Java Solution to the "Best Time to Buy and Sell Stock" LeetCode DSA Problem

Introduction

Stock trading can be complex, but understanding the right time to buy and sell can be the key to maximizing profit. For coders, this principle translates into one of the most popular coding challenges on LeetCode: Best Time to Buy and Sell Stock. In this blog, we’ll break down an efficient Java solution that solves this problem with an optimal time complexity of O(n).

This article is designed not just to guide you through the solution but also to help you gain insights into solving similar coding challenges efficiently. Whether you're prepping for coding interviews or sharpening your algorithmic skills, this solution will add value to your journey.

Problem Overview

LeetCode's "Best Time to Buy and Sell Stock" problem can be summarized as follows:

You are given an array of stock prices, where each element represents the stock's price on a given day.
Your task is to identify the maximum profit you can gain by selecting one day to buy and a future day to sell.
You can only complete one transaction: buying one share and then selling it.

For example:
int[] prices = {5, 8, 4, 9, 1, 10};

The maximum profit would be 9 (buy at 1 and sell at 10).

Java Solution: Step-by-Step Breakdown

1. Initialize Variables:

We begin by setting buy to the maximum possible value and maxProfit to zero. This sets the groundwork for tracking the lowest buy price and the maximum profit.

2. Iterate Over Prices:

We loop through the array of prices, checking if the current price is lower than our buy price. If it is, we update buy. Otherwise, we calculate the profit if we were to sell at the current price.

3. Maximize Profit:

During each iteration, if the calculated profit exceeds the maxProfit recorded so far, we update it.

4. Return the Maximum Profit:

After the loop, the maxProfit variable will hold the highest possible profit, which we return as the final answer.

Here’s the code:

public class BestTimeToBuyAndSell {

public static void main(String[] args) {
int [] arr = {5,8,4,9,1,10};

int max = getMaximumProfit(arr);

System.out.println("maximum profit gained is: "+max);
}

private static int getMaximumProfit(int[] arr) {
int buy = Integer.MAX_VALUE;
int maxProfit = 0;

for (int i : arr) {
if (i<buy) {
buy = i;
}
else {
int profit = i-buy;

if (profit>maxProfit) {
maxProfit = profit;
}
}
}
return maxProfit;
}

}

Solution Breakdown

Tracking the Lowest Buy Price: The variable buy keeps track of the lowest price seen so far. If the current price is less than buy, it updates, ensuring you always buy at the minimum possible price.
Calculating Profit: Each time the price is higher than buy, you calculate the profit by subtracting buy from the current price. This ensures you're calculating profit only when selling the stock is viable.
Optimizing Profit: By comparing the current profit with maxProfit, you ensure you're always recording the maximum profit.

Time Complexity:

This solution has a time complexity of O(n), meaning it only requires a single pass through the price array. This makes it highly efficient for large datasets, a crucial aspect for competitive programming and real-world applications.

Key Takeaways

Optimal Buy and Sell Strategy: This algorithm guarantees that you're identifying the best day to buy and sell for maximum profit in one pass through the array.
Efficiency: The solution is not only intuitive but also efficient, running in linear time with O(n) complexity. This makes it perfect for high-stakes coding interviews and performance-intensive applications.
Real-World Relevance: Understanding the fundamentals of how to track optimal buy and sell points has real-world implications, from algorithmic trading to investment strategies.

Conclusion

The "Best Time to Buy and Sell Stock" problem is an excellent way to build your problem-solving and optimization skills. With this efficient Java solution, you're well on your way to mastering the logic needed to crack not only coding interviews but also broader algorithmic challenges.

By optimizing both the buying and selling strategies in just O(n) time, this approach balances simplicity and performance, making it a go-to solution for interviewers and developers alike.

Looking to Sharpen Your Coding Skills?

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This blog combines practical problem-solving techniques with a real-world coding challenge, enhancing your understanding of stock trading algorithms while strengthening your coding interview preparation!