This notebook was prepared by Donne Martin. Source and license info is on GitHub.

# Challenge Notebook¶

## Problem: Given a list of stock prices, find the max profit from 1 buy and 1 sell.¶

See the LeetCode problem page.

## Constraints¶

• Are all prices positive ints?
• Yes
• Is the output an int?
• Yes
• If profit is negative, do we return the smallest negative loss?
• Yes
• If there are less than two prices, what do we return?
• Exception
• Can we assume the inputs are valid?
• No
• Can we assume this fits memory?
• Yes

## Test Cases¶

• None -> TypeError
• Zero or one price -> ValueError
• No profit
• [8, 5, 3, 2, 1] -> -1
• General case
• [5, 3, 7, 4, 2, 6, 9] -> 7

## Algorithm¶

Refer to the Solution Notebook. If you are stuck and need a hint, the solution notebook's algorithm discussion might be a good place to start.

## Code¶

In [ ]:
class Solution(object):

def find_max_profit(self, prices):
# TODO: Implement me
pass


## Unit Test¶

The following unit test is expected to fail until you solve the challenge.

In [ ]:
# %load test_max_profit.py
import unittest

class TestMaxProfit(unittest.TestCase):

def test_max_profit(self):
solution = Solution()
self.assertRaises(TypeError, solution.find_max_profit, None)
self.assertRaises(ValueError, solution.find_max_profit, [])
self.assertEqual(solution.find_max_profit([8, 5, 3, 2, 1]), -1)
self.assertEqual(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7)
print('Success: test_max_profit')

def main():
test = TestMaxProfit()
test.test_max_profit()

if __name__ == '__main__':
main()


## Solution Notebook¶

Review the Solution Notebook for a discussion on algorithms and code solutions.