 1. Suppose the volume and adjusted closing price of a trading day are V1 and C1, respectively. 
+# 2. The total sale of that day equals V1 x C1. 
+# 3. Now, suppose the volume and adjusted closing price of another trading day are V2 and C2, respectively. 
+# 4. 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.
+
+def get_monthly_averages(data_list):
+  '''calculate the monthly average prices of the stock'''
+
+  monthly_averages_list=[]
+  data_list_data_only = data_list[1:]
+  month_available = []
+  
+  # data cleaning
+  for i in range(len(data_list_data_only)):
+    # V1 x C1, calculate the total sale, append into column
+    data_list_data_only[i].append(float(data_list_data_only[i][COL_VOLUME]) * float(data_list_data_only[i][COL_ADJ_CLOSE]))
+
+    # mark the row by YYYY-MM for easy monthly sum calculation, COL_MONTH_ONLY
+    data_list_data_only[i].append(data_list_data_only[i][COL_DATE][0:7])
+
+  # get the month in the list YYYY-MM
+  month_available = set(list(map(lambda x: x[COL_MONTH_ONLY], data_list_data_only)))
+
+  # literate the whole list, calculate the total_sale and total volume
+  # get the average sale by total_sale / total_volume
+  for month in sorted(month_available):
+    filtered_month = list(filter(lambda x: x[COL_MONTH_ONLY] == month, data_list_data_only))
+    total_sale = sum(list( map(lambda x: x[COL_TOTAL_SALE_OF_DAY], filtered_month)))
+    total_volume = sum(list( map(lambda x: float(x[COL_VOLUME]), filtered_month)))
+    monthly_averages_list.append([month, total_sale/total_volume])
+
+  return list(monthly_averages_list)
+
+# 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 get_monthly_averages() as the argument
+# to this function and then calculate the 5-month exponential moving average (EMA) stock prices.
+# In general, the EMA for a particular month can be calculated by the following formula:
+# 
+#     EMA = (Monthly average price – previous month’s EMA) x smoothing constant + previous month’s EMA
+# 
+# where
+# 
+#     smoothing constant = 2 / (number of time periods in months + 1)
+# 
+# Initial SMA = 20-period sum / 20
+# Multiplier = (2 / (Time periods + 1) ) = (2 / (20 + 1) ) = 0.0952(9.52%)
+# EMA = {Close – EMA(previous day)} x multiplier + EMA(previous day).
+def get_moving_averages(monthly_averages_list):
+  '''
+    get moving averages from montyly_average_list
+    input:
+    [ [YYYY-MM, monthly average price],
+      [YYYY-MM, monthly average price],
+      ...]
+
+    output: 
+    [ [YYYY-MM, monthly average price, EMA],
+      [YYYY-MM, monthly average price, EMA],
+      ...]
+  '''
+
+  # by ref, the first 5 month EMA were given by SMA
+  monthly_averages_list[0].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)
+  monthly_averages_list[1].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)
+  monthly_averages_list[2].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)
+  monthly_averages_list[3].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)
+  monthly_averages_list[4].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)
+
+  # smoothing constant = 2 / (number of time periods in months + 1)
+  smoothing_constant = 2 / (5 + 1)
+
+  # main loop to calculate EMA, start from the 6th month available till the end of the list
+  for i in range(5, len(monthly_averages_list)):
+    previous_month_EMA = monthly_averages_list[i-1][2]
+    Monthly_average_price = monthly_averages_list[i][1]
+
+    EMA = (Monthly_average_price - previous_month_EMA) * smoothing_constant + previous_month_EMA
+    monthly_averages_list[i].append(EMA)
+
+  return monthly_averages_list
+
+# Based on the entered 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 EMA price) and 
+#   - the worst month (with the lowest EMA 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. 
+def format_date_string(yyyy_mm):
+  '''rearrange date string from csv file YYYY-MM => MM-YYYY'''
+  [yyyy, mm] = yyyy_mm.split('-')
+  return '-'.join([mm, yyyy])
+
+def write_output_file(filename_to_write, monthly_averages_list_w_ema, report_name):
+  '''get output string from template and write to output file
+  input:
+    filename_to_write: txt file name with path to be written to
+    monthly_averages_list_w_ema: list provided with EMA
+    report_name: report name to be written to report
+  '''
+
+  RESULT_TEMPLATE='''
+# The best month for ^report_name^:
+# ^best_month^, ^best_EMA^
+
+# The worst month for ^report_name^:
+# ^worst_month^, ^worst_EMA^
+  '''.strip()
+
+  # get the max EMA of the list
+  best_EMA = max(map(lambda x: x[2], monthly_averages_list_w_ema[5:]))
+  # get the month(s) by the EMA wanted
+  best_months = list(map(lambda x: format_date_string(x[0]), filter(lambda x: x[2] == best_EMA, monthly_averages_list_w_ema[5:])))
+
+  # get the min(worst) EMA of the list
+  worst_EMA = min(map(lambda x: x[2], monthly_averages_list_w_ema[5:]))
+  # get the month(s) by the EMA wanted
+  worst_months = list(map(lambda x: format_date_string(x[0]), filter(lambda x: x[2] == worst_EMA, monthly_averages_list_w_ema[5:])))
+
+  # assemble the output string
+  result_string = RESULT_TEMPLATE
+  result_string = result_string\
+    .replace('^best_month^', ','.join(best_months))\
+    .replace('^best_EMA^', str('%.2f' % best_EMA))\
+    .replace('^worst_month^', ','.join(worst_months))\
+    .replace('^worst_EMA^', str('%.2f' % worst_EMA)) \
+    .replace('^report_name^', report_name) 
+
+  # write output file
+  with open(filename_to_write, 'w+') as file_write:
+    file_write.truncate(0)
+    file_write.writelines(result_string)
+
+def main():
+    # Main function starts here
+
+    print('start')
+
+    # gather csv file with path from user
+    input_filename = input("Please input a csv filename: ")
+    
+    csv_filename = os.path.basename(input_filename)
+    csv_path = os.path.dirname(input_filename)
+
+    # transform to the output file path by csv file name got
+    txt_filename = csv_filename.replace('.csv','_output.txt')
+    if (csv_path != ''):
+      txt_filename = '/'.join([csv_path, txt_filename])
+    else:
+      # by default keep into current directory
+      txt_filename = '/'.join(['.', txt_filename])
+    
+    # grep the corp_name from the filename google.csv => google
+    corp_name = os.path.basename(input_filename).split('.')[0]
+
+    # process the data_list by csv file as stateed in assignment
+    print(f'processing {csv_filename}')
+    csv_list=get_data_list(input_filename)
+    monthly_averages_list = get_monthly_averages(csv_list)
+    monthly_averages_list_w_EMA = get_moving_averages(monthly_averages_list)
+
+    # write output file
+    write_output_file(txt_filename, monthly_averages_list_w_EMA, corp_name)
+    print('wrote to {file} done'.format(file = txt_filename))
+
+if __name__ == "__main__":
+    main()

1st_copy/src/test.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+set -ex
+
+
+python3 ./main.py

2nd_copy/NOTES.md

@@ -0,0 +1,112 @@
+### objective
+
+env: Windows
+deadline 23/12
+
+### CAUTION
+
+Do not include any code not written by you in your
+project. You are NOT allowed to import any Python libraries in your solution except the
+modules namely os (https://docs.python.org/3/library/os.html), sys
+(https://docs.python.org/3/library/sys.html) and csv (https://docs.python.org/3/library/csv.html). If
+cheating is found or the import requirement is violated, you will receive a zero mark.
+
+### 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.
+
+### drill down
+
+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 get_monthly_averages() as the argument
+to this function and then calculate the 5-month exponential moving average (EMA) stock prices.
+In general, the EMA for a particular month can be calculated by the following formula:
+EMA = (Monthly average price – previous month’s EMA) x smoothing constant
+
+- previous month’s EMA
+  where
+  smoothing constant = 2 / (number of time periods in months + 1)
+  3
+  For example, the following table shows the stock prices between Oct 2020 and Apr 2021:
+  Month Monthly Average Price
+  Oct 2020 14
+  Nov 2020 13
+  Dec 2020 14
+  Jan 2021 12
+  Feb 2021 13
+  Mar 2021 12
+  Apr 2021 11
+  The initial 5-month EMA for Feb 2021 can be calculated by the simple average formula, as
+  shown below:
+  5-month EMA for Feb 2021 = (14 + 13 + 14 + 12 + 13) / 5 = 13.2
+  The 5-month EMA for Mar 2021 can be calculated by the EMA formula, as shown below:
+  5-month EMA for Mar 2021 = (Monthly average price – previous month’s EMA) x
+  smoothing constant + previous month’s EMA
+  = (12 – 13.2) x (2 / 6) + 13.2
+  = 12.8
+  The 5-month EMA for Apr 2021 can be calculated by the EMA formula, as shown below:
+  5-month EMA for Apr 2021 = (Monthly average price – previous month’s EMA) x
+  smoothing constant + previous month’s EMA
+  = (11 – 12.8) x (2 / 6) + 12.8
+  = 12.2
+  The resulting 5-month EMA stock prices are shown below:
+  Month Average Price 5-month EMA Price
+  Oct 2020 14 -
+  Nov 2020 13 -
+  Dec 2020 14 -
+  Jan 2021 12 -
+  Feb 2021 13 13.2
+  Mar 2021 12 12.8
+  Apr 2021 11 12.2
+  4
+  A tuple with 2 items, including the date (year and month only) and the 5-month EMA price for
+  that month, will be generated for each month except the first 4 months. 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:
+Based on the entered 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 EMA price) and the worst month (with the lowest EMA 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 (please note the values are not true, which are for reference only)
+
+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

2nd_copy/_ref/test.csv

@@ -0,0 +1,15 @@
+Date,Open,High,Low,Close,Adj Close,Volume
+2008-07-04,460,463.24,449.4,450.26,450.26,4848500
+2008-07-03,468.73,474.29,459.58,464.41,464.41,4314600
+2008-06-02,476.77,482.18,461.42,465.25,465.25,6111500
+2008-06-29,469.75,471.01,462.33,463.29,463.29,3848200
+2008-05-08,452.02,452.94,417.55,419.95,419.95,9017900
+2008-05-05,445.49,452.46,440.08,444.25,444.25,4534300
+2008-04-04,460,463.24,449.4,450.26,450.26,4848500
+2008-04-03,468.73,474.29,459.58,464.41,464.41,4314600
+2008-03-02,476.77,482.18,461.42,465.25,465.25,6111500
+2008-03-29,469.75,471.01,462.33,463.29,463.29,3848200
+2008-02-28,472.49,476.45,470.33,473.78,473.78,3029700
+2008-02-27,473.73,474.83,464.84,468.58,468.58,4387100
+2008-01-28,472.49,476.45,470.33,473.78,473.78,3029700
+2008-01-27,473.73,474.83,464.84,468.58,468.58,4387100

2nd_copy/build.sh

@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+rm -rf _temp/*
+rm -rf delivery2.zip
+
+mkdir -p _temp
+
+set -ex
+
+cp src/main.py _temp/XXXXXXXX_project.py
+
+pushd _temp
+  7za a -tzip ../delivery2.zip *
+popd
+
+rm -rf _temp

2nd_copy/src/Pipfile

@@ -0,0 +1,11 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+
+[dev-packages]
+
+[requires]
+python_version = "3.11"

2nd_copy/src/main.py

@@ -0,0 +1,262 @@
+# Objective:
+# This scripts aims to analyze the historical prices of a stock
+import os
+import sys
+import csv
+
+# define error constant
+CSV_FILE_NOT_FOUND='csv_file_not_found'
+
+# column assignment by CSV definition
+[ C_DATE, 
+  C_OPEN, 
+  C_HIGH, 
+  C_LOW, 
+  C_CLOSE, 
+  C_ADJ_CLOSE, 
+  C_VOLUME, 
+  C_MONTH_AVG_PRICE,
+  C_EMA
+  ] = list(range(0,8+1))
+
+# NOTE: get_data_list(csv_file_name)
+# NOTE: This function has one parameter, namely csv_file_name. 
+# NOTE: 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
+# NOTE: file. 
+# NOTE: After reading each row, it will be split into a list. The list will then be appended into a main
+# NOTE: list (a list of lists), namely data_list. The data_list will be returned at the end of the
+# NOTE: function.
+
+# NOTE: file tested found as protected by outer try except structure
+def clean_data(data_list):
+  """clean and bloat data"""
+
+  out_list = []
+  for data in sorted(data_list):
+    out_list.append([
+      data[C_DATE],
+      float(data[C_OPEN]),
+      float(data[C_HIGH]),
+      float(data[C_LOW]),
+      float(data[C_CLOSE]),
+      float(data[C_ADJ_CLOSE]),
+      float(data[C_VOLUME]),
+    ])
+  return out_list
+
+def get_data_list(csv_file_name):
+  '''parse csv file, bloat it into list object'''
+
+  data_list = []
+  with open(csv_file_name, newline='') as f_csv:
+    data_list = list(csv.reader(f_csv, delimiter=',', quotechar='"'))
+  
+  # NOTE: skip the very first row as that is names
+  # NOTE: bloat the column accordingly
+  return clean_data(data_list[1:])
+
+# NOTE: get_monthly_averages(data_list)
+# NOTE: This function has one parameter, namely data_list. You need to pass the data_list
+# NOTE: generated by the get_data_list() function as the argument to this function and then
+# NOTE: calculate the monthly average prices of the stock. The average monthly prices are calculated in
+# NOTE: the following way. 
+# NOTE: 
+# NOTE: 1. Suppose the volume and adjusted closing price of a trading day are V1 and C1, respectively. 
+# NOTE: 2. The total sale of that day equals V1 x C1. 
+# NOTE: 3. Now, suppose the volume and adjusted closing price of another trading day are V2 and C2, respectively. 
+# NOTE: 4. The average of these two trading days is the sum of the total sales divided by the total volume:
+# NOTE: 
+# NOTE:                        Average price = (V1 x C1 + V2 x C2) / (V1 + V2)
+# NOTE: 
+# NOTE: To average a whole month, you need to 
+# NOTE:   - add up the total sales (V1 x C1 + V2 x C2 + ... + Vn x Cn) for each day and 
+# NOTE:   - divide it by the sum of all volumes (V1 + V2 + ... + Vn) where n is the number of trading days in the month.
+# NOTE: A tuple with 2 items, including the date (year and month only) and the average for that month,
+# NOTE: will be generated for each month. The tuple for each month will be appended to a main list,
+# NOTE: namely monthly_averages_list. The monthly_averages_list will be returned at the end of the function.
+
+def get_available_month(data_list):
+  '''get the unique month from the list
+  input: 
+    data_list
+  '''
+  return sorted(set([data[0][0:7] for data in data_list]))
+
+def get_monthly_averages(data_list):
+  '''get the average price by month
+  input: 
+    data_list
+  '''
+  month_in_list = get_available_month(data_list)
+  month_average_price = {}
+  monthly_averages_list = data_list
+
+  # get total volume by month
+  for month in month_in_list:
+    filtered_month_transaction = list(filter(lambda row: row[C_DATE][0:7] == month, monthly_averages_list))
+
+    # NOTE: (V1 x C1 + V2 x C2 ...)
+    sum_total_sale_by_month = sum(map(lambda row: row[C_VOLUME] * row[C_ADJ_CLOSE], filtered_month_transaction))
+
+    # NOTE: (V1 + V2 ...)
+    sum_volume_by_month = sum(map(lambda t: t[C_VOLUME], filtered_month_transaction))
+
+    # NOTE: Average price = (V1 x C1 + V2 x C2 ...) / (V1 + V2 ... )
+    month_average_price[month] = sum_total_sale_by_month/sum_volume_by_month
+
+  # NOTE: append to main list -> C_MONTH_AVG_PRICE
+  for data in monthly_averages_list:
+    data.append(month_average_price[data[C_DATE][0:7]])
+
+  return monthly_averages_list
+
+# NOTE: get_moving_averages(monthly_averages_list)
+# NOTE: This function has one parameter, namely monthly_averages_list. You need to pass the
+# NOTE: monthly_averages_list generated by get_monthly_averages() as the argument
+# NOTE: to this function and then calculate the 5-month exponential moving average (EMA) stock prices.
+# NOTE: In general, the EMA for a particular month can be calculated by the following formula:
+# NOTE: 
+# NOTE:     EMA = (Monthly average price – previous month’s EMA) x smoothing constant + previous month’s EMA
+# NOTE: 
+# NOTE: where
+# NOTE: 
+# NOTE:     smoothing constant = 2 / (number of time periods in months + 1)
+# NOTE: 
+# NOTE: Initial SMA = 20-period sum / 20
+# NOTE: Multiplier = (2 / (Time periods + 1) ) = (2 / (20 + 1) ) = 0.0952(9.52%)
+# NOTE: EMA = {Close – EMA(previous day)} x multiplier + EMA(previous day).
+def get_monthly_average(data_list, month_wanted):
+  '''
+  get monthly average from the list
+  input: 
+    data_list: data_list
+    month_wanted: YYYY-MM
+  '''
+  return list(filter(lambda d: d[C_DATE][0:7] == month_wanted, data_list) )[0][C_MONTH_AVG_PRICE]
+
+
+def get_SMA(data_list, month_to_get_SMA):
+  '''calculate SMA from the beginning(oldest) of the list
+  input:
+    data_list: data_list
+    month_to_get_SMA : number of month to initialize the SMA (i.e. 5)
+  '''
+  sum_of_months = 0
+  
+  for month in month_to_get_SMA:
+    sum_of_months = sum_of_months + get_monthly_average(data_list, month)
+
+  return sum_of_months / len(month_to_get_SMA)
+
+def get_extreme_EMA(ema_list, max_min= 'min', skip_month=0):
+  '''get max/min EMA from the list
+  input:
+    ema_list: month list with ema
+    max_min: max / min selector (default: min)
+    skip_month: month to skip as initialized as SMA (i.e. the first 5 month)
+  '''
+  if (max_min == 'max'):
+    return max(map(lambda r: r[2], ema_list[skip_month:]))
+
+  return min(map(lambda r: r[2], ema_list[skip_month:]))
+
+def get_month_by_EMA(ema_list, ema_value):
+  '''get months(value) specified by the EMA value wanted
+  input:
+    ema_list: month list with ema
+    ema_value: ema value to select the month (i.e. max EMA)
+  '''
+  return list(map(lambda r: r[0], filter(lambda x: x[2] == ema_value, ema_list)))
+
+def get_output_content(max_ema, min_ema, max_ema_months, min_ema_months, report_name=""):
+  '''get the output content, return with a formatted string
+  input:
+    max_ema: max ema to report
+    min_ema: min ema to report
+    max_ema_months: month(s) to report with max ema
+    min_ema_months: month(s) to report with min ema
+  '''
+  # reformat to MM-YYYY before out to file
+  reformat_max_ema_months = list(map(lambda m: m.split('-')[1]+'-'+m.split('-')[0] , max_ema_months))
+  reformat_min_ema_months = list(map(lambda m: m.split('-')[1]+'-'+m.split('-')[0] , min_ema_months))
+
+  return '''
+# The best month for {report_name}:
+# {best_ema_months}, {best_EMA}
+
+# The worst month for {report_name}:
+# {worst_ema_months}, {worst_EMA}
+  '''.format(
+    best_ema_months=','.join(reformat_max_ema_months), 
+    best_EMA=round(max_ema, 2), 
+    worst_ema_months=','.join(reformat_min_ema_months), 
+    worst_EMA=round(min_ema, 2),
+    report_name=report_name).strip()
+  
+
+def get_moving_averages(monthly_averages_list):
+  '''get moving averages
+  input:
+    monthly_averages_list
+  '''
+  month_available = get_available_month(monthly_averages_list)
+  # NOTE: initialize first 0 to 4 SMA
+  monthly_averages_list_w_EMA = [[c, get_monthly_average(monthly_averages_list, c)] for c in month_available]
+  initial_SMA = sum(map(lambda x: x[1], monthly_averages_list_w_EMA[0:5]))/5
+  
+  smoothing_constant = 2 / (5 + 1)
+
+  for i in range(0,len(monthly_averages_list_w_EMA)):
+    if (i < 5):
+      # first 5 month were given by SMA
+      monthly_averages_list_w_EMA[i].append( initial_SMA)
+
+    else:
+      month_average_this_month = monthly_averages_list_w_EMA[i][1]
+      EMA_last_month = monthly_averages_list_w_EMA[i-1][2]
+      EMA_this_month = (month_average_this_month - EMA_last_month) * smoothing_constant + EMA_last_month
+
+      monthly_averages_list_w_EMA[i].append( EMA_this_month  )
+
+  return monthly_averages_list_w_EMA
+
+# get input from user
+csv_filepath = input("Please input a csv filename: ")
+
+try:
+  # NOTE: get csv file from user
+  csv_filename = csv_filepath
+  txt_filename = csv_filename.split('.csv')[0]+'_output.txt'
+  report_name = os.path.basename(csv_filename).replace('.csv','')
+  
+  # NOTE: process file
+  data_list = get_data_list(csv_filename)
+  monthly_average_list = get_monthly_averages(data_list)
+  ema_list = get_moving_averages(monthly_average_list)
+
+  # NOTE: output txt file
+  max_ema = get_extreme_EMA(ema_list,'max', 5)
+  min_ema = get_extreme_EMA(ema_list, 'min',5)
+  best_ema_months = get_month_by_EMA(ema_list, max_ema)
+  worst_ema_months = get_month_by_EMA(ema_list, min_ema)
+
+  output_string = get_output_content(max_ema, min_ema, best_ema_months, worst_ema_months, report_name)
+
+  with open(txt_filename, 'w+') as f_output:
+    f_output.truncate(0)
+    f_output.writelines(output_string)
+
+  print('output wrote '+txt_filename)
+  print('done !')
+
+except IsADirectoryError as e:
+  # NOTE: if input is a directory, drop here
+    print('sorry the path is a directory')
+
+except FileNotFoundError as e:
+  # NOTE: if csv file not found, drop here
+  print('sorry cannot find the file wanted')
+
+except Exception as e:
+  #   # cast outside if exception definition not found
+  raise e

2nd_copy/src/test.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+set -ex
+
+clear
+python3 ./main.py

gitUpdate.bat

@@ -0,0 +1,7 @@
+git status .
+
+@pause
+
+git add .
+git commit -m"update hyhl_1022,"
+start git push

jupyter/jupyter-helloworld/1st_copy.ipynb

@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "83041d33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%rm -rf google_output.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9b24b4df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os,sys, csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c2b90953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# column from csv file\n",
+    "# COL_DATE: the day of trading\n",
+    "# COL_OPEN: the stock price at the beginning of the trading day\n",
+    "# COL_HIGH: the highest price the stock achieved on the trading day\n",
+    "# COL_LOW: the lowest price the stock achieved on the trading day\n",
+    "# COL_CLOSE: the stock price at the end of the trading day\n",
+    "# COL_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)\n",
+    "# COL_VOLUME: the total number of shares were traded on the trading day\n",
+    "COL_DATE=0\n",
+    "COL_OPEN=1\n",
+    "COL_HIGH=2\n",
+    "COL_LOW=3\n",
+    "COL_CLOSE=4\n",
+    "COL_ADJ_CLOSE=5\n",
+    "COL_VOLUME=6\n",
+    "\n",
+    "# append at middle stage\n",
+    "COL_TOTAL_SALE_OF_DAY=7\n",
+    "COL_MONTH_ONLY=8\n",
+    "COL_EMA=9\n",
+    "\n",
+    "# monthly_averages_list\n",
+    "COL_MONTHLY_AVERAGE_PRICE=1\n",
+    "COL_EMA=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "09a9417f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get_data_list(csv_file_name)\n",
+    "# This function has one parameter, namely csv_file_name. \n",
+    "# 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\n",
+    "# file. \n",
+    "# After reading each row, it will be split into a list. The list will then be appended into a main\n",
+    "# list (a list of lists), namely data_list. The data_list will be returned at the end of the\n",
+    "# function.\n",
+    "def get_data_list(csv_file_name):\n",
+    "  '''read data list from csv file'''\n",
+    "  data_list = []\n",
+    "  try:\n",
+    "    with open(csv_file_name, newline='') as csvfile:\n",
+    "      temp = []\n",
+    "      temp = csv.reader(csvfile, delimiter=',', quotechar='\"')\n",
+    "      data_list = list(temp)\n",
+    "    \n",
+    "    return data_list\n",
+    "  except Exception as e:\n",
+    "    print('error during reading csv file ')\n",
+    "    print('exitting...')\n",
+    "    sys.exit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cd616e6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get_monthly_averages(data_list)\n",
+    "# This function has one parameter, namely data_list. You need to pass the data_list\n",
+    "# generated by the get_data_list() function as the argument to this function and then\n",
+    "# calculate the monthly average prices of the stock. The average monthly prices are calculated in\n",
+    "# the following way. \n",
+    "# \n",
+    "# 1. Suppose the volume and adjusted closing price of a trading day are V1 and C1, respectively. \n",
+    "# 2. The total sale of that day equals V1 x C1. \n",
+    "# 3. Now, suppose the volume and adjusted closing price of another trading day are V2 and C2, respectively. \n",
+    "# 4. The average of these two trading days is the sum of the total sales divided by the total volume:\n",
+    "# \n",
+    "#                        Average price = (V1 x C1 + V2 x C2) / (V1 + V2)\n",
+    "# \n",
+    "# To average a whole month, you need to \n",
+    "#   - add up the total sales (V1 x C1 + V2 x C2 + ... + Vn x Cn) for each day and \n",
+    "#   - divide it by the sum of all volumes (V1 + V2 + ... + Vn) where n is the number of trading days in the month.\n",
+    "# A tuple with 2 items, including the date (year and month only) and the average for that month,\n",
+    "# will be generated for each month. The tuple for each month will be appended to a main list,\n",
+    "# namely monthly_averages_list. The monthly_averages_list will be returned at the end of the function.\n",
+    "\n",
+    "def get_monthly_averages(data_list):\n",
+    "  '''calculate the monthly average prices of the stock'''\n",
+    "\n",
+    "  monthly_averages_list=[]\n",
+    "  data_list_data_only = data_list[1:]\n",
+    "  month_available = []\n",
+    "  \n",
+    "  # data cleaning\n",
+    "  for i in range(len(data_list_data_only)):\n",
+    "    # V1 x C1, calculate the total sale, append into column\n",
+    "    data_list_data_only[i].append(float(data_list_data_only[i][COL_VOLUME]) * float(data_list_data_only[i][COL_ADJ_CLOSE]))\n",
+    "\n",
+    "    # mark the row by YYYY-MM for easy monthly sum calculation, COL_MONTH_ONLY\n",
+    "    data_list_data_only[i].append(data_list_data_only[i][COL_DATE][0:7])\n",
+    "\n",
+    "  # get the month in the list YYYY-MM\n",
+    "  month_available = set(list(map(lambda x: x[COL_MONTH_ONLY], data_list_data_only)))\n",
+    "\n",
+    "  # literate the whole list, calculate the total_sale and total volume\n",
+    "  # get the average sale by total_sale / total_volume\n",
+    "  for month in sorted(month_available):\n",
+    "    filtered_month = list(filter(lambda x: x[COL_MONTH_ONLY] == month, data_list_data_only))\n",
+    "    total_sale = sum(list( map(lambda x: x[COL_TOTAL_SALE_OF_DAY], filtered_month)))\n",
+    "    total_volume = sum(list( map(lambda x: float(x[COL_VOLUME]), filtered_month)))\n",
+    "    monthly_averages_list.append([month, total_sale/total_volume])\n",
+    "\n",
+    "  return list(monthly_averages_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dfe29847",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get_moving_averages(monthly_averages_list)\n",
+    "# This function has one parameter, namely monthly_averages_list. You need to pass the\n",
+    "# monthly_averages_list generated by get_monthly_averages() as the argument\n",
+    "# to this function and then calculate the 5-month exponential moving average (EMA) stock prices.\n",
+    "# In general, the EMA for a particular month can be calculated by the following formula:\n",
+    "# \n",
+    "#     EMA = (Monthly average price – previous month’s EMA) x smoothing constant + previous month’s EMA\n",
+    "# \n",
+    "# where\n",
+    "# \n",
+    "#     smoothing constant = 2 / (number of time periods in months + 1)\n",
+    "# \n",
+    "# Initial SMA = 20-period sum / 20\n",
+    "# Multiplier = (2 / (Time periods + 1) ) = (2 / (20 + 1) ) = 0.0952(9.52%)\n",
+    "# EMA = {Close – EMA(previous day)} x multiplier + EMA(previous day).\n",
+    "def get_moving_averages(monthly_averages_list):\n",
+    "  '''\n",
+    "    get moving averages from montyly_average_list\n",
+    "    input:\n",
+    "    [ [YYYY-MM, monthly average price],\n",
+    "      [YYYY-MM, monthly average price],\n",
+    "      ...]\n",
+    "\n",
+    "    output: \n",
+    "    [ [YYYY-MM, monthly average price, EMA],\n",
+    "      [YYYY-MM, monthly average price, EMA],\n",
+    "      ...]\n",
+    "  '''\n",
+    "\n",
+    "  # by ref, the first 5 month EMA were given by SMA\n",
+    "  monthly_averages_list[0].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)\n",
+    "  monthly_averages_list[1].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)\n",
+    "  monthly_averages_list[2].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)\n",
+    "  monthly_averages_list[3].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)\n",
+    "  monthly_averages_list[4].append(sum(map(lambda x: x[1], monthly_averages_list[0:5]))/5)\n",
+    "\n",
+    "  # smoothing constant = 2 / (number of time periods in months + 1)\n",
+    "  smoothing_constant = 2 / (5 + 1)\n",
+    "\n",
+    "  # main loop to calculate EMA, start from the 6th month available till the end of the list\n",
+    "  for i in range(5, len(monthly_averages_list)):\n",
+    "    previous_month_EMA = monthly_averages_list[i-1][2]\n",
+    "    Monthly_average_price = monthly_averages_list[i][1]\n",
+    "\n",
+    "    EMA = (Monthly_average_price - previous_month_EMA) * smoothing_constant + previous_month_EMA\n",
+    "    monthly_averages_list[i].append(EMA)\n",
+    "\n",
+    "  return monthly_averages_list\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c89cbae8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_date_string(yyyy_mm):\n",
+    "  '''rearrange date string from csv file YYYY-MM => MM-YYYY'''\n",
+    "  [yyyy, mm] = yyyy_mm.split('-')\n",
+    "  return '-'.join([mm, yyyy])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8d646beb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def write_output_file(filename_to_write, monthly_averages_list_w_ema, report_name):\n",
+    "  '''get output string from template and write to output file\n",
+    "  input:\n",
+    "    filename_to_write: txt file name with path to be written to\n",
+    "    monthly_averages_list_w_ema: list provided with EMA\n",
+    "    report_name: report name to be written to report\n",
+    "  '''\n",
+    "\n",
+    "  RESULT_TEMPLATE='''\n",
+    "# The best month for ^report_name^:\n",
+    "# ^best_month^, ^best_EMA^\n",
+    "\n",
+    "# The worst month for ^report_name^:\n",
+    "# ^worst_month^, ^worst_EMA^\n",
+    "  '''.strip()\n",
+    "\n",
+    "  # get the max EMA of the list\n",
+    "  best_EMA = max(map(lambda x: x[2], monthly_averages_list_w_ema[5:]))\n",
+    "  # get the month(s) by the EMA wanted\n",
+    "  best_months = list(map(lambda x: format_date_string(x[0]), filter(lambda x: x[2] == best_EMA, monthly_averages_list_w_ema[5:])))\n",
+    "\n",
+    "  # get the min(worst) EMA of the list\n",
+    "  worst_EMA = min(map(lambda x: x[2], monthly_averages_list_w_ema[5:]))\n",
+    "  # get the month(s) by the EMA wanted\n",
+    "  worst_months = list(map(lambda x: format_date_string(x[0]), filter(lambda x: x[2] == worst_EMA, monthly_averages_list_w_ema[5:])))\n",
+    "\n",
+    "  # assemble the output string\n",
+    "  result_string = RESULT_TEMPLATE\n",
+    "  result_string = result_string\\\n",
+    "    .replace('^best_month^', ','.join(best_months))\\\n",
+    "    .replace('^best_EMA^', str('%.2f' % best_EMA))\\\n",
+    "    .replace('^worst_month^', ','.join(worst_months))\\\n",
+    "    .replace('^worst_EMA^', str('%.2f' % worst_EMA)) \\\n",
+    "    .replace('^report_name^', report_name) \n",
+    "\n",
+    "  # write output file\n",
+    "  with open(filename_to_write, 'w+') as file_write:\n",
+    "    file_write.truncate(0)\n",
+    "    file_write.writelines(result_string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1917aaef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    # Main function starts here\n",
+    "\n",
+    "    print('start')\n",
+    "\n",
+    "    # gather csv file with path from user\n",
+    "    input_filename = input(\"Please input a csv filename: \")\n",
+    "    \n",
+    "    csv_filename = os.path.basename(input_filename)\n",
+    "    csv_path = os.path.dirname(input_filename)\n",
+    "\n",
+    "    # transform to the output file path by csv file name got\n",
+    "    txt_filename = csv_filename.replace('.csv','_output.txt')\n",
+    "    if (csv_path !=''):\n",
+    "        txt_filename = '/'.join([csv_path, txt_filename])\n",
+    "    else:\n",
+    "        txt_filename = '/'.join(['.', txt_filename])\n",
+    "    \n",
+    "    # grep the corp_name from the filename google.csv => google\n",
+    "    corp_name = os.path.basename(input_filename).split('.')[0]\n",
+    "\n",
+    "    # process the data_list by csv file as stateed in assignment\n",
+    "    print(f'processing {csv_filename}')\n",
+    "    csv_list=get_data_list(input_filename)\n",
+    "    monthly_averages_list = get_monthly_averages(csv_list)\n",
+    "    monthly_averages_list_w_EMA = get_moving_averages(monthly_averages_list)\n",
+    "\n",
+    "    # write output file\n",
+    "    write_output_file(txt_filename, monthly_averages_list_w_EMA, corp_name)\n",
+    "    print('wrote to {file} done'.format(file = txt_filename))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7d3e814",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start\n"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == \"__main__\":\n",
+    "    main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "325de646",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de467460",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cat google_output.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41f834e6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

jupyter/jupyter-helloworld/Pipfile

@@ -0,0 +1,21 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+jupyter = "*"
+notebook = "*"
+pandas = "*"
+quandl = "*"
+seaborn = "*"
+sklearn = "*"
+scikit-learn = "*"
+pydot = "*"
+bokeh = "*"
+jupyter-bokeh = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3"

jupyter/jupyter-helloworld/dev.sh

@@ -0,0 +1,14 @@
+#!/usr/bin/env bash
+
+set -ex
+
+python -m pip install --upgrade pip
+
+python -m pip install pipenv
+
+pipenv sync
+
+pipenv run \
+  jupyter-notebook \
+    --allow-root \
+    --ip=0.0.0.0

jupyter/jupyter-helloworld/helloworld-requests.ipynb

@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b6bf609f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: requests in /root/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages (2.28.1)\n",
+      "Requirement already satisfied: charset-normalizer<3,>=2 in /root/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages (from requests) (2.1.1)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /root/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages (from requests) (1.26.13)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /root/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages (from requests) (3.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /root/.local/share/virtualenvs/app-4PlAip0Q/lib/python3.10/site-packages (from requests) (2022.9.24)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9b24b4df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "r = requests.get('https://www.example.com')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1cc7f2dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.status_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a6bd95b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Goog.csv\n",
+      "Goog.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = input()\n",
+    "print(a)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

jupyter/jupyter-helloworld/helloworld.ipynb

@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6bf609f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b24b4df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.get('https://www.example.com')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cc7f2dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r.status_code"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

jupyter/jupyter-helloworld/init.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+set -ex
+
+pipenv install jupyter
+pipenv install jupyter notebook
+
+pipenv install pandas
+pipenv install quandl
+
+pipenv install seaborn
+pipenv install scikit-learn
+
+# jupyter-notebook
+

jupyter/jupyter-helloworld/journal.md

@@ -0,0 +1,27 @@
+### to spin up dev environment
+
+```
+./start_docker.sh
+
+// inside docker
+
+./dev.sh
+
+open host browser: 
+http://127.0.0.1:8888/?token=98ab80de026fe83fd8e03c8e344b31e7575ec4a084c59f21
+
+```
+
+### to develop
+
+
+start from fresh python docker image
+
+```
+./start_docker.sh
+
+./init.sh
+```
+
+
+

jupyter/jupyter-helloworld/start_docker.sh

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+
+set -ex
+
+docker run -it \
+  -v $PWD:/app \
+  -w /app \
+  -v /var/run/docker.sock:/var/run/docker.sock \
+  -v ~/.ssh/id_rsa:/home/node/.ssh/id_rsa:ro \
+  -v ~/.ssh/known_host:/home/node/.ssh/known_hosts:ro \
+  -p 8888:8888 \
+  --rm \
+  python:3.10 \
+  bash
+  
+  # -u 1000:1000 \
+  # -e XDG_CACHE_HOME=/app/.cache \

meta.md

@@ -0,0 +1,8 @@
+---
+tags: [python, jupyter, INT3075]
+---
+
+# hyhl_1022
+
+[[1st_copy/NOTES]]
+[[2nd_copy/NOTES]]

package.json

@@ -0,0 +1,13 @@
+{
+  "name": "notes",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1",
+    "gitUpdate": "git add . && git commit -m \"update,\" && git pull && git push"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC"
+}