def test_stocks_best_buy_empty():
from interviews.stocks import stocks_best_buy
# Given an empty list of stock prices,
prices = []
# when we look for the best buy/sell,
result = stocks_best_buy(prices)
# then we get no solution.
assert result == 'No Solution'
def test_stocks_best_buy_hill():
from interviews.stocks import stocks_best_buy
# Given a stock price hill,
prices = [
float(i) for i in range(1, 11, 1)
]
# when we look for the best buy/sell,
result = stocks_best_buy(prices)
# then we get the first price and the last price.
assert result == (1.0, 10.0,)
def test_stocks_best_buy_cliff():
from interviews.stocks import stocks_best_buy
# Given a stock price cliff,
prices = [
float(i) for i in range(10, 0, -1)
]
# when we look for the best buy/sell,
result = stocks_best_buy(prices)
# then we get no solution.
assert result == 'No Solution'
def test_stocks_best_buy_plateau():
from interviews.stocks import stocks_best_buy
# Given a plateau of stock prices,
prices = [
float(10) for _ in range(10)
]
# when we look for the best buy/sell,
result = stocks_best_buy(prices)
# then we get no solution.
assert result == 'No Solution'
def test_stocks_best_buy_single_price():
from interviews.stocks import stocks_best_buy
# Given a list containing a single stock price,
prices = [
10.0,
]
# when we look for the best buy/sell,
result = stocks_best_buy(prices)
# then we get no solution.
assert result == 'No Solution'
def test_stocks_best_buy_plateau_to_cliff():
from interviews.stocks import stocks_best_buy
# Given a stock price cliff following a plateau,
prices = list(chain(
[float(10) for _ in range(5)],
[float(i) for i in range(9, 4, -1)],
))
# when we look for the best buy/sell,
result = stocks_best_buy(prices)
# then we get no solution.
assert result == 'No Solution'