Question 1: Dynamic Programming in Data Science Problem Statement: You are given a dataset representing daily stock prices for a company over n days in an array prices[]. Your task is to write a Python function to calculate the maximum profit you can achieve by performing at most two buy-sell transactions. The second transaction must start only after the first is completed. Requirements: Implement a dynamic programming solution. Provide an explanation of the time complexity of your algorithm.
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Problem Statement: Given an unordered list of future stock prices, what is the maximum amount of profit that you could generate from a starting amount of $1,000.00? Rules: • You can trade fractional shares (e.g. if there were shares for $400.00, you could buy/sell 2.5 of them for $1,000.00). • All trades occur instantaneously and do not incur any transaction costs. • Shares may only be bought/sold on a date that you have a known price. • Short selling is not allowed. • You do not need to have a position at all times (at any time, if you cannot identify a profitable trade, you do not have to trade). • Round final answer to the nearest dollar. • Do not assume input tuple will be sorted in any manner. • Future prices will be given as a list in the following format: [Stock, Date, Price] Example 1: Input: CSCO, 10/18/2024, 41.89 AMZN, 10/10/2024, 113.67 AMZN, 10/18/2024, 120.5 CSCO, 10/10/2024, 43.12 Solution: Buy 8.797 AMZN @$113.67 on 10/10/2024 and sell @$120.5 on 10/18/2024. Profit = $60 Expected Output: 60 Example 2: Input: IBM, 12/01/2023, 132.05 IBM, 12/03/2023, 135.19 IBM, 12/18/2023, 134.07 AAPL, 12/01/2023, 187.19 AAPL, 12/04/2023, 164.33 AAPL, 12/20/2023, 180.94 AAPL, 12/21/2023, 179.65 GOOG, 12/01/2023, 116.41 GOOG, 12/07/2023, 111.36 GOOG, 12/19/2023, 112.19 Solution: Buy 7.573 IBM @$132.05 on 12/1/2023 and sell @$135.19 on 12/3/2023. Buy 6.230 AAPL on 12/4/2023 @$164.33 and sell on 12/20/2023 @$180.94. Profit = $127
Akash M.
Texts: For the following questions, the files sp500.csv and prices.csv are provided, which contain a list of stocks in the S&P 500 and certain data about each. For the constituent data, Symbol is a unique key column. For the prices data, Symbol and Date together uniquely define a row. I just need the Python code for the following questions. a) Use read_csv() to load two files, sp500.csv and prices.csv, each into their own DataFrame. b) Rotate data: Transform the data organization for the price DataFrame, so prices for each date are each in their own dated column. For example, price-20201031, price-20200930, and so on. c) Join data: Use join() to combine the constituents data and rotated price data from part (b) into one DataFrame. The resulting DataFrame should have one row for each row in the constituents data. d) Augment data: Add a column that is the difference between the price on the last available date and the price on the prior available date. e) Missing values: Determine which stocks do not have price data.
A department store has purchased 5,000 swimsuits to be sold during the summer sales season. The season lasts three months, and the store manager forecasts that customers buying early in the season are likely to be less price sensitive, and those buying later in the season are likely to be more price sensitive. The demand curves in each of the three months are forecast to be as follows: d1 = 2,000 - 10p1, d2 = 2,000 - 20p2, and d3 = 2,000 - 30p3. If the department store is to charge a fixed price over the entire season, what should it be? What is the resulting revenue? If the department store wants dynamic prices that vary by month, what should they be? How does this affect profits relative to charging fixed prices? If each swimsuit costs $40 and the store plans to charge dynamic prices, how many swimsuits should it purchase at the beginning of the season? 1. If the department store is to charge a fixed price over the entire season, what should it be? a) $20 b) $30 c) $40 d) $50 2. If the department store is to charge a fixed price over the entire season, what is the resulting revenue? a) $140,000 b) $150,000 c) $160,000 d) $170,000 3. If the department store wants dynamic prices that vary by month, what should the price be in month 1? a) 80 b) 90 c) 100 d) 110 4. If the department store wants dynamic prices that vary by month, what is the resulting revenue? a) 169,667 b) 172,833 c) 183,333 d) 195,233 5. If each swimsuit costs $40 and the store plans to charge dynamic prices, how many swimsuits should it purchase at the beginning of the season to maximize its profit? a) 1500 b) 1600 c) 1700 d) 1800
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