Add No_121: Best Time to Buy and Sell Stock

Author: brianchiang-tw

No_121_Best Time to Buy and Sell Stock/No_121_Best Time to Buy and Sell Stock_description.md

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+'''
+
+Description:
+
+Say you have an array for which the ith element is the price of a given stock on day i.
+
+If you were only permitted to complete at most one transaction (i.e., buy one and sell one share of the stock), design an algorithm to find the maximum profit.
+
+Note that you cannot sell a stock before you buy one.
+
+Example 1:
+
+Input: [7,1,5,3,6,4]
+Output: 5
+Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5.
+             Not 7-1 = 6, as selling price needs to be larger than buying price.
+Example 2:
+
+Input: [7,6,4,3,1]
+Output: 0
+Explanation: In this case, no transaction is done, i.e. max profit = 0.
+
+'''

No_121_Best Time to Buy and Sell Stock/best_time_to_buy_and_sell_stock_by_valley_and_peak.py

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+'''
+
+Description:
+
+Say you have an array for which the ith element is the price of a given stock on day i.
+
+If you were only permitted to complete at most one transaction (i.e., buy one and sell one share of the stock), design an algorithm to find the maximum profit.
+
+Note that you cannot sell a stock before you buy one.
+
+Example 1:
+
+Input: [7,1,5,3,6,4]
+Output: 5
+Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5.
+             Not 7-1 = 6, as selling price needs to be larger than buying price.
+Example 2:
+
+Input: [7,6,4,3,1]
+Output: 0
+Explanation: In this case, no transaction is done, i.e. max profit = 0.
+
+''' 
+
+
+from typing import List
+class Solution:
+    def maxProfit(self, prices: List[int]) -> int:
+        
+        if len(prices) == 0:
+            # corner case
+            return 0
+        
+        min_price, min_index = 2**31, 0
+        max_price, max_index = 0, 0
+        
+        max_profit = 0
+        
+        for i, p in enumerate(prices):
+            
+            if p <= min_price:
+                # update valley
+                min_price = p
+                min_index = i
+            
+            if p >= min_price:
+                # update peak
+                max_price = p
+                max_index = i
+                
+            
+            if max_index > min_index and (max_price - min_price) > max_profit :
+                # update max_profit
+                max_profit = max_price - min_price
+                
+                
+        return max_profit
+            
+
+
+# n : the length of input array, prices.
+
+## Time Complexity: O( n )
+#
+# The major overhead in time is the for loop iterating on (i, p), which is of O( n ).
+
+## Space Complexity: O( 1 )
+#
+# The major overhead in space is the variables for price computation, which is of O( 1 ).
+
+
+
+def test_bench():
+
+    test_data = [
+                    [7,1,5,3,6,4],
+                    [7,6,4,3,1],
+                    [2,4,1],
+                    []
+                ]
+
+    # expected output:
+    '''
+    5
+    0
+    2
+    0
+    '''
+
+    for stock_price in test_data:
+
+        print( Solution().maxProfit(stock_price) )
+
+    return
+
+
+
+if __name__ == '__main__':
+
+    test_bench()