apple_stocks.py

def get_best_profit_brute_force(stock_prices_yesterday):

    max_profit = 0

    # go through every time
    for outer_time in xrange(len(stock_prices_yesterday)):

        # for every time, go through every OTHER time
        for inner_time in xrange(len(stock_prices_yesterday)):

            # for each pair, find the earlier and later times
            earlier_time = min(outer_time, inner_time)
            later_time   = max(outer_time, inner_time)
            
            # and use those to find the earlier and later prices
            earlier_price = stock_prices_yesterday[earlier_time]
            later_price   = stock_prices_yesterday[later_time]

            # see what our profit would be if we bought at the
            # earlier price and sold at the later price
            potential_profit = later_price - earlier_price

            # update max_profit if we can do better
            max_profit = max(max_profit, potential_profit)

    return max_profit

def get_best_profit_smarter_brute_force(stock_prices_yesterday):

    max_profit = 0

    # go through every price (with it's index as the time)
    for earlier_time, earlier_price in enumerate(stock_prices_yesterday):

        # and go through all the LATER prices
        for later_price in stock_prices_yesterday[earlier_time:]:

            # see what our profit would be if we bought at the
            # earlier price and sold at the later price
            potential_profit = later_price - earlier_price

            # update max_profit if we can do better
            max_profit = max(max_profit, potential_profit)

    return max_profit

def get_best_profit_positive(stock_prices_yesterday):

    min_price = stock_prices_yesterday[0]
    max_profit = 0

    for current_price in stock_prices_yesterday:

        # ensure min_price is the lowest price we've seen so far
        min_price = min(min_price, current_price)

        # see what our profit would be if we bought at the
        # min price and sold at the current price
        potential_profit = current_price - min_price

        # update max_profit if we can do better
        max_profit = max(max_profit, potential_profit)

    return max_profit

def get_best_profit(stock_prices_yesterday):

    # make sure we have at least 2 prices
    if len(stock_prices_yesterday) < 2:
        raise IndexError('Getting a profit requires at least 2 prices')

    # we'll greedily update min_price and max_profit, so we initialize
    # them to the first price and the first possible profit
    min_price = stock_prices_yesterday[0]
    max_profit = stock_prices_yesterday[1] - stock_prices_yesterday[0]

    for index, current_price in enumerate(stock_prices_yesterday):

        # skip the first (0th) time
        # we can't sell at the first time, since we must buy first,
        # and we can't buy and sell at the same time!
        # if we took this out, we'd try to buy /and/ sell at time 0.
        # this would give a profit of 0, which is a problem if our
        # max_profit is supposed to be /negative/--we'd return 0!
        if index == 0:
            continue

        # see what our profit would be if we bought at the
        # min price and sold at the current price
        potential_profit = current_price - min_price

        # update max_profit if we can do better
        max_profit = max(max_profit, potential_profit)

        # update min_price so it's always
        # the lowest price we've seen so far
        min_price  = min(min_price, current_price)

    return max_profit


# tests

functions = [
    get_best_profit_brute_force,
    get_best_profit_smarter_brute_force,
    get_best_profit_positive,
    get_best_profit
]

def test(function, input, output):
    assert function(input) == output

positive_tests = [
    ([10, 20, 5],     10),  # simple buy and sell
    ([10, 5, 10, 20], 15),  # wait to buy and sell
    ([10, 10, 10],    0),   # no change
]

negative_tests = [
    ([35, 20, 10],  -10),   # decrease in value all day
    ([30, 20, 10],  -10),   # steady decrease
    ([100, 70, 50], -20),   # decrease, wait to buy
]

for function in functions:
    for input, output in positive_tests:
        test(function, input, output)

for input, output in negative_tests:
    test(get_best_profit, input, output)

import unittest

class EnsureErrors(unittest.TestCase):

    def test(self):
        self.assertRaises(IndexError, get_best_profit, [1])
        self.assertRaises(IndexError, get_best_profit, [])

if __name__ == '__main__':
    unittest.main()