Two Stock Arrays with Switching Cost

Learning Outcome

Turn the Two Stock Arrays with Switching Cost interview variant into a clear brute-force baseline, optimized pattern, and implementation plan.

Original Interview Statement

Given daily values for two stocks and a percentage cost to switch, maximize final money.

Examples

Brute Force Approach

Try every sequence of stock choices across days.

Optimized Approach

Maintain best money if currently holding stock 1 or stock 2. Transition either stay or switch after applying cost.

Exact Pseudocode

dp1, dp2 = initial holdings
for each day:
  next1 = max(stay1, switch2to1)
  next2 = max(stay2, switch1to2)
return max(dp1, dp2)
  

Reference Code

def max_money(stock1, stock2, switch_percent):
    fee = switch_percent / 100.0
    hold1 = float(stock1[0])
    hold2 = float(stock2[0])
    prev1 = stock1[0]
    prev2 = stock2[0]
    for a, b in zip(stock1[1:], stock2[1:]):
        next1 = max(hold1 * a / prev1, hold2 * (1 - fee) * a / prev2)
        next2 = max(hold2 * b / prev2, hold1 * (1 - fee) * b / prev1)
        hold1, hold2 = next1, next2
        prev1, prev2 = a, b
    return max(hold1, hold2)
  

Complexity

Edge Cases

• Zero switching cost
• Very high switching cost
• Equal prices

Follow-ups

• Transaction fee per switch as fixed amount
• Allow cash state

Nearest Practice References

• Best Time to Buy and Sell Stock with Transaction Fee
• State DP

Common Mistakes

• Copying the nearest LeetCode solution without checking the changed rule.
• Skipping duplicate or boundary cases.
• Not stating the brute force before the optimized approach.