← Jump Trading Interview Insights
I spent way too long trying to figure out the exact pattern-matching logic before even touching the output array.
First, clarify the problem details: how patterns are matched (exact sequence of up/down movements), the initial number of shares, and the order of buy/sell when both trigger. Then, design an efficient algorithm that precomputes pattern matches using rolling hashes or KMP, and simulates the trading day by day, updating the share count based on triggered actions.
Pro tip: Discuss the time and space complexity trade-offs: for example, precomputing pattern matches with KMP gives O(n + m) per pattern, which is optimal for large inputs. Also, mention edge cases like empty patterns or insufficient history, showing thoroughness.
Ask about initial shares, whether buy/sell quantities are fixed (e.g., 1 share), and the order of operations if both patterns match on the same day. Confirm that patterns must match exactly the sequence of price movements immediately before the day.
Convert the price array into a sequence of 1s and -1s representing daily up/down movements. Then, for each pattern, find all starting indices where it matches using an efficient string matching algorithm like KMP or rolling hash.
Iterate through each day, checking if a buy or sell pattern ends at the previous day (i.e., matches the movements immediately before the current day). Update the share count accordingly, handling simultaneous triggers as per the clarified order.
Consider cases where patterns are longer than available history, patterns are empty, or multiple matches occur. Validate with small examples to ensure correctness.
Discuss the time complexity: O(n + m) for preprocessing each pattern and O(n) for simulation, where n is the number of days and m is the pattern length. Space complexity O(n) for storing movements and match indices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Stack-based approach clicked pretty fast for me.
Clarify the problem constraints (e.g., string length, number of substrings, whether substrings can overlap) and discuss the naive approach of repeatedly scanning and removing substrings, which can be inefficient. Then propose an optimized solution using a stack to process the string in a single pass, checking for removable suffixes and handling cascading removals.
Pro tip: Mention that the order of removal doesn't affect the final result, so you can process left-to-right. Also, consider using a trie or Aho-Corasick for multiple substrings to optimize matching, but be prepared to implement a simpler stack-based solution if time is limited.
Ask about input sizes, character set, whether substrings can be empty, and if removals should be applied simultaneously or sequentially. Confirm that the final string should be returned after no more removals are possible.
Explain that repeatedly scanning the string for each substring and removing them can be O(n * m * k) where n is string length, m is number of substrings, and k is average substring length. This may be too slow for large inputs.
Use a stack to build the result character by character. After each push, check if the top of the stack matches any removable substring; if so, pop the matched characters. This handles cascading removals efficiently in O(n * L) where L is the maximum substring length.
For multiple substrings, check all possible suffixes of the stack against the set. To optimize, group substrings by length or use a trie to quickly identify matches. Ensure that after a removal, you re-check the new top for further removals.
State time complexity O(n * L) and space O(n). Discuss edge cases: empty string, no removals, all characters removed, substrings that are prefixes/suffixes of each other, and very long strings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.