 Open in App
Not now

# Python Program For Stock Buy Sell To Maximize Profit

• Difficulty Level : Expert
• Last Updated : 27 Dec, 2022

The cost of a stock on each day is given in an array, find the max profit that you can make by buying and selling in those days. For example, if the given array is {100, 180, 260, 310, 40, 535, 695}, the maximum profit can earned by buying on day 0, selling on day 3. Again buy on day 4 and sell on day 6. If the given array of prices is sorted in decreasing order, then profit cannot be earned at all.

Naive approach: A simple approach is to try buying the stocks and selling them on every single day when profitable and keep updating the maximum profit so far.

Below is the implementation of the above approach:

## Python3

 `# Python3 implementation of the approach`   `# Function to return the maximum profit` `# that can be made after buying and` `# selling the given stocks` `def` `maxProfit(price, start, end):`   `    ``# If the stocks can't be bought` `    ``if` `(end <``=` `start):` `        ``return` `0``;`   `    ``# Initialise the profit` `    ``profit ``=` `0``;`   `    ``# The day at which the stock` `    ``# must be bought` `    ``for` `i ``in` `range``(start, end, ``1``):`   `        ``# The day at which the` `        ``# stock must be sold` `        ``for` `j ``in` `range``(i``+``1``, end``+``1``):`   `            ``# If buying the stock at ith day and` `            ``# selling it at jth day is profitable` `            ``if` `(price[j] > price[i]):` `                `  `                ``# Update the current profit` `                ``curr_profit ``=` `price[j] ``-` `price[i] ``+` `maxProfit(price, start, i ``-` `1``)``+` `maxProfit(price,  j ``+` `1``, end);`   `                ``# Update the maximum profit so far` `                ``profit ``=` `max``(profit, curr_profit);`   `    ``return` `profit;`   `# Driver code` `if` `__name__ ``=``=` `'__main__'``:` `    ``price ``=` `[``100``, ``180``, ``260``, ` `             ``310``, ``40``, ``535``, ``695``];` `    ``n ``=` `len``(price);` `    ``print``(maxProfit(price, ``0``, n ``-` `1``));` `# This code is contributed by Rajput-Ji`

Output

```865
```

Time Complexity: O(N2)
Auxiliary Space: O(1)

Efficient approach: If we are allowed to buy and sell only once, then we can use following algorithm. Maximum difference between two elements. Here we are allowed to buy and sell multiple times.
Following is the algorithm for this problem.

1. Find the local minima and store it as starting index. If not exists, return.
2. Find the local maxima. and store it as an ending index. If we reach the end, set the end as the ending index.
3. Update the solution (Increment count of buy-sell pairs)
4. Repeat the above steps if the end is not reached.

## Python3

 `# Python3 Program to find ` `# best buying and selling days`   `# This function finds the buy sell` `# schedule for maximum profit` `def` `stockBuySell(price, n):` `    `  `    ``# Prices must be given for at ` `    ``# least two days` `    ``if` `(n ``=``=` `1``):` `        ``return` `    `  `    ``# Traverse through given price array` `    ``i ``=` `0` `    ``while` `(i < (n ``-` `1``)):` `        `  `        ``# Find Local Minima` `        ``# Note that the limit is (n-2) as ` `        ``# we are comparing present element ` `        ``# to the next element` `        ``while` `((i < (n ``-` `1``)) ``and` `                ``(price[i ``+` `1``] <``=` `price[i])):` `            ``i ``+``=` `1` `        `  `        ``# If we reached the end, break` `        ``# as no further solution possible` `        ``if` `(i ``=``=` `n ``-` `1``):` `            ``break` `        `  `        ``# Store the index of minima` `        ``buy ``=` `i` `        ``i ``+``=` `1` `        `  `        ``# Find Local Maxima` `        ``# Note that the limit is (n-1) as we are` `        ``# comparing to previous element` `        ``while` `((i < n) ``and` `               ``(price[i] >``=` `price[i ``-` `1``])):` `            ``i ``+``=` `1` `            `  `        ``# Store the index of maxima` `        ``sell ``=` `i ``-` `1` `        `  `        ``print``(``"Buy on day: "``,buy,``"    "``,` `                ``"Sell on day: "``,sell)` `        `  `# Driver code`   `# Stock prices on consecutive days` `price ``=` `[``100``, ``180``, ``260``, ` `         ``310``, ``40``, ``535``, ``695``]` `n ``=` `len``(price)`   `# Function call` `stockBuySell(price, n)` `# This is code contributed by SHUBHAMSINGH10`

Output

```Buy on day:  0      Sell on day:  3
Buy on day:  4      Sell on day:  6
```

Time Complexity: The outer loop runs till I become n-1. The inner two loops increment value of I in every iteration. So overall time complexity is O(n)
Auxiliary Space : O(1) since using constant variables

Valley Peak Approach:

In this approach, we just need to find the next greater element and subtract it from the current element so that the difference keeps increasing until we reach a minimum. If the sequence is a decreasing sequence so the maximum profit possible is 0.

## Python3

 `# Python3 program for the ` `# above approach` `def` `max_profit(prices: ``list``, ` `               ``days: ``int``) ``-``> ``int``:` `    ``profit ``=` `0`   `    ``for` `i ``in` `range``(``1``, days):`   `        ``# Checks if elements are adjacent ` `        ``# and in increasing order` `        ``if` `prices[i] > prices[i``-``1``]:`   `            ``# Difference added to 'profit'` `            ``profit ``+``=` `prices[i] ``-` `prices[i``-``1``]`   `    ``return` `profit`   `# Driver Code` `if` `__name__ ``=``=` `'__main__'``:`   `    ``# Stock prices on consecutive days` `    ``prices ``=` `[``100``, ``180``, ``260``, ` `              ``310``, ``40``, ``535``, ``695``]`   `    ``# Function call` `    ``profit ``=` `max_profit(prices, ``len``(prices))` `    ``print``(profit)`   ` ``# This code is contributed by vishvofficial.`

Output

```865
```

Time Complexity: O(n)
Auxiliary Space: O(1)

Please refer complete article on Stock Buy Sell to Maximize Profit for more details!

My Personal Notes arrow_drop_up
Related Articles