Stock Span Problem in Python
Introduction to Stock Span Problem in Python
The stock span problem in Python is a classic algorithmic challenge that involves calculating the “span” of stock prices over a series of days. The span of a stock on a particular day is defined as the maximum number of consecutive days (including the current day) for which the stock price is less than or equal to the price on that day.
In this page, we will delve into this problem, exploring stock span problem (naive and optimized approach) in python.
What is the Stock Span Problem in Python?
The Stock Span Problem revolves around calculating the span of stock’s price for a given day. The span of a stock’s price on a particular day is defined as the maximum number of consecutive days just before the current day for which the price of the stock is less than or equal to the current day’s price.
In simpler terms, it helps us determine how many days a stock’s price remained consistent or rose leading up to a given day. This information is valuable for assessing trends and volatility in the stock market.
Calculating Stock Span
The stock span of a particular day can be calculated using a simple algorithm. For each day, we start from that day and move backward in time, checking how many consecutive days the stock price was less than or equal to the current day’s price. This process continues until we find a day where the stock price exceeds the current day’s price or until we reach the first day of available data.
Pseudo Code : Stock Span Problem in Python
- Here’s a Python implementation of the Stock Span Problem:
def calculate_span(prices):
stack = []
span = []
for i in range(len(prices)):
while stack and prices[i] >= prices[stack[-1]]:
stack.pop()
if not stack:
span.append(i + 1)
else:
span.append(i - stack[-1])
stack.append(i)
return span
| Approach | Description | Time Complexity |
|---|---|---|
| Naïve Approach | In this approach, for each day, we iterate through all the previous days until we find a day with a stock price greater than or equal to the current day. We then calculate the span by counting the number of days between the current day and the found day. | O(n^2) |
| Optimized Approach | In the optimized approach, we use a stack to keep track of the indices of previously seen days with stock prices. We start with an empty stack and iterate through the days. For each day, we pop elements from the stack while the current day’s stock price is greater than the stock price of the day at the top of the stack. We calculate the span as the difference between the current day’s index and the index at the top of the stack, and then push the current day’s index onto the stack. | O(n) |
Stock Span Problem (Naïve and Optimized Approach) in Python
The Naïve Approach
The naive approach to solving the Stock Span Problem involves iterating through the stock prices for each day and, for each day, going back to the previous days to count how many consecutive days have lower or equal prices. Here’s a simplified Python implementation of the naive approach:
def stock_span_naive(prices):
n = len(prices)
spans = [0] * n
for i in range(n):
j = i - 1
while j >= 0 and prices[i] >= prices[j]:
spans[i] += 1
j -= 1
return spans
The Optimized Approach
To overcome the inefficiency of the naive approach, we can employ a more optimized solution using a stack data structure. This approach has a time complexity of O(n) and is much more efficient.
def stock_span_optimized(prices):
n = len(prices)
spans = [0] * n
stack = []
for i in range(n):
while stack and prices[i] >= prices[stack[-1]]:
stack.pop()
if not stack:
spans[i] = i + 1
else:
spans[i] = i - stack[-1]
stack.append(i)
return spans
Advantages of Mastering the Stock Span Problem
- Informed Decision-Making : By understanding the Stock Span Problem, you can make data-driven decisions when buying or selling stocks, increasing your chances of profitability.
- Risk Mitigation : Identify stocks with consistent upward trends to minimize your exposure to market volatility.
- Algorithm Development : Build sophisticated trading algorithms that leverage the Stock Span Problem to execute profitable trades.
Conclusion
In the world of stock trading, understanding the historical performance of a stock is essential. The Stock Span Problem offers a valuable tool to assess the continuity and trends in stock prices. While the naive approach provides accurate results, the optimized approach is far more efficient, making it a preferred choice for analyzing large datasets.
In conclusion, whether you’re a seasoned trader or a budding investor, mastering algorithms like the Stock Span Problem can provide you with a competitive edge in the world of finance.
Prime Course Trailer
Related Banners
Get PrepInsta Prime & get Access to all 200+ courses offered by PrepInsta in One Subscription
Get over 200+ course One Subscription
Courses like AI/ML, Cloud Computing, Ethical Hacking, C, C++, Java, Python, DSA (All Languages), Competitive Coding (All Languages), TCS, Infosys, Wipro, Amazon, DBMS, SQL and others
Login/Signup to comment