Stock buy and sell problem in DSA

Introduction

A well-known algorithmic riddle with applications in algorithmic trading, business sectors, and different regions is the stock buy and sell problem. The situation of trading stocks to amplify benefits is at the focal point of the stock buy and sell controversy. Work out the maximum benefit that can be made by buying and selling a solitary portion of the stock while considering a scope of stock values, every one of which reflects the worth of the stock on a particular day.

Approach

To settle the Stock Buy and Sell problem effectively, we should think of an arrangement that utilizes the accessible data. A straightforward and efficient method entails navigating the range of stock values, noting the lowest price thus far, and adjusting the maximum profit correspondingly.

Algorithm

• Set the initial values of two variables: max_profit, which will hold the maximum profit, and min_price, which will record the lowest price thus far.
• Iterate through the array of stock prices.
• If the current price is less than min_price, adjust min_price at each iteration.
• Subtract min_price from the current price to determine the potential profit.
• If the computed profit exceeds the existing max_profit, update max_profit.
• Give max profit back as the outcome.

Code

Output:

Code Explanation

Function maxProfit(int prices[], int n)

• This function expects two parameters: a number n, which shows the number of elements in the array, and an array prices[], which addresses the stock prices on different days.
• The highest profit that can be made by buying and selling a single portion of the stock is returned as a number.
• It returns 0 since there can be no profit if the number of elements in the array is less than or equal to 1 (n <= 1). This indicates that no transactions are feasible.
• min_price: used to record the lowest price that has been found thus far when looping through the array.
• max_profit: used to record the highest possible profit.
• Since we've already taken into account the element at index 0 as the initial minimum price, it then iterates over the array starting at index 1 (for (int i = 1; i< n; i++)).
• The current price (prices[i]) is compared to the min_price during the loop. If so, the current price is updated in min_price.
• It determines the profit that may be obtained by selling the stock at the current price (prices[i] - min_price) if the current price is not less than min_price.
• It adjusts max_profit to reflect the computed profit if this profit exceeds the current max_profit.
• The final max profit is returned once the full array has been iterated through.

main() function

• It declares an array called prices[] that holds the daily stock prices.
• By utilizing the sizeof the operator, it determines the array's length (n).
• To find the greatest profit, it invokes the maxProfit() function with prices[] and n as inputs.
• By using printf(), the greatest profit is printed.
• The code has an O(n) time complexity, where n is the number of elements in the pricing array. This is because the function only makes one iteration through the array. Because it requires a fixed amount of additional space regardless of the size of the input, the space complexity is O(1).

Conclusion

A basic issue in algorithmic trading & financial research is the stock buy and sell dilemma. Through the use of ideas from Data Structures and Algorithms, we can make effective answers for these sorts of issues. Here, we checked out a system for buying and offering stocks to maximize profit.We also included a C implementation and an explanation of the results. Understanding more intricate trading methods and optimization techniques starts with this problem.